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Economic reforms and agricultural supply response in Jamaica

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Title:
Economic reforms and agricultural supply response in Jamaica
Creator:
Ballayram, 1950-
Publication Date:
Language:
English
Physical Description:
xvi, 192 leaves : ill. ; 29 cm.

Subjects

Subjects / Keywords:
Agrarian reform ( jstor )
Agriculture ( jstor )
Coffee industry ( jstor )
Crops ( jstor )
Economic reform ( jstor )
Fertilizers ( jstor )
Mathematical variables ( jstor )
Price shocks ( jstor )
Prices ( jstor )
Supply ( jstor )
Agriculture and state -- Jamaica ( lcsh )
Crops -- Jamaica ( lcsh )
Dissertations, Academic -- Food and Resource Economics -- UF ( lcsh )
Economic policy -- Jamaica ( lcsh )
Food and Resource Economics thesis, Ph.D ( lcsh )
Genre:
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Thesis:
Thesis (Ph.D.)--University of Florida, 2001.
Bibliography:
Includes bibliographical references (leaves 183-191).
General Note:
Printout.
General Note:
Vita.
Statement of Responsibility:
by Ballayram.

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University of Florida
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University of Florida
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Copyright [name of dissertation author]. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Resource Identifier:
026974320 ( ALEPH )
47260198 ( OCLC )

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ECONOMIC REFORMS AND AGRICULTURAL SUPPLY RESPONSE
IN JAMAICA
















By

BALLAYRAM


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2001















ACKNOWLEDGEMENTS

First, I would like to express my deepest gratitude to

my wife for all her support throughout my studies here at

the University of Florida. Her support in this process was

overwhelming and unselfish. My daughters were also very

understanding, supportive, and encouraging, and I thank

them sincerely for the sacrifices they had to make so that

I could complete my graduate studies. My parents and in-

laws (all now deceased) gave me encouragement and support,

both financial and otherwise, in my quest to achieve a

higher education. Without their love, confidence and moral

support, none of this would have been possible. I owe them

an eternal debt of gratitude, because their support came at

a time when the cost of higher education was far beyond the

reach which their economic resources could accommodate.

I would also like to express my deepest gratitude to

Dr. Carlton G. Davis, chairman of my supervisory committee.

He opened the door for me at the University of Florida, and

provided me with countless hours of individual attention

during my course of studies. On numerous occasions he went

beyond what would normally be the call of duty of an









academic supervisor, to provide me with the support that

enabled me to continue my studies.

I would also like to acknowledge the contributions

from the other members of my supervisory committee: Dr.

Robert Emerson, Dr. Clyde Kiker, Dr. Richard Kilmer and Dr.

David Denslow, who provided me with valuable suggestions

during the preparation of this dissertation.

I also benefited immensely, in my course of studies at

U.F., from discussions with Dr. Max Langham, Dr. Charles

Moss, Dr. Ronald Ward and Dr. James Seale, Jr.

Financial support from the Food and Resource Economics

Department is greatly acknowledged. Dr. Carlton Davis'

efforts in securing this funding for me are greatly

appreciated.

Finally, I wish to acknowledge the excellent computer

guidance and support services which Alex Heyman, Roger and

Laura Clemons, Ed Howard and Juan Carlos, of the Food and

Resource Economics Support Center, provided throughout my

studies here at U.F.


iii















TABLE OF CONTENTS


page


ACKNOWLEDGEMENTS ...... .................. ii

LIST OF TABLES ....... .................... vi


LIST OF FIGURES ........


ABSTRACT . . . . . .

CHAPTERS

1. INTRODUCTION ...... ...................

1.1 Background to Jamaica's Economic
Reforms ..... .................
1.2 Problem Statement and Study
Justification ... ................
1.3 Objectives of the Study .. ...........
1.4 Hypotheses ...............
1.5 Conceptual Issues ..... ............
1.6 Procedural and Methodological
Considerations ............
1.7 Summary ....... ..................

2. ECONOMIC STRUCTURE, GROWTH, AND POLICY
REFORMS IN THE JAMAICAN ECONOMY, 1962-1999


2.1 Structure of the Economy and
Economic Growth: An Overview ...
2.2 Output Trends and Policies in
Jamaican Agriculture ........
2.3 The Economic Reforms .........
2.4 Summary ....... ................

3. METHODOLOGICAL AND EMPIRICAL ISSUES IN
ECONOMIC REFORMS AND SUPPLY RESPONSE .

3.1 Review of the Economic Reform
Literature . . . .


* 26

* 30
* 38
* 46


. . ix


. xv


2

8
* 10
. 11
* 12.

* 20
* 22









Before-After Approach ....
With-Without Approach ....
Comparison of Simulations


Approach ....


3.2 Preliminary Issues in Modeling Supply
Response in Jamaica .. ...........
3.3 Error Correction Model ........
3.4 An Error Correction Model for Crop
Supply Response in Jamaica .....
3.5 The Data . . . .
3.6 Summary ....... ................

4. EMPIRICAL ESTIMATION OF CROP SUPPLY RESPONSE

4.1 Motivations for Using Cointegration
Analysis . . . .
4.2 Definition of Variables ............
4.3 Testing for Stationarity .......
4.4 Estimating Long-run Supply Response .
4.5 Analysis of Short-run Dynamics ....
4.6 Summary ....... ................

5. SUPPLY RESPONSE AND COUNTERFACTUAL ANALYSIS

5.1 Before-After Analysis ... ..........
5.2 Counterfactual Analysis ............
5.3 Summary ....... ................

6. CONCLUSIONS AND POLICY IMPLICATIONS ...


. 75
. 79
* 87
* 90
* 106
. 136

* 137


137
145
164


. 165


APPENDICES


A. PRICE OF SELECTED CROPS PRODUCED IN JAMAICA,
1962-1999 (J$) ..... ...............


. 174


B. QUANTITY OF SELECTED CROPS PRODUCED IN
JAMAICA, 1962-1999 (METRIC TONES) .. ....... ..175

C. SELECTED ECONOMIC STATISTICS ON JAMAICA ..... ..176

D. DIAGNOSTIC STATISTICS FOR MODEL SPECIFICATION
AND TEST STATISTICS FOR COINTEGRATION ..... ..177

REFERENCES ......... ...................... 183

BIOGRAPHICAL SKETCH ....... .................. .192


3.1.1
3.1.2
3.1.3


. . 54















LIST OF TABLES


page


Table


2.1 Sectoral Contribution to Real Gross
Domestic Product (Period Averages,
Percentages) ....... .................. 27

2.2 GDP and Sectoral Growth (1995=100)
(Percentage) ....... .................. 28

2.3 Growth Rates of Total Agricultural and
Broad Agricultural Aggregate Indexes ...... ..30

2.4 Composition of Agricultural Output
(J$M, 1995=100), Selected Years ... ......... .32

2.5 Agriculture Sub-sectors as a Percentage
of Total Agriculture (Period Averages).... 32

2.6 Growth Rates of Agricultural Components
(Percentages) .................... ...... 34

2.7 Average Rates of Growth of Farmgate
and F.O.B. Prices, Output and the
Nominal Protection Coefficient (NPC)
for the Period 1970-78, Jamaica ... ......... .36

2.8 Net Barter Terms of Trade and Agricultural
Income Terms of Trade (1980=100)
(Period Averages) ...... ................ .38

2.9 Outcomes of Economic Reform Policies
in Jamaica, 1986 vs. 1995 .... ............ .45


3.1 Empirical Studies on Agriculture
Supply Responses in Jamaica ..........

4.1 Alternative (Substitute) Crops in Each ECM

4.2 Unit Root Tests--Prices and Quantities
in Levels ...... .................


. 56

. 84


. 88









4.3 Unit Root Tests--Prices and Quantities
in First Difference ...... ............... ..89

4.4 Diagnostic Statistics for Residual
Tests for the Banana ECM .... ............ 91

4.5 Tests of Cointegration Rank for Banana ECM . 95

4.6 Estimated Long-run and Adjustment
Coefficients, P's, a's--Banana ECM .. ....... ..96

4.7 Estimated Long-run and Adjustment
Coefficients, P's, a's for all Crops ........ ..100

4.8 Endogenous and Exogenous Variables in Crops'
Impulse Response Functions .... ........... .109

4.9 Responses of Banana Quantity to Shocks
in the Banana VAR ...... ............... 111

4.10 Variance Decomposition Percentage of One-
period and Three-period Forecast Error
Variance--Banana VAR ..... .............. .114

4.11 Responses of Quantity to Shocks in Exogenous
Variables ....... ................... 117

4.12 Summary of Forecast Error Variance in
Crop Quantities Explained by Variables
in the VAR (Percentage) .... ............ 119

4.13 Responses of Sugar Quantity to Shocks
in the Sugar VAR ...... ................ ..121

4.14 Variance Decomposition Percentage of One-
period and Three-period Forecast Error
Variance--Sugar VAR ..... .............. 121

4.15 Responses of Coffee Quantity to Shocks
in the Coffee VAR ..... ............... ..122

4.16 Variance Decomposition Percentage of One-
period and Three-period Forecast Error
Variance--Coffee VAR ..... .............. ..122

4.17 Responses of Pimento Quantity to Shocks
in the Pimento VAR ...... ............... ..123

vii









4.18 Variance Decomposition Percentage of One-
period and Three-period Forecast Error
Variance--Pimento VAR .... ............. .123

4.19 Responses of Yam Quantity to Shocks
in the Yam VAR ....... ................. ..124

4.20 Variance Decomposition Percentage of One-
period and Three-period Forecast Error
Variance--Yam VAR ..... ............... ..124

4.21 Responses of Orange Quantity to Shocks
in the Orange VAR ..... ......... ..125

4.22 Variance Decomposition Percentage of One-
period and Three-period Forecast Error
Variance--Orange VAR ..... .............. .125

4.23 Responses of Cocoa-bean Quantity to Shocks
in the Cocoa-bean VAR .... ............. .126

4.24 Variance Decomposition Percentage of One-
period and Three-period Forecast Error
Variance--Cocoa-bean VAR ..... ............ .126


4.25 Responses of Potato Quantity to Shocks
in the Potato VAR ...........

4.26 Variance Decomposition Percentage of One-
period and Three-period Forecast Error
Variance--Potato VAR ... ...........

5.1 Descriptive Price and Quantity Statistics
--1962-1979 and 1980-1999 .......

5.2 Descriptive Price and Quantity Statistics
--1975-1979 and 1980-1984 .......

5.3 Diagnostic Statistics for Forecasts of
Fitted Values .............


* 127



* 127


. .. 140


. 141


. 155


5.4 Estimated Long-run and Adjustment
Coefficients, P's, a's for all Crops,
1962-1979 . . . . .

5.5 Estimated Long-run and Adjustment
Coefficients, P's, a's for all Crops,
1980-1999 . . . . .
viii


* 157



159















LIST OF FIGURES


Figure

1.1 Conceptualization of the Jamaican
Agricultural Sector ...........

2.1 GDP and Sectoral Contribution to GDP
in Constant (1995) Dollars ............

2.2 GDP Growth (Percentage Change Over
Previous Year, 1995=100) ... ..........

2.3 Growth of Total Agriculture, Industry
and Services (Percentage Change Over
Previous Year, 1995=100) ... ..........

2.4 Total Agricultural Output Index (1995=100)

2.5 Output Indexes for Broad Agricultural
Aggregates (1995=100) ..........

2.6 Sub-sectors as a Proportion of Agriculture

2.7 Selected Economic Policy Reforms
in Jamaica, 1977-98 ...........

4.1 Residuals for Banana Price in Banana ECM .

4.2 Residuals for Banana Quantity in Banana ECM

4.3 Residuals for Sugar Price in Banana ECM .

4.4 Residuals for Wage in Banana ECM .........

4.5 Cross- and Autocorrelograms in Banana ECM .

4.6 Plot of Eigenvalues for the Banana ECM .

4.7 Impulse Responses to an Innovation in Sugar
Price--Banana VAR ............


page


. 14


. 27


* 29



* 29

. 31


31

* 33


* 43

* 92

* 92

* 93

* 93

* 94

* 94


* 112









4.8 Impulse Responses to an Innovation in
Fertilizer Price--Banana VAR ... ........

4.9 Impulse Responses to an Innovation in Wage
--Banana VAR . . . .

4.10 Impulse Responses to an Innovation in
Banana Price--Banana VAR ... ..........

4.11 Impulse Responses to an Innovation in Banana
Quantity--Banana VAR ..... ...........

4.12 Impulse Responses to an Innovation in Sugar
Quantity--Sugar VAR .... ............

4.13 Impulse Responses to an Innovation in Coffee
Quantity--Coffee VAR .... ............

4.14 Impulse Responses to an Innovation in Pimento
Quantity--Pimento VAR .............

4.15 Impulse Responses to an Innovation in Banana
Price--Sugar VAR ..... ..............


112


. 112


. 112


. 113


. 113


. 113


. 113


. 128


4.16 Impulse Responses to an Innovation in
Fertilizer Price--Sugar VAR .......

4.17 Impulse Responses to an Innovation in Wage
--Sugar VAR ........ ............

4.18 Impulse Responses to an Innovation in
Sugar Price--Sugar VAR .... ...........

4.19 Impulse Responses to an Innovation in Banana
Price--Coffee VAR ............

4.20 Impulse Responses to an Innovation in
Fertilizer Price--Coffee VAR ..........

4.21 Impulse Responses to an Innovation in Wage
--Coffee VAR .............

4.22 Impulse Responses to an Innovation in


Coffee Price--Coffee VAR


* 128


* 128


* 128


* 129


* 129


* 129


. ........ 129


4.23 Impulse Responses to an Innovation in Banana
Price--Pimento VAR ... .............


. 130









4.24 Impulse Responses to an Innovation in
Fertilizer Price--Pimento VAR ... ......... .130

4.25 Impulse Responses to an Innovation in Wage
--Pimento VAR ...... ................. .130

4.26 Impulse Responses to an Innovation in
Pimento Price--Pimento VAR .... ........... .130

4.27 Impulse Responses to an Innovation in Cassava
Price--Yam VAR ....... ................. .131

4.28 Impulse Responses to an Innovation in
Fertilizer Price--Yam VAR .... ........... .131

4.29 Impulse Responses to an Innovation in Wage
--Yam VAR ....... ................... .131

4.30 Impulse Responses to an Innovation in
Yam Price--Yam VAR ...... ............... ..131

4.31 Impulse Responses to an Innovation in Yam
Quantity--Yam VAR ..... ............... ..132

4.32 Impulse Responses to an Innovation in Orange
Quantity--Orange VAR ...... ........... 132

4.33 Impulse Responses to an Innovation in Cocoa-
bean Quantity--Cocoa-bean VAR ... ......... .132

4.34 Impulse Responses to an Innovation in Potato
Quantity--Potato VAR ......... ......... 132

4.35 Impulse Responses to an Innovation in
Grapefruit Price--Orange VAR .... .......... .133

4.36 Impulse Responses to an Innovation in
Fertilizer Price--Orange VAR .... .......... .133

4.37 Impulse Responses to an Innovation in Wage
--Orange VAR ....... ................. 133

4.38 Impulse Responses to an Innovation in
Orange Price--Orange VAR .... ............ .133

4.39 Impulse Responses to an Innovation in Banana
Price--Cocoa-bean VAR ..... ............. 134

xi









4.40 Impulse Responses to an Innovation in
Fertilizer Price--Cocoa-bean VAR ... ........ ..134

4.41 Impulse Responses to an Innovation in Wage
--Cocoa-bean VAR ...... ................ ..134

4.42 Impulse Responses to an Innovation in
Cocoa-bean Price--Cocoa-bean VAR ... ........ ..134

4.43 Impulse Responses to an Innovation in Cassava
Price--Potato VAR ..... ............... ..135

4.44 Impulse Responses to an Innovation in
Fertilizer Price--Potato VAR .... .......... .135

4.45 Impulse Responses to an Innovation in Wage
--Potato VAR ....... .................. .135

4.46 Impulse Responses to an Innovation in
Potato Price--Potato VAR ..... ............ .135

5.1 Nominal Price Changes--Banana (BANP)
and Sugar (SUGP) ...... ................ .143

5.2 Nominal Price Changes--Coffee (COFP)
and Pimento (PIMP) ...... ............... .143

5.3 Nominal Price Changes--Yam (YAMP)
and Orange (ORP) ...... ................ 143

5.4 Nominal Price Changes--Cocoa-bean (COBP)
and Potato (POTP) ..... ............... .143

5.5 Real Price Changes--Banana and Sugar
(1980=100) ....... ................... ..144

5.6 Real Price Changes--Coffee and Pimento
(1980=100) ....... ................... ..144

5.7 Real Price Changes--Yam and Orange
(1980=100) ....... ................... ..144

5.8 Real Price Changes--Cocoa-bean and Potato
(1980=100) ....... ................... ..144

5.9 Banana Quantity--Actual (LBANQ) and
Forecasted (FOLBANQ) ..... .............. .148

xii









5.10 Banana Price--Actual (LBANPR) and
Forecasted (FOLBANPR) ..... ............. 148

5.11 Sugar Price--Actual (LSUGPR) and
Forecasted (FOLSUGPR) ..... ............. 148

5.12 Wage--Actual (LWAGE) and Forecasted
(FOLWAGE) ........ ................... 148

5.13 Fertilizer Price--Actual (LFERP) and
Forecasted (FOLFERP) ..... .............. .149

5.14 Sugar Quantity--Actual (LSUGQ) and
Forecasted (FOLSUGQ) ..... .............. .149

5.15 Coffee Quantity--Actual (LCOFQ) and
Forecasted (FOLCOFQ) ..... .............. .149

5.16 Coffee Price--Actual (LCOFPR) and
Forecasted (FOLCOFPR) ..... ............ 149

5.17 Pimento Quantity--Actual (LPIMQ) and
Forecasted (FOLPIMQ) ..... .............. .150

5.18 Pimento Price--Actual (LPIMPR) and
Forecasted (FOLPIMPR) ..... ............. 150

5.19 Yam Quantity--Actual (LYAMQ) and
Forecasted (FOLYAMQ) ..... .............. .150

5.20 Yam Price--Actual (LYAMPR) and
Forecasted (FOLYAMPR) ..... ............. 150

5.21 Cassava Price--Actual (LCASPR) and
Forecasted (FOLCASPR) ..... ............. 151

5.22 Orange Quantity--Actual (LORQ) and
Forecasted (FOLORQ) ..... .............. 151


5.23 Orange Price--Actual (LORPR) and
Forecasted (FOLORPR) ... ..........

5.24 Grapefruit Price--Actual (LGRPR) and
Forecasted (FOLGRPR) ... ..........

5.25 Cocoa-bean Quantity--Actual (LCOBQ) and
Forecasted (FOLCOBQ) ... ..........

xiii


. . 151


. . 151


. . 152









5.26 Cocoa-bean Price--Actual (LCOBPR) and
Forecasted (FOLCOBPR) ..... ............. 152

5.27 Potato Quantity--Actual (LPOTQ) and
Forecasted (FOLPOTQ) ..... .............. .152

5.28 Potato Price--Actual (LPOTPR) and
Forecasted (FOLPOTPR) ...... .............. .152

5.29 Banana--Fitted Output for Reform Period
(BANFITA) and Counterfactual (BANFITC) ...... .161

5.30 Sugar--Fitted Output for Reform Period
(SUGFITA) and Counterfactual (SUGFITC) ...... ..161

5.31 Coffee--Fitted Output for Reform Period
(COFFITA) and Counterfactual (COFFITC) ...... ..161

5.32 Pimento--Fitted Output for Reform Period
(PIMFITA) and Counterfactual (PIMFITC) ...... .161

5.33 Yam--Fitted Output for Reform Period
(YAMFITA) and Counterfactual (YAMFITC) ...... .162

5.34 Orange--Fitted Output for Reform Period
(ORFITA) and Counterfactual (ORFITC) ........ ..162

5.35 Cocoa-bean--Fitted Output for Reform Period
(COBFITA) and Counterfactual (COBFITC) ...... ..162

5.36 Potato--Fitted Output for Reform Period
(POTFITA) and Counterfactual (POTFITC) ...... .162


xiv














Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

ECONOMIC REFORMS AND AGRICULTURAL SUPPLY RESPONSE
IN JAMAICA

By

Ballayram

May 2001

Chairman: Dr. Carlton G. Davis
Major Department: Food and Resource Economics

A number of economic reform programs have been

undertaken in Jamaica, over the past two decades. Designed

largely by the International Monetary Fund (IMF) and the

World Bank, these reforms focused on correcting internal

policy weaknesses and creating an environment conducive to

sustained growth. The reforms emphasized liberal trade and

exchange rate regimes, a less intrusive and smaller public

sector, and reliance on market forces to determine

agricultural prices and quantities.

Against this background, this study investigates the

impact of these recent economic reforms on agricultural

crop supply responses in Jamaica. The estimation technique

used an error correction modeling framework based on


xv









cointegration theory, within an estimation framework

developed by Johansen. The results of the crop supply

response estimation confirm that there is a long-run

relationship between agricultural crop output and price

incentives. Most of the estimated crop price elasticities

are low, statistically significant, and fall within the

range estimated by other studies on Jamaica. The adjustment

process of the short-run to the long-run was found to be

slow for some crops and higher for others.

Using a counterfactual, which assumed no change in

policy regime, fitted series of supply response functions

from the pre-reform period were forecasted within a

univariate ARMA(p,q) framework and compared to fitted

series of supply responses from the reform period. The

results are mixed. It was found that the impacts of the

economic reforms in Jamaica are crop and time specific.

Mean output was higher in the reform period for four of the

eight crops analyzed. Higher real price shifts were

observed in the reform period for some crops but these

price shifts were also accompanied by higher price-

variability. This suggests that the pro-competitive effects

that were expected to accompany the reforms may have

outweighed the stability impulses of administered prices in

the pre-reform era.


xvi















CHAPTER 1
INTRODUCTION


The past two decades have seen a shift in economic

policies throughout Latin American and Caribbean countries.

Generally, these policy changes have been in response to

debt crises, persistent payments-imbalances, and years of

negative and slow growth. By the 1990s a new orthodoxy on

development thinking could be discerned. This is manifested

in reductions in public sector activities and greater

reliance on private sector initiatives and market forces, a

departure from the policy regimes of the 1960s and 1970s.

In Jamaica, as elsewhere, the new orthodoxy has

influenced economic policies in agriculture. In particular,

policies have shifted away from far-reaching government

interventions and control, to increased reliance on market

signals to allocate resources and form prices. In addition,

economy-wide macro-economic policy changes have been made

which can potentially influence agricultural incentives,

income, and output. Designed largely by the International

Monetary Fund (IMF) and the World Bank in consultation with







2

the Government of Jamaica, these reforms' are expected to

favor Jamaican agriculture under the presumption by the

Bretton Woods institutions that previous policies severely

discriminated against the sector. (This point is elaborated

in Chapter 2, section 2.4). This study pertains to the

impacts that these economic policy reforms have on

agricultural supply responses in Jamaica, as the

agricultural sector emerges from a heavily regulated sector

to one that is more open2 and liberal. This chapter

discusses the problems that this study addresses.

1.1 Background to Jamaica's Economic Reforms

For purposes of this study, the reform period is

defined as the years 1980-1999, while the pre-reform period

is 1962-1979. The year 1962 is sufficiently far back to

facilitate a meaningful analysis of economic policies and

conditions that led up to the dramatic changes in policies

in the reform period. The reform period marks a re-

orientation of economic policy. This change arose from the

stabilization programs of the IMF in Jamaica in the late

1970s. It gathered momentum in the 1980s and 1990s in the



I In this study, "policy reforms," "economic policy reforms," "economic
reforms," and "structural reforms," are used interchangeably.
2 Openness is defined here in terms of trade/GDP ratio. A high
trade/GDP ratio can coexist with high import tariffs, export subsidies,
and non-tariff barriers, signaling a non-liberal trade regime.







3

form of structural adjustment programs cum reforms for

economic liberalization, under the direction of the IMF,

the World Bank and other leading external funding agencies,

notably the Inter-American Development Bank (IDB) and the

United States Agency for International Development (USAID).

In effect, therefore, for the purposes of this study,

economic reforms refer to the policies stipulated in the

stabilization and structural adjustment programs which

Jamaica has been pursuing since the late 1970s.

When the People's National Party (PNP) took office in

1972 under Prime Minister Michael Manley, it was under

pressure to keep its election campaign promises to increase

real wages and government spending, and to reduce social

inequalities. The responses of the new administration to

this situation, coupled with subsequent external and

domestic events, were to plunge the economy into a

sustained recession and major disequilibrium. During the

previous administration under the Jamaica Labor Party

(JLP), the performance of the economy was impressive. GDP

growth rates were positive and relatively high (5.1 percent

per annum over the 1960-1970 period), inflation was less

than five percent per annum and the balance of payments

showed modest surpluses (Jefferson, 1972; IMF, 1998). Over

the 1970-1980 period, however, real GDP declined by -0.9







4

percent. In fact, with the exception of a negligible 0.7

percent GDP growth in 1978, negative growth rates were

recorded for all other six years between 1974 and 1980.

Similarly, in the productive sectors, agricultural value-

added growth declined from 6.7 percent in 1970-1972 to -0.4

percent in 1973-1980. For these same periods, declines in

growth rates were recorded for services from 9.2 percent to

-0.08 percent, manufacturing from 6.7 percent to -4.2

percent, construction from 6.0 to -10.6 percent, and mining

from 13.2 percent to 1.3 percent, respectively (Singh,

1995; IMF, 1998).

The external account reflected the production crisis

described above. Between 1972 and 1980, the accumulated

deficit on the balance of payments was US$679.2 million,

compared to an accumulated surplus of US$95 million between

1960 and 1971 (Jamaica, Bank of Jamaica, 1985). Further,

the deficit on the current account increased almost three-

fold from US$570.2 million in 1962-1970 to US$1,620.0

million in 1972-1980, and the capital account became

negative in 1977, signaling capital flight. At the same

time, the dwindling net capital inflows could not match the

current account deficit. This depleted international

reserves, which plummeted sharply from US$103.1 million in

1972 to minus US$461 million in 1980 (Sharpley, 1984). The







5

foreign exchange shortage harmed the economy given its

relative openness and dependence on foreign intermediate

imports.

Finally, the inflation rate, which was 4.2 percent per

annum over the 1960-1970 period, recorded double-digit

rates throughout the following decade. Domestic prices, as

measured by the consumer price index, rose sharply,

recording an annual increase of 25 percent and 47 percent

over the 1974-1975 and 1978-1979 periods respectively

(Sharpley, 1984). By the late 1970s economic conditions had

deteriorated to such an extent that, when combined with

manifest signs of social distress (such as a dramatic

increase in violent crimes and emigration of skilled

workers), they generated a sense of instability and crises

(Stephens and Stephens, 1986; Kaufman, 1985).

The crises indicated a need for policy changes, and it

appears that the government was thinking along those lines

as reflected in a Stand-by Agreement it negotiated with the

IMF just before the December 1976 elections. The Agreement

included, inter alia, a wage freeze, fiscal restraint and a

currency devaluation of 20 to 40 percent (Boyd, 1988).

However, in the December 1976 election the PNP was returned

to office and the new administration promptly rejected the

IMF Stand-by Agreement as being inconsistent with the







6

mandate obtained in the recent election. Instead, and as a

way of avoiding agreements with the IMF (Boyd, 1988), and

after significant slippages in the external and domestic

accounts (Sharpley, 1984), the government implemented its

own adjustment policy measures in January 1977. These

included (Boyd, 1988)

(1) The reversal of the previous wage indexation policy;

(2) Implementation of import and exchange controls, a

dual exchange rate system and foreign exchange

rationing;

(3) Suspension of foreign debts for 18 months; and,

(4) A search for funding from sympathetic governments.

However, it became increasingly clear to the

government that in order to attract foreign capital some

formal agreement with the IMF was necessary. In this

regard, the government signed three agreements with the IMF

between August 1977 and June 1979. The first was suspended

after four months for failure to meet the stipulated

quarterly tests. The second, signed in May 1978, was

pursued in all its aspects by the government for a year.

Although economic performance under this agreement was poor

(Sharpley, 1984; Boyd, 1988), it was re-negotiated in June

1979. However, the agreement collapsed in December 1979







7

after the government failed to meet the program's

performance criteria.

In 1980, in light of increasing social unrest,

continued slippages in the fiscal deficits and external

accounts, severe foreign exchange shortages, and rising

inflation (Sharpley, 1984; Boyd, 1988; Thomas, 1999), Prime

Minister Manley called a general election so that the

nation could decide on a path of economic development and

"what part the IMF should play; or whether it should play

any part at all" (Jamaica, API, Manley, 1980). Dubbed "the

IMF election", the October 1980 election was won by the

strongly pro-IMF JLP, led by Edward Seaga. Expectedly,

therefore, both the IMF and the government began

negotiations on funding and policies aimed at ameliorating

the country's economic and social crises.

The new administration relied heavily on funding from

external sources. Several loan agreements were negotiated

with the leading collaborating lending institutions, the

IMF, World Bank, IDB and USAID which have developed a

system of cross-conditionalities on their loans, given

their basic set of shared objectives on economic policies

and on Jamaica (Anderson and Witter, 1994).

The prescription by the IMF and the World Bank

(Fund/Bank) on how best to deal with the economic crisis in







8

Jamaica was a series of economic policy reforms. These

reforms went beyond the expenditure-switching and

expenditure-reducing policies which macroeconomic policy

would normally prescribe under these circumstances, to

include a built-in bias in favor of a more liberal economic

system. The latter aimed at, inter alia, fiscal discipline,

liberalizing the domestic market and the external trade

sector, privatizing of State Owned Enterprises (SOEs) and

other social services, and generally, greater reliance on

market signals to allocate resources. Over all, between

1981 and 1992, IMF financial support totaled US$1,036.9

million (under 10 agreements), and that of the World Bank

US$360.4 million (under 8 agreements).

1.2 Problem Statement and Study Justification

The cumulative effect of the reforms mentioned above

is that the Jamaican agricultural sector is now expected to

operate within an economic framework that differs vastly

from that of the 1960s and 1970s. Historically, the sector

has played an important role in the economy. Agricultural

output to GDP ratio is about eight percent, agricultural

food export in total export is approximately 20 percent,

and agriculture's share of the total labor force exceeds 26

percent (Davis et al., 1999). Given the importance of

agriculture in the economy, how the sector performs in this







9

new policy environment demands urgent attention. In this

study, agricultural sector performance is evaluated in

terms of crop supply responses. Quantitative analyses of

the impact of these reforms on Jamaican agricultural supply

responses are both scarce and generally not particularly

robust. In addition, the literature on similar economic

reforms in other countries suggests that the outcomes of

these reforms are ambiguous, which justifies more empirical

work (Khan, 1990).

Additionally, this study is justified on the following

grounds. First, the economic reforms have generated major

changes in the long-term prospects for the national economy

within which agriculture will operate. After two decades a

good set of data on these policy reforms is now available

which should facilitate empirical research on the impact of

these reforms on agricultural crop supply responses. This

evaluation can throw light on courses of action which

policy makers in Jamaica and elsewhere can consider as they

seek to reactivate and sustain long-term growth in the

agricultural sector.

Second, while a few studies have analyzed specific

aspects of the impact of the economic policy reforms on

Jamaican agriculture (Singh, 1995; Anderson and Witter,

1994; Newman and Le Franc, 1994; and Brown, 1994), what is







10

lacking is an over-all analytically rigorous assessment of

agricultural supply responses within the context of the new

policy framework as defined by the recent economic reforms.

Finally, analysis of the impact of these reforms on

agricultural supply responses is both timely and urgent in

light of an on-going debate. A number of writers in the

Caribbean and elsewhere have been advancing the view that

the kinds of economic reforms implemented in Jamaica and in

other countries have harmed agriculture (Anderson and

Witter, 1994; Green, 1989; Bates, 1989). In contrast a

recent series of studies sponsored by the World Bank

suggests that policy reforms similar to Jamaica's should

boost agriculture in countries which have traditionally

discriminated against the sector (Schiff and Vald6s, 1992a;

Krueger, 1992).

1.3 Objectives of the study

The general objective of this research is to evaluate

the impact of economic reforms on selected agricultural

crop supply responses in Jamaica over the 1980-1999 period.

The specific objectives are to provide

(1) A statistical evaluation of the performance of the

agricultural sector in the pre-reform (1962-1979)

and reform (1980-1999) periods.







11

(2) A descriptive, diagnostic and statistical analysis

of the economic reforms that have taken place over

the past two decades within the agricultural sector.

(3) An econometric (modeling) evaluation of selected

agricultural crop supply responses over the 1962-

1999 period, with the number and types of crops

chosen for study to be determined by data

availability.

(4) A comparative analysis of agricultural crop supply

responses in the reform period with a counter-

factual, the latter defined as crop supply responses

that would have resulted in the absence of the

reforms.

1.4 Hypotheses

The following hypotheses are advanced in the study:

(1) Structural reforms significantly improve agri-

cultural supply responses.

(2) Sustained improvements in agricultural supply

responses require further broadening and deepening

of the reform process.

1.5 Conceptual Issues

Agriculture features prominently in policy discussions

when (1) it is believed that agricultural policies and

institutions help precipitate an economic problem which







12

policy makers are addressing; and (2) when agriculture is

an important contributor to employment, GDP, export

earnings, domestic food supply, and revenue for the

government (Binswanger and Deininger, 1997). Various

publications by the government of Jamaica suggest that both

of these factors have featured prominently in the recent

reforms, while (2) was a major consideration in the pre-

reform (1962-1979) period. Consequently, the conceptual-

ization is based on two perspectives that are believed to

have influenced the design of the policy reforms:

(1) The Jamaican government perspective. From numerous

government publications, it would appear that this

perspective is clustered around three main goals:

(a) increasing food supply; (b) developing rural

areas; and (c) increasing agriculture's contribution

to the over-all economy, through, inter alia,

employment, contribution to GDP, and export earn-

ings; and,

(2) The IMF/World Bank perspective, whose focus on the

agricultural sector clusters around (a) getting

prices right; (b) improving efficiency; and (c)

rationalizing public expenditure in the agricultural

sector.







13

These two perspectives are not mutually exclusive. On

the contrary, the choice of policies in a particular

program package results from extensive discussions between

officials of the IMF/World Bank and the Jamaican

government. The program policy mix therefore reflects the

particular economic situation in the country as well as the

preferences of the government (Khan, 1990). Despite this, a

review of the literature on Jamaican agricultural policy

formation suggests that since the late 1970s the influence

of the IMF/World Bank perspective has exceeded the Jamaican

government's.

Given the two perspectives mentioned above, and a

review of the content of the reforms since the late 1970s,

the evidence suggests that the reforms have focused on

three inter-related issues of relevance to the agricultural

sector: (i) agricultural (inter-sectoral) terms of trade;

(ii) agricultural growth, its adjustment, and supply

response; and (iii) efficiency in the agricultural sector

(Nallari, 1992; Singh, 1995; World Bank, 1996).

Figure 1.1 conceptualizes the Jamaican agricultural

sector, dividing the factors impacting agriculture into

external (e.g., world prices) and internal. The latter can

be separated into those that are exogenous (weather,

terrain) and those that are policy induced. Another useful












































Policy Instruets:
P1. Agriculture Projects:
infrastructure, land
development, mechanization,
crop improvement
P2. Rural industries
P3. Monetary Policies: interest
rates, exchange rates.
P4. Tax Policies: income,
indirect, customs duties.
P5. Public consumption, rural
invest.
P6. Rural health, education,
welfare.
P7. Import/Export controls,
tariffs, subsidies.
P8. Price controls, input
subsidies.
P9. Agriculture credit
PlO.Marketing: infrastructure,
information.


Agriculture Sector
Performance Indicators:
1. Economic Outcomes
(a) quantity flows
supply response
agri. in GDP
agri. growth rate
(b) agri. multi-factor
productivity
(c) relative prices
(TOT, RER).
2. Accumulation
Indicators
coefficient of
investment
capital formation
agri. employment
agri. tax revenues
rural nutrition
farm incomes


Figure 1.1:
Sector.


Conceptualization of the Jamaican Agricultural


T I I







15

distinction is between prices and non-price factors

(including exogenous shocks) that influence agricultural

output and income. At the micro level, the price variables

include prices of the particular crops, prices of

alternative crops, input prices, and the general price

level. Government policies exercise both direct and

indirect influence on agricultural prices and thus on

agricultural performance. Direct (sectoral) policies such

as price ceilings, guaranteed prices and trade

taxes/subsidies provide incentives to shift resources among

crops and sectors of the economy (Binswanger, 1989).

Government policies also can influence output indirectly

through macroeconomic (or economy-wide) policies. The most

critical components in this regard are fiscal policies and

exchange rate policies (Mamingi, 1997).

The terms of trade as a policy variable turns on the

hypothesis that if all prices were determined in markets,

and effective rates of taxation were equalized across

commodities, then agriculture's supply responses, growth,

income, and contribution to GDP would be higher compared to

situations of negative effective protection of the sector

(Schiff and Vald~s, 1992b). A negative effective protection

of agriculture arises when:









(1.1)i Pf)+ 5 C,] < o
P

where P is a general price deflator, Pi denotes the producer

price of crop i, Pf is the free-market price for crop i,

and 8 is a subsidy to producers as a proportion of cost, Ci.

For many developing countries, agricultural pricing

policies have consistently kept Pi below P and subsidies

have not sufficiently counterbalanced this disparity

(Schiff and Vald6s, 1992b). Given severe budgetary

constraints, to achieve at least zero effective protection,

the policy implication of (1.1) is to raise the real


P


eliminating the disparity between Pi and P. In effect,

improving agriculture's terms of trade.

The exchange rate sets an upper limit on agricultural

export earnings and, when combined with input taxes and

subsidies, affects input prices and competing agricultural

imports. Changes in the real exchange rate can affect

agricultural output and growth by altering the terms of

trade between agriculture and non-agricultural sectors.

Further, policies that lead to over-valuation of the

exchange rate can adversely affect export crops, encourage







17

rent-seeking activities and generate unproductive uses of

resources (Jaeger, 1991).

In addition to the price policies, non-price factors

constitute an important influence on agricultural output.

These factors reflect the material conditions of production

in the agricultural sector. Some of these factors include,

inter alia, government expenditure and investment in the

agricultural sector, construction of rural infrastructure

(roads, drainage and irrigation works), extension services,

rural credit institutions, and dissemination of relevant

scientific information to farmers. The aim of agricultural

sector-specific programs is to find a mix of policies for

increasing efficiency and productivity in the sector. It is

assumed that these two outcomes are necessary (if not

sufficient) conditions for increasing agricultural output

and growth by reducing costs of production and/or

increasing product prices. These policies focus on using

public investment in agriculture efficiently, reducing

marketing and transport bottlenecks, improving agricultural

extension services, health and education (capital formation

in agriculture), rationalizing input prices, and enhancing

the efficiency of parastatals in the agriculture sector.

Behrman (1990) has advanced an important perspective

on the channels through which policies affect performance







18

indicators. He argues that analysis of the impact of policy

reforms on performance indicators must explicitly consider

the conduits through which the effects of the policies are

transmitted to the observed (or desired) outcome. These

"meso-level" variables, identified as markets (product,

input and financial) and infrastructure (economic and

social), are the interface between the policies (sector-

specific and economy-wide) and targets. In Figure 1.1, for

example, various policies, Pi, i = 1,2,3,...10, are

identified. Some are macroeconomic (e.g., monetary policies

and exchange rate adjustments), whereas others are sector

specific (e.g., agriculture credit, tariff reductions in

the trade sector and elimination of price controls in

agriculture). These policies are then combined to achieve

specific targets, either at the sectoral or macroeconomic

levels.

Behrman (1990) emphasizes, however, that the meso-

level variables condition the effectiveness of policies on

the target variables and that analyses of policy impacts

must also evaluate how policies have influenced these meso-

level variables. For example, if poor transportation, lack

of effective irrigation, or in-efficient research and

extension services restrain farmers' responses to higher







19

prices, then improving these meso-level variables may do

more for the farmer than price increases (Chhibber, 1989).

The influence of the factors mentioned above on

agricultural supply responses has been well documented in

the empirical literature. However, the recent economic

reforms have had an enduring effect on these factors. This

points to an important issue raised in the literature,

viz., how to measure the magnitude of reforms (IDB, 1997).

Economic statistics such as exchange rate differentials,

inflation rates, tax changes and so on deal with outcomes

rather than the policies that gave rise to them. In order

to address this issue, structural policy indexes have been

constructed by Lora (1997) and others.

The index by Lora (1997) was constructed for twenty

countries in Latin America and the Caribbean. This index

seeks to measure the extent of market freedom accorded to

economic policies in areas of trade, tax, finance,

privatization and labor. In each of these areas indices of

market freedom are identified. For example, in trade policy

the indices are average tariffs and tariff spreads; in tax

policy the indices include, inter alia, tax rates on

companies and on individuals; and on financial policy,

indices include freedom of interest rates on deposits,

loans, reserves on bank deposits, etc. (IDB, 1997). The







20
structural policy index is a simple average of the indices

in the five areas. The index can range from 0 to 1, based

on the worst and best observations respectively, on market

freedom in the country. Further, the index is an important

indicator of the extent to which countries are departing

from past ways of operating their economies. Jamaica's

structural policy index has shown continuous movement

towards market freedom, increasing from 0.426 in 1985 to

0.684 in 1995. These index values are higher than the

average reported for the twenty Latin American and

Caribbean countries in the sample (Lora, 1997).

1.6 Procedural and Methodological Considerations

In approaching the general and specific objectives of

the study, critical analysis of the Jamaican agricultural

sector's performance is first undertaken. To achieve the

general objectives and, more specifically, objectives (2),

(3) and (4), requires four specific tasks. The first task

is to identify and evaluate the structural reforms

undertaken since the late 1970s. Both the stabilization and

the structural reform policies are evaluated according to

their (1) theoretical bases; (2) breath of vision; and (3)

logical consistency. Second, supply response models are

developed and estimated. Finally, specific objective (4) is







21

addressed within the context of simulating an alternative

path to that of the reforms.

Any appraisal of the reforms must compare their

impacts not to the pre-reform indicators but rather to some

specified, hypothetical alternative. This requires some

simulation exercises. The idea here is to generate

simulated time series over the reform period for the

performance indicators. The simulated series then

constitute the counterfactual to which the actual outcomes

in the reform period are compared. For the counterfactuals,

at least three scenarios appear to be logical extensions.

Scenario 1 assumes that the policies pursued over the pre-

reform period continued into the reform period, i.e., that

the reforms over the 1980-1999 period were not instituted.

Scenario 2 assumes policies based on the Jamaican

government's critique of the IMF/World Bank programs were

implemented. Finally, Scenario 3 assumes that IMF/World

Bank assistance came with no conditionalities. These

components form the basis of the data series for the

simulation exercise.

The three scenarios appear logical and plausible

options given (1) the initial defiance by the government of

the mandated reforms in the late 1970s; (2) the frequent

failures to meet conditionality tests because the reforms







22

were not implemented; and (3) government's own protest-

ations over the years of what are considered "acceptable"

reforms. While numerous instances of disagreements exist

between the Jamaican government and the IMF/World bank with

respect to appropriate reform policies, the clearest

statement to this effect is captured in the following

quotation:

Even though lender and borrower therefore shared the
common concern regarding the need for economic reform,
there is still much debate about appropriate social
policies and about the specific effects of
manipulating major economic variables such as exchange
rates and interest rates. This was illustrated by the
lengthy and tortuous negotiations between the
government of Jamaica and the IDB and World Bank on
the 1989-90 Agricultural Sector Adjustment Loan.
(Anderson and Witter, 1994, p.14)


1.7 Summary

It is generally believed that developing countries

have historically discriminated against their agricultural

sectors in favor of industrial development. It is further

felt that policies that reverse that discrimination should

boost agricultural output and income. Over the past two

decades far-reaching economic reforms have been undertaken

in Jamaica leading to a new economic framework within which

the agricultural sector must now operate. This study seeks

to assess the impact of these reforms on agriculture crop

supply responses, an undertaking that is both timely and







23

urgent given the importance of the sector in the economy

and the absence of rigorous analytical studies on the

Jamaican experience with these reforms.















CHAPTER 2
ECONOMIC STRUCTURE, GROWTH, AND POLICY REFORMS IN THE
JAMAICAN ECONOMY, 1962-1999


It is useful, at the outset, to situate the discussions

in this chapter against the backdrop of three distinct

phases of economic policy that characterize the economic

history of Jamaica over the period 1962-1999. In the first

phase, 1962-1972, the Jamaica Labor Party (JLP) emphasized

free markets. However, Bonnick (1984) argues that despite

the rhetoric of economic liberalism, in reality the

government pursued an import-substitution (IS) strategy

based on protectionism, trade restrictions, and price

controls.

In the second phase, 1972-1980, the People's National

Party (PNP) under Prime Minister Michael Manley pursued

economic populism and state directed control (dirigisme) in

an effort to build democratic socialism. This phase was

marked by extensive government intervention, which included

nationalization of major industries, price controls, and

subsidization of basic foods and some agricultural imported

inputs. These policies made significant demands on national

resources and created a large bureaucratic economic

24







25

structure. Consequently, the government was unable to

respond to the crisis in the world economy in the mid-

1970s, and was forced to approach the IMF for stabilization

funding in 1977.

The third phase, 1980-present, is directly linked to

the policies of the previous phases, particularly the 1977

IMF stabilization funding. Phase three can be viewed as

having two sub-phases. The first is the return of the JLP

to government in 1980-1989 and the implementation of

various Fund/Bank stabilization and structural adjustment

programs. The second, 1989-present, marks the return to

government by the PNP, the continuation of the market led

policies of the preceding JLP administration, but more

importantly, the intensification of the country's commit-

ment to liberalization of the economy. The rest of this

chapter is organized as follows: Section 2.1 provides an

overview of the structure of the economy and its growth

performance. Section 2.2 highlights trends and policies in

the agriculture sector. Sector 2.3 describes the economic

reforms, and Section 2.4 provides a summary of issues

raised in this chapter.









2.1 Structure of the Economy and Economic Growth:
An Overview

The relative contribution of economic sectors to GDP in

Jamaica has not changed much over the past two decades. The

data presented in Table 2.1 and Figure 2.1 show that

agriculture's share has been fairly stable, ranging from an

average of 8.4 percent over the period 1969-1979 to 8.0

percent over 1990-1998. Although agricultural contribution

to GDP is lower than that of the other sectors, these

comparatively small percentage contributions are deceptive.

Davis et al. (1999) have shown that the sector plays an

important part in the country's employment, food

production, and foreign exchange earnings.

The industrial sector, which includes mining,

manufacturing and construction, contributed on average 39.1

percent to GDP in the period 1969-1979 but declined to 37.3

percent in 1990-1998. The manufacturing sub-sector also

shows a decline from 18.4 percent in 1969-1979 to 16.3

percent in 1990-1998. Finally, services constitute the

largest sector of the Jamaican economy, accounting for over

50 percent of GDP over the period 1969-1998. Its

contribution to real GDP has increased from 48.5 percent in

1969-1979 to 54.7 percent in 1990-1998.









Table 2.1: Sectoral Contribution to Real Gross Domestic
Product (Period Averages, Percentage).


1969-79 1980-89 1990-98 1969-98
Agriculture 8.4 6.4 8.0 7.6
Industry 39.1 34.5 37.3 37.0
Manufacturing 18.4 20.9 16.3 17.1
Services 48.5 59.1 54.7 53.9


Source: Computed using data from Jamaica, Pannlng
Institute of Jamaica (various issues).
Note: The 1998 data used were preliminary estimates.


1973 1977 1981 1985
Years 1969-1998


1989 1993 1997


--Real GDP ---Agri. -Indus. -*-Serv.

Figure 2.1: GDP and Sectoral Contribution to GDP in
Constant 1995 Dollars.



Real sectoral and GDP growth rates are estimated and

shown in Table 2.2. The estimation uses a log-linear model,

Yt = ao0 + P*Time + Et and corrected for autocorrelation

whenever it exists. Over the 1969-1998 period, growth rates

were positive but consistently low, while sub-period growth

reveal mixed results. GDP growth rates were just over two


2000

1600

1200

800

400

0
19


69









Table 2.2: GDP and Sectoral Growth (1995=100)
(Percentage).
1969-79 1980-89 1990-98 1969-98
GDP 2.3a 2.4b -0.2 I. 'M
Agriculture -0.3 1.1 2.3b 0.6b
Industry 0.9 4_._3_ 3 -3.8a 0.8a
Services 4.6a 0.1 2.4b 1.6a
Source: Computed using data from Jamaica, Planning
Institute of Jamaica (various issues).
Note: The 1998 data used were preliminary estimates.
aI b indicate statistical significance at the five
and 10 percent levels, respectively.


percent in the 1969-1979 and 1980-1989 periods. Services

growth was reduced from 4.6 percent over 1969-1979 to 2.4

percent over 1990-1998, and was insignificant over the

1980-1989 period. Industrial growth, which was 4.3 percent

in the 1980-1989 period, declined significantly to -3.8

percent over the 1990-1998 period. Agriculture growth over

the 1990-1998 period was 2.3 percent, compared to its

insignificant growth in the two previous sub-periods. Over

the entire 1969-1998 period agriculture and industry

recorded less than one percent growth, while GDP and

services grew just over one percent, respectively.

These periodic growth rates mask the highly volatile

annual growth rates in these economic series as shown in

Figures 2.2 and 2.3. Over the period 1969-1998, annual

growth rates for GDP ranged between -9.0 and 13.0 percent,

and were negative in 12 of the 30 years. For the











15

10

tit 5

e 0
0
54 -5

10

-15
1970 1974 1978 1982 1986 1990 1994 1998
Years 1969-98

---GDP Growth

Figure 2.2: GDP Growth (Percentage Change Over Previous
Year, 1995-100).


40
30 A
20 A

4 10
00
54 -10
0 -20

-30
-40

1970 1974 1978 1982 1986 1990 1994 1998
Years 1969-98
----Agri. Growth mInd. Growth ----Ser. Growth

Figure 2.3: Growth of Total Agriculture, Industry and
Services (Percentage Change over Previous Year, 1995=100).




1969-1998 period, agriculture growth rates ranged between

-12.6 and 29.3 percent, and were negative in 15 years.







30

Similar annual volatilities characterize the industrial and

services growth rate series.

2.2 Output Trends and Policies in
Jamaican Agriculture

The growth rates of volume indexes for total

agriculture and broad aggregates (food, crops, livestock

and cereals) and graphs for these series are shown in Table

2.3 and Figures 2.4 and 2.5. With the exception of the

cereals index, which declined over 1971-1998 at an annual

rate of -3.2 percent, the other indexes grew but at less

than two percent over the 1971-1998 period.



Table 2.3: Growth Rates of Total Agricultural and
Broad Agricultural Aggregate Indexes.
1971-79 1980-89 1990-98 1971-98
Total Agriculture 1.6a 1.7 1.9a 1.7a
Food 1.6a 1.6 1. 9a 1.7a
Crops 2.6lb 1.5a -3.6 1.7a
Livestock 1.6a -16.5 0.7 1.3a
Cereals 16.9a -9.0 -4.5- -3.2]
Source: Computed using data from Food and Agriculture
Organization, FAO Production Yearbook (various issues).
aI b, indicate statistical significance at five and ten
percent levels, respectively.



The composition of agricultural output is shown in

Table 2.4. Export agriculture, domestic agriculture, and

livestock, forestry and fishing constitute the aggregative

components of agriculture in GDP. The data in Table 2.5 and

the graph in Figure 2.6 show that domestic agriculture











o 120

90
I i


60
60

30



0 1971 1976 1981 1986 1991 1996
0
Years 1971-98

-*-Total Agriculture

Figure 2.4: Total Agricultural Output Index (1995=100).


Figure 2.5: Output
Aggregates (1995=100).


Indexes


for Broad Agricultural


250
0
200

150

100

H 50
4J

0
o 1971 1976 1981 1986 1991 1996
Years 1971-1998

-Crops a.Livestock -nCereal --Food









Table 2.4: Composition of Agricultural Output
1995=100). Selected Years.


Source: uamaica, Pannlng
issues).


instLIUte 01 Jamaica


Table 2.5: Agriculture Sub-sectors as a Percentage of Total
Agriculture (Period Averaqes).


Source: Computed using data from Jamaica,
Institute of Jamaica (various issues).
Note: Figures in parentheses are standard deviation.


Planning


(J$M,


kVdiOUs


Agriculture Sub-sectors 1969-79 1980-89 1990-98 1969-98
23.47 15.95 11.21 7.29
Export Agriculture (5.50) (2.24) (1.46) (6.26)
44.78 57.21 68.79 56.13
Domestic Agriculture (5.32) (6.35) (3.62) (11.17)
Livestock, Forestry & 31.73 26.85 20.03 26.59
Fishing (1.35) (4.49) (2.50) (5.65)


Agriculture
and Sub-
sectors 1969 1975 1980 1985 1990 1995 1998
Total 95.9 105.9 101.3 72.2 104.4 154.4 120.4
Agriculture
Export 34.3 25.9 14.8 13.9 13.6 15.4 12.3
Agriculture
Sugar 22.6 17.7 9.9 7.8 9.2 9.4 7.9
Cane
Other 11.6 8.2 4.8 6.1 4.4 5.9 4.4
Main
Exports
Domestic 33.0 47.1 56.9 38.0 66.2 114.5 82.8
Agriculture
Root 14.4 26.9 25.3 14.6 35.1 62.8 42.7
Crops
Other 18.6 20.2 31.7 23.4 31.1 51.7 40.1
Primary
Products
Livestock, 28.6 32.9 29.5 20.3 24.6 24.5 25.3
Forestry &
Fishing






















0 1 I- i . 1 -
1969 1973 1977 1981 1985 1989 1993 1997
Years 1969-98
s-Export Agriculture
-n-Domestic Agriculture
---Livestock, Fishing & Forestry

Figure 2.6: Sub-sectors as a Proportion of Agriculture.



constitutes the largest proportion of total agriculture in

the 1969-1998 period and other sub-periods. In 1969-1979

the domestic agriculture sub-sector averaged 44.8 percent

in total agriculture and increased to 68.8 percent in 1990-

1998. Both of the other two sub-sectors, export agriculture

and livestock, forestry and fishing, have declined over the

period 1990-1998 compared to the two previous decades. In

terms of growth rates, Table 2.6 shows that domestic

agriculture and its sub-groups are the only sub-sectors

within the agri-sub-sector that have positive growth rates

over the 1969-1998 period. This has compensated somewhat

for the negative and low growth in the other agriculture

sub-sectors.


80


60


S40


20







34

Table 2.6: Growth Rates of Agricultural Components
(Percentage).
Agriculture Sub-sectors 1969-79 1980-89 1990-98 1969-98
Export Agriculture -4.4a -I. 3D -T. 1 -2. a
Sugar -4 2a -2.6a -5.4a -2.0a
Other main exports -4.6a 1.3 1.0 -2.7a
Domestic Agriculture 1.8 11.2a 25.6a 2.6a
Root crops -2.7 19.7a -1.2b 2.6b
Other domestic crops 4.0a 3.4b -145 2.7a
Livestock, forestry, & -1.5 -3.3a 0.1 -1.7a
fishing II I
Source: Computed using data from Jamaica, Planning
Institute of Jamaica (various issues).
a, b, indicate statistical significance at the five and ten
percent levels, respectively.



The stagnation of agriculture in Jamaica, which can be

inferred from the growth rates in Table 2.6 reflects, to a

large degree, the state of the agricultural sectors in many

less developing countries (LDCs). A plausible explanation

for this, is that the development literature in the 1950s

and 1960s viewed agriculture as a static sector, from which

resources could be shifted to promote industry, considered

as the dynamic sector. To a large extent, this view has

influenced the kinds of agricultural policies that have

been pursued in the past by LDCs. In this regard, Schiff

and Vald~s (1992a, p.59) state:

In many developing countries, the high rate of
agricultural taxation has been part of an explicit or
implicit policy of industrialization-led growth,
justified in part by the belief that industry was the
dynamic sector while agriculture was static and not
very responsive to incentives. That means that
economic growth could be accelerated by shifting







35

resources from agriculture to industry, by taxing
agriculture directly, and by protecting the industrial
sector.

Many governments in the developing countries have

intervened in their agricultural sectors, both directly

through agricultural sector policies, and indirectly,

through economy-wide policies such as industrial

protection. Direct interventions take numerous forms. Some

of these include procurement measures (e.g., government

marketing boards as sole buyers of agricultural output, and

suppliers of major agricultural input); quotas and direct

taxation on various agricultural export crops; subsidies on

farm credit and farm inputs; and quantitative restrictions

and tariffs on imported agricultural imports. While some of

the direct interventions have benefited agricultural

producers, some are tantamount to an implicit tax on

agriculture, depressing farmgate prices and farm incomes

below levels that would otherwise prevail (Schiff and

Vald6s, 1992a). Indirect forms of interventions affect

agricultural production incentives via macroeconomic

policies (e.g., overvaluation of the exchange rate) and

industrial protection policies (Krueger, 1992).

Various measures have been employed by the Jamaican

government to extract surpluses from the agricultural

sector. These include taxing agricultural exports,







36

controlling farmgate prices by the state marketing boards,

over valuating exchange rates and reducing internal

agricultural terms of trade relative to manufacturing.

Studies by Gafar (1980, 1997) and Pollard and Graham (1985)

strongly support this proposition. Table 2.7 shows that



Table 2.7: Average Rates of Growth of Farmgate and F.O.B.
Prices, Output and the Nominal Protection Coefficient (NPC)
for the Period 1970-1978, Jamaica.
Growth Rates (%)1970-78 a
Farmgate F.O.B. Nominal Protection
Commodities Prices Prices Output Coefficient b
Sugar Cane -1.25 4.13 -2.90 0.82
Banana 5.06 7.77 -7.43 0.77
Cocoa 1.64 14.06 -0.54 0.72
Coffee 9.80 8.34 2.02 0.61
Coconut 7.06 4.67 -21.00 n.a.
a Adapted from Pollard and Graham (1985), Table 2.
b Based on the data in Pollard and Graham (1985), Table 4
for 1970-1979. The NPC is defined as the ratio of farmgate
prices (PF) to F.O.B. prices (Pw) received minus marketing
and processing costs (C): NPC = PF / (Pw C).



over the 1970-1978 period, the nominal protection

coefficient (NPC), for Jamaican agriculture was less than

one, indicating the extent to which the sector was taxed.

For example, a NPC of 0.82 means that the commodity is

taxed at a rate of 18 percent. Subtracting one from the NPC

gives the nominal protection rate, NPR, which, according to

Table 2.7, is negative, suggesting that producers of the

crops reported were not supported, but were taxed instead.







37

It should be emphasized also, that developing countries

have a tendency to overvalue their domestic currencies, so

that the official exchange rates used to calculate the NPC

overstate internal prices. Hence government taxation of

agriculture is even greater than is usually captured by the

NPC (Gardner, 1987).

More recent data compiled for this study also support

the proposition that Jamaican agriculture has been taxed.

Table 2.8 reports on the net barter and income terms of

trade between agriculture and manufacturing for Jamaica.

The net barter terms of trade, N, is defined here as


N = L, where PA and PM are the price indexes of agriculture


and manufacturing, respectively. Values of N greater than

one indicate that prices of agricultural commodities have

risen relative to those in manufacturing (Tsakok, 1990).

The estimates in Table 2.8 reveal that over the 1966-1978

and 1989-1999 periods, the net barter terms of trade were

unfavorable to Jamaican agriculture, but were favorable

over the decade 1979-1988.

Although the net barter terms of trade may move

against agriculture, the sector can still increase its

purchasing power if agricultural output increases pro-

portionately more than the decrease in agricultural prices.







38

The purchasing power of agriculture is captured in the net


income terms of trade, I, estimated as: I = PA x QAI where QA
PM

is the agricultural output index. The index I is therefore

the net barter terms of trade adjusted for marketed

amounts. Increases in I indicate a rising purchasing power

of agriculture for buying manufactured goods (Tsakok,

1990). The data in Table 2.8 suggest that agriculture's

ability to purchase manufactured goods have peaked in the

1979-1988 period.



Table 2.8: Not Barter Terms of Trade and Agricultural
Income Terms of Trade (1980=100; Period Averages).
1966-78 1979-88 1989-98
Net Barter Terms of Trade 0.60 1.39 0.86
Income Terms of Trade 58.20 159.18 134.03
Source: Compiled by author.



2.3 The Economic Reforms

After the late 1970s, Jamaica undertook major changes

in economic policies, both sector-specific and economy-

wide. These policy changes have been an integral part of

the conditionalities usually attached to program packages

by external funding agencies. The reforms vary in

intensity, both inter-temporally and in terms of the areas

in which policy reforms were enacted, such as foreign

trade, taxation, and liberalization measures. Underlying







39

the reforms has been a conviction by funding agencies that

freer markets allow for a more productive and efficient use

of scarce resources, a condition not necessarily accepted

uncritically by the Jamaican government. Nevertheless, a

number of market restrictions were eliminated, regulations

deemed necessary were simplified and made more transparent,

and private enterprises were encouraged. The most

conspicuous reforms were the liberalization efforts to

facilitate foreign trade and financing activities through

exchange rate and tariff adjustments.

The reform period, 1980-1999, has been punctuated by

successive IMF/World Bank stabilization and structural

adjustment programs, as well as programs financed by other

international funding agencies. The stabilization and

structural adjustment programs of the IMF and the World

Bank for Jamaica were developed as programs of policy

reforms, aimed at reversing, within a few years, the

economic crisis in the country. Three facets of this crisis

emphasized in the IMF/World Bank literature are structural

factors, external factors and poor domestic policies,

including an anti-agricultural bias in pricing policies.

However, it is a matter of public record that these

IMF/World Bank reforms have been resisted by the Jamaican

government at various times, and have been hotly debated in







40

the popular media regarding the social impacts of

externally imposed structural adjustment programs.

Despite this apparent dissatisfaction, the government

has utilized operational policies along the lines of those

prescribed by the Bretton Woods institutions. While in

principle the early reforms were aimed at stabilizing the

economy, from the outset there was a built-in bias in the

policies for a more liberalized economic system. For

example, the three year Extended Fund Facility (EFF), which

the Jamaican government signed with the IMF in May 1978,

contained specific conditions for exchange rate devaluation

as well as the liberalization of domestic prices (Boyd,

1988). More extensive economic reforms were advanced by the

new administration under Prime Minister Edward Seaga who

held office from 1980-1989.

Under the 1982 and 1983 structural adjustment

programs, quantitative restrictions (QR), on trade were

replaced by tariff equivalents, and by 1984 all QRs were

eliminated. Exchange rate devaluation was pursued in 1984-

1985, followed by the unification of the official and

parallel rates. At the same time, the government began to

reduce the public sector via the divestment of state owned

enterprises (SOEs).







41

The 1990s witnessed the most protracted reforms aimed

at deregulation and liberalization of the Jamaican economy.

The PNP, which took office in 1989 under Prime Minister

Michael Manley, had campaigned in the general elections on

a platform similar to the JLP, vowing to continue the JLP's

market oriented policies. This was an opportunity for a

fresh departure for the PNP, which was noted for its

previous support for democratic socialism and extensive

government intervention. Garrity (1996, p.52) argues that

...the Manley-led government made the decision in 1990
to truly embrace the market and proceed with economic
liberalization reforms. For Manley... [t]o continue the
reforms of the previous JLP government would not be
sufficient to bring about the needed structural
changes, nor were there sufficient resources to
continue the previous reforms....

Based on the limited available options, Manley's

commitment to the liberalization process represented a

turning point in state-society relations in Jamaica. In

particular, the administration sought an accommodative

pattern of governance between state and social actors by

energizing the private sector. In this sense, the

liberalization process can be viewed as "state-sponsored

disengagement from a command role in the economy ... and the

creation of an enabling environment for private sector-led

growth and development." (Garrity, 1996, p.52)







42

Against this background, the sectoral adjustment loan,

which the government signed with the World Bank in 1989,

focused on liberalizing the agriculture sector along the

following lines:

(1) Deregulation of the coffee and cocoa boards by 1990;

(2) Shifts in government focus from production and

markets to support services and infrastructure;

(3) Continuation of the divestment of agro-enterprises

(e.g., sugar);

(4) Elimination of subsidies on agricultural credit;

(5) Reduction of tariffs; and

(6) Elimination of the Generalized Food Subsidies;

As a result, by 1991 the generalized food subsidies

were eliminated, and complete liberalization of the

exchange market and credit interest rate were achieved.

Consequently, the overall liberalization process, since the

first agreement with the IMF in 1977, opened markets to

more competition and reduced the role of the public sector.

As would be expected, the conditions that accompanied

the loans from the Bretton Woods Institutions involved

reforms at both the macroeconomic and the sectoral levels.

Figure 2.7 is a schematic presentation of the kinds of

economic reforms and a partial list of specific tasks

undertaken since the late 1970s. For convenience, the





























Domestic Fiscal Monetary/Fin. Trade Other
Deregulation Reforms Reforms Liberalization Reforms


UL goVL. 01 SUDSl].1es k ]rea.lon
assets on interest, Agriculti
New pricing loans and Credit Bi
system for food and Nati
coffee, Introduction DevelopmE
cocoa, of Bank
citrus and Generalized
pimento and Consumption
more Tax
appropriate
sharing of
export
earnings





Figure 2.7: Selected Economic
1977-98.


Policy Reforms in Jamaica,







44

policy reforms are organized into five blocks: (1) Domestic

Deregulation; (2) Fiscal Reforms; (3) Monetary/Financial

Reforms; (4) Trade Liberalization and (5) Other Reforms.

Within each block, specific tasks are listed, reflecting

implicitly and explicitly policy changes, which seek to

enhance the role of market forces, encourage private sector

initiatives and reduce government regulation and

intervention in the economy. It is interesting to note that

in spite of intense criticisms regarding the reforms' lack

of success in producing the economic recovery predicted by

its proponents, the trend of economic reforms has not been

reversed in periods of economic stress. Such periods have

instead been met with the broadening and deepening of

reform efforts.

Table 2.9 shows selected economic statistics in the

pre-reform and reform periods in Jamaica. In the area of

trade, average tariffs in 1986 were 56 percent but fell to

11 percent in 1995. Exchange rate systems have been an

important policy instrument in Jamaica to establish

restrictions on capital outflows and restrictions for

repatriating export revenues and foreign exchange.

Following the exchange rate liberalization, many of these

restrictions have been dismantled. As evidence of the

process of exchange rate unification and deregulation,







45

Table 2.9 shows that the exchange rate differential (i.e.,

the difference between the average market price for foreign

exchange--inclusive of transaction costs and exchange rate

taxes--and the official rate) was 25 percent in 1986 but

fell to 5 percent in 1995.



Table 2.9: Outcomes of Economic Reform Policies in Jamaica,
1986 vs. 1995.
Structural
Maximum Tax Rate Policy
Tariff Exchange
Reduction Differential Companies Individuals Index, I
Net aver.
Tariffs % Percent Percent Percent 0 198611995 1986 1995 1986 11995 1986 1995 1985 1995
56 JF 25 5 45 32 50 25 0.402 0.684
Source: Adapted from IDB (1997).



In the area of tax reforms, Jamaica adopted various

changes aimed at administrative simplicity and ease of tax

collection. Taxes for companies were reduced from 45

percent in 1986 to 32 percent in 1995. The maximum taxes

for individuals were reduced from 50 percent to 25 percent

over the same period. Finally, the structural policy index,

which is a summary statistic of the extent to which market

forces are allowed to operate in the economy, increased

from 0.402 in 1985 to 0.684 in 1995.









2.4 Summary

The evidence presented in this chapter suggests that

the direction of change of economic structure of the

Jamaican economy that began around 1980 continued over the

past two decades. Over the period 1966-1998, growth rates

of GDP and economic sectors were low but positive. With few

exceptions, sectoral growth rates in the reform period,

1980-1999, were higher than in the pre-reform (1962-1979)

period.

Some structural change within the agricultural sector

was discerned from the data. In particular, export

agriculture declined relative to other agri-sub-sectors.

Domestic agriculture recorded impressive growth, and it is

this agri-sub-sector's overall growth performance that

partially compensated for the negative and low growth in

the other agri-sub-sectors. Significant economic reforms

were implemented, and analysis of their impact on

agriculture will be undertaken in a later chapter.















CHAPTER 3
METHODOLOGICAL AND EMPIRICAL ISSUES IN ECONOMIC
REFORMS AND SUPPLY RESPONSE


The purpose of this chapter is four-fold. First, it

reviews the methods that have been used in the literature

to analyze the impact of economic reforms in developing

countries. Second, it reviews the literature on supply

response analysis. Third, it develops a crop supply

response model for Jamaica; and finally, it presents the

data sources for the analysis.

3.1 Review of the Economic Reform Literature

In this review of the economic reform literature two

sets of issues are addressed. First, attention is directed

at the way the economic reforms are conceptualized. Second,

the analytical methods used to evaluate the impact of the

reforms are examined.

One of the most challenging problems in capturing the

impact of economic reforms on target variables is how to

isolate the effect of each of the reforms undertaken. Since

the reforms were undertaken in various areas (trade

reforms, fiscal/monetary reforms, etc), at different points

in time, and with varying levels of intensities, their

47







48

effects on particular variables in the economy become

compounding and difficult to isolate. Khan (1990) argues

that it is theoretically and empirically difficult to link

all the policy reform measures to the ultimate targets of

the policies. Hence most studies have attempted to assess

the effects of the overall policy package on particular

target outcomes. This is the thrust of the studies by

Chadha et al. (1998), and some of the studies in Tshibaka

(1998), Campbell and Stein (1991), La Guerre (1994), Vald6s

and Muir-Leresche (1993), Commander (1989) and Weeks

(1995). In these studies, neither the precise nature of the

underlying economic relationships, nor the specific

policies adopted are made explicit. Instead, the attention

is on whether or not the program package, which in effect

gives rise to a particular policy environment, has been

"effective" in the sense of achieving broad macroeconomic

objectives (Khan, 1990).

Most of the studies reviewed proceeded from the

premise that the economic reforms originated from the

conditionalities that accompanied Fund/Bank-supported

packages. In all country experiences the problem of

economic instability provided the initial imperative to

seek IMF assistance, which, when it came took the form of

stabilization policies. However, in most of the studies







49

reviewed the stabilization policies are treated en passant,

or not at all, and instead attention is focused exclusively
3
on economic outcomes associated with structural adjustment

For most developing countries that have been pursuing

the Fund/Bank economic programs, the last decade has been

one in which the reforms have intensified the process of

liberalizing the economic system. A frequently raised

question with respect to Fund/Bank-supported structural

adjustment programs cum structural reforms for economic

liberalization, has been whether these programs are

effective in achieving stated economic objectives. A second

and related question is how to measure the effects of these

reform policies on the target variables identified in the

programs. With respect to this latter question, Guitidn

(1981) has argued that economic performance under a policy

package should be compared to a counterfactual. The latter

is defined as economic outcomes that would have taken place

in the absence of the program package. The concept of a

counterfactual is intuitively appealing and is a standard

yardstick widely used in economics to measure the impact of

policy interventions. Since the counterfactual is



3 Stabilization policies are designed to put the economy back on its
equilibrium path, whereas, structural adjustment policies aim at
putting the economy on a new (higher) equilibrium.







50

unobservable, it has to be estimated, hence alternative

methodologies used to evaluate the program effects are

judged in terms of their estimates of the counterfactual.

Although the preceding two questions have generated a

large body of literature over the past two decades, there

is little consensus in the economics profession either

about the impact of past programs on target variables, or

about how to estimate the effects of program packages

(Khan, 1990). With respect to measuring the impact of

reforms, there are three main approaches that have been

applied in the empirical literature. These are

(1) The before-after approach, which compares the

behavior of key macro-economic variables before and

during, or after, any particular reform period, or

policy package.

(2) The with-without approach compares the performance

of macro-economic variables of non-program countries

(the control group) with those from program

countries. A modified version of this approach is a

reduced form regression estimate that controls for

initial conditions in program and non-program

countries.

(3) The comparison-of-simulations approach. This

approach simulates performance of Fund/Bank-type







51

programs then compares them with simulated outcomes

from another set of policies.

Approaches (2) and (3) have been used extensively in cross-

country studies to assess the impact of Fund/Bank-supported

programs. The before-after approach has been the most

prevalent in country case studies using time series data.

3.1.1 Before-After Approach

The before-after approach has been the most popular in

the literature on Fund/Bank-support programs. Reichmann and

Stillson (1978) were the first to use this approach in an

examination of 79 Fund/Bank-supported programs over the

period 1963-1972. They compared growth, balance of payments

and inflation in the two years prior to and after the

implementation of the program. Connors (1979) also used the

before-after approach to evaluate 31 programs in 23

countries over the period 1973-1977. More recent studies

include Singh (1995) and the papers in Le Franc (1994), on

Jamaica. Similar studies on other developing countries

include most of the papers in Tshibaka (1998), Campbell and

Stein (1991), La Guerre (1994), Vald~s and Muir-Leresche

(1993), Commander (1989) and Weeks (1995).

The before-after approach is viewed by some analysts

as providing a relatively poor estimate of the counter-

factual to program effects by assuming that non-program







52
determinants of economic variables remain constant between

non-program and program periods. This assumption has been

questioned in the literature in light of the fact that non-

program determinants of economic outcomes (e.g., terms of

trade variations, changes in international interest rates,

weather, etc.) typically change year after year. Conse-

quently, Goldstein and Montiel (1986) have demonstrated

that the before-after estimates of program effects are

biased, since all economic changes in program period are

incorrectly attributed to program factors. These authors

suggested further that the before-after estimates are

unsystematic over time, since in any particular year

program effects will often be dominated by non-program

factors. For example, if a hurricane damages infra-

structural works and agricultural crops, then agricultural

growth may decline, causing all programs in that year to

appear to have performed poorly.

3.1.2 With-Without Approach

The with-without approach seeks to overcome the

shortcomings of the before-after approach by comparing

economic outcomes between program and non-program

countries. Since both groups of countries are subjected to

the similar external environments, it is argued that this

comparison cancels out the non-program determinants, so







53

that any observable differences in outcomes in the two

groups of countries are attributable to the Fund/Bank-

supported programs. In other words, the economic outcomes

observed in non-program countries are taken as the

counterfactual of what would have occurred in program

countries in the absence of Fund/Bank-supported programs.

Donovan (1981, 1982) was the first to use this

approach on a sample of Fund/Bank-supported programs

implemented between 1970-1980. Loxley (1984) also applied

this approach to 38 less developed economies (income of

$690 or less) with Fund/Bank program during 1971-1982.

Other similar studies include Gylfason (1987) and Pastor

(1987).

A major problem with the with-without approach is that

countries in the sample of non-program and program

countries are not randomly selected. They are program

countries because of poor economic conditions prior to

entering into a Fund/Bank-support program. Consequently

there is a systematic difference between these two groups

of countries and the non-random selection of program

countries produces a biased estimate of program effects.

This is the result of attributing observable differences in

economic outcomes between program and non-program countries

to program status, when in fact the initial economic







54
position of the two groups of countries is an important

determinant of economic performance.

To overcome this problem requires identifying the

specific differences between program and non-program

countries in the pre-program period and controlling for

these differences prior to comparing economic outcomes.

This is the idea underlying a modified version of the with-

without approach. In this regard, Goldstein and Montiel

(1986) proposed a generalized evaluation estimator to

control for pre-program differences between program and

non-program countries. This estimator is a reduced-form

relationship that links changes in macroeconomic outcome

(program target variables) to lagged values of the target

variables, lagged values of policy variables and variables

that represent exogenous effects on the target variables.

These authors applied this approach to a sample of 58

developing countries in which 68 programs were implemented

over the period 1974-1981. The approach was extended by

Serieux (1999) to include the effect of democratization in

Fund/Bank-supported program countries.

3.1.3 Comparison of Simulations Approach

Finally, the simulation approach relies on simulations

of economic models to make inferences on hypothetical

outcomes of Fund/Bank-type policy packages. Khan and Knight







55

(1981), created a simulation using panel data for 29

developing countries in a dynamic econometric model. The

objective was to investigate the hypothetical effects of

pursuing a stabilization program with similar policy

characteristics as a Fund/Bank-supported one. The authors

later (Khan and Knight, 1985) extended their simulation

exercise to include comparisons of alternative packages.

More recent studies include Robinson and Gehlhar (1996),

and Chadha et al. (1998), who use computable general

equilibrium (CGE) models for the Egyptian and Indian

experiences with recent economic reforms, respectively.

3.2 Preliminary Issues in Modeling Supply
Response in Jamaica

A number of empirical studies have been conducted in

the past to estimate crop supply response in Jamaica. A

partial list of these studies is shown in Table 3.1. With

the exception of Gafar (1997), all of the studies were

conducted prior to the period 1980-1999. In addition, none

of the studies addressed the issue of the impact of

economic reforms on agricultural supply response.

One approach to capturing the long-run and short-run

changes in agriculture supply response is to use the

Nerlovian-type partial adjustment models. Both quantity and

prices can be modeled to adjust to their long-run or

equilibrium path, and the model is capable of estimating










Table 3.1: Empirical Studies on Agriculture
mme i,% Tnniminsk


56

Supply


both long-run and short-run parameters as well as the speed

of adjustment towards the long run equilibrium. A potential

complication in such an analysis of long-run and short-run

changes is that most economic time series data are non-

stationary and usually characterized by a unit root.

This means that the linear properties of the series

such as its mean and variance, are not constant over the

sample but change over time (Greene, 1993; Gujarati, 1995).

Nelson and Plosser (1982) have shown that the economic

implications of an economic time series that is


Crop/ Funct. Price Elasticity
Category Period Author & Year Form Short-run Long-run
Banana 1954-1972 Gafar (1980) Linear 0.16 0.57
1961-1979 Pollard & Graham log 0.49 -2.72
(1985)
Cocoa 1954-1972 Gafar (1980) Linear 0.41 2.56
1961-1979 Pollard & Graham Log 0.74 0.76
(1985)
Coffee 1953-1968 Williams (1972) Log 0.70 -0.80
1954-1972 Gafar (1980) Linear 0.92 1.15
1961-1979 Pollard & Graham Log 0.10 0.07
(1985) 1
Citrus 1961-1979 Pollard & Graham Log 0.24 -1.33
(1985)
Sugar 1954-1972 Gafar (1980) Linear 0.17-0.29 0.31-0.7
1961-1979 Pollard & Graham log 0.24 1.41
(1985)
Broad Agri.
Aggregates 1964-1990 Gafar (1997) log
Export 0.20 0.35
Domestic 0.15 1.08
Livestock 0.15 0.21
Forestry &
Fishing 0.02 0.21
Total Agri. 0.12 0.23
Source: Adapted from Gafar (1997, p.213).







57

characterized by a unit root are different from those of a

stationary process. In particular, an economic time series

with a unit root will have a permanent response consequent

upon any shock in the system. In contrast, a stationary

series will reflect only a transitory response. Therefore,

the response to economic reform policies is not simply a

"policy on", "policy off" or one-shot choice. Even if the

reforms were temporary, so long as the economic series

possesses a unit root, there will be a permanent response.

Non-stationarity of variables poses problems of

estimation of functional relationships using conventional

econometric methods. In the first place, Fuller (1976) has

shown that under non-stationarity the limiting distribution

of the asymptotic variance of the parameter estimates is

not finitely defined, hence the conventional t and F tests

are inappropriate. Secondly, non-stationarity gives rise to

spurious correlation among variables (Greene, 1993). In

macroeconomic time series it is not unusual to find that a

variable is non-stationary in its level4 but stationary in

first differences. In technical terms, if Yt is non-

stationary, but AYt (the first difference) is stationary,

then Yt is integrated of order one, i.e., Yt~I(1). If two


4 Data that have not been transformed in any way (such as logarithmic
transformation, first differences, etc.), are said to be in 'level'
form.







58

series Yt and Xt are 1(1) then a linear combination of them,

e.g., Zt = Yt aXt may also be 1(1). However, there may also

exist, a value for a that ensures that Zt is stationary. In

such an event, the two series are said to be cointegrated,

and the cointegrating vector is denoted (1, a).

It is tempting to conclude that in order to estimate

meaningful relationships among non-stationary variables,

all that is necessary is to difference the variables until

they achieve stationarity and then estimate the

relationship. However, Johansen and Juselius (1990) argue

that unless the difference operator is also explicitly

applied to the error process, such differencing results in

loss of information. In this event, resorting only to

estimating the relationship in difference form captures

only the short-term effects, while the long-term

relationship among the variables is left undetected

(Nickell, 1985). Finally, differencing the economic series

may not be appropriate, such as when economic theory

postulates a relationship among variables in levels, not in

difference forms.

To overcome these problems, econometricians have

developed an approach known as error correction models

based on cointegration. Hylleberg and Mizon (1989, p.124)

claim that







59

...when estimating structural models it is our
experience from practical applications that the error
correction formulation provides an excellent framework
within which, it is possible to apply both the data
information and the information obtainable from
economic theory.

Error correction modeling requires two conditions

(1) All variables must be integrated to the same order,

i.e., Yi-I(d), where d is the number of times Y has

to be differenced to achieve stationarity; and,

(2) All variables must be cointegrated of order (d b),

where b>O.

The idea underlying cointegration is that one or more

linear combinations of non-stationary variables are

stationary. That is, cointegration approaches the station-

arity issue as linear combinations of economic series,

rather than by differencing the series. The implication of

this is that if a set of variables is cointegrated then,

following the Granger Representation Theorem (Fuller, 1985)

a valid error correction representation of the data exists.

In effect, then, cointegration is a test of existence of a

long-run relationship of variables that are integrated of

the same order (Greene, 1993; Gujarati, 1995). However, an

important feature of error correction models based on

cointegration is that the data in both levels and

differences are included, thereby facilitating investi-

gation of both short-run and long-run effects in the data.









3.3 Error Correction Model

The error correction model (ECM) can be derived from a

re-parameterization of an Autoregressive Distributed Lagged

(ADL) model (Hendry et al., 1984). Alternatively, the ECM

can be derived from the dynamic optimizing behavior of

economic agents. This latter approach is presented here.

Following Nickell (1985), suppose economic agents

optimize their behavior with respect to an inter-temporal

quadratic loss function:


(3.1) L = t Y Y 8
s-0

(Y- tsy*s- Yt+S-1 A

where Y* is the desired or long-run equilibrium value of Y

that the economic agent can control to minimize L,

conditional on information at time t, and subject to

movements in Y*. The discounting and weighting factors are

a, (OO), respectively.

Minimizing (3.1) with respect to Yt+s gives a second

order difference equation whose solution at time t is:

(3.2) Ayt = 02AYt + ( Pl)


[( + (1 X- (2)0 (aq1)N+ -
EI J t







61
where pi is the stable root (i.e., the root that is < 1)

from solving the characteristic equation of the general

Euler equation which was derived from minimizing (3.1).

Equation (3.2) is an optimal rule and has an error

correction term in brackets [.]; the coefficient (I-Ii) is

the speed of adjustment, i.e., the speed of closure to any

discrepancy between desired and actual values of Y. In the

error correction term in (3.2) the long-run target is a

convex combination of all target values from Y-, onwards.

Nickell (1985) has shown that when 02 = 0 in the loss

function (3.1) then equation (3.2) nests the forward-

looking partial adjustment model (PAM):


(3.3) AY = ( I %i ( tiNY$s }Yt-
s-0II

It is important to note that the dynamic equation

(3.3) does not necessarily have to be of a standard PAM

type. To estimate (3.3) empirically requires the para-

meterization of Yj ,. One way is to model this sequence of

expected target values as a stochastic process such that

Yt.S are expressed in terms of current and lagged values and

substituted into (3.3). The resulting equation may not

necessarily result in a PAM. Indeed, Nickell demonstrates

that when







62

...we allow the target to follow anything more
complex than a first order autoregression, the
structural equation [3.3], which is fundamentally a
partial adjustment model, will reduce to an error
correction mechanism in terms of observable variables
(Nickell, 1985, p.124).

Suppose that the actual values of the series follow a

second order autoregressive scheme with a unit root and a

drift:

(3.4) Yt+s= g + PYts-1 + (1 P t+S-2 + Et+S

where s>O, et+, is white noise, and g is the drift. Denoting

the expected value with an (*), the expectation of (3.4)

is:

(3.5) Y s = g + PYt+s-1 + (1 P t s-2

Following the derivations by Nickell (1985) and

Alogoskoufis and Smith (1991) the solution to the second-

order difference equation (3.5) is:

*.= yt-+1 0 4 A4
(3.6) Y = S2-- gs + ( Y


Substituting (3.6) into (3.2) yields the decision rule in

the form:


(3.7) =y C + + +y + iii1 PIAY -y- Y1)
(1 + cxp1( -F t3

where c =g( PX2 P0101 P1X1 (2) glcX1 -
(2 P)[1 + ai(1 P)] (2 PX plp)

Equation (3.7) is written in the form of an ECM. The

parameterization of Y* in terms of exogenous variables would







63
reflect the long-run cointegrating relationship. According

to Nickell (1985, p.124),

since it is almost a stylized fact that aggregate
quantity variables in economics follow second order
autoregression with a root close to unity, we may find
the error correction mechanism appearing in many
different contexts.

3.4 An Error Correction Model for Crop Supply
Response in Jamaica

The dynamically unrestricted version of the ECM in

(3.7) can be expressed as:

(3.8) = *0 + 1AQ + 2(Qtl Qt

where Qt is output in logarithm and Vo, *1, *2 are straight-

forward analogues of the intercept and coefficients in

(3.7). Generally, Q* is parameterized in terms of other

(weakly) exogenous variables. This parameterization shows

the long-run cointegrating relationship between the

exogenous variables and the dependent variable, Qt. Given

the unrestricted nature of (3.8) a wide range of possible

processes that describe the law of motion of Qt can be

accommodated. Following previous agricultural supply

response models, quantity supplied is postulated as a

function of the expected values of a set of variables that

are believed to capture agricultural incentives. These

exogenous variables are, the price of the crop, Pc, the

price of substitute crops, Psi, i=1,2... and the prices of







64

inputs. In this study, two inputs are considered,

fertilizer and labor, whose prices are denoted as F and W,

respectively. With these specifications of the exogenous

variables, the parameterization of the long-run equilibrium

supply function of the cth crop is:

(3.9) Q. + O + 02iPit + O + 04F +

c = l...n; i=1,2,...; t=l...T; et-iid(0,a).

where all variables are measured in logarithms (to

facilitate interpretation of estimated coefficients), and

have been previously defined. The superscript e denotes

expected value of the variables. Given the parameterization

in 3.9, the general error correction model, with all

variables as previously defined, can be written as:

(3.10) AQt = X0 + XIAQtI + X2Apt_1 + \31AP1,t + X4AWe_

+ X5AF t1 + X6(Q +
+ es I+A t- Qt-J +


To give empirical content to these models, the

specification of expected values must be addressed. Clearly

if economic agents had full information about the current

variables at the time they are set, then actual values of

the variables would be substituted for their expected

values. When this is not the case, some process of

expectations formation has to be assumed. The applied

literature on ECM has largely ignored issues of







65

expectations. Because of its simplicity in practice, actual

values are usually substituted for expected values. An

alternative to this procedure is to assume that crop and

input prices are determined by policy changes for one year

in advance. With this assumption, expectations about

current economic variables have to be based on information

that is available up to the end of the previous period,

(t- I). This can be expressed as:

(3.11) x=e =(XtjZj =

where Xt denotes the economic variable of interest, and 0 is

the information set available to the economic agent.

Previous studies of crop supply response in Jamaica

assumed naive expectation, based on past (simple) lagged or

polynomial lagged prices. Under rational expectations,

expected current and future prices of crops, wages and

inputs will reflect the generation process for these

explanatory-forcing variables. As noted previously, a

second order auto-regressive process with a unit root with

drift seems adequate to describe the process followed by

many economic series. The data on quantities, prices and

inputs for this study have been analyzed and found to

follow this process. Consequently, the expected prices of

crops and inputs are characterized as follows:

(3.12) <:"I-, = ao + a, '-, + 0 a, ,-









(3.13) P-, = bo + bIP.1 + (I bj)P. ,-

(3.14) Ft= co +cF-, +(1-c )F,

(3.15) Wt =do +dW.-1 +(-dl)W,.2

Using (3.8)-(3.15), the general error correction model for

the cth crop is:

2 2
(3.16) AQ ct = a0c + .,t6Q.,t-j + Y a2j c,t-J + E a3ik APi,t-k
jal k-1

2 2
+ a.lawt 1 + E (mA -. + ajQc 51pc (21psi
1==1

-83W, -840),, ;


where, i=1,2, all variables as previously defined, and the

random error term is suppressed.

The last term in (3.16) is the error correction term. In

the empirical estimation of the ECM (3.16), the error

correction term is usually specified as the residual from

the cointegrating relationship.

The Engle-Granger (1987) two-step method has been used

extensively in the applied literature to estimate the ECM

(3.16). However this method assumes that the cointegration

vector is unique. Except in the bivariate model, this

assumption may be violated in multivariate models. To test

for, and estimate multiple cointegrating vectors, Johansen

(1988) and Johansen and Juselius (1990) have devised an

appropriate method within the following framework. Define a







67

standard vector autoregressive (VAR) model with lag length

k as:

(3.17) Xt = i1x + H2Xt_2 + ... + [kXt-k + Et

t=l1..., T

where X is an Nxl vector of N endogenous variables, et-iid


(0,A) with dimension NxN. The long-run, or cointegrating

matrix is:
(3.18) I nI n2 nk = n

The number of distinct cointegrating vectors, r, which

exists between the variables of X, is given by Rank (n).

Most economic time series appear to be integrated to

the order of one, in which case, r N-1, where N is the

number of variables in the vector X. In the case of a

bivariate model, N=2, and therefore if the variables are

cointegrated, then there is a unique cointegrating vector.

The matrix n is then decomposed as:

(3.19) H = ap'

where I represents the matrix containing the r

cointegrating vector, and a is the matrix of weights with

which each cointegrating vector enters each of the

differenced X equations. A large a value implies that the

system will respond to any deviation from the long-run

equilibrium path with a rapid adjustment. If a's are zero







68

in some equations, this is a sign of a weak exogeneity,

implying that the variable does not respond to the

disequilibrium in the system. The parameters a and P form

an over-parameterization of the model. However, the space

spanned by P, sp(P), can be estimated, and shown to be the

empirical canonical variates of Xt-k with respect to AXt.

This is in effect the following theorem advanced by

Johansen:

The maximum likelihood estimator of the space spanned
by P is the space spanned by the r canonical variates
corresponding to the r largest squared canonical
correlations between the residuals of Xt-k and AXt
corrected for the effect of the lagged differences of
the X process. (Johansen, 1988, p.233)

Implementation of this theorem begins by re-para-

meterizing (3.17) (detailed derivations are in Johansen,

(1988) and Enders, (1995)), into the following error

correction model:

(3.20) AXt = FAXt- + --+ Fk-AXt-k+1 + FkXt-k + St

where F = -I + R1 + H2 + ... + Ili, i=l ...k

Without any loss of information, the ECM in (3.20) is

therefore a transformation of the VAR(k) model in equation

(3.17), and is expressed in first differences and augmented

by the error correction term, fkXt-k. The long-run equi-

librium or impact matrix is the matrix F, and is equivalent

to rI = a' 0 in (3.19). The rank of fl is the basis of







69

determining the number of cointegrating relationship

between the variables in the ECM (3.20). Johansen (1988)

identifies three possibilities with regards to Rank(l),

(1) rank(I) = 0. This means that the variables are not

cointegrated and the model is basically a VAR in

first differences.

(2) 0 < Rank(11) < p. In this case, the variables are

cointegrated and the number of cointegrating

relationship(s) is less than the number of

variables, p, in the model.

(3) rank() = p. This means that all variables are

stationary and the model is in effect a VAR in

levels.

The loglikelihood representation of (3.20) is:

(3.21) L ) =


I (Rot + P'R + a'R)


Johansen's procedure begins by regressing AXt on the lagged

differences of AXt and generating fitted residuals Rot, then

regressing Xt-k on the lagged differences and generating

fitted residuals, Rkt. These fitted residuals are then used

to construct the following product moment matrices:

1
(3.22) S= T 1 Rjt i, j = 0, k
Tj Rit
i-i







70

The product moment matrices (3.22) are then used to find

the cointegrating vectors by solving the determinant:

(3.23) 1xSkk SkOSOOSOkj = 0

This will yield the estimated eigenvalues (xi, ..., n) and

eigenvectors (vl,...,vn), which are normalized such that:

(3.24) V'SkkV = I

where V is the matrix of eigenvectors. The most significant

eigenvectors then constitute the r cointegrating vectors,

i.e.,

(3.25) 1 = V '"" "Vr)

Using (3.25), a is then estimated from (3.19).

The critical issue in all of this is to determine

which, and how many, of the eigenvectors in (3.24)

represent significant cointegrating relationships. First,

the 0 vectors that have the largest partial correlation

with AXt, conditional on the lags of AXt, are identified.

Second, the eigen vectors that correspond to the r largest

eigen values are chosen. Finally, to determine the value of

r the following test statistics suggested by Johansen

(1988) are employed:

n
(3.26) 1(q, n) = -T : ln(l X)
i=q+l

(3.27) 2(q, q + 1) = -Tln( q+1)







71

The null hypothesis HO: r~q is tested with (3.26), while

HO: r = q is tested against HI: r = q + 1 with (3.27). The

critical values for these tests are taken from Osterwald-

Lenum (1992). The critical values from this source

recalculates and extends those critical values from

Johansen (1988) and Johansen and Juselius (1990), to handle

a full test sequence from full rank (r = p, i.e., Xt is

stationary) to zero rank (r = 0, i.e., all linear

combinations of X are I(1)), for at most 11-dimensional

systems.

The cointegration technique is used to determine the

long-run relationships among variables. This co-movement of

variables in a long-run supply function has not been

explored for Jamaica. Cointegration analysis is appropriate

in this regard. It will also suggest which variables in the

supply function are in the long-run equilibrium.

3.5 The Data

The principal sources of annual time series data,

which are used in this study, are the Food and Agriculture

Organization (FAO) agricultural database (FAOSTAT), avail-

able on the internet, and annual publications of various

government agencies in Jamaica. These include, Economic and

Social Survey of Jamaica (Jamaica, Planning Institute of

Jamaica); Production Statistics (Jamaica, Statistical







72

Institute of Jamaica (STATIN)); Statistical Digest

(Jamaica, Bank of Jamaica), Statistical Year Book of

Jamaica (Jamaica, Statistical Institute of Jamaica

(STATIN)); Census of Agriculture, 1968, 1978, 1996

(Jamaica, Statistical Institute of Jamaica (STATIN));

International Financial Statistics (IMF); and data from

published and unpublished documents from the Ministry of

Agriculture in Jamaica.

It should be noted that the data on Jamaica contained

in the FAO database, FAOSTAT, IMF and World Bank sources

are based on publications and data supplied by STATIN,

Ministry of Agriculture, and other agencies of the

Government of Jamaica. The data used to estimate the supply

functions in this study are taken from the FAO FAOSTAT

database. This is the most comprehensive data set on crop

output and prices in Jamaica. However, in several areas the

data are less than ideal. For example, whenever data are

not available the FAO provides its own estimates based on

past crop performance, other crops and other country's

data. Jamaican officials claim that production data for

some crops reported on the FAO data base are not monitored

in Jamaica.

The crop output and price data are annual series, and

are collected at the farmgate through periodic production







73

surveys and agricultural censuses. Farmers in the Caribbean

rarely keep records of production, cost expenditures and so

on. Hence, for these farmers it is difficult to recall data

for production surveys that are conducted between long

periods. Consequently, this casts some amount of suspicion

on the reliability of the data. Nonetheless, these are the

data reported as official statistics, and are used by the

government as a basis for policy analysis/discussion.

Several important economic aggregates were not

adequately covered in the data sources. In particular, data

on agricultural wages are not available on Jamaica. As a

result, a proxy for this variable was constructed from data

on compensation to agricultural employees recorded in the

national accounts. Data on consumer price indexes--the

basis for deflating crop and input prices in this study--

were not available as continuous series over the 1962-1999

period. Consequently, a combination of splicing indexes and

converting the final 1962-1999 series to a 1980 base year

was undertaken.

Although the data are less than ideal in several

areas, and are bound to contain some noise, the advantages

of using them are:







74

(1) they are from a common source and are characterized

by a common accounting/estimating procedure in their

derivation; and

(2) the data are the most comprehensive time series

available on Jamaican crop supply in a single

source.

3.6 Summary

There are competing approaches in the literature on

how to evaluate the impacts of economic reforms associated

with Fund/Bank-type policy packages. Each approach utilizes

the idea of a counterfactual against which actual economic

outcomes in the program period are compared. Estimating

crop supply responses over a period in which significant

policy changes have occurred, requires a modeling framework

that is capable of capturing both the long-run and short-

run changes. The Nerlovian-type supply response models have

been used in the literature for this purpose. However, in

the context of data series that are non-stationary, this

approach can produce spurious regressions. A more

appropriate analytical framework is that provided by error

correction models based on cointegration analysis.















CHAPTER 4
EMPIRICAL ESTIMATION OF CROP SUPPLY RESPONSE


The aim of this chapter is four-fold. First, the

central motivations for using error correction modeling

based on cointegration theory in this study are presented.

Second, the time-series data on prices and quantities to be

used in the ECMs to estimate crop supply responses in

Jamaica are tested for stationarity. Third, long-run supply

responses are estimated for eight agricultural crops.

Finally, the short-run dynamics of the crop supply

responses are analyzed.

4.1 Motivations for Using Cointegration Analysis

Chapter 3 provides a fairly elaborate treatment of the

theoretical and statistical aspects of error correction

modeling based on cointegration theory. Against that

background, it is useful to recall in a cryptic, condensed

and fairly non-technical way, the central motivations for

using this approach to address the issues with which this

study is concerned.

The empirical purpose of this study is to investigate

the impact of economic reforms on crop supply responses in







76

Jamaica. Generally, supply response models in agriculture

postulate a long-run relationship between output and

agricultural incentives (Askari and Cummings, 1976).

Deviations from this long-run equilibrium occur in the

short-run and may involve considerable adjustment costs to

the economic agent. This is especially the case when

significant policy changes are implemented. Jamaica

provides a good case for evaluating the connections between

economic reforms and supply responses. The country has

traditionally been heavily dependent upon agriculture for

food, employment and export earnings. A combination of

excesses in state intervention and adverse world economic

conditions prompted major economic reforms in the late

1970s in the form of serious macroeconomic stabilization

policies under the direction of the IMF.

During the early 1980s more far-ranging structural

reforms were instituted in an effort to reverse the current

account and fiscal deficits, reduce inflation and monetary

growth to achieve financial stability, restore economic

growth, and so on. Implicitly in these early reforms, but

more explicitly in the late 1980s and throughout the 1990s,

the aim has been to re-orient the economy towards a more

liberal economic system. These reforms took expression in

progressive devaluation of the Jamaican dollar, elimination







77

of state marketing boards, liberalization of agricultural

input and output prices and privatization of state held

monopolies and public enterprises. Intuitively, therefore,

the analysis of supply responses in these situations would

require a modeling framework that is capable of incor-

porating both the long-run and short-run changes.

A preliminary analysis of the Jamaican crop output and

price data series over the 1962-1999 period reveals sub-

stantial fluctuations, especially since the early 1980s.

The strongly time trended data imply a statistical problem

that has not been addressed in applied analysis on Jamaican

crop supply responses. This statistical problem is referred

to, in the literature, as non-stationarity of the time

series.

The analytical approach chosen in this study to deal

with the above mentioned issues, emphases the importance of

considering the interactions between the variables in the

system in a simultaneous equation model, and to distinguish

between the short-run and long-run effects. The modeling

approach differs from those previously used to model

Jamaican crop supply responses in two very important ways.

First, the data are analyzed as a full system of equations.

This allows for possible interactions in determining the

precise relationships among the variables in the system.







78

Second, the multivariate cointegration modeling which

is used in this study, is designed for this type of

empirical work by explicitly classifying the non-stationary

and stationary components and facilitating an inter-

pretation in terms of the dynamics of short-run and long-

run effects. There are two general motivations for using

error correction models based on cointegration theory in

applied economic analyses. First, when time-series data are

non-stationary, i.e., their linear properties, such as mean

and variance, are time dependent, then conventional

econometric analysis on such data may produce "spurious"

results.

Second, even though a set of time series may

individually be non-stationary, there may be a linear

combination among them that is stationary. Such series are

said to be cointegrated, that is, they have a tendency to

move together in the long-run, even though in the short-run

they may diverge from each other. This co-movement of

related series suggests the existence of long-run

relationship between them. So when data are non-stationary

there is the additional possibility that the data

generating process contains information about the

equilibrium process that makes the process adjust toward

the long-run steady state or the equilibrium path.







79

There is, therefore, additional economic insights

gained from the recognition that the data are non-

stationary. The component FkXt-1, (=- ap'Xt-i, since II = ap'),

in equation (3.20), is directly related to the non-

stationarity in the data. Since this component contains

information about the speed of adjustment, a, to some long-

run relations, P'Xt, and if the data are cointegrated, then

the economic process can generally be understood within a

theoretical model that assumes some adjustment behavior.

This means that theoretical models such as the partial

adjustment model (PAM), which assume static equilibrium,

cannot be used when data are non-stationary.

4.2 Definition of Variables

In this chapter, error correction models based on

cointegration theory are used to test the hypothesis that

long-run relationships exist between crop output and price

incentives in Jamaica. The estimation procedure is based on

the work of Johansen (1988) and Johansen and Juselius

(1990). There are basically three steps in this estimation

procedure

(1) test the order of integration of the variables and

specify the lag length of the variables using a

standard vector auto-regressive (VAR) specification.







80

(2) estimate the ECM and the number of cointegrating

relationship(s) among the variables included in the

ECM.

(3) perform short-run analysis by conducting innovation

accounting on the ECM.

For each crop a p-variable vector autoregression (VAR)

of lag k is specified. The model is re-parameterized into

an error correction model (ECM) as specified in equation

(3.20) and estimated according to the methodology of

Johansen (1988) and Johansen and Juselius (1990). Esti-

mation is done using the Regression Analysis for Time

Series (RATS), version 4.3 (Doan, 1996), and Cointegration

Analysis for Time Series (CATS) in RATS, (Hansen and

Juselius, 1995), computer programs. The variables in each

ECM include the output (quantity) of the crop of interest,

the price of the crop, the price of a substitute (or

alternative) crop, and two input prices, average agri-

cultural wage rate and average fertilizer price.

The choice of a crop price variable is critical for

the estimation of crop supply responses. In this regard,

Askari and Cummings (1976) suggest using any one of the

following prices:

(1) Nominal farmgate price;

(2) Farmgate price deflated by any one of the following:









(a) a price index of farmer's inputs;

(b) a consumer price index; and

(c) some index of the prices of competitive crops (or

the price of the most competitive crop).

An additional issue that is related to the choice of

an appropriate price variable for the crop supply functions

especially within the context of the IMF/World Bank

programs in Jamaica, is that agricultural price incentives

are influenced by various macroeconomic policies. Of

particular importance, in this regard, is the real exchange

rate (RER), defined as the ratio of prices of tradable (PT)

to non-tradable (PN) goods (Tsakok, 1990). Krueger (1992)

and Schiff and Vald6s (1992a, 1992b), have shown

extensively how macroeconomic policies in developing

countries generally have a RER effect, which ultimately

affect output price and agricultural supply. In the

empirical literature the RER is usually approximated as

RER=e*WPI/CPI, where WPI is the foreign wholesale price

index, CPI is the domestic consumer price index, and e is

the official exchange rate. The World Bank's approach to

showing the link between macroeconomic policies (as

represented by the RER) and real crop prices, decomposes

real producer price as follows:

RPP = PF/CPI = PF/PBe = e(WPI/CPI) = NPC*RER*pB







82

where RPP is real producer price, PF is the farmgate

producer price, PB is the border price, NPC is the nominal

protection coefficient, and PB is the real border price of

the country's exports (World Bank, 1994). This definition

of RPP shows that it contains information on the RER and

also reveals that it is really hazardous to include both

the RER and RPP in the same equation (Mamingi, 1997).

In addition to the issues raised above, two additional

considerations were taken into account in the choice of the

crop price variable. First, since farm producers sell most

of their marketable surplus immediately after reaping,

farmgate prices seem to be a good approximation for prices

received. Second, since farmers purchase most of their

requirements from retail markets, the consumer price index

seems to be an appropriate deflator for producer prices.

For these reasons, therefore, the farmgate price deflated

by the CPI was used as the real producer price (RPP)

variable in the supply functions. In effect, this price

variable reflects not only price incentives to the

producers, but also macroeconomic (reform) policies (as

represented by the RER). In particular, this link between

the price variable and macroeconomic policies is important

in order to pursue an investigation of the hypotheses

advanced on page 11.







83

The question regarding which alternative (or

substitute) crop to include in a particular crop's ECM

proved to be difficult. In the absence of recorded data on

this issue, the final choice of the substitute crop

resulted from a consideration of a number of factors. Among

these were, the nature of the crops (traditional export

versus domestic crops); the terrain where the crops are

grown (mountainous versus flat lands); tree-crops versus

annuals; and, root-crops versus vine-crops. The difficulty

of identifying an appropriate substitute crop (or crops)

can be demonstrated in the case of banana. Possible

substitute crops for banana are coffee and sugar, since

these are all traditional export crops. Banana is grown

extensively on flat lands, but is also cultivated on hilly

terrains as is coffee, whereas, sugar growing has been

confined to the relatively flat plains in Jamaica. Hence,

both sugar and coffee are plausible substitutes for banana,

and the choice of either one or both becomes an empirical

issue.

A further complication in the choice of a substitute

crop arises. In the early 1990s, this researcher observed

two cases where lands which were previously used to

cultivate sugar and banana were converted into papaya

groves. Telephone interviews conducted with officials of







84
the Ministry of Agriculture, Government of Jamaica,

indicate that these are not isolated cases, and that

similar land conversions have been observed into cut-

flowers, decorative foliage, aloe-vera, ochro, and other

non-traditional export crops.

Several substitute crops were initially included in

each ECM but in all cases it was found that the inclusion

of only one substitute crop price improved the statistical

properties of the model. The alternative crops in each

crop's ECM are reported in Table 4.1.



Table 4.1: Alternative (Substitute) Crops in Each ECM.
Crop Alternative Crops Considered
Banana Sugar coffee, papaya
Sugar Banana papaya
Coffee Banana', pimento, sugar, orange
Pimento Banana', coffee, cocoa bean
Yam Cassava potato, sweet potato
Orange Grapefruit', tangerine, coffee
Cocoa Bean Banana, pimento, coffee
Potato Cassava', yam, sweet potato
Crop chosen as substitute crop.



Two input prices are included in each crop's ECM,

namely, fertilizer price and average wage in the

agriculture sector. The fertilizer price variable is a

weighted price index of all types of fertilizers imported

into Jamaica, with quantities as weights. With respect to

the wage variable, there are no data on agriculture wages




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ECONOMIC REFORMS AND AGRICULTURAL SUPPLY RESPONSE IN JAMAICA By BALLAYRAM A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2001

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ACKNOWLEDGEMENTS First, I would like to express my deepest gratitude to my wife for all her support throughout my studies here at the University of Florida. Her support in this process was overwhelming and unselfish. My daughters were also very understanding, supportive, and encouraging, and I thank them sincerely for the sacrifices they had to make so that I could complete my graduate studies. My parents and in laws (all now deceased) gave me encouragement and support, both financial and otherwise, in my quest to achieve a higher education. Without their love, confidence and moral support, none of this would have been possible. I owe them an eternal debt of gratitude, because their support came at a time when the cost of higher education was far beyond the reach which their economic resources could accommodate. I would also like to express my deepest gratitude to Dr. Carlton G. Davis, chairman of my supervisory committee. He opened the door for me at the University of Florida, and provided me with countless hours of indi victual attention during my course of studies. On numerous occasions he went beyond what would normally be the call of duty of an ii

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academic supervisor, to provide me with the support that enabled me to continue my studies. I would also like to acknowledge the contributions from the other members of my supervisory committee: Dr. Robert Emerson, Dr. Clyde Kiker, Dr. Richard Kilmer and Dr. David Denslow, who provided me with valuable suggestions during the preparation of this dissertation. I also benefited immensely, in my course of studies at U. F., from discussions with Dr. Max Langham, Dr. Charles Moss, Dr. Ronald Ward and Dr. James Seale, Jr. Financial support from the Food and Resource Economics Department is greatly acknowledged. Dr. Carlton Davis' efforts in securing this funding for me are greatly appreciated. Finally, I wish to acknowledge the excellent computer guidance and support services which Alex Heyman, Roger and Laura Clemons, Ed Howard and Juan Carlos, of the Food and Resource Economics Support Center, provided throughout my studies here at U.F. iii

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TABLE OF CONTENTS page ii ACKNOWLEDGEMENTS LIST OF TABLES LIST OF FIGURES ABSTRACT CHAPTERS . . . . . . . . . . vi 1. INTRODUCTION 1.1 Background to Jamaica's Economic Reforms . . . . . . 1.2 Problem Statement and Study Justification . . . . . 1. 3 Objectives of the Study . . 1.4 Hypotheses . . . . 1.5 Conceptual Issues . . . . . 1. 6 Procedural and Methodological Considerations . . . . 1. 7 Summary . . . . . . . 2. ECONOMIC STRUCTURE, GROWTH, AND POLICY REFORMS IN THE JAMAICAN ECONOMY, 1962-1999 2.1 Structure of the Economy and Economic Growth: An Overview 2.2 Output Trends and Policies in Jamaican Agriculture . . 2.3 The Economic Reforms . . . 2.4 Summary . . . . 3. METHODOLOGICAL AND EMPIRICAL ISSUES IN ECONOMIC REFORMS AND SUPPLY RESPONSE 3.1 Review of the Economic Reform Literature iv . . . . ix xv 1 2 8 10 . 11 11 . 20 22 24 . 26 . 30 38 . 46 47 47

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3 .1.1 3 .1. 2 3 .1. 3 Before-After Approach .. With-Without Approach .. Comparison of Simulations Approach ...... Preliminary Issues in Modeling Supply Response in Jamaica ..... 51 52 54 3.2 3.3 3.4 Error Correction Model . . . . 55 60 3.5 3.6 An Error Correction Model for Crop Supply Response in Jamaica ..... The Data . . . . . . . Summary ..... 4. EMPIRICAL ESTIMATION OF CROP SUPPLY RESPONSE. 4.1 Motivations for Using Cointegration Analysis . . . . . . 4.2 Definition of Variables . . . . 4.3 Testing for Stationarity . . . 4.4 Estimating Long-run Supply Response 4.5 Analysis of Short-run Dynamics 4 6 Summary . . . . . . . . 5. SUPPLY RESPONSE AND COUNTERFACTUAL ANALYSIS . . 63 71 74 75 75 79 87 90 106 136 137 5.1 Before-After Analysis ... ........ 137 5.2 Counterfactual Analysis ........... 145 5.3 Summary . . . . . . . . 164 6. CONCLUSIONS AND POLICY IMPLICATIONS APPENDICES A. PRICE OF SELECTED CROPS PRODUCED IN JAMAICA, . 165 1962-1999 (J$) ............. ... 174 B. QUANTITY OF SELECTED CROPS PRODUCED IN JAMAICA, 1962-1999 (METRIC TONES) 175 C. SELECTED ECONOMIC STATISTICS ON JAMAICA 176 D. DIAGNOSTIC STATISTICS FOR MODEL SPECIFICATION AND TEST STATISTICS FOR COINTEGRATION REFERENCES BIOGRAPHICAL SKETCH. V .. 177 183 192

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LIST OF TABLES Table page 2.1 Sectoral Contribution to Real Gross Domestic Product (Period Averages, Percentages) .................. 27 2.2 GDP and Sectoral Growth (1995=100) (Percentage) . . . . . ..... 28 2.3 Growth Rates of Total Agricultural and Broad Agricultural Aggregate Indexes ...... 30 2.4 Composition of Agricultural Output (J$M, 1995=100), Selected Years . ..... 32 2.5 Agriculture Sub-sectors as a Percentage of Total Agriculture (Period Averages) ..... 32 2.6 Growth Rates of Agricultural Components (Percentages) . . . 34 2.7 Average Rates of Growth of Farmgate and F.O.B. Prices, Output and the Nominal Protection Coefficient (NPC) for the Period 1970-78, Jamaica . . ... 36 2.8 Net Barter Terms of Trade and Agricultural Income Terms of Trade (1980=100) (Period Averages) ................ 38 2.9 Outcomes of Economic Reform Policies in Jamaica, 1986 vs. 1995 . . ...... 45 3.1 Empirical Studies on Agriculture Supply Responses in Jamaica .. 4.1 Alternative (Substitute) Crops in Each ECM 4.2 Unit Root Tests--Prices and Quantities 56 8 4 in Levels . . . . . . . . . 88 vi

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4.3 Unit Root Tests--Prices and Quantities in First Difference. . . . ..... 89 4.4 Diagnostic Statistics for Residual Tests for the Banana ECM 4.5 Tests of Cointegration Rank for Banana ECM 4.6 Estimated Long-run and Adjustment . 91 95 Coefficients, P's, a's--Banana ECM ....... 96 4.7 Estimated Long-run and Adjustment Coefficients, P's, a's for all Crops 4.8 Endogenous and Exogenous Variables in Crops' . 100 Impulse Response Functions . . ..... 109 4.9 Responses of Banana Quantity to Shocks in the Banana VAR .............. 111 4.10 Variance Decomposition Percentage of One period and Three-period Forecast Error Variance--Banana VAR .............. 114 4.11 Responses of Quantity to Shocks in Exogenous Variables . . . . . . ..... 117 4.12 Summary of Forecast Error Variance in Crop Quantities Explained by Variables in the VAR (Percentage) .......... 119 4.13 Responses of Sugar Quantity to Shocks in the Sugar VAR ...... 4.14 Variance Decomposition Percentage of One period and Three-period Forecast Error . . 121 Variance--Sugar VAR . . . . . . 121 4.15 Responses of Coffee Quantity to Shocks in the Coffee VAR ............... 122 4.16 Variance Decomposition Percentage of One period and Three-period Forecast Error Variance--Cof fee VAR . . . . . . . 122 4.17 Responses of Pimento Quantity to Shocks in the Pimento VAR . . . . . . 123 vii

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4.18 Variance Decomposition Percentage of One period and Three-period Forecast Error Variance--Pirnento VAR ........... 123 4.19 Responses of Yarn Quantity to Shocks in the Yarn VAR ......... 4.20 Variance Decomposition Percentage of One period and Three-period Forecast Error . . 12 4 Variance--Yarn VAR . . . . . ... 124 4.21 Responses of Orange Quantity to Shocks in the Orange VAR . . . . ... 125 4.22 Variance Decomposition Percentage of One period and Three-period Forecast Error Variance--Orange VAR .............. 125 4.23 Responses of Cocoa-bean Quantity to Shocks in the Cocoa-bean VAR . . ...... 126 4.24 Variance Decomposition Percentage of One period and Three-period Forecast Error Variance--Cocoa-bean VAR. . . . ... 126 4.25 Responses of Potato Quantity to Shocks in the Potato VAR . . . . . . 127 4.26 Variance Decomposition Percentage of One period and Three-period Forecast Error Variance--Potato VAR ............. 127 5.1 Descriptive Price and Quantity Statistics --1962-1979 and 1980-1999 ........... 140 5.2 Descriptive Price and Quantity Statistics --1975-1979 and 1980-1984 . . . ... 141 5.3 Diagnostic Statistics for Forecasts of Fitted Values . . . . . ..... 155 5.4 Estimated Long-run and Adjustment Coefficients, P's, a's for all Crops, 1962-1979 . . . ..... 5.5 Estimated Long-run and Adjustment Coefficients, P's, a's for all Crops, 157 1980-1999 . . . . ........ 159 viii

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LIST OF FIGURES Figure page 1.1 Conceptualization of the Jamaican Agricultural Sector .............. 14 2.1 GDP and Sectoral Contribution to GDP in Constant ( 1995) Dollars . . . . . 27 2.2 GDP Growth (Percentage Change Over Previous Year, 1995=100) . . 2.3 2.4 2.5 2.6 2.7 4.1 4.2 4.3 4.4 4.5 4.6 4.7 Growth of Total Agriculture, Industry and Services (Percentage Change Over Previous Year, 1995=100) ..... Total Agricultural Output Index (1995=100) Output Indexes for Broad Agricultural Aggregates (1995=100) ...... Sub-sectors as a Proportion of Agriculture Selected Economic Policy Reforms in Jamaica, 1977-98 ..... Residuals for Banana Price in Banana ECM . Residuals for Banana Quantity in Banana ECM Residuals for Sugar Price in Banana ECM Residuals for Wage in Banana ECM . . Crossand Autocorrelograms in Banana ECM Plot of Eigenvalues for the Banana ECM . Impulse Responses to an Innovation in Sugar Price--Banana VAR . . . . . . ix . . . . . . . . . 29 29 31 31 33 43 92 92 93 93 94 94 112

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4.8 Impulse Responses to an Innovation in Fertilizer Price--Banana VAR. . ... 112 4.9 Impulse Responses to an Innovation in Wage --Banana VAR . . . . . 112 4.10 Impulse Responses to an Innovation in Banana Price--Banana VAR .... 4.11 Impulse Responses to an Innovation in Banana ... 112 Quantity--Banana VAR .............. 113 4.12 Impulse Responses to an Innovation in Sugar Quantity--Sugar VAR. .......... 113 4.13 Impulse Responses to an Innovation in Coffee Quan ti ty--Coffee VAR . . . . . . 113 4.14 Impulse Responses to an Innovation in Pimento Quantity--Pimento VAR .............. 113 4.15 Impulse Responses to an Innovation in Banana Price--Sugar VAR. . . . . ..... 128 4.16 Impulse Responses to an Innovation in Fertilizer Price--Sugar VAR .... 128 4.17 Impulse Responses to an Innovation in Wage --Sugar VAR. . . . . . ... 128 4.18 Impulse Responses to an Innovation in Sugar Price--Sugar VAR ..... 4.19 Impulse Responses to an Innovation in Banana . 12 8 Price--Cof fee VAR . . . . . . 12 9 4.20 Impulse Responses to an Innovation in Fertilizer Price--Coffee VAR. . .. 129 4.21 Impulse Responses to an Innovation in Wage --Coffee VAR . . . . ... 129 4.22 Impulse Responses to an Innovation in Coffee Price--Coffee VAR .... 4.23 Impulse Responses to an Innovation in Banana . 12 9 Price--Pimento VAR ............. 130 X

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4.24 Impulse Responses to an Innovation in Fertilizer Price--Pirnento VAR . . ... 130 4.25 Impulse Responses to an Innovation in Wage --Pimento VAR. . . . . ... 130 4.26 Impulse Responses to an Innovation in Pimento Price--Pirnento VAR ... 4.27 Impulse Responses to an Innovation in Cassava . 130 Price--Yarn VAR. . . . . ..... 131 4.28 Impulse Responses to an Innovation in Fertilizer Price--Yarn VAR . . ... 131 4.29 Impulse Responses to an Innovation in Wage --Yarn VAR . . . . . . . 131 4.30 Impulse Responses to an Innovation in Yam Price--Yarn VAR. . . . ... 131 4.31 Impulse Responses to an Innovation in Yam Quantity--Yarn VAR ............... 132 4.32 Impulse Responses to an Innovation in Orange Quantity--Orange VAR ........... 132 4.33 Impulse Responses to an Innovation in Cocoabean Quantity--Cocoa-bean VAR ......... 132 4.34 Impulse Responses to an Innovation in Potato Quantity--Potato VAR .............. 132 4.35 Impulse Responses to an Innovation in Grapefruit Price--Orange VAR. . ..... 133 4.36 Impulse Responses to an Innovation in Fertilizer Price--Orange VAR. . ... 133 4.37 Impulse Responses to an Innovation in Wage --Orange VAR . . . . ... 133 4.38 Impulse Responses to an Innovation in Orange Price--Orange VAR .... 4.39 Impulse Responses to an Innovation in Banana . 133 Price--Cocoa-bean VAR ............. 134 xi

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4.40 Impulse Responses to an Innovation in Fertilizer Price--Cocoa-bean VAR .... 134 4.41 Impulse Responses to an Innovation in Wage --Cocoa-bean VAR. . . . . ... 134 4.42 Impulse Responses to an Innovation in Cocoa-bean Price--Cocoa-bean VAR . 134 4.43 Impulse Responses to an Innovation in Cassava Price--Potato VAR . . . . . . 135 4.44 Impulse Responses to an Innovation in Fertilizer Price--Potato VAR .. 4.45 Impulse Responses to an Innovation in Wage --Potato VAR ........ 4.46 Impulse Responses to an Innovation in Potato Price--Potato VAR ..... 5.1 Nominal Price Changes--Banana (BANP) . . 135 135 135 and Sugar (SUGP) ................ 143 5.2 Nominal Price Changes--Coffee (COFP) and Pimento (PIMP) ............... 143 5.3 Nominal Price Changes--Yam (YAMP) and Orange (ORP) ....... . 143 5.4 Nominal P~ice Changes--Cocoa-bean (COBP) and Potato ( POTP) . . . . . 143 5.5 Real Price Changes--Banana and Sugar (1980=100) . . . . ......... 144 5.6 Real Price Changes--Coffee and Pimento (1980=100) . . . . ..... 144 5.7 Real Price Changes--Yam and Orange (1980=100) ........ . . 144 5.8 Real Price Changes--Cocoa-bean and Potato (1980=100) ................... 144 5.9 Banana Quantity--Actual (LBANQ) and Forecasted (FOLBANQ) ............ 148 xii

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5.10 Banana Price--Actual (LBANPR) and Forecasted (FOLBANPR) 5.11 Sugar Price--Actual (LSUGPR) and Forecasted (FOLSUGPR) 5.12 Wage--Actual (LWAGE) and Forecasted 148 148 (FOLWAGE) . . . . ...... 148 5.13 Fertilizer Price--Actual (LFERP) and Forecasted (FOLFERP) . . . ...... 149 5.14 Sugar Quantity--Actual (LSUGQ) and Forecasted (FOLSUGQ) .............. 149 5.15 Coffee Quantity--Actual (LCOFQ) and Forecasted (FOLCOFQ) .............. 149 5.16 Coffee Price--Actual (LCOFPR) and Forecasted (FOLCOFPR) . . . . . .. 149 5.17 Pimento Quantity--Actual (LPIMQ) and Forecasted (FOLPIMQ) . . . ...... 150 5.18 Pimento Price--Actual (LPIMPR) and Forecasted (FOLPIMPR) ........... 150 5.19 Yam Quantity--Actual (LYAMQ) and Forecasted (FOLYAMQ) .............. 150 5.20 Yam Price--Actual (LYAMPR) and Forecasted ( FOLYAMPR) . . . . . 150 5.21 Cassava Price--Actual (LCASPR) and Forecasted (FOLCASPR) . . ....... 151 5.22 Orange Quantity--Actual (LORQ) and Forecasted (FOLORQ) ...... 151 5.23 Orange Price--Actual (LORPR) and Forecasted (FOLORPR) . ...... 151 5.24 Grapefruit Price--Actual (LGRPR) and Forecasted (FOLGRPR) . . . .... 151 5.25 Cocoa-bean Quantity--Actual (LCOBQ) and Forecasted (FOLCOBQ) .............. 152 xiii

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5.26 Cocoa-bean Price--Actual (LCOBPR) and Forecasted (FOLCOBPR) ............. 152 5.27 Potato Quantity--Actual (LPOTQ) and Forecasted (FOLPOTQ) .............. 152 5.28 Potato Price--Actual (LPOTPR) and Forecasted (FOLPOTPR) ..... 5.29 Banana--Fitted Output for Reform Period . . 152 (BANFITA) and Counterfactual (BANFITC) ..... 161 5.30 Sugar--Fitted Output for Reform Period (SUGFITA) and Counterfactual (SUGFITC) ..... 161 5.31 Coffee--Fitted Output for Reform Period (COFFITA) and Counterfactual (COFFITC) ..... 161 5.32 Pimento--Fitted Output for Reform Period (PIMFITA) and Counterfactual (PIMFITC) ..... 161 5.33 Yam--Fitted Output for Reform Period (YAMFITA) and Counterfactual (YAMFITC) ..... 162 5.34 Orange--Fitted Output for Reform Period (ORFITA) and Counterfactual (ORFITC) . .. 162 5.35 Cocoa-bean--Fitted Output for Reform Period (COBFITA) and Counterfactual (COBFITC) ... 162 5.36 Potato--Fitted Output for Reform Period (POTFITA) and Counterfactual (POTFITC) ..... 162 xiv

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ECONOMIC REFORMS AND AGRICULTURAL SUPPLY RESPONSE IN JAMAICA By Ballayram May 2001 Chairman: Dr. Carlton G. Davis Major Department: Food and Resource Economics A number of economic reform programs have been undertaken in Jamaica, over the past two decades. Designed largely by the International Monetary Fund ( IMF) and the World Bank, these reforms focused on correcting internal policy weaknesses and creating an environment conducive to sustained growth. The reforms emphasized liberal trade and exchange rate regimes, a less intrusive and smaller public sector, and reliance on market forces to determine agricultural prices and quantities. Against this background, this study investigates the impact of these recent economic reforms on agricultural crop supply responses in Jamaica. The estimation technique used an error correction modeling framework based on xv

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cointegration theory, within an estimation framework developed by Johansen. The results of the crop supply response estimation confirm that there is a long-run relationship between agricultural crop output and price incentives. Most of the estimated crop price elasticities are low, statistically significant, and fall within the range estimated by other studies on Jamaica. The adjustment process of the short-run to the long-run was found to be slow for some crops and higher for others. Using a counterfactual, which assumed no change in policy regime, fitted series of supply response functions from the pre-reform period were forecasted within a univariate ARMA(p,q) framework and compared to fitted series of supply responses from the reform period. The results are mixed. It was found that the impacts of the economic reforms in Jamaica are crop and time specific. Mean output was higher in the reform period for four of the eight crops analyzed. Higher real price shifts were observed in the reform period for some crops but these price shifts were also accompanied by higher price variability. This suggests that the pro-competitive effects that were expected to accompany the reforms may have outweighed the stability impulses of administered prices in the pre-reform era. xvi

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CHAPTER 1 INTRODUCTION The past two decades have seen a shift in economic policies throughout Latin American and Caribbean countries. Generally, these policy changes have been in response to debt crises, persistent payments-imbalances, and years of negative and slow growth. By the 1990s a new orthodoxy on development thinking could be discerned. This is manifested in reductions in public sector activities and greater reliance on private sector initiatives and market forces, a departure from the policy regimes of the 1960s and 1970s. In Jamaica, as elsewhere, the new orthodoxy has influenced economic policies in agriculture. In particular, policies have shifted away from far-reaching government interventions and control, to increased reliance on market signals to allocate resources and form prices. In addition, economy-wide macro-economic policy changes have been made which can potentially influence agricultural incentives, income, and output. Designed largely by the International Monetary Fund (IMF) and the World Bank in consultation with 1

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2 the Government of Jamaica, these reforms 1 are expected to favor Jamaican agriculture under the presumption by the Bretton Woods institutions that previous policies severely discriminated against the sector. (This point is elaborated in Chapter 2, section 2. 4) This study pertains to the impacts that these economic policy reforms have on agricultural supply responses in Jamaica, as the agricultural sector emerges from a heavily regulated sector to one that is more open 2 and liberal. This chapter discusses the problems that this study addresses. 1.1 Background to Jamaica's Economic Reforms For purposes of this study, the reform period is defined as the years 1980-1999, while the pre-reform period is 1962-1979. The year 1962 is sufficiently far back to facilitate a meaningful analysis of economic policies and conditions that led up to the dramatic changes in policies in the reform period. The reform period marks a re orientation of economic policy. This change arose from the stabilization programs of the IMF in Jamaica in the late 1970s. It gathered momentum in the 1980s and 1990s in the In this study, "policy reforms," "economic policy reforms," "economic reforms," and "structural reforms," are used interchangeably. 2 Openness is defined here in terms of trade/GDP ratio. A high trade/GDP ratio can coexist with high import tariffs, export subsidies, and non-tariff barriers, signaling a non-liberal trade regime.

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3 form of structural adjustment programs cum reforms for economic liberalization, under the direction of the IMF, the World Bank and other leading external funding agencies, notably the Inter-American Development Bank (IDB) and the United States Agency for International Development (USAID). In effect, therefore, for the purposes of this study, economic reforms refer to the policies stipulated in the stabilization and structural adjustment programs which Jamaica has been pursuing since the late 1970s. When the People's National Party (PNP) took office in 1972 under Prime Minister Michael Manley, it was under pressure to keep its election campaign promises to increase real wages and government spending, and to reduce social inequalities. The responses of the new administration to this situation, domestic events, coupled with subsequent external and were to plunge the economy into a sustained recession and major disequilibrium During the previous administration under the Jamaica Labor Party ( JLP) the performance of the economy was impressive. GDP growth rates were positive and relatively high (5.1 percent per annum over the 1960-1970 period), inflation was less than five percent per annum and the balance of payments showed modest surpluses (Jefferson, 1972; IMF, 1998). Over the 1970-1980 period, however, real GDP declined by -0. 9

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4 percent. In fact, with the exception of a negligible O. 7 percent GDP growth in 1978, negative growth rates were recorded for all other six years between 1974 and 1980. Similarly, in the productive sectors, agricultural value added growth declined from 6.7 percent in 1970-1972 to -0.4 percent in 1973-1980. For these same periods, declines in growth rates were recorded for services from 9.2 percent to -0.08 percent, manufacturing from 6.7 percent to -4.2 percent, construction from 6.0 to -10.6 percent, and mining from 13.2 percent to 1.3 percent, respectively (Singh, 1995; IMF, 1998). The external account reflected the production crisis described above. Between 1972 and 1980, the accumulated deficit on the balance of payments was US$ 67 9. 2 million, compared to an accumulated surplus of US$95 million between 1960 and 1971 (Jamaica, Bank of Jamaica, 1985). Further, the deficit on the current account increased almost three fold from US$570.2 million in 1962-1970 to US$1,620.0 million in 1972-1980, and the capital account became negative in 1977, signaling capital flight. At the same time, the dwindling net capital inflows could not match the current account deficit. This depleted international reserves, which plummeted sharply from US$103.l million in 1972 to minus US$461 million in 1980 (Sharpley, 1984). The

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5 foreign exchange shortage harmed the economy given its relative openness and dependence on foreign intermediate imports. Finally, the inflation rate, which was 4.2 percent per annum over the 1960-1970 period, recorded double-digit rates throughout the following decade. Domestic prices, as measured by the consumer price index, rose sharply, recording an annual increase of 25 percent and 47 percent over the 1974-1975 and 1978-1979 periods respectively (Sharpley, 1984). By the late 1970s economic conditions had deteriorated to such an extent that, when combined with manifest signs of social distress (such as a dramatic increase in violent crimes and emigration of skilled workers), they generated a sense of instability and crises (Stephens and Stephens, 1986; Kaufman, 1985). The crises indicated a need for policy changes, and it appears that the government was thinking along those lines as reflected in a Stand-by Agreement it negotiated with the IMF just before the December 1976 elections. The Agreement included, inter alia, a wage freeze, fiscal restraint and a currency devaluation of 20 to 40 percent (Boyd, 1988). However, in the December 1976 election the PNP was returned to office and the new administration promptly rejected the IMF Stand-by Agreement as being inconsistent with the

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6 mandate obtained in the recent election. Instead, and as a way of avoiding agreements with the IMF (Boyd, 1988), and after significant slippages in the external and domestic accounts (Sharpley, 198 4) the government implemented its own adjustment policy measures in January 1977. These included (Boyd, 1988) (1) The reversal of the previous wage indexation policy; ( 2) Implementation of import and exchange controls, a dual exchange rate system and foreign exchange rationing; (3) Suspension of foreign debts for 18 months; and, (4) A search for funding from sympathetic governments. However, it became increasingly clear to the government that in order to attract foreign capital some formal agreement with the IMF was necessary. In this regard, the government signed three agreements with the IMF between August 1977 and June 1979. The first was suspended after four months for failure to meet the stipulated quarterly tests. The second, signed in May 1978, was pursued in all its aspects by the government for a year. Although economic performance under this agreement was poor (Sharpley, 1984; Boyd, 1988), it was re-negotiated in June 1979. However, the agreement collapsed in December 1979

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after the government performance criteria. failed to meet the 7 program's In 1980, in light of increasing social unrest, continued slippages in the fiscal deficits and external accounts, severe foreign exchange shortages, and rising inflation (Sharpley, 1984; Boyd, 1988; Thomas, 1999), Prime Minister Manley called a general election so that the nation could decide on a path of economic development and "what part the IMF should play; or whether it should play any part at all" (Jamaica, API, Manley, 1980). Dubbed "the IMF election", the October 1980 election was won by the strongly pro-IMF JLP, led by Edward Seaga. Expectedly, therefore, both the IMF and the government began negotiations on funding and policies aimed at ameliorating the country's economic and social crises. The new administration relied heavily on funding from external sources. Several loan agreements were negotiated with the leading collaborating lending institutions, the IMF, World Bank, IDB and USAID which have developed a system of cross-conditionalities on their loans, given their basic set of shared objectives on economic policies and on Jamaica (Anderson and Witter, 1994). The prescription by the IMF and the World Bank (Fund/Bank) on how best to deal with the economic crisis in

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8 Jamaica was a series of economic policy reforms. These reforms went beyond the expenditure-switching and expenditure-reducing policies which macroeconomic policy would normally prescribe under these circumstances, to include a built-in bias in favor of a more liberal economic system. The latter aimed at, inter alia, fiscal discipline, liberalizing the domestic market and the external trade sector, privatizing of State Owned Enterprises (SOEs) and other social services, and generally, greater reliance on market signals to allocate resources. Over all, between 1981 and 1992, IMF financial support totaled US$1,036.9 million (under 10 agreements), and that of the World Bank OS$360.4 million (under 8 agreements). 1.2 Problem Statement and Study Justification The cumulative effect of the reforms mentioned above is that the Jamaican agricultural sector is now expected to operate within an economic framework that differs vastly from that of the 1960s and 1970s. Historically, the sector has played an important role in the economy. Agricultural output to GDP ratio is about eight percent, agricultural food export in total export is approximately 20 percent, and agriculture's share of the total labor force exceeds 26 percent (Davis et al., 1999). Given the importance of agriculture in the economy, how the sector performs in this

PAGE 25

9 new policy environment demands urgent attention. In this study, agricultural sector performance is evaluated in terms of crop supply responses. Quan ti tati ve analyses of the impact of these reforms on Jamaican agricultural supply responses are both scarce and generally not particularly robust. In addition, the literature on similar economic reforms in other countries suggests that the outcomes of these reforms are ambiguous, which justifies more empirical work (Khan, 1990). Additionally, this study is justified on the following grounds. First, the economic reforms have generated major changes in the long-term prospects for the national economy within which agriculture will operate. After two decades a good set of data on these policy reforms is now available which should facilitate empirical research on the impact of these reforms on agricultural crop supply responses. This evaluation can throw light on courses of action which policy makers in Jamaica and elsewhere can consider as they seek to reactivate and sustain long-term growth in the agricultural sector. Second, while a few studies have analyzed specific aspects of the impact of the economic policy reforms on Jamaican agriculture (Singh, 1995; Anderson and Witter, 1994; Newman and Le Franc, 1994; and Brown, 1994), what is

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10 lacking is an over-all analytically rigorous assessment of agricultural supply responses within the context of the new policy framework as defined by the recent economic reforms. Finally, analysis of the impact of these reforms on agricultural supply responses is both timely and urgent in light of an on-going debate. A number of writers in the Caribbean and elsewhere have been advancing the view that the kinds of economic reforms implemented in Jamaica and in other countries have harmed agriculture (Anderson and Witter, 1994; Green, 1989; Bates, 1989). In contrast a recent series of studies sponsored by the World Bank suggests that policy reforms similar to Jamaica's should boost agriculture in countries which have traditionally discriminated against the sector (Schiff and Valdes, 1992a; Krueger, 1992). 1.3 Objectives of the study The general objective of this research is to evaluate the impact of economic reforms on selected agricultural crop supply responses in Jamaica over the 1980-1999 period. The specific objectives are to provide ( 1) A statistical evaluation of the performance of the agricultural sector in the pre-reform (1962-1979) and reform (1980-1999) periods.

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11 (2) A descriptive, diagnostic and statistical analysis of the economic reforms that have taken place over the past two decades within the agricultural sector. (3) An econometric (modeling) evaluation of selected agricultural crop supply responses over the 19621999 period, with the number and types of crops chosen for study to be determined by data availability. ( 4) A comparative analysis of agricultural crop supply responses in the reform period with a counter factual, the latter defined as crop supply responses that would have resulted in the absence of the reforms. 1.4 Hypotheses The following hypotheses are advanced in the study: (1) Structural reforms significantly improve agricultural supply responses. (2) Sustained improvements in agricultural supply responses require further broadening and deepening of the reform process. 1.5 Conceptual Issues Agriculture features prominently in policy discussions when (1) it is believed that agricultural policies and institutions help precipitate an economic problem which

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12 policy makers are addressing; and ( 2) when agriculture is an important contributor to employment, GDP, export earnings, domestic food supply, and revenue for the government (Binswanger and Deininger, 1997). Various publications by the government of Jamaica suggest that both of these factors have featured prominently in the recent reforms, while ( 2) was a major consideration in the pre reform (1962-1979) period. Consequently, the conceptual ization is based on two perspectives that are believed to have influenced the design of the policy reforms: ( 1) The Jamaican government perspective. From numerous government publications, it would appear that this perspective is clustered around three main goals: (a) increasing food supply; (b) developing rural areas; and (c) increasing agriculture's contribution to the over-all economy, through, inter alia, employment, contribution to GDP, and export earn ings; and, (2) The IMF /World Bank perspective, whose focus on the agricultural sector clusters around (a) getting prices right; (b) improving efficiency; and (c) rationalizing public expenditure in the agricultural sector.

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13 These two perspectives are not mutually exclusive. On the contrary, the choice of policies in a particular program package results from extensive discussions between officials of the IMF/World Bank and the Jamaican government. The program policy mix therefore reflects the particular economic situation in the country as well as the preferences of the government (Khan, 1990). Despite this, a review of the literature on Jamaican agricultural policy formation suggests that since the late 1970s the influence of the IMF/World Bank perspective has exceeded the Jamaican government's. Given the two perspectives mentioned above, and a review of the content of the reforms since the late 1970s, the evidence suggests that the reforms have focused on three inter-related issues of relevance to the agricultural sector: (i) agricultural (inter-sectoral) terms of trade; (ii) agricultural growth, its adjustment, and supply response; and (iii) efficiency in the agricultural sector (Nallari, 1992; Singh, 1995; World Bank, 1996). Figure 1.1 conceptualizes the Jamaican agricultural sector, dividing the factors impacting agriculture into external (e.g., world prices) and internal. The latter can be separated into those that are exogenous (weather, terrain) and those that are policy induced. Another useful

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,..._ P4 pg P6 P3 P4 P7 Bxternal Trada imports exports Bxogenoua: Weather, Terrain. PS bogenOU8: World Prices P2 P4 PS P6 l I Pl I i PB Pl0 1_,___ ._______. Non-l'ara BOU8e Bolds: resources L---. Agriculture l Production consumption rarm Bouae Bolds Input resources consumption i I---+ :+crops livestock fisheries i H Agri. Product Marketing Non-Agri. Production/ Marketing Agricultural :Input Marketing L------......J~ PB, PlO I Policy Ina~ta: Pl. Agriculture Projects: infrastructure, land development, mechanization, crop improvement P2. Rural industries P3. Monetary Policies: interest rates, exchange rates. P4. Tax Policies: income, indirect, customs duties. PS. Public consumption, rural invest. P6. Rural health, education, welfare. P7. Import/Export controls, tariffs, subsidies. PB. Price controls, input subsidies. P9. Agriculture credit Pl0.Marketing: infrastructure, information. i Agriculture Sector Per~ormance :Indicators: 1. Economic Outcomes (a) quantity flows supply response agri. in GDP i agri. growth rate (b) agri. multi-factor productivity (c) relative prices (TOT, RER). 2. Accumulation Indicators coefficient of investment capital formation agri. employment agri. tax revenues rural nutrition farm incomes a 14 i Figure 1.1: Conceptualization of the Jamaican Agricultural Sector.

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distinction is between prices and non-price 15 factors (including exogenous shocks) that influence agricultural output and income. At the micro level, the price variables include prices of the particular crops, prices of alternative crops, input prices, and the general price level. Government policies exercise both direct and indirect influence on agricultural prices and thus on agricultural performance. Direct (sectoral) policies such as price ceilings, guaranteed prices and trade taxes/subsidies provide incentives to shift resources among crops and sectors of the economy (Binswanger, 198 9) Government policies also can influence output indirectly through macroeconomic (or economy-wide) policies. The most critical components in this regard are fiscal policies and exchange rate policies (Mamingi, 1997). The terms of trade as a policy variable turns on the hypothesis that if all prices were determined in markets, and effective rates of taxation were equalized across commodities, then agriculture's supply responses, growth, income, and contribution to GDP would be higher compared to situations of negative effective protection of the sector (Schiff and Valdes, 1992b). A negative effective protection of agriculture arises when:

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16 ( 1.1) where Pis a general price deflator, Pi denotes the producer price of crop i, P! is the free-market price for crop i, and o is a subsidy to producers as a proportion of cost, Ci. For many developing countries, agricultural pricing policies have consistently kept Pi below and subsidies have not sufficiently (Schiff and Valdes, counterbalanced this 1992b). Given severe disparity budgetary constraints, to achieve at least zero effective protection, the policy implication of (1.1) is to raise the real producer price, or to liberalize all markets thereby eliminating the disparity between Pi and In effect, improving agriculture's terms of trade. The exchange rate sets an upper limit on agricultural export earnings and, when combined with input taxes and subsidies, affects input prices and competing agricultural imports. Changes in the real exchange rate can affect agricultural output and growth by altering the terms of trade between agriculture and non-agricultural sectors. Further, policies that lead to over-valuation of the exchange rate can adversely affect export crops, encourage

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17 rent-seeking activities and generate unproductive uses of resources (Jaeger, 1991). In addition to the price policies, non-price factors constitute an important influence on agricultural output. These factors reflect the material conditions of production in the agricultural sector. Some of these factors include, inter alia, government expenditure and investment in the agricultural sector, construction of rural infrastructure (roads, drainage and irrigation works), extension services, rural credit institutions, and dissemination of relevant scientific information to farmers. The aim of agricultural sector-specific programs is to find a mix of policies for increasing efficiency and productivity in the sector. It is assumed that these two outcomes are necessary (if not sufficient) conditions for increasing agricultural output and growth by reducing costs of production and/or increasing product prices. These policies focus on using public investment in agriculture efficiently, reducing marketing and transport bottlenecks, improving agricultural extension services, health and education (capital formation in agriculture), rationalizing input prices, and enhancing the efficiency of parastatals in the agriculture sector. Behrman (1990) has advanced an important perspective on the channels through which policies affect performance

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18 indicators. He argues that analysis of the impact of policy reforms on performance indicators must explicitly consider the conduits through which the effects of the policies are transmitted to the observed (or desired) outcome. These umeso-level" variables, identified as markets (product, input and financial) and infrastructure (economic and social) are the interface between the policies ( sector specific and economy-wide) and targets. In Figure 1.1, for example, various policies, i = 1,2,3, ... 10, are identified. Some are macroeconomic (e.g., monetary policies and exchange rate adjustments) whereas others are sector specific (e.g., agriculture credit, tariff reductions in the trade sector and elimination of price controls in agriculture) These policies are then combined to achieve specific targets, either at the sectoral or macroeconomic levels. Behrman (1990) emphasizes, however, that the meso level variables condition the effectiveness of policies on the target variables and that analyses of policy impacts must also evaluate how policies have influenced these meso level variables. For example, if poor transportation, lack of effective irrigation, or in-efficient research and extension services restrain farmers' responses to higher

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19 prices, then improving these meso-level variables may do more for the farmer than price increases (Chhibber, 1989). The influence of the factors mentioned above on agricultural supply responses has been well documented in the empirical literature. However, the recent economic reforms have had an enduring effect on these factors. This points to an important issue raised in the literature, viz., how to measure the magnitude of reforms (IDB, 1997). Economic statistics such as exchange rate differentials, inflation rates, tax changes and so on deal with outcomes rather than the policies that gave rise to them. In order to address this issue, structural policy indexes have been constructed by Lora (1997) and others. The index by Lora ( 1997) was constructed for twenty countries in Latin America and the Caribbean. This index seeks to measure the extent of market freedom accorded to economic policies in areas of trade, tax, finance, privatization and labor. In each of these areas indices of market freedom are identified. For example, in trade policy the indices are average tariffs and tariff spreads; in tax policy the indices include, inter alia, tax rates on companies and on individuals; and on financial policy, indices include freedom of interest rates on deposits, loans, reserves on bank deposits, etc. (IDB, 1997). The

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20 structural policy index is a simple average of the indices in the five areas. The index can range from Oto 1, based on the worst and best observations respectively, on market freedom in the country. Further, the index is an important indicator of the extent to which countries are departing from past ways of operating their economies. Jamaica's structural policy index has shown continuous movement towards market freedom, increasing from O. 426 in 1985 to 0.684 in 1995. These index values are higher than the average reported for the twenty Latin American and Caribbean countries in the sample (Lora, 1997). 1.6 Procedural and Methodological Considerations In approaching the general and specific objectives of the study, critical analysis of the Jamaican agricultural sector's performance is first undertaken. To achieve the general objectives and, more specifically, objectives (2), (3) and (4), requires four specific tasks. The first task is to identify and evaluate the structural reforms undertaken since the late 1970s. Both the stabilization and the structural reform policies are evaluated according to their ( 1) theoretical bases; ( 2) breath of vision; and ( 3) logical consistency. Second, supply response models are developed and estimated. Finally, specific objective (4) is

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21 addressed within the context of simulating an alternative path to that of the reforms. Any appraisal of the reforms must compare their impacts not to the pre-reform indicators but rather to some specified, hypothetical alternative. This requires some simulation exercises. The idea here is to generate simulated time series over the reform period for the performance indicators. The simulated series then constitute the counterfactual to which the actual outcomes in the reform period are compared. For the counterfactuals, at least three scenarios appear to be logical extensions. Scenario 1 assumes that the policies pursued over the pre reform period continued into the reform period, i.e., that the reforms over the 1980-1999 period were not instituted. Scenario 2 assumes policies based on the Jamaican government's critique of the IMF /World Bank programs were implemented. Finally, Scenario 3 assumes that IMF/World Bank assistance came with no conditionalities. These components form the basis of the data series for the simulation exercise. The three scenarios appear logical and plausible options given (1) the initial defiance by the government of the mandated reforms in the late 1970s; (2) the frequent failures to meet conditionality tests because the reforms

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22 were not implemented; and (3) government's own protest ations over the years of what are considered "acceptable" reforms. While numerous instances of disagreements exist between the Jamaican government and the IMF/World bank with respect to appropriate reform policies, the clearest statement to this effect is captured in the following quotation: Even though lender and borrower the ref ore shared the common concern regarding the need for economic reform, there is still much debate about appropriate social policies and about the specific effects of manipulating major economic variables such as exchange rates and interest rates. This was illustrated by the lengthy and tortuous negotiations between the government of Jamaica and the IDB and World Bank on the 1989-90 Agricultural Sector Adjustment Loan. (Anderson and Witter, 1994, p.14) 1.7 Summary It is generally believed that developing countries have historically discriminated against their agricultural sectors in favor of industrial development. It is further felt that policies that reverse that discrimination should boost agricultural output and income Over the past two decades far-reaching economic reforms have been undertaken in Jamaica leading to a new economic framework within which the agricultural sector must now operate. This study seeks to assess the impact of these reforms on agriculture crop supply responses, an undertaking that is both timely and

PAGE 39

23 urgent given the importance of the sector in the economy and the absence of rigorous analytical studies on the Jamaican experience with these reforms

PAGE 40

CHAPTER 2 ECONOMIC STRUCTURE, GROWTH, AND POLICY REFORMS IN THE JAMAICAN ECONOMY, 1962-1999 It is useful, at the outset, to situate the discussions in this chapter against the backdrop of three distinct phases of economic policy that characterize the economic history of Jamaica over the period 1962-1999. In the first phase, 1962-1972, the Jamaica Labor Party (JLP) emphasized free markets. However, Bonnick (1984) argues that despite the rhetoric of economic liberalism, in reality the government pursued an import-substitution (IS) strategy based on protectionism, trade restrictions, and price controls. In the second phase, 1972-1980, the People's National Party (PNP) under Prime Minister Michael Manley pursued economic populism and state directed control (dirigisme) in an effort to build democratic socialism. This phase was marked by extensive government intervention, which included nationalization of major industries, price controls, and subsidization of basic foods and some agricultural imported inputs. These policies made significant demands on national resources and created a large bureaucratic economic 24

PAGE 41

25 structure. Consequently, the government was unable to respond to the crisis in the world economy in the mid1970s, and was forced to approach the IMF for stabilization funding in 1977. The third phase, 1980-present, is directly linked to the policies of the previous phases, particularly the 1977 IMF stabilization funding. Phase three can be viewed as having two sub-phases. The first is the return of the JLP to government in 1980-1989 and the implementation of various Fund/Bank stabilization and structural adjustment programs. The second, 1989-present, marks the return to government by the PNP, the continuation of the market led policies of the preceding JLP administration, but more importantly, the intensification of the country's commit ment to liberalization of the economy. The rest of this chapter is organized as follows: Section 2 .1 provides an overview of the structure of the economy and its growth performance. Section 2.2 highlights trends and policies in the agriculture sector. Sector 2. 3 describes the economic reforms, and Section 2.4 provides a summary of issues raised in this chapter.

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26 2.1 Structure of the Economy and Economic Growth: An Overview The relative contribution of economic sectors to GDP in Jamaica has not changed much over the past two decades. The data presented in Table 2.1 and Figure 2.1 show that agriculture's share has been fairly stable, ranging from an average of 8.4 percent over the period 1969-1979 to 8.0 percent over 1990-1998. Although agricultural contribution to GDP is lower than that of the other sectors, these comparatively small percentage contributions are deceptive. Davis et al. ( 1999) have shown that the sector plays an important part in the country's employment, food production, and foreign exchange earnings. The industrial sector, which includes mining, manufacturing and construction, contributed on average 39.1 percent to GDP in the period 1969-1979 but declined to 37.3 percent in 1990-1998. The manufacturing sub-sector also shows a decline from 18.4 percent in 1969-1979 to 16.3 percent in 1990-1998. Finally, services constitute the largest sector of the Jamaican economy, accounting for over 50 percent of GDP over the period 1969-1998. Its contribution to real GDP has increased from 48.5 percent in 1969-1979 to 54.7 percent in 1990-1998.

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Table 2.1: Sectoral Contribution to Real Gross Domestic Product (Period Averages, Percentage). 1969-79 1980-89 1990-98 1969-98 Agriculture 8.4 6.4 8.0 7.6 Industry 39.1 34.5 37.3 37.0 Manufacturing 18.4 20.9 16.3 17.1 Services 48.5 59.1 54.7 53.9 Source: Computed using data from Jamaica, Planning Institute of Jamaica (various issues). Note: The 1998 data used were preliminary estimates. 2000 1600 .-1200 a 41> ., 800 400 0 1969 1973 1977 1981 1985 1989 1993 Years 1969-1998 I -+Real GDP -+-Agri. --Indus. -.Serv. I 1997 27 Figure 2.1: GDP and Sectoral Contribution to GDP in Constant 1995 Dollars. Real sectoral and GDP growth rates are estimated and shown in Table 2.2. The estimation uses a log-linear model, Yt = o + P*Time + Et and corrected for autocorrelation whenever it exists. Over the 1969-1998 period, growth rates were positive but consistently low, while sub-period growth reveal mixed results. GDP growth rates were just over two

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Table 2.2: GDP and Sectoral Growth (1995=100) (Percentage) 1969-79 1980-89 1990-98 1969-98 GDP 2. 3a 2. 4 -0.2 1.10 Agriculture -0.3 1.1 2. 3 0. 6 Industry 0.9 4. 3 -3. Ba 0. 8a Services 4. 6a 0.1 2. 4 1. 6a Source: Computed using data from Jamaica, Planning Institute of Jamaica (various issues). Note: The 1998 data used were preliminary estimates. a, b, indicate statistical significance at the five and 10 percent levels, respectively. 28 percent in the 1969-1979 and 1980-1989 periods. Services growth was reduced from 4.6 percent over 1969-1979 to 2.4 percent over 1990-1998, and was insignificant over the 1980-1989 period. Industrial growth, which was 4.3 percent in the 1980-1989 period, declined significantly to -3.8 percent over the 1990-1998 period. Agriculture growth over the 1990-1998 period was 2.3 percent, compared to its insignificant growth in the two previous sub-periods. Over the entire 1969-1998 period agriculture and industry recorded less than one percent growth, while GDP and services grew just over one percent, respectively. These periodic growth rates mask the highly volatile annual growth rates in these economic series as shown in Figures 2.2 and 2.3. Over the period 1969-1998, annual growth rates for GDP ranged between -9.0 and 13.0 percent, and were negative in 12 of the 30 years. For the

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15 10 5 t,I II .IJ i:: 0 0 J,f -5 Pl -10 -15 1970 1974 1978 1982 1986 Years 1969-98 1 -.-GDP Growth I 29 1990 1994 1998 Figure 2.2: GDP Growth (Percentage Change Over Previous Year, 1995-100). 40 30 20 t,I 10 Ill .IJ i:: 0 0 J,f -10 Pl -20 -30 -40 1970 1974 1978 1982 1986 1990 1994 1998 Years 1969-98 1 -+-Agri. Growth -aInd. Growth _.,_Ser. Growth Figure 2.3: Growth of Total Agriculture, Industry and Services (Percentage Change over Previous Year, 1995=100). 1969-1998 period, agriculture growth rates ranged between -12.6 and 29.3 percent, and were negative in 15 years.

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30 Similar annual volatilities characterize the industrial and services growth rate series. 2.2 Output Trends and Policies in Jamaican Agriculture The growth rates of volume indexes for total agriculture and broad aggregates (food, crops, livestock and cereals) and graphs for these series are shown in Table 2. 3 and Figures 2. 4 and 2. 5. With the exception of the cereals index, which declined over 1971-1998 at an annual rate of -3. 2 percent, the other indexes grew but at less than two percent over the 1971-1998 period. Table 2.3: Growth Rates of Total Agricultural and Broad Agricultural Aggregate Indexes. 1971-79 1980-89 1990-98 1971-98 Total Agriculture 1. 6a 1.7 1. 9a 1.7a Food 1. 6a 1.6 1. 9a 1. 7a Crops 2. 6 1. 5a -3.6 1.7a Livestock 1. 6a -16.5 0.7 1. 3a Cereals 16. ga -9.0 -4. 5 -3. 2 Source: Computed using data from Food and Agriculture Organization, FAO Production Yearbook (various issues). a b, indicate statistical significance at five and ten percent levels, respectively. The composition of agricultural output is shown in Table 2.4. Export agriculture, domestic agriculture, and livestock, forestry and fishing constitute the aggregative components of agriculture in GDP. The data in Table 2.5 and the graph in Figure 2.6 show that domestic agriculture

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31 0 120 0 It) 90 OI OI 60 M 't1 s:: 30 H 4.1 ::, 0 4.1 ::, 1971 1976 1981 1986 1991 1996 0 Years 1971-98 1 -+-Total Agriculture I Figure 2.4: Total Agricultural Output Index (1995=100). 250 0 0 ... 200 It) OI OI 150 M 100 ,:j s:: 1-1 50 4.1 ::, '11 0 4.1 ::, 1971 1976 0 1981 1986 1991 1996 Years 1971-1998 J ---.-crops -+-Livestock --cereal -+-Food Figure 2.5: Output Aggregates (1995=100). Indexes for Broad Agricultural

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32 Table 2. 4: Composition of Agricultural Output (J$M, 1995=100), Selected Years. Agriculture and Subsectors 1969 1975 1980 1985 1990 1995 1998 Total 95.9 105.9 101. 3 72.2 104.4 154.4 120.4 Agriculture Export 34.3 25.9 14.8 13.9 13.6 15.4 12.3 Agriculture Sugar 22.6 17.7 9.9 7.8 9.2 9.4 7.9 Cane Other 11. 6 8.2 4.8 6.1 4.4 5.9 4.4 Main Exports Domestic 33.0 47.1 56.9 38.0 66.2 114.5 82.8 Agriculture Root 14.4 26.9 25.3 14.6 35.1 62.8 42.7 Crops Other 18.6 20.2 31. 7 23.4 31.1 51. 7 40.1 Primary Products Livestock, 28.6 32.9 29.5 20.3 24.6 24.5 25.3 Forestry & Fishing Source: Jamaica, Planning Institute of Jamaica (various issues). Table 2.5: Agriculture Sub-sectors as a Percentage of Total Agriculture (Period Averages). Agriculture Sub-sectors 1969-79 1980-89 1990-98 1969-98 23.47 15.95 11. 21 7.29 Export Agriculture (5.50) (2.24) (1.46) (6.26) 44.78 57.21 68.79 56.13 Domestic Agriculture (5.32) (6.35) (3.62) (11.17) Livestock, Forestry & 31. 73 26.85 20.03 26.59 Fishing ( 1. 35) (4.49) (2.50) (5.65) Source: Computed using data from Jamaica, Planning Institute of Jamaica (various issues). Note: Figures in parentheses are standard deviation.

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80 60 t,I .. i:: 4 0 u l,f 20 0 1969 1973 1977 1981 1985 1989 1993 1997 Years 1969-98 -+-Export Agriculture -Dorne3tic Agriculture --.-Livestock, Fishing & Forestry Figure 2.6: Sub-sectors as a Proportion of Agriculture. 33 constitutes the largest proportion of total agriculture in the 1969-1998 period and other sub-periods. In 1969-1979 the domestic agriculture sub-sector averaged 44. 8 percent in total agriculture and increased to 68.8 percent in 19901998. Both of the other two sub-sectors, export agriculture and livestock, forestry and fishing, have declined over the period 1990-1998 compared to the two previous decades. In terms of growth rates, Table 2.6 shows that domestic agriculture and its sub-groups are the only sub-sectors within the agri-sub-sector that have positive growth rates over the 1969-1998 period. This has compensated somewhat for the negative and low growth in the other agriculture sub-sectors.

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Table 2.6: Growth Rates of Agricultural (Percentage). Agriculture Sub-sectors 1969-79 1980-89 1990-98 Export Agriculture -4. 4a -1. 3 -4 .1 D Sugar -4.2a -2. 6a -5.4a Other main exports -4. 6a 1. 3 1.0 Domestic Agriculture 1. 8 11. 2a 25. 6a Root crops -2.7 19. 7a -1.2D Other domestic crops 4. oa 3. 4 -1.4 Livestock, forestry, & -1.5 -3. 3a 0.1 fishing Jamaica, 34 Components 1969-98 -2 .1 a -2. oa -2.7a 2. 6a 2 6 2. 7a -1. 7a Planning Source: Computed using data from Institute of Jamaica (various issues). a, b' indicate statistical significance percent levels, respectively. at the five and ten The stagnation of agriculture in Jamaica, which can be inferred from the growth rates in Table 2.6 reflects, to a large degree, the state of the agricultural sectors in many less developing countries (LDCs) A plausible explanation for this, is that the development literature in the 1950s and 1960s viewed agriculture as a static sector, from which resources could be shifted to promote industry, considered as the dynamic sector. To a large extent, this view has influenced the kinds of agricultural policies that have been pursued in the past by LDCs. In this regard, Schiff and Valdes (1992a, p.59) state: In many developing countries, the high rate of agricultural taxation has been part of an explicit or implicit policy of industrialization-led growth, justified in part by the belief that industry was the dynamic sector while agriculture was static and not very responsive to incentives. That means that economic growth could be accelerated by shifting

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35 resources from agriculture to industry, by taxing agriculture directly, and by protecting the industrial sector. Many governments in the developing countries have intervened in their agricultural sectors, both directly through agricultural sector policies, and indirectly, through economy-wide policies such as industrial protection. Direct interventions take numerous forms. Some of these include procurement measures (e.g., government marketing boards as sole buyers of agricultural output, and suppliers of major agricultural input) ; quotas and direct taxation on various agricultural export crops; subsidies on farm credit and farm inputs; and quantitative restrictions and tariffs on imported agricultural imports. While some of the direct interventions have benefited agricultural producers, some are tantamount to an implicit tax on agriculture, depressing farmgate prices and farm incomes below levels that would otherwise prevail (Schiff and Valdes, 1992a). Indirect forms of interventions affect agricultural production incentives via macroeconomic policies (e.g., overvaluation of the exchange rate) and industrial protection policies (Krueger, 1992). Various measures have been employed by the Jamaican government to extract surpluses from the agricultural sector. These include taxing agricultural exports,

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36 controlling farmgate prices by the state marketing boards, over valuating exchange rates and reducing internal agricultural terms of trade relative to manufacturing. Studies by Gafar (1980, 1997) and Pollard and Graham (1985) strongly support this proposition. Table 2.7 shows that Tabla 2 7 : Average Ra tas of Growth of Farmgata and F. O. B. Prices, output and the Nominal Protection Coa~~icient (NPC) for the Period 1970-1978, Jamaica. Growth Rates (%)1970-78 a Farmgate F.O.B. Nominal Protection Commodities Prices Prices Output Coefficient b Sugar Cane -1. 25 4.13 -2.90 0.82 Banana 5.06 7.77 -7.43 0.77 Cocoa 1. 64 14.06 -0.54 0.72 Coffee 9.80 8.34 2.02 0.61 Coconut 7.06 4.67 -21.00 n.a. a Adapted from Pollard and Graham (1985), Table 2. b Based on the data in Pollard and Graham (1985), Table 4 for 1970-1979. The NPC is defined as the ratio of farmgate prices (Pr) to F.O.B. prices (Pw) received minus marketing and processing costs (C): NPC =Pr/ (Pw C). over the 1970-1978 period, the nominal protection coefficient (NPC), for Jamaican agriculture was less than one, indicating the extent to which the sector was taxed. For example, a NPC of O. 82 means that the commodity is taxed at a rate of 18 percent. Subtracting one from the NPC gives the nominal protection rate, NPR, which, according to Table 2. 7, is negative, suggesting that producers of the crops reported were not supported, but were taxed instead.

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37 It should be emphasized also, that developing countries have a tendency to overvalue their domestic currencies, so that the official exchange rates used to calculate the NPC overstate internal prices. Hence government taxation of agriculture is even greater than is usually captured by the NPC (Gardner, 1987). More recent data compiled for this study also support the proposition that Jamaican agriculture has been taxed. Table 2. 8 reports on the net barter and income terms of trade between agriculture and manufacturing for Jamaica. The net barter terms of trade, N, is defined here as where PA and PM are the price indexes of agriculture and manufacturing, respectively. Values of N greater than one indicate that prices of agricultural commodities have risen relative to those in manufacturing (Tsakok, 1990). The estimates in Table 2. 8 reveal that over the 1966-1978 and 1989-1999 periods, the net barter terms of trade were unfavorable to Jamaican agriculture, but were favorable over the decade 1979-1988. Although the net barter terms of trade may move against agriculture, the sector can still increase its purchasing power if agricultural output increases pro portionately more than the decrease in agricultural prices.

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38 The purchasing power of agriculture is captured in the net income terms of trade, I, estimated as: I is the agricultural output index. The index I is therefore the net barter terms of trade adjusted for marketed amounts. Increases in I indicate a rising purchasing power of agriculture for buying manufactured goods (Tsakok, 1990) The data in Table 2. 8 suggest that agriculture's ability to purchase manufactured goods have peaked in the 1979-1988 period. Table 2.8: Net Barter Terms of Trade and Agricultural Income Terms of Trade (1980=100; Period Averages). 1966-78 1979-88 1989-98 Net Barter Terms of Trade 0.60 1. 39 0.86 Income Terms of Trade 58.20 159.18 134.03 Source: Compiled by author. 2.3 The Economic Reforms After the late 1970s, Jamaica undertook major changes in economic policies, both sector-specific and economy wide. These policy changes have been an integral part of the conditionalities usually attached to program packages by external funding agencies. The reforms vary in intensity, both inter-temporally and in terms of the areas in which policy reforms were enacted, such as foreign trade, taxation, and liberalization measures. Underlying

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39 the reforms has been a conviction by funding agencies that freer markets allow for a more productive and efficient use of scarce resources, a condition not necessarily accepted uncritically by the Jamaican government. Nevertheless, a number of market restrictions were eliminated, regulations deemed necessary were simplified and made more transparent, and private enterprises were encouraged. The most conspicuous reforms were the liberalization efforts to facilitate foreign trade and financing activities through exchange rate and tariff adjustments. The reform period, 1980-1999, has been punctuated by successive IMF/World Bank stabilization and structural adjustment programs, as well as programs financed by other international funding agencies. The stabilization and structural adjustment programs of the IMF and the World Bank for Jamaica were developed as programs of policy reforms, aimed at reversing, within a few years, the economic crisis in the country. Three facets of this crisis emphasized in the IMF/World Bank literature are structural factors, external factors and poor domestic policies, including an anti-agricultural bias in pricing policies. However, it is a matter of public record that these IMF/World Bank reforms have been resisted by the Jamaican government at various times, and have been hotly debated in

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the popular media regarding the social impacts 40 of externally imposed structural adjustment programs. Despite this apparent dissatisfaction, the government has utilized operational policies along the lines of those prescribed by the Bretton Woods institutions. While in principle the early reforms were aimed at stabilizing the economy, from the outset there was a built-in bias in the policies for a more liberalized economic system. For example, the three year Extended Fund Facility {EFF), which the Jamaican government signed with the IMF in May 1978, contained specific conditions for exchange rate devaluation as well as the liberalization of domestic prices {Boyd, 1988). More extensive economic reforms were advanced by the new administration under Prime Minister Edward Seaga who held office from 1980-1989. Under the 1982 and 1983 structural adjustment programs, quantitative restrictions {QR), on trade were replaced by tariff equivalents, and by 1984 all QRs were eliminated. Exchange rate devaluation was pursued in 19841985, followed by the unification of the official and parallel rates. At the same time, the government began to reduce the public sector via the divestment of state owned enterprises (SOEs).

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41 The 1990s witnessed the most protracted reforms aimed at deregulation and liberalization of the Jamaican economy. The PNP, which took off ice in 198 9 under Prime Minister Michael Manley, had campaigned in the general elections on a platform similar to the JLP, vowing to continue the JLP's market oriented policies. This was an opportunity for a fresh departure for the PNP, which was noted for its previous support for democratic socialism and extensive government intervention. Garrity (1996, p.52) argues that ... the Manley-led government made the decision in 1990 to t~uly embrace the market and proceed with economic liberalization reforms. For Manley ... [t] o continue the reforms of the previous JLP government would not be sufficient to bring about the needed structural changes, nor were there sufficient resources to continue the previous reforms .... Based on the limited available options, Manley's commitment to the liberalization process represented a turning point in state-society relations in Jamaica. In particular, the administration sought an accommodative pattern of governance between state and social actors by energizing the private sector. In this sense, the liberalization process can be viewed as "state-sponsored disengagement from a command role in the economy ... and the creation of an enabling environment for private sector-led growth and development." (Garrity, 1996, p.52)

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42 Against this background, the sectoral adjustment loan, which the government signed with the World Bank in 1989, focused on liberalizing the agriculture sector along the following lines: (1) Deregulation of the coffee and cocoa boards by 1990; (2) Shifts in government focus from production and markets to support services and infrastructure; ( 3) Continuation of the di vestment of agro-enterprises (e.g., sugar); (4) Elimination of subsidies on agricultural credit; (5) Reduction of tariffs; and (6) Elimination of the Generalized Food Subsidies; As a result, by 1991 the generalized food subsidies were eliminated, and complete liberalization of the exchange market and credit interest rate were achieved. Consequently, the overall liberalization process, since the first agreement with the IMF in 1977, opened markets to more competition and reduced the role of the public sector. As would be expected, the conditions that accompanied the loans from the Bretton Woods Institutions involved reforms at both the macroeconomic and the sectoral levels. Figure 2.7 is a schematic presentation of the kinds of economic reforms and a partial list of specific tasks undertaken since the late 1970s. For convenience, the

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\ Overall Objectives Means Components I Domestic 1 Economic Stability, Growth, Improved Living Standard 1 Fiscal Economic/Structural Reforms Monetary/Fin. l Trade Deregulation Reforms Reforms Liberalization Specific Changes Elimination of govt. marketing board Removal of price controls Improve land tenure Sell/lease state land Divestment of govt. assets New pricing system for coffee, cocoa, citrus and pimento and more appropriate sharing of export earnings a, Wage restraint Reduced subsidies Cost recovery Tax changes Expansion of agriculture non-price incentives Elimination of subsidies on interest, loans and food Introduction of Generalized Consumption Tax ', Devaluation Elimination Exchange rate of liberalization quantitative Open capital restrictions account Tariff Banking reductions reforms Elimination Interest rate of exports and credit restraints liberalization Rationaliz* Capital market ing of Reforms import* Creation of licensing Agriculture system Credit Bank Elimination and National of subsidies Development on imported Bank foods. 43 l Other Reforms Social safety net program Land capability classificati on Soil conservation Irrigation rehab. Planned rehab. of the sugar Industry Increase availability of foreign inputs Agro-21 program Hillside project Figure 2.7: 1977-98. Selected Economic Policy Reforms in Jamaica,

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44 policy reforms are organized into five blocks: (1) Domestic Deregulation; (2) Fiscal Reforms; (3) Monetary/Financial Reforms; ( 4) Trade Liberalization and ( 5) Other Reforms. Within each block, specific tasks are listed, reflecting implicitly and explicitly policy changes, which seek to enhance the role of market forces, encourage private sector initiatives and reduce government regulation and intervention in the economy. It is interesting to note that in spite of intense criticisms regarding the reforms' lack of success in producing the economic recovery predicted by its proponents, the trend of economic reforms has not been reversed in periods of economic stress. Such periods have instead been met with the broadening and deepening of reform efforts. Table 2. 9 shows selected economic statistics in the pre-reform and reform periods in Jamaica. In the area of trade, average tariffs in 1986 were 56 percent but fell to 11 percent in 1995. Exchange rate systems have been an important policy instrument in Jamaica to establish restrictions on capital outflows and restrictions for repatriating export revenues and foreign exchange. Following the exchange rate liberalization, many of these restrictions have been dismantled. As evidence of the process of exchange rate unification and deregulation,

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45 Table 2.9 shows that the exchange rate differential (i.e., the difference between the average market price for foreign exchange--inclusive of transaction costs and exchange rate taxes--and the official rate) was 25 percent in 1986 but fell to 5 percent in 1995. Tabla 2.9: Outcomes of Economic Reform Policies in Jamaica, 1986 vs. 1995. Structural Maximum Tax Rate Policy Tariff Exchange Reduction Differential Companies Individuals Index, I Net aver. Tariffs% Percent Percent Percent O
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46 2.4 Summary The evidence presented in this chapter suggests that the direction of change of economic structure of the Jamaican economy that began around 1980 continued over the past two decades. Over the period 1966-1998, growth rates of GDP and economic sectors were low but positive. With few exceptions, sectoral growth rates in the reform period, 1980-1999, were higher than in the pre-reform (1962-1979) period. Some structural change within the agricultural sector was discerned from the data. In particular, export agriculture declined relative to other agri-sub-sectors. Domestic agriculture recorded impressive growth, and it is this agri-sub-sector's overall growth performance that partially compensated for the negative and low growth in the other agri-sub-sectors. Significant economic reforms were implemented, and analysis of their impact on agriculture will be undertaken in a later chapter.

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CHAPTER 3 METHODOLOGICAL AND EMPIRICAL ISSUES IN ECONOMIC REFORMS AND SUPPLY RESPONSE The purpose of this chapter is four-fold. First, it reviews the methods that have been used in the literature to analyze the impact of economic reforms in developing countries. Second, it reviews the literature on supply response analysis. Third, it develops a crop supply response model for Jamaica; and finally, it presents the data sources for the analysis. 3.1 Review of the Economic Reform Literature In this review of the economic reform literature two sets of issues are addressed. First, attention is directed at the way the economic reforms are conceptualized. Second, the analytical methods used to evaluate the impact of the reforms are examined. One of the most challenging problems in capturing the impact of economic reforms on target variables is how to isolate the effect of each of the reforms undertaken. Since the reforms were undertaken in various areas (trade reforms, fiscal/monetary reforms, etc), at different points in time, and with varying levels of intensities, their 47

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48 effects on particular variables in the economy become compounding and difficult to isolate. Khan ( 1990) argues that it is theoretically and empirically difficult to link all the policy reform measures to the ultimate targets of the policies. Hence most studies have attempted to assess the effects of the overall policy package on particular target outcomes. This is the thrust of the studies by Chadha et al. ( 1998) and some of the studies in Tshibaka (1998), Campbell and Stein (1991), La Guerre (1994), Valdes and Muir-Leresche (1993), Commander ( 198 9) and Weeks (1995). In these studies, neither the precise nature of the underlying economic relationships, nor the specific policies adopted are made explicit. Instead, the attention is on whether or not the program package, which in effect gives rise to a particular policy environment, has been "effective" in the sense of achieving broad macroeconomic objectives (Khan, 1990). Most of the studies reviewed proceeded from the premise that the economic reforms originated from the conditionalities packages. In all that accompanied country experiences Fund/Bank-supported the problem of economic instability provided the initial imperative to seek IMF assistance, whi~h, when it came took the form of stabilization policies. However, in most of the studies

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49 reviewed the stabilization policies are treated en passant, or not at all, and instead attention is focused exclusively on economic outcomes associated with structural adjustment 3 For most developing countries that have been pursuing the Fund/Bank economic programs, the last decade has been one in which the reforms have in tens if ied the process of liberalizing the economic system. A frequently raised question with respect to Fund/Bank-supported structural adjustment programs cum structural reforms for economic liberalization, has been whether these programs are effective in achieving stated economic objectives. A second and related question is how to measure the effects of these reform policies on the target variables identified in the programs. With respect to this latter question, Guitian (1981) has argued that economic performance under a policy package should be compared to a counterfactual. The latter is defined as economic outcomes that would have taken place in the absence of the program package. The concept of a counterfactual is intuitively appealing and is a standard yardstick widely used in economics to measure the impact of policy interventions. Since the counterfactual is Stabilization policies are designed to put the economy back on its equilibrium path, whereas, structural adjustment policies aim at putting the economy on a new (higher) equilibrium.

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50 unobservable, it has to be estimated, hence alternative methodologies used to evaluate the program effects are judged in terms of their estimates of the counterfactual. Although the preceding two questions have generated a large body of literature over the past two decades, there is little consensus in the economics profession either about the impact of past programs on target variables, or about how to estimate the effects of program packages (Khan, 1990). With respect to measuring the impact of reforms, there are three main approaches that have been applied in the empirical l i terature. These are ( 1) The before-after approach, which compares the behavior of key macro-economic variables before and during, or after, any particular reform period, or policy package. ( 2) The with-without approach compares the performance of macro-economic variables of non-program countries (the control group) with those from program countries. A modified version of this approach is a reduced form regression estimate that controls for initial conditions in program and non-program countries. (3) The comparison-of-simulations approach. This approach simulates performance of Fund/Bank-type

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51 programs then compares them with simulated outcomes from another set of policies. Approaches (2) and (3) have been used extensively in cross country studies to assess the impact of Fund/Bank-supported programs. The before-after approach has been the most prevalent in country case studies using time series data. 3.1.1 Before-After Approach The before-after approach has been the most popular in the literature on Fund/Bank-support programs. Reichmann and Stillson ( 197 8) were the first to use this approach in an examination of 79 Fund/Bank-supported programs over the period 1963-1972. They compared growth, balance of payments and inflation in the two years prior to and after the implementation of the program. Connors (1979) also used the before-after approach to evaluate 31 programs in 23 countries over the period 1973-1977. More recent studies include Singh (1995) and the papers in Le Franc (1994), on Jamaica. Similar studies on other developing countries include most of the papers in Tshibaka (1998), Campbell and Stein (1991), La Guerre (1994), Valdes and Muir-Leresche (1993), Commander (1989) and Weeks (1995). The before-after approach is viewed by some analysts as providing a relatively poor estimate of the counter f actual to program effects by assuming that non-program

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52 determinants of economic variables remain constant between non-program and program periods. This assumption has been questioned in the literature in light of the fact that non program determinants of economic outcomes (e.g., terms of trade variations, changes in international interest rates, weather, etc.) typically change year after year. Conse quently, Goldstein and Montiel (1986) have demonstrated that the before-after estimates of program effects are biased, since all economic changes in program period are incorrectly attributed to program factors. These authors suggested further that the before-after estimates are unsystematic over time, since in any particular year program effects will often be dominated by non-program factors. For example, if a hurricane damages infrastructural works and agricultural crops, then agricultural growth may decline, causing all programs in that year to appear to have performed poorly. 3.1.2 With-Without Approach The with-without approach seeks to overcome the shortcomings of the before-after approach by comparing economic outcomes between program and non-program countries. Since both groups of countries are subjected to the similar external environments, it is argued that this comparison cancels out the non-program determinants, so

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53 that any observable differences in outcomes in the two groups of countries are attributable to the Fund/Bank supported programs. In other words, the economic outcomes observed in non-program countries are taken as the counterfactual of what would have occurred in program countries in the absence of Fund/Bank-supported programs. Donovan (1981, 1982) was the first to use this approach on a sample of Fund/Bank-supported programs implemented between 197 0-198 0. Loxley ( 198 4) also applied this approach to 38 less developed economies (income of $690 or less) with Fund/Bank program during 1971-1982. Other similar studies include Gylfason ( 1987) and Pastor (1987). A major problem with the with-without approach is that countries in the sample of non-program and program countries are not randomly selected. They are program countries because of poor economic conditions prior to entering into a Fund/Bank-support program. Consequently there is a systematic difference between these two groups of countries and the non-random selection of program countries produces a biased estimate of program effects. This is the result of attributing observable differences in economic outcomes between program and non-program countries to program status, when in fact the initial economic

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54 position of the two groups of countries is an important determinant of economic performance. To overcome this problem requires identifying the specific differences between program and non-program countries in the pre-program period and controlling for these differences prior to comparing economic outcomes. This is the idea underlying a modified version of the with without approach. In this regard, Goldstein and Montiel (1986) proposed a generalized evaluation estimator to control for pre-program differences between program and non-program countries. This estimator is a reduced-form relationship that links changes in macroeconomic outcome (program target variables) to lagged values of the target variables, lagged values of policy variables and variables that represent exogenous effects on the target variables. These authors applied this approach to a sample of 58 developing countries in which 68 programs were implemented over the period 1974-1981. The approach was extended by Serieux (1999) to include the effect of democratization in Fund/Bank-supported program countries. 3.1.3 Comparison of Simulations Approach Finally, the simulation approach relies on simulations of economic models to make inferences on hypothetical outcomes of Fund/Bank-type policy packages. Khan and Knight

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55 (1981), created a simulation using panel data for 29 developing countries in a dynamic econometric model. The objective was to investigate the hypothetical effects of pursuing a stabilization program with similar policy characteristics as a Fund/Bank-supported one. The authors later (Khan and Knight, 1985) extended their simulation exercise to include comparisons of alternative packages. More recent studies include Robinson and Gehlhar (1996), and Chadha et al. (1998), who use computable general equilibrium (CGE) models for the Egyptian and Indian experiences with recent economic reforms, respectively. 3.2 Preliminary Issues in Modeling Supply Response in Jamaica A number of empirical studies have been conducted in the past to estimate crop supply response in Jamaica. A partial list of these studies is shown in Table 3.1. With the exception of Ga far ( 1997), all of the studies were conducted prior to the period 1980-1999. In addition, none of the studies addressed the issue of the impact of economic reforms on agricultural supply response. One approach to capturing the long-run and short-run changes in agriculture supply response is to use the Nerlovian-type partial adjustment models. Both quantity and prices can be modeled to adjust to their long-run or equilibrium path, and the model is capable of estimating

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Table 3 1: Empirical Responses in Jamaica. Studies Crop/ Category Period Author & Year Banana 1954-1972 Gafar (1980) 1961-1979 Pollard & Graham (1985) Cocoa 1954-1972 Gafar (1980) 1961-1979 Pollard & Graham (1985) Coffee 1953-1968 Williams (1972) 1954-1972 Gafar (1980) 1961-1979 Pollard & Graham (1985) Citrus 1961-1979 Pollard & Graham (1985) Sugar 1954-1972 Gafar (1980) 1961-1979 Pollard & Graham (1985) Broad Ag:ri. Ag:g:reg:ates 1964-1990 Gafar (1997) Export Domestic Livestock Forestry & Fishing Total Agri. on Agricul. ture 56 Supply Funct. Price Elasticity Form Short-run Long-run Linear 0.16 0.57 log 0.49 -2. 72 Linear 0.41 2.56 Log 0.74 0.76 Log 0.70 -0.80 Linear 0.92 1.15 Log 0.10 0.07 Log 0.24 -1.33 Linear 0.17-0.29 0.31-0.7 log 0.24 1. 41 log 0.20 0.35 0.15 1.08 0.15 0.21 0.02 0.21 0.12 0.23 Source: Adapted from Gafar (1997, p.213). both long-run and short-run parameters as well as the speed of adjustment towards the long run equilibrium. A potential complication in such an analysis of long-run and short-run changes is that most economic time series data are non stationary and usually characterized by a unit root. This means that the linear properties of the series such as its mean and variance, are not constant over the sample but change over time (Greene, 1993; Gujarati, 1995). Nelson and Plosser (1982) have shown that the economic implications of an economic time series that is

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57 characterized by a unit root are different from those of a stationary process. In particular, an economic time series with a unit root will have a permanent response consequent upon any shock in the system. In contrast, a stationary series will reflect only a transitory response. Therefore, the response to economic reform policies is not simply a "policy on", "policy off" or one-shot choice. Even if the reforms were temporary, so long as the economic series possesses a unit root, there will be a permanent response. Non-stationarity of variables poses problems of estimation of functional relationships using conventional econometric methods. In the first place, Fuller (1976) has shown that under non-stationarity the limiting distribution of the asymptotic variance of the parameter estimates is not finitely defined, hence the conventional t and F tests are inappropriate. Secondly, non-stationarity gives rise to spurious correlation among variables (Greene, 1993). In macroeconomic time series it is not unusual to find that a variable is non-stationary in its level 4 but stationary in first differences. In technical terms, if Yt is nonstationary, but /J.Yt (the first difference) is stationary, then Yt is integrated of order one, i.e., Yt~I(l). If two Data that have not been transformed in any way (such as logarithmic transformation, first differences, etc.), are said to be in 'level' form.

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58 series Yt and Xt are I(l) then a linear combination of them, e.g., Zt = Yt ClXt may also be I ( 1) However, there may also exist, a value for a that ensures that Zt is stationary. In such an event, the two series are said to be cointegrated, and the cointegrating vector is denoted (1, a). It is tempting to conclude that in order to estimate meaningful relationships among non-stationary variables, all that is necessary is to difference the variables until they achieve stationarity and then estimate the relationship. However, Johansen and Juselius ( 1990) argue that unless the difference operator is also explicitly applied to the error process, such differencing results in loss of information. In this event, resorting only to estimating the relationship in difference form captures only the short-term relationship among the effects, variables while is the left long-term undetected (Nickell, 1985) Finally, differencing the economic series may not be appropriate, such as when economic theory postulates a relationship among variables in levels, not in difference forms. To overcome these problems, econometricians have developed an approach known as error correction models based on cointegration. Hylleberg and Mizon (1989, p.124) claim that

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59 ... when estimating structural models it is our experience from practical applications that the error correction formulation provides an excellent framework within which, it is possible to apply both the data information and the information obtainable from economic theory. Error correction modeling requires two conditions (1) All variables must be integrated to the same order, i.e., Yi~I(d), where dis the number of times Y has to be differenced to achieve stationarity; and, (2) All variables must be cointegrated of order (d b), where b>O. The idea underlying cointegration is that one or more linear combinations of non-stationary variables are stationary. That is, cointegration approaches the station arity issue as linear combinations of economic series, rather than by differencing the series. The implication of this is that if a set of variables is cointegrated then, following the Granger Representation Theorem (Fuller, 1985) a valid error correction representation of the data exists. In effect, then, cointegration is a test of existence of a long-run relationship of variables that are integrated of the same order (Greene, 1993; Gujarati, 1995). However, an important feature of error correction models based on cointegration is that the data in both levels and differences are included, thereby facilitating investi gation of both short-run and long-run effects in the data.

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60 3.3 Error Correction Model The error correction model (ECM) can be derived from a re-parameterization of an Autoregressive Distributed Lagged (ADL) model (Hendry et al., 1984). Alternatively, the ECM can be derived from the dynamic optimizing behavior of economic agents. This latter approach is presented here. Following Nickell (1985), suppose economic agents optimize their behavior with respect to an inter-temporal quadratic loss function: ( 3. 1) L == f CX. 5 ~l (Yt+s Y:+s} + {Yt+s Yt+s-1 ) 2 2 02 s=O where y* is the desired or long-run equilibrium value of Y that the economic agent can control to minimize L, conditional on information at time t, and subject to movements in Y. The discounting and weighting factors are a, (OO), respectively. Minimizing ( 3 .1) with respect to Yt+s gives a second order difference equation whose solution at time tis: (3.2)

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61 where 1 is the stable root {i.e., the root that is < 1) from solving the characteristic equation of the general Euler equation which was derived from minimizing (3.1 ) Equation ( 3. 2) is an optimal rule and has an error correction term in brackets [.] ; the coefficient ( l-) is the speed of adjustment, i.e., the speed of closure to any discrepancy between desired and actual values of Y. In the error correction term in ( 3. 2) the long-run target is a convex combination of all target values from Y ; 1 onwards. Nickell (1985) has shown that when 8 2 = 0 in the loss function (3.1) then equation (3.2) nests the forward looking partial adjustment model (PAM): (3.3) It is important to note that the dynamic equation ( 3. 3) does not necessarily have to be of a standard PAM type. To estimate (3.3) empirically requires the parameterization of Y ; +s. One way is to model this sequence of expected target values as a stochastic process such that Y ; +s are expressed in terms of current and lagged values and substituted into (3.3). The resulting equation may not necessarily result in a PAM. Indeed, Nickell demonstrates that when

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62 ... we allow the target [Y ; +J to follow anything more complex than a first order autoregression, the structural equation [ 3. 3] which is fundamentally a partial adjustment model, will reduce to an error correction mechanism in terms of observable variables (Nickell, 1985, p.124). Suppose that the actual values of the series follow a second order autoregressive scheme with a unit root and a drift: ( 3. 4) where s>O, Et+s is white noise, and g is the drift. Denoting the expected value with an (*), the expectation of (3.4) is: (3.5) Following the derivations by Nickell (1985) and Alogoskoufis and Smith (1991) the solution to the second order difference equation (3.5) is: ( 3. 6) Substituting (3.6) into (3.2) yields the decision rule in the form: ( 3. 7) where c 0 2 + (1 1 X1 eJ ( ) tlYt = C + ( ( )\ tlYt + 1 AYt-l yt-1 1 + 0'. 1 1 J = g(l ~x2 ~0'.X1 X1 eJ (2 ~)2[1 + 0'. 1 (1 ~)] g(a X1 1 ) (2 ~xl 0'.) Equation (3.7) is written in the form of an ECM. The parameterization of y in terms of exogenous variables would

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63 reflect the long-run cointegrating relationship. According to Nickell (1985, p.124), since it is almost a stylized fact that aggregate quantity variables in economics follow second order autoregression with a root close to unity, we may find the error correction mechanism appearing in many different contexts. 3.4 An Error Correction Model for Crop Supply Response in Jamaica The dynamically unrestricted version of the ECM in (3.7) can be expressed as: ( 3. 8) where Otis output in logarithm and Wo, W1, W2 are straight forward analogues of the intercept and coefficients in (3. 7) Generally, o: is parameterized in terms of other (weakly) exogenous variables. This parameterization shows the long-run cointegrating relationship between the exogenous variables and the dependent variable, Ot. Given the unrestricted nature of (3.8) a wide range of possible processes that describe the law of motion of Ot can be accommodated. Following previous agricultural supply response models, quantity supplied is postulated as a function of the expected values of a set of variables that are believed to capture agricultural incentives. These exogenous variables are, the price of the crop, Pc, the price of substitute crops, Psi, i=l, 2. . and the prices of

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inputs. In this study, two inputs are 64 considered, fertilizer and labor, whose prices are denoted as F and W, respectively. With these specifications of the exogenous variables, the parameterization of the long-run equilibrium supply function of the c th crop is: ( 3. 9) c = l...n ; i = 1 2 ... ; t = 1... T ; &t ~ii d ( 0 er) where all variables are measured in logarithms (to facilitate interpretation of estimated coefficients) and have been previously defined. The superscript e denotes expected value of the variables. Given the parameterization in 3.9, the general error correction model, with all variables as previously defined, can be written as: (3.10) b.Qt = E\.o + f\.lb.Qt-1 + f\.zb.P:,t-1 + E\.31b.P:i,t + f\.4b.w:-1 + f\.5b.F; _l + f\.6(Q~-1 Qt_i) + \) To give empirical content to these models, the specification of expected values must be addressed. Clearly if economic agents had full information about the current variables at the time they are set, then actual values of the variables would be substituted for their expected values. When this is not the case, some process of expectations formation has to be assumed. The applied literature on ECM has largely ignored issues of

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. ~. 65 expectations. Because of its simplicity in practice, actual values are usually substituted for expected values. An alternative to this procedure is to assume that crop and input prices are determined by policy changes for one year in advance. With this assumption, expectations about current economic variables have to be based on information that is available up to the end of the previous period, (t 1). This can be expressed as: ( 3. 11) x ~e = E(x t l
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66 (3.13) (3.14) (3.15) Using (3.8)-(3.15), the general error correction model for the c th crop is: 2 2 (3.16) !:J.Q c ,t = O'.oc + 0'.1c,tf:J.Qc,t-l + L 0'.2jf:J.Pc,t-j + L 0'.3 i k f:J.Ps i, tk j = l k = l 2 2 + L 0'.41!:J.Wtl + L 0'. 5m f:J.F t m + aio c <5 1 P c 0 2 1 P si 1 = 1 m= l where, i=l,2, all variables as previously defined, and the random error term is suppressed. The last term in (3 .16) is the error correction term. In the empirical estimation of the ECM (3.16), the error correction term is usually specified as the residual from the cointegrating relationship. The Engle-Granger (1987) two-step method has been used extensively in the applied literature to estimate the ECM (3.16). However this method assumes that the cointegration vector is unique. Except in the bivariate model, this assumption may be violated in multivariate models. To test for, and estimate multiple cointegrating vectors, Johansen (1988) and Johansen and Juselius (1990) have devised an appropriate method within the following framework. Define a

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67 standard vector autoregressive (VAR) model with lag length k as: (3.17) t=l.. T where X is an Nxl vector of N endogenous variables, Et~iid ( O, A) with dimension NxN. The long-run, or cointegrat i ng matrix is: ( 3 18) I Il l II 2 ... Il k = II The number of distinct cointegrating vectors, r, which exists between the variables of X, is given by Rank ( IT ). Most economic time series appear to be integrated to the order of one, in which case, r~N-1, where N is the number of variables in the vector X. In the case of a bivariate model, N = 2, and therefore if the variables are cointegrated, then there is a unique cointegrating vector. The matrix rr is then decomposed as: (3.19) II = a.W where represents the matrix containing the r cointegrating vector, and a. is the matrix of weights with which each cointegrating vector enters each of the differenced X equations. A large a value implies that the system will respond to any deviation from the long-run equilibrium path with a rapid adjustment. If a' s are zero

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68 in some equations, this is a sign of a weak exogenei ty, implying that the variable does not respond to the disequilibrium in the system. The parameters a. and form an over-parameterization of the model. However, the space spanned by~, sp{~), can be estimated, and shown to be the empirical canonical variates of Xt-k with respect to AXt. This is in effect the following theorem advanced by Johansen: The maximum likelihood estimator of the space spanned by~ is the space spanned by the r canonical variates corresponding to the r largest squared canonical correlations between the residuals of Xt-k and AXt corrected for the effect of the lagged differences of the X process. {Johansen, 1988, p.233) Implementation of this theorem begins by re-para meterizing {3.17) (detailed derivations are in Johansen, (1988) and Enders, (1995)), into the following error correction model: (3.20) where ri = -I + TI1 + II2 + ... + IIi, i=l .. k Without any loss of information, the ECM in (3.20) is therefore a transformation of the VAR{k) model in equation (3.17), and is expressed in first differences and augmented by the error correction term, rkxt-k The long-run equilibrium or impact matrix is the matrix rk and is equivalent to IT = a'~ in (3 .19). The rank of IT is the basis of

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determining the number of 69 cointegrating relationship between the variables in the ECM (3.20). Johansen (1988) identifies three possibilities with regards to Rank(IT ) (1) rank(Il) = 0. This means that the variables are not cointegrated and the model is basically a VAR in first differences. ( 2) 0 < Rank (IT) < p. In this case, the variables are cointegrated relationship(s) and is the number less than variables, p, in the model. of the cointegrating number of (3) rank(Il) = p. This means that all variables are stationary and the model is in effect a VAR in levels. The loglikelihood representation of (3.20) is: (3.21) L(et, ~, 0) = ,ni -T/2 Johansen's procedure begins by regressing ~Xt on the lagged differences of M{t and generating fitted residuals Rot, then regressing Xt k on the lagged differences and generating fitted residuals, Rkt These fitted residuals are then used to construct the following product moment matrices: (3.22) 1 T -R j t' TL R it i = l i, j = 0, k

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70 The product moment matrices (3. 22) are then used to find the cointegrating vectors by solving the determinant: (3.23) This will yield the estimated eigenvalues ... An) and eigenvectors (v 1 ... Vn), which are normalized such that: (3.24) v's k'.k'. v = r where Vis the matrix of eigenvectors. The most significant eigenvectors then constitute the r cointegrating vectors, i.e. (3.25) Using (3.25), a is then estimated from (3.19). The critical issue in all of this is to determine which, and how many, of the eigenvectors in (3.24) represent significant cointegrating relationships. First, the vectors that have the largest partial correlation with Axt, conditional on the lags of AXt, are identified. Second, the eigenvectors that correspond to the r largest eigenvalues are chosen. Finally, to determine the value of r the following test statistics suggested by Johansen (1988) are employed: n (3.26) n 1 (q, n) = -T L ln(l li) i =q+l (3.27)

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71 The null hypothesis HO: r:$q is tested with ( 3. 2 6) while HO: r = q is tested against Hl: r = q + 1 with (3.27). The critical values for these tests are taken from OsterwaldLenum (1992). The critical values from this source recalculates and extends those critical values from Johansen (1988) and Johansen and Juselius (1990), to handle a full test sequence from full rank ( r = p, i.e., Xt is stationary) to zero rank ( r = 0, i.e., all linear combinations of X are I ( 1) ) for at most 11-dimensional systems. The cointegration technique is used to determine the long-run relationships among variables. This co-movement of variables in a long-run supply function has not been explored for Jamaica. Cointegration analysis is appropriate in this regard. It will also suggest which variables in the supply function are in the long-run equilibrium. 3.5 The Data The principal sources of annual time series data, which are used in this study, are the Food and Agriculture Organization (FAO) agricultural database (FAOSTAT), avail able on the internet, and annual publications of various government agencies in Jamaica. These include, Economic and Social Survey of Jamaica (Jamaica, Planning Institute of Jamaica); Production Statistics (Jamaica, Statistical

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Institute of Jamaica (STATIN)); Statistical 72 Digest (Jamaica, Bank of Jamaica), Statistical Year Book of Jamaica (Jamaica, Statistical Institute of Jamaica (STATIN)); Census of Agriculture, 1968, 1978, 1996 (Jamaica, Statistical Institute of Jamaica (STATIN)) ; International Financial Statistics (IMF); and data from published and unpublished documents from the Ministry of Agriculture in Jamaica. It should be noted that the data on Jamaica contained in the FAO database, FAOSTAT, IMF and World Bank sources are based on publications and data supplied by STATIN, Ministry of Agriculture, and other agencies of the Government of Jamaica. The data used to estimate the supply functions in this study are taken from the FAO FAOSTAT database. This is the most comprehensive data set on crop output and prices in Jamaica. However, in several areas the data are less than ideal. For example, whenever data are not available the FAO provides its own estimates based on past crop performance, other crops and other country's data. Jamaican officials claim that production data for some crops reported on the FAO data base are not monitored in Jamaica. The crop output and price data are annual series, and are collected at the farmgate through periodic production

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73 surveys and agricultural censuses. Farmers in the Caribbean rarely keep records of production, cost expenditures and so on. Hence, for these farmers it is difficult to recall data for production surveys that are conducted between long periods. Consequently, this casts some amount of suspicion on the reliability of the data. Nonetheless, these are the data reported as official statistics, and are used by the government as a basis for policy analysis/discussion. Several important economic aggregates were not adequately covered in the data sources. In particular, data on agricultural wages are not available on Jamaica. As a result, a proxy for this variable was constructed from data on compensation to agricultural employees recorded in the national accounts. Data on consumer price indexes--the basis for def la ting crop and input prices in this studywere not available as continuous series over the 1962-1999 period. Consequently, a combination of splicing indexes and converting the final 1962-1999 series to a 198 0 base year was undertaken. Although the data are less than ideal in several areas, and are bound to contain some noise, the advantages of using them are:

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7 4 (1) they are from a common source and are characterized by a common accounting/estimating procedure in their derivation; and (2) the data are the most comprehensive time series available on Jamaican crop supply in a single source. 3.6 Summary There are competing approaches in the literature on how to evaluate the impacts of economic reforms associated with Fund/Bank-type policy packages. Each approach utilizes the idea of a counterfactual against which actual economic outcomes in the program period are compared. Estimating crop supply responses over a period in which significant policy changes have occurred, requires a modeling framework that is capable of capturing both the long-run and short run changes. The Nerlovian-type supply response models have been used in the literature for this purpose. However, in the context of data series that are non-stationary, this approach can produce spurious regressions. A more appropriate analytical framework is that provided by error correction models based on cointegration analysis.

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CHAPTER 4 EMPIRICAL ESTIMATION OF CROP SUPPLY RESPONSE The aim of this chapter is four-fold. First, the central motivations for using error correction modeling based on cointegration theory in this study are presented. Second, the time-series data on prices and quantities to be used in the ECMs to estimate crop supply responses in Jamaica are tested for stationarity. Third, long-run supply responses are estimated for eight agricultural crops. Finally, the short-run dynamics of the crop supply responses are analyzed. 4.1 Motivations for Using Cointegration Analysis Chapter 3 provides a fairly elaborate treatment of the theoretical and statistical aspects of error correction modeling based on cointegration theory. Against that background, it is useful to recall in a cryptic, condensed and fairly non-technical way, the central motivations for using this approach to address the issues with which this study is concerned. The empirical purpose of this study is to investigate the impact of economic reforms on crop supply responses in 75

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76 Jamaica. Generally, supply response models in agriculture postulate a long-run relationship between output and agricultural incentives (Askari and Cummings, 197 6) Deviations from this long-run equilibrium occur in the short-run and may involve considerable adjustment costs to the economic agent. This is especially the case when significant policy changes are implemented. Jamaica provides a good case for evaluating the connections between economic reforms and supply responses. The country has traditionally been heavily dependent upon agriculture for food, employment and export earnings. A combination of excesses in state intervention and adverse world economic conditions prompted major economic reforms in the late 1970s in the form of serious macroeconomic stabilization policies under the direction of the IMF. During the early 1980s more far-ranging structural reforms were instituted in an effort to reverse the current account and fiscal deficits, reduce inflation and monetary growth to achieve financial stability, restore economic growth, and so on. Implicitly in these early reforms, but more explicitly in the late 1980s and throughout the 1990s, the aim has been to re-orient the economy towards a more liberal economic system. These reforms took expression in progressive devaluation of the Jamaican dollar, elimination

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77 of state marketing boards, liberalization of agricultural input and output prices and privatization of state held monopolies and public enterprises. Intuitively, therefore, the analysis of supply responses in these situations would require a modeling framework that is capable of incor porating both the long-run and short-run changes. A preliminary analysis of the Jamaican crop output and price data series over the 1962-1999 period reveals sub stantial fluctuations, especially since the early 1980s. The strongly time trended data imply a statistical problem that has not been addressed in applied analysis on Jamaican crop supply responses. This statistical problem is referred to, in the literature, as non-stationarity of the time series. The analytical approach chosen in this study to deal with the above mentioned issues, emphases the importance of considering the interactions between the variables in the system in a simultaneous equation model, and to distinguish between the short-run and long-run effects. The modeling approach differs from those previously used to model Jamaican crop supply responses in two very important ways. First, t?e data are analyzed as a full system of equations. This allows for possible interactions in determining the precise relationships among the variables in the system.

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78 Second, the multi variate cointegration modeling which is used in this study, is designed for this type of empirical work by explicitly classifying the non-stationary and stationary components and facilitating an inter pretation in terms of the dynamics of short-run and long run effects. There are two general motivations for using error correction models based on cointegration theory in applied economic analyses. First, when time-series data are non-stationary, i.e., their linear properties, such as mean and variance, are time dependent, then conventional econometric analysis on such data may produce "spurious" results. Second, even though a set of time series may individually be non-stationary, there may be a linear combination among them that is stationary. Such series are said to be cointegrated, that is, they have a tendency to move together in the long-run, even though in the short-run they may diverge from each other. This co-movement of related series suggests the existence of long-run relationship between them. So when data are non-stationary there is generating the additional possibility that process contains information the data about the equilibrium process that makes the process adjust toward the long-run steady state or the equilibrium path.

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There is, 79 therefore, additional economic insights gained from the recognition that the data are non stationary. The component jkXt-1, (= cx[3' Xt-1, since II = cx[3'), in equation (3.20), is directly related to the non stationarity in the data. Since this component contains information about the speed of adjustment, ex, to some long run relations, [3'Xt, and if the data are cointegrated, then the economic process can generally be understood within a theoretical model that assumes some adjustment behavior. This means that theoretical models such as the partial adjustment model (PAM), which assume static equilibrium, cannot be used when data are non-stationary. 4.2 Definition of Variables In this chapter, error correction models based on cointegration theory are used to test the hypothesis that long-run relationships exist between crop output and price incentives in Jamaica. The estimation procedure is based on the work of Johansen (1988) and Johansen and Juselius (1990). There are basically three steps in this estimation procedure ( 1) test the order of integration of the variables and specify the lag length of the variables using a standard vector auto-regressive (VAR) specification.

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80 (2) estimate the ECM and the number of cointegrating relationship(s) among the variables included in the ECM. (3) perform short-run analysis by conducting innovation accounting on the ECM. For each crop a p-variable vector autoregression (VAR) of lag k is specified. The model is re-parameterized into an error correction model (ECM) as specified in equation (3.20) and estimated according to the methodology of Johansen (1988) and Johansen and Juselius (1990). Esti mation is done using the Regression Analysis for Time Series (RATS), version 4. 3 (Doan, 1996), and Cointegration Analysis for Time Series (CATS) in RATS, (Hansen and Juselius, 1995), computer programs. The variables in each ECM include the output (quantity) of the crop of interest, the price of the crop, the price of a substitute (or alternative) crop, and two input prices, average agri cultural wage rate and average fertilizer price. The choice of a crop price variable is critical for the estimation of crop supply responses. In this regard, Askari and Cummings (1976) suggest using any one of the following prices: (1) Nominal farmgate price; (2) Farmgate price deflated by any one of the following:

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(a) a price index of farmer's inputs; (b) a consumer price index; and 81 (c) some index of the prices of competitive crops (or the price of the most competitive crop). An additional issue that is related to the choice of an appropriate price variable for the crop supply functions especially within the context of the IMF/World Bank programs in Jamaica, is that agricultural price incentives are influenced by various macroeconomic policies. Of particular importance, in this regard, is the real exchange rate (RER), defined as the ratio of prices of tractable (PT) to non-tractable (PN) goods (Tsakok, 1990). Krueger (1992) and Schiff and Valdes (1992a, 1992b), have shown extensively how macroeconomic policies in developing countries generally have a RER effect, which ultimately affect output price and agricultural supply. In the empirical literature the RER is usually approximated as RER=e*WPI/CPI, where WPI is the foreign wholesale price index, CPI is the domestic consumer price index, and e is the official exchange rate. The World Bank's approach to showing the link between macroeconomic policies (as represented by the RER) and real crop prices, decomposes real producer price as follows: RPP =PF/CPI= PF/Pae= e(WPI/CPI) = NPC*RER*p 5

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82 where RPP is real producer price, PF is the farmgate producer price, PB is the border price, NPC is the nominal protection coefficient, and PB is the real border price of the country's exports (World Bank, 1994). This definition of RPP shows that it contains information on the RER and also reveals that it is really hazardous to include both the RER and RPP in the same equation (Mamingi, 1997). In addition to the issues raised above, two additional considerations were taken into account in the choice of the crop price variable. First, since farm producers sell most of their marketable surplus immediately after reaping, farmgate prices seem to be a good approximation for prices received. Second, since farmers purchase most of their requirements from retail markets, the consumer price index seems to be an appropriate def la tor for producer prices. For these reasons, therefore, the farmgate price deflated by the CPI was used as the real producer price (RPP) variable in the supply functions. In effect, this price variable reflects not only price incentives to the producers, but also macroeconomic (reform) policies (as represented by the RER) In particular, this link between the price variable and macroeconomic policies is important in order to pursue an investigation of the hypotheses advanced on page 11.

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The question regarding which alternative 83 (or substitute) crop to include in a particular crop's ECM proved to be difficult. In the absence of recorded data on this issue, the final choice of the substitute crop resulted from a consideration of a number of factors. Among these were, the nature of the crops (traditional export versus domestic crops) ; the terrain where the crops are grown (mountainous versus flat lands) ; tree-crops versus annuals; and, root-crops versus vine-crops. The difficulty of identifying an appropriate substitute crop (or crops) can be demonstrated in the case of banana. Possible substitute crops for banana are coffee and sugar, since these are all traditional export crops. Banana is grown extensively on flat lands, but is also cultivated on hilly terrains as is coffee, whereas, sugar growing has been confined to the relatively flat plains in Jamaica. Hence, both sugar and coffee are plausible substitutes for banana, and the choice of either one or both becomes an empirical issue. A further complication in the choice of a substitute crop arises. In the early 1990s, this researcher observed two cases where lands which were previously used to cultivate sugar and banana were converted into papaya groves. Telephone interviews conducted with officials of

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84 the Ministry of Agriculture, Government of Jamaica, indicate that these are not isolated cases, and that similar land conversions have been observed into cutflowers, decorative foliage, aloe-vera, ochro, and other non-traditional export crops. Several substitute crops were initially included in each ECM but in all cases it was found that the inclusion of only one substitute crop price improved the statistical properties of the model. The alternative crops in each crop's ECM are reported in Table 4.1. Table 4.1: Alternative (Substitute) Crops in Each EOf. Crop Alternative Crops Considered Banana Sugarw, coffee, papaya Sugar Banana w, papaya Coffee Banana, pimento, sugar, orange Pimento Banana, coffee, cocoa bean Yam Cassava, potato, sweet potato Orange Grapefruit w, tangerine, coffee Cocoa Bean Bananaw, pimento, coffee Potato Cassavaw, yam, sweet potato Crop chosen as substitute crop. Two input prices are included in each crop's ECM, namely, fertilizer price and average wage in the agriculture sector. The fertilizer price variable is a weighted price index of all types of fertilizers imported into Jamaica, with quantities as weights. With respect to the wage variable, there are no data on agriculture wages

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85 in Jamaica. A proxy for this variable was constructed from data on compensation to agricultural employees recorded in the national income accounts. Compensation of agricultural employees is defined to include all payments by producers of wages and salaries to their employees in kind and in cash, and of contributions paid or imputed, in respect of their employees to social security schemes and to private pensions, family allowance, casualty insurance, life insurance and similar schemes. (Jamaica, National Income and Product, 1970, p.50) Total compensation to agricultural workers is divided by total agricultural labor force to yield a proxy for the average nominal wage rate. The latter is then divided by the consumer price index and used as the proxy for average real wage rate in the agricultural sector. All variables are measured in logari thrns and indexed at 1980=100. The logarithmic transformation of the data was done because of ease of interpretation of the estimated coefficients and the frequency of its use in the econometric literature. In addition, Doan (1996) has argued that preliminary data transformation helps to straighten out trends and eliminate systematic tendency of the variance of the series to depend on the magnitude of the data. To save on degrees of freedom, the smallest lag length was sought while at the same time seeking to pass the diagnostic tests on the residuals. After some

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86 experimentation and testing, a lag-length of k=2 was settled upon. This is the minimal lag length required for an ECM, and is sufficiently long to accommodate the issues of expectations discussed in Chapter 3. The residual analysis for adequacy of model specification combines statistical testing and inter pretation of graphs. Tests for autocorrelation utilize the Ljung-Box (L-B), LM(l) and LM(4) tests. The (L-B) test is a test of the null hypothesis that the residuals from the first T / 4 observations are not autocorrelated. The LM ( 1) and LM(4) are tests of the null hypotheses of no firstand fourth-order autocorrelation. A multivariate test using the piocedure of Doornik and Hansen (1994) is used to test the null hypothesis that the residuals from the system of equations are normally distributed. Finally, the auto regressive conditional heterosckedasticity (ARCH) test is used to test the null hypothesis that the residuals of each equation in the system are normally distributed. All of the tests mentioned above are distributed as x 2 with the appropriate degrees of freedom and corrected for small sample bias when necessary. A sample of graphs that aid in the residual analysis is presented in Section 4. 4. These residual graphs and residual test statistics for the

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87 tests mentioned above, are normal output from the RATS/CATS program when estimating ECMs, using the Johansen approach. 4.3 Testing for Stationarity A preliminary step in the estimation of the ECM models is to test the data for stationarity. Three tests are used in this study, the Dickey-Fuller (D-F) and Augrnented Dickey-Fuller (ADF), the Weighted Symmetric (W-S), and the Phillips-Perron (P-P) tests. The Time Series Processor (TSP), (version 4.4), was used to conduct these three tests on all quantity and price data. First, tests were conducted on the variables in levels, then on their first difference. The results are shown in Tables 4.2 and 4.3. For the Dickey-Fuller test, the following models were used: ( 4 1) ( 4 2) where Tis time, Xt is a time series, ai are coefficients to be estimated, t is white noise, and ~Xt the first difference. The ADF tests were conducted on the following equations: m ( 4 3) M{t = ao + a1 Xt-1 + J31 L Xt-1 + t i=l m ( 4 4) M{t = ao + a1Xt-1 + a2T + J31L M{t-1 + t i=l

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88 Table 4.2: Unit Root Tests--Prices and Quantities in Levels. Tests for Price Tests Variable W-S 3 DF" P-Pc W-S 3 Banana -2.85 -2.49 -10.22 -1. 74 (0.13) (0.34) (0.42) (0.80) Sugar -1. 66 -1.24 -4.24 -1.39 (0.83) (0.90) (0.87) (0.92) Coffee -2.91 -2.13 -9.08 -1.08 (0.11) (0.53) (0.50) (0.97) Pimento -2.96 -2.57 -15.83 -2.21 (0.09) (0.29) (0.16) (0.49) Yam -1. 96 -1.59 -7.81 -2.06 ( 0. 67) (0. 80) ( 0. 60) ( 0. 61) Orange -1.55 -1.34 -4.95 -1.33 (0.88) (0.88) (0.82) ( 0. 93) Cocoa Bean -2.36 -2.07 -7.94 -2.89 (0.38) (0.56) (0.59) (0.12) Potato -2.36 -2.02 10.74 -3.12 (0.39) (0.59) (0.39) (0. 06) Fertilizer -1. 972 -1.142 -13.460 (0.667) (0.922) (0.141) Wage -2.092 0.743 -16.639 (0.135) (1.000) (0.120) Note: Numbers in parentheses are P-values. a Weighted Symmetric Test. b Dickey-Fuller Test. c Phillips-Perron Test. for Quantity DF" P-Pc -3.70 -5.39 (0.02) (0. 79) -1.14 -5.41 (0.92) (0. 79) -3.08 -25.21 (0.11) (0.02) -2.40 -12.44 (0.38) (0.29) -4.10 -9.46 (0. 01) (0.47) -2.43 -10.63 (0.36) (0.40) -2.53 -20.69 (0.32) (0.06) -0.58 -20.94 (0.98) (0. 06) The order of the ADF test (i.e., the value of m) was chosen on the basis of residual whiteness, since the ADF test is not valid when serial correlation among the errors exists. The test statistics from equations ( 4 .1) ( 4. 4) are not distributed as at-distribution because under Ho: non stationarity, the variance is not finite. Fuller (1976) has simulated the critical values of the distribution for equations (4.1)-(4.4). The (W-S) test is sometimes recommended over the (D-F) tests due to its higher power ( Pantula et al., 1994) The

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89 Table 4.3: Unit Root Tests--Prices and Quantities in First Difference. Tests for Price Tests for Quantity Variable w-sa DF/AD~ P-Pc w-sa Banana -3.80 -2.12 -18.21 -4.81 (0.01) (0.54) ( 0. 10) (0.00) Sugar -3.67 -2 .11 -17.33 -3.65 (0.01) (0.54) (0.12) (0.01) Coffee -3.92 -3.61 -29.02 -3.88 (0.01) (0.03) (0.01) (0.01) Pimento -3.25 -3.00 -44.74 -3.66 (0.04) (0.13) (0. 00) (0.01) Yam -3.39 -2.97 -24.80 -3.32 (0.03) (0.14) (0.03) (0.03) Orange -2.90 -3.56 -24.54 -4.17 (0.11) (0.03) (0.03) (0.00) Cocoa Bean -3.24 -2.94 -31.13 -3.54 (0.04) (0.15) (0.01) (0.02) Potato -3.99 -3.70 -28.38 -2.47 (0.00) (0.02) (0.01) (0.31) Fertilizer -6 .112 -5.180 -42.821 (0.063) (0.052) (0.000) Wage -6. 4 72 -6.639 -43.081 (0.024) (0.017) (0.000) Note: Numbers in parentheses are P-values. a Weighted Symmetric Test. b Dickey-Fuller/Augmented Test. c Phillips-Perron Test. OF/AD~ P-Pc -3.23 -18.32 (0.08) (0.10) -3.36 -42.73 (0.06) (0.00) -3.65 -43.70 (0.03) (0.00) -3.47 -39.44 (0.04) (0.00) -4.05 -24.77 (0.01) (0.03) -3.90 -42.52 (0. 01) (0.00) -3.29 -39.41 (0.07) (0.00) -3.12 -28.49 (0.10) (0.01) (W-S) test uses a weighted double-length regression. The first half of the regression regresses Yt on Yt-1 and lags of ~Yt, with weights (t-1)/T, where Tis the full sample size, and t the size of the sub-sample used for the first half of the regression. In the second half of the regression Yt is regressed on Yt+1 and leads of Yt-Yt-1 with weights [1(t1)] /T (Hall and Cummins, 1998). Phillips and Perron (1988) have suggested a variant to the Dickey-Fuller test by using a non-parametric General Methods of Moments (GMM) type

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90 method to compute a residual variance which is robust to serial correlation. The unit root tests indicate that for each variable tested, at least one test shows that the variable is non stationary in levels, but stationary in first differences. The conflicting test results reported in Table 4. 3 derive from the different power of these tests, which in turn depend upon the estimators used ( Pantula et al., 1994) This should not pose any serious estimation problem, since Hansen and Juselius (1995, p.l), argue that ... not all indi victual variables included in [a multivariate cointegration model] need be I(l), as is often incorrectly assumed. To find cointegrat i on between non-stationary variables, only two of the variables have to be I(l). Consequently, the estimation of crop supply response which follows is based on the conclusion that the variables included in the models are all integrated of order one, i.e., Y i ~I(l), which implies that, ~Yi~I(O). 4.4 Estimating Long-run Supply Response For the banana ECM, the vector of stochastic variables is: Xt = (lbanq, lbanpr, lsugpr, lwage, lferp)' where lbanq = quantity of banana; lbanpr = price of banana; sugpr = sugar price (the alternative crop to banana); lwage = average wage in the agricultural sector; lferp = average

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91 price of fertilizer. The 'l' that begins each variable name indicates that the variables are measured in logarithms. The diagnostic statistics for residual analysis for the unrestricted model for banana are shown in Table 4. 4 and suggest that the assumed model for the banana ECM is supported by the data. This is also supported by the graphs in Figures 4 .1-4. 5, where in particular, the standardized residuals all show a zero-reverting pattern. The next step Table 4.4: Diagnostic Statistics for Residual Tests for the Banana ECM. Tests Dfa x2 p-value For normality of residuals 10 10.322 0.21 For autocorrelation L-B ( 9) 124 102.050 0.06 LM (1) 25 23.629 0.54 LM(4) 25 20.661 0.71 For ARCH(2) Lbanq 2 1. 961 Lbanpr 2 1. 492 Lsuqpr 2 0.575 Lferp 2 0.638 Lwage 2 0.913 Notes: The critical value for X 2 (2) = 5.99 at the 95 percent significance level. a Df = degrees of freedom. estimates the number of cointegrating relationships, r, in the system. In Table 4.5 the likelihood ratio test statistics for the rank of Il (from equation 3.19) are presented along with

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Actual and Fitted for DLBANPR 0 6 -------~----~ 0 4 0 2 -0 0 -+-~++--+->---',.....--i-->-+-+--+-t-+-..-+--< -0 2 -0 4 -0 6 -+--rr-rrncrn"TTTTTTTTTTTTrrrrrnrrn"TTT-,,--' 1968 1974 1982 1990 Standardized Residuals 2 -1 1968 1974 1982 1990 Histogram of Standardized Residuals 0 5 ------------~ 0 4 0 3 0 2 Correlogram of INiduala 1 00 ------------~ 0 75 0 50 0 25 0 00 -0 25 -0 50 -0 75 -1 00 -+-----,-~,.......~--. -~~--. ~ 5 Lag Figure 4.1: Residuals for Banana Price in Banana ECM. Actual and Fitted for DLBANQ 0 15 ------------~ 0 10 0 05 -0 00 -t--+-,=-++-+-'-lrl---\----++-+......._---1 -0.05 -0 10 -0 15 -0 20 -0 25 -+-7T1TTTTTTTTTirrMTTTTTTrrrrrn"TTTTTTr-' 1968 1975 1984 1993 -1 -2 1968 1974 1982 1990 Histogram of Standardized Residuals 0 7 ---= =-----.-------~ ..__, 0 5 -1-----~ 0 5 0 4 0 3 0.2 0 1 0 0 -'___ .__.__.__ __ ,_,___ Correlogram of residuals 1 00 ------------~ 0 75 0 50 0.25 0 00 + --_,, ___ ,.... -0 25 -0 50 -0 75 1 00 ~--r-~~-----,--~~---.~ 5 Lag Figure 4.2: Residuals for Banana Quantity in Banana ECM. 92

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93 Actual and Fitted for DLSUGPR Histogram of Standardized 0 8 0.5 0 6 0.4 0 4 0 2 0 3 0 0 0 2 -0 2 0 1 -0 4 1966 1974 1962 1990 0 0 Standardized Residuals 3 1.00 Comtlogram ct 2 0 75 0 50 0 25 0 0 00 -0 25 -1 -0 50 -2 -0 75 -1.00 -3 5 1966 1974 1962 1990 Lag Figure 4.3: Residuals for Sugar Price in Banana ECM. Actual and Fitted for DLWAGE Histogram of Residuals 0 2 0 5 ....... 0 1 0 4 -0 0 -0 1 0 3 -0 2 0 2 -0 3 0 1 -0.4 1966 1974 1962 1990 0 0 Standardized Reliduala Comtlogram of 2 1 .00 0 75 0 50 0 0 25 -1 0 00 -0 25 -2 -0 50 -3 -0 75 -1 00 -4 5 Lag Figure 4.4: Residuals for Wage in Banana ECM.

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Crossand autocorrelograms of the residuals DI..SANQ DLBANPR DLSUGPR DLWAGE ~E:EB El B ~BBEB -EJEEElE ~E:J83 EJ Lags 1 to 9 Figure 4.5: Crossand Autocorrelograms in Banana ECM. The eigenvalues of the companion matrix 1 00 D D u u D D 0.75 0.50 co Dao 000 OD DO D 0 D D 0 D D 0 D 0 D D D D 0.25 l De:, p D D 0.00 ---~----+--------..---~~ -----I J D -0.25 ~a f D D D D -0.50 DD D D D D D D DD Op D D -0.75 D D ODO ODO 1 00 a n ,... _ ,... n 0 '-----~~-~'-=----------' -1.0 0.0 1.0 Figure 4.6: Plot of Eigenvalues for the Banana ECM. 94

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Table 4.5: Tests of Cointegration Rank for Banana ECM. Eigen Values Amax Trace Ho:r P-r Amax 90 Trace 90 0.649 37.66 80.99 0 5 21. 74 71.66 0.369 16.56 43.33 1 4 18.03 49.92 0.332 14.54 26.27 2 3 14.09 31. 88 0.022 8.96 12.23 3 2 10.29 17.79 0.087 3.27 3.27 4 1 7.50 7.50 95 the 90 percent quantiles of the appropriate limiting distributions. The estimated maximum eigenvalue and trace statistics are shown in the columns Amax and Trace, respectively. The corresponding critical values at the 90 percent level are in the columns Amax 90 and Trace 90. Testing is done sequentially (i.e., row by row), beginning with the first row in Table 4.5, until non-rejection of the null hypothesis is found. This sequential testing begins by testing the null hypothesis Ho:r = 0, (i.e., there is no cointegrating relationship in the system) in row one of Table 4.5. Testing is done by comparing the estimated Amax and Trace statistics with their theoretical (tabular) counterparts, Amax 90 and Trace 90, respectively. The null hypothesis Ho: r = 0 is rejected since the estimated test statistics, (Amax = 37. 66 and Trace = 80. 99), are greater than the corresponding critical values for these statistics (Amax 90 = 21. 74 and Trace 90 = 71. 66), respectively. Row

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96 two is then examined. In Table 4. 5, Ho: r = 1 cannot be rejected, since the estimated test statistics (Amax= 16.56 and Trace = 43. 33) are less than their critical values (Amax 90 = 18.03 and Trace 90 = 49.92), respectively. This suggests that there is one long-run or cointegrating relationship in the banana ECM. This is also supported by the graph in Figure 4. 6 which shows that the eigenvalues are all within the unit circle. The model is then re-estimated under the restriction that only one cointegrating relationship exists in the system. The resulting cointegrating relationship is: ( 4 5) -6.158lbanq 3.943lbanpr + 0.957lsugpr + 0.655lwage + 3.464lferp 23.365 = 0 After normalizing on the banana variable and transferring the banana quantity variable to the left hand side of the equal sign the estimates for the cointegrating vector, P, and the vector of weights, a, are shown in Table 4.6. Table 4 6: Estimated Long-run and Adjustment Coefficients, P's, a's--Banana EOf. lbanq lbanpr lsugpr lwage lferp constant ~s 1 0.640 -0.156 -0.567 -0 .106 -3.795 a.' s -0.141 -0.352 0. 511 0.169 0.097 (-1.909)c (-1.949)c (2.452)b (3.329)a (0.641) Note: Figures in parentheses are t-values. a b, c, indicate statistical significance at one, five and ten percent levels, respectively.

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97 The cointegrating parameters can be interpreted as long-run elasticities. Thus, a ten percent increase in the price of banana will lead to a 6. 40 percent increase in banana supply in the long run, other things remaining constant. Similarly, a ten percent increase in the average price of fertilizer will lead to a 1.06 percent decrease in banana supply. With respect to the substitute crop, a ten percent increase in sugar price will lead to a 1.60 percent decrease in banana quantity. Finally, a ten percent increase in agricultural wage rate will lead to a 5. 70 percent decrease in banana output. The coefficients in the a-vector are the weights with which the cointegrating vector(s) enter(s) each of the indi victual equations in the system. This can be seen from the estimated rr matrix (4.6): ( 4 6) rr = ai,, = lbanq lbanpr rr = lsugpr = lferpr lwage In terms of an coefficients can be 0 .141 0.352 0.511 [1 0.640 0.156 0.567 0.106 3 795] 0.097 0.169 0.14 1 0.091 0.022 0.080 0.015 0.536 0.352 0.225 0.055 0.198 0.037 1.334 0.511 0.327 0.079 0.287 0.054 1.938 0.097 0.062 0.015 0.055 0.010 0.369 0.169 0.108 0.026 0.095 0 018 0.642 economic interpretation, these a considered as the average speed of adjustment towards the estimated equilibrium. Thus, a small

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98 coefficient indicates a slow adjustment whereas a large coefficient indicates rapid adjustment. At the indicated levels of statistical significance, all variables in the banana ECM (except fertilizer price) adjust to a deviation from the long-run equilibrium relationship. In the equation that estimates the long-run supply response of banana ( lbanq in equation 4. 6) the average speed of adjustment is cx1banq = -0 .141, implying that about 15 percent of any deviation from the long-run equilibrium in quantity is corrected within the first period. When the speed of adjustment is low, this might be caused by government regulations, high cost of adjustment, lags between planting and harvesting, inadequate supportive infrastructure in the agricultural sector, and other factors that tend to slow down the process of adjustment to the equilibrium growth path. The estimation and diagnostic testing of the long-run cointegrating relationship for the other crops that are included in this study, follows the same format and procedure as for banana. First, the vector of stochastic variables for each crop's ECM was specified as follows: Sugar ECM: Xt = (lsugq, lsugpr, lbanpr, lwage, lferp)'; Coffee ECM: Xt = (lcofq, lcofpr, lbanpr, lwage, lferp)'; Pimento ECM: Xt = (lpimq, lpimpr, lbanpr, lwage, lferp)';

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99 Yam ECM: Xt = (lyamq, lyampr, lcaspr, lwage, lferp)'; Orange ECM: Xt = (lorq, lorgpr, lbanpr, lwage, lferp)'; Cocoa-bean ECM: Xt = (lcobq, lcobpr, lbanpr, lwage, lferp)'; Potato ECM: Xt = (lpotq, lpotpr, lcaspr, lwage, lferp)'; where lsugq = sugar quantity; lsugpr = price of sugar; lcofq = coffee quantity; lcofpr = price of coffee; lpimq = pimento quantity; lpimpr = price of pimento; lyamq = yam quantity; lyampr = price of yam; lorq = orange quantity; lorpr = price of orange; lcobq = cocoa-bean quantity; lcobpr = price of cocoa bean; lpotq = potato quantity; lpotpr = price of potato. The 'l' that begins each variable name indicates logarithms, and all other variables are as previously defined. Second, residual analysis was conducted to ensure that the models were appropriate for the data. Finally, the hypothesis of reduced rank is then tested on the matrix II= a~', which defines the cointegrating vectors and adjustment coefficients a. A normalization on the crop variable is taken, and the normalized~ and a vector(s) is (are) reported. The estimates of the~ and a vectors for each crop are presented in Table 4.7. The first column in Table 4.7 lists

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100 Table 4. 7: Estimated Long-run and Adjustment Coefficients, P's, 's for all Crops. Crops Q Po Pa w F C Banana P's 1 0.640 -0.156 -0.567 -0.106 -3.795 ct' s -0.141 -0.352 0. 511 0.169 0.097 (-1.909)b (-1.949)b (2.452)a (3.469)a (0.641) Sugar P's 1 0.155 -0.640 -0.106 -0.563 -3.290 ct' s -0 .141 0. 511 0.352 0.169 0.097 (-2.899)a (2.452)a (1.949)b (3.329)a (2.64l)a Coffee CE *1 p;-;1 0.969 0.291 -0.213 -0. 724 -5.348 ct' s -0.069 -0.202 -0.664 0.088 0.107 (-2.420) (-1.316) (-4.823)a (1.617) (1.326) CE #2 p;-;1 0.474 -0.005 -0.448 -2.938 6.503 ct' s 0.049 -0.091 -0.100 -0.002 -0.193 (2.717)b (-1.911)c (-1.930)c (-2.091)b (-5.697) 4 Pimento CE *1 P's 1 0.576 -0.849 -0.534 -1.526 1.683 ct' s -0.047 -0.318 0.031 0.004 0.070 (-2.763)a (-6.173)a (0.400) (2.156)b (2.455)a CE #2 -P's 1 -0.225 -0.026 0.542 0. 468 -10 579 ct' s -0.820 0.140 0.200 -0.106 -0.036 (-4.588) (0.932) (0.889) (-1.575) (-0.252) Yaa CE *1 1 -3.755 5.138 0.484 1.225 -16.819 ct' s -0.060 0.128 -0.124 -0.012 -0.098 (-2.184)b (2.813)b (-4.377)a (-0.539) (-2.125)b CE #2 -Jl's 1 0.418 -0.967 1.860 1. 941 -23.344 ct' s -0.186 -0.273 0.156 -0.045 -0.028 (-4.718)a (-4.147)a (3.808)a (-1. 730)c (-0.427) CE #3 P's 1 1.706 -2.958 -5.880 -1. 700 23.210 ct' s -0.033 -0.062 0.026 0.021 -0.042 (-2.851)a (-3.240)a (2.184)b (2.257)b (-2.189)b Orange J3' s 1 2.347 -1. 620 -1. 979 -1. 492 11.270 ct' s -0 .139 -0.366 0.021 0.062 0.174 (-1.472) (-1.858) (0.094) (2.134) (3.047) Cocoa Bean P's 1 0.359 -0.081 -1. 405 -0.983 -12.350 ct' s -0.797 0.676 -0.841 -0.284 -0.062 (-3.971) (2.103) (-3.449) (-1. 621) (-0.694)

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Table 4.7: Cont'd. Crops Q P o Pa w Potato CE #1 -Ws 1 1. 952 -1.030 -5.197 a's -0.053 -0.043 0.077 0.031 (-1.108) (-1.273) (4.035)a (3.052)a CE #2 1 -0.251 0.220 0.127 a's -1.008 0.071 -0.056 -0.018 (-3.569)a (2.364)a (-2.504)a (-1. 999)b Note: Figures in parentheses are t-values. CE indicates 'cointegration equation'. 101 F C 0.704 -38.815 -0.068 (-2.793)a 0.323 -5.860 -0 .117 (-2.815)a a, b, c, indicate statistical significance at one, five and ten percent levels, respectively. Q = quantity; Po= own-price; Pa= price of the alternative crop; W = average wage rate in agriculture sector; F = average price of fertilizer; C = constant (intercept). the crops, which are inc l uded in this study, followed by the~ and a vectors. When there are more than one long-run cointegrating relationships, these are numbered as "CE # (.)" with the associated and a vectors. Columns 2-6 indicate the variables and coefficients in the ECM for each crop, and column 7 records the intercepts. Thus, in the case of sugar, there is one long-run or cointegrating relationship, which, from Table 4.7, can be expressed as: Q = -3.290 + 0.957Po 0.640Pa 0.106W 0.563F where Q = quantity of sugar; P 0 = own-price (i.e., price of sugar); Pa= price of alternative crop; W = average wage in agriculture; F = average price of fertilizer. A number of inferences can be drawn from the information presented in Table 4.7. First, the long-run

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102 relationship between supply and own-price is inelastic for five of the eight crops. Crops that are price elastic are yam, orange and potato, with own-price elasticities of 1.7, 2.3, and 2. 0, respectively. The own-price elasticity coefficients reported in Table 4.7 are generally within the range estimated by other studies for Jamaica (see Table 3. 1) A second point to note with respect to the information in Table 4.7 is the relatively low elasticities with respect to the substitute crop price. For the banana ECM it is -0 .16, for CE #2 in the coffee ECM, -0. 01, and for sugar and cocoa-bean ECMs, -0. 64 and -0. 08 respectively. Relatively higher elasticities are recorded for the pimento -0.85, yam -3.0, orange -1.62 and potato -1.03. Third, the relationship between quantity and input prices is generally inelastic. The exception are for wages in the yam and potato ECMs, and for fertilizer price in coffee, pimento, orange, and yam. Thus, in the coin tegration equation #3 for yam, in the long-run a one percent increase in average agricultural wage will lead to a 6 percent fall in yam supply, other things remaining constant. A similar conclusion is arrived at for the first cointegrating relationship for potato. Similarly, the long run relationship between supply and fertilizer price is

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103 elastic for coffee (CE #2), pimento (CE #1) orange and yarn (CE #3) A fourth point to note with respect to Table 4. 7 is about the adjustment coefficients a. Generally, the adjust ment coefficients are significant. The exceptions include fertilizer price in the banana ECM, own-price, and wages in CE #1 for the coffee ECM, price of the alternative crop in CE #1 for the pimento ECM, and a few others. An insignificant adjustment coefficient, ai, is a sign of weak exogenei ty of the variable Xi in the vector of stochastic variables, Xt, in the ECM. Weak exogeneity means that although a long-run relationship exists between Xi and the other variables in Xt, Xi does not adjust to deviations from the long-run equilibrium. As such, any deviation from the long-run equilibrium in the system after a shock, is restored by adjustments made by variables other than Xi in the system (Johansen and Juselius, 1990, 1992). According to Johansen (1995), this does not preclude Xi from the cointegrating relationship in Xt, An issue of some importance is raised here regarding the results in Table 4. 7. This relates to the negative relationship observed between quantity and own-price for some of the crops, and what interpretation is possible in these cases. An examinat i on of the data on quantity and

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104 own-price reveals strong negative correlations for banana (-0. 737), and sugar (-0.760), and modest negative quantity-own-price correlations for coffee (-.420) and potato (-0.390). The quantity-own-price correlations for the other crops are positive. In Table 4. 7 negative relations between quantity and own-price show up for pimento (CE #2), yarn (CE #1) and potato (CE #2). Given the strong negative quantity-own price correlation coefficients for banana and sugar, it is surprising that negative relations between quantity and price do not show up in the estimated cointegrating relationships for these two crops. A number of possible explanations can be offered for these results. First, graphical examination of the quantity-own-price relationship reveals that the negative correlations are less pronounced than what the magnitude of the estimates would imply. Second, and as an extension of the previous point, the negative relationship is observed for specific and short period(s), rather than for the entire 1962-1999 period. In fact, in chapter 5 where supply functions are estimated for the eight crops over two subsample periods, 1962-1979 and 1980-1999, the negative quantity-own-price relations are more prevalent (see Tables 5.4, and 5.5, pp.157-160).

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105 Third, error correction models based on cointegration account for the effects of not one or two, but of all the cointegrated variables in the system. In particular, the ECM technique provides a way of separating the long-run relationship between the economic variables (Yt = ~Xt ) from the short-run responses (i.e., the llyt, AXt terms) In this regard, Engle and Granger (1987) have demonstrated that, asymptotically, these effects are dominated by the long-run relationships, and as the sample size becomes larger, the estimates of the cointegrating vector converges to the true Value at a rate equal to T 1 h T h 1 w ere 1.s t e samp e size. This rate of convergence is faster than the case where the variables are stationary, in which case the rate of convergence is T112 This is ref erred to as the superconsistency property of the estimates produced by ECMs. In effect, therefore, the estimates of the cointegrating vectors produced by ECMs based on cointegration, are not confined to, but rather, go beyond the simple correlations which may be observed between variables. Finally, cases where the cointegrating relationships reveal a negative relationship between quantity and own price, need to be commented on. It is tempting to interpret these cases as reflecting relationships on a demand function, i.e., a negative relationship between quantity

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106 and own-price. However, this might not be a plausible explanation, since variables which would usually be included in a demand function such as consumer income, an index for consumer tastes, and other demand forcing variables, are not included in the crops' ECM. For those cases observed in Table 4.7, the strongest statement which could be offered for the negative relationships between quantity and own-price is that there is a long-run relationship observed for some crops, with possible properties of an incompletely specified real demand relationship. The diagnostic statistics for model specification and tests for cointegration rank for each estimated ECM are shown in Appendix D. Graphs for residual analysis (not reported) similar to those for banana, were examined in conjunction with the diagnostic statistics. The residual analysis suggests that for all crops, appropriate for the data. the ECMs are 4.5 Analysis of Short-run Dynamics The previous section suggests that the series in the crop ECMs are cointegrated. Since the cointegrated variables are in equilibrium, exogenous shocks to one of the variables in time to, will result in time paths of the system which will eventually stabilize to a new equilibrium

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107 unless further shocks occur in the system. Lutkepohl and Reimers (1992), contend that these time paths provide insights into the short-run and long-run relationship between/among the variables in the system, especially in cases where there are more than one cointegrating relationships in an ECM. In this regard, innovation accounting (analysis of impulse response functions and variance decompositions) has become a useful tool in the analysis of cointegrated systems Lutkepohl and Reimers, 1992). (Enders, 1995, 1996; The impulse response functions, specified as VARs, generate impact or dynamic multipliers. These coefficients capture the effects of exogenous shocks of each variable on its own time path, and on those of the other variables in the system, by imposing a recursive structure on the moving average representation of the VAR model (Enders, 1995). The moving average representation facilitates analysis of the interaction between sequences in the series of a VAR. Impulse response functions can be plotted to provide a visual representation of the behavior of each series in response to shocks in the system. Identification of the imposing some necessitates restrictions) on the system. parameters in the VAR structure (via parameters One method of imposing

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108 restrictions uses the Choleski decomposition (Doan, 1996). In this method, the ordering of the variables in the VAR is important and is based on statistical properties of the data as well as theoretical insights (Enders, 1995). However, the method can be tedious since, for example, in a five variable system there are 5 (=120) possible orderings of the variables. Sims (1980) and Bernanke (1986) have proposed an alternative method of imposing restrictions for the identification of the VAR. In this scheme, exact identification of the structural model for an n-variable VAR necessitates (n 2 n)/2 restrictions but additional ( over identifying) restrictions can be imposed and tested statistically. The restrictions are based on economic theory. The VAR in standard form (Enders, 1996), which is used for the impulse response analysis is specified as: ( 4 7) where: Xt = (6Q, 6P 0 6Pa, 6F, 6W) ; Xti = (6Ot-i, 6Po,t-i, 6Pa,t-i, 6F,t-i, 6W,t-d', i = 1,2 ... Ai= parameters to be estimated; et= vector of error terms. All other variables as previously defined.

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109 Restrictions on ( 4. 7) are based on the relationships that are specified in Table 4 8 In effect, the specification suggests that Po, Pa, F and W are exogenous variables, and further, that innovations in any one of these variables will induce contemporaneous changes in itself and on Q, but on no other variable in the system. However, this specification, when combined with the Sims Bernanke error variance decomposition, does not rule out induced changes in the other variables in future periods. This is to be expected, given that the variables are cointegrated. Table 4.8: Endogenous and Exogenous Variables in Crops' Impulse Response Functions. The Contemporaneous Is Affected by the Value of: Contemporaneous value of: Q All variables in the system Po No other variable Pa No other variable F No other variable w No other variable Notes: Q = crop output; Po= (own-price), i.e., price of output; Pa= price of substitute crop; F = fertilizer price; W = average agriculture wage rate. Related to impulse response functions are forecast error variance decompositions, which provide information about the proportion of the movements in a variable due to its own shocks and to those from other variables in the

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110 system (Enders, 1996). A twelve-period forecasting horizon is specified, and the Sims-Bernanke forecast error variance decompositions are used based on the restrictions implied in Table 4.8. Impulses for 12 periods were examined for each crop's VAR and the first and second period results are reported in table format, while the plots for the entire forecast period are reported graphically. For all variables, the effects of the shocks after the fourth and fifth periods converge to zero or the equilibrium path, which is to be expected from a cointegrated system. The results for the banana VAR are reported in Table 4. 9. Thus, a one-standard-deviation shock in banana price (equal to 0.220 units) induces contemporaneous decrease of 0.165 units in banana quantity. By model specification, there is no contemporaneous change in the other variables. After one period, banana price is still O .112 uni ts above its mean, while banana quantity has increased by 0.076 units. In the second period, the shock in banana price has induced an increase in sugar price of 0.081 units, a decrease in fertilizer price of 0.007 units, and an increase in wage of O. 041 units. These effects are also shown graphically. As seen in Figures 4.7-4.14, equilibrium is achieved between the fourth and sixth periods.

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Table 4.9: Responses of Banana Quantity to Shocks in the Banana VAR. Responses to Shock in sugar price Period dlsugpr dlferp dlwage dlbanpr Dlbanq 1 0.210 0 0 0 -0.162 2 0.071 0.020 -0.051 0.171 0.092 Responses to Shock in fertilizer price Period dlsugpr dlferp dlwage dlbanpr dlbanq 1 0 0.150 0 0 -0.039 2 -0.007 0.023 -0.047 0.014 0.018 Responses to Shock in wage rate Period dlsugpr dlferp dlwage dlbanpr dlbanq 1 0 0 0.055 0 0.002 2 -0.014 0.020 0.031 -0.018 0 Responses to Shock in banana price Period dlsugpr dlferp dlwaqe dlbanpr dlbanq 1 0 0 0 0.220 -0.165 2 0.081 -0.007 0.041 0.112 0.076 Notes: The prefix 'd' to the variable names denotes change in the variable. All variables as previously defined. 111 To complement the results from the impulse response functions, the forecast error decompositions of the banana VAR are reported in Table 4.10. Enders (1996) argues that in applied research, it is typical for a variable to explain almost all of its forecast error variance at short horizons and smaller proportions at longer horizons. The Sims-Bernanke forecast error variance decompositions (based on restrictions specified in Table 4. 8) reported in Table 4 .10 show that each variable explains 100 percent of its forecast error variance in the first period. However, after the first period, the forecast error variance of a variable, which is explained by its own shock, falls. In

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1.0 --,-----\--,-/\-------.-----------, ,, ~ ,,N._ g 0 0 -+-+ : c:,rc......:....._ -s ~ v= ,:; P. ,:,. ,;:; :::~ ~ -""" "" ~-!.cc.:==== ~ =====I \f r -0 5 -1.0 -1 5 i 1 ... / I \ : \ I \ I '., ,' ! i -2 0 ~--,,,----r--., --,.---,----r---,--,,,----r--..--,--,---' 0 2 4 6 8 10 Figure 4. 7: Impulse Responses to Innovation in Sugar Price--Banana VAR. 1 0 --,---r-----------,----------~ \ ' 0 8 0.6 0 -4 0.2 ' ' '. ' \ \ ' ' ,,,, --.. -'::..... 0 0 -r--~::-=----~-:--: =-=--""'~=~-==-=-~-------, -0 2 --'---,,--r--..--,--,--~,----.,--r--,,----r--..-,--.---' 0 2 -4 6 8 10 an Figure 4. 9: Impulse Responses Innovation in Wage--Banana VAR. to an 1 00 -,------------,-----------, 0 7 0. 0 25 > :~ 0 00 -t------,-.:;-..;~ ---;; ~ :~~~:;;,-~ --~~------l \; ::--=---;. ;.------0.25 i;' / : \ / / \ ,; -0 50 \ ,' -0 75 \ ,, \ 1.00 ~--.---,,----r--.----r--,----,--,----,--.--., -...,..-~ 0 2 4 6 8 10 Figure 4. 8: Impulse Responses to an Innovation in Fertilizer Price--Banana VAR. 1.0 --,--~--------,------------, \ ., (-:, ,, \ ._ ........ ...... \ ... ...... 0 5 / ,,' ::--_ .. _ 0.0 +--.L_~~~:;=~;;.;.. ....... --!= ========j -/-~ -0.5 -1.0 -1.5 -2 0 --'-----,--,----,--.---,----r---,----,--,----,--.---,-~ 0 2 6 8 10 Figure 4.10: Impulse Responses to an Innovation in Banana Price--Banana VAR.

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3 2 cllugpr chi) 2.4 .-ge -.,, cbnq 1 6 0 8 0 0 -0 8 -1 6 0 2 4 6 6 10 Figure 4.11: Impulse Responses to an Innovation in Banana Quantity--Banana VAR. 1 00 \ ... "" \ chi) 0.75 \ .-ge \ clcofpr \ dcotq 0 50 \ I 0 25 0 00 -0 25 -0 50 0 2 4 6 8 10 Figure 4.13: Impulse Responses to an Innova t ion in Coffee Quan t i t y--Coffee VAR. 3.2 dlbanpr dlfllfl) 2.4 dlwage laugpr 1 6 dleugq 0.8 0 0 -0 8 -1 6 0 2 4 6 8 10 Figure 4.12: Impulse Responses to an Innovation in Sugar Quantity--Sugar VAR. 1 00 I \ 0 75 i I 0.50 \ 0 25 0 00 -0 25 -0 50 0 Figure 4.14: Innova t ion in VAR. 2 4 Impulse Pimen t o _,, .-ge dpfl1II" dlplmq 6 6 10 Responses to an Quan ti ty -Pimen t o

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114 the case of the banana VAR, in the third period, sugar price explains 75.7 percent of its own forecast error variance, fertilizer price 71.2 percent, wage 33.0 percent, banana price 42.9 percent, and banana quantity 57.3 percent. These percentage distribution of the forecast error variance among the variables remain constant after the second and third forecast periods. Table 4.10: Variance Decomposition Percentage of One-period and Three-period Forecast Error Variance--Banana VAR. Forecast error Percentage forecast error variance in: explained by shocks in: Sugar price dlsugpr dlferpr dlwage dlbanpr dlbanq 1 Yr 100 0 0 0 0 3 yrs 75.712 0.105 0.613 10.395 13.176 Fertilizer price 1 Yr 0 100 0 0 0 3 yrs 11.999 71.220 1.999 1.119 13.663 Wage 1 Yr 0 0 100 0 0 3 yrs 24.430 24.341 32.961 18.196 0.072 Banana price 1 Yr 0 0 0 100 0 3 yrs 21.262 0.149 0.340 42.880 35.368 Banana quantity 1 Yr 19.407 1.105 0.004 19.940 59.543 3 yrs 21. 416 1.147 0.006 20.094 57.337 The cross-variable effects are zero in the first period (by model specification), but change with the time horizon. In Table 4 .10, the cross-variable effects in the third period show that the shock in banana price can

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115 explain 43 percent of its own forecast error variance, shocks in banana quantity and sugar price account for 35 percent and 21 percent, respectively. Shocks in fertilizer price and the wage rate account for only 0 .14 9 and 0. 340 percent, respectively, of the forecast error variance in banana price. Similarly, in period three, the cross-price effect of a shock in fertilizer price can explain 71 percent of its own forecast error variance, while 14 percent and 12 percent are accounted for by shocks in banana quantity and sugar price, respectively. Finally, a shock in wage rate explains 33 percent of its own forecast error variance, while shocks in banana, fertilizer and sugar prices account for 18.2, 24.3 and 24.4 percent, respectively. The last two rows in Table 4 .10 show the forecast error variance decompositions when the exogenous variables (Po, Pa, W, F) are modeled to impact banana quantity in all time periods. Thus, the banana quantity variable explains 60 percent of its own 1-step ahead forecast error variance, and 57 percent in the 3-step ahead forecast error variance. Similarly, in the first period, banana and sugar prices account for 20 percent and 19 percent of the forecast error variance in banana quantity, respectively. These percent ages increase slightly in the 3-year ahead forecast.

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Fertilizer price and wages account for 116 negligible percentage variations in banana forecast error variance in both of the periods, suggesting thereby, the relative importance of own-price and price of the substitute crop on the short-run variations of banana quantity. The estimates from the impulse response functions and forecast error decompositions for the other crops are reported in Tables 4 .13-4. 2 6. The associated plots of the impulse response functions are shown in Figures 4 .15-4. 4 6. In order to present the information in a manner that is succinct and easy to read, a major portion of the estimates that are reported in Tables 4 .13-4. 26 are summarized in Tables 4 .11 and 4 .12. Table 4 .11 shows the responses of quantity to shocks in the exogenous variables in the system in periods one and two. Table 4.12 reproduces the last two rows of the tables that record the forecast error variance decompositions for the crops' ECM. That table shows the percentage forecast error variance in quantity, which is explained by the variables in the system. The response of quantity to positive shocks in own price (Po} is expected to be positive. However, Table 4.11 shows that in period one, five of the eight crops (banana, sugar, yam, orange, and potato} show a decrease in output, which is induced by an own-price shock. Thus, in period one

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117 Table 4 .11: Responses of Quantity to Shocks in Exogenous Variables. Responses of Quantity to Shocks in: Variable Period Po Pa w F Banana 1 -0.165 -0.162 0.002 -0.039 Quantity 2 0.076 0.092 0 0.018 Sugar 1 -0.162 -0.165 0.002 -0.039 Quantity 2 0.092 0.076 0 0.018 Coffee 1 0.021 -0.006 -0.016 0.004 Quantity 2 -0.005 0.021 0.009 0.004 Pimento 1 0.022 -0.006 -0.023 -0.021 Quantity 2 0.033 0.078 0.019 -0.090 Yam 1 -0.087 -0.008 -0.046 -0.019 Quantity 2 0.100 0.018 0.014 0.026 Orange 1 -0.038 0.061 0.097 0.025 Quantity 2 0.210 -0.148 -0.026 0.023 Cocoa Bean 1 0.007 0.028 0.031 0 Quantity 2 -0.062 -0.005 -0.046 0.006 Potato 1 -0.194 -0.026 -0.050 0.049 Quantity 2 0.177 -0.028 0.021 -0.012 Source: Tables 4.13, 4.15, 4.17, 4.19, 4.21, 4.23 and 4.25. a one-standard-deviation-shock in banana price (equal to 0.220 units--see Table 4.13), induces a decrease in banana supply of 0.165 units. Other crops whose supply decrease in period one consequent upon a one-standard-deviation-shock in own-price are sugar (-0. 162 uni ts) and potato (-0 .194 uni ts) However, with the exception of coffee and cocoa bean, all crops increase supply in the second period following a shock in own-price. The response of quantity to positive shocks in the substitute crop price, Pa, wage, W, and fertilizer price, F, is expected to be negative. In period one this expectation is confirmed for shocks in Pa on all crop supply except

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118 orange and cocoa-bean. Thus, in the case of coffee supply response, a one-standard-deviation-shock in banana price (equal to 0.223 units--see Table 4 .15) induces a contemporaneous decrease in coffee supply of 0. 006 uni ts. Interestingly, in the second period, with the exception of orange, cocoa-bean and potato supply, shocks in the substitute crops' price induce positive response in crop supply response for the other crops. Wage rate shocks appear to have negligible contemporaneous effect and no effect in period two on banana and sugar responses. Wage shocks do have negative contemporaneous effect on coffee, pimento, yam, and potato supply responses. However, in period two, it appears that wage shocks generally have a positive impact on short-run supply response. The exceptions are orange and cocoa-bean. Finally, shocks in fertilizer price have negative contemporaneous effect on banana, sugar, pimento and yam supply response. However, with the exception of potato, and pimento, supply response of crops to a shock in fertilizer price are positive in period two. Table 4.12 shows the proportion of forecast error variances in quantity, which are attributable to the variables that are specified as exogenous in the VARs for each crop. As previously mentioned, it is common for a

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119 variable to explain forecast a larger proportion of its contemporaneous error variance and smaller proportions at later periods. This is generally the case for the crops shown in Table 4 .12. Thus, 92 percent of coffee's contemporaneous forecast error variance is explained by its own shock, five percent by own-price (Po), three percent by wage shocks and by negligible percentages in fertilizer and the substitute crop prices. However, in the third period, the percentage forecast error variance Table 4 .12 : Summary of Forecast Error Variance in Crop Quantities Explained by Variables in the VAR (Percentage). Forecast Percentage of Forecast Error Variance Error in Quantity Explained by Shocks in: Variance in: Period Pa F w Po Q Banana 1 19.407 1.105 0.004 19.940 59.543 3 21. 416 1.147 0.006 20.094 57.337 Sugar 1 19.940 1.105 0.004 19.407 59.543 3 20.094 1.147 0.006 21. 416 57.337 Coffee 1 0.394 0.150 2.727 4.564 92.165 3 4.732 0.805 2.857 3.950 87.655 Pimento 1 0.070 0.839 0.948 0.917 97.226 3 7.285 10.403 2.914 1. 959 77.439 Yarn 1 0.127 0.653 3.691 13.330 82.199 3 1. 869 2.100 3.244 25.605 67.183 Orange 1 6.461 1.110 16.195 2.465 73.769 3 21. 606 1.548 5.842 43.470 27.534 Cocoa Bean 1 2.026 0 2.451 0.141 95.383 3 1. 588 0.133 6.502 11.047 80.730 Potato 1 0.714 2. 626 2.711 40.583 53.367 3 1. 485 2.440 2.459 54.282 39.334 Source: Tables 4.14, 4.16, 4.18, 4.20, 4.22, 4.24 and4.26.

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120 explained by coffee quantity shock reduces to 88 percent, while the proportion explained by the substitute crop price shock increases to five percent. Enders (1995, 1996) argues that if X1 shocks do not explain any of the forecast error variance in X2 in a VAR at all time horizons, then the X 2 sequence is exogenous. This means that the X2 sequence evolves independently of shocks in X1. On the other hand, if X1 shocks explain all the forecast error variance in X 2 sequence at all forecast horizons, then X2 is said to be endogenous. Neither of these two extreme cases are observed in Table 4.12. With respect to the issues of endogeniety / exogeniety and the results reported in Table 4 .12, one point must be noted. With only a few exceptions, shocks in fertilizer price and wages appear to explain relatively small proportions of the forecast error variance in quantity. The exceptions are pimento, orange and to some extent cocoa bean. This is also true for the price of the substitute crop in the cases of coffee, cocoa-bean and potato.

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Table 4.13: Responses of Sugar Quantity to Shocks in the Sugar VAR. Responses to Shock in banana price Period dlbanpr dlferp dlwage dlsugpr dlsugq 1 0.220 0 0 0 -0.165 2 0.112 -0.007 0.041 0.081 0.076 Responses to Shock in fertilizer price Period dlbanpr dlferp dlwage dlsugpr dlsugq 1 0 0.150 0 0 -0.039 2 0.014 0.023 -0.047 -0.007 0.018 Responses to Shock in wage Period dlbanpr dlferp dlwage dlsugpr dlsugq 1 0 0 0.055 0 0.002 2 -0.018 0.020 0.031 -0.014 0 Responses to Shock in sugar price Period dlbanpr dlferp dlwage dlsugpr dlsugq 1 0 0 0 0.210 -0.162 2 0.171 0.020 -0.051 0.071 0.092 Notes: The prefix 'd' to the variable names denotes change in the variable. All variables previously defined. 121 Table 4.14: Variance Decomposition Percentage of One-period and Three-period Forecast Error Variance--Sugar VAR. Forecast error Percentage forecast error variance in: explained by shocks in: Banana price dlbanpr dlferpr dlwage dlsugpr dlsugq 1 Yr 100 0 0 0 0 3 yrs 42.880 0.149 0.340 21.262 35.368 Fertilizer price 1 Yr 0 100 0 0 0 3 yrs 1.119 71.220 1. 999 11.999 13.663 Wage 1 Yr 0 0 100 0 0 3 yrs 18.196 24.341 32.961 24.430 0.072 Sugar price 1 Yr 0 0 0 100 0 3 yrs 10.395 0.105 0.613 75.712 13.176 Sugar quantity 1 Yr 19.940 1.105 o 004 19.407 59.543 3 yrs 20.094 1.147 0.006 21.416 57.337

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Table 4.15: Responses of Coffee Quantity to Shocks in the Coffee VAR. Responses to Shock in banana price Period dlbanpr dlferp dlwage dlcofpr dlcofq 1 0.223 0 0 0 -0.006 2 0.004 -0.034 0.023 0.003 0.021 Responses to Shock in fertilizer price Period dlbanpr dlferp dlwage dlcofpr dlcofq 1 0 0.150 0 0 0.004 2 -0.006 0.014 -0.058 -0.082 0.007 Responses to Shock in wage Period dlbanpr dlferp dlwage dlcofpr dlcofq 1 0 0 0.073 0 -0.016 2 -0.014 0.032 0.034 -0.005 0.009 Responses to Shock in coffee price Period dlbanpr dlferp dlwage dlcofpr dlcofq 1 0 0 0 0.178 0.021 2 0.009 -0.015 0.014 -0.034 -0.005 Notes: The prefix 'd' to the variable names denotes change in the variable. All variables previously defined. 122 Table 4.16: Variance Decomposition Percentage of One-period and Three-period Forecast Error Variance--Coffee VAR. Forecast error Percentage forecast error variance in: explained by shocks in: Banana price dlbanpr dlferpr dlwage dlcofpr dlcofq 1 Yr 100 0 0 0 0 3 yrs 87.482 0.080 1.039 0.180 11.219 Fertilizer price 1 Yr 0 100 0 0 0 3 yrs 4.411 87.721 5.209 1.034 1.625 Wage 1 Yr 0 0 100 0 0 3 yrs 8.878 38.064 50.623 2.393 0.041 Coffee price 1 Yr 0 0 0 100 0 3 yrs 1. 898 16.105 0.419 76.390 5 .188Coffee quantity 1 Yr 0.394 0.150 2.727 4.564 92.165 3 yrs 4.732 0.805 2.857 3.950 87.655

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Table 4.17: Responses of Pimento Quantity to Shocks in the Pimento VAR. Responses to Shock in banana price Period dlbanpr dlferp dlwage dlpimpr dlpimq 1 0.234 0 0 0 -0.006 2 0.001 -0.037 0.025 0.019 0.078 Responses to Shock in fertilize price Period dlbanpr dlferp dlwage dlpimpr dlpimq 1 0 0.150 0 0 -0.021 2 -0.006 0.013 -0.056 -0.048 -0.090 Responses to Shock in wage Period dlbanpr dlferp dlwage dlpimpr dlpimq 1 0 0 0.071 0 -0.023 2 -0.008 0.030 0.035 -0.063 0.019 Responses to Shock in pimento price Period dlbanpr dlferp dlwage dlpimpr dlpimq 1 0 0 0 0.199 0.022 2 0.004 -0.010 0.015 -0.017 0.033 Notes: The prefix 'd' to the variable names denotes change in the variable. All variables previously defined. 123 Table 4.18: Variance Decomposition Percentage of One-period and Three-period Forecast Error Variance--Pimento VAR. Forecast error variance in: Percentage forecast error explained by shocks in: Banana price dlbanpr dlferpr dlwage dlpimpr dlpimq 1 Yr 100 0 0 0 0 3 yrs 97.005 0.622 0.354 0.108 1. 911 Fertilizer price 1 Yr 0 100 0 0 0 3 yrs 5.401 87.773 4.943 0.456 1.427 Waqe 1 Yr 0 0 100 0 0 3 yrs 11.946 35.878 48.819 2.842 0.516 Pimento price 1 Yr 0 0 0 100 0 3 yrs 0.913 8.551 10.265 79.974 0.298 Pimento quantity 1 Yr 0.070 0.839 0.948 0.917 97.226 3 yrs 7.285 10.403 2.914 1.959 77.439

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Table 4.19: Responses of Yam Quantity to Shocks in the Yam VAR. Responses to Shock in cassava price Period dlcaspr dlferp dlwage dlyampr dlyamq 1 0.119 0 0 0 -0.008 2 0.046 0.044 -0.033 0.051 0.018 Responses to Shock in fertilizer price Period dlcaspr dlferp dlwage dlyampr dlyamq 1 0 0.147 0 0 -0.019 2 -0.037 0.002 -0.047 -0.018 0.026 Responses to Shock in wage Period dlcaspr dlferp dlwage dlyampr dlyamq 1 0 0 0.064 0 -0.046 2 0.007 0.022 0.035 0.007 0.014 Responses to Shock in yam price Period dlcaspr dlferp dlwage dlyampr dlyamq 1 0 0 0 0.179 -0.087 2 -0.013 -0.031 0.038 -0.023 0.100 Notes: The prefix 'd' to the variable names denotes change in the variable. All variables previously defined. 1 24 Table 4.20: Variance Decomposition Percentage of One-period and Three-period Forecast Error Variance--Yam. VAR. Forecast error Percentage forecast error variance in: explained by shocks in: Cassava price dlcaspr dlferpr dlwage dlyampr dlyamq 1 Yr 100 0 0 0 0 3 yrs 84.304 8.617 0.466 1.210 5.404 Fertilizer price 1 Yr 0 100 0 0 0 3 yrs 7.353 83.702 2.931 4.949 1.064 Wage 1 Yr 0 0 100 0 0 3 yrs 17.798 20.621 43.241 17.782 0.558 Yam price 1 Yr 0 0 0 100 0 3 yrs 6.503 1.294 0.321 83.129 8.752 Yam quantity 1 Yr 0.127 0.653 3.691 13.330 82.199 3 yrs 1. 869 2.100 3.244 25.605 6 7 .183

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Table 4.21: Responses of Orange Quantity to Shocks in the Orange VAR. Responses to Shock in grapefruit price Period dlgrpr dlferp dlwage dlorpr dlorq 1 0.523 0 0 0 0.061 2 -0.103 0.027 -0.038 0.081 -0.148 Responses to Shock in fertilizer price Period dlgrpr dlferp dlwage dlorpr dlorq 1 0 0.148 0 0 0.025 2 -0.088 0.012 -0.045 -0.122 0.023 Responses to Shock in wage Period dlgrpr dlferp dlwage dlorpr dlorq 1 0 0 0.072 0 0.097 2 -0.052 0.042 0.039 -0.107 -0.026 Responses to Shock in orange price Period dlqrpr dlferp dlwage dlorpr dlorq 1 0 0 0 0.477 -0.038 2 0.124 0.023 0.042 -0.201 0.210 Notes: The prefix 'd' to the variable names denotes change in the variable. All variables previously defined. 125 Table 4.22: Variance Decomposition Percentage of One-period and 'l'hree-period Forecast Error Variance--Orange VAR. Forecast error Percentage forecast error variance in: explained by shocks in: Grapefruit price dlgrpr dlferpr dlwage dlorpr dlorq 1 Yr 100 0 0 0 0 3 yrs 84.559 2.263 1. 511 10.961 0.705 Fertilizer price 1 Yr 0 100 0 0 0 3 yrs 4.577 83.741 7.159 4.251 0.272 Wage 1 Yr 0 0 100 0 0 3 yrs 12.886 22.566 50.088 13.979 0.480 Orange price 1 Yr 0 0 0 100 0 3 yrs 2.306 5.298 3.512 83.017 5.867 Orange quantity 1 Yr 6.461 1.110 16.195 2.465 73.769 3 yrs 21. 606 1. 548 5.842 43.470 27.534

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Table 4.23: Responses of Cocoa-bean Quantity to Shocks in the Cocoa-bean VAR. Responses to Shock in banana price Period dlbanpr dlferp dlwage dlcobpr dlcobq 1 0.225 0 0 0 0.028 2 0.013 -0.048 0.022 -0.030 -0.005 Responses to Shock in fertilizer price Period dlbanpr dlferp dlwage dlcobpr dlcobq 1 0 0.146 0 0 0 2 -0.015 0.025 -0.056 0.093 0.006 Responses to Shock in wage Period dlbanpr dlferp dlwage dlcobpr dlcobq 1 0 0 0.072 0 0.031 2 -0.011 0.027 0.037 0.073 -0.046 Responses to Shock in cocoa-bean price Period dlbanpr dlferp dlwage dlcobpr dlcobq 1 0 0 0 0.275 0.007 2 0.032 -0.031 -0.004 -0.083 -0.062 Notes: The prefix 'd' to the variable names denotes change in the variable. All variables previously defined. 126 Table 4.24: Variance Decomposition Percentage of One-period and Three-period Forecast Error Variance--Cocoa-bean VAR. Forecast error Percentage forecast error variance in: explained by shocks in: Banana price dlbanpr dlferpr dlwage dlcobpr dlcobq 1 Yr 100 0 0 0 0 3 yrs 90.042 1.424 0.760 2.612 5.163 Fertilizer price 1 Yr 0 100 0 0 0 3 yrs 8.454 81.851 3.994 3.370 2.331 Wage 1 Yr 0 0 100 0 0 3 yrs 11.070 35.763 48.791 2.466 1.910 Cocoa bean price 1 Yr 0 0 0 100 0 3 yrs 0.857 11. 874 5.585 77.946 3.738 Cocoa-bean quantity 1 Yr 2.026 0 2.451 0.141 95.383 3 yrs 1. 588 0.133 6.502 11.047 80.730

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Table 4.25: Responses of Potato Quantity to Shocks in the Potato VAR. Responses to Shock in cassava price Period dlcaspr dlferp dlwage dlpotpr dlpotq 1 0.125 0 0 0 -0.026 2 0.045 0.049 -0.036 0.050 -0.028 Responses to Shock in fertilizer price Period dlcaspr dlferp dlwage dlpotpr dlpotq 1 0 0.148 0 0 0.049 2 -0.037 0.001 -0.046 -0.020 -0.012 Responses to Shock in wage Period dlcaspr dlferp dlwage dlpotpr dlpotq 1 0 0 0.062 0 -0.050 2 0.003 0.025 0.030 0.008 0.021 Responses to Shock in potato price Period dlcaspr dlferp dlwage dlpotpr dlpotq 1 0 0 0 0.187 -0.194 2 -0.017 -0.030 0.039 -0.029 0.177 Notes: The prefix 'd' to the variable names denotes change in the variable. All variables previously defined. 1 27 Table 4.26: Variance Decomposition Percentage of One-period and Three-period Forecast Error Variance--Potato VAR. Forecast error Percentage forecast error variance in: explained by shocks in: Cassava price dlcaspr dlferpr dlwage dlpotpr dlpotq 1 Yr 100 0 0 0 0 3 yrs 90.133 7.985 0.168 1.591 0.123 Fertilizer price 1 Yr 0 100 0 0 0 3 yrs 8.889 84.140 2.715 3.706 0.550 Wage 1 Yr 0 0 100 0 0 3 yrs 19.565 19.787 36.307 22.798 1.543 Potato price 1 Yr 0 0 0 100 0 3 yrs 6.058 1.034 0.269 89.234 3.405 Potato quantity 1 Yr 0.714 2.626 2.711 40.583 53.367 3 yrs 1. 485 2.440 2.459 54.282 39.334

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1.0 -,---r--------,----------, 0 5 --dlbanpr dlferp dlwage l1ugpr -0 0 -r-~~:-::-"5::-~ ~ ..,,. --::,,, --.;. ;..__ _____ dl ugq ___ --l -0 5 -1 0 -1 5 2 0 ~----r--,---.---,-----.---,-----,---,--~-.---,-----.---' 0 2 6 8 10 Figure 4.15: Impulse Responses to Innovation in Banana Price--Sugar VAR. 1 0 dlbanpr I ', dlferp 0 8 I I dlwage '. leugpr I I dl1ugq 0 6 I I ', '. I 0 4 ' ' 0 2 ', ' ,, ,----.. ~0 0 -' :::::... -_.... ----..... -0 2 I I I I I I 0 2 .. 6 8 10 an Figure 4.17: Impulse Responses Innovation in Wage--Sugar VAR. to an 1.00 -,-----------,----------~ 0.75 0 50 0 25 ;,\ o oo -+-......-= ;::::;-----.,.t:. ::;; s --= .:;::::-~:..:... ...., __ __ ____ -J ; :-; -;., _.,... -0 25 -0 50 -0 75 \ 'I i;' / \ I '. I I '. \ I _, I I I I I I I I -1.00 --'---~ ---,--~-~ -.--I I 0 2 .. I I I 6 8 10 Figure 4.16: Impulse Responses to an Innovation in Fertilizer Price--Sugar VAR. 1.0 0 5 0 0 I I I ) -0.5 1 ,., '. -1 0 ! i -1 5 i -2.0 0 2 .. 6 dlbanpr dlfllfJ) dlwage l1ugpr dlaugq 8 10 Figure 4.18: Impulse Responses to Innova t ion in Sugar Price -Suga r VAR. an t-' l'v CX)

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1 00 -,----r----------,-----------, 0 75 0 50 0 25 0 2 6 8 10 Figure 4.19: Impulse Responses to an Innova t ion in Banana Price--Cof f ee VAR. 1 0 -,----,---------,----------~ 0 8 0 6 0 4 0 2 I I \ \ I \, \ I I \. .......... \ dlbanpr dlle,p dtwage dk:olpr dk:olq ...... ,.,.. ~.. \~ ........... 0 0 -+---....-f----'>'"7'r-,.....,.. -'-~-=---~-~""' ,.. -.::-==~ ----, ------0 2 ~--.---,---,---,---,---,---,---,---,---,---,---,-~ 0 2 4 6 8 10 Figure 4.21: Impulse Responses to an I nnova t ion i n Wage C o ff ee V AR. 1 0 0 8 0 6 0 4 0 2 -0 0 -,. -0 2 ,, .. \ '. -0.4 \ \ / -0 6 \,' -0 8 0 2 6 8 dlbenpr dlfe,p dtwage dk:olpr dk:olq 10 Figure 4.20: Impulse Responses to an Innovation in Fertilizer Price--Coffee VAR. 1 0 -,---,----------.-----------, 0 8 0.6 0 4 0 2 I '. i ..... -. > I~' .. dlbanpr dlle,p dlwage dk:olpr dk:olq >;t---.... -:..-:.,--":.. ....., ___ 0 0 -+-~_:::J_-:-~-~;, '? e:,:=:;;, '3"' ,.,;; ~~,,,..,.. -------j "' I I -0 2 0 2 4 6 8 10 Figure 4.22: Impulse Responses to an I nno v a t ion in Coffee P r iceCof f ee VAR. I-' N \.0

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1 00 --.---r--------~--------~ 0 75 0 50 0 25 0 2 4 6 8 10 Figure 4.23: Impulse Responses to an Innovation in Banana Price--Pimento VAR. 1.0 --.---.----------r----------, 0.8 0.6 0.4 0 2 ,"' ... _,,. ''. 0.0 -t---1 / ~ ., ~:::.;: __ ::::;;,,,. _~ 7~-""--~--~-~-::':; ,?. __ !',' _'_ ;;,...;;:~ ------i '/ '.------0 2 / \' --0 4 ~---.---,---.---,---.----,---.---,---.---,---.---,-~ 0 2 4 6 8 10 Figure 4.25: Impulse Responses to an Innovation in Wage--Pimento VAR. 1 0 --.----------~--------~ 0.8 0 6 0.4 0 2 -~ ..,,,;:: -_-:--~. --0 0 --+ -,\""", /+_-_ ----==, .. == --~ ;"'-_ ~~~~-......,=-------i --0.2 / / ', I ,' -0 . \ ,.. ,' \ / \ .,/ --0 6 \ ,' --0 8 ~---.---,'-,, --,---.---,---.---,,----.--,----,,----,-~ 0 2 6 8 10 Figure 4.24: Impulse Responses to an Fertilizer Price--Pimento Innovation in VAR. 1.0 --.-~:-------~---cbnpr ---~ I cS'9IJ) 0.8 0 6 0.4 0 2 I .-ge I clplffll' I I I I ~ -, ', _/ _i_ ,. --'-' ... ..::.--0 0 --+--<-:: ~_ ,:1._,_-::.:<.: __ :=_===-_--~ri~ ~ :!'7!1 --------j --0 2 ......l...-~1----,1-----.,---,---.--,---.--,-......--~ 0 2 4 6 8 10 Figure 4.26: Impulse Responses to an Innovation in Pimento P ri ce--Pimento VAR. I-' w 0

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1.0 -.---.---------,----------~ 0.8 0.6 0 -4 0 2 _,,, chi) .-ge dlyamq -0 0 +---b,--~---'k_~ --,,,,.,::::;;::.-::;1_.,.-----------l -0.2 -0.-4 _1 / "'----:;;..... ,,,,,,' -0.6 ~----r-----.----r-----.-~----.-~----.----r-----.----r-----.--' 0 2 6 8 10 Figure 4.27: Impulse Responses to an Innovation in Cassava Price--Yam VAR. 1 0 \ 0.8 \ \ 0.6 \ ' I I 0.-4 ' ' 0.2 \ , ... ...... ', 0.0 ~ -="' I ....... --~ I -0 2 ,' I I I 0 2 -4 6 8 Figure 4.29: Impulse Responses Innovation in Wage -Yam VAR. to an 1.00 dcaapr 0.75 chi) .-ge ... 0 50 dyamq 0 25 0.00 -0 25 -0 50 -0.75 0 2 6 8 10 Figure 4.28: Impulse Responses to an Innovation in Fertilizer Price--Yam VAR. 1.0 ~----r----------r-----------, 0 8 0 6 \ / ', 0 -4 0.2 ' './ 1\ \ i. i \ ', / y \ \\ / !\ \ ,.: ---' :::;:,,.,.. 0.0 -+--~~f, ~ "':--7 :::;:, ~ ~~ -------j ; .. .._ .. -0 2 I i -0.-4 -'---~,-~----. -~----.,-~----.,-~--,,~~ .--,-r-~ 0 2 -4 6 8 10 Figure 4.30: Impulse Responses Innovation in Yam Pr i ce -Yam VAR. to an

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1.0 \ .-.p 0 8 \ ..._ \ 0.6 clyWnq \ 0.4 \ I 0 2 \ i 0 0 .... -\ ........... -0.2 -0 -1 0 2 .. 6 8 10 Figure 4.31: Impulse Responses to an Innovation in Yam Quantity--Yam VAR. 1.0 -,----,---------~-------~ 0 8 0 6 0.-1 0 Figure 4.33: Innovation in bean VAR. 2 .. 6 8 10 Impulse Responses to an Cocoa bean Quan t ity -Cocoa 1.0 dlD'P' .-.p 0 8 ..._ clo
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1.00 <111,J)r 0 75 cl0fpr 0 50 clorq 0.25 0.00 ---0.25 -0 50 -0 75 0 2 6 8 10 Figure 4.35: Impulse Responses to an Innovation in Grapefruit Price--Orange VAR. 1.00 ~-~--------~--------~ 0 75 0 50 0 25 ' ' \, \ ' \ I \ \ \ . ..... :\ \ ' : ', ... .... \ \. .. .... : , 0.00 -+-...,,~-'1-------i:;,...~ ........ "" ~-------, \ I -0 25 ~-,, --.------, ,..--.------,..---.---,..----,-----..--,--,-----..--,--,----~ 0 2 4 6 8 10 Figure 4.37: Impulse Responses Innovation in Wage -Orange VAR. to an 1 00 <111,J)r 0 75 cl0fpr 0 50 clorq 0 25 0 00 ,,__ -0 25 -0.50 -0 75 0 2 6 8 10 Figure 4.36: Impulse Responses to an Innovation in Fertilizer Price--Orange VAR. 1.0 d91lr 0 8 chi> 0 6 ctorpr clorq 0 4 '\ I 0 2 -0.0 ,,._ -0 2 -0 4 -0.6 -0 8 0 2 6 8 10 Figure 4.38: Impulse Responses to an Innovation in O r ange Price--Orange VA R I-' w w

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1 0 -,----,---------~-----------, 0 8 0 6 0.-4 0.2 0 2 6 8 10 Figure 4.39: Impulse Responses to an Innovation in Banana Price--Cocoa-bean VAR. 1.00 -,-----,----------,------------, 0 75 0.50 0.25 ' ' '. ' \ '. ' \ \\ \ . \ \ ~---.. \ ... \ ): : '':.~~--:: .. 0 00 -+-----'-<------'T::--=:-~ .__,,, =:'!!_~-~~=-------i I / \ /. -0.25 ~---.---,--,----,---,,.-----,--,----,--.----,--r-,--r--~ b 2 e a 10 Figure 4.41: Impulse Responses to Innovation in WageCocoa-bean VAR. an 1 0 0.8 0.6 0.-4 \., 0 2 1; I .... .,.._ -0.0 ' -0 2 '. -0 '4 \ -0 6 \ \ ,,, -0.8 \/ 0 2 6 8 10 Figure 4.40: Impulse Responses to an Innovation in Fertilizer Price--Cocoa-bean VAR. 1 0 0 8 I I 0.6 0 -4 I I 0.2 I 0 0 -0 2 -0 '4 0 2 6 8 10 Figure 4.42: Impulse Responses to an Innovation in Cocoa-bean Price--Co c oa-bean VAR.

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1.0 -,---,---------~--------~ 0 8 0 6 0 4 0.2 dcaapr ..._ .,_,, ::: -+---1.:._L\1-~ _,, ---..<'.1=,,,;,_""' /: _. _:~ ~ ; 7: ::: ,:;; .,;.=!,= =-----------l -0 4 ' \_ _,, -0.6 ~--.---,--,---,--,---,--,---,--,---,--.---,-~ 0 2 4 6 8 10 Figure 4.43: Impulse Responses to an Innovation in Cassava Pr i ce--Potato VAR. 1 0 dlcaapr ' 0 8 ..._ I .,_,, \ 0 6 \ '. ' I I 0 4 ' ' 0 2 ' ' 0 0 ,, .. ~--:..::~~--~ I I -0 2 I I I I I I 0 2 4 6 8 10 Figure 4.45: Impulse Responses t o an I nnovation in Wage -Pota t o VAR. 1 0 dlcaapr 0 8 ..._ 0 6 ._,, 0.4 0 2 -0 0 -0 2 -0 4 -0 6 -0 8 0 2 4 6 8 10 Figure 4.44: Impulse Responses to an Innovation in Fertilizer Price--Potato VAR. 1 00 0 75 I I \ {--\. 0 50 li '. \ \ . . J \ 0 25 ,.' \ ,' '. \ . / !\ \, .. --~--, .. __ 0 00 / f ',. ,,:. -0 25 ! -0 50 i i -0.75 I I I I I 0 2 4 6 8 Figure 4.46: Impulse Responses to an I nno v a t ion i n Pota t o P r i ce -Po t a to VAR

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136 4.6 Summary This chapter provides empirical evidence of long-run equilibrium or cointegrating relationships between crop output and price incentives for eight crops grown in Jamaica. It was found that the data series relevant to the estimation of the long-run relationships are nonstationary. This statistical quality of the data has not been taken into consideration in previous studies on crop supply responses in Jamaica's agricultural sector. To deal with this data problem, an error correction modeling approach based on cointegration theory was adopted, within an estimation framework developed by Johansen (1988). The results from the estimation found low own-price elasticities as well as low adjustment coefficients. Additionally, impulse functions and forecast error variances were estimated in order to analyze the short-run dynamics of the output-price incentive relationships. It was found that overall, it appears that short-run crop responses are low, and respond more to crop price variations than on input-price changes. Slow adjustments in the short-run may be a sign of constraining government regulations, inadequate supportive infrastructure, lags between planting and harvesting, and other reasons.

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CHAPTER 5 SUPPLY RESPONSE AND COUNTERFACTUAL ANALYSIS In this chapter the before-after approach and counter factual analysis are used to assess the impact of the economic reforms on crop supply response in Jamaica. The before-after analysis is done in Section 5.1, and the counterfactual analysis in Section 5.2. 5.1 Before-After Analysis The before-after approach compares the performances of target economic variables during a particular program regime with their performances prior to the program. The approach assumes that any changes in the variables in the reform period are attributed solely to the reforms in that period. Two before-after estimators, which have been used extensively in the literature to evaluate Fund/Bank economic reform programs, are the mean change in the target variable and its variance (Khan, 1990). The mean estimator can be expressed as fj,y = Y2 Yi, where Y 2 and Y 1 are the means of the variable in the reform and pre-reform periods, respectively. If fj,y > 0, this is interpreted to mean that the reforms impacted positively on the variables. 137

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138 Price-variability is measured by the coefficient of variation, standard expressed as CV= s =, X deviation and mean where s, and X are the respectively. A brief justification for using these estimators is warranted here. One of the primary aims of the economic reforms in agriculture is to provide farmers with prices that are remunerative enough to induce output growth. Indeed, the observation that government interventions have been historically biased against agriculture (Schiff and Valdes, 1992a), led naturally to the belief by the IMF and World Bank that the economic reforms would reduce government distortion of prices, and improve price and non-price incentives to agriculture (World Bank, 1996). In this regard, it seems logical to investigate whether the reforms have ushered-in stimulative prices in agriculture. However, a related but antecedent question is what constitutes a stimulative price shift? Given the biological lag between planting and harvesting in agriculture, and the observation that in developing countries few farmers can fully mitigate against temporal price risk because of lack of resources, modern microeconomic theory provides little predictive confidence on agricultural production response to a given shift in the price distribution. However, micro

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139 economic theory of the firm under price uncertainty does postulate a positive relationship between output and the mean of output price, and a negative relationship between output and the variance of output price (Baron, 1970; Sandmo, 1971; Ishii, 1977). The issues raised above therefore justify an empirical examination of the first two moments of the price and quantity series used in the crop supply response models. In order to facilitate comparative analysis of variability among the different crops, the coefficient of variation is used instead of the variance. As mentioned in chapter 1, the period 1962-1979 is considered as the pre-reform period, and 1980-1999 the reform period. Two sets of estimates of the means and coefficients of variation for all eight crops' output and prices are computed. The first set covers the entire pre-reform and the reform periods, and the second set uses observations from the last five years in the pre-reform, and the first five years in the reform periods, respectively. These estimates are reported in Tables 5 .1 and 5. 2, and are robust with respect to the time periods used. With respect to quantity, Table 5 .1 shows that the mean output was higher in the reform period for four of the eight crops analyzed. These are coffee, pimento, yam and

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Table 5.1: Descriptive Price and Quantity Statistics --1962-1979 and 1980-1999. Pre-Reform Period Reform Period 1962-1979 1980-1999 Variable Mean CV Mean CV banq 134.771 0.189 98.274 0.086 banpr 2.978 0.398 8.436 0.242 sugq 175.214 0.204 93.300 0.088 suqpr 0.419 0.169 5.542 0.577 cofq 70.603 0.218 92.263 0.309 cofpr 41.021 0.219 21.485 0.229 pimq 189.586 0.197 339.261 0.148 pimpr 63.750 0.429 21. 555 0.238 yamq 74.936 0.357 135.259 0.226 yampr 7.297 0.141 9.227 0.593 orq 142.996 0.331 135.266 0.315 orpr 1. 575 0.364 11. 000 0.967 cobq 104.918 0.133 120.861 0.240 cobpr 19.774 0. 464 16.600 0.494 potq 162.886 0.237 152.036 0.307 potpr 8.654 0.212 12.390 0.382 ferp 9.432 0.219 8.215 0.214 wage 283.077 0.191 246.531 0.190 Note: CV= six, is the coefficient of variation, where, sand x are the standard deviation and mean respectively. All other variables as previously defined. 140 cocoa-bean. These increases in output are likely due to the government expansion programs in these crop activities, such as coffee and cocoa-bean rehabilitation schemes by the government, and to the stimulus of increased prices and export opportunities following the liberalization of prices in the reform period. With regards to the crops whose mean output did not increase over the reform period (banana, sugar, orange and potato), this is likely due to the

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Table 5.2: Descriptive Price and Quantity Statistics --1975-1979 and 1980-1984. Last 5 years of First 5 years of Pre-Reform Period Reform Period 1975-1979 1980-1984 Variable Mean CV Mean CV banq 105.833 0.118 109.286 0.055 banpr 4.428 0.186 7.771 0.393 sugq 141. 758 0.130 89.588 0.094 sugpr 0.482 0.161 1. 525 0.592 cofq 62.004 0.245 76.606 0.189 cofpr 48.084 0.165 21.103 0.152 pimq 193.388 0.241 214.163 0.425 pimpr 40.569 0.257 20.414 0.035 yamq 102.466 0.107 100.227 0.087 yampr 8.513 0.147 10.572 0.123 orq 94.350 0.145 78.325 0.017 orpr 1.116 0.591 1.061 0.089 cobq 94.169 0.095 125.868 0.207 cobpr 25.660 0.735 24.911 0.071 potq 162.018 0.266 133.925 0.321 potpr 10.746 0.112 11.568 0.139 ferp 10.020 0.246 9.175 0.219 wage 328.513 0.119 233.430 0.036 Notes: CV= six, is the coefficient of variation, where, sand x are the standard deviation and mean respectively. All other variables as previously defined. 141 persistence of structural factors, which slow down the adjustment process of output to policy changes. Table 5 .1 also shows that mean real prices for five crops (banana, sugar, yam, orange, and potato), increased over the reform period. The exceptions are coffee, pimento and cocoa-bean. However, real price-variability appears to have increased for most crop prices over the reform period. The exceptions are banana and pimento. Thus, the

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142 coefficient of variation for sugar price was 17 percent in the pre-reform period, but increased to 58 percent in the reform period. Similar high coefficients of variations are recorded for other crop prices. The observations made above in connection to the information in Table 5.1 are in general agreement with the information in Table 5.2. In addition to the information in Tables 5.1 and 5.2, Figures 5.1-5.8 show changes in nominal and real crop prices over the period 1962-1999. For most crops, nominal prices shifted upwards from the late 1970s and early 1980s, but the most dramatic increases are observed from the late 1980s onwards. While nominal prices have been increasing in the reform period, real prices show mixed changes as shown in Figures 5. 5-5. 8. Real prices for coffee and pimento have declined steadily over the 1962-1999 period. Some price stability and windfall gains have been observed for cocoa bean and potato prices but they seem to be on the descent, since 1987, below levels observed in the earlier years. The real prices for yam and orange increased briefly after the announced price liberalization policies by the government in 1985. By 1994 both prices were on the down turn but trending above the pre-liberalized price regime. Finally, real prices for banana and coffee showed dramatic increases

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25000 -----.--------~-------~ 20000 15000 10000 5000 1965 MNP SUGP 1975 1985 1995 Figure 5.1: Nominal Price Changes--Banana (BANP) and Sugar (SUGP). 40000 30000 20000 10000 1965 1975 1985 1995 Figure 5.3: Nominal Price Changes--Yam (YAMP) and Orange (ORP). 56000 ----r--------~---------, COFP 48000 --+.-----PIM P __ __, 40000 32000 24000 16000 8000 0 _J_-f 'r=i=F?i-'FF'#'i:::;::.,r-rr-.-.-,r,-rm-.,-rrrrn-rrm___J 1965 1975 1985 1995 Figure 5.2: Nominal Price Changes--Coffee (COFP) and Pimento (PIMP). 25000 COBP POTP 20000 15000 ,\: 10000 5000 0 1965 1975 1985 1995 Figure 5.4: Nominal Price Changes--Cocoa bean (COBP) and Potato (POTP).

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10.0 7.5 5.0 2.5 1964 BANPR SUGPR 1973 1982 1991 Figure 5.5: Real Price Changes--Banana and Sugar (1980=100). 35 ~-------~-------YR.FR /:, ORPR : 30 -i-------------' : \ 25 / \ : ... 20 i \ i \ 15 f ',, 10 5 .. .. 0 ---'-~~~~~~~..........:;..:.:.....~~~~~~~~-_J 1964 1973 1982 1991 Figure 5. 7: Real Price Changes--Yam and Orange (1980 = 100). 140 COFPR 120 PIMPR 100 80 60 40 20 0 1964 1973 1982 1991 Figure 5.6: Real Price Changes--Coffee and Pimento (1980=100). 60 ~---------~---------, COBPR POTPR 50 _J...-------~-,--J 40 30 20 10 ... __ __ __ ..... 1964 1973 1982 1991 Figure 5.8: Real Price Changes--Cocoa-bean and Potato (1980 = 100).

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145 in the early 1980s, and while sugar price continued upwards until 1991, banana price peaked in 1985, fluctuated somewhat, and along with sugar, have been on a decline, with some evidence of an upturn by 1998. In effect, therefore, these observations suggest that the pro-competitive effects that were expected to accompany the reforms as well as from the demise of state monopolies, may have outweighed the stability impulses of controlled prices of the pre-reform era. 5.2 Counterfactual Analysis One of the premises upon which the long-run supply elasticities reported in chapter 4 is based, is that the recent economic reforms in Jamaica influenced the variation of the economic time series which are included in the various crops' error correction models. On the basis of this premise, the long-run crop supply elasticities reported in chapter 4 could be interpreted as what has happened to supply response under the economic reforms. However, the true effect of the economic reforms on crop supply response must be based upon a comparison of the supply response estimates in chapter 4 with estimates derived under the assumption that the economic reforms, which were observed over the period 1980-1999 did not occur. Crop supply response under this assumption would

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146 constitute a counterfactual, against which the supply response estimates in chapter 4 should be compared. A relevant issue, in this regard, is what would have been the policies pursued by the government if the reforms were not undertaken? Answers to this question would provide the counterfactual situations. The most manageable way of constructing a counter factual using time series data, is to assume that the policies, which the government was pursuing over the 19621979 period, continued over the 1980-1999 period. Under this assumption, the time series modeling technique would forecast the 1980-1999 period by extrapolating techniques, which incorporate no information other than that available in the past. A central issue, however, is whether the forecasts should be made on individual series or as a system. Since in each crop supply response ECM the variables are shown to be in long-run equilibrium, it seems justifiable to use a systems approach to forecasting the economic time series. As such, a VAR approach is utilized and in each VAR the stochastic variables are the same as those that were included in the supply response ECM in chapter 4. The forecasts of the economic time series are shown in Figures 5.9-5.28. Although the graphs show clear distinct

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147 ions between the actual and forecasted variables, for some crops the two sets of series show mixed results. For example, Figure 5.9 shows that the forecast of banana quantity is above the actual series over the 1980-1999 period (the exception is in 1983 when actual and forecast quantities are equal). Similarly, Figure 5.11 shows that actual price of sugar has been above the forecast for the entire 1980-1999 period. Given the assumption under which the forecasts were made, this suggests that, in the case of Figure 5.9, if past (1962-1979) policies had continued over the 1980-1999 period, banana output would have been higher than what actually prevailed during the period of economic reforms. This would have been the case despite the fact that the forecasted banana price is lower than actual price. The one-step ahead forecasts shown in Figures 5.9-5.28 suffer from two short-comings. First, they reflect the data trend of the last few years before the end of 1980. Further, the forecasts are smooth and do not reflect the fluctuations in the actual series. Finally, the forecasts were plagued with large standard errors, and therefore provide only a possible indication rather than a precise picture of the counterfactuals.

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5.2 LBANQ 5 1 FOLBANQ 5.0 4.9 4.8 4.7 4.6 4 5 4 4 1964 1973 1982 1991 Figure 5.9: Banana Quantity--Actual (LBANQ) and Forecasted (FOLBANQ). 3.0 -..,...----------------~ 2.5 2.0 1 5 1.0 0.5 0.0 ----------------------< -0.5 -1 0 -1 5 ---'-~--r-r-m--r--r-m-,-,-m-r-r ............. m~m~~~~,-----J 1964 1973 1982 1991 Figure 5.11: Sugar Price--Actual (LSUGPR) and Forecasted (FOLSUGPR). 2.50 ---r-----------,-----,------, 2.25 2 00 1.75 1.50 1.25 1 00 0.75 0.50 0 25 ---'-~.,......-,-r-r-.----.----r-T""T"--r-r-m--r-r-r-r-1---r--r-m--r-r-m--r-r-m""T""T'~ 1964 1973 1982 1991 Figure 5.10: Banana Price--Actual (LBANPR) and Forecasted (FOLBANPR). 5.9 -.-------=-------------, 5 8 5 7 5 6 5.5 5.4 5.3 5.2 5 1 ---'-~--r-r-m-r-r--r-r-m-,-,-......-m--r+-m-r-r.--.-m-r-r-r,-rr-~ 1964 1973 1982 Figure 5.12: Wage--Actual Forecasted (FOLWAGE). 1991 (LWAGE) and

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2.6 ---,------------------2.5 2.4 2.3 2 2 2.1 2 0 1.9 1.8 1, 7 ~---.--r-r-m---,-,--.--,-m---rr-m---,-,-,--r'-m--r-r-..---,-,m---rr-m~-' 1964 1973 1982 1991 Figure 5.13: Fertilizer Price--Actual (LFERP) and Forecasted (FOLFERP). 5.25 ---,---------~-------~ 5 00 1964 1973 1982 LCOFQ FOI.COFQ 1991 Figure 5.15: Coffee Quantity--Actual (LCOFQ) and Forecasted (FOLCOFQ). 5 50 ---,----------~--L SUOQ --~ FOLSUOQ 5.25 5.00 4.75 4 50 1964 1973 1982 1991 Figure 5.14: Sugar Quantity--Actual (LSUGQ) and Forecasted (FOLSUGQ). 4 2 ---,------------------~ 4 0 3 8 3.6 3.4 3.2 3 0 2.8 2 6 ....J....---,-.,.....-,m---rr-..---,-,--,.--r---rr-m---,-l-,-,--r-m--r-r-...--r-,m---rr-..---,-,-,--' 1964 1973 1982 1991 Figure 5.16: Coffee Price--Actual (LCOFPR) and Forecasted (FOLCOFPR).

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6.50 ---.-----------.---------~ 6.25 6.00 5 75 5.50 5 25 5.00 4.75 LPIIIQ FOLPIIIQ 4,50 __,__-,--.--,--,,--,-,--.--,--,,--,-,--.--,--,,--,-,-~m...,...,....~~~~~~--' 1964 1973 1982 1991 Figure 5.17: Pimento Quantity--Actual (LPIMQ) and Forecasted (FOLPIMQ). 5.4 LYAMQ 5.2 FOLYAMQ 5.0 4.8 4.6 4.4 4 2 4.0 3.8 1964 1973 1982 1991 Figure 5.19: Yarn Quantity--Actual (LYAMQ) and Forecasted (FOLYAMQ). 11 0 ~------------------, 10.5 10 0 9 5 9.0 8.5 8 0 7.5 7 0 6 5 ----'--,--,---r,,--,-,--.--,--,,--,-,-...,...,....,-r-r...,...,....,..-'r-,-...,...,....m...,...,....m...,...,....~~---' 1964 1973 1982 1991 Figure 5.18: Pimento Price--Actual (LPIMPR) and Forecasted (FOLPIMPR). 3.00 ----.------------------, 2.75 2.50 2.25 2 00 1.75 1.50 1 25 __,__-r~m~m...,...,....m...,...,....r+-....,...,....m...,...,....~~~,--,-,---' 1964 1973 1982 1991 Figure 5.20: Yarn Price--Actual (LYAMPR) and Forecasted (FOLYAMPR). I-' (.Tl 0

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1964 1973 1982 1991 Figure 5.21: Cassava Price--Actual (LCASPR) and Forecasted (FOLCASPR). 3.6 ~----------------~ 3 0 2.4 1.8 1.2 0.6 0.0 --+-----~r-+-+--.,.._~-------0.6 2 ~---,--r-,--r-r-,--,-,-m-,-,-m-,----,-,....,,_,~-r-r--m-,-,-m-,----,-~--' 1964 1973 1982 1991 Figure 5.23: Orange Price--Actual (LORPR) and Forecasted (FOLORPR). 5 40 LORQ 5.22 FOI.ORQ 5 04 4.86 4.68 4 50 4 32 4.14 3 96 1964 1973 1982 1991 Figure 5.22: Orange Quantity--Actual (LORQ) and Forecasted (FOLORQ). 3 5 ~----------------~ 2.8 2 1 1.4 0.7 0.0 -+-----~----........-=--------,---~--0.7 -1.4 1 ~---.---r-r-m-,-,-m-,----,--r,--,c-r-r-r',---m-,-,-m""T""T""-r,--,c-r-r--r-r-.--' 1964 1973 1982 1991 Figure 5.24: Grapefruit Price--Actual (LGRPR) and Forecasted (FOLGRPR).

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5.25 ~--------~-------~ 5 00 4.75 4.50 1964 1973 1982 L008Q FOL008Q 1991 Figure 5.25: Cocoa-bean Quantity--Actual (LCOBQ) and Forecasted (FOLCOBQ). 5.60 LPOTQ 5.44 FOi.POTO 5.28 5 12 4.96 4.80 4.64 4.48 4.32 1964 1973 1982 1991 Figure 5.27: Orange Price--Actual (LORPR) and Forecasted (FOLORPR). 1964 1973 1982 1991 Figure 5.26: Cocoa-bean Price--Actual (LCOBPR) and Forecasted (FOLCOBPR). 1964 1973 1982 1991 Figure 5.28: Grapefruit Price--Actual (LGRPR) and Forecasted (FOLGRPR). I-' (J1 N

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153 A second approach was used to construct a counterfactual in order to assess the impact of the economic reforms in Jamaica. In this approach, the actual observations were divided into two separate sub-samples, 1962-1979 and 1980-1999, corresponding to the pre-reform and reform periods, respectively. Supply functions for each crop were estimated for each of the two sub-samples. The estimation procedure followed the Johansen method, which was outlined in chapter 4. Fitted values from the 1962-1979 period were then forecasted over the 1980-1999 period and these forecasts were used as the counterfactuals against which the fitted values from the 1980-1999 supply functions were compared graphically. With respect to the forecasting exercise, the BOXJENK, CORRELATE, and FORECAST instructions in the RATS computer software were used to identify, estimate and forecast the fitted series which were used as the counterfactual series. After some experimentation, the forecasts were modeled as mixed autoregressive-moving average processes, denoted as ARMA(p, q), where p and q are the autoregressive and moving average orders, respectively (Pindyck and Rubinfeld, 1991). Following the Box-Jenkins methodology (Enders, 1995; Doan, 1996), the orders of p and q were determined by examining the sample autocorrelation functions (ACF)

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154 partial auto-correlations functions ( PACF) and graphs of the fitted series. For each series, several models were fitted to the data and the final choice was determined on the basis of parsimony, and various diagnostic statistics. The test criteria used are the root-mean-square error (RMSE), Theil' s Inequality Coefficient (Theil U), and the Ljung-Box Q-statistics. Low RMSE is a desirable quality from a forecasting model. The Theil U statistic is independent of the units of measurement. It is the ratio of the RMSE for the forecast model to the RMSE for a "naive" forecast of no change in the dependent variable from the previous value (Doan, 1996) Values of Theil U that are less than one indicate that a model performs better (i.e., has a lower RMSE) than the naive model. Doan (1996) claims that a Theil U of 0.8 or less is reasonable for a univariate forecast model. The values of (p, q) were determined by the ACF and PACF. For an ARMA(p, q) process, the ACF begins to decay at lag q, and the PACF begins to decay at lag p. In addition to the ACF and PACF, the Ljung-Box Q-statistic was used in model selection. The Q-statistic is a chi-square (X 2 ) test used to test the null hypothesis of no significant autocorrelations. The diagnostic statistics for the fore casts of fitted values are reported 1n Table 5.3.

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Table 5.3: Diagnostic Statistics for Forecasts of Fitted Values. Crop RMSE Theil u Q (2) a Q ( 4) a Banana 0.167 0.353 3.397 3.963 Sugar 4.799 0.524 0.062 0.266 Coffee 0.378 0.880 0.061 0.368 Pimento 0.094 0.838 2.304 3.559 Yam 0.190 0.538 0.177 3.596 Orange 0.710 0.598 2.324 5.878 Cocoa-bean 5.769 1.188 0.902 4.558 Potato 0.325 0. 262 1.957 3.095 a Ljung-Box Q-statistics. x~ and x~ critical values at 95% level are 5.99 and 9.49 respectively. RMSE = Root Mean Square Error. 155 The estimated a and 13 vectors for each crop for the two sample periods are reported in Tables 5. 4 and 5. 5, respectively. From Table 5.4 the most economically plausible equations (i.e., equations that meet a priori (sign) expectations) were selected to generate fitted values for the dependent variable in each crops' ECM for the 1962-79 period. For example, the fitted values for banana for the 1962-79 period were estimated as follows: BANFIT = .300 + 0.781*P 0 0.242*Pa 0.758*W 0.854*F where all coefficients are taken from the CE #1 relation for 13 for banana in Table 5. 4. These fitted values were then forecasted, using the methodology described above, over the 1980-99 period, and used as the counterfactual for banana.

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156 In a manner similar to that described above, the fitted values for each crops' ECM were estimated for the 1980-99 period. For example, the fitted values for banana for the 1980-99 period were estimated as follows: BANFITA = -4.140 + 0.172*P 0 0.103*Pa 0.040*W 0.003*F where all coefficients are taken from the CE #2 relation for for banana in Table 5.5. The 'A' appended to 'BANFITA' is used to denote that the actual observations, Po, Pa, Wand F, were used to estimate the fitted series. The two sets of fitted values, the (forecasted) 'counterfactual' and 'actual', for each crop, were then plotted on the same graph. These graphs are shown in Figures 5.29-5.36. From a visual inspection of those graphs the following observations are noted. First, banana is the only crop in the sample for which the fitted values from the reform period are clearly above the fitted values for the counterfactual over the entire 1980-1999 period. Excluding the years 1987-1988, this observation is also applicable for pimento. The interpretation of this observation, based upon the assumptions that have been used to generate the fitted and forecasted series, is that if there were no change in the policy regime in Jamaican over

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157 Table S. 4: Estimated Long-run and Adjustment Coefficients, P's, a's for all Crops, 1962-1979. Crops Q Po Pa w F C Banana CE #1 [}' s 1 0.781 -0.242 -0.758 -0.854 0.300
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Table 5.4: Cont'd. Crops Q Po Pa w Cocoabean CE *1 -P's 1 -0.313 0.035 0.603
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159 Table 5.5: Estimated Long-run and Adjustment Coefficients, P's, u's for all Crops, 1980-1999. Crops Q Po Pa w F C Banana CE #1 1 -0.249 0.255 -0.006 0.921 -6.383 a's -0 .135 0.093 -1. 942 0.344 -0.805 (-1.534) (0.230) (-3.814)a (2.532)b (-3.89l)a CE #2 P's 1 0 .172 -0.103 -0.040 -0.003 -4.140 a's -1.047 -2.308 1.043 0.310 0.559 (-4.456)a (-2.150)b (0. 769) (0.858) (1.015) CE #3 P's 1 0.306 0.069 0.029 0.916 -7.344 a's -0.064 -1.064 -0.176 -0.162 -1.122 (-2.788)b (-2.880)a (-0.376) (-1. 301) (-5.918)a Sugar CE #1 1 0.255 -0.249 -0.006 -0.921 5.879 a's -0.135 -1. 942 0.093 0.344 -0.805 (-2.534)b (-3.814)a (0.230) (2.532)b (-3.89l)a CE #2 1 0.103 -0 .172 -0.040 -0.003 -3.636 a's -1.047 -1.043 2.308 0.310 0.559 (-4.456)a (-0.769) (2.150)b (0.858) ( 1. 015) CE #3 1 0.069 0.306 0.029 0.916 -6.839 a' s -0.064 -0 .176 -1. 064 -0.162 -1.122 (-0.788) (-0.376) (-2.880)a (-1.301) (-5.918)a Coffee CE #1 -~'s 1 -1.140 2.259 0.956 6.905 -25.382 a's -0.127 0.087 -0.143 -0.018 -0.193 (-1.792)c ( 1. 4 67) (-2.749)b (-0.600) (-7.023)a CE #2 -P's 1 1.108 -0.231 -0.440 -0 .116 -6.146 a's -0.016 -0. 711 o. 717 0.031 0.223 (-0.066) (-3.460)a (3.972)a (0.301) (2.339)b Pimento CE #1 p;-;1 -0.190 1.410 0.907 1.680 -15.165 a's -0.048 0.286 -0.380 -0.015 -0.127 (-0.446) (4.779)a (-3. 760)a (-0.281) (-2.lll)b CE #2 1 0.282 -0.406 -1.181 -0.388 -9.764 a.' s -0.769 -0.096 0.087 0.021 0.010 (-5.208)a (-l.186)c (2.629)b (0.293) (0.125)

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Table 5.5: Cont'd. Crops Q P o Pa w Yam CE *1 P's 1 2.543 -3.168 -0.052 s -0 .131 -0.391 0.030 0.075 (-2.183)b (-3.944)a (2.464)b (1.301) CE #2 -P's 1 0.356 -0.248 1.805 s -0.253 -0.744 0.384 -0 .179 (-3.344)a (-5.960)a (4.703)a (-2.470)a CE #3 -P's 1 -0.301 0.469 0.122 a's -0.336 0.564 -0.099 -0.024 (-3.850)a (3.928)a (-1.049) (-0.286) Orang CE U pr-;1 -5.652 4.301 -4.485 a's -0.047 0.089 -0.306 0.059 (-1.001) (0.816) (-5.225) a (2.889)a CE #2 pr-;1 0.224 -0.477 -0 .115 a's -0.271 -1.347 1.369 0.017 (-1.789) c (-3.846)a (7 .267) a (0. 260) CE #3 -P's 1 0.050 -0.307 1.185 a's -0.197 -0.576 0.453 0.049 (-1.147) (-1. 446) (2.114)b (0.655) Cocoabean CE U 6's 1 0.016 -1.159 -0.505 a's -0.153 -0.384 0.432 0.098 (-0.884) (-2.326)b (2.107)b ( 1. 027) CE #2 pr-;1 -0.302 -0.362 1.106 s -1. 831 0.558 0.532 -0.179 (-5.336) 8 (l.708)c (1.312) (-0.952) Potato CE U -P's 1 0. 971 -1.274 -2.240 s -0.081 -0.083 0.136 0.032 (-0.942) (-1.295) (5.318) 8 (l.928)c CE #2 -P's 1 -0.008 0.612 0.030 s -0.917 0. 472 -0.225 -0.179 (-1.239) (0. 855) (-1.016) (-1. 263) Note: Figures in parentheses are t-values. CE indicates 'cointegration equation'. 160 F C -0.543 -7.369 0.012 (0.100) 0.503 -18.315 -0.193 (-1.246) 0.980 -11.136 -0.453 (-2.533)b -4.097 32.444 0.049 (1.496) -1. 609 -0.501 0.237 (2.240)b 0.468 1.084 -0.527 (-4.348) 8 -2.212 -0.353 0.603 (4.688) 8 0. 471 -10.230 -0.216 (-0.851) -0.702 17.404 0.049 (1.019) 0.303 -6.541 -0.138 (-0.331) a, b, c, indicate statistical significance at one, five and ten percent levels, respectively.

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6.2 --,---------------------, 6.0 5 8 5 6 5 4 5 2 5 0 --------------' ....... 4 8 ............. .,// '.,/ IANF I TA 4.6 -'------ir--r--.-.--r-r--.---r-r-r---,----.-l,---,-----,--~T""""""T IA NF ~ ITC -.-~_J 1980 1985 1990 1995 Figure 5.29: Banana--Fitted Output for Reform Period (BANFITA) and Counterfactual (BANFITC) 150 --,----------.....-----------, 125 100 75 50 25 COFFITA COFFITC ,-. : ," .:' -.,; 0 ~---,--.---.--r-r--.-----.--.----r----,---.--.----r----,---.--.----r----,---.--.----r~ 1980 1985 1990 1995 Figure 5.31: Coffee--Fitted Output for Reform Period (COFFITA) and Counterfactual (COFFITC) 160 -,.------------,.-----8 UG FIT A _-, SUGFITC 155 150 145 140 135 130 : : / \ : : ,. 125 -'---r--r~~r--r~~~-~~-.-~~-.-~--' 1980 1985 1990 1995 Figure 5.30: Sugar--Fitted Output for Reform Period (SUGFITA) and Counterfactual (SUGFITC). 12800 -,.-----~ w ,r ,.. --.....---------, ~MFITC 11200 ---1--------~ 9600 8000 6400 4800 3200 .... ,./ \ __ ........ 1600 --'-------,-----.-~~-,---,----,--~~---,-~~-.--,---,----,--~ 1981 1986 1991 1996 Figure 5.32: Pimento--Fitted Output for Reform Period (PIMFITA) and Counterfactual (PIMFITC).

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8.40 ---.----------------~ 8.00 7.60 7 20 6.80 6.40 6.00 --t-----------, YAMFITA S : SO ~-.------r-r--r-~ Y~ ITC -~~'-,--~~~-,......,..---' 1981 1986 1991 1996 Figure 5.33: Yam--Fitted Output for Reform Period (YAMFITA) and Counterfactual (YAMFITC) 14 -,-----------,-----------, 12 10 8 6 4 2 COBFITA COBFITC 0 --'--~~--r-~~--r--~-~-,......,..~~-_J 1980 1985 1990 1995 Figure 5.35: Cocoa-bean--Fitted Output for Reform Period (COBFITA) and Counterfactual (COBFITC) 70 60 50 40 30 20 10 .. 0 1980 Figure 5.34: Reform Period (ORFITC) ORFITA ORFITC 1985 1990 1995 Orange--Fitted Output for (ORFITA) and Counterfactual 8 ~---------------------, 7 6 5 4 POTFITA POTFITC ' . ' . 3 -L--~~~-~ ~~~~-r--~~~~-.,...-~~ 1981 1986 1991 1996 Figure 5.36: Potato--Fitted Output for Reform Period (POTFITA) and Counterfactual (POTFITC).

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163 the 1980-1999 period, then the output of banana and pimento, with given price incentives, would have been lower than that which were actually observed under the reform policy regime. In other words, it appears that the reforms impacted positively on these two crops over the 1980-1999 period. Second, for some crops there are two distinct periods over 1980-1999 in which the fitted values from the reform period are either above or below the counterfactual. For example, over the 1980-1992 period the fitted values for sugar for the reform period were below those for the counterfactual. The reverse is observed for the series since 1992. Similarly, fitted values for coffee for the reform period have been above the counterfactual over the 1980-1995 period but the situation reversed after 1995. Similar observations can be made for orange and cocoa-bean. Finally, the cases of yarn and potato are different from the two patterns observed for the other crops. Yarn shows an oscillating pattern. Fitted values for the counterfactual were below fitted values for the reform period over 1980-1985. Potato showed similar oscillations but with relatively wider gaps within each oscillation. In effect, therefore, based on the counterfactual analysis, the data seem to suggest that the impact of the

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164 reforms are crop and time specific. For some crops it would appear that the reforms impacted negatively on output trends in the 1990s. This is the case of coffee, orange, cocoa-bean and potato. For banana and pimento the reforms appear to impact positively on output responses. For yam and potato, similar straightforward conclusions cannot be made since the reforms appear to impact positively on output response in some periods and negatively in others. 5.3 Summary Comparative analysis of crop output and prices between the pre-reform and reform periods suggests a mixed record regarding the impacts of economic reforms in Jamaican agriculture. For some crops, mean output increased under the reform periods, while for others, output contracted. Clear evidence of greater price variability is observed in the reform period compared to the pre-reform era. The counterfactual analysis suggests that the effects of the reforms are crop and time dependent.

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CHAPTER 6 CONCLUSIONS AND POLICY IMPLICATIONS Jamaica embarked upon a process of economic policy re orientation in the late 1970s, at a time when the economy was showing signs of acute economic crisis. Mainly the IMF and World Bank sponsored the economic reforms in consultation with the Jamaican government. Although, at various points in time, there were clear signs of opposition by the government towards the economic reforms emanating from the Bretton Woods Institutions, by 1990, Jamaica had, to a large degree, embraced most of the reform policies. Essentially pragmatic, the reforms highlighted the need for a more outward orientation of the Jamaican economy. Much emphasis has been placed on being competitive in world markets and also upon relying on market forces to determine prices and to allocate increasingly scarce capital resources. The reforms also entailed a critical re assessment of the role of the state in terms of its size, overall efficiency, and its relationships with the private sector. As a result of these considerations, topics such as 165

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166 deregulation, privatization and economic liberalization acquired increased attention in the policy debates over the past two decades. The reforms that began in the late 1980s engendered an entirely new economic environment for agriculture. However, it posed numerous challenges to economic agents such as small farmers and other agricultural producers generally, who had hitherto operated under various subsidies and guaranteed marketing and pricing schemes. In this regard, one government agency reports that Since the liberalization of the economy and the sub sequent restructuring of government support for essential services in the agricultural sector, farmers have had some problems remaining competitive. (Jamaica, Planning Institute of Jamaica, 1996, p.7.12) The quotation above is instructive insofar as it may have identified an important aspect of economic reality in Jamaica, namely, the difficulty of farm producers to make a successful transition between policy regimes, which in many respects make differential demands on economic actors. This conclusion achieves some degree of plausibility in light of the observation that the transformation or re-ordering of economic policy priorities constitutes a fundamental departure from the basic policy directives that had been adopted in Jamaica from the 1960s through to the late 1970s. These policies encouraged inward looking industrial

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167 ization; state-sponsored import substitution and nationalization of foreign enterprises; extensive use of price controls and subsidies; and, generally, a relatively strong populist approach to the solution of poverty, low income, unemployment, and other socio-economic problems. One of the principal tasks of this study has been to investigate the impact of the economic reforms on crop supply response in Jamaica. The estimates on crop supply responses presented in this study suggest that long-run equilibrating relationships exist which link crop output and price incentives for the eight crops studied. This result has important policy implications insofar as it indicates a significant relationship between agricultural supply and price incentives. In particular, and on the basis of the analytical method used in this study, the link between output and price incentives means that these variables move together over time and more importantly, respond to the same shocks in the system, albeit with varying degrees. Further, as markets become more competitive, this link becomes stronger since market signals are transmitted more effectively and efficiently. Although economic theory is generally silent as to what constitutes a stimulative price shift for agricultural producers, the econometric evidence presented in this study

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168 indicates a positive relationship between output supply and own-price. Crops such as banana and sugar, whose prices are still determined by preferential agreements, enjoyed increased real prices over the reform period. Jamaican sugar and banana have guaranteed access to the European Union (EU) market via the Lome Conventions. The prices paid for Jamaican exports in this market are significantly higher than world market prices. However, the EU' s Common Agricultural Policy (CAP) requires progressive reductions of subsidies to African Caribbean and Pacific (ACP) countries. This is expected to reduce the prices paid for ACP exports. In addition, with respect to bananas, EU's arrangements with the ACP countries are constantly being challenged by Central and Latin American (Dollar) banana countries. Proposals are for the indi victual ACP quotas to be replaced by a single global ACP group quota. While the tariff preference is expected to continue in the future, it is under constant review. The main implication is that Jamaican farmers must reduce cost of production in order to compete in the EU market. The twin issues of reducing cost of production and achieving international competitiveness appear to be major goals in the government's National Industrial Policy which

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169 was tabled in 1996. In this regard, J$750 million in international funding was sourced and allocated to various projects in response to the frequent debates in the EU to reduce preferential treatments of ACP exports (Jamaica, Planning Institute of Jamaica, 1998). In the case of banana, various projects have been implemented since the mid-1990s. These include programs for improved management of water systems and the banana disease, Sigatoka. For small and medium size farms, high cost and unavailability of inputs are major constraints to improving productivity. For these farmers the government has invested J$38 million to set up a supply store and has established a revolving loan scheme for improvements in irrigation and other infrastructural works. In addition, other projects have been implemented which aim at improving banana quality, such as experiments in de-handling, deflowering, hardening, and packaging and storage. bunch There is strong evidence to suggest that there are considerable constraints in the economic system, which slow down the adjustment process for economic variables. This is evidenced by the prevalence of low adjustment coefficients, which were estimated along with the long-run relationships. Own-price and quantity adjustments appear to exercise the major weight in the adjustment of the short-run to the

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long-run equilibrium process. 170 While significant elasticities were observed for the input prices, generally these were low, but fall within the range of estimates reported in other studies on supply response in Jamaica. The low input price elasticities are not surprising since inputs such as wages and fertilizer constitute important components in the production process. However, if these inputs are not to appear as constraints to production, policy initiatives have to be undertaken to make them appealing to producers. Given the biological lag between planting and harvesting seasons in agriculture, adjustments in the sector are necessarily slow processes. Additional factors that contribute to slow adjustments are the institutional and structural framework within which agricultural producers must operate. Slow adjustments in the short-run are signs of constraining government regulations, inadequate supportive infrastructure, and lack of credit to farmers. Although not explicitly incorporated in the modeling framework used in this study, these factors have been listed by farmers in coffee, cocoa-bean, citrus and yam production as major constraints to increasing output (Jamaica, Planning Institute of Jamaica, 1994, 1996, 1998).

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171 In an earlier section of this study, some of these factors were identified as the conduits through which policies are transmitted to target variables. It appears that the government has been aware of these problem as can be gleaned from the following quotation: For positive growth in the sector to be sustained, there is urgent need for infrastructural development and a strengthening of research and extension services ... This need has assumed an increasing sense of urgency in light of pending changes on the global economic landscape with the new General Agreement on Tariffs and Trade (GATT). (Jamaica, Planning Institute of Jamaica, 1994, p.7.1) To the extent that this is true, further improvements in crop supply response would be conditional upon the intensification and broadening of the reform process in agriculture. Indeed, the general decline in agricultural production since the mid-1990s, prompted the government to outline policy guidelines aimed at correcting some of the problems impacting the sector. These guidelines emphasized improving the competitiveness of traditional and non traditional exports in international markets; creating an enabling environment for farmers through additional capital funding over the long, medium and short-run period; implementation of projects/programs aimed at improving production and productivity for local and export markets; and improving support services (Jamaica, Planning Institute of Jamaica, 1998).

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172 While it may appear that government policies are being implemented to facilitate the farmer to operate in the new policy environment, budgetary constraints may not always permit an effective solution. For example, in light of increasing demands by farmers for agricultural credit at concessionary rates, the government offered agricultural loans at subsidized rate (13 percent). However, loans allocated to farmers have dropped almost 50 percent over the past five years because of budgetary constraints. The empirical evidence provides only partial support for the hypothesis that the reforms impacted significantly on agricultural supply responses. In particular, even when controlling for policy regime changes by constructing counterfactuals against which the reforms could be compared, it was found that for the eight crops included in this study, the effects are crop and time dependent. Although nominal crop prices have increased dramatically during the reform period, real crop prices have remained stable or have declined generally. Movements in the exchange rate and the general price level seem to be the immediate causes for these phenomena. It appears also that price variability increased significantly during the reform period. Given that farmers in Jamaica, as in most developing countries, can rarely mitigate against temporal

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173 price risk, the evidence seems to suggest that the procompetitive effects of the reforms may have acted more as a depressive factor on supply response, compared to the stability of the pre-reform period characterized by stable and guaranteed markets and prices by government commodity boards. A relevant question which may be posed here is how representative is the sample of crops which was chosen for the study. The eight crops were not randomly chosen but were selected the same way a researcher might have chosen to study just one of the crops. However, the crops included in the study include the most important traditional export crops of Jamaica. It is unlikely that any of these crops would have been insulated from the economic reforms over the past two decades. However, whether the conclusions from this study can be generalized for the other crops that are grown in Jamaica, is an empirical question. Further insights into the impact of the reforms on crop supply responses in Jamaica would be obtained by more intensive study of single crops. This will allow more in depth study, and guard against using the same modeling framework to accommodate crops whose supply dynamics may be different.

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APPENDIX A PRICE DATA USED IN CROP'S ECM ON JAMAICA, 1962-1999 (J$) Year banana 1962 28 1963 29 1964 29 1965 30 1966 30 1967 34 1968 34 1969 29 1970 30 1971 51 1972 49 1973 73 1974 159 1975 188 1976 196 1977 196 1978 175 1979 347 1980 520 1981 640 1982 700 1983 1,190 1984 1,998 1985 2,065 1986 2,249 1987 2,866 1988 2,668 1989 2,337 1990 3,748 1991 7,046 1992 8,818 1993 11,000 1994 17,637 1995 20,073 1996 17,813 1997 17,680 1998 18,510 1999 22,522 Source: ( 1) Institute of Food and Agricultural Cocoasugar coffee pimento yam orange bean potato 5 542 1050 80 18 210 99 5 549 1052 86 21 219 99 5 560 1065 88 25 228 102 6 570 1075 90 27 232 102 6 570 1,102 91 33 236 104 6 641 2,121 96 38 263 107 6 425 1,047 101 31 276 114 6 572 1,102 107 29 265 120 6 569 1,165 118 31 272 133 7 613 1,102 140 31 344 141 8 674 1,146 141 25 350 132 9 839 1,005 176 37 369 242 11 1,272 1,397 273 57 451 309 19 1,784 1,947 342 26 559 350 19 1,966 1,994 399 64 566 466 21 2,483 1,837 424 78 616 573 32 3,560 1,900 424 21 3,590 651 45 3,000 2,010 551 42 3,000 816 71 2,205 2,100 1,168 104 2,208 1,230 102 2,021 2,205 1,224 108 2,991 1,100 130 2,572 2,425 1,168 122 2,991 1,320 239 2,940 2,425 1,345 122 2,991 1,587 483 3,123 3,417 1,499 208 4,228 1,852 762 5,144 5,071 1,631 380 5,104 2,601 908 8,157 7,716 1,764 454 6,022 4,189 1,249 7,716 8,047 2,557 381 6,482 5,754 1,354 7,716 8,818 3,527 536 6,941 5,225 1,849 7,800 9,149 5,842 3,960 6,556 5,688 4,943 8,000 9,590 4,410 2,860 6,756 4,670 5,324 11,684 11,464 7,980 8,800 7,706 10,780 9,743 27,925 23,148 12,050 30,800 8,000 16,950 10,957 27,355 27,550 14,700 44,660 9,000 19,880 14,825 30,433 33,460 15,000 45,000 10,000 20,000 18,120 43,333 34,171 11,400 45,522 21,000 15,200 20,456 43,333 36,376 11,562 45,521 21,987 16,982 17,170 43,333 45,194 11,256 45,987 21,558 16,989 16,752 46,065 50,706 11,789 45,887 22,154 17,586 17,635 48,221 52,602 12,156 45,978 22,546 17,892 1962-65, 1995-1999: Jamaica, Statistical Jamaica, (STATIN), (Data Files) (2) 1966-1995: Agricultural Organization (FAO). FAOSTAT Data. http://fao.org. August, 1998. 174

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APPENDIX B QUANTITY DATA USED IN CROPS' ECM, 1962-1999 (METRIC TONNES) Year banana sugar coffee pimento yarn 1962 221741 523215 1721 3012 61245 1963 210523 521000 1785 3012 62546 1964 226331 512663 1798 3111 62946 1965 231046 505621 1852 3125 63012 1966 240,400 508,247 1,861 3,200 63,486 1967 234,200 455,815 1,991 4,500 74,580 1968 198,000 451,895 1,972 4,800 66,306 1969 196,000 389,124 1,589 3,800 62,556 1970 195,000 376,057 1,805 2,671 80,825 1971 187,000 385,029 1,358 3,510 122,864 1972 198,000 379,214 1,040 3,290 127,489 1973 169,000 331,181 1,146 3,410 127,650 1974 132,000 372,391 1,486 3,570 131,323 1975 127,000 360,578 1,186 3,320 134,254 1976 140,000 368,737 1,929 4,970 119,566 1977 160,000 295,237 1,208 3,320 124,941 1978 160,000 291,541 1,477 3,830 149,767 1979 170,000 283,084 958 2,340 157,169 1980 140,000 231,800 2,216 1,840 132,893 1981 150,000 225,000 1,379 4,266 136,410 1982 160,000 195,531 1,516 2,759 116,978 1983 160,000 198,202 1,632 4,729 130,633 1984 155,000 187,791 1,745 6,109 149,060 1985 150,000 225,492 1,274 5,390 163,763 1986 145,000 206,136 1,697 4,162 165,633 1987 140,000 188,985 1,658 4,412 175,628 1988 135,000 221,706 2,231 3,834 166,864 1989 130,000 204,915 1,260 5,444 133,281 1990 127,660 208,592 1,560 3,770 161,462 1991 134,000 235,904 2,280 3,750 186,104 1992 130,000 228,024 1,920 5,730 214,386 1993 125,000 219,046 1,500 7,120 221,928 1994 120,000 223,041 2,460 9,370 233,912 1995 130,000 248,558 2,580 9,630 240,371 1996 130,000 237,943 2,580 10,370 253,371 1997 130,000 236,510 2,887 10,400 212,567 1998 130,000 179,251 3,104 10,542 198,402 1999 130,000 223,000 3,412 11,221 201,354 Source: (1) 1962-65, 1995-1999: Institute of Jamaica, (STATIN), (Data Food and Agricultural Organization cultural Data. http://fao.org. August, 175 oranqe Cocoa-bean potato 73125 1542 10257 74552 1945 11450 75489 1854 12453 78562 1945 10254 79,772 2,174 13,608 82,236 1,642 13,297 85,085 1,864 7,454 73,304 1,857 8,717 65, 142 2,163 8,440 72,726 1,872 13,207 83,160 2,378 16,132 23,408 1,952 8,941 39,309 1,618 14,606 40,849 1,780 13,617 41,234 1,646 7,643 42,042 1,692 7,579 49,665 1,797 11,794 31,224 1,366 11,331 43,159 1,752 6,848 33,000 1,752 12,428 32,879 2,408 6,667 31,686 2,302 7,603 28,298 2,812 12,310 42,119 2,380 7,075 41,850 2,406 5,439 75,460 3,186 9,443 50,820 1,549 9,893 57,750 1,386 10,818 82,236 2,104 14,296 61,908 1,750 7,548 70,000 2,478 6,935 72,000 2,522 9,134 72,546 2,576 12,188 72,892 2,538 17,036 73,452 1,407 13,775 74,561 1,653 12,661 74,987 1,687 12,875 75,984 1,702 13,256 Jamaica, Statistical Files) (2) 1966-1995: (FAO). FAOSTAT Agri1998.

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APPENDIX C SELECTED ECONOMIC STATISTICS ON JAMAICA Year Adef ferp 1962 13.50 144 1963 13.80 138 1964 14 .10 139 1965 14.50 138 1966 15.90 124 1967 16.70 137 1968 17.40 132 1969 19.70 142 1970 20.50 146 1971 20.40 144 1972 20.10 149 1973 23.90 193 1974 31.37 229 1975 38.13 298 1976 44.75 339 1977 52.40 492 1978 72.09 778 1979 86.54 1,007 1980 100.00 1,158 1981 103.67 1,228 1982 110. 58 983 1983 133.06 966 1984 198.67 1,149 1985 249.27 1,582 1986 316.83 1,820 1987 354.07 2,013 1988 413.28 1,913 1989 442.73 2,697 1990 546.16 4,636 1991 835.26 6,776 1992 869.95 7,704 1993 893.32 7,725 1994 804.54 13,904 1995 841. 98 19,017 1996 879.42 18,136 1997 886.67 21,176 1998 897.56 20,254 1999 909.78 21,924 Sources: (1) Data files, Jamaica (STATIN); (2) Jamaica (various issues). rwage 208.74 209.58 210.28 212.34 216.42 255.69 289 .11 300.95 289.13 299.33 325.47 307.26 302.97 362.47 361.25 354.16 326.04 264.19 234.02 221. 24 231.40 244.66 235.83 262. 21 277.54 316.75 340.99 314.55 268.05 182.63 175.35 179.30 206.60 218.66 213.69 238.27 270.68 298.20 Jamaica, Jamaica, Cpi80 gdef ER 11. 60 0.40 0. 71 11. 95 0. 40 0. 71 12.20 0.40 0. 71 12.71 0.40 0.71 14 .60 0.50 0. 71 15.16 0.50 0.83 16.43 0.60 0.84 17.78 0.80 0.83 19.01 0.90 0.84 19.58 0.90 0.78 21.03 0.90 0.85 24.81 1.10 0.91 31.53 1. 40 0.91 37.04 1. 80 0.91 40.64 1. 90 0.91 45.02 2.20 0.91 60.97 2.70 1. 70 78.79 3.20 1. 78 100.00 3.80 1. 78 112. 75 4.10 1. 78 119. 92 4.50 1. 78 113. 86 5.20 3.28 170.91 7.00 4.93 215.14 9.20 5.48 247.41 10.80 5.48 263.75 12.00 5.50 285.66 13.60 5.48 326.27 15.30 6.48 398.45 18.90 8.04 601.99 27.90 21.49 1,066.93 44.40 22.19 1,302.79 59.70 32.48 1,759.36 78.40 33.20 2,109.56 100.00 39. 62 2,666.53 121.10 34.87 2,934.30 134.30 41.02 2,942.14 154.00 42.40 3,023.26 159.00 42.84 Statistical Institute of Planning Institute of Notes: Adef = Agricultural deflator (1980=100); ferp = Average price of fertilizer; rwage = index of average agricultural real wage (1980=100); CPI80 = consumer price index (1980=100); gdef = GDP deflater (1980=100); ER = Jamaican exchange rate ($J/US). 176

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APPENDIX D DIAGNOSTIC STATISTICS FOR MODEL SPECIFICATION AND TEST STATISTICS FOR COINTEGRATION Table D-1: Tests of Cointegration Rank for Sugar Ea. Eigen values Amax Trace Ho:r P-r Amax 90 Trace 90 0.649 37.66 80.99 0 5 21.73 71. 66 0.369 16.56 43.33 1 4 18.03 49.92 0.332 14.54 26.77 2 3 14.09 31.88 0.220 8.96 12.23 3 2 10.29 17.79 0.087 3.27 3.27 4 1 7.50 7.50 Table D-2: Diagnostic Statistics for Residual Tests for the Sugar Ea. Tests De x2 p-value For normality of residuals 10 15.108 0.10 For autocorrelation L-B(9) 195 124.208 0.21 LM (1) 25 23.084 0.57 LM ( 4) 25 21.299 0.68 For ARCH(2) lsugq 2 1. 825 lsugpr 2 0.441 lbanpr 2 1. 555 lwage 2 0.919 lferp 2 4.600 Notes: The critical value for 'X, 2 (2) = 5.99 At the 95 percent significance level. a Df = degrees of freedom. 177

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Table D-3: Tests of Cointegration Rank for Coffee ECM. Eigen value lMax Trace Ho:r P-r lMax 90 Trace 90 0.675 40.44 88.74 0 5 21. 74 71. 66 0.497 24.72 48.30 1 4 18.03 49.92 0.289 12.30 23.58 2 3 14.09 31. 88 0.189 7.56 11.28 3 2 10.29 17.79 0.098 3.72 3.72 4 1 7.50 7.50 Table D-4: Diagnostic Statistics for Residual Tests for the Coffee ECM. Tests oe x2 p-value For normality of residuals 10 11.349 0.33 For autocorrelation L-B ( 9) 190 238.190 0.20 LM (1) 25 38.821 0.06 LM ( 4) 25 26.303 0.39 For ARCH(2) Lcofq 2 2.763 Lcofpr 2 4.747 Lbanpr 2 0.086 Lferp 2 2.478 Lwage 2 1.345 Notes: The critical value for X 2 (2) = 5.99 at the 95 percent significance level. a Df = degrees of freedom. Table D-5: Tests of Cointegration Rank for Pimento ECM. Eigen value lMax Trace Ho:r P-r lMax 90 Trace 90 0.608 33.75 81.34 0 5 21.74 71. 66 0.438 20.74 47.59 1 4 18.03 49.92 0.284 12.00 26.85 2 3 14.09 31. 88 0.263 10.99 14.85 3 2 10.29 17. 79 0.102 3.86 3.86 4 1 7.50 7.50 178

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Table D-6: Diagnostic Statistics for Residual Tests for the Pimento ECM. Tests oe 'X.2 p-value For normality of residuals 10 19.394 0.09 For autocorrelation L-B ( 9) 190 212.972 0.06 LM (1) 25 23.861 0.53 LM (4) 25 36.915 0.06 For ARCH(2) Lpimq 2 3.255 Lpimpr 2 1.131 Lbanpr 2 2.209 Lferp 2 9.196 Lwage 2 4.575 Notes: The critical value for 'X, 2 (2) = 5.99 at the 95 percent significance level. a Df = degrees of freedom. Table D-7: Diagnostic Statistics for Residual Tests for the Yam ECM. Tests Dfa 'X.2 p-value For normality of residuals 10 7.472 0.68 For autocorrelation L-B ( 9) 185 214.194 0.07 LM (1) 25 28.044 0.31 LM ( 4) 25 22.418 0.61 For ARCH(2) Lyamq 2 0.337 Lyampr 2 2.800 Lcaspr 2 3.434 Lferp 2 5.101 Lwage 2 0.065 Notes: The critical value for 'X, 2 (2) = 5.99 at the 95 percent significance level. a Df = degrees of freedom. 179

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Table D-8: Tests of Cointegration Rank for the Yam EOI. Eigen value 1Max Trace Ho:r P-r 1..Max 90 Trace 90 0.682 41.23 110 0 5 21. 74 71. 66 0.549 28.66 68.67 1 4 18.03 49.92 0.462 22.31 40.01 2 3 14.09 31. 88 0.246 10.17 17.70 3 2 10.29 17.79 0.189 7.53 7.53 4 1 7.50 7.50 Table D-9: Diagnostic Statistics for Residual Tests for the Orange EOI. Tests oe 'X.2 p-value For normality of residuals 10 5.691 0.10 For autocorrelation L-B(9) 195 217.078 0.12 LM (1) 25 26.658 0.37 LM(4) 25 30.412 0.21 For ARCH(2) Lorq 2 0.088 Lorpr 2 0.405 Lgrpr 2 6.827 Lferp 2 0.797 Lwage 2 6.931 Notes: The critical value for x_ 2 (2) = 5.99 at the 95 percent significance level. a Df = degrees of freedom. Table D-10: Tests of Cointegration Rank for Orange EOI. Eigen value 1Max Trace Ho:r P-r 1..Max 90 Trace 90 0.679 40.90 75.90 0 5 21. 74 71.66 0.341 15.01 34.99 1 4 18.03 49.92 0.315 13.63 19.99 2 3 14.09 31.88 0.142 5.53 6.36 3 2 10.29 17.79 0.021 0.82 0.82 4 1 7.50 7.50 180

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Table D-11: Diagnostic Statistics for Residual Tests for the Cocoa-bean ECM. Tests oe x2 p-value For normality of residuals 10 15.772 0.09 For autocorrelation L-B ( 9) 195 171.387 0.10 LM (1) 25 18.188 0.83 LM(4) 25 35.577 0.08 For ARCH(2) Lcobq 2 0.910 Lcobpr 2 0.438 Lbanpr 2 1.278 Lferp 2 6.781 Lwage 2 3.322 Notes: The critical value for X 2 (2) = 5.99 at the 95 percent significance level. a Df = degrees of freedom. Table 0-12: Tests of Cointegration Rank for Cocoa-bean ECM. Eigen value A.Max Trace Ho:r P-r A.Max 90 Trace 90 0.665 39.41 84.17 0 5 23.72 82.68 0.404 18.65 44.77 1 4 19.88 58.96 0.298 12.74 26.12 2 3 16.13 39.08 0.263 10.98 13.38 3 2 12.39 22.95 0.064 2.39 2.39 4 1 10.56 10.56 Table D-13: Tests for Cointegration Rank for Potato ECM. Eigen value AMax Trace Ho:r P-r 1Max 90 Trace 90 0.713 44.92 93.40 0 5 21.74 71. 66 0.415 19.30 48.47 1 4 18.03 49.92 0.302 12.92 29.17 2 3 14.09 31. 88 0.254 10.55 16.25 3 2 10.29 17.79 0.146 5.70 5.70 4 1 7.50 7.50 181

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Table D-14: Diagnostic Statistics for Residual Tests for the Potato E04. Tests oe t p-value For normality of residuals 10 14.295 0.16 For autocorrelation L-B ( 9) 190 218.901 0.07 LM(l) 25 24.219 0.51 LM(4) 25 22.342 0.62 For ARCH(2) Lpotq 2 1.816 Lpotpr 2 3.973 Lcaspr 2 1.283 Lferp 2 1.237 Lwage 2 1.401 Notes: The critical value for X 2 (2) = 5.99 at the 95 percent significance level. a Df = degrees of freedom. 1 82

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191 Singh, R. "The Impact of Structural Adjustment Policies on the Performance of Agriculture: The Case of Jamaica." Structural Adjustment and the Agricultural Sector in Latin America and the Caribbean. John Weeks, ed., pp. 229-257. New York: St. Martin's Press Inc., 1995. Stephens, E.H., and J.D. Stephens. Democratic Socialism in Jamaica: The Political Movement and Social Transformation in Dependent Capitalism. Princeton, New Jersey: Princeton University Press, 1986. Thomas, Desmond. "Anatomy of a Stabilization Process: The Case of Jamaica, 1984 to the Present. Canadian Journal of Development Studies. 20(1999) :159-179. Tsakok, Isabelle. Agricultural Price Policy: A Practioner's Guide to Partial-Equilibrium Analysis. Ithaca, New York: Cornell University Press, 1990. Tshibaka, T.B., ed. Structural Adjustment and Agriculture in West Africa. Dakar, Senegal: Council for the Development of Social Science Research in Africa (CODESRIA), 1998. Valdes, Alberto, and Kay Muir-Leresche, eds. Agricultural Policy Reforms and Regional Market Integration in Malawi, Zambia, and Zimbabwe. Washington, D.C.: International Food Policy Research Institute ( IFPRI) 1993. Weeks, John. Structural Adjustment and the Agricultural Sector in Latin America and the Caribbean. New York: St. Martin's Press, 1995. World Bank. Adjustment in Africa: Reforms, results, and the road ahead. New York: Oxford University Press, 1994. Trends in Developing Economies. Washington, D.C.: World Bank, 1996.

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BIOGRAPHICAL SKETCH Ballayram of Essequibo Coast, Guyana, worked in the primary educational and civil service systems in Guyana before pursuing higher education. He completed his B.S.Soc. degree with distinction in economics at the University of Guyana in 1980. After three years as an Assistant Lecturer in the Department of Economics at the University of Guyana, he proceeded to pursue a Master of Arts degree in development economics, under a Fulbright Scholarship, at the University of Notre Dame, Indiana. On completion of his master's degree he lectured first in the Economics Department at the University of Guyana, and then subsequently, in the Economics Department at the University of the West Indies, Mona Campus, Jamaica. Ballayram was awarded a Graduate Research Assistantship, in 1994, to pursue a Ph.D. in the Food and Resource Economics Department at the University of Florida. His areas of specialty are environmental economics, and international trade and economic development. 192

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I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree Doctor of ~ iloSORhy . OM" G. Davis, Chair Distinguished Professor of Food and Resource Economics I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, and a i ty, as a dissertation for the degree of ---~ OB y. Dr Professor of Food and Resource Economics I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Ph'losophy. I certify that Professor of Food and Resource Economics opinion it conforms presentation and is fully ad as a dissertation for the d Robert D. Emerson Professor of Food and Resource Economics

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I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. a: Dr. David A. Denslow, J Distinguished Service Professor of Food and Resource Economics This dissertation was submitted to the Graduate Faculty of the College of Agricultural and Life Sciences and to the Graduate School and was accepted as partial fulfillment of the requirement for the degree of Doctor of Philosophy. May 2001 Dean, College of Agri and Life Sciences Dean, Graduate School