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Evaluating returns to postharvest research and development in the fresh-winter tomato industry

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Title:
Evaluating returns to postharvest research and development in the fresh-winter tomato industry
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Ansoanuur, James S., 1949-
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English
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ix, 120 leaves : ill. ; 28 cm.

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Agriculture ( jstor )
Capital expenditures ( jstor )
Financial investments ( jstor )
Market prices ( jstor )
Marketing ( jstor )
Mathematical variables ( jstor )
Prices ( jstor )
Supply ( jstor )
Tomatoes ( jstor )
Vegetables ( jstor )
Dissertations, Academic -- Food and Resource Economics -- UF
Food and Resource Economics thesis Ph. D
Tomato industry -- Harvesting -- Research ( lcsh )
Tomatoes -- Marketing -- Mexico ( lcsh )
Tomatoes -- Postharvest technology ( lcsh )
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bibliography ( marcgt )
non-fiction ( marcgt )

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Thesis:
Thesis (Ph. D.)--University of Florida, 1988.
Bibliography:
Includes bibliographical references (leaves 110-119)
General Note:
Typescript.
General Note:
Vita.
Statement of Responsibility:
by James S. Ansoanuur.

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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.
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EVALUATING RETURNS TO POSTHARVEST
RESEARCH AND DEVELOPMENT
IN THE FRESH-WINTER TOMATO INDUSTRY












By


JAMES S. ANSOANUUR


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


1988




EVALUATING RETURNS TO POSTHARVEST
RESEARCH AND DEVELOPMENT
IN THE FRESH-WINTER TOMATO INDUSTRY
By
JAMES S. ANSOANUUR
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
1988


ACKNOWLEDGEMENTS
I wholeheartedly express my sincere gratitude to my chairman, Dr.
Max R. Langham, for his support, guidance and patience during the
course of my doctoral studies and the preparation of my dissertation.
At times when it was rough, his experienced and timely counsel put me
at ease. He has been my mentor, treated me like a godson and exposed
me to new knowledge, leaving me more confident as a professional
economist as I move on.
I would also like to thank Drs. Robert Emerson, Tim Taylor and
John VanSickle, the other members of my supervisory committee, for
their patience, suggestions and guidance during the course of the
preparation of my dissertation. I wish to also thank Audrey Sharp and
Lavon Mikell for their help in typing some portions of the text,
particularly the tables. I also wish to acknowledge the USDA, which
supported this research under IR-6 and the USDA/CSRS Cooperative
Agreement No. 58-32R6-2-143 entitled "Evaluation of Agricultural
Marketing Research."
Finally I would like to express my deepest appreciation to my
wife, Elizabeth, and children, Frieda, Mwitse and George, for their
support, understanding and endurance during the course of my doctoral
studies and particularly when I was preparing this dissertation.
ii


TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS ii
LIST OF TABLES vi
LIST OF FIGURES vii
ABSTRACT viii
CHAPTERS
I INTRODUCTION 1
Problem Statement 3
Objectives 5
Production and Marketing of Fresh-Winter Tomatoes
in Florida and Mexico 5
Tomato Production in
Florida 6
Tomato Production in Sinaloa, Mexico 10
Mexico's and Florida's Share of the Winter-
Tomato Market 13
Marketing Channels 13
Fresh-Market Winter-Tomato Research 18
IILITERATURE REVIEW 23
Consumers' and Producers' Surplus (CS-PS)
Approach 24
Econometric Method 29
III THEORETICAL FRAMEWORK 36
Motivation for a Simultaneous-Equations
Approach 36
Model Specification 37
Florida Supply 43
Mexican Export Supply 44
Marketing Margin Between the Grower
Level and the U.S. Retail Supply 46
iii


Demand in the U.S. Market 46
Consumers' and Producers' Surplus
Analysis 47
IV MODEL ESTIMATED 49
Modeling Expectation 49
The Rational Expectations Model 49
Naive Price Expectations Model 50
Extrapolative Expectations Model 50
Adaptive Expectations Model 51
Revisional Price Expectations Model 52
Estimates of the Model Parameters 54
Estimation Method 59
Data Sources 60
Mexican Growing Season
Temperature 60
Real Agricultural Interest Rate in Mexico 60
Mexican Rural Daily Wage Rate 61
Quantity Shipped from Mexico
and the FOB Price in Mexico 61
U. S Population 62
Mexican Fertilizer
Price Index 62
Real Per Capita National
Income in Mexico 62
Mexico's Population 62
Per Capita Quantity Shipped from Florida
and FOB Price 62
Florida Real Daily
Wage Rate 63
Real Agricultural Interest Rate
for Florida 63
Florida Growing Season
Weather 63
Research Expenditures 64
Total Quantity Supplied
at U.S. Retail Level 66
Real Retail Price 67
Retail Price of Substitutes 67
Real Cost Index for Fresh
Fruits and Vegetables 67
Florida Fertilizer Price 67
Weighted Average Price at the
Grower Level 68
V EMPIRICAL RESULTS OF THE MODEL 69
Florida Shipping-Point Supply 69
Mexican Export Supply 75
Marketing Margin 77
U.S. Retail Demand 78
iv


Measuring Returns of Research 79
Grower Level 82
Retail Level 83
VI SUMMARY, CONCLUSIONS AND RECOMMENDATIONS 89
Summary 89
Results 91
Conclusions 92
Recommendations 93
Area for Future Emphasis 94
APPENDICES
A DATA USED IN THE MODEL 97
B NL2SLS AND 3SLS PARAMETER ESTIMATES
OF THE MODEL 102
REFERENCES 110
BIOGRAPHICAL SKETCH 120
v


LIST OF TABLES
Table Page
1 2SLS Structural Parameter Estimates 70
2 Discounted Values of R&D1 and R&D2 Impacts on the
Florida Supply, Mexican Export Supply, Marketing
Margin and U.S. Retail Demand (at a discount rate
of 4 percent) 80
3 Rate of Change of Surpluses with Respect
to One Unit Change in R&D Expenditure at
the Retail Level 85
4 Estimates of Marginal Rates of Return to R&D
Investments in the Fresh-Winter
Tomato Industry at the Retail Level 86
5 Estimates of NPV of Average Rates of
Return to R&D Investments in the
Fresh-Winter Tomato Industry 88
A.l Data Set for Fresh-Winter Tomatoes in Florida 97
A. 2 Data Set for Fresh-Winter Tomatoes in Mexico 98
A.3 Data Set for Fresh-Winter Tomatoes in U.S.
Retail Market 99
A.4 Real Research Expenditures on Fresh-Winter
Tomatoes in Florida 100
B.l NL2SLS Structural Parameter Estimates 102
B.2 3SLS Structural Parameter Estimates 106
vi


LIST OF FIGURES
Figure Page
1 Major Growing Areas for Fresh-Winter Vegetables
in Florida 7
2 Growing Areas for Fresh-Winter Vegetables
in Sinaloa, Mexico 11
3 Marketing Channels for Florida Fresh
Vegetables from Grower to U.S. Consumer 15
4 Marketing Channels for Mexican Fresh
Vegetables from Grower to U.S. Consumer 17
5 Shift in Supply Due to Adoption of New
Improved Input 25
vii


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
EVALUATING RETURNS TO POSTHARVEST
RESEARCH AND DEVELOPMENT
IN THE FRESH-WINTER TOMATO INDUSTRY
By
James S. Ansoanuur
December, 1988
Chairman: Dr. Max R. Langham
Major Department: Food and Resource Economics Department
Evaluation of agricultural research has generally focused on the
effects of biological or technological improvements in production;
however, technological advancements in food and fiber processing,
quality improvements and marketing are often integral factors
affecting efficient utilization of perishable food resources, improved
marketing methods and demand shifts.
The most widely used econometric methods of evaluating returns to
agricultural research have generally focused on the impact on supply
and cost reduction. Furthermore, the returns are often expressed in
terms of internal rate of return, which does not address the
distribution of the returns to the different groups in the economic
viii


system. A model for evaluating returns to postharvest research and
development (R&D) and preharvest marketing-related R&D, which
addresses both the supply and demand impacts of these variables and
also the distribution of the benefits to the different economic
groups, was formulated for the fresh-winter tomato industry.
Using a partial equilibrium, simultaneous-equations approach, a
Florida shipping-point supply equation, a Mexican export supply
equation and a marketing margin equation for fresh-winter tomatoes
were estimated. The estimated coefficients were then used to derive
supply and demand equations at two levels (grower and retail) in the
fresh-winter tomato market. These supply and demand equations were
used to derive consumer surplus (CS) and producer surplus (PS)
relationships as a function of the preharvest and postharvest R&D
expenditures. These CS and PS relationships enabled estimation of the
present value of marginal and average rates of return to preharvest
and postharvest R&D investments to tomato growers and distributors, to
U.S. consumers of fresh-winter tomatoes, and to society as a whole.
The results show that expenditures allocated to postharvest
activity have a very high rate of return at the margin and that
growers and distributors receive an estimated 33 percent of the
returns while consumers receive 67 percent of the returns.
ix


CHAPTER I
INTRODUCTION
Since World War II, impressive growth in productivity has been
recorded in both the agricultural and industrial sectors in the U.S.
This trend emanated from technological breakthroughs in industry and
agriculture in the form of improved and superior factors of
production, improved crop varieties, high-quality livestock breeds,
improved managerial efficiency and organization, and improved channels
of communication and information flows for the efficient utilization
of scarce resources. These improvements in technology are not
accidental but the result of investments in research and development
(R&D) programs.
Estimating the value of research and education has received
considerable attention by economists since the mid-1950s. The results
indicate that under a wide range of circumstances the economic returns
to agricultural research have been high relative to other investments
available to society (Ruttan, 1982, p.237). This research evaluation
has been largely directed towards preharvest or production-oriented
research and little on postharvest research.
Recently, there has been a growing interest in the returns to
postharvest technology and marketing economic research. Work by
Jorgenson et al. (1987, pp.198-200) indicates that growth rates in
total factor productivities of industries associated with providing
1


2
services to agriculture have been slightly negative. With food and
kindred products, and trucking and warehousing, estimates of
productivity growth rates have been less than those for production
agriculture. One hypothesis is that the U.S. has devoted too few
resources (both public and private) to basic research affecting
efficiency in industries associated with providing farm inputs and
food and fiber derived from farm products.
Freebairn et al. have developed a conceptual model which suggests
that there is no reason to believe that research opportunities are
greater or research costs less at the farm production level than at
the marketing level. White and Havlicek, in a study which focused on
farm production, concluded that underfunding of agricultural research
has serious implications on the future cost of food to consumers --
particularly if underfunding (below optimal levels) persists rather
than being made up. If the White and Havlicek conclusions hold for
research beyond the farm gate, and Freebairn et al. are correct,
consumers can expect a trend toward increased relative prices for food
as a consequence of present and past underinvestments in research on
problems beyond the farm gate.
Further studies are needed to confirm or reject these findings
and also assess the contribution of postharvest research and
development efforts in the efficient marketing of farm produce and
other postharvest processes, and the subsequent impact on consumers'
and producers' welfare. This research seeks to augment the knowledge
relating to the returns to postharvest R&D investments. Postharvest
R&D is generally geared toward technological improvements in the


3
packaging, processing, transportation, organizational efficiency and
information flow which are meant to enhance the market power of the
conducting industry. R&D is often an integral factor affecting demand
shifts, marketing costs and strategies, and efficient utilization of
perishable food resources to enhance profits.
Some biological or technological improvements in farm production
techniques do not only impact the production side but may also have a
bearing on the marketability of the produce. For example, genetic
research to develop varieties of tomatoes that will withstand physical
damage, which have disease resistance, and which yield fruit of a
desired size is not only production-oriented but also results in
desirable marketing qualities. The expenditures produce not only the
scientific and technological improvements at the production level, but
also at the marketing level. Such marketing-related attributes of
production-oriented R&D investments need to be recognized in the
evaluation of returns to research investments.
Problem Statement
This research is directed at evaluating the impact of postharvest
and marketing-related preharvest R&D investments on the marketing of
fresh-winter tomatoes from Florida and Mexico in the U.S. domestic
market. It is hoped that the techniques developed in this case study
will be useful for measurement of returns to research in other areas
of the food and fiber marketing channels.
Winter tomatoes were selected because they are an important
winter vegetable crop in the United States and the leading vegetable
export to the U.S. from Mexico, the major foreign supplier of winter


4
vegetables. Because of its climate, Florida is the leading producer
of fresh-winter tomatoes in the U.S. Tomatoes represent approximately
30 percent of the total cash farm value of the Florida vegetable
income and are grown on 11 percent of the total vegetable acreage of
Florida. The hot and dry, desert climate of northwestern Mexico
favors vegetable production in the fall, winter and spring seasons.
Over 30 percent of the total dollar value of vegetables imported from
Mexico is comprised of tomatoes. In 1983-84 the total dollar value of
imports of vegetables from Mexico was $576 million, of which $224
million (38.9 percent) was from tomatoes, 28 percent from cucumbers,
17 percent from peppers, 13 percent from squash, 2 percent from egg
plant and 2 percent from green beans (Buckley et al.). The tomatoes
produced and exported to the U.S. must meet U.S. marketing standards
in terms of grade, size, weight, containers and maturity. The impact
of research has been paramount in the tomato industry in Florida.
Several varieties have proved outstanding. Economic and marketing
research have resulted in more efficient and low-cost methods of
handling tomatoes in the marketing channel. What impact do these
developments have on consumer and producer welfare?
The agricultural research facilities in Mexico are not able to
meet the needs of vegetable growers. Consequently the Mexican
industry imports seeds, chemicals, equipment and other technology from
the U.S. (Gutierrez). Some new technology applied in Florida and
California is almost immediately adopted by Mexican growers and new
postharvest technology also benefits Mexican growers since they use
much the same marketing procedures as U.S. growers. Thus U.S. based


5
R&D investments impact the Mexican fresh-winter tomato industry as
well, and this influence needs to be captured in any measurement of
returns to tomato-related research in the U.S.
Objectives
The overall objective of this study is to estimate returns to
research on winter tomatoes -- especially as this research impacts
postharvest processes. Specific objectives are to:
1. Develop an econometric model describing the
interrelationship of several important variables affecting
the fresh-winter tomato industry.
2. Empirically estimate the model using data pertaining to
fresh-winter tomato production and marketing in Florida and
Mexico.
3. Evaluate the distributional effects (consumer and producer
surplus), and the overall returns to postharvest technology
and marketing research.
Production and Marketing of Fresh-Winter
Tomatoes in Florida and Mexico
This section draws on material presented in Buckley et al.;
Bredahl et al.; Gutierrez; Emerson; Froman; and Zepp and Simmons.
Fresh-winter tomatoes for the U.S. market during the winter season,
December through June, come from two major production regions: Florida
in the U.S., and Sinaloa in northwest Mexico. Sinaloa has become the
only major foreign supplier of fresh market tomatoes owing to a
combination of factors, which include: favorable climate; an extensive
irrigation infrastructure; railway lines and good roads connecting


6
producing areas to marketing centers; proximity to western U.S.
markets; and the availability of inexpensive labor. Political factors
also contributed to the decline in other production areas. Cuba was
eliminated by the 1962 U.S. trade embargo. The termination of the
bracero program in 1964 reduced the availability of cheap labor in
the U.S. vegetable-production areas. Financial and technological
resources therefore flowed out of the United States and into Mexico in
response to these events.
Tomato Production in Florida
In Florida, winter tomatoes are the largest vegetable crop in
value, and rank second to citrus in total revenue of all Florida
agricultural commodities. According to Buckley et al.(p,15):
Tomatoes accounted for 34.7 percent of the total value of
all vegetables produced in Florida during the 1983/84
growing season.
Tomato production is concentrated around west-central and east-
central Florida during the fall and spring and moves south to
southwest Florida and around Homestead in Dade County during the
winter (Figure 1).
Tomatoes are planted between the last week in July and the third
week in March and harvested in October and November in the central
areas of the state. Production occurs further south with the approach
of winter. Technological change has played a major role in Florida's
competitive edge over Mexico in the production and marketing of fresh-
winter tomatoes, despite Mexico's lower cost of production.
Florida growers make widespread use of stake and ground culture.
In stake culture, tomato plants are supported upright with stakes and


7
Figure 1. Major Growing Areas for Fresh-Winter Vegetables in Florida.
Source:
Zepp and Simmons (1979).


8
string. West-central and southwest are the principal mature-green,
stake-production areas. Ground tomatoes grow without the benefit of
upright support. Dade County has the largest acreage of ground
tomatoes. The tomatoes are grown under irrigation and mostly over
plastic mulch, an improved technology wherein plastic mulch covers the
soil surface, thereby maintaining uniform soil moisture and
temperature conditions, and aiding in weed control and reducing
fertilizer leaching.
Although some vine-ripe tomatoes are marketed, the majority of
fresh- winter tomatoes produced in Florida are picked and marketed as
mature greens. Mature green tomatoes stay firm longer and have a
prolonged shelf life. Ripening can, however, be quickened with
ethylene gas after packing.
According to Buckley et al., the area planted, the yield, the
production and the total value of tomatoes produced in Florida have
shown an upward trend during the past 15 years. This has been
attributed to the adoption of staked-tomato culture, full-bed plastic
mulch, and improved disease-resistant and high-yielding varieties
developed through research. Some of the improved varieties include
FTE-12, Duke, and Sunny. They are high yielding, and produce fruits
which are much firmer and larger than traditional varieties. Laser
leveling of fields, which provides greater uniformity of soil
moisture, has also contributed to increases in tomato yields. In
order to reduce frost during the winter, most tomato growers have
acquired sprinkler irrigation systems. The tomatoes are either
transplanted or directly seeded mechanically; however, operations


9
such as thinning, pruning, tying plants and harvesting are performed
by hand labor.
During harvest the number of pickings depends on the market and
field conditions and the yield. Buckley et al. contend that when
production is concentrated as a result of widespread use of hybrid
varieties, fewer pickings take place. Fields once picked three to
five times, depending on market and field conditions, are now picked
two to three times.
From the field, tomatoes are sent to the packinghouse, where
they are washed, waxed, sized, artificially ripened if necessary, and
packed mechanically before being sold. The tomatoes are sold by the
packinghouses through brokers or hired salesmen.
The interests of tomato growers in Florida and Mexico are
represented by grower organizations. The Florida Tomato Committee and
the Florida Tomato Exchange support and protect the interests of
Florida tomato growers. The Florida Tomato Committee regulates the
marketing of fresh tomatoes through a federal marketing order, which
requires certain grade and size standards to be maintained during the
marketing season. Tomatoes grown in Florida and all tomatoes imported
are expected to meet these standards. The size, grade, container and
inspection requirements are set by policymakers based on the
recommendations of the Tomato Committee. All tomatoes produced in
Florida and those imported must adhere to these regulations. The
Florida Tomato Exchange, which is a nonprofit cooperative association
of first handlers of fresh tomatoes in Florida, provides collective
action with respect to the orderly marketing and distribution of fresh


10
tomatoes. The Tomato Exchange complements the activities of the
Tomato Committee, with major emphasis placed on production research,
promotion of tomatoes through advertising, legislative activities,
legal aid on items affecting the tomato industry, and items not
covered under the marketing order.
Tomato Production in Sinaloa. Mexico
Tomato production in Sinaloa is mainly for the export market.
However, the domestic market may be used as a secondary or residual
market for quantities and sizes that do not meet export requirements.
Market conditions determine export quantities, with more being
exported when export prices are high and exceed the export marketing
costs. Low prices can result in more being shipped to the domestic
market, fed to livestock or simply discarded.
Figure 2 depicts the major fresh-tomato-producing areas in the
state of Sinaloa, the largest producing area being Culiacan which
produces and ships primarily vine-ripe tomatoes. Planting in Culiacan
takes place in the late fall (September to November), with harvesting
peaking in the winter months of January to March. The Guasave and Los
Mochis areas produce and export roughly half vine-ripe and half mature
greens. Winter production in these areas is limited because of
frequent frost. Thus, production is directed toward two marketing
seasons -- the late fall and early spring markets. Planting time for
the fall crop is in August and September, with harvest in the months
of November and December, while the spring crop is planted during
late February and March, with harvest in the months of May and June.


11
Figure 2. Growing Areas for Fresh-Winter Vegetables in Sinaloa,
Mexico.
Source:
Zepp and Simmons (1979).


12
Advances in vegetable production and harvest and postharvest
technology in the U.S., are imported and adopted by Mexican producers
with the aid of the U.S. importers of their produce. Both staked and
ground-grown tomatoes are produced. Staked tomatoes are primarily
produced around Culiacan; ground-grown tomatoes are produced around
Gausave and Los Mochis. Staked tomatoes are normally harvested as
vine-ripe tomatoes while ground-grown are harvested as mature greens.
Popular varieties such as Sunny and Contessa, as well as much of
the planting media and forms, are imported from the U.S. (Buckley, et
al.).
At harvest time, the number of pickings depends on the market
conditions (i.e., export price vis-a-vis harvesting and export costs).
From the field, tomatoes are sent to the packinghouses, where they are
dumped into a water tank to remove field heat and to clean them. After
cleaning they are waxed, sorted as export quality or domestic quality,
packed by color and size, banded in pallets, and precooled.
Like in Florida, Mexican grower associations support and protect
grower interests with regard to the production and orderly marketing
of tomatoes from Sinaloa. The Union Nacional de Productores de
Hortalizas (UNPH) and the Confederacin de Asociaciones Agricolas del
Estado de Sinaloa (CAADES) are two associations that govern tomato
production in Sinaloa. They recommend maximum planting acreages,
establish regulations governing types of containers used in shipping,
determine quality standards for exports, and issue export permits
based on acreage allotments. They adjust quality standards according
to prevailing U.S. prices. These regulations are intended to avoid


13
overproduction and low export prices (Froman). The acreages allotted
for planting are strictly enforced through the allocation of water by
the Agricultural Department. Excess planting may result in a
reduction in water supply, and excess export production may result in
cancellation of the export license.
In general, lower than acceptable prices result in stricter
quality requirements and, if this does not suffice to raise prices,
smaller sizes are restricted. Also, shipment of tomatoes to the U.S
market through Nogales is determined by maturity and to the rate of
movement. If movement is slow, the more mature tomatoes are
restricted. An inspection system at Nogales enforces the
restrictions. Certificates of origin are required and any truck or
rail lot that does not meet the restrictions is turned back.
Mexico's and Florida's Share of the Winter-Tomato Market
The U.S. fresh-winter tomato market is roughly split between
Florida and Mexico. Weather conditions in Florida play an important
role in the annual fluctuations in the respective market shares. In
general, supplies from Mexico dominate during the peak of the winter
season (January through April) while supplies from Florida are largest
in the early and later part of the season (Zepp and Simmons).
Florida supplies mainly the eastern United States and Mexico
supplies the west of the United States. Both areas supply the mid
west. Domestic weather and crop conditions influence the geographic
distributions with the result that Mexico supplies more to markets in
the east and mid-west when Florida supplies are reduced by a killing
frost.


14
Marketing Channels
Fresh-winter tomatoes from Florida and Mexico (after clearing
Mexican and U.S. custom agents) go through the same marketing channel
before reaching the final consumer at the retail level in the U.S. In
this process, marketing services such as packing, repacking, wrapping
and transporting are provided by marketing agents.
From the field, fresh tomatoes are sent to the packing plants,
where they are washed, waxed, sized, sorted, graded, and packed. Some
degreening or ripening of mature-green tomatoes may be done at this
point by storing them in temperature controlled rooms for one to three
weeks (Buckley, et al). The degreening process is accelerated with
ethylene gas.
From the packing plants the fresh tomatoes are transported by
truck or rail depending on the distance between packing plant and
terminal or wholesale markets. Often vegetables shipped long
distances go by rail. At the terminal or wholesale markets the fresh
tomatoes are stored in warehouses and then delivered to retail
markets, restaurants, and institutions. Mature-green tomatoes may go
from the packing plants to repackers, where they are ripened, resorted
and repacked according to color before being transported to the
terminal and wholesale markets (Figure 3).
According to Buckley et al. fresh tomatoes are also marketed
through alternative routes which involve direct movement of the
vegetables from the packing plant to the warehouse of an integrated
wholesale-retail grocery chain before being distributed to retail
stores and finally to the consumer. Secondary wholesalers may also


15
Figure 3. Marketing Channels for Florida Fresh Vegetables
from Grower to U.S. Consumer.
Source:
Mongelli, Robert (1984).


16
purchase the produce from primary wholesalers and then resell to
jobbers and truck jobbers.
To facilitate shipping logistics and assure marketing outlets for
the highly perishable vegetables, contractual agreements are made over
the telephone between contractual operators and local buyers or
customers in the terminal markets.
Fresh tomatoes from Mexico go from the field to the packing shed
or plant where they go through the same process as in Florida (i.e.
washing, waxing, sorting, grading and packing). From here they are
transported by truck or rail through Mexican and U.S. customs, where
they are inspected to make sure that they meet export and import
requirements. Export documentation as well as paperwork for
repatriating export earnings are processed at the Mexican side of the
border. Export fees are also collected at this point. After this,
the fresh vegetables are transported to wholesale warehouses in
Nogales, Arizona. U.S. customs agents then collect import tariffs,
process export documents and issue certificates testifying that the
produce have met U.S. import standards. These export and import
transactions are being handled by customs brokers on both sides of the
border on behalf of the Mexican producers.
Distributors in Nogales resort the vegetables according to
maturity before shipping them to terminal markets, wholesalers and
chain store warehouses. From here the vegetables go to retail
outlets, restaurants, institutions and the final consumer in the U.S.
(Figure 4). There is a partnership relationship between the
distributors and growers. Through this partnership distributors


17
Figure 4. Marketing Channels for Mexican Fresh Vegetables from
Grower to U.S. Consumer.
Source: Adopted from Buckley et al., and Mongelli, (1984).


18
provide seed, other inputs, technical and market information from the
United States and preharvest and harvest financing to the growers.
According to Buckley, et al. (p.8):
approximately 60 percent of the distributors in Nogales are
partners with one or more Mexican growers. These firms handle an
estimated 60 percent of the Mexican produce.
Of the remaining 40 percent of the distributorships 20 percent are
owned outright by Mexican growers and managed by a U.S. citizen.
Independent contractors also do business with Mexican growers and they
form the remaining 20 percent of the distributorships. Chain store
buyers may also operate in Nogales, but they usually do not have
physical storage or handing facilities in the area; thus their
purchases are shipped directly to chain store warehouses. From here
the produce is then sent to retail stores for distribution to the
consumer.
Fresh-Market Winter Tomato Research
In Florida, research on tomatoes began in the 1920s. Areas of
research focus have included the development of disease resistant
varieties, improved cultural practices, improved methods of preventing
postharvest decay of fruits, uniform ripening techniques, cultivars
that are amenable to machine harvest, mechanized harvesting of fresh-
market tomatoes, and handling and transportation of tomatoes to lower
the cost of marketing.
Some production-level research and development benefits both
production and marketing of tomatoes. Plant breeders have developed
varieties which have resistance to multiple diseases, concentrated
ripening of fruit, uniform maturity, and at least 90 percent


19
marketable yields (Villanlon and Bryan). Tomatoes have been developed
with fruits which are firm and resistant to cracking, rupturing, and
bruising during harvesting and handling (Cargill and Rossmiller).
Improved cultural practices such as plastic or paper mulching to
reduce soil temperatures and conserve soil moisture also affect the
marketability of the tomato fruits by reducing the amount of sand and
number of blemishes on them. Tomatoes for fresh market must be
relatively free of defects to meet grade requirements. Sand particles
can cause abrasions or punctures during harvest and packinghouse
handling, resulting in decay or surface defects (Ramsey et al.; Halsey
et al.).
A great amount of research has been done to develop cultivars
with characteristics that will allow machine harvesting of tomatoes.
Mechanical harvesting is desired inorder to reduce costs and enable
Florida producers to be more competitive, and considerable effort has
been made in this direction (Everett et al.; Navarro and Locascio;
Deen et al.). Researchers from the Institute of Food and Agricultural
Sciences (IFAS) have been developing and evaluating equipment for
mechanical harvesting of mature-green tomatoes for fresh market.
Consumer acceptance tests for mechanically-harvested tomatoes handled
through commercial channels have been used to evaluate the
marketability of machine-harvested tomatoes (Hicks et al.). In these
tests, taste panel evaluations of flavor, texture, appearance and
general acceptance of newly-developed varieties compared with well-
established varieties were performed. To be feasible mechanized
systems must allow growers to deliver high quality fresh tomatoes to


20
market channels. To date, mechanical harvesting of fresh market
tomatoes in Florida has not proved to be economical. The machines are
designed for once-over harvesting and the varieties do not produce
fruit that ripens uniformly at one time; thus there are considerable
losses.
Research has been carried out to reduce postharvest decay
resulting from contamination by bacteria which grow in bruises and
punctures that occur during harvesting (Bartz and Crill). Ethephon
treatment of green-harvested fruit, applied at the packinghouse,
appears to have promise as a new technique for further improving the
quality of winter tomatoes. Chlorine compounds have been used
successfully for some time to control postharvest decay by reducing
bacterial inoculum during postharvest washing (Segall).
Marketing agreements and orders have been used for several
decades by various commodity groups, including tomato growers, in an
effort to stabilize and increase the level of farm income. Brooker
and Pearson evaluated the aggregate effects of these marketing orders
or supply management policies in terms of 1) the net revenue obtained
by domestic growers, 2) the volume of tomatoes marketed and consumed
in the U.S., and 3) consumer expenditures. Such information has
helped the Florida tomato industry in making marketing decisions. It
has also been of value to other commodity groups faced with similar
circumstances, and to government agencies responsible for marketing
policy. In 1979, Degner and Cubenas provided the information that, it
was hoped, would promote the development and expansion of direct
marketing of agricultural commodities from farmers to consumers on an


21
economically sustainable basis. Container specification is an
important factor in the marketing of tomatoes. Sherman et al.
identified suitable containers for shipping Florida tomatoes,
particularly during the warm weather.
Consumers of tomatoes prefer firm and fully-red tomatoes.
Several researchers (Ben-Yehoshua et al.; Hobson; Kittagawa et al.;
Risse et al., 1985) have studied the packing of tomatoes either in
polyethylene bags or individually wrapped in film. Mature-green
tomatoes, individually wrapped in heat-shrinkable plastic film after
ethylene treatment, had less weight loss and were firmer than non-
wrapped tomatoes stored for up to three weeks at 12.8C and held an
additional seven days at 21C. Studies have also been done to
determine the effect that film wrapping of mature-green tomatoes,
before and after ethylene treatment, has on ripening and shelf-life
(Risse, et al., 1984). Wrapping before ethylene treatment may be a
useful measure of prolonging quality and freshness of tomatoes during
export shipment or extended storage. Least-cost methods of repacking
tomatoes have been identified through research (Meyer). Mongelli
(1980), made a comparative study of handling systems for fresh
tomatoes from packinghouse to retail store. He found that a
synthesized pallet-pool system was the lowest-cost system for moving
tomatoes from packing plant to wholesaler. Pallet delivery was the
lowest-cost system for movement from wholesaler to retailer. The
lower cost of handling translated to lower prices for the consumer.
Studies on shipping alternatives for moving Florida produce to eastern
and midwestern markets were done by Klindworth and Brooks. In a 1984


22
study Mongelli studied methods for harvesting and handling tomatoes
from field to packinghouse and found bulk bins for handling tomatoes
from field to packinghouse, and hand stacking transport from
packinghouse to wholesale warehouse, to be the most cost-efficient
combination.
Both public and private funds are spent on tomato research at the
state and federal level. At the state level tomato research has been
conducted by IFAS. Much of the earlier research on tomatoes in
Florida was conducted at the Gulf Coast Research and Education Center
at Bradenton, Florida. At the federal level the U.S.D.A. has a
sizeable research program in tomatoes, which includes the work by
Mongelli; Meyer; Fahey; Jesse; Worthington, et al.; Zepp and Simmons
and Zepp.
The Florida tomato industry, through the Florida Tomato Exchange,
invests funds on tomato research, tomato promotion through
advertising, legislative activities and legal aid on items affecting
the fresh-market tomato industry.


CHAPTER II
LITERATURE REVIEW
This section draws on material presented in Norton and Davis,
Peterson and Hayami, and Stranahan. New technology is created through
investments in research and development (R&D) and results in
productivity increases. Economic evaluation of the returns as they
relate to investments in R&D has been an important area of study in
economics. Numerous reviews concerning technological change in
general and the productivity of research in particular have been done
(Peterson (1971), Shumway (1973, 1977), Sim and Gardner, Schuh and
Tollini, Ruttan (1980), Nelson, Norton and Davis, and Evenson (1982)).
Techniques employed to quantify and evaluate the returns to R&D
have been, primarily, the consumers' surplus (CS), the producers'
surplus (PS), and the econometric method^-. The CS-PS technique has
focused on the impact of R&D on the supply of agricultural commodities
(Griliches (1958), Peterson (1967) and Evenson (1969)) and on the
social and distributional implications of R&D benefits (Schmitz and
Seckler). Econometric estimation methods have employed the production
1 Most consumers' and producers' surplus analyses have employed the
graphical approach, with some assumptions about the shape, the nature
of shift, and the elasticity of the demand and supply curves. On the
other hand, econometric estimation methods have specified a
production function relationship, or cost function, with R&D as one of
the explanatory variables, and its impact is seen as the contribution
to production or cost at the margin.
23


24
function approach or, through duality, the cost function and the
profit function approach to estimate the effect of R&D on value added
in food production (Griliches, 1964; Evenson, 1967) and manufacturing
(Mansfield, Terleckyj).
Consumers' and Producers' Surplus (CS-PS) Approach
Basically, this approach attempts to quantify the changes in
consumers' and producers' surplus which can be attributed to
technological change. Research and development generate new
knowledge which may result in resource or cost saving in the industry.
Technological change thus lowers the marginal costs of production and
shifts the supply curve to the right. This shift gives rise to
benefits as it causes changes in consumer and producer surplus. The
idea is to determine the (discounted) benefits and costs of
technological change over time and thus obtain a benefit/cost ratio
and/or an average internal rate of return to research. The cost of
technological change is reflected in the R&D expenditures.
Estimates of internal rates of return using this methodology have
ranged from 30 to 60 percent. The thrust of this method can be
illustrated as in Figure 5. The figure shows a conventional,
downward-sloping demand curve (D) and an initial supply curve (SG)
which shifts to position as the result of the adoption of a new
improved input developed through research. Before this shift,
consumers' surplus equaled area a; afterward, it is represented by
(a+b+c). Thus, the net gain to consumers from the research-induced
shift in supply is (b+c). Similarly, before the supply shift,
producers' surplus equaled area (b+d); afterward, the producers'


25
Price ($)
Figure 5. Shift in Supply Due to Adoption of New Improved Input.


26
surplus equals (f+d). Their net gain from the supply shift is then
(f-b). The net gain to society (consumers' plus producers' surplus)
is (b+c+f-b = c+f), which also constitutes the gross benefits of
research.
The area under SQ out to Qj_, minus the area under out to
represents the value of resources which are released after adopting
the improved technology of producing the particular good, and which
can then be employed in their next best use. Alternatively, one might
consider the effects of consequent unemployment of these resources as
in Schmitz and Seckler. The net change in economic benefits depends
upon the assumed elasticities of supply and demand and the nature of
the supply shift (Eddleman).
According to Norton and Davis the first attempt to quantify the
benefits from agricultural research investments was by Schultz in
1953. Under the special assumptions of perfectly elastic supply
curves and a perfectly inelastic demand curve, he found that 1950
techniques of production were more productive than 1910 techniques by
about at least 32 percent and that producing the same amount of 1910
output with 1950 techniques would have saved about $10 billion in
input cost.
After Schultz, Griliches (1958), under the assumption of unitary
demand elasticity and a parallel shift to the right in the supply
curve by adoption of hybrid corn, estimated returns for the case of
perfectly elastic and perfectly inelastic supply curves, and obtained
the widely quoted 743 percent rate of return to investment in hybrid
corn research. Peterson, in his 1967 evaluation of poultry research,


27
assumed a proportional shift in the supply curve and, relaxing
Griliches' supply and demand elasticity restrictions, found a 21 to 25
percent annual internal rate of return to investment in poultry
research.
Ayer and Schuh estimated a demand equation and a supply equation
for improved and unimproved cotton varieties in their evaluation of
the returns to research on cotton in Brazil. Employing a similar
approach as Ayer and Schuh, Akino and Hayami estimated the social
benefits of rice breeding research in Japan together with the
distributional effects of a rice import policy.
Scobie and Posada evaluated the impact of technical change on
income distribution in Colombian rice production by the CS-PS
approach. They looked at different categories of rice producers and
consumers in various income groups and concluded that while consumers
benefited most and producers suffered overall losses, small producers
lost the most. Widmer et al., directly estimated the supply function
of beef cattle in Canada with time series data. Lagged research
expenditures were included as explanatory variables, making it
possible to estimate the rate at which research has been shifting the
aggregate supply function through time. Direct estimation of the
supply function also permitted the estimation of research benefits at
the margin. After the supply curve was estimated a new hypothetical
supply curve was generated by adding small increments to the actual
research expenditures. Then the area between this supply function and
the actual supply function, below the demand function, constitutes the
gross benefit of this incremental expenditure. Comparison of this


28
gross benefit with the changes in the actual research expenditures
yielded an estimate of net benefits at the margin. Widmer et al.
found an average internal rate of return of nearly 66 percent and a
marginal rate of return of 63 percent on beef cattle research in
Canada. To eliminate the biases resulting from specific assumptions
about supply shifts and elasticities, Linder and Jarrett (1978)
provided a general formula for measuring research benefits. Rose and
Wise and Fell, in comments on Linder and Jarrett's paper, suggested a
kink in the supply curve to handle the assumption of linearity in the
demand and supply curves made by Linder and Jarrett in their analysis.
The direct estimation of the supply curve as in Widmer et al. can
overcome this problem of arbitrarily assuming the nature of shift,
since the shift through time can actually be estimated if research is
one of the explanatory variables (shifters) in the supply equation
estimated. The CS-PS studies have differed in specification of supply
and demand functions and in the nature of supply function shifts. The
nature of the shift assumed affects the distribution of benefits to
producers and consumers. Producers' benefits are smaller with
divergent shifts than with either parallel or convergent shifts
(Norton and Davis).
Norton and Davis (p. 690) also contend that:
The demand elasticity is also important because the more
inelastic the demand curve, the more likely producers will lose
following technical change. Also, if the supply elasticity is
absolutely larger than the demand elasticity, consumers will tend
to receive a larger share of the benefits than producers. In
addition, those technologies which do not directly displace labor
can do so indirectly as a result of a fall in the product price
if the demand elasticity is low.


29
The Widmer et al. study found a supply elasticity of beef cattle
with respect to research expenditure to be 0.36, and that resulted in
90 percent of the benefits going to producers with only 10 percent
going to consumers. Their study underscores the importance of the
supply and demand elasticities in the distribution of benefits among
consumers and producers. The importance of general equilibrium
effects on factoral income distribution was stressed by Binswanger
(1980); these have been ignored in CS-PS evaluation. The basic
flexibility of the CS-PS approach can be a liability if underlying
relationships and policies are not accurately reflected in the
analysis. This approach is best for aggregate analysis because it
aggregates all consumers and producers of a given product and looks at
only those two groups. There are many types of producers of a given
commodity -- small-scale farmers, large-scale farmers, landowners,
sharecroppers, and farmers with unmechanized and mechanized units. An
aggregate producers' surplus sheds little light on how research
affects each group. To better understand the distribution of benefits
and costs, it would help to gain insights into who would benefit and
who would lose within the producing and consuming sectors. Another
problem is that of determining the costs of research and development.
Since knowledge builds on knowledge, it is difficult to know how far
in the past to consider the costs which occurred to produce a given
shift in the supply curve.
Econometric Method
The econometric method involves specifying and estimating the
relationship between supply and R&D investments and then determining


30
the marginal rates of return to investments in R&D. The average rates
of return can also be measured as shown in the work of Widmer et al.
Further, this method can be used to assign parts of the return to
different sources, such as scientific research and extension advice,
education and conventional inputs. The statistical significance of
the estimated returns from research can be tested. In earlier studies
the most commonly used econometric models were the production function
(PF) and productivity index models. The theory of duality has made it
possible to employ the cost function to econometrically evaluate
returns to R&D (Stranahan and Shonkwiler). Stranahan (p. 18) contends
that:
The production function model is usually used in
cross-sectional aggregate studies whereas the productivity
index is used most often with aggregate time series or
pooled data.
Griliches (1964) used the Cobb-Douglas production function
formulation in his pioneering work to analyze cross-sectional
aggregate data of the U.S. for the years 1949 to 1959. He found that
lagged public research and extension expenditures (the average of R&D
expenditures lagged one and six years) were both significant and
important sources of aggregate output growth. His estimation showed
the elasticity of production with respect to R&D to be about .06, and
implied a very high social rate of return of about 1300 percent to
investment in agricultural R&D. Even after adjusting for private
research expenditures and their contribution to aggregate agricultural
output he still estimated a 300 percent rate of return.
Peterson (1967), found a comparable output response and a gross
return of $18.52 per dollar of public research on poultry production


31
when he used a similar cross-sectional model. Assuming a ten-year lag
in the impact of R&D expenditures and treating private R&D allocations
as comparable to public R&D expenditures, he estimated a substantially
smaller internal rate of return of 33 percent.
Minasian in a non-agricultural study, analyzed the contribution
of R&D to value added in a cross-sectional study of the U.S. chemical
industry. He estimated a Cobb-Douglas function with value added as
the dependent variable and capital, labor, firm constants, a time
trend and the technology of the it'ri firm during time period t as
independent variables. Minasian estimated an elasticity of 0.11 for
R&D, resulting in a gross return of 54 percent on investments in R&D.
Applying the PF model to commodity groups, Bredahl and Peterson and
Norton estimated the marginal internal rate of return (MIRR) to each
of four commodity groups (cash grains, dairy, poultry, and livestock)
and suggested reallocating research dollars from relatively low to
relatively high payoff commodities so as to increase the overall rate
of return.
The productivity index approach is used in time series studies.
The use of a productivity index as the dependent variable avoids the
problem of high intercorrelation problems with time-series data for
conventional production inputs and the general lack of sufficient data
for the important conventional inputs (Norton and Davis). The change
in productivity index is a suitable indicator of the effect of
research on efficiency because it measures change in efficiency, not
change in farm income or prices (Evenson, Waggoner and Ruttan).
Evenson (1978) analyzed the relationships between productivity and


32
investment in (a) agricultural invention, (b) education, and (c)
research and extension. There were two distinct categories of
research -- science-oriented research and technology-oriented
research. Evenson divided the U.S. into geoclimatic regions and
attempted to isolate spillover effects of research between different
states. The spillover effects were estimated by interaction of south,
north and west variables with the technology-oriented research
variable. Technology-oriented research yielded a rate of return of 95
percent; science-oriented research yielded a 110 percent rate of
return for the period of 1927 to 1950. Evenson found that 55 percent
of the change in productivity attributed to technology-oriented
research from a typical state was realized within that state, with 45
percent being realized in other states with similar soils and climate.
The spillover from science-oriented research was considerably greater.
In the econometric approach, the R&D variable enters the model
as a distributed lag. Several factors influence the lag structure
between R&D investment and the resulting increase in technology.
These include the time lag between R&D and the actual invention of a
newer and more productive process (Griliches, 1980). Previous
investigators have assumed no or little lag and no depreciation. The
lag effect is accounted for, based on the nature of the sector, by
appropriately adjusting the marginal product or internal rate of
return associated with R&D (Bredahl and Peterson; Griliches, 1964).
Lags between R&D and the realized benefits tend to be shorter in
industries where R&D focuses on development and applied issues than in


33
industries where efforts focus on basic research. Basic research is
longer term and more uncertain (Mansfield).
Evenson (1967) first investigated the question of lags between
R&D and realized benefits econometrically with various R&D lag
structures. Using aggregate data for U.S. agriculture he found that
an inverted V distributed lag gave the best fit, with the peak
influence coming with an average lag of six to eight years and the
total effect dying out in about ten to sixteen years.
The econometric studies cited above have generally focused on
the effects of biological or technological improvements in farm
production techniques (i.e., evaluating production-oriented or
preharvest research). In response to growing interest in returns to
postharvest R&D, Stranahan and Shonkwiler evaluated the returns to
postharvest R&D in the Florida frozen concentrated orange juice
market. Because productivity and input quantity data were either
unavailable or more difficult to obtain, while cost and price
information was more accurate in the citrus processing subsector, a
cost function in conjunction with share equations was estimated. The
indirect cost function was specified as in the following equation:
C-c(Y,P,Z), (2.1)
where Y is output, P is vector of input prices, and Z is quasi-fixed
input included in the production processes. They assumed that the firm
or industry minimizes cost of producing a given output Y, with respect
to input prices and the level of the quasi-fixed input Z, where Z may
characterize the state of technical progress, degree of learning, or
contain environmental or behavioral parameters. In earlier studies


34
Caves, Christensen, and Swanson employed Z as a short-run fixed factor
representing capital structures in a translog cost function.
Differentiating the indirect cost function with respect to (2.1)
gives the negative of the shadow price of Z (Diewert; Lau).
aC(Y,P,Z)/3Zi = -WiCY.P.Z). (2.2)
At the margin, the total cost of producing Y, given the
cost-minimizing levels of input usage, will be reduced by an amount
equal to the implicit market value (or imputed value) of input Z. In
the citrus-processing subsector study Z was taken to be the average of
expenditures on citrus-processing research, lagged one and six years,
divided by the GNP implicit price deflator, lagged one and six years.
R&D can affect input usage neutrally or can bias input levels through
time depending on the magnitude of the parameter on research in the
share equations (Binswanger, 1974). Zero parameter values imply that
research impacts input usage neutrally through time. Thus the study
also investigated the effect of R&D on input usage and found that on
average, research has had a positive effect on labor and other input
usage and a negative effect on materials usage. The rate of return to
citrus research was found to be 57.4 percent, which is somewhat higher
than those rates calculated using the productivity index approach.
All the econometric studies discussed above used R&D
expenditures as the measure of research, with considerable variation
in specific items included. Some U.S. studies have used only
commodity-specific R&D expenditures by the state experiment stations
(e.g. Bredahl and Peterson) and some have used total R&D expenditures
by experiment stations, the USDA, the Soil Conservation Service, and


35
private research organizations (e.g. Cline and Lu). Others (e.g.,
Evenson and Kislev 1973, 1975, and Evenson, 1974) have used the number
of scientific publications as a proxy for research. R&D expenditures
were further separated into commodity-specific applied research and
noncommodity-specific applied, agriculturally-related basic research.
Evenson and Binswanger included separate variables to measure effects
of applied research and basic science-oriented research.


CHAPTER III
THEORETICAL FRAMEWORK
Motivation for a Simultaneous-Equations Approach
The various econometric approaches to evaluating returns to R&D
outlined in the literature review, though plausible in terms of
overcoming certain data and estimation problems, do not go far enough
in evaluating the returns to research. In general, the methodology
assumes R&D affects only the supply side. However, certain kinds of
research beyond the farm gate and perhaps even some production-
oriented research, can affect the demand for the commodity, for
example, if product quality is improved or if the research leads to
more effective use of expenditures for advertising and promotion.
Such payoff to research should not be overlooked in any evaluation of
returns to R&D. Another shortcoming of approaches based on profit and
value-added functions is that they break down when prices of output
can not be taken as fixed. Such a situation normally exists in an
aggregative approach to measuring returns.
These problems provided the primary motivation for employing a
simultaneous-equations model to evaluate returns to postharvest R&D
investments in the U.S. market for fresh-winter tomatoes from Florida
and Mexico. The nature of the problem is such that we can not apply a
restricted profit function because the assumption of exogenous output
prices is invalid. The two major producing areas are considered and
36


37
the action of each can affect the output price. Simultaneity in
inputs and outputs also precludes using a direct production function
methodology. The quantity supplied and demanded and the price are
jointly determined in the system, and such joint determination
suggests the need for a structural equations model.
The cost function approach could be employed since the output
price is not one of the arguments and the endogeneity problem could be
avoided however, the approach ignores any demand-side impacts of the
R&D. Also, these duality approaches cannot adequately address the
distributional effects of the R&D investment expenditure. These
latter effects can, to a limited extent, be evaluated within a market
clearing framework.
Model Specification
The market for fresh-winter tomatoes in the U.S. is represented
here by a set of four behavioral equations, an implicit price
relationship, and four identities. Behavioral equations on a per
capita basis are specified for the Florida shipping point supply, the
export supply from Mexico, a marketing margin equation for the U.S.
market and aggregate U.S. domestic demand at the retail level.
Identities are used to define the marketing margin, as well as the
weighted average shipping point price, and the aggregate supply in the
U.S. domestic market, and to state the market clearing conditions.
Annual crop supply responses have commonly been modeled by
specifying an acreage planted equation, yield per acre equation and
the actual total production or supply as the product of the two
(Shonkwiler and Emerson; Shonkwiler; Chern and Just; Brandt and


38
French; and Gutierrez). Alternatively, the Nerlove model in its basic
form or a modified version which treats acreage planted as a proxy for
physical output, has been employed to model crop supply responses.
However, the variables which affect acreage and yield are those
which affect the quantity supplied. In this study supply was
estimated directly rather than indirectly through acreage and yield.
Expected and actual output prices, and prices of inputs employed in
production, and other factors such as weather are examples of
variables that affect the decisions and final supply of the profit-
maximizing farmer. The period between planting and harvesting can see
drastic dislocations in market conditions facing farmers, and result
in seemingly radical harvesting decisions. In fresh-winter tomato
production, unharvested acreages can be abandoned if output price at
harvest is less than the cost of harvesting, packing and marketing at
the shipping point.
At planting time, farmers therefore gauge what output levels
would maximize their profits based on their past experiences, physical
and technical environment, expected output prices, current prices of
inputs used for production, and other market conditions. Their plans
may of course not be realized because of weather and the influences of
other exogenous forces.
Based on the above reasoning, the supply relationships for fresh-
winter tomatoes from Florida and the export supply from Mexico were
modeled as a straightforward relationship between the physical output
and the major economic and noneconomic factors believed to affect
output.


39
The marketing margin for fresh-winter tomatoes is the result of
demand and supply forces for marketing services required to move
fresh-winter tomatoes to the retail market by distributors. It is
assumed that distributors are profit-maximizers in the competitive
fresh-winter tomato market and that they employ levels of marketing
services to achieve this objective. The marketing margin for fresh-
winter tomatoes was specified as a function of the per capita total
quantity shipped from Florida and Mexico, a price index of marketing
inputs, and the preharvest and postharvest R&D expenditures
appropriately lagged.
Following the tenets of demand theory (utility maximization
subject to budget constraint), the per capita demand for fresh-winter
tomatoes at the retail level in the U.S. was postulated to depend on
the retail price, U.S. per capita disposable income, prices of
substitutes at retail level, R&D expenditures "appropriately" lagged,
and expenditures on advertisement to promote fresh-winter tomatoes.
Research affects demand for fresh-winter tomatoes by improving product
quality and increasing shelf-life, among other things.
The general structural model was specified in the following
equations:
PCQFLt f(Pit*, DWRlt, IRlt, Plt, FPlt Wlt, RDlt_i, PCQMEXt)
+ ult (3.1)
PCQMEXt f(DWR2t, IR2t. p2t- pp2t- w2t- RDlt-j
RD2t.j, MPCINCt, PCQFLt) + u2t (3.2)
MMt f(PCQSUSR, MCIt, RDlt-j RD2t-j) + u3t (3-3)
MMt RPt WAPt
(3.4)


40
PCQSUSRt PCQFLt + PCQMEXt (3.5)
RPt f(PCQDUSRt> RPSt, IncUSt, RDit-i, RD2t-i,
ATOMt) + u4t (3.6)
PCQSUSRt PCQDUSRt (3.7)
The endogenous variables in this simultaneous equations system are
PCQFL, P]_, PCQMEX, P2, PCQSUSR, PCQDUSR, WAP, MM and RP. In addition
to the seven equations specified in (3.1) through (3.7), WAP was
computed using the definition of weighted average price, and P2 was
eliminated from the model with an implicit function of P^. The
Florida FOB price and the Nogales price were highly correlated. The
rest of the variables in the system were assumed to be exogenously
determined. Definitions of the individual variables are:
PCQFLt = per capita quantity of tomatoes offered for shipment in
pounds in Florida at time t;
Pit real expected price per pound of tomatoes in cents at time
t in Florida;
DWRit real daily wage rate of labor in dollars used in
production of fresh-winter tomatoes at time t in Florida;
IRlt = real interest rate charged farmers during planting time
(July through January) in Florida;
Plt = real season average price per pound of tomatoes in cents at
the shipping point in Florida at time t;
FPit real price of fertilizer in Florida in dollars per ton, at
time of planting;
Wit weather index for the winter tomato-growing region in
Florida;
RDit-i real R&D expenditures on farm level technology, in
dollars, "appropriately" lagged;
PCQMEXt export supply of tomatoes from Mexico through Nogales
in pounds per capita in the U.S. market at time t;


41
DWR2t real daily wage rate of labor used in production of
fresh-winter tomatoes at time t in Mexico, in dollars;
IR.2t = real agricultural interest rate at time of planting, in
Mexico;
?2t = real domestic supply price in Mexico (approximated by the
real FOB price in Nogales Arizona) in cents per pound at
time t;
FP2t real price index of fertilizer used in production of
fresh-winter tomatoes at time t in Sinaloa, Mexico;
W2t = average temperature in Mexico's tomato-producing region;
MPCINCt = real Mexican per capita national income in dollars at
time t;
MMt marketing margin for fresh-winter tomatoes in the U.S. in
cents per pound at time t;
PCQSUSRt = per capita retail level supply of fresh-winter
tomatoes in the U.S. at time t in pounds;
MCIt real price index of marketing inputs (labor,
transportation charges, packaging materials) employed in
moving tomatoes from the shipping point in Florida to the
retail market at time t;
RI>2t-i real postharvest research expenditures on fresh-winter
tomatoes in dollars "appropriately" lagged;
RPt real retail price of tomatoes at time t in cents per pound;
WAPt weighted average shipping point price for fresh-winter
tomatoes shipped from Florida and Mexico in cents per
pound at time t;
RPSt real retail price of substitutes (green peppers) at time t
in cents per pound;
IncUSt real U.S. per capita disposable income at time t
(dollars);
ATOMt real expenditures on advertisement to promote fresh-
winter tomato consumption;
ult> u2t> u3t> u4t = random error terms.
The grower level supply for fresh-winter tomatoes in the U.S.
market was obtained by adding the supply from Florida and the export


42
supply from Mexico. By adding the marketing margin to the grower
level supply the retail supply was obtained. The grower level demand
was obtained by subtracting the marketing margin from the retail
demand. Consumers' surplus (CS) and producers' surplus (PS) at the
equilibrium quantities and prices for the different levels (grower and
retail) were estimated from the resulting demand and supply equations.
The impact of R&D on CS and PS can be evaluated by differentiating CS,
PS or (CS+PS) with respect to R&D since the CS and PS relationships
will include R&D at the farm level and marketing level as explanatory
variables. The contribution of preharvest and postharvest R&D
investments to the surplus accruing to producers and consumers was
estimated using the demand and supply equations at both the grower and
the retail levels. Estimates were derived at the equilibrium prices,
and quantities; CS, PS, and (CS+PS) relationships were derived and
then differentiated with respect to the preharvest and postharvest R&D
variables. The distributional effects among producers and consumers
and the rate of return to R&D investments were also estimated. The
rationale for the specification of the variables in the above model is
discussed below.
Florida Supply
The farmer is assumed to act rationally, varying levels of inputs
with the ultimate objective of profit maximization. For agricultural
crop production there is a fixed biological lag between production
efforts and the final output. For fresh-winter tomato production the
time that passes between planting and harvesting is three to four
months. Thus planting decisions must be based on the price that


43
producers expect to receive at harvest time, the current costs of
inputs employed in production, and the interest rate observed at
planting time reflecting t^ cost of capital used for tomato
production.
As stated earlier, the farmer may be confronted with changing
market conditions after planting decisions have been made. Thus the
farmer has to make decisions at harvest time so as to maximize
profits. The decision to harvest and the yield level are therefore
based on the prevailing product price, the harvesting, packing and
marketing cost at the shipping point, and noneconomic factors such as
weather and technology. If the product price at harvest time relative
to the harvesting, packing and marketing cost is unfavorable, planted
acreage may be abandoned. The tomato plant can be destroyed by
freezing winter temperatures, or fruit setting can be inhibited,
thereby reducing yield. Improvement in technology such as development
of high-yielding disease-resistant varieties and improved cultural
practices (e.g., plastic mulching) can affect yields. R&D expenditure
variables help to explain variations of effects such as these. The
effect of weather on yield was accounted for by including a weather
variable.
Florida and Mexico are the only two major winter-tomato-
producing regions; thus the quantity shipped from one area will affect
the other. The per capita quantities shipped from each area were
therefore included in the other's supply response relationship and
were expected to have negative impact on the competing region's
supply.


44
Mexican Export Supply
Since the early 1960s the Mexicans have adhered to a planned
supply program (Fliginger et al.; Goldberg; Simmons et al.; Buckley et
al.). A description of this planned supply program was given in
Chapter I.
Some macroeconomic factors also have a bearing on the production
and shipment of tomatoes to the export market. For example, the
cyclical overvaluation of the peso which in turn affects the export
price and domestic input costs has not favored tomato production and
export. Devaluation of the peso may increase net returns for Mexican
tomatoes exported to the U.S. in the short-run because it raises the
price (in pesos) Mexican producers receive relative to costs.
However, imported input costs will increase and the advantages
initially provided by increased returns are thus reduced.
Based on the structure and conduct of the fresh-winter tomato
industry the export supply from Mexico is regarded as an excess supply
to meet U.S. excess demand. The U.S. excess demand for fresh-market
winter tomatoes is the difference between the aggregate quantity
demanded in the United States during December through June and the
quantity of fresh-market winter tomatoes produced in Florida. The
Mexican export supply is the difference between the domestic supply
and the domestic demand. Thus the Mexican export supply will be a
function of the variables that enter the domestic demand and supply
relationships. The export supply from Mexico was therefore specified
as a function of the Mexican daily wage rate; the interest rate
charged Mexican vegetable farmers; the price of fertilizer;


45
temperature during the growing season; the price of fresh-winter
tomatoes (which was approximated by the FOB price at Nogales since the
domestic price could not be obtained); the per capita disposable
income in Mexico; external factors such as U.S. R&D expenditures on
tomato technology "appropriately" lagged; and the quantity shipped
from Florida. Acreage planted in Mexico is allocated by the
government through the recommendations of the growers' Unions, and the
quantity actually exported is based on prevailing market conditions.
Therefore, the expected price at planting time does not enter the
export supply equation; it is the current price that explains export
supply. As mentioned in Chapter I, Mexican growers depend to a great
extent on progress in the United States for technical improvement.
Technological advances are mainly acquired from U.S. technicians and
consultants and from publications of universities in the U.S. and the
U.S.D.A. Many of these publications are translated and published by
the growers' association (Firch and Young). It is therefore
appropriate to include U.S. R&D expenditures on tomato production and
marketing technology in the Mexican export supply equation. The lag
structure of the impact of R&D investments may be the same for Mexican
producers as for U.S. producers since technology developed in the
U.S. is almost immediately available to Mexican producers because of
their association with U.S. agents. A weather variable is included to
account for the effect of weather on tomato yields.
Marketing Margin Between the Grower Level and the U.S. Retail Supply
Fresh-winter tomatoes go through a marketing channel from the
shipping points in Florida and Mexico to the final consumer at the


46
retail level. In this process distributors are providing marketing
services. It is assumed that these distributors behave in such a way
as to maximize their profits. The marketing margin, which is the
difference between the retail price and the grower level price
(represented here as the weighted average of Florida and Mexican
supply prices), reflects the demand for the marketing services. This
was therefore specified as a function of the per capita quantity
shipped from Florida and Mexico to the retail market, the cost of the
marketing services, and the preharvest and postharvest R&D
expenditures in the U.S. The grower level demand was then obtained by
subtracting the marketing margin from the retail demand equation.
The U.S. retail supply was estimated by first horizontally
summing the grower level supplies from Florida and the export supply
from Mexico and then vertically summing this result and the marketing
margin. There were no data available to adjust quantities supplied
for any losses which may occur between the grower and the retail
levels.
Demand in the U.S. Market
The basic theory underlying the specification of the retail
demand for fresh-winter tomatoes in the U.S. is the familiar one of
utility maximization (Henderson and Quandt; Silberberg). Fresh-winter
tomatoes are mostly consumed in salads with other vegetables. It is
therefore assumed the consumers of fresh-winter vegetables strive to
maximize the utility derived from consuming a bundle of fresh-winter
vegetables (tomatoes, cucumbers, lettuce, green peppers, celery,
carrots, etc.) subject to a budget. The resulting utility-maximizing


47
relationship expressed in price-dependent form is a function of per
capita quantity demanded, the retail prices of substitutes and
complements, the R&D expenditures on preharvest and postharvest
technology "appropriately" lagged, the U.S. per capita disposable
income and expenditures on advertising for the promotion of fresh-
winter tomatoes. The latter five exogenous variables are demand
shifters. R&D affects demand through improved techniques that enhance
or preserve the product quality at retail, which includes increased
shelf-life and improvements in palatability. Preharvest and
postharvest R&D variables are therefore included to capture these
effects on the demand for fresh-winter tomatoes. Advertising and
promotional campaigns are information-oriented and make consumers
aware of fresh-winter tomatoes and their nutritional value in diets.
Consumers' and Producers' Surplus Analysis
Shifts in supply and demand may occur at all levels in the
marketing channel due to production-oriented R&D investments and
marketing-related R&D investments on fresh-winter tomatoes. Estimates
of the effects on consumers' and producers' surplus were obtained at
the grower level as follows:
CS
G
DG(Q) dQ PG.QG
0
f(Q, R&D1( R&D2) dQ PG.QG ,
0
(3.8)
where DG(Q) is the grower level demand and (QG) and (PG) are the
market clearing quantity and price respectively. Holding all


48
exogenous variables with the exception of the research variables
constant at their means, the consumer surplus at the grower level is a
function of the preharvest and postharvest R&D expenditures with the
lagged effects discounted to the present time.
Similarly, on the supply side, by holding the exogenous variables
constant at their means with the exception of the research variables,
the producer surplus may be expressed as a function of the preharvest
and postharvest R&D expenditures and total surplus = (CS + PS)
The discounted marginal rates of returns of R&D investments were
estimated by partially differentiating CS, PS, and (CS+PS);
and, estimates of the discounted average rates of returns to R&D
investments were obtained by comparing the total returns to the total
research expenditures.
By the same procedure as in equation (3.8), alternative estimates
of CS, PS and (CS+PS) at the U.S. retail market were estimated, and
the discounted marginal and average rates of returns to U.S.-based
preharvest and postharvest R&D investments were evaluated.


CHAPTER IV
MODEL ESTIMATED
A partial equilibrium, simultaneous equations model was developed
in Chapter III for the fresh-winter tomato market in the U.S. during
the months of December through June. This chapter includes a
discussion of different price expectation models, the estimation
technique and the data set.
Modeling Expectations
An expected price variable appears in the supply response
equation for Florida. Because expectations are not directly
observable, additional information is needed. Several strategies for
providing additional information have been proposed to handle this
nonobservable variable and these include: rational expectations, simple
naive expectations, extrapolative expectations, Nerlove's adaptive
expectations and revisional price expectations.
The Rational Expectations Model
The rational expectations model was first introduced by Muth in
terms of market supply and demand relationships and maintains that
participants in the market act as if they were solving the supply and
demand system in forming their price expectations. Generally, the
rational expectations interpretation of the expected price, Pt*, is the
mathematical expectation of Pt given all information (It-l) available
when the expectation is formed, i.e., Pt* E(Pt|lt.^). In a
49


50
structural econometric model this information consists of the
predetermined variables and the model's reduced-form parameters
(Wallis). Thus the model can be solved for the expected price as a
function of the expected values of the exogenous variables. This
function can then be substituted into the model, leading to a
specification which contains the original endogenous and exogenous
variables plus the expected values of the exogenous variables. In
general, following this substitution, the model will be highly non
linear in the parameters and also have parameter restrictions across
equations. Thus a system method of estimation would be most
appropriate since a limited information method of estimation will be
less attractive because of the cross equation restrictions (Shonkwiler
and Emerson). Time series analysis is utilized to generate the
necessary forecasts of the exogenous variables.
Naive Price Expectations Model
Naive or static expectations define expectations of the current
period price as the previous period's price, i.e.,
Pt* Pt-1 (4.1)
This model has a rich history in economic analysis and has comprised
the basis for the cobweb model used in the analysis of commodity cycles
in agriculture. An advantage is that information required is simple to
obtain.
Extrapolative Expectations Model
Extrapolative expectations require a longer time-series for the
variable in order to "extrapolate" how the variable changes over


51
time, (Moore and Meyers). There are several specifications under this
classification;
a. linear time-trend
Pt* a + bt-1 (4.2a)
b. exponential growth curve
Pt* = aert'l (4.2b)
c. auto regressive trend
Pt* -a + bPt-.i +....+ bpPt.p (4.2c)
Adaptive Expectations Model
The adaptive expectation model was developed by Marc Nerlove on the
premise that:
farmers react to expected price and this expected price
depends only to a limited extent on what last year's price
was (Nerlove, p. 498).
Adaptive expectations can be represented as:
Pt* Pt-1* Mpt-1 pt-l*) 0 < b < 1 (4.3)
and stated as the revision in the expectation of Pt is proportional to
the error made in the forecast of Pp.]_. We can represent adaptive
expectations as an infinite weighted-average of previous levels of the
variable, with the weights declining geometrically as the lag length
increases:
Pt* bPt.i + b(l-b)Pt_2 + b(l-b)2Pt_3 + (4.4)
Nerlove's adaptive expectations model can be empirically applied
to the case of one explanatory variable rather easily. However, when
there are several explanatory variables, the estimation procedure of
the reduced-form equation becomes complex, and the number of degrees
of freedom is reduced (Nerlove and Addison; Chern and Just).


52
Revisional Price Expectations Model
The revisional price expectation model which appeared in the
literature only recently reflects the situation wherein the beginning
period price expectation may be revised once during the production
process (Taylor and Shonkwiler). The other models of price
expectations outlined above are conditioned by the information
available when the production decision is made. Such definitions of
expectations are perhaps appropriate if the measure of supply is
planted acreage, a fairly common practice in agricultural supply
studies (Askari and Cummings; Shonkwiler and Emerson). However, if
supply measures are in terms of physical output (e.g., supply of
livestock or total crop production), the amount actually harvested will
depend on prevailing market conditions, thus price expectations
conditioned by the information available when the production decision
is made may be inadequate. For example, the number of pickings of
fresh vegetables or the weight at which livestock should be marketed
can all be influenced by the information acquired subsequent to the
beginning of the production period. The sequential nature of
agricultural production processes thus affects the way supply prices
are imputed. Under the assumption that additional information can be
utilized to improve the accuracy of formulating price expectations,
Taylor and Shonkwiler defined a price expectation consistent with
intended production and marketing decisions which allows unobserved^
1 It should, however, be noted that this information is not directly
observable in the data but is observed by producers (Taylor and
Shonkwiler, p. 289).


53
information to be utilized in obtaining a measure of price expectations
formulated by producers.
Following Taylor and Shonkwiler (pp. 289-290), let flfl+ct be:
the information set available to the economic agent at the time t-
1+a. The a parameter being constrained on the closed interval
[0,1] indexes the information which becomes available subsequent
to period t-1 up to and including period t. If the main
components of the information set are prices, a revisional
expectation may be defined by Pt' Et_i+a(Ptl^t-1+a) This
revisional expectation is the conditional expectation of Pt given
information available at time t-l+a. Since a is contained in the
closed interval [0,1], the realized price Pt (a=l) and the
beginning period expectation Pt* Et.^(Pt|nt.^) (a-0), are
special cases of the revisional price expectation.... If a-0, the
imputed price which yields the observed output as optimal is the
beginning period price expectation. This would imply that the
major response of supply to price rests with the decision to
commit a given set of resources to production. Conversely, if
a-1, observed price is the optimizing supply price with the
implication that economic responses in supply occur primarily
through marketing decisions.
Given the assumption that additional information can potentially
improve the ability of the economic agent to conjecture what the actual
price will be, the revisional expectation may be expressed in an
empirical framework as:
Pt' aPt + (1 a)Pt* ae[0,l] (4.5),
where Pt denotes the actual price obtained in period t and Pt* denotes
the expected price conditioned by information set available at time
t-1.
Thus revisional price expectation is here defined as a convex
combination of a beginning period expectation and the observed price.
The weights assigned to the beginning period price expectation and the
observed price in imputing the optimal supply price will depend on this
value of a. If a is unconstrained and can take on any positive value
in the real number system the revisional price expectation will be an


54
affine combination of the beginning period price and the observed
price.
Estimates of the Model Parameters
The highly commercial and concentrated nature of the fresh-winter
tomato industry may produce a situation more conducive to the use of
rational expectations by producers (DeCanio). Also the competition
between Florida and Mexican growers and the information collection and
dissemination service of the Florida Tomato Committee suggest that
growers take important supply and demand forces into account when
making production decisions at planting time. Thus the rationally
expected prices are informed predictions of future events and are
assumed to be based on the underlying economic forces. These forces
would be consistent with those described by appropriate economic theory
(Muth). Shonkwiler and Emerson, were able to implement the rational
expectations hypothesis in a two simultaneous equations model of fresh-
winter tomato-imports and supply. However, we could not implement
the rational expectations hypothesis in our model of four equations
because of limited observations. A revisional price expectation was
finally adopted after considerable efforts to use the rational
expectations approach.
Preliminary estimation using different lag structures
(geometrically and linearly declining lags, second-degree Almon lag and
the inverted V) and different lag lengths of the two R&D expenditure
variables were tried. These trials provided a basis for specifying the
lag length and structure for the preharvest and postharvest R&D
expenditure variables in the model estimated.


55
The lag structure between R&D investments, which is a proxy for
the resulting increase in technology and the subsequent impact on
output, is influenced by several factors. These factors include: the
time lag between R&D expenditures and the development of a new variety
or production process; between research and commercial development and
adoption of the technique or variety; and the depreciation rate of the
new technology.
In the agricultural sector, technology depreciates because of the
biological environment. The lags between R&D investments and the
realized benefits in agriculture will thus vary with the type of
technology forthcoming from the research and the commodity involved.
Evenson (1967) found the impact of R&D on aggregate agriculture was
best described by an inverted V lag with a mean lag of 5 to 7 years.
Lags of longer lengths have been used. There is evidence that quite
long lags, at least 30 years, must be allowed if it is hoped to capture
all of the impact of research on agricultural output (Pardey and
Craig). A very long series of data would be required to measure the
impact of research with such long lags in benefits. The present value
of benefits in the distant future would be low.
Lags of 4,5,6, 7, 8, 9, 10 and 11 years for the R&D expenditure
variable, following a second-degree Almon polynomial with zero end
point restrictions, were tried in the preliminary runs of the model
with the two different price expectation models (i.e., the naive price
expectation and the revisional price expectation). Lags of 6, 8 and 10
years following the inverted V lag with zero end-point restrictions
were also tried. The revisional price expectation was implemented by


56
including both the current and lagged prices in the supply equation
(i.e., beginning period expectation was taken to be the price lagged
one period) and then restricting the sum of their coefficients to be
between 0 and 1; i.e., a convex combination of the two prices (Taylor
and Shonkwiler). The coefficient estimates for both the current price
and the lagged price were negative. The model was reestimated without
any restrictions on the current and lagged price coefficients (i.e.,
affine combination of the two prices). The input prices and output
price were also deflated by the interest rate, i.e., homogeneity of
degree zero was imposed on the Florida and Mexican excess supply
equations. The parameter estimates of the deflated current and lagged
price were positive as expected. A 10-year lag following a second-
degree Almon polynomial-distributed lag was believed most appropriate
for the preharvest research expenditure variable affecting the supply
in Florida. An 11-year lag following a second-degree Almon polynomial-
distributed lag had the smallest estimated standard error relative to
the respective estimated coefficient for both the preharvest and
postharvest research expenditure variables affecting the Mexican excess
supply, the marketing margin and the retail demand in the U.S.
Generally the impact of research on output could be described by
the following lagged relationship:
Yt a + b()Xt + b]Xt_i + - + b^Xj-.^ + Ut, (4.6)
where Yt is a measure of output at time t and Xt_ i = 0,1, ,k
the values of the R&D expenditures during the current and past k years
respectively. Ut is the disturbance term which satisfies the usual
assumptions (Kelejian and Oates). A degree-of-freedom problem and


57
multicollinearity would be encountered if the model were estimated as
specified in equation (4.6)). The Almon and the inverted V lag both
provide ways to reduce the number of parameters to be estimated. The
Almon lag assumes that the pattern of the impact of R&D on output (the
b's in equation (4.6) follow a polynomial which shows that the b's are
expected to increase at first and then decrease. In this study the
impact of R&D on output was approximated by a second-degree polynomial,
or:
bi a0 + Qli + a2i2 1 0.1 k, (4.7)
where oq, and 02 are constants to be determined. If we replace the
b's in (4.6) by their expressions in (4.7), we have:
Yt = a + oo^t + (Q0 + 1 + 2 )Xt-i +(<*0 +2a\ + 4a2)Xt_2 + ...(4.8)
+ (qq +ka^ + k^c*2)Xt-k + Ut.
Rearranging terms in (4.8) gives us:
k
Yt a + q0 2 Xt-.i
i-0
k k
Q1 SiXfi + a2 Si2Xt.i + Ut.
i-1 i-1
To simplify further, define
k k k
Zlt = Z X(-., %2t EiXfi > an i-0 i-1 i-1
(4.9)
(4.10)
(4.10) is substituted into (4.9) to give:
Yt a + a0Zlt + aiZ2t + <*2z3t + ut- (4.11)
This equation can be simplified by assuming b_]_ and bk+q are zero
(zero-end point restrictions), i.e., now
i -1, 0, 1, . ,m, m-t-1 and
b-l Q0 Q1 + a2 0 > anc*
bk+l = Q0 + al(k+1) + a2(k+l)2 0.
These expressions can be solved in terms of qq and and substituted
back into (4.11) to eliminate two parameters, i.e.,


58
(k+2)Q1 + q2[(k+1)2 1] = 0
Q1 -a2 [(k+1)2 l]/(k+2) a2B
ocq a2 [-[(k+1)2 1]/(k+2) -1] a2A
Yj- a + a2AZit + a2BZ2(- + a2Z3t + Ut (4.12)
or
Yt a + a2[AZlt + BZ2t + Z3t] + Ut (4.13)
Yt a + a2Zt + Ut. (4.14)
After estimating (4.14) ag an<^ al are obtained from the relationships
above, then estimators for the b's are obtained as follows:
A A
b0 a0 (4-15)
A A A A
bi = ag + a3 + a2
A A A A
b2 = QQ + 2at]_ + 4a2
AAA A
O
bk ag + kai + kza2.
The inverted V lag was suggested and used by DeLeeuw. The technique
assumes zero end-point restrictions and an even lag length. That is,
for an even lag length k, bg 0 and b^ 0. Then
b ib for 0 5 i < k/2 (4.16)
- (k-i)b for k/2 < i < k.
Substituting these values into (4.6), we get
Yt bZt + Ut (4.17)
where
k/2 k
Zt 2 iXt_i + 2 (k-i)Xt.i.
i-0 (k/2)+l


59
After estimating b from (4.17) and using (4.16) one can obtain
estimates of b^.
Estimation Method
The model was estimated in the linear form and the natural logarithmic
form. The model in the natural logarithm form was estimated by non
linear two-stage least squares (NL2SLS) using all the exogenous
variables in the system as instruments. The marketing margin and
retail demand equations which contain a nonlinear variable (InQSUSR)
were estimated by Amemiya's (1974) nonlinear two-stage least squares
estimator, where the instruments are low-order polynomials of all the
exogenous variables in the system. A second-degree polynomial of all
the exogenous variables was used. For all the other equations the
instruments were all the exogenous variables in the system. There was
a degree-of-freedom problem with the nonlinear approach due to the
fact that the number of observations was fewer than the number of
parameters in the second-degree polynomial used in the first stage of
the estimation of the marketing margin and retail demand.
The linear model was estimated by linear two-stage least squares
and as a system by 3SLS. The system of equations contain endogenous
variables as explanatory variables and since there could be correlation
of the stochastic disturbance terms across structural equations because
of the simultaneous nature of the model, these endogenous variables
would be correlated with the stochastic disturbance terms across
equations. The 3SLS uses estimated information on the correlation of
the stochastic disturbance terms of the structural equations from 2SLS
residuals in order to improve asymptotic efficiency. The 3SLS


60
estimates are consistent and are asymptotically efficient but are
recognized as being sensitive to specification errors which may exist
in the model. The results using 2SLS were more consistent with
expectations than those obtained with 3SLS -- perhaps due to
sensitivity to specification errors.
Data Sources
Mexican Growing Season Temperature
The major production of fresh-winter tomatoes in Mexico occurs
around Culiacan, which lies on latitude 25N and longitude 80W, and
Los Mochis, lying between latitude 25 30'N and longitude 82 12'N in
the State of Sinaloa; thus the average temperature data that should
enter the model would be that prevailing in these areas during the
growing season. The closest location from which temperature data were
available was for Guadalajara, lying between 27N and 76 30'W. These
average temperature data in degrees Fahrenheit were obtained from the
U.S. National Weather Data Center.^
Real Agricultural Interest Rate in Mexico
The data on interest rate charged farmers in Mexico were obtained
from various sources. The figures for 1964-1970 were obtained from
Commission Nacional Bancaria, Boletin Estadistico Secretaria de
Hacienda Y Crdito Publico, Mexico 1964-1970 and those for 1971-1984
were obtained from FIRA, Banco Nacional de Mexico. The interest rate
was then deflated by the CPI in Mexico to give the real agricultural
interest rate. The CPI data for Mexico were obtained from the
International Monetary Fund, International Financial Statistics (1964-
2
Paul Dyke provided diskettes containing temperature data.


61
1984). The Mexican agricultural interest rate was very highly
correlated with the interest rate charged farmers in Florida, so the
U.S. interest rate was used in place of the Mexican interest rate in
the Mexican supply equation to reduce the number of exogenous variables
in the model.
Mexican Rural Daily Wage Rate
The Mexican rural daily wage rate data for the 1964/65-1980/81
seasons were obtained from Gutierrez, and for the 1981/82-1983/84
seasons from Buckley et al. These were deflated with the Mexican CPI
for the season to give the real Mexican rural daily wage rate.
Quantity Shipped from Mexico and the FOB Price in Mexico
The quantity shipped for the 1964/65-1983/84 seasons (December-
June) in millions of pounds were obtained from various issues of the
Florida Tomato Committee, Annual Report. The FOB price is the
weighted average price per pound for generally good quality tomatoes,
including duty and crossing charges at Nogales, Arizona. These were
also obtained from various issues of the Florida Tomato Committee,
Annual Report and were deflated by the CPI to give the real FOB price.
The FOB price was used as a proxy for the Mexican domestic supply price
since the domestic price could not be obtained. The Mexican FOB price
was very highly correlated with the Florida FOB price, therefore the
latter was used in the Mexican export supply equation; i.e., an
implicit relationship among these two prices was used to eliminate an
endogenous variable from the Mexican export supply equation.


62
U.S.Population
The total quantities shipped were divided by the U.S. total
population as of July 1 of each year to obtain the per capita quantity
shipped to the U.S. The U.S. population data were obtained from
U.S.D.A. Statistical Bulletin No.713,
Mexican Fertilizer Price Index
The indices of fertilizer prices for Mexico were obtained from the
World Bank through personal communication.
Real Per Capita National Income in Mexico
Personal income data were not available for Mexico. Consequently,
Mexico's national income deflated by Mexico's CPI and the population
were used in the Mexican export supply equation. Data were obtained
from the U.N. Department of International Economic and Social Affairs,
Monthly Bulletin of Statistics (1965-1984).
Mexico's Population
Mexico's mid-year population for each year was used. The population
data were taken from the U.N. Department of International Economic and
Social Affairs, Monthly Bulletin of Statistics (1965-1984).
Per Capita Quantity Shipped from Florida and FOB Price
The total quantity shipped from Florida in million of pounds for
the 1964/65-1983/84 seasons (December-June) and the seasonal weighted
average shipping-point price, or FOB price, for generally good-quality
tomatoes were obtained from various issues of the Florida Tomato
Committee, Annual Report. The CPI for all commodities in the U.S. for
each season were obtained from various issues of the Survey of Current
Business and used to deflate the FOB price to give the real deflated


63
FOB price in Florida. The total quantities shipped from Florida were
divided by the total U.S. population to give the per capita quantity
shipped.
Florida Real Daily Wage Rate
The Florida real daily wage rate was obtained by multiplying the
hourly wage rate for field workers during the first week in October of
each year by 8 and then deflating by the CPI for all commodities. The
labor wages for 1966-1981 were obtained from Gutierrez; the daily wage
rate for 1983 was obtained from Buckley et al. The wage rates for
1964 and 1965 were obtained from U.S.D.A. Farm Labor, 1964, 1965.
There were no data available for 1982 and 1984. The missing values
were filled by running a simple regression of the log of data against
time with intercept (i.e., lnWRt a + rt). The estimates of a and r
were then used to forecast the missing values of the wage rate.
Real Agricultural Interest Rate for Florida
The interest rate charged tomato farmers was represented by the
interest on non-real-estate debt which was obtained from U.S.D.A.
Statistical Bulletin No.740. The interest rate was then deflated by
the U.S., CPI for all commodities to give the real interest rate.
Florida Growing Season Weather
The weather variable for Florida was represented by the number of
days below freezing at Homestead. The largest production of fresh-
winter tomatoes in Florida occurs in the Dade County area and the area
around Immokalee in Collier County. Since winter production is
concentrated in the southern part of the state, freezing temperatures
in Homestead should provide a good proxy for cold weather in the


64
growing area. Number of days below freezing point was selected because
of the devastating effect of freezing temperatures on the tomato plant
and consequently on the production. The Florida weather data were
obtained from various issues of the U.S. Weather Bureau, Climatological
Data, Monthly and Annual Summary Florida Section.
Research Expenditures
Much of the preharvest agricultural research expenditures data were
obtained from the Current Research Information Service (CRIS) of the
U.S.D.A. Preharvest research expenditures for 1953-1964 and 1970-1984
were obtained from this source. Some preharvest research expenditures
for 1976 to 1984 were obtained from the Florida Tomato Exchange.
Postharvest research expenditures for 1970-1984 were obtained from CRIS
and the Florida Tomato Exchange. Some postharvest research expenditures
on fresh tomatoes in Florida were obtained from Inventory of
Agricultural Research of SAES Forestry Schools, Research Agencies of
the U.S.D.A. Vol.II (Tables II,III,IV) 1966-1983 and from various
issues of Funds for Research at State Agricultural Experiment Stations
CSRS-U.S.D.A. Postharvest research expenditures from these sources
were determined by looking at the Research Problem Area Classification
Code. There are different classification codes for different research
activities ( i.e., production, breeding, efficient marketing activity,
quality improvement and consumer acceptance activities). The missing
values for the preharvest research expenditures from 1965-1969 were
obtained by a simple regression of the log of the current expenditures
on the log of the expenditures lagged one period with no intercept


65
(i.e., lnR&Dt = alnR&Dt_i). The estimate was then used to forecast the
missing values.
The research expenditure figure for 1965 was obtained from this
simple model; the model was run again with the 1965 estimated missing
value as a new data point and a new estimate for the coefficient (a)
was obtained and used to forecast the value for 1966. This stepwise
procedure was repeated until all the missing values were obtained. The
figure for 1970 was forecasted by this procedure and the forecasted
value was compared with the actual figure for 1970. The ratio of the
actual to the forecasted value was about 0.40 which was then used to
scale all the forecasted values for the other years.
The preharvest research data for 1953 to 1964 were for vegetable
research in general and not specifically for tomatoes. The amount
devoted to tomato research was obtained by multiplying the research
expenditures times the ratio of the total value of fresh-winter
tomatoes to the total value of vegetables produced in Florida. This
ratio was crosschecked by finding the proportion of fresh-tomato-
related research projects among all vegetable research projects in
Florida, from various issues of the Florida Agricultural Experiment
Station, Annual Reports. This proportion was almost the same as the
ratio of the value of fresh tomatoes to the value of all vegetables in
Florida (about 0.35). The research expenditures for 1961-1964 showed
a big jump in spending between this period and the earlier period
(1953-1960). Considerable effort was made to attempt to smooth the
data across this apparent flaw in the data. However, the resulting
smoothed data gave results with several estimated coefficients


66
inconsistent in sign. As a consequence, the original preharvest
research data were used in the final estimation of the model.
The preharvest and postharvest research expenditure data series
were then deflated with the U.S. agricultural research deflator series
constructed from factor level price indices weighted with time varying
weights which capture the shifting factor mix of research spending by
the State Agricultural Experiment Stations (SAES) (Pardey et al.).
Many analytical studies of agricultural research have deflated the R&D
expenditure figures with single-price indices based on the implicit GDP
deflator and have assumed all of the appropriate price series move as
one. Generally, total research expenditures have been deflated by a
salaries-based price series; some have used the federal implicit GDP
deflator and others the CPI
Most of the other commonly-used deflator series use two expenditure
categories, labor and nonlabor, with either fixed or variable index
weights. These single factor price index deflators have tended to
overstate or understate the amount of research expenditures. In this
study the deflator used was a four factor research deflator constructed
by Pardey et al. The four factors in the deflator are: labor expenses,
operating expenses, expenditures for land and building, and
expenditures for equipment. The deflated research expenditures were
used to construct series of varying lag lengths. These series were
then used in the trial runs of the model to specify the length of lags.'
Total Quantity Supplied at U.S, Retail Level
The total supply at the U.S. retail level was obtained by summing
the quantity shipped from Florida and Mexico and then dividing by the


67
total U.S. population as of July 1 of each year to get the per capita
retail supply. Data on marketing losses were not available to adjust
the series.
Real Retail Price (RP)
The retail price for fresh-winter tomatoes in cents per pound were
obtained from U.S.D.A. Fresh Market Vegetables Statistics 1949-80 and
later issues. Prices were deflated with the U.S., CPI for all
commodities.
Retail Price of Substitutes
The retail price in cents per pound for green peppers, which were
treated as a substitute for fresh tomatoes, was obtained from U.S.D.A.,
ERS-Statistical Bulletin No.688, and U.S.D.A., Fresh Market Vegetable
Statistics, 1949-80, and later issues. These prices were also deflated
with the U.S., CPI for all commodities.
Real Cost Index for Fresh Fruits and Vegetables
The retail cost index for marketing fresh fruits and vegetables was
obtained from U.S.D.A. Statistical Bulletin No.713.
Data on expenditures for advertisement and promotion of fresh-winter
tomatoes were obtained from the Florida Tomato Exchange. U.S.
disposable income data were obtained from the U.S. Department of
Commerce, Survey of Current Business. These were deflated with the
U.S., CPI and the total U.S. population to estimate the real per
capita disposable income.
Florida Fertilizer Price
Florida fertilizer prices in dollars per ton were obtained from
U.S.D.A., Statistical Bulletin No.750.


68
Weighted Average Price at the Grower Level
A weighted average price series at the grower level was constructed
by multiplying the quantities shipped from Florida and Mexico by the
respective FOB prices for each season and then divided by the total
quantity shipped from the two supply areas. This weighted average
price was then subtracted from the retail price, which is also a
weighted average price, to obtain the marketing margin.
The final data set used is presented in appendix A.


CHAPTER V
EMPIRICAL RESULTS OF THE MODEL
The model was fitted to data for the 1964/65-1983/84 seasons
(December-June). Table 1 shows the 2SLS parameter estimates for the
model in linear form. These were used in the final analysis and are
discussed below. N2SLS parameter estimates for the model in log form
and 3SLS parameter estimates for the model in linear form are reported
in appendix B, tables B.l and B.2 respectively. There were more
instruments than number of observations for the first stage of the
N2SLS estimation and therefore there was a linear dependency in the
estimation of the instrument for the quantity supplied at retail
level.
Florida Shipping-Point Supply
Parameter estimates for the Florida shipping-point supply all
carry the signs suggested by theory-'-, except the deflated fertilizer
price (deflated with the interest rate charged farmers in Florida),
which was positive instead of negative. The deflated wage rate had a
negative impact on supply as expected. The deflated current price
parameter was positive (1.055), i.e., an elasticity of supply with
respect to deflated current price of 0.698. The deflated lagged price
parameter estimate was 0.037, which translated into a long-run
l The expected sign is shown in parentheses by the name of the
variables in column 2 of Table 1.
69


70
Table 1 2SLS Structural Parameter Estimates3
Equation
Variable
Coeff.
t-
Elast.
Estim.
Stat.
Florida Shipping
Point Supply
intercept(- +)
0.802
0.434
(per capita
deflated*3
quantity)
wage rate(-)
-0.499
-0.291
-0.315
deflated
fertilizer
price(-)
0.211
1.901
0.735
number of
-0.166
-1.097
days below
freezing
point in
Homestead(-)
deflated
current price(+)
1.055
1.167
0.698
deflated
lagged price(+)
0.037
0.065
0.024
per capita
quantity from
Mexico(-)
-0.935
-3.117
-0.752
preharvest
R&D expenditure
t(+)
0.392
1.125
t-l(+)
0.713
1.125
t-2(+)
0.963
1.125
t-3(+)
1.141
1.125
t-4(+)
1.248
1.125
t-5(+)
1.284
1.125
t-6(+)
1.248
1.125
t-7(+)
1.141
1.125
t-8(+)
0.963
1.125
t-9(+)
0.713
1.125
t-10(+)
0.392
1.125
Mexican Export
intercept(+)
15.653
1.239
Supply(per
capita
deflated
5.131
1.124
0.597
quantity)
wage rate(-)
deflated
0.167
0.056
0.016
fertilizer price(-)


71
Table 1 cont.
deflated
current price(+)
1.153
2.782
0.948
average growing-
season tempera
ture (+)
-0.092
-0.590
per capita quant,
from Florida(-)
-0.924
-3.265
-1.148
per capita
national income(-)
-0.227
-0.763
-0.357
preharvest
R&D expenditures
t(+)
-0.451
-1.168
t-l<+)
-0.826
-1.168
t-2(+)
-1.127
-1.168
t-3(+)
-1.352
-1.168
t-4(+)
-1.502
-1.168
t-5(+)
-1.578
-1.168
t-6(+)
-1.578
-1.168
t-7(+)
-1.502
-1.168
t-8(+)
-1.352
-1.168
t-9(+)
-1.127
-1.168
t-10(+)
-0.826
-1.168
t-ll(+)
-0.451
-1.168
postharvest
R&D expenditures
t(+)
-1.180
-1.842
t-l(+)
-2.163
-1.842
t-2(+)
-2.950
-1.842
t-3(+)
-3.539
-1.842
t-4(+)
-3.933
-1.842
t-5(+)
-4.129
-1.842
t-6(+)
-4.129
-1.842
t-7(+)
-3.933
-1.842
t-8(+)
-3.539
-1.842
t-9(+)
-2.950
-1.842
t-10(+)
-2.163
-1.842
t-ll(+)
-1.180
-1.842
Marketing Margin intercept(- +)
Equation
(marketing per capita
margin) quantity at
retail(-)
marketing
cost index(+)
-6.118 -0.297
-0.074 -0.071
-0.114 -0.033


72
Table 1 cont.
preharvest
R&D expenditures
t(+)
3.747
1.446
t-l(+)
6.870
1.446
t-2(+)
9.369
1.446
t-3<+)
1.242
1.446
t-4(+)
12.492
1.446
t-5(+)
13.116
1.446
t-6(+)
13.116
1.446
c-7(+)
12.492
1.446
t-8(+)
11.242
1.446
t-9(+)
9.369
1.446
t-10(+)
6.870
1.446
t-ll(+)
3.747
1.446
postharvest
R&D expenditures
t(+)
3.796
2.544
t-l(+)
6.960
2.544
t-2(+)
9.491
2.544
t-3(+)
11.389
2.544
t-4(+)
12.654
2.544
t-5(+)
13.287
2.544
t-6(+)
13.287
2.544
t-7(+)
12.654
2.544
t-8(+)
11.389
2.544
t-9(+)
9.491
2.544
t-10(+)
6.960
2.544
t-ll(+)
3.796
2.544
U.S. Retail
intercept(+-)
5.981
0.407
Demand(average
US retail
per capita
-1.768
-1.011
price)
quantity at
retail(-)
price of
0.115
0.732
green peppers
(substitute)(+)
per capita
US disposable
income(+)
0.101
0.127
expenditures
on advertising
to promote
tomato
consumption(+)
0.297
1.151
-6.320
0.777
0.052


73
Table 1 cont.
preharvest
R&D expenditures
t( + )
3.956
4.895
t-l(+)
7.253
4.895
t-2(+)
9.890
4.895
t-3(+)
11.868
4.895
t-4(+)
13.187
4.895
t-5(+)
13.846
4.895
t-6(+)
13.846
4.895
t-7(+)
13.187
4.895
t-8(+)
11.868
4.895
t-9(+)
9.890
4.895
t-10(+)
7.253
4.895
t-ll(+)
3.956
4.895
postharvest
R&D expenditures
t( + )
4.351
4.000
t-l(+)
7.976
4.000
t- 2(+)
10.877
4.000
t-3(+)
13.052
4.000
t-4(+)
14.502
4.000
t-5(+)
15.227
4.000
t-6(+)
15.227
4.000
t-7(+)
14.502
4.000
t-8(+)
13.052
4.000
t-9(+)
10.877
4.000
t-10(+)
7.976
4.000
t-ll(+)
4.351
4.000
a. Expected signs of coefficients are indicated by the
variables.
b. Deflated with the interest rate.


74
elasticity of supply with respect to price of 0.722. These results
suggest that current price carries more weight than the lagged price
in supply decisions. From an economic standpoint, this suggests that
supply response to price occurs primarily through yield variations
rather than planting decisions in the case of fresh-winter tomatoes.
This result can be interpreted as the revisional price expectation,
where price expectations formed at the beginning of the production
period (lagged price is beginning period expected price) do not affect
shipping decisions considerably. As more information becomes
available price expectations are being revised, resulting in current
price carrying most of the weight regarding supply decisions. The
weights to be attached to the beginning period expected price and the
current price were not restricted to the interval (0,1) before
estimation, as in Taylor and Shonkwiler, it could be any value on the
real positive number system, i.e., an affine combination of the
planting time and marketing time price.
The preharvest research variable entered the Florida supply
equation as a 10-year second-degree Almon polynomial-distributed lag.
Coefficient estimates had the right signs, with the impact rising to a
peak and then declining. The elasticity of the total undiscounted
impact was (0.277). Increases in the per capita quantity shipped from
Mexico were associated with a decline in the Florida supply as
expected. A 1 percent increase in per capita quantity shipped from
Mexico was associated with a 0.752 percent decline in the per capita
quantity shipped from Florida.


75
The weather was proxied by the number of days below freezing in
Homestead, the major winter-tomato producing area. Thus, this
variable was expected to have a negative impact on supply since tomato
plants are quite sensitive to freezing temperatures.
Mexican Export Supply
The Mexican supply specified as an excess supply relationship has
as arguments production input prices, output price, demand-related
variables (e.g., per capita income), per capita quantity shipped from
Florida, and the preharvest and postharvest R&D expenditures on tomato
research in Florida. The FOB price in Florida and Mexico were highly
correlated, as were the U.S. and the Mexican agricultural interest
rates. As a consequence, the Florida FOB price and the U.S. interest
rate were used in the Mexican export supply equation. As in the
Florida supply equation, output price and input prices on the supply
side of the Mexican market were relative to the interest rate. The
parameter estimates for the daily wage and fertilizer price, both
relative to the interest rate, were positive 5.131 and 0.167,
respectively. Since interest costs are an important component of
cost, it is not clear whether these signs are inconsistent; however,
the estimate of the coefficient for the wage rate is opposite in sign
to that estimated in the Florida supply equation. The impact of
temperature on supply was negative (-0.092), which was inconsistent
because rises in temperature between the ranges of 65F and 85F are
conducive to tomato production. The average temperatures were
observed in the low 60s. The tomato plant does best in moderately
dry areas with temperatures ranging between 65F and 85F. Foliage


76
diseases are induced by high temperature coupled with humidity (Ware
and McCollum) but high temperatures were not observed in Mexico.
The parameter estimate for the supply price was 1.153 with a
small standard error. The lagged price was not specified to enter the
supply relationship because the Mexican government imposes
restrictions on acreage planted, so beginning period price
expectations of farmers is not an important factor in planting
decisions. The parameter estimate for per capita national income was
negative (0.227) as expected, because an increase in per capita
national income will result in increased domestic consumption and,
since tomato is a normal good, less for export. The parameter
estimate for the per capita quantity shipped from Florida was negative
(-0.924), with a small standard error, and consistent with theory.
Since the quantities shipped from Mexico are to meet the U.S. excess
demand, they were expected to be negatively associated with quantities
supplied from Florida.
The estimated coefficients of the preharvest and postharvest U.S.
based R&D expenditure variables in the Mexican export supply equation
represented by an 11-year second-degree Almon polynomial-distributed
lag were negative. Implying the U.S. based R&D investments had
negative impact on export supply, which is inconsistent with
expectation. Considering the fact that much of the technology
employed in the production and marketing of fresh-winter tomatoes in
Mexico comes from the U.S., one would expect a positive impact on
Mexican supply and consequently on the excess supply. However, since


77
Mexico controls shipments based on quantities shipped from Florida,
the effect may be theoretically indeterminant.
Marketing Margin
The parameter estimates for the marketing margin equation all had
the expected signs except the marketing cost index represented by the
price of marketing services. Tomek and Robinson define the marketing
margin as the price of a collection of marketing services, which is
the outcome of the demand for and the supply of such services. Hence
higher input prices for a service ceteris paribus would result in a
decrease in supply and a higher margin. It is therefore expected that
the estimate for the parameter for the marketing cost index should be
positive in sign. The estimated coefficient was (-0.114) with a large
standard error (3.480), indicating the marketing cost index did not
significantly affect the marketing margin. The per capita quantity
shipped had an estimated parameter of (-0.0743), with a large standard
error (1.051), indicating nonsignificant impact on the marketing
margin. From a conceptual point of view, this sign may be positive or
negative.
Preharvest R&D investments increased supply at the farm level
thereby increasing demand for marketing services to move the produce
to the retail level. This will increase the price of the marketing
services and thus the marketing margin. There should therefore be a
positive relationship between the preharvest R&D expenditure variable
and the marketing margin. The parameter estimate for this variable
was positive (0.312), with a standard error of (0.216). The
postharvest R&D parameter estimate in the margin equation was positive


78
(0.316), with a standard error of (0.124). This sign is consistent
with expectations since it was believed that research in postharvest
activities increases the number and level of services in the marketing
channels and hence the margin.
U.S, Retail Demand
The retail demand parameter estimates all had the expected signs.
The estimated own price elasticity of demand was (-6.32) and falls
within the upper range of elasticities obtained in previous studies.
The demand price elasticities in previous studies varied from -0.181
(Hamming and Mittelhammer) to -0.79 (Shonkwiler and Emerson) to -1.07
and -3.25 to -5.5 (Simons and Pomareda) and -6.4 (Firch and Young).
As in Shonkwiler and Emerson, the price of substitutes was
proxied by the price of green peppers and this variable had an
estimated coefficient of (0.115) or an elasticity of (0.777). A
positive elasticity of less than 1 between the retail price of fresh
tomatoes and its substitute, green peppers, is consistent with Buse's
conclusion that the elasticity of the price of good "j" with respect
to changes in the price of good "i" is usually positive and less than
1 for substitute commodities. The elasticity of demand with respect
to real per capita disposable income was positive but near zero
(0.052). Fresh tomatoes are a normal good and one would expect its
demand to increase as income increases, though one would expect a
stronger effect on retail demand than was estimated. The preharvest
R&D expenditures variable, which was represented by an 11-year second-
degree Almon polynomial-distributed lag in the retail demand, had a
positive impact which is consistent with the earlier argument that


79
some preharvest research will affect the demand since breeding
programs and cultural practices to produce tomato fruit of good
quality, taste and longer shelf-life would be expected over time to
increase the demand. The postharvest R&D expenditure variable, which
is geared toward preservation and improving the quality in the
marketing process, would impact demand positively. This variable was
also represented by an 11-year second-degree Almon polynomial-
distributed lag and the parameter estimates all had the correct signs.
Lastly, the impact of advertisement and promotional activity on
the retail demand of fresh tomatoes was positive (0.279) as expected
because advertisement and promotion are supposed to increase the
demand for fresh tomatoes. However, the estimated effect was quite
weak.
Measuring Returns to Research
Estimates of parameters reported in Table 1 were used to define
supply and demand functions at the grower and retail levels. The
lagged effects of the preharvest and postharvest R&D variables were
discounted at a real rate of 4 percent to obtain the present value of
the impact of the research expenditures. The discounted, lagged
effects of the research expenditures are reported in table 2. The sum
of the discounted, lagged effects were then used in place of the
undiscounted research expenditure variable coefficient estimates in
the model.
The Florida shipping-point supply function was added to the
Mexican excess supply function to give the grower level supply
function. The supply of fresh-winter tomatoes at the retail level is


80
Table 2. Discounted Values of R&D1 and R&D2 Impacts on the Florida
Supply, Mexican Export Supply, Marketing Margin and U.S.
Retail Demand (at a real discount rate of 4 percent)
R&D1
R&D2
Year
FL
Mex.
Market.
U.
S.
Mex.
Market
U.
S.
Margin
Margin
t
0
.392
-0.
.451
3.
.748
3.
,956
-1
.180
3.
.796
4.
,351
t-1
0
.686
-0.
.794
6.
.606
6.
.974
-2
.080
6.
.692
7.
,669
t-2
0
.890
-1.
.042
8.
, 662
9.
, 144
-2
.727
8.
.775
10.
.056
t-3
1
.014
-1.
.202
9.
.995
10.
.551
-3
.147
10.
.124
11.
.603
t-4
1
.067
-1.
.284
10.
,678
11.
,272
-3
.362
10.
.817
12.
.396
t-5
1
.055
-1.
.297
10.
.781
11.
,381
-3
.394
10.
.921
12.
.516
t-6
0
.986
-1,
.247
10.
.366
10.
,943
-3
.264
10.
.501
12.
.034
t-7
0
.867
-1.
.142
9.
.493
10.
,021
-2
.989
9.
.616
11.
.020
t-8
0
.704
-0.
.988
8.
.215
8,
.672
-2
.586
8.
.322
9.
.537
t-9
0
.501
-0.
.792
6.
,582
6.
.949
-2
.072
6.
.668
7.
.642
t-10
0
.265
-0.
.558
4,
,641
4.
.900
-1
.461
4.
,702
5.
.388
t-11
-0.
.293
2.
.434
2.
,570
-0
.766
2.
,466
2.
.826
Sum
8
.428
-11.
.089
92.
.200
97.
,331
-29
.027
93.
.398
107.
.038


81
the supply at the grower level plus the marketing cost expended to
move them to the retail level; and the marketing cost is the marketing
margin. Thus the retail level supply was obtained by adding the
marketing margin to the grower level supply.
There was a very high correlation between the Florida FOB price
(which was used in both the Florida supply and Mexican excess supply
equations) and the weighted average grower level price. A simple
linear regression of the Florida FOB price on the weighted average
price was run without an intercept, resulting in a coefficient
estimate of 0.9282 with a t-statistic of 33.034 for the weighted
average price. This simple linear relationship between the Florida
FOB price and the weighted average price was substituted for the FOB
price in the grower level supply equation. This resulted in a
weighted average price in both the marketing margin equation and the
grower level supply equation, i.e., the same grower level price in
both equations. When the marketing margin was then added to the
grower level supply the weighted average price dropped out, leaving
the retail price in the retail supply equation.
The demand at the grower level is a derived demand for the raw
product less the demand for marketing services; thus the marketing
margin was subtracted from the retail demand for fresh-winter tomatoes
to give the grower level demand. Holding all other variables in the
model constant at their means, except the weighted average grower
level price, the total quantity supplied at the grower level and the
preharvest and postharvest research variables, the grower level and
retail level supply and demand equations were:


82
WAP -7.8123 + O.9449PCQSUSR + 0.5566RD! + 13.1046RD2 (5.1)
(grower level supply)
WAP 17.7811 1.6941PCQSUSR + 5.1313RD]_ + 13.6401RD2 (5.2)
(grower level demand)
RP = -14.14 + 0.8706PCQSUSR + 92.7562RDX + 106.5028RD2 (5.3)
(retail level supply)
RP = 11.4534 1.7684PCQSUSR + 97.3309RDX + 1007.0383RD2 (5.4)
(retail level demand)
Equations (5.1) to (5.4) were used to derive the consumers' and
producers' surplus relationships at the grower and retail levels in
the U.S. These surplus measures were used to estimate the benefits of
research on tomatoes.
The approach used was to estimate the effect of changing the
level of R&D expenditures and then letting the full effect work itself
out.
Grower Level
Equations (5.1) and (5.2) were solved for the equilibrium
quantity and price at the grower level as:
PCQSUSRE 9.6981 + 1.7335RDX + 0.2029RD2 and (5.5)
WAPe 1.3515 + 2.1946RDX + 13.2963RD2 (5.6)
Following the procedure in (3.8) the consumers and producers' surplus
relationships at the grower level were obtained as follows:
CSG 79.668 + 28.4801RDX + 3.3339RD2 + 2.5453RDX2 +
0.0349RD22 + 0.5959RD]RD2 (5.7)
PSG = 44.4357 + 15.8854RDX + 1.8588RD2 + 1.4197RDX2 +
0.0194RD22 + 0.3323RD1RD2
(5.8)


83
Differentiating (5.7) and 5.8) with respect to R&D^ and R&D2 we obtain
the change in the consumers' and producers' surplus at the grower
level per unit change in these variables.2 These derivatives were
evaluated at the mean values of the R&D^ and R&D2 expenditures for the
period 1965-84.
Retail Level
Following a procedure similar to that used for the grower level,
the retail level consumers' and producers' surplus relationships were
obtained by first solving equations (5.3) and (5.4) for the
equilibrium quantity and price as follows:
PSQSUSRE 9.6981 + 1.7335RD]_ + 0.2029RD2 (5.9)
RPe -5.6969 + 94.2667RD! + 106.681RD2; (5.10)
and, then, by using equation (3.8) the consumers' and producers'
surplus relationships at the retail level were obtained as:
CSR = 83.1631 + 29.7168RD! + 3.4652RD2 + 2.6547R!2 +
0.0361RD22 + 0.6191RD]RD2 (5.11)
PSR 40.9405 + 14.6488RD! + 1.7284RD2 + 1.3104RD2 +
0.0182RD22 + 0.3092RD^RD2. (5.12)
By differentiating (5.11) and (5.12) with respect to R&D^ and R&D2 the
per unit change in the consumers' and producers' surplus with respect
to these variables at the retail level was obtained. These first
derivatives were then evaluated at the means of the research
expenditures.
2
1 unit for R&Di = $10 million and 1 unit for R&D2 = $1 million.


84
The rate of change of the surpluses with respect to a one unit
O
change in R&D expenditures in cents per capita^ are reported in
Table 3. Estimates of the marginal rates of returns to R&D
investments in the fresh-winter tomato industry are reported in
Table 4. These were obtained by adjusting the values in Table 3 by
the U.S. population and units of research expenditures ($10 million
in the case of preharvest and $1 million in the case of postharvest).
Using estimates at the retail level, marginal rates of return to
R&D investments indicate that for every additional dollar of
investment made in preharvest R&D on tomato research, a gross return
of $10.85 was realized by society, of which $3.58 or 33 percent
accrued to distributors and growers, and $7.27 or 67 percent to U.S.
consumers. Similarly, an additional unit of investment made in
postharvest R&D on tomato research yielded a gross return of $12.70
to society (table 4). The percentage distribution of these returns
between producers and consumers was nearly identical to that for
preharvest research. Thus, benefits from both preharvest and
postharvest research investments on fresh tomatoes were estimated to
accrue mostly to consumers as a group. Economic theory would suggest
that to maximize surplus, additional dollars of research should be
added until the present value of marginal return was $1. The
estimates indicate that additional dollars of both preharvest and
postharvest research are needed to reach the social optimum.
The grower and retail level prices were in cents per pound and
the quantities were measured on per capita basis, so the
surpluses were in cents per capita.
3


85
Table 3. Rate of Change of Surplus with Respect to a One Unit Change
in R&D Expenditures at the Retail Level (cents per capita)
Preharvest
R&D (1 unit-
-SI mil.)
Postharvest
R&D (1 unit=$l mil.)
Change
Change
Change
Change
Change
Change
in
in
in
in
in
in
total
producer
consumer
total
producer
consumer
surplus
surplus
surplus
surplus
surplus
surplus
4.52042 1.4926 3.0278 5.2918 1.7611 3.5306


Table 4.
Estimates of Marginal Rates of Returns to R&D Investments
in the Fresh-Winter Tomato Industry at the Retail Level (in dollars)
PVa of returns per $1.00 PV of returns per $1.00
preharvest R&D investments postharvest R&D investments
PVa of
returns to
society
PV of
returns to
producers
PV of
returns to
consumers
PV of
returns to
societv
PV of
returns to
producers
PV of
returns to
consumers
10.85
3.58
CVJ
12.70
4.23
8.47
a. Present Value
oo


87
Estimates of the net present value of average rates of returns to
preharvest and postharvest research investments on fresh winter
tomatoes are presented in table 5. They show higher rates of return
on average to postharvest research than preharvest and indicate that
society is being very well rewarded for its research investments in
both preharvest and postharvest research. These values were obtained
by evaluating equations (5.11) and (5.12) at the means of the
research expenditures and adjusting for population and the units of
research expenditures.
It had been hoped to measure the impact on Mexican producers
(spillover effects) of U.S.-based preharvest and postharvest research
investments, however, the model did not permit separate estimation of
benefits on the Mexican side.


Table 5.
Estimates of NPV*5 Average Rates of Returns to R&D Investments
in the Fresh-Winter Tomato Industry at the Retail Level (in dollars)
Preharvest R&D Investments
Postharvest
R&D Investments
NPV of
average
returns to
societv
NPV of
average
returns to
producers
NPV of
average
returns to
consumers
NPV of
average
returns to
societv
NPV of
average
returns to
producers
NPV of
average
returns to
consumers
348.70
114.70
234.00
2,055.46
677.79
1,377.67
b. Net Present Value
CO
CO


CHAPTER VI
SUMMARY, CONCLUSION AND RECOMMENDATIONS
Summary
Considerable effort has been devoted in the past to evaluating
returns to R&D investments in agriculture and manufacturing. Most of
these studies, however, have been directed at production level R&D
investments, with little emphasis on postharvest and marketing-related
R&D investments. Lately there has been an increased interest in
measuring the returns to postharvest R&D investments.
The studies that have been done thus far have largely ignored the
impact of both preharvest and postharvest R&D expenditures on the
demand side. They tend to focus on the impact on productivity and
cost saving. Producers' and consumers' welfare analyses have been
done assuming shifts in the supply curve and supply elasticities
resulting from the R&D-induced productivity and cost changes. The
supply shift is the net effect of a combination of factors, including
R&D expenditures. Thus, by attributing the shift in supply only to
R&D expenditures leads to overestimation of the benefits of research
investments. The impact of R&D investments needs to be separated from
that of other factors. Also, the returns have often been expressed in
terms of internal rates of return, which does not reveal much about
the distribution of benefits. Impacts on producers and consumers
provide useful information to policymakers.
89


90
This study has attempted to address these problems by specifying
a simultaneous equations model which encompasses a shipping-point
supply equation for tomato producers in Florida, a Mexican export
supply equation, a marketing margin equation and the U.S. retail
demand equation for fresh-winter tomatoes. Both preharvest and
postharvest R&D expenditures entered the retail demand equation, the
marketing margin equation and the Mexican export supply equation.
This specification provides a basis for measuring the impact of
research expenditures on demand. It also enabled estimation of the
separate impact of research expenditures on supply and demand.
Supply and demand elasticities were also estimated as well as the
nature of shift in the supply curve, in the analysis of consumers' and
producers' welfare.
The model was estimated in linear form by 2SLS, by nonlinear 2SLS
in log form and by 3SLS. The 2SLS estimates were used in estimating
the benefits from pre and postharvest R&D. The research expenditures
entered the estimated model as 10- and 11-year Almon polynomial-
distributed lags. The supply price was a combination of current and
lagged prices reflecting the sequential nature of supply decisions,
i.e., the decision to plant and then the decision to harvest and
market. The model was fitted to seasonal (December-June) winter-
tomato data from Florida and Mexico from 1965-1984. The parameter
estimates were then used to define both grower- and retail-level
supply and demand equations. The grower-level supply was the sum of
the Florida and the Mexican export supply equations; the grower-level
demand was obtained by subtracting the marketing margin from the


Full Text
EVALUATING RETURNS TO POSTHARVEST
RESEARCH AND DEVELOPMENT
IN THE FRESH-WINTER TOMATO INDUSTRY
By
JAMES S. ANSOANUUR
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
1988

ACKNOWLEDGEMENTS
I wholeheartedly express my sincere gratitude to my chairman, Dr.
Max R. Langham, for his support, guidance and patience during the
course of my doctoral studies and the preparation of my dissertation.
At times when it was rough, his experienced and timely counsel put me
at ease. He has been my mentor, treated me like a godson and exposed
me to new knowledge, leaving me more confident as a professional
economist as I move on.
I would also like to thank Drs. Robert Emerson, Tim Taylor and
John VanSickle, the other members of my supervisory committee, for
their patience, suggestions and guidance during the course of the
preparation of my dissertation. I wish to also thank Audrey Sharp and
Lavon Mikell for their help in typing some portions of the text,
particularly the tables. I also wish to acknowledge the USDA, which
supported this research under IR-6 and the USDA/CSRS Cooperative
Agreement No. 58-32R6-2-143 entitled "Evaluation of Agricultural
Marketing Research."
Finally I would like to express my deepest appreciation to my
wife, Elizabeth, and children, Frieda, Mwitse and George, for their
support, understanding and endurance during the course of my doctoral
studies and particularly when I was preparing this dissertation.
ii

TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS ii
LIST OF TABLES vi
LIST OF FIGURES vii
ABSTRACT viii
CHAPTERS
I INTRODUCTION 1
Problem Statement 3
Objectives 5
Production and Marketing of Fresh-Winter Tomatoes
in Florida and Mexico 5
Tomato Production in
Florida 6
Tomato Production in Sinaloa, Mexico 10
Mexico's and Florida's Share of the Winter-
Tomato Market 13
Marketing Channels 13
Fresh-Market Winter-Tomato Research 18
IILITERATURE REVIEW , 23
Consumers' and Producers' Surplus (CS-PS)
Approach 24
Econometric Method 29
III THEORETICAL FRAMEWORK 36
Motivation for a Simultaneous-Equations
Approach 36
Model Specification 37
Florida Supply 43
Mexican Export Supply 44
Marketing Margin Between the Grower
Level and the U.S. Retail Supply 46
iii

Demand in the U.S. Market 46
Consumers' and Producers' Surplus
Analysis 47
IV MODEL ESTIMATED 49
Modeling Expectation 49
The Rational Expectations Model 49
Naive Price Expectations Model 50
Extrapolative Expectations Model 50
Adaptive Expectations Model 51
Revisional Price Expectations Model 52
Estimates of the Model Parameters 54
Estimation Method 59
Data Sources 60
Mexican Growing Season
Temperature 60
Real Agricultural Interest Rate in Mexico 60
Mexican Rural Daily Wage Rate 61
Quantity Shipped from Mexico
and the FOB Price in Mexico 61
U. S . Population 62
Mexican Fertilizer
Price Index 62
Real Per Capita National
Income in Mexico 62
Mexico's Population 62
Per Capita Quantity Shipped from Florida
and FOB Price 62
Florida Real Daily
Wage Rate 63
Real Agricultural Interest Rate
for Florida 63
Florida Growing Season
Weather 63
Research Expenditures 64
Total Quantity Supplied
at U.S. Retail Level 66
Real Retail Price 67
Retail Price of Substitutes 67
Real Cost Index for Fresh
Fruits and Vegetables 67
Florida Fertilizer Price 67
Weighted Average Price at the
Grower Level 68
V EMPIRICAL RESULTS OF THE MODEL 69
Florida Shipping-Point Supply 69
Mexican Export Supply 75
Marketing Margin 77
U.S. Retail Demand 78
iv

Measuring Returns of Research 79
Grower Level 82
Retail Level 83
VI SUMMARY, CONCLUSIONS AND RECOMMENDATIONS 89
Summary 89
Results 91
Conclusions 92
Recommendations 93
Area for Future Emphasis 94
APPENDICES
A DATA USED IN THE MODEL 97
B NL2SLS AND 3SLS PARAMETER ESTIMATES
OF THE MODEL 102
REFERENCES 110
BIOGRAPHICAL SKETCH 120
v

LIST OF TABLES
Table Page
1 2SLS Structural Parameter Estimates 70
2 Discounted Values of R&D1 and R&D2 Impacts on the
Florida Supply, Mexican Export Supply, Marketing
Margin and U.S. Retail Demand (at a discount rate
of 4 percent) 80
3 Rate of Change of Surpluses with Respect
to One Unit Change in R&D Expenditure at
the Retail Level 85
4 Estimates of Marginal Rates of Return to R&D
Investments in the Fresh-Winter
Tomato Industry at the Retail Level 86
5 Estimates of NPV of Average Rates of
Return to R&D Investments in the
Fresh-Winter Tomato Industry 88
A.l Data Set for Fresh-Winter Tomatoes in Florida 97
A. 2 Data Set for Fresh-Winter Tomatoes in Mexico 98
A.3 Data Set for Fresh-Winter Tomatoes in U.S.
Retail Market 99
A.4 Real Research Expenditures on Fresh-Winter
Tomatoes in Florida 100
B.l NL2SLS Structural Parameter Estimates 102
B.2 3SLS Structural Parameter Estimates 106
vi

LIST OF FIGURES
Figure Page
1 Major Growing Areas for Fresh-Winter Vegetables
in Florida 7
2 Growing Areas for Fresh-Winter Vegetables
in Sinaloa, Mexico 11
3 Marketing Channels for Florida Fresh
Vegetables from Grower to U.S. Consumer 15
4 Marketing Channels for Mexican Fresh
Vegetables from Grower to U.S. Consumer 17
5 Shift in Supply Due to Adoption of New
Improved Input 25
vii

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
EVALUATING RETURNS TO POSTHARVEST
RESEARCH AND DEVELOPMENT
IN THE FRESH-WINTER TOMATO INDUSTRY
By
James S. Ansoanuur
December, 1988
Chairman: Dr. Max R. Langham
Major Department: Food and Resource Economics Department
Evaluation of agricultural research has generally focused on the
effects of biological or technological improvements in production;
however, technological advancements in food and fiber processing,
quality improvements and marketing are often integral factors
affecting efficient utilization of perishable food resources, improved
marketing methods and demand shifts.
The most widely used econometric methods of evaluating returns to
agricultural research have generally focused on the impact on supply
and cost reduction. Furthermore, the returns are often expressed in
terms of internal rate of return, which does not address the
distribution of the returns to the different groups in the economic
viii

system. A model for evaluating returns to postharvest research and
development (R&D) and preharvest marketing-related R&D, which
addresses both the supply and demand impacts of these variables and
also the distribution of the benefits to the different economic
groups, was formulated for the fresh-winter tomato industry.
Using a partial equilibrium, simultaneous-equations approach, a
Florida shipping-point supply equation, a Mexican export supply
equation and a marketing margin equation for fresh-winter tomatoes
were estimated. The estimated coefficients were then used to derive
supply and demand equations at two levels (grower and retail) in the
fresh-winter tomato market. These supply and demand equations were
used to derive consumer surplus (CS) and producer surplus (PS)
relationships as a function of the preharvest and postharvest R&D
expenditures. These CS and PS relationships enabled estimation of the
present value of marginal and average rates of return to preharvest
and postharvest R&D investments to tomato growers and distributors, to
U.S. consumers of fresh-winter tomatoes, and to society as a whole.
The results show that expenditures allocated to postharvest
activity have a very high rate of return at the margin and that
growers and distributors receive an estimated 33 percent of the
returns while consumers receive 67 percent of the returns.
ix

CHAPTER I
INTRODUCTION
Since World War II, impressive growth in productivity has been
recorded in both the agricultural and industrial sectors in the U.S.
This trend emanated from technological breakthroughs in industry and
agriculture in the form of improved and superior factors of
production, improved crop varieties, high-quality livestock breeds,
improved managerial efficiency and organization, and improved channels
of communication and information flows for the efficient utilization
of scarce resources. These improvements in technology are not
accidental but the result of investments in research and development
(R&D) programs.
Estimating the value of research and education has received
considerable attention by economists since the mid-1950s. The results
indicate that under a wide range of circumstances the economic returns
to agricultural research have been high relative to other investments
available to society (Ruttan, 1982, p.237). This research evaluation
has been largely directed towards preharvest or production-oriented
research and little on postharvest research.
Recently, there has been a growing interest in the returns to
postharvest technology and marketing economic research. Work by
Jorgenson et al. (1987, pp.198-200) indicates that growth rates in
total factor productivities of industries associated with providing
1

2
services to agriculture have been slightly negative. With food and
kindred products, and trucking and warehousing, estimates of
productivity growth rates have been less than those for production
agriculture. One hypothesis is that the U.S. has devoted too few
resources (both public and private) to basic research affecting
efficiency in industries associated with providing farm inputs and
food and fiber derived from farm products.
Freebairn et al. have developed a conceptual model which suggests
that there is no reason to believe that research opportunities are
greater or research costs less at the farm production level than at
the marketing level. White and Havlicek, in a study which focused on
farm production, concluded that underfunding of agricultural research
has serious implications on the future cost of food to consumers --
particularly if underfunding (below optimal levels) persists rather
than being made up. If the White and Havlicek conclusions hold for
research beyond the farm gate, and Freebairn et al. are correct,
consumers can expect a trend toward increased relative prices for food
as a consequence of present and past underinvestments in research on
problems beyond the farm gate.
Further studies are needed to confirm or reject these findings
and also assess the contribution of postharvest research and
development efforts in the efficient marketing of farm produce and
other postharvest processes, and the subsequent impact on consumers'
and producers' welfare. This research seeks to augment the knowledge
relating to the returns to postharvest R&D investments. Postharvest
R&D is generally geared toward technological improvements in the

3
packaging, processing, transportation, organizational efficiency and
information flow which are meant to enhance the market power of the
conducting industry. R&D is often an integral factor affecting demand
shifts, marketing costs and strategies, and efficient utilization of
perishable food resources to enhance profits.
Some biological or technological improvements in farm production
techniques do not only impact the production side but may also have a
bearing on the marketability of the produce. For example, genetic
research to develop varieties of tomatoes that will withstand physical
damage, which have disease resistance, and which yield fruit of a
desired size is not only production-oriented but also results in
desirable marketing qualities. The expenditures produce not only the
scientific and technological improvements at the production level, but
also at the marketing level. Such marketing-related attributes of
production-oriented R&D investments need to be recognized in the
evaluation of returns to research investments.
Problem Statement
This research is directed at evaluating the impact of postharvest
and marketing-related preharvest R&D investments on the marketing of
fresh-winter tomatoes from Florida and Mexico in the U.S. domestic
market. It is hoped that the techniques developed in this case study
will be useful for measurement of returns to research in other areas
of the food and fiber marketing channels.
Winter tomatoes were selected because they are an important
winter vegetable crop in the United States and the leading vegetable
export to the U.S. from Mexico, the major foreign supplier of winter

4
vegetables. Because of its climate, Florida is the leading producer
of fresh-winter tomatoes in the U.S. Tomatoes represent approximately
30 percent of the total cash farm value of the Florida vegetable
income and are grown on 11 percent of the total vegetable acreage of
Florida. The hot and dry, desert climate of northwestern Mexico
favors vegetable production in the fall, winter and spring seasons.
Over 30 percent of the total dollar value of vegetables imported from
Mexico is comprised of tomatoes. In 1983-84 the total dollar value of
imports of vegetables from Mexico was $576 million, of which $224
million (38.9 percent) was from tomatoes, 28 percent from cucumbers,
17 percent from peppers, 13 percent from squash, 2 percent from egg¬
plant and 2 percent from green beans (Buckley et al.). The tomatoes
produced and exported to the U.S. must meet U.S. marketing standards
in terms of grade, size, weight, containers and maturity. The impact
of research has been paramount in the tomato industry in Florida.
Several varieties have proved outstanding. Economic and marketing
research have resulted in more efficient and low-cost methods of
handling tomatoes in the marketing channel. What impact do these
developments have on consumer and producer welfare?
The agricultural research facilities in Mexico are not able to
meet the needs of vegetable growers. Consequently the Mexican
industry imports seeds, chemicals, equipment and other technology from
the U.S. (Gutierrez). Some new technology applied in Florida and
California is almost immediately adopted by Mexican growers and new
postharvest technology also benefits Mexican growers since they use
much the same marketing procedures as U.S. growers. Thus U.S. based

5
R&D investments impact the Mexican fresh-winter tomato industry as
well, and this influence needs to be captured in any measurement of
returns to tomato-related research in the U.S.
Objectives
The overall objective of this study is to estimate returns to
research on winter tomatoes -- especially as this research impacts
postharvest processes. Specific objectives are to:
1. Develop an econometric model describing the
interrelationship of several important variables affecting
the fresh-winter tomato industry.
2. Empirically estimate the model using data pertaining to
fresh-winter tomato production and marketing in Florida and
Mexico.
3. Evaluate the distributional effects (consumer and producer
surplus), and the overall returns to postharvest technology
and marketing research.
Production and Marketing of Fresh-Winter
Tomatoes in Florida and Mexico
This section draws on material presented in Buckley et al.;
Bredahl et al.; Gutierrez; Emerson; Froman; and Zepp and Simmons.
Fresh-winter tomatoes for the U.S. market during the winter season,
December through June, come from two major production regions: Florida
in the U.S., and Sinaloa in northwest Mexico. Sinaloa has become the
only major foreign supplier of fresh market tomatoes owing to a
combination of factors, which include: favorable climate; an extensive
irrigation infrastructure; railway lines and good roads connecting

6
producing areas to marketing centers; proximity to western U.S.
markets; and the availability of inexpensive labor. Political factors
also contributed to the decline in other production areas. Cuba was
eliminated by the 1962 U.S. trade embargo. The termination of the
bracero program in 1964 reduced the availability of cheap labor in
the U.S. vegetable-production areas. Financial and technological
resources therefore flowed out of the United States and into Mexico in
response to these events.
Tomato Production in Florida
In Florida, winter tomatoes are the largest vegetable crop in
value, and rank second to citrus in total revenue of all Florida
agricultural commodities. According to Buckley et al.(p,15):
Tomatoes accounted for 34.7 percent of the total value of
all vegetables produced in Florida during the 1983/84
growing season.
Tomato production is concentrated around west-central and east-
central Florida during the fall and spring and moves south to
southwest Florida and around Homestead in Dade County during the
winter (Figure 1).
Tomatoes are planted between the last week in July and the third
week in March and harvested in October and November in the central
areas of the state. Production occurs further south with the approach
of winter. Technological change has played a major role in Florida's
competitive edge over Mexico in the production and marketing of fresh-
winter tomatoes, despite Mexico's lower cost of production.
Florida growers make widespread use of stake and ground culture.
In stake culture, tomato plants are supported upright with stakes and

7
Figure 1. Major Growing Areas for Fresh-Winter Vegetables in Florida.
Source:
Zepp and Simmons (1979).

8
string. West-central and southwest are the principal mature-green,
stake-production areas. Ground tomatoes grow without the benefit of
upright support. Dade County has the largest acreage of ground
tomatoes. The tomatoes are grown under irrigation and mostly over
plastic mulch, an improved technology wherein plastic mulch covers the
soil surface, thereby maintaining uniform soil moisture and
temperature conditions, and aiding in weed control and reducing
fertilizer leaching.
Although some vine-ripe tomatoes are marketed, the majority of
fresh- winter tomatoes produced in Florida are picked and marketed as
mature greens. Mature green tomatoes stay firm longer and have a
prolonged shelf life. Ripening can, however, be quickened with
ethylene gas after packing.
According to Buckley et al., the area planted, the yield, the
production and the total value of tomatoes produced in Florida have
shown an upward trend during the past 15 years. This has been
attributed to the adoption of staked-tomato culture, full-bed plastic
mulch, and improved disease-resistant and high-yielding varieties
developed through research. Some of the improved varieties include
FTE-12, Duke, and Sunny. They are high yielding, and produce fruits
which are much firmer and larger than traditional varieties. Laser
leveling of fields, which provides greater uniformity of soil
moisture, has also contributed to increases in tomato yields. In
order to reduce frost during the winter, most tomato growers have
acquired sprinkler irrigation systems. The tomatoes are either
transplanted or directly seeded mechanically; however, operations

9
such as thinning, pruning, tying plants and harvesting are performed
by hand labor.
During harvest the number of pickings depends on the market and
field conditions and the yield. Buckley et al. contend that when
production is concentrated as a result of widespread use of hybrid
varieties, fewer pickings take place. Fields once picked three to
five times, depending on market and field conditions, are now picked
two to three times.
From the field, tomatoes are sent to the packinghouse, where
they are washed, waxed, sized, artificially ripened if necessary, and
packed mechanically before being sold. The tomatoes are sold by the
packinghouses through brokers or hired salesmen.
The interests of tomato growers in Florida and Mexico are
represented by grower organizations. The Florida Tomato Committee and
the Florida Tomato Exchange support and protect the interests of
Florida tomato growers. The Florida Tomato Committee regulates the
marketing of fresh tomatoes through a federal marketing order, which
requires certain grade and size standards to be maintained during the
marketing season. Tomatoes grown in Florida and all tomatoes imported
are expected to meet these standards. The size, grade, container and
inspection requirements are set by policymakers based on the
recommendations of the Tomato Committee. All tomatoes produced in
Florida and those imported must adhere to these regulations. The
Florida Tomato Exchange, which is a nonprofit cooperative association
of first handlers of fresh tomatoes in Florida, provides collective
action with respect to the orderly marketing and distribution of fresh

10
tomatoes. The Tomato Exchange complements the activities of the
Tomato Committee, with major emphasis placed on production research,
promotion of tomatoes through advertising, legislative activities,
legal aid on items affecting the tomato industry, and items not
covered under the marketing order.
Tomato Production in Sinaloa. Mexico
Tomato production in Sinaloa is mainly for the export market.
However, the domestic market may be used as a secondary or residual
market for quantities and sizes that do not meet export requirements.
Market conditions determine export quantities, with more being
exported when export prices are high and exceed the export marketing
costs. Low prices can result in more being shipped to the domestic
market, fed to livestock or simply discarded.
Figure 2 depicts the major fresh-tomato-producing areas in the
state of Sinaloa, the largest producing area being Culiacan which
produces and ships primarily vine-ripe tomatoes. Planting in Culiacan
takes place in the late fall (September to November), with harvesting
peaking in the winter months of January to March. The Guasave and Los
Mochis areas produce and export roughly half vine-ripe and half mature
greens. Winter production in these areas is limited because of
frequent frost. Thus, production is directed toward two marketing
seasons -- the late fall and early spring markets. Planting time for
the fall crop is in August and September, with harvest in the months
of November and December, while the spring crop is planted during
late February and March, with harvest in the months of May and June.

11
Figure 2. Growing Areas for Fresh-Winter Vegetables in Sinaloa,
Mexico.
Source:
Zepp and Simmons (1979).

12
Advances in vegetable production and harvest and postharvest
technology in the U.S., are imported and adopted by Mexican producers
with the aid of the U.S. importers of their produce. Both staked and
ground-grown tomatoes are produced. Staked tomatoes are primarily
produced around Culiacan; ground-grown tomatoes are produced around
Gausave and Los Mochis. Staked tomatoes are normally harvested as
vine-ripe tomatoes while ground-grown are harvested as mature greens.
Popular varieties such as Sunny and Contessa, as well as much of
the planting media and forms, are imported from the U.S. (Buckley, et
al.).
At harvest time, the number of pickings depends on the market
conditions (i.e., export price vis-a-vis harvesting and export costs).
From the field, tomatoes are sent to the packinghouses, where they are
dumped into a water tank to remove field heat and to clean them. After
cleaning they are waxed, sorted as export quality or domestic quality,
packed by color and size, banded in pallets, and precooled.
Like in Florida, Mexican grower associations support and protect
grower interests with regard to the production and orderly marketing
of tomatoes from Sinaloa. The Union Nacional de Productores de
Hortalizas (UNPH) and the Confederación de Asociaciones Agricolas del
Estado de Sinaloa (CAADES) are two associations that govern tomato
production in Sinaloa. They recommend maximum planting acreages,
establish regulations governing types of containers used in shipping,
determine quality standards for exports, and issue export permits
based on acreage allotments. They adjust quality standards according
to prevailing U.S. prices. These regulations are intended to avoid

13
overproduction and low export prices (Froman). The acreages allotted
for planting are strictly enforced through the allocation of water by
the Agricultural Department. Excess planting may result in a
reduction in water supply, and excess export production may result in
cancellation of the export license.
In general, lower than acceptable prices result in stricter
quality requirements and, if this does not suffice to raise prices,
smaller sizes are restricted. Also, shipment of tomatoes to the U.S
market through Nogales is determined by maturity and to the rate of
movement. If movement is slow, the more mature tomatoes are
restricted. An inspection system at Nogales enforces the
restrictions. Certificates of origin are required and any truck or
rail lot that does not meet the restrictions is turned back.
Mexico's and Florida's Share of the Winter-Tomato Market
The U.S. fresh-winter tomato market is roughly split between
Florida and Mexico. Weather conditions in Florida play an important
role in the annual fluctuations in the respective market shares. In
general, supplies from Mexico dominate during the peak of the winter
season (January through April) while supplies from Florida are largest
in the early and later part of the season (Zepp and Simmons).
Florida supplies mainly the eastern United States and Mexico
supplies the west of the United States. Both areas supply the mid¬
west. Domestic weather and crop conditions influence the geographic
distributions with the result that Mexico supplies more to markets in
the east and mid-west when Florida supplies are reduced by a killing
frost.

14
Marketing Channels
Fresh-winter tomatoes from Florida and Mexico (after clearing
Mexican and U.S. custom agents) go through the same marketing channel
before reaching the final consumer at the retail level in the U.S. In
this process, marketing services such as packing, repacking, wrapping
and transporting are provided by marketing agents.
From the field, fresh tomatoes are sent to the packing plants,
where they are washed, waxed, sized, sorted, graded, and packed. Some
degreening or ripening of mature-green tomatoes may be done at this
point by storing them in temperature controlled rooms for one to three
weeks (Buckley, et al). The degreening process is accelerated with
ethylene gas.
From the packing plants the fresh tomatoes are transported by
truck or rail depending on the distance between packing plant and
terminal or wholesale markets. Often vegetables shipped long
distances go by rail. At the terminal or wholesale markets the fresh
tomatoes are stored in warehouses and then delivered to retail
markets, restaurants, and institutions. Mature-green tomatoes may go
from the packing plants to repackers, where they are ripened, resorted
and repacked according to color before being transported to the
terminal and wholesale markets (Figure 3).
According to Buckley et al. fresh tomatoes are also marketed
through alternative routes which involve direct movement of the
vegetables from the packing plant to the warehouse of an integrated
wholesale-retail grocery chain before being distributed to retail
stores and finally to the consumer. Secondary wholesalers may also

15
Figure 3. Marketing Channels for Florida Fresh Vegetables
from Grower to U.S. Consumer.
Source:
Mongelli, Robert (1984).

16
purchase the produce from primary wholesalers and then resell to
jobbers and truck jobbers.
To facilitate shipping logistics and assure marketing outlets for
the highly perishable vegetables, contractual agreements are made over
the telephone between contractual operators and local buyers or
customers in the terminal markets.
Fresh tomatoes from Mexico go from the field to the packing shed
or plant where they go through the same process as in Florida (i.e.
washing, waxing, sorting, grading and packing). From here they are
transported by truck or rail through Mexican and U.S. customs, where
they are inspected to make sure that they meet export and import
requirements. Export documentation as well as paperwork for
repatriating export earnings are processed at the Mexican side of the
border. Export fees are also collected at this point. After this,
the fresh vegetables are transported to wholesale warehouses in
Nogales, Arizona. U.S. customs agents then collect import tariffs,
process export documents and issue certificates testifying that the
produce have met U.S. import standards. These export and import
transactions are being handled by customs brokers on both sides of the
border on behalf of the Mexican producers.
Distributors in Nogales resort the vegetables according to
maturity before shipping them to terminal markets, wholesalers and
chain store warehouses. From here the vegetables go to retail
outlets, restaurants, institutions and the final consumer in the U.S.
(Figure 4). There is a partnership relationship between the
distributors and growers. Through this partnership distributors

17
Figure 4. Marketing Channels for Mexican Fresh Vegetables from
Grower to U.S. Consumer.
Source: Adopted from Buckley et al., and Mongelli, (1984).

18
provide seed, other inputs, technical and market information from the
United States and preharvest and harvest financing to the growers.
According to Buckley, et al. (p.8):
approximately 60 percent of the distributors in Nogales are
partners with one or more Mexican growers. These firms handle an
estimated 60 percent of the Mexican produce.
Of the remaining 40 percent of the distributorships 20 percent are
owned outright by Mexican growers and managed by a U.S. citizen.
Independent contractors also do business with Mexican growers and they
form the remaining 20 percent of the distributorships. Chain store
buyers may also operate in Nogales, but they usually do not have
physical storage or handing facilities in the area; thus their
purchases are shipped directly to chain store warehouses. From here
the produce is then sent to retail stores for distribution to the
consumer.
Fresh-Market Winter Tomato Research
In Florida, research on tomatoes began in the 1920s. Areas of
research focus have included the development of disease resistant
varieties, improved cultural practices, improved methods of preventing
postharvest decay of fruits, uniform ripening techniques, cultivars
that are amenable to machine harvest, mechanized harvesting of fresh-
market tomatoes, and handling and transportation of tomatoes to lower
the cost of marketing.
Some production-level research and development benefits both
production and marketing of tomatoes. Plant breeders have developed
varieties which have resistance to multiple diseases, concentrated
ripening of fruit, uniform maturity, and at least 90 percent

19
marketable yields (Villanlon and Bryan). Tomatoes have been developed
with fruits which are firm and resistant to cracking, rupturing, and
bruising during harvesting and handling (Cargill and Rossmiller).
Improved cultural practices such as plastic or paper mulching to
reduce soil temperatures and conserve soil moisture also affect the
marketability of the tomato fruits by reducing the amount of sand and
number of blemishes on them. Tomatoes for fresh market must be
relatively free of defects to meet grade requirements. Sand particles
can cause abrasions or punctures during harvest and packinghouse
handling, resulting in decay or surface defects (Ramsey et al.; Halsey
et al.).
A great amount of research has been done to develop cultivars
with characteristics that will allow machine harvesting of tomatoes.
Mechanical harvesting is desired inorder to reduce costs and enable
Florida producers to be more competitive, and considerable effort has
been made in this direction (Everett et al.; Navarro and Locascio;
Deen et al.). Researchers from the Institute of Food and Agricultural
Sciences (IFAS) have been developing and evaluating equipment for
mechanical harvesting of mature-green tomatoes for fresh market.
Consumer acceptance tests for mechanically-harvested tomatoes handled
through commercial channels have been used to evaluate the
marketability of machine-harvested tomatoes (Hicks et al.). In these
tests, taste panel evaluations of flavor, texture, appearance and
general acceptance of newly-developed varieties compared with well-
established varieties were performed. To be feasible mechanized
systems must allow growers to deliver high quality fresh tomatoes to

20
market channels. To date, mechanical harvesting of fresh market
tomatoes in Florida has not proved to be economical. The machines are
designed for once-over harvesting and the varieties do not produce
fruit that ripens uniformly at one time; thus there are considerable
losses.
Research has been carried out to reduce postharvest decay
resulting from contamination by bacteria which grow in bruises and
punctures that occur during harvesting (Bartz and Crill). Ethephon
treatment of green-harvested fruit, applied at the packinghouse,
appears to have promise as a new technique for further improving the
quality of winter tomatoes. Chlorine compounds have been used
successfully for some time to control postharvest decay by reducing
bacterial inoculum during postharvest washing (Segall).
Marketing agreements and orders have been used for several
decades by various commodity groups, including tomato growers, in an
effort to stabilize and increase the level of farm income. Brooker
and Pearson evaluated the aggregate effects of these marketing orders
or supply management policies in terms of 1) the net revenue obtained
by domestic growers, 2) the volume of tomatoes marketed and consumed
in the U.S., and 3) consumer expenditures. Such information has
helped the Florida tomato industry in making marketing decisions. It
has also been of value to other commodity groups faced with similar
circumstances, and to government agencies responsible for marketing
policy. In 1979, Degner and Cubenas provided the information that, it
was hoped, would promote the development and expansion of direct
marketing of agricultural commodities from farmers to consumers on an

21
economically sustainable basis. Container specification is an
important factor in the marketing of tomatoes. Sherman et al.
identified suitable containers for shipping Florida tomatoes,
particularly during the warm weather.
Consumers of tomatoes prefer firm and fully-red tomatoes.
Several researchers (Ben-Yehoshua et al.; Hobson; Kittagawa et al.;
Risse et al., 1985) have studied the packing of tomatoes either in
polyethylene bags or individually wrapped in film. Mature-green
tomatoes, individually wrapped in heat-shrinkable plastic film after
ethylene treatment, had less weight loss and were firmer than non-
wrapped tomatoes stored for up to three weeks at 12.8°C and held an
additional seven days at 21°C. Studies have also been done to
determine the effect that film wrapping of mature-green tomatoes,
before and after ethylene treatment, has on ripening and shelf-life
(Risse, et al., 1984). Wrapping before ethylene treatment may be a
useful measure of prolonging quality and freshness of tomatoes during
export shipment or extended storage. Least-cost methods of repacking
tomatoes have been identified through research (Meyer). Mongelli
(1980), made a comparative study of handling systems for fresh
tomatoes from packinghouse to retail store. He found that a
synthesized pallet-pool system was the lowest-cost system for moving
tomatoes from packing plant to wholesaler. Pallet delivery was the
lowest-cost system for movement from wholesaler to retailer. The
lower cost of handling translated to lower prices for the consumer.
Studies on shipping alternatives for moving Florida produce to eastern
and midwestern markets were done by Klindworth and Brooks. In a 1984

22
study Mongelli studied methods for harvesting and handling tomatoes
from field to packinghouse and found bulk bins for handling tomatoes
from field to packinghouse, and hand stacking transport from
packinghouse to wholesale warehouse, to be the most cost-efficient
combination.
Both public and private funds are spent on tomato research at the
state and federal level. At the state level tomato research has been
conducted by IFAS. Much of the earlier research on tomatoes in
Florida was conducted at the Gulf Coast Research and Education Center
at Bradenton, Florida. At the federal level the U.S.D.A. has a
sizeable research program in tomatoes, which includes the work by
Mongelli; Meyer; Fahey; Jesse; Worthington, et al.; Zepp and Simmons
and Zepp.
The Florida tomato industry, through the Florida Tomato Exchange,
invests funds on tomato research, tomato promotion through
advertising, legislative activities and legal aid on items affecting
the fresh-market tomato industry.

CHAPTER II
LITERATURE REVIEW
This section draws on material presented in Norton and Davis,
Peterson and Hayami, and Stranahan. New technology is created through
investments in research and development (R&D) and results in
productivity increases. Economic evaluation of the returns as they
relate to investments in R&D has been an important area of study in
economics. Numerous reviews concerning technological change in
general and the productivity of research in particular have been done
(Peterson (1971), Shumway (1973, 1977), Sim and Gardner, Schuh and
Tollini, Ruttan (1980), Nelson, Norton and Davis, and Evenson (1982)).
Techniques employed to quantify and evaluate the returns to R&D
have been, primarily, the consumers' surplus (CS), the producers'
surplus (PS), and the econometric method^-. The CS-PS technique has
focused on the impact of R&D on the supply of agricultural commodities
(Griliches (1958), Peterson (1967) and Evenson (1969)) and on the
social and distributional implications of R&D benefits (Schmitz and
Seckler). Econometric estimation methods have employed the production
1 Most consumers' and producers' surplus analyses have employed the
graphical approach, with some assumptions about the shape, the nature
of shift, and the elasticity of the demand and supply curves. On the
other hand, econometric estimation methods have specified a
production function relationship, or cost function, with R&D as one of
the explanatory variables, and its impact is seen as the contribution
to production or cost at the margin.
23

24
function approach or, through duality, the cost function and the
profit function approach to estimate the effect of R&D on value added
in food production (Griliches, 1964; Evenson, 1967) and manufacturing
(Mansfield, Terleckyj).
Consumers' and Producers' Surplus (CS-PS) Approach
Basically, this approach attempts to quantify the changes in
consumers' and producers' surplus which can be attributed to
technological change. Research and development generate new
knowledge which may result in resource or cost saving in the industry.
Technological change thus lowers the marginal costs of production and
shifts the supply curve to the right. This shift gives rise to
benefits as it causes changes in consumer and producer surplus. The
idea is to determine the (discounted) benefits and costs of
technological change over time and thus obtain a benefit/cost ratio
and/or an average internal rate of return to research. The cost of
technological change is reflected in the R&D expenditures.
Estimates of internal rates of return using this methodology have
ranged from 30 to 60 percent. The thrust of this method can be
illustrated as in Figure 5. The figure shows a conventional,
downward-sloping demand curve (D) and an initial supply curve (SG)
which shifts to position as the result of the adoption of a new
improved input developed through research. Before this shift,
consumers' surplus equaled area a; afterward, it is represented by
(a+b+c). Thus, the net gain to consumers from the research-induced
shift in supply is (b+c). Similarly, before the supply shift,
producers' surplus equaled area (b+d); afterward, the producers'

25
Price ($)
Figure 5. Shift in Supply Due to Adoption of New Improved Input.

26
surplus equals (f+d). Their net gain from the supply shift is then
(f-b). The net gain to society (consumers' plus producers' surplus)
is (b+c+f-b = c+f), which also constitutes the gross benefits of
research.
The area under SQ out to Q]_, minus the area under out to
represents the value of resources which are released after adopting
the improved technology of producing the particular good, and which
can then be employed in their next best use. Alternatively, one might
consider the effects of consequent unemployment of these resources as
in Schmitz and Seckler. The net change in economic benefits depends
upon the assumed elasticities of supply and demand and the nature of
the supply shift (Eddleman).
According to Norton and Davis the first attempt to quantify the
benefits from agricultural research investments was by Schultz in
1953. Under the special assumptions of perfectly elastic supply
curves and a perfectly inelastic demand curve, he found that 1950
techniques of production were more productive than 1910 techniques by
about at least 32 percent and that producing the same amount of 1910
output with 1950 techniques would have saved about $10 billion in
input cost.
After Schultz, Griliches (1958), under the assumption of unitary
demand elasticity and a parallel shift to the right in the supply
curve by adoption of hybrid corn, estimated returns for the case of
perfectly elastic and perfectly inelastic supply curves, and obtained
the widely quoted 743 percent rate of return to investment in hybrid
corn research. Peterson, in his 1967 evaluation of poultry research,

27
assumed a proportional shift in the supply curve and, relaxing
Griliches' supply and demand elasticity restrictions, found a 21 to 25
percent annual internal rate of return to investment in poultry
research.
Ayer and Schuh estimated a demand equation and a supply equation
for improved and unimproved cotton varieties in their evaluation of
the returns to research on cotton in Brazil. Employing a similar
approach as Ayer and Schuh, Akino and Hayami estimated the social
benefits of rice breeding research in Japan together with the
distributional effects of a rice import policy.
Scobie and Posada evaluated the impact of technical change on
income distribution in Colombian rice production by the CS-PS
approach. They looked at different categories of rice producers and
consumers in various income groups and concluded that while consumers
benefited most and producers suffered overall losses, small producers
lost the most. Widmer et al., directly estimated the supply function
of beef cattle in Canada with time series data. Lagged research
expenditures were included as explanatory variables, making it
possible to estimate the rate at which research has been shifting the
aggregate supply function through time. Direct estimation of the
supply function also permitted the estimation of research benefits at
the margin. After the supply curve was estimated a new hypothetical
supply curve was generated by adding small increments to the actual
research expenditures. Then the area between this supply function and
the actual supply function, below the demand function, constitutes the
gross benefit of this incremental expenditure. Comparison of this

28
gross benefit with the changes in the actual research expenditures
yielded an estimate of net benefits at the margin. Widmer et al.
found an average internal rate of return of nearly 66 percent and a
marginal rate of return of 63 percent on beef cattle research in
Canada. To eliminate the biases resulting from specific assumptions
about supply shifts and elasticities, Linder and Jarrett (1978)
provided a general formula for measuring research benefits. Rose and
Wise and Fell, in comments on Linder and Jarrett's paper, suggested a
kink in the supply curve to handle the assumption of linearity in the
demand and supply curves made by Linder and Jarrett in their analysis.
The direct estimation of the supply curve as in Widmer et al. can
overcome this problem of arbitrarily assuming the nature of shift,
since the shift through time can actually be estimated if research is
one of the explanatory variables (shifters) in the supply equation
estimated. The CS-PS studies have differed in specification of supply
and demand functions and in the nature of supply function shifts. The
nature of the shift assumed affects the distribution of benefits to
producers and consumers. Producers' benefits are smaller with
divergent shifts than with either parallel or convergent shifts
(Norton and Davis).
Norton and Davis (p. 690) also contend that:
The demand elasticity is also important because the more
inelastic the demand curve, the more likely producers will lose
following technical change. Also, if the supply elasticity is
absolutely larger than the demand elasticity, consumers will tend
to receive a larger share of the benefits than producers. In
addition, those technologies which do not directly displace labor
can do so indirectly as a result of a fall in the product price
if the demand elasticity is low.

29
The Widmer et al. study found a supply elasticity of beef cattle
with respect to research expenditure to be 0.36, and that resulted in
90 percent of the benefits going to producers with only 10 percent
going to consumers. Their study underscores the importance of the
supply and demand elasticities in the distribution of benefits among
consumers and producers. The importance of general equilibrium
effects on factoral income distribution was stressed by Binswanger
(1980); these have been ignored in CS-PS evaluation. The basic
flexibility of the CS-PS approach can be a liability if underlying
relationships and policies are not accurately reflected in the
analysis. This approach is best for aggregate analysis because it
aggregates all consumers and producers of a given product and looks at
only those two groups. There are many types of producers of a given
commodity -- small-scale farmers, large-scale farmers, landowners,
sharecroppers, and farmers with unmechanized and mechanized units. An
aggregate producers' surplus sheds little light on how research
affects each group. To better understand the distribution of benefits
and costs, it would help to gain insights into who would benefit and
who would lose within the producing and consuming sectors. Another
problem is that of determining the costs of research and development.
Since knowledge builds on knowledge, it is difficult to know how far
in the past to consider the costs which occurred to produce a given
shift in the supply curve.
Econometric Method
The econometric method involves specifying and estimating the
relationship between supply and R&D investments and then determining

30
the marginal rates of return to investments in R&D. The average rates
of return can also be measured as shown in the work of Widmer et al.
Further, this method can be used to assign parts of the return to
different sources, such as scientific research and extension advice,
education and conventional inputs. The statistical significance of
the estimated returns from research can be tested. In earlier studies
the most commonly used econometric models were the production function
(PF) and productivity index models. The theory of duality has made it
possible to employ the cost function to econometrically evaluate
returns to R&D (Stranahan and Shonkwiler). Stranahan (p. 18) contends
that:
The production function model is usually used in
cross-sectional aggregate studies whereas the productivity
index is used most often with aggregate time series or
pooled data.
Griliches (1964) used the Cobb-Douglas production function
formulation in his pioneering work to analyze cross-sectional
aggregate data of the U.S. for the years 1949 to 1959. He found that
lagged public research and extension expenditures (the average of R&D
expenditures lagged one and six years) were both significant and
important sources of aggregate output growth. His estimation showed
the elasticity of production with respect to R&D to be about .06, and
implied a very high social rate of return of about 1300 percent to
investment in agricultural R&D. Even after adjusting for private
research expenditures and their contribution to aggregate agricultural
output he still estimated a 300 percent rate of return.
Peterson (1967), found a comparable output response and a gross
return of $18.52 per dollar of public research on poultry production

31
when he used a similar cross-sectional model. Assuming a ten-year lag
in the impact of R&D expenditures and treating private R&D allocations
as comparable to public R&D expenditures, he estimated a substantially
smaller internal rate of return of 33 percent.
Minasian in a non-agricultural study, analyzed the contribution
of R&D to value added in a cross-sectional study of the U.S. chemical
industry. He estimated a Cobb-Douglas function with value added as
the dependent variable and capital, labor, firm constants, a time
trend and the technology of the it'ri firm during time period t as
independent variables. Minasian estimated an elasticity of 0.11 for
R&D, resulting in a gross return of 54 percent on investments in R&D.
Applying the PF model to commodity groups, Bredahl and Peterson and
Norton estimated the marginal internal rate of return (MIRR) to each
of four commodity groups (cash grains, dairy, poultry, and livestock)
and suggested reallocating research dollars from relatively low to
relatively high payoff commodities so as to increase the overall rate
of return.
The productivity index approach is used in time series studies.
The use of a productivity index as the dependent variable avoids the
problem of high intercorrelation problems with time-series data for
conventional production inputs and the general lack of sufficient data
for the important conventional inputs (Norton and Davis). The change
in productivity index is a suitable indicator of the effect of
research on efficiency because it measures change in efficiency, not
change in farm income or prices (Evenson, Waggoner and Ruttan).
Evenson (1978) analyzed the relationships between productivity and

32
investment in (a) agricultural invention, (b) education, and (c)
research and extension. There were two distinct categories of
research -- science-oriented research and technology-oriented
research. Evenson divided the U.S. into geoclimatic regions and
attempted to isolate spillover effects of research between different
states. The spillover effects were estimated by interaction of south,
north and west variables with the technology-oriented research
variable. Technology-oriented research yielded a rate of return of 95
percent; science-oriented research yielded a 110 percent rate of
return for the period of 1927 to 1950. Evenson found that 55 percent
of the change in productivity attributed to technology-oriented
research from a typical state was realized within that state, with 45
percent being realized in other states with similar soils and climate.
The spillover from science-oriented research was considerably greater.
In the econometric approach, the R&D variable enters the model
as a distributed lag. Several factors influence the lag structure
between R&D investment and the resulting increase in technology.
These include the time lag between R&D and the actual invention of a
newer and more productive process (Griliches, 1980). Previous
investigators have assumed no or little lag and no depreciation. The
lag effect is accounted for, based on the nature of the sector, by
appropriately adjusting the marginal product or internal rate of
return associated with R&D (Bredahl and Peterson; Griliches, 1964).
Lags between R&D and the realized benefits tend to be shorter in
industries where R&D focuses on development and applied issues than in

33
industries where efforts focus on basic research. Basic research is
longer term and more uncertain (Mansfield).
Evenson (1967) first investigated the question of lags between
R&D and realized benefits econometrically with various R&D lag
structures. Using aggregate data for U.S. agriculture he found that
an inverted V distributed lag gave the best fit, with the peak
influence coming with an average lag of six to eight years and the
total effect dying out in about ten to sixteen years.
The econometric studies cited above have generally focused on
the effects of biological or technological improvements in farm
production techniques (i.e., evaluating production-oriented or
preharvest research). In response to growing interest in returns to
postharvest R&D, Stranahan and Shonkwiler evaluated the returns to
postharvest R&D in the Florida frozen concentrated orange juice
market. Because productivity and input quantity data were either
unavailable or more difficult to obtain, while cost and price
information was more accurate in the citrus processing subsector, a
cost function in conjunction with share equations was estimated. The
indirect cost function was specified as in the following equation:
C-c(Y,P,Z), (2.1)
where Y is output, P is vector of input prices, and Z is quasi-fixed
input included in the production processes. They assumed that the firm
or industry minimizes cost of producing a given output Y, with respect
to input prices and the level of the quasi-fixed input Z, where Z may
characterize the state of technical progress, degree of learning, or
contain environmental or behavioral parameters. In earlier studies

34
Caves, Christensen, and Swanson employed Z as a short-run fixed factor
representing capital structures in a translog cost function.
Differentiating the indirect cost function with respect to (2.1)
gives the negative of the shadow price of Z (Diewert; Lau).
aC(Y,P,Z)/3Zi = -WiCY.P.Z). (2.2)
At the margin, the total cost of producing Y, given the
cost-minimizing levels of input usage, will be reduced by an amount
equal to the implicit market value (or imputed value) of input Z. In
the citrus-processing subsector study Z was taken to be the average of
expenditures on citrus-processing research, lagged one and six years,
divided by the GNP implicit price deflator, lagged one and six years.
R&D can affect input usage neutrally or can bias input levels through
time depending on the magnitude of the parameter on research in the
share equations (Binswanger, 1974). Zero parameter values imply that
research impacts input usage neutrally through time. Thus the study
also investigated the effect of R&D on input usage and found that on
average, research has had a positive effect on labor and other input
usage and a negative effect on materials usage. The rate of return to
citrus research was found to be 57.4 percent, which is somewhat higher
than those rates calculated using the productivity index approach.
All the econometric studies discussed above used R&D
expenditures as the measure of research, with considerable variation
in specific items included. Some U.S. studies have used only
commodity-specific R&D expenditures by the state experiment stations
(e.g. Bredahl and Peterson) and some have used total R&D expenditures
by experiment stations, the USDA, the Soil Conservation Service, and

35
private research organizations (e.g. Cline and Lu). Others (e.g.,
Evenson and Kislev 1973, 1975, and Evenson, 1974) have used the number
of scientific publications as a proxy for research. R&D expenditures
were further separated into commodity-specific applied research and
noncommodity-specific applied, agriculturally-related basic research.
Evenson and Binswanger included separate variables to measure effects
of applied research and basic science-oriented research.

CHAPTER III
THEORETICAL FRAMEWORK
Motivation for a Simultaneous-Equations Approach
The various econometric approaches to evaluating returns to R&D
outlined in the literature review, though plausible in terms of
overcoming certain data and estimation problems, do not go far enough
in evaluating the returns to research. In general, the methodology
assumes R&D affects only the supply side. However, certain kinds of
research beyond the farm gate and perhaps even some production-
oriented research, can affect the demand for the commodity, for
example, if product quality is improved or if the research leads to
more effective use of expenditures for advertising and promotion.
Such payoff to research should not be overlooked in any evaluation of
returns to R&D. Another shortcoming of approaches based on profit and
value-added functions is that they break down when prices of output
can not be taken as fixed. Such a situation normally exists in an
aggregative approach to measuring returns.
These problems provided the primary motivation for employing a
simultaneous-equations model to evaluate returns to postharvest R&D
investments in the U.S. market for fresh-winter tomatoes from Florida
and Mexico. The nature of the problem is such that we can not apply a
restricted profit function because the assumption of exogenous output
prices is invalid. The two major producing areas are considered and
36

37
the action of each can affect the output price. Simultaneity in
inputs and outputs also precludes using a direct production function
methodology. The quantity supplied and demanded and the price are
jointly determined in the system, and such joint determination
suggests the need for a structural equations model.
The cost function approach could be employed since the output
price is not one of the arguments and the endogeneity problem could be
avoided however, the approach ignores any demand-side impacts of the
R&D. Also, these duality approaches cannot adequately address the
distributional effects of the R&D investment expenditure. These
latter effects can, to a limited extent, be evaluated within a market
clearing framework.
Model Specification
The market for fresh-winter tomatoes in the U.S. is represented
here by a set of four behavioral equations, an implicit price
relationship, and four identities. Behavioral equations on a per
capita basis are specified for the Florida shipping point supply, the
export supply from Mexico, a marketing margin equation for the U.S.
market and aggregate U.S. domestic demand at the retail level.
Identities are used to define the marketing margin, as well as the
weighted average shipping point price, and the aggregate supply in the
U.S. domestic market, and to state the market clearing conditions.
Annual crop supply responses have commonly been modeled by
specifying an acreage planted equation, yield per acre equation and
the actual total production or supply as the product of the two
(Shonkwiler and Emerson; Shonkwiler; Chern and Just; Brandt and

38
French; and Gutierrez). Alternatively, the Nerlove model in its basic
form or a modified version which treats acreage planted as a proxy for
physical output, has been employed to model crop supply responses.
However, the variables which affect acreage and yield are those
which affect the quantity supplied. In this study supply was
estimated directly rather than indirectly through acreage and yield.
Expected and actual output prices, and prices of inputs employed in
production, and other factors such as weather are examples of
variables that affect the decisions and final supply of the profit-
maximizing farmer. The period between planting and harvesting can see
drastic dislocations in market conditions facing farmers, and result
in seemingly radical harvesting decisions. In fresh-winter tomato
production, unharvested acreages can be abandoned if output price at
harvest is less than the cost of harvesting, packing and marketing at
the shipping point.
At planting time, farmers therefore gauge what output levels
would maximize their profits based on their past experiences, physical
and technical environment, expected output prices, current prices of
inputs used for production, and other market conditions. Their plans
may of course not be realized because of weather and the influences of
other exogenous forces.
Based on the above reasoning, the supply relationships for fresh-
winter tomatoes from Florida and the export supply from Mexico were
modeled as a straightforward relationship between the physical output
and the major economic and noneconomic factors believed to affect
output.

39
The marketing margin for fresh-winter tomatoes is the result of
demand and supply forces for marketing services required to move
fresh-winter tomatoes to the retail market by distributors. It is
assumed that distributors are profit-maximizers in the competitive
fresh-winter tomato market and that they employ levels of marketing
services to achieve this objective. The marketing margin for fresh-
winter tomatoes was specified as a function of the per capita total
quantity shipped from Florida and Mexico, a price index of marketing
inputs, and the preharvest and postharvest R&D expenditures
appropriately lagged.
Following the tenets of demand theory (utility maximization
subject to budget constraint), the per capita demand for fresh-winter
tomatoes at the retail level in the U.S. was postulated to depend on
the retail price, U.S. per capita disposable income, prices of
substitutes at retail level, R&D expenditures "appropriately" lagged,
and expenditures on advertisement to promote fresh-winter tomatoes.
Research affects demand for fresh-winter tomatoes by improving product
quality and increasing shelf-life, among other things.
The general structural model was specified in the following
equations:
PCQFLt - f(Pit*, DWRlt, IRlt, Plt, FPlt Wlt, RDlt_i, PCQMEXt)
+ ult (3.1)
PCQMEXt - f(DWR2t, IR2t* p2t- pP2t, W2t, RDlt_j
RD2t.j, MPCINCt, PCQFLt) + u2t (3.2)
MMt - f(PCQSUSR, MCIt, RDit.j, RD2t.j) + u3t (3.3)
MMt - RPt - WAPt
(3.4)

40
PCQSUSRt ~ PCQFLt + PCQMEXt (3.5)
RPt - f(PCQDUSRt> RPSt, IncUSt, RDit-i, RD2t-i,
ATOMt) + u4t (3.6)
PCQSUSRt ” PCQDUSRt (3.7)
The endogenous variables in this simultaneous equations system are
PCQFL, P]_, PCQMEX, P2, PCQSUSR, PCQDUSR, WAP, MM and RP. In addition
to the seven equations specified in (3.1) through (3.7), WAP was
computed using the definition of weighted average price, and P2 was
eliminated from the model with an implicit function of P^. The
Florida FOB price and the Nogales price were highly correlated. The
rest of the variables in the system were assumed to be exogenously
determined. Definitions of the individual variables are:
PCQFLt = per capita quantity of tomatoes offered for shipment in
pounds in Florida at time t;
Pit — real expected price per pound of tomatoes in cents at time
t in Florida;
DWRit - real daily wage rate of labor in dollars used in
production of fresh-winter tomatoes at time t in Florida;
IRlt = real interest rate charged farmers during planting time
(July through January) in Florida;
Plt = real season average price per pound of tomatoes in cents at
the shipping point in Florida at time t;
FPit - real price of fertilizer in Florida in dollars per ton, at
time of planting;
Wit “ weather index for the winter tomato-growing region in
Florida;
RDit-i = real R&D expenditures on farm level technology, in
dollars, "appropriately" lagged;
PCQMEXt “ export supply of tomatoes from Mexico through Nogales
in pounds per capita in the U.S. market at time t;

41
DWR2t - real daily wage rate of labor used in production of
fresh-winter tomatoes at time t in Mexico, in dollars;
IR.2t = real agricultural interest rate at time of planting, in
Mexico;
P2t ” real domestic supply price in Mexico (approximated by the
real FOB price in Nogales Arizona) in cents per pound at
time t;
FP2t = real price index of fertilizer used in production of
fresh-winter tomatoes at time t in Sinaloa, Mexico;
W2t = average temperature in Mexico's tomato-producing region;
MPCINCt = real Mexican per capita national income in dollars at
time t;
MMt - marketing margin for fresh-winter tomatoes in the U.S. in
cents per pound at time t;
PCQSUSRt = per capita retail level supply of fresh-winter
tomatoes in the U.S. at time t in pounds;
MCIt - real price index of marketing inputs (labor,
transportation charges, packaging materials) employed in
moving tomatoes from the shipping point in Florida to the
retail market at time t;
RÜ2t-i “ teal postharvest research expenditures on fresh-winter
tomatoes in dollars "appropriately" lagged;
RPt " real retail price of tomatoes at time t in cents per pound;
WAPt - weighted average shipping point price for fresh-winter
tomatoes shipped from Florida and Mexico in cents per
pound at time t;
RPSt - real retail price of substitutes (green peppers) at time t
in cents per pound;
IncUSt - real U.S. per capita disposable income at time t
(dollars);
ATOMt - real expenditures on advertisement to promote fresh-
winter tomato consumption;
ult> u2t> u3t> u4t = random error terms.
The grower level supply for fresh-winter tomatoes in the U.S.
market was obtained by adding the supply from Florida and the export

42
supply from Mexico. By adding the marketing margin to the grower
level supply the retail supply was obtained. The grower level demand
was obtained by subtracting the marketing margin from the retail
demand. Consumers' surplus (CS) and producers' surplus (PS) at the
equilibrium quantities and prices for the different levels (grower and
retail) were estimated from the resulting demand and supply equations.
The impact of R&D on CS and PS can be evaluated by differentiating CS,
PS or (CS+PS) with respect to R&D since the CS and PS relationships
will include R&D at the farm level and marketing level as explanatory
variables. The contribution of preharvest and postharvest R&D
investments to the surplus accruing to producers and consumers was
estimated using the demand and supply equations at both the grower and
the retail levels. Estimates were derived at the equilibrium prices,
and quantities; CS, PS, and (CS+PS) relationships were derived and
then differentiated with respect to the preharvest and postharvest R&D
variables. The distributional effects among producers and consumers
and the rate of return to R&D investments were also estimated. The
rationale for the specification of the variables in the above model is
discussed below.
Florida Supply
The farmer is assumed to act rationally, varying levels of inputs
with the ultimate objective of profit maximization. For agricultural
crop production there is a fixed biological lag between production
efforts and the final output. For fresh-winter tomato production the
time that passes between planting and harvesting is three to four
months. Thus planting decisions must be based on the price that

43
producers expect to receive at harvest time, the current costs of
inputs employed in production, and the interest rate observed at
planting time reflecting t^ cost of capital used for tomato
production.
As stated earlier, the farmer may be confronted with changing
market conditions after planting decisions have been made. Thus the
farmer has to make decisions at harvest time so as to maximize
profits. The decision to harvest and the yield level are therefore
based on the prevailing product price, the harvesting, packing and
marketing cost at the shipping point, and noneconomic factors such as
weather and technology. If the product price at harvest time relative
to the harvesting, packing and marketing cost is unfavorable, planted
acreage may be abandoned. The tomato plant can be destroyed by
freezing winter temperatures, or fruit setting can be inhibited,
thereby reducing yield. Improvement in technology such as development
of high-yielding disease-resistant varieties and improved cultural
practices (e.g., plastic mulching) can affect yields. R&D expenditure
variables help to explain variations of effects such as these. The
effect of weather on yield was accounted for by including a weather
variable.
Florida and Mexico are the only two major winter-tomato-
producing regions; thus the quantity shipped from one area will affect
the other. The per capita quantities shipped from each area were
therefore included in the other's supply response relationship and
were expected to have negative impact on the competing region's
supply.

44
Mexican Export Supply
Since the early 1960s the Mexicans have adhered to a planned
supply program (Fliginger et al.; Goldberg; Simmons et al.; Buckley et
al.). A description of this planned supply program was given in
Chapter I.
Some macroeconomic factors also have a bearing on the production
and shipment of tomatoes to the export market. For example, the
cyclical overvaluation of the peso which in turn affects the export
price and domestic input costs has not favored tomato production and
export. Devaluation of the peso may increase net returns for Mexican
tomatoes exported to the U.S. in the short-run because it raises the
price (in pesos) Mexican producers receive relative to costs.
However, imported input costs will increase and the advantages
initially provided by increased returns are thus reduced.
Based on the structure and conduct of the fresh-winter tomato
industry the export supply from Mexico is regarded as an excess supply
to meet U.S. excess demand. The U.S. excess demand for fresh-market
winter tomatoes is the difference between the aggregate quantity
demanded in the United States during December through June and the
quantity of fresh-market winter tomatoes produced in Florida. The
Mexican export supply is the difference between the domestic supply
and the domestic demand. Thus the Mexican export supply will be a
function of the variables that enter the domestic demand and supply
relationships. The export supply from Mexico was therefore specified
as a function of the Mexican daily wage rate; the interest rate
charged Mexican vegetable farmers; the price of fertilizer;

45
temperature during the growing season; the price of fresh-winter
tomatoes (which was approximated by the FOB price at Nogales since the
domestic price could not be obtained); the per capita disposable
income in Mexico; external factors such as U.S. R&D expenditures on
tomato technology "appropriately" lagged; and the quantity shipped
from Florida. Acreage planted in Mexico is allocated by the
government through the recommendations of the growers' Unions, and the
quantity actually exported is based on prevailing market conditions.
Therefore, the expected price at planting time does not enter the
export supply equation; it is the current price that explains export
supply. As mentioned in Chapter I, Mexican growers depend to a great
extent on progress in the United States for technical improvement.
Technological advances are mainly acquired from U.S. technicians and
consultants and from publications of universities in the U.S. and the
U.S.D.A. Many of these publications are translated and published by
the growers' association (Firch and Young). It is therefore
appropriate to include U.S. R&D expenditures on tomato production and
marketing technology in the Mexican export supply equation. The lag
structure of the impact of R&D investments may be the same for Mexican
producers as for U.S. producers since technology developed in the
U.S. is almost immediately available to Mexican producers because of
their association with U.S. agents. A weather variable is included to
account for the effect of weather on tomato yields.
Marketing Margin Between the Grower Level and the U.S. Retail Supply
Fresh-winter tomatoes go through a marketing channel from the
shipping points in Florida and Mexico to the final consumer at the

46
retail level. In this process distributors are providing marketing
services. It is assumed that these distributors behave in such a way
as to maximize their profits. The marketing margin, which is the
difference between the retail price and the grower level price
(represented here as the weighted average of Florida and Mexican
supply prices), reflects the demand for the marketing services. This
was therefore specified as a function of the per capita quantity
shipped from Florida and Mexico to the retail market, the cost of the
marketing services, and the preharvest and postharvest R&D
expenditures in the U.S. The grower level demand was then obtained by
subtracting the marketing margin from the retail demand equation.
The U.S. retail supply was estimated by first horizontally
summing the grower level supplies from Florida and the export supply
from Mexico and then vertically summing this result and the marketing
margin. There were no data available to adjust quantities supplied
for any losses which may occur between the grower and the retail
levels.
Demand in the U.S. Market
The basic theory underlying the specification of the retail
demand for fresh-winter tomatoes in the U.S. is the familiar one of
utility maximization (Henderson and Quandt; Silberberg). Fresh-winter
tomatoes are mostly consumed in salads with other vegetables. It is
therefore assumed the consumers of fresh-winter vegetables strive to
maximize the utility derived from consuming a bundle of fresh-winter
vegetables (tomatoes, cucumbers, lettuce, green peppers, celery,
carrots, etc.) subject to a budget. The resulting utility-maximizing

47
relationship expressed in price-dependent form is a function of per
capita quantity demanded, the retail prices of substitutes and
complements, the R&D expenditures on preharvest and postharvest
technology "appropriately" lagged, the U.S. per capita disposable
income and expenditures on advertising for the promotion of fresh-
winter tomatoes. The latter five exogenous variables are demand
shifters. R&D affects demand through improved techniques that enhance
or preserve the product quality at retail, which includes increased
shelf-life and improvements in palatability. Preharvest and
postharvest R&D variables are therefore included to capture these
effects on the demand for fresh-winter tomatoes. Advertising and
promotional campaigns are information-oriented and make consumers
aware of fresh-winter tomatoes and their nutritional value in diets.
Consumers' and Producers' Surplus Analysis
Shifts in supply and demand may occur at all levels in the
marketing channel due to production-oriented R&D investments and
marketing-related R&D investments on fresh-winter tomatoes. Estimates
of the effects on consumers' and producers' surplus were obtained at
the grower level as follows:
CS
G
DG(Q) dQ - PG.QG
0
f(Q, R&D1( R&D2) dQ - PG.QG ,
0
(3.8)
where DG(Q) is the grower level demand and (QG) and (PG) are the
market clearing quantity and price respectively. Holding all

48
exogenous variables with the exception of the research variables
constant at their means, the consumer surplus at the grower level is a
function of the preharvest and postharvest R&D expenditures with the
lagged effects discounted to the present time.
Similarly, on the supply side, by holding the exogenous variables
constant at their means with the exception of the research variables,
the producer surplus may be expressed as a function of the preharvest
and postharvest R&D expenditures and total surplus = (CS + PS)
The discounted marginal rates of returns of R&D investments were
estimated by partially differentiating CS, PS, and (CS+PS);
and, estimates of the discounted average rates of returns to R&D
investments were obtained by comparing the total returns to the total
research expenditures.
By the same procedure as in equation (3.8), alternative estimates
of CS, PS and (CS+PS) at the U.S. retail market were estimated, and
the discounted marginal and average rates of returns to U.S.-based
preharvest and postharvest R&D investments were evaluated.

CHAPTER IV
MODEL ESTIMATED
A partial equilibrium, simultaneous equations model was developed
in Chapter III for the fresh-winter tomato market in the U.S. during
the months of December through June. This chapter includes a
discussion of different price expectation models, the estimation
technique and the data set.
Modeling Expectations
An expected price variable appears in the supply response
equation for Florida. Because expectations are not directly
observable, additional information is needed. Several strategies for
providing additional information have been proposed to handle this
nonobservable variable and these include: rational expectations, simple
naive expectations, extrapolative expectations, Nerlove's adaptive
expectations and revisional price expectations.
The Rational Expectations Model
The rational expectations model was first introduced by Muth in
terms of market supply and demand relationships and maintains that
participants in the market act as if they were solving the supply and
demand system in forming their price expectations. Generally, the
rational expectations interpretation of the expected price, Pt*, is the
mathematical expectation of Pt given all information (It-l) available
when the expectation is formed, i.e., Pt* - E(Pt|lt_i). In a
49

50
structural econometric model this information consists of the
predetermined variables and the model's reduced-form parameters
(Wallis). Thus the model can be solved for the expected price as a
function of the expected values of the exogenous variables. This
function can then be substituted into the model, leading to a
specification which contains the original endogenous and exogenous
variables plus the expected values of the exogenous variables. In
general, following this substitution, the model will be highly non¬
linear in the parameters and also have parameter restrictions across
equations. Thus a system method of estimation would be most
appropriate since a limited information method of estimation will be
less attractive because of the cross equation restrictions (Shonkwiler
and Emerson). Time series analysis is utilized to generate the
necessary forecasts of the exogenous variables.
Naive Price Expectations Model
Naive or static expectations define expectations of the current
period price as the previous period's price, i.e.,
Pt* “ Pt-1 (4-1)
This model has a rich history in economic analysis and has comprised
the basis for the cobweb model used in the analysis of commodity cycles
in agriculture. An advantage is that information required is simple to
obtain.
Extrapolative Expectations Model
Extrapolative expectations require a longer time-series for the
variable in order to "extrapolate" how the variable changes over

51
time, (Moore and Meyers). There are several specifications under this
classification;
a. linear time-trend
Pt* - a + bt-1 (4.2a)
b. exponential growth curve
Pt* = aert'l (4.2b)
c. auto regressive trend
Pt* =a + bPt-.i +....+ bpPt.p (4.2c)
Adaptive Expectations Model
The adaptive expectation model was developed by Marc Nerlove on the
premise that:
farmers react to expected price and this expected price
depends only to a limited extent on what last year's price
was (Nerlove, p. 498).
Adaptive expectations can be represented as:
Pt* - Pt-1* - Mpt-1 - pt-l*) 0 < b < 1 (4.3)
and stated as the revision in the expectation of Pt is proportional to
the error made in the forecast of Pp.]_. We can represent adaptive
expectations as an infinite weighted-average of previous levels of the
variable, with the weights declining geometrically as the lag length
increases:
Pt* - bPt.i + b(l-b)Pt_2 + b(l-b)2Pt_3 + (4.4)
Nerlove's adaptive expectations model can be empirically applied
to the case of one explanatory variable rather easily. However, when
there are several explanatory variables, the estimation procedure of
the reduced-form equation becomes complex, and the number of degrees
of freedom is reduced (Nerlove and Addison; Chern and Just).

52
Revisional Price Expectations Model
The revisional price expectation model which appeared in the
literature only recently reflects the situation wherein the beginning
period price expectation may be revised once during the production
process (Taylor and Shonkwiler). The other models of price
expectations outlined above are conditioned by the information
available when the production decision is made. Such definitions of
expectations are perhaps appropriate if the measure of supply is
planted acreage, a fairly common practice in agricultural supply
studies (Askari and Cummings; Shonkwiler and Emerson). However, if
supply measures are in terms of physical output (e.g., supply of
livestock or total crop production), the amount actually harvested will
depend on prevailing market conditions, thus price expectations
conditioned by the information available when the production decision
is made may be inadequate. For example, the number of pickings of
fresh vegetables or the weight at which livestock should be marketed
can all be influenced by the information acquired subsequent to the
beginning of the production period. The sequential nature of
agricultural production processes thus affects the way supply prices
are imputed. Under the assumption that additional information can be
utilized to improve the accuracy of formulating price expectations,
Taylor and Shonkwiler defined a price expectation consistent with
intended production and marketing decisions which allows unobserved^
1 It should, however, be noted that this information is not directly
observable in the data but is observed by producers (Taylor and
Shonkwiler, p. 289).

53
information to be utilized in obtaining a measure of price expectations
formulated by producers.
Following Taylor and Shonkwiler (pp. 289-290), let flfl+a be:
the information set available to the economic agent at the time t-
1+a. The a parameter being constrained on the closed interval
[0,1] indexes the information which becomes available subsequent
to period t-1 up to and including period t. If the main
components of the information set are prices, a revisional
expectation may be defined by Pt' - Et_i+a(Ptl^t-1+a)• This
revisional expectation is the conditional expectation of Pt given
information available at time t-l+a. Since a is contained in the
closed interval [0,1], the realized price Pt (a=l) and the
beginning period expectation Pt* — ^t-l^tl^t-l) (<*“0), are
special cases of the revisional price expectation.... If a-0, the
imputed price which yields the observed output as optimal is the
beginning period price expectation. This would imply that the
major response of supply to price rests with the decision to
commit a given set of resources to production. Conversely, if
a-1, observed price is the optimizing supply price with the
implication that economic responses in supply occur primarily
through marketing decisions.
Given the assumption that additional information can potentially
improve the ability of the economic agent to conjecture what the actual
price will be, the revisional expectation may be expressed in an
empirical framework as:
Pt' - aPt + (1 - a)Pt* ae[0,l] (4.5),
where Pt denotes the actual price obtained in period t and Pt* denotes
the expected price conditioned by information set available at time
t-1.
Thus revisional price expectation is here defined as a convex
combination of a beginning period expectation and the observed price.
The weights assigned to the beginning period price expectation and the
observed price in imputing the optimal supply price will depend on this
value of a. If a is unconstrained and can take on any positive value
in the real number system the revisional price expectation will be an

54
affine combination of the beginning period price and the observed
price.
Estimates of the Model Parameters
The highly commercial and concentrated nature of the fresh-winter
tomato industry may produce a situation more conducive to the use of
rational expectations by producers (DeCanio). Also the competition
between Florida and Mexican growers and the information collection and
dissemination service of the Florida Tomato Committee suggest that
growers take important supply and demand forces into account when
making production decisions at planting time. Thus the rationally
expected prices are informed predictions of future events and are
assumed to be based on the underlying economic forces. These forces
would be consistent with those described by appropriate economic theory
(Muth). Shonkwiler and Emerson, were able to implement the rational
expectations hypothesis in a two simultaneous equations model of fresh-
winter tomato-imports and supply. However, we could not implement
the rational expectations hypothesis in our model of four equations
because of limited observations. A revisional price expectation was
finally adopted after considerable efforts to use the rational
expectations approach.
Preliminary estimation using different lag structures
(geometrically and linearly declining lags, second-degree Almon lag and
the inverted V) and different lag lengths of the two R&D expenditure
variables were tried. These trials provided a basis for specifying the
lag length and structure for the preharvest and postharvest R&D
expenditure variables in the model estimated.

55
The lag structure between R&D investments, which is a proxy for
the resulting increase in technology and the subsequent impact on
output, is influenced by several factors. These factors include: the
time lag between R&D expenditures and the development of a new variety
or production process; between research and commercial development and
adoption of the technique or variety; and the depreciation rate of the
new technology.
In the agricultural sector, technology depreciates because of the
biological environment. The lags between R&D investments and the
realized benefits in agriculture will thus vary with the type of
technology forthcoming from the research and the commodity involved.
Evenson (1967) found the impact of R&D on aggregate agriculture was
best described by an inverted V lag with a mean lag of 5 to 7 years.
Lags of longer lengths have been used. There is evidence that quite
long lags, at least 30 years, must be allowed if it is hoped to capture
all of the impact of research on agricultural output (Pardey and
Craig). A very long series of data would be required to measure the
impact of research with such long lags in benefits. The present value
of benefits in the distant future would be low.
Lags of 4,5,6, 7, 8, 9, 10 and 11 years for the R&D expenditure
variable, following a second-degree Almon polynomial with zero end¬
point restrictions, were tried in the preliminary runs of the model
with the two different price expectation models (i.e., the naive price
expectation and the revisional price expectation). Lags of 6, 8 and 10
years following the inverted V lag with zero end-point restrictions
were also tried. The revisional price expectation was implemented by

56
including both the current and lagged prices in the supply equation
(i.e., beginning period expectation was taken to be the price lagged
one period) and then restricting the sum of their coefficients to be
between 0 and 1; i.e., a convex combination of the two prices (Taylor
and Shonkwiler). The coefficient estimates for both the current price
and the lagged price were negative. The model was reestimated without
any restrictions on the current and lagged price coefficients (i.e.,
affine combination of the two prices). The input prices and output
price were also deflated by the interest rate, i.e., homogeneity of
degree zero was imposed on the Florida and Mexican excess supply
equations. The parameter estimates of the deflated current and lagged
price were positive as expected. A 10-year lag following a second-
degree Almon polynomial-distributed lag was believed most appropriate
for the preharvest research expenditure variable affecting the supply
in Florida. An 11-year lag following a second-degree Almon polynomial-
distributed lag had the smallest estimated standard error relative to
the respective estimated coefficient for both the preharvest and
postharvest research expenditure variables affecting the Mexican excess
supply, the marketing margin and the retail demand in the U.S.
Generally the impact of research on output could be described by
the following lagged relationship:
Yt - a + b()Xt + b]Xt_i + - - - + b^Xj-.^ + Ut, (4.6)
where Yt is a measure of output at time t and Xt_¿ i = 0,1, ,k
the values of the R&D expenditures during the current and past k years
respectively. Ut is the disturbance term which satisfies the usual
assumptions (Kelejian and Oates). A degree-of-freedom problem and

57
multicollinearity would be encountered if the model were estimated as
specified in equation (4.6)). The Almon and the inverted V lag both
provide ways to reduce the number of parameters to be estimated. The
Almon lag assumes that the pattern of the impact of R&D on output (the
b's in equation (4.6) follow a polynomial which shows that the b's are
expected to increase at first and then decrease. In this study the
impact of R&D on output was approximated by a second-degree polynomial,
or:
t>i - qq + Q]_i + c*2i2 i - 0,1 k, (4.7)
where qq, and a2 are constants to be determined. If we replace the
b's in (4.6) by their expressions in (4.7), we have:
Yt = a + oo^t + (Q0 + «1 + “2 )Xt-i +(<*0 +2a^ + 4a2)Xt_2 + ...(4.8)
+ (qq +ka^ + k^a2)X(-.jc + Ut.
Rearranging terms in (4.8) gives us:
k
Yt ” a + qq Z Xt-.i
i-0
k k
Q1 SiXt-i + a2 Zi2Xt-i + Ut.
i-1 i-1
To simplify further, define
k k k
Zlt = Z X(-.¿, Z2t ~ 2iX(-.¿ , and Z3{- — Zi^X^.£.
i-0 i-1 i-1
(4.9)
(4.10)
(4.10) is substituted into (4.9) to give:
Yt “ a + a0Zlt + <*iZ2t + <*2z3t + Ut. (4.11)
This equation can be simplified by assuming b.3 and b^i are zero
(zero-end point restrictions), i.e., now
i — -1, 0, 1, . . . ,m, m-t-1 and
b-i - qq - Q]_ + Q2 — 0 , and
bk+l = Q0 + Ql(k+1) + a2(k+l)2 - 0.
These expressions can be solved in terms of qq and <33 and substituted
back into (4.11) to eliminate two parameters, i.e.,

58
(k+2)Q1 + q2[(k+1)2 - 1] = 0
Q1 - -a2 [(k+1)2 - l]/(k+2) - a2B
erg - a2 [-[(k+1)2 - 1]/(k+2) -1] - a2A
Yj- - a + a2AZit + a2BZ2t + a2Z3t + Ut (4.12)
or
Yt - a + a2[AZlt + BZ2t + Z3t] + Ut (4.13)
Yt “ a + a2Zt + Ut. (4.14)
After estimating (4.14) ag an<^ al are obtained from the relationships
above, then estimators for the b's are obtained as follows:
A A
b0 - a0 (4-15)
A A A A
bi = erg + a3 + a2
A A A A
^2 = Q0 + 2at]_ + 4a2
AAA A
O
bk - ag + kai + kza2.
The inverted V lag was suggested and used by DeLeeuw. The technique
assumes zero end-point restrictions and an even lag length. That is,
for an even lag length k, bg - 0 and b^ - 0. Then
b¿ - ib for 0 <, i < k/2 (4.16)
- (k-i)b for k/2 < i < k.
Substituting these values into (4.6), we get
Yt - bZt + Ut (4.17)
where
k/2 k
Zt - 2 iXt_i + 2 (k-i)Xt.i.
i-0 (k/2)+l

59
After estimating b from (4.17) and using (4.16) one can obtain
estimates of b^.
Estimation Method
The model was estimated in the linear form and the natural logarithmic
form. The model in the natural logarithm form was estimated by non¬
linear two-stage least squares (NL2SLS) using all the exogenous
variables in the system as instruments. The marketing margin and
retail demand equations which contain a nonlinear variable (InQSUSR)
were estimated by Amemiya's (1974) nonlinear two-stage least squares
estimator, where the instruments are low-order polynomials of all the
exogenous variables in the system. A second-degree polynomial of all
the exogenous variables was used. For all the other equations the
instruments were all the exogenous variables in the system. There was
a degree-of-freedom problem with the nonlinear approach due to the
fact that the number of observations was fewer than the number of
parameters in the second-degree polynomial used in the first stage of
the estimation of the marketing margin and retail demand.
The linear model was estimated by linear two-stage least squares
and as a system by 3SLS. The system of equations contain endogenous
variables as explanatory variables and since there could be correlation
of the stochastic disturbance terms across structural equations because
of the simultaneous nature of the model, these endogenous variables
would be correlated with the stochastic disturbance terms across
equations. The 3SLS uses estimated information on the correlation of
the stochastic disturbance terms of the structural equations from 2SLS
residuals in order to improve asymptotic efficiency. The 3SLS

60
estimates are consistent and are asymptotically efficient but are
recognized as being sensitive to specification errors which may exist
in the model. The results using 2SLS were more consistent with
expectations than those obtained with 3SLS -- perhaps due to
sensitivity to specification errors.
Data Sources
Mexican Growing Season Temperature
The major production of fresh-winter tomatoes in Mexico occurs
around Culiacan, which lies on latitude 25°N and longitude 80°W, and
Los Mochis, lying between latitude 25° 30'N and longitude 82° 12'N in
the State of Sinaloa; thus the average temperature data that should
enter the model would be that prevailing in these areas during the
growing season. The closest location from which temperature data were
available was for Guadalajara, lying between 27°N and 76° 30'W. These
average temperature data in degrees Fahrenheit were obtained from the
U.S. National Weather Data Center.^
Real Agricultural Interest Rate in Mexico
The data on interest rate charged farmers in Mexico were obtained
from various sources. The figures for 1964-1970 were obtained from
Commission Nacional Bancaria, Boletin Estadistico Secretaria de
Hacienda Y Crédito Publico, Mexico 1964-1970 and those for 1971-1984
were obtained from FIRA, Banco Nacional de Mexico. The interest rate
was then deflated by the CPI in Mexico to give the real agricultural
interest rate. The CPI data for Mexico were obtained from the
International Monetary Fund, International Financial Statistics (1964-
2
Paul Dyke provided diskettes containing temperature data.

61
1984). The Mexican agricultural interest rate was very highly
correlated with the interest rate charged farmers in Florida, so the
U.S. interest rate was used in place of the Mexican interest rate in
the Mexican supply equation to reduce the number of exogenous variables
in the model.
Mexican Rural Daily Wage Rate
The Mexican rural daily wage rate data for the 1964/65-1980/81
seasons were obtained from Gutierrez, and for the 1981/82-1983/84
seasons from Buckley et al. These were deflated with the Mexican CPI
for the season to give the real Mexican rural daily wage rate.
Quantity Shipped from Mexico and the FOB Price in Mexico
The quantity shipped for the 1964/65-1983/84 seasons (December-
June) in millions of pounds were obtained from various issues of the
Florida Tomato Committee, Annual Report. The FOB price is the
weighted average price per pound for generally good quality tomatoes,
including duty and crossing charges at Nogales, Arizona. These were
also obtained from various issues of the Florida Tomato Committee,
Annual Report and were deflated by the CPI to give the real FOB price.
The FOB price was used as a proxy for the Mexican domestic supply price
since the domestic price could not be obtained. The Mexican FOB price
was very highly correlated with the Florida FOB price, therefore the
latter was used in the Mexican export supply equation; i.e., an
implicit relationship among these two prices was used to eliminate an
endogenous variable from the Mexican export supply equation.

62
U.S.Population
The total quantities shipped were divided by the U.S. total
population as of July 1 of each year to obtain the per capita quantity
shipped to the U.S. The U.S. population data were obtained from
U.S.D.A. Statistical Bulletin No.713,
Mexican Fertilizer Price Index
The indices of fertilizer prices for Mexico were obtained from the
World Bank through personal communication.
Real Per Capita National Income in Mexico
Personal income data were not available for Mexico. Consequently,
Mexico's national income deflated by Mexico's CPI and the population
were used in the Mexican export supply equation. Data were obtained
from the U.N. Department of International Economic and Social Affairs,
Monthly Bulletin of Statistics (1965-1984).
Mexico's Population
Mexico's mid-year population for each year was used. The population
data were taken from the U.N. Department of International Economic and
Social Affairs, Monthly Bulletin of Statistics (1965-1984).
Per Capita Quantity Shipped from Florida and FOB Price
The total quantity shipped from Florida in million of pounds for
the 1964/65-1983/84 seasons (December-June) and the seasonal weighted
average shipping-point price, or FOB price, for generally good-quality
tomatoes were obtained from various issues of the Florida Tomato
Committee, Annual Report. The CPI for all commodities in the U.S. for
each season were obtained from various issues of the Survey of Current
Business and used to deflate the FOB price to give the real deflated

63
FOB price in Florida. The total quantities shipped from Florida were
divided by the total U.S. population to give the per capita quantity
shipped.
Florida Real Daily Wage Rate
The Florida real daily wage rate was obtained by multiplying the
hourly wage rate for field workers during the first week in October of
each year by 8 and then deflating by the CPI for all commodities. The
labor wages for 1966-1981 were obtained from Gutierrez; the daily wage
rate for 1983 was obtained from Buckley et al. The wage rates for
1964 and 1965 were obtained from U.S.D.A. Farm Labor, 1964, 1965.
There were no data available for 1982 and 1984. The missing values
were filled by running a simple regression of the log of data against
time with intercept (i.e., lnWRt - a + rt). The estimates of a and r
were then used to forecast the missing values of the wage rate.
Real Agricultural Interest Rate for Florida
The interest rate charged tomato farmers was represented by the
interest on non-real-estate debt which was obtained from U.S.D.A.
Statistical Bulletin No.740. The interest rate was then deflated by
the U.S., CPI for all commodities to give the real interest rate.
Florida Growing Season Weather
The weather variable for Florida was represented by the number of
days below freezing at Homestead. The largest production of fresh-
winter tomatoes in Florida occurs in the Dade County area and the area
around Immokalee in Collier County. Since winter production is
concentrated in the southern part of the state, freezing temperatures
in Homestead should provide a good proxy for cold weather in the

64
growing area. Number of days below freezing point was selected because
of the devastating effect of freezing temperatures on the tomato plant
and consequently on the production. The Florida weather data were
obtained from various issues of the U.S. Weather Bureau, Climatological
Data, Monthly and Annual Summary - Florida Section.
Research Expenditures
Much of the preharvest agricultural research expenditures data were
obtained from the Current Research Information Service (CRIS) of the
U.S.D.A. Preharvest research expenditures for 1953-1964 and 1970-1984
were obtained from this source. Some preharvest research expenditures
for 1976 to 1984 were obtained from the Florida Tomato Exchange.
Postharvest research expenditures for 1970-1984 were obtained from CRIS
and the Florida Tomato Exchange. Some postharvest research expenditures
on fresh tomatoes in Florida were obtained from Inventory of
Agricultural Research of SAES Forestry Schools, Research Agencies of
the U.S.D.A. Vol.II (Tables II,III,IV) 1966-1983 and from various
issues of Funds for Research at State Agricultural Experiment Stations
CSRS-U.S.D.A. Postharvest research expenditures from these sources
were determined by looking at the Research Problem Area Classification
Code. There are different classification codes for different research
activities ( i.e., production, breeding, efficient marketing activity,
quality improvement and consumer acceptance activities). The missing
values for the preharvest research expenditures from 1965-1969 were
obtained by a simple regression of the log of the current expenditures
on the log of the expenditures lagged one period with no intercept

65
(i.e., lnR&Dt = alnR&Dt.^). The estimate was then used to forecast the
missing values.
The research expenditure figure for 1965 was obtained from this
simple model; the model was run again with the 1965 estimated missing
value as a new data point and a new estimate for the coefficient (a)
was obtained and used to forecast the value for 1966. This stepwise
procedure was repeated until all the missing values were obtained. The
figure for 1970 was forecasted by this procedure and the forecasted
value was compared with the actual figure for 1970. The ratio of the
actual to the forecasted value was about 0.40 which was then used to
scale all the forecasted values for the other years.
The preharvest research data for 1953 to 1964 were for vegetable
research in general and not specifically for tomatoes. The amount
devoted to tomato research was obtained by multiplying the research
expenditures times the ratio of the total value of fresh-winter
tomatoes to the total value of vegetables produced in Florida. This
ratio was crosschecked by finding the proportion of fresh-tomato-
related research projects among all vegetable research projects in
Florida, from various issues of the Florida Agricultural Experiment
Station, Annual Reports. This proportion was almost the same as the
ratio of the value of fresh tomatoes to the value of all vegetables in
Florida (about 0.35). The research expenditures for 1961-1964 showed
a big jump in spending between this period and the earlier period
(1953-1960). Considerable effort was made to attempt to smooth the
data across this apparent flaw in the data. However, the resulting
smoothed data gave results with several estimated coefficients

66
inconsistent in sign. As a consequence, the original preharvest
research data were used in the final estimation of the model.
The preharvest and postharvest research expenditure data series
were then deflated with the U.S. agricultural research deflator series
constructed from factor level price indices weighted with time varying
weights which capture the shifting factor mix of research spending by
the State Agricultural Experiment Stations (SAES) (Pardey et al.).
Many analytical studies of agricultural research have deflated the R&D
expenditure figures with single-price indices based on the implicit GDP
deflator and have assumed all of the appropriate price series move as
one. Generally, total research expenditures have been deflated by a
salaries-based price series; some have used the federal implicit GDP
deflator and others the CPI
Most of the other commonly-used deflator series use two expenditure
categories, labor and nonlabor, with either fixed or variable index
weights. These single factor price index deflators have tended to
overstate or understate the amount of research expenditures. In this
study the deflator used was a four factor research deflator constructed
by Pardey et al. The four factors in the deflator are: labor expenses,
operating expenses, expenditures for land and building, and
expenditures for equipment. The deflated research expenditures were
used to construct series of varying lag lengths. These series were
then used in the trial runs of the model to specify the length of lags.'
Total Quantity Supplied at U.S, Retail Level
The total supply at the U.S. retail level was obtained by summing
the quantity shipped from Florida and Mexico and then dividing by the

67
total U.S. population as of July 1 of each year to get the per capita
retail supply. Data on marketing losses were not available to adjust
the series.
Real Retail Price (RP)
The retail price for fresh-winter tomatoes in cents per pound were
obtained from U.S.D.A. Fresh Market Vegetables Statistics 1949-80 and
later issues. Prices were deflated with the U.S., CPI for all
commodities.
Retail Price of Substitutes
The retail price in cents per pound for green peppers, which were
treated as a substitute for fresh tomatoes, was obtained from U.S.D.A.,
ERS-Statistical Bulletin No.688, and U.S.D.A., Fresh Market Vegetable
Statistics, 1949-80, and later issues. These prices were also deflated
with the U.S., CPI for all commodities.
Real Cost Index for Fresh Fruits and Vegetables
The retail cost index for marketing fresh fruits and vegetables was
obtained from U.S.D.A. Statistical Bulletin No.713.
Data on expenditures for advertisement and promotion of fresh-winter
tomatoes were obtained from the Florida Tomato Exchange. U.S.
disposable income data were obtained from the U.S. Department of
Commerce, Survey of Current Business. These were deflated with the
U.S., CPI and the total U.S. population to estimate the real per
capita disposable income.
Florida Fertilizer Price
Florida fertilizer prices in dollars per ton were obtained from
U.S.D.A., Statistical Bulletin No.750.

68
Weighted Average Price at the Grower Level
A weighted average price series at the grower level was constructed
by multiplying the quantities shipped from Florida and Mexico by the
respective FOB prices for each season and then divided by the total
quantity shipped from the two supply areas. This weighted average
price was then subtracted from the retail price, which is also a
weighted average price, to obtain the marketing margin.
The final data set used is presented in appendix A.

CHAPTER V
EMPIRICAL RESULTS OF THE MODEL
The model was fitted to data for the 1964/65-1983/84 seasons
(December-June). Table 1 shows the 2SLS parameter estimates for the
model in linear form. These were used in the final analysis and are
discussed below. N2SLS parameter estimates for the model in log form
and 3SLS parameter estimates for the model in linear form are reported
in appendix B, tables B.l and B.2 respectively. There were more
instruments than number of observations for the first stage of the
N2SLS estimation and therefore there was a linear dependency in the
estimation of the instrument for the quantity supplied at retail
level.
Florida Shipping-Point Supply
Parameter estimates for the Florida shipping-point supply all
carry the signs suggested by theory-'-, except the deflated fertilizer
price (deflated with the interest rate charged farmers in Florida),
which was positive instead of negative. The deflated wage rate had a
negative impact on supply as expected. The deflated current price
parameter was positive (1.055), i.e., an elasticity of supply with
respect to deflated current price of 0.698. The deflated lagged price
parameter estimate was 0.037, which translated into a long-run
l The expected sign is shown in parentheses by the name of the
variables in column 2 of Table 1.
69

70
Table 1 2SLS Structural Parameter Estimates3
Equation
Variable
Coeff.
t-
Elast.
Estim.
Stat.
Florida Shipping
Point Supply
intercept(- +)
0.802
0.434
(per capita
deflated*3
quantity)
wage rate(-)
-0.499
-0.291
-0.315
deflated
fertilizer
price(-)
0.211
1.901
0.735
number of
-0.166
-1.097
days below
freezing
point in
Homestead(-)
deflated
current price(+)
1.055
1.167
0.698
deflated
lagged price(+)
0.037
0.065
0.024
per capita
quantity from
Mexico(-)
-0.935
-3.117
-0.752
preharvest
R&D expenditure
t(+)
0.392
1.125
t-l(+)
0.713
1.125
t-2(+)
0.963
1.125
t-3(+)
1.141
1.125
t-4(+)
1.248
1.125
t-5(+)
1.284
1.125
t-6(+)
1.248
1.125
t-7(+)
1.141
1.125
t-8(+)
0.963
1.125
t-9(+)
0.713
1.125
t-10(+)
0.392
1.125
Mexican Export
intercept(+)
15.653
1.239
Supply(per
capita
deflated
5.131
1.124
0.597
quantity)
wage rate(-)
deflated
0.167
0.056
0.016
fertilizer price(-)

71
Table 1 cont.
deflated
current price(+)
1.153
2.782
0.948
average growing-
season tempera¬
ture (+)
-0.092
-0.590
per capita quant,
from Florida(-)
-0.924
-3.265
-1.148
per capita
national income(-)
-0.227
-0.763
-0.357
preharvest
R&D expenditures
t(+)
-0.451
-1.168
t-l<+)
-0.826
-1.168
t-2(+)
-1.127
-1.168
t-3(+)
-1.352
-1.168
t-4(+)
-1.502
-1.168
t-5(+)
-1.578
-1.168
t-6(+)
-1.578
-1.168
t-7(+)
-1.502
-1.168
t-8(+)
-1.352
-1.168
t-9(+)
-1.127
-1.168
t-10(+)
-0.826
-1.168
t-ll(+)
-0.451
-1.168
postharvest
R&D expenditures
t(+)
-1.180
-1.842
t-l(+)
-2.163
-1.842
t-2(+)
-2.950
-1.842
t-3(+)
-3.539
-1.842
t-4(+)
-3.933
-1.842
t-5(+)
-4.129
-1.842
t-6(+)
-4.129
-1.842
t-7(+)
-3.933
-1.842
t-8(+)
-3.539
-1.842
t-9(+)
-2.950
-1.842
t-10(+)
-2.163
-1.842
t-ll(+)
-1.180
-1.842
Marketing Margin intercept(- +)
Equation
(marketing per capita
margin) quantity at
retail(-)
marketing
cost index(+)
-6.118 -0.297
-0.074 -0.071
-0.114 -0.033

72
Table 1 cont.
preharvest
R&D expenditures
t(+)
3.747
1.446
t-l(+)
6.870
1.446
t-2(+)
9.369
1.446
t-3<+)
1.242
1.446
t-4(+)
12.492
1.446
t-5(+)
13.116
1.446
t-6(+)
13.116
1.446
t-7(+)
12.492
1.446
t-8(+)
11.242
1.446
t-9(+)
9.369
1.446
t-10(+)
6.870
1.446
t-ll(+)
3.747
1.446
postharvest
R&D expenditures
t(+)
3.796
2.544
t-l(+)
6.960
2.544
t-2(+)
9.491
2.544
t-3(+)
11.389
2.544
t-4(+)
12.654
2.544
t-5(+)
13.287
2.544
t-6(+)
13.287
2.544
t-7(+)
12.654
2.544
t-8(+)
11.389
2.544
t-9(+)
9.491
2.544
t-10(+)
6.960
2.544
t-ll(+)
3.796
2.544
U.S. Retail
intercept(+-)
5.981
0.407
Demand(average
US retail
per capita
-1.768
-1.011
price)
quantity at
retail(-)
price of
0.115
0.732
green peppers
(substitute)(+)
per capita
US disposable
income(+)
0.101
0.127
expenditures
on advertising
to promote
tomato
consumption(+)
0.297
1.151
-6.320
0.777
0.052

73
Table 1 cont.
preharvest
R&D expenditures
t( + )
3.956
4.895
t-l(+)
7.253
4.895
t-2(+)
9.890
4.895
t-3(+)
11.868
4.895
t-4(+)
13.187
4.895
t-5(+)
13.846
4.895
t-6(+)
13.846
4.895
t-7(+)
13.187
4.895
t-8(+)
11.868
4.895
t-9(+)
9.890
4.895
t-10(+)
7.253
4.895
t-ll(+)
3.956
4.895
postharvest
R&D expenditures
t( + )
4.351
4.000
t-l(+)
7.976
4.000
t- 2(+)
10.877
4.000
t-3(+)
13.052
4.000
t-4(+)
14.502
4.000
t-5(+)
15.227
4.000
t-6(+)
15.227
4.000
t-7(+)
14.502
4.000
t-8(+)
13.052
4.000
t-9(+)
10.877
4.000
t-10(+)
7.976
4.000
t-ll(+)
4.351
4.000
a. Expected signs of coefficients are indicated by the
variables.
b. Deflated with the interest rate.

74
elasticity of supply with respect to price of 0.722. These results
suggest that current price carries more weight than the lagged price
in supply decisions. From an economic standpoint, this suggests that
supply response to price occurs primarily through yield variations
rather than planting decisions in the case of fresh-winter tomatoes.
This result can be interpreted as the revisional price expectation,
where price expectations formed at the beginning of the production
period (lagged price is beginning period expected price) do not affect
shipping decisions considerably. As more information becomes
available price expectations are being revised, resulting in current
price carrying most of the weight regarding supply decisions. The
weights to be attached to the beginning period expected price and the
current price were not restricted to the interval (0,1) before
estimation, as in Taylor and Shonkwiler, it could be any value on the
real positive number system, i.e., an affine combination of the
planting time and marketing time price.
The preharvest research variable entered the Florida supply
equation as a 10-year second-degree Almon polynomial-distributed lag.
Coefficient estimates had the right signs, with the impact rising to a
peak and then declining. The elasticity of the total undiscounted
impact was (0.277). Increases in the per capita quantity shipped from
Mexico were associated with a decline in the Florida supply as
expected. A 1 percent increase in per capita quantity shipped from
Mexico was associated with a 0.752 percent decline in the per capita
quantity shipped from Florida.

75
The weather was proxied by the number of days below freezing in
Homestead, the major winter-tomato producing area. Thus, this
variable was expected to have a negative impact on supply since tomato
plants are quite sensitive to freezing temperatures.
Mexican Export Supply
The Mexican supply specified as an excess supply relationship has
as arguments production input prices, output price, demand-related
variables (e.g., per capita income), per capita quantity shipped from
Florida, and the preharvest and postharvest R&D expenditures on tomato
research in Florida. The FOB price in Florida and Mexico were highly
correlated, as were the U.S. and the Mexican agricultural interest
rates. As a consequence, the Florida FOB price and the U.S. interest
rate were used in the Mexican export supply equation. As in the
Florida supply equation, output price and input prices on the supply
side of the Mexican market were relative to the interest rate. The
parameter estimates for the daily wage and fertilizer price, both
relative to the interest rate, were positive 5.131 and 0.167,
respectively. Since interest costs are an important component of
cost, it is not clear whether these signs are inconsistent; however,
the estimate of the coefficient for the wage rate is opposite in sign
to that estimated in the Florida supply equation. The impact of
temperature on supply was negative (-0.092), which was inconsistent
because rises in temperature between the ranges of 65°F and 85°F are
conducive to tomato production. The average temperatures were
observed in the low 60s. The tomato plant does best in moderately
dry areas with temperatures ranging between 65°F and 85°F. Foliage

76
diseases are induced by high temperature coupled with humidity (Ware
and McCollum) but high temperatures were not observed in Mexico.
The parameter estimate for the supply price was 1.153 with a
small standard error. The lagged price was not specified to enter the
supply relationship because the Mexican government imposes
restrictions on acreage planted, so beginning period price
expectations of farmers is not an important factor in planting
decisions. The parameter estimate for per capita national income was
negative (0.227) as expected, because an increase in per capita
national income will result in increased domestic consumption and,
since tomato is a normal good, less for export. The parameter
estimate for the per capita quantity shipped from Florida was negative
(-0.924), with a small standard error, and consistent with theory.
Since the quantities shipped from Mexico are to meet the U.S. excess
demand, they were expected to be negatively associated with quantities
supplied from Florida.
The estimated coefficients of the preharvest and postharvest U.S.
based R&D expenditure variables in the Mexican export supply equation
represented by an 11-year second-degree Almon polynomial-distributed
lag were negative. Implying the U.S. based R&D investments had
negative impact on export supply, which is inconsistent with
expectation. Considering the fact that much of the technology
employed in the production and marketing of fresh-winter tomatoes in
Mexico comes from the U.S., one would expect a positive impact on
Mexican supply and consequently on the excess supply. However, since

77
Mexico controls shipments based on quantities shipped from Florida,
the effect may be theoretically indeterminant.
Marketing Margin
The parameter estimates for the marketing margin equation all had
the expected signs except the marketing cost index represented by the
price of marketing services. Tomek and Robinson define the marketing
margin as the price of a collection of marketing services, which is
the outcome of the demand for and the supply of such services. Hence
higher input prices for a service ceteris paribus would result in a
decrease in supply and a higher margin. It is therefore expected that
the estimate for the parameter for the marketing cost index should be
positive in sign. The estimated coefficient was (-0.114) with a large
standard error (3.480), indicating the marketing cost index did not
significantly affect the marketing margin. The per capita quantity
shipped had an estimated parameter of (-0.0743), with a large standard
error (1.051), indicating nonsignificant impact on the marketing
margin. From a conceptual point of view, this sign may be positive or
negative.
Preharvest R&D investments increased supply at the farm level
thereby increasing demand for marketing services to move the produce
to the retail level. This will increase the price of the marketing
services and thus the marketing margin. There should therefore be a
positive relationship between the preharvest R&D expenditure variable
and the marketing margin. The parameter estimate for this variable
was positive (0.312), with a standard error of (0.216). The
postharvest R&D parameter estimate in the margin equation was positive

78
(0.316), with a standard error of (0.124). This sign is consistent
with expectations since it was believed that research in postharvest
activities increases the number and level of services in the marketing
channels and hence the margin.
U.S, Retail Demand
The retail demand parameter estimates all had the expected signs.
The estimated own price elasticity of demand was (-6.32) and falls
within the upper range of elasticities obtained in previous studies.
The demand price elasticities in previous studies varied from -0.181
(Hamming and Mittelhammer) to -0.79 (Shonkwiler and Emerson) to -1.07
and -3.25 to -5.5 (Simons and Pomareda) and -6.4 (Firch and Young).
As in Shonkwiler and Emerson, the price of substitutes was
proxied by the price of green peppers and this variable had an
estimated coefficient of (0.115) or an elasticity of (0.777). A
positive elasticity of less than 1 between the retail price of fresh
tomatoes and its substitute, green peppers, is consistent with Buse's
conclusion that the elasticity of the price of good "j" with respect
to changes in the price of good "i" is usually positive and less than
1 for substitute commodities. The elasticity of demand with respect
to real per capita disposable income was positive but near zero
(0.052). Fresh tomatoes are a normal good and one would expect its
demand to increase as income increases, though one would expect a
stronger effect on retail demand than was estimated. The preharvest
R&D expenditures variable, which was represented by an 11-year second-
degree Almon polynomial-distributed lag in the retail demand, had a
positive impact which is consistent with the earlier argument that

79
some preharvest research will affect the demand since breeding
programs and cultural practices to produce tomato fruit of good
quality, taste and longer shelf-life would be expected over time to
increase the demand. The postharvest R&D expenditure variable, which
is geared toward preservation and improving the quality in the
marketing process, would impact demand positively. This variable was
also represented by an 11-year second-degree Almon polynomial-
distributed lag and the parameter estimates all had the correct signs.
Lastly, the impact of advertisement and promotional activity on
the retail demand of fresh tomatoes was positive (0.279) as expected
because advertisement and promotion are supposed to increase the
demand for fresh tomatoes. However, the estimated effect was quite
weak.
Measuring Returns to Research
Estimates of parameters reported in Table 1 were used to define
supply and demand functions at the grower and retail levels. The
lagged effects of the preharvest and postharvest R&D variables were
discounted at a real rate of 4 percent to obtain the present value of
the impact of the research expenditures. The discounted, lagged
effects of the research expenditures are reported in table 2. The sum
of the discounted, lagged effects were then used in place of the
undiscounted research expenditure variable coefficient estimates in
the model.
The Florida shipping-point supply function was added to the
Mexican excess supply function to give the grower level supply
function. The supply of fresh-winter tomatoes at the retail level is

80
Table 2. Discounted Values of R&D1 and R&D2 Impacts on the Florida
Supply, Mexican Export Supply, Marketing Margin and U.S.
Retail Demand (at a real discount rate of 4 percent)
R&D1
R&D2
Year
FL
Mex.
Market.
U.
S.
Mex.
Market
U.
S.
Margin
Margin
t
0
.392
-0.
.451
3.
.748
3.
,956
-1
.180
3.
.796
4.
,351
t-1
0
.686
-0.
.794
6.
.606
6.
.974
-2
.080
6.
.692
7.
,669
t-2
0
.890
-1.
.042
8.
, 662
9.
, 144
-2
.727
8.
,775
10.
.056
t-3
1
.014
-1.
.202
9.
.995
10.
.551
-3
.147
10.
.124
11.
.603
t-4
1
.067
-1.
.284
10.
,678
11.
,272
-3
.362
10.
.817
12.
.396
t-5
1
.055
-1.
.297
10.
.781
11.
,381
-3
.394
10.
,921
12.
.516
t-6
0
.986
-1,
.247
10.
.366
10.
,943
-3
.264
10.
.501
12.
.034
t-7
0
.867
-1.
.142
9.
.493
10.
,021
-2
.989
9.
.616
11.
.020
t-8
0
.704
-0.
.988
8.
.215
8,
.672
-2
.586
8.
.322
9.
.537
t-9
0
.501
-0.
.792
6.
,582
6.
.949
-2
.072
6.
.668
7.
.642
t-10
0
.265
-0.
.558
4.
,641
4.
.900
-1
.461
4.
,702
5.
.388
t-11
-0.
.293
2.
.434
2.
,570
-0
.766
2.
,466
2.
.826
Sum
8
.428
-11.
.089
92.
.200
97.
,331
-29
.027
93.
.398
107.
.038

81
the supply at the grower level plus the marketing cost expended to
move them to the retail level; and the marketing cost is the marketing
margin. Thus the retail level supply was obtained by adding the
marketing margin to the grower level supply.
There was a very high correlation between the Florida FOB price
(which was used in both the Florida supply and Mexican excess supply
equations) and the weighted average grower level price. A simple
linear regression of the Florida FOB price on the weighted average
price was run without an intercept, resulting in a coefficient
estimate of 0.9282 with a t-statistic of 33.034 for the weighted
average price. This simple linear relationship between the Florida
FOB price and the weighted average price was substituted for the FOB
price in the grower level supply equation. This resulted in a
weighted average price in both the marketing margin equation and the
grower level supply equation, i.e., the same grower level price in
both equations. When the marketing margin was then added to the
grower level supply the weighted average price dropped out, leaving
the retail price in the retail supply equation.
The demand at the grower level is a derived demand for the raw
product less the demand for marketing services; thus the marketing
margin was subtracted from the retail demand for fresh-winter tomatoes
to give the grower level demand. Holding all other variables in the
model constant at their means, except the weighted average grower
level price, the total quantity supplied at the grower level and the
preharvest and postharvest research variables, the grower level and
retail level supply and demand equations were:

82
WAP - -7.8123 + O.9449PCQSUSR + 0.5566RDX + 13.1046RD2 (5.1)
(grower level supply)
WAP - 17.7811 - 1.6941PCQSUSR + 5.1313RD]_ + 13.6401RD2 (5.2)
(grower level demand)
RP = -14.14 + 0.8706PCQSUSR + 92.7562RDx + 106.5028RD2 (5.3)
(retail level supply)
RP = 11.4534 - 1.7684PCQSUSR + 97.3309RDx + 1007.0383RD2 (5.4)
(retail level demand)
Equations (5.1) to (5.4) were used to derive the consumers' and
producers' surplus relationships at the grower and retail levels in
the U.S. These surplus measures were used to estimate the benefits of
research on tomatoes.
The approach used was to estimate the effect of changing the
level of R&D expenditures and then letting the full effect work itself
out.
Grower Level
Equations (5.1) and (5.2) were solved for the equilibrium
quantity and price at the grower level as:
PCQSUSRE - 9.6981 + 1.7335RD]_ + 0.2029RD2 and (5.5)
WAPe - 1.3515 + 2.1946RDx + 13.2963RD2 (5.6)
Following the procedure in (3.8) the consumers’ and producers' surplus
relationships at the grower level were obtained as follows:
CSG - 79.668 + 28.4801RDX + 3.3339RD2 + 2.5453RDx2 +
0.0349RD22 + 0.5959RD1RD2 (5.7)
PSG = 44.4357 + 15.8854RDx + 1.8588RD2 + 1.4197RDx2 +
0.0194RD22 + 0.3323RD;jRD2
(5.8)

83
Differentiating (5.7) and 5.8) with respect to R&Dq and R&D2 we obtain
the change in the consumers' and producers' surplus at the grower
level per unit change in these variables.2 These derivatives were
evaluated at the mean values of the R&D^ and R&D2 expenditures for the
period 1965-84.
Retail Level
Following a procedure similar to that used for the grower level,
the retail level consumers' and producers' surplus relationships were
obtained by first solving equations (5.3) and (5.4) for the
equilibrium quantity and price as follows:
PSQSUSRE - 9.6981 + 1.7335RD]_ + 0.2029RD2 (5.9)
RPe - -5.6969 + 94.2667RD! + 106.681RD2; (5.10)
and, then, by using equation (3.8) the consumers' and producers'
surplus relationships at the retail level were obtained as:
CSR = 83.1631 + 29.7168RD! + 3.4652RD2 + 2.6547RÜ!2 +
0.0361RD22 + 0.6191RD]RD2 (5.11)
PSR - 40.9405 + 14.6488RD! + 1.7284RD2 + 1.3104RD2 +
0.0182RD22 + 0.3092RD^RD2. (5.12)
By differentiating (5.11) and (5.12) with respect to R&D^ and R&D2 the
per unit change in the consumers' and producers' surplus with respect
to these variables at the retail level was obtained. These first
derivatives were then evaluated at the means of the research
expenditures.
2
1 unit for R&Di = $10 million and 1 unit for R&D2 = $1 million.

84
The rate of change of the surpluses with respect to a one unit
O
change in R&D expenditures in cents per capita^ are reported in
Table 3. Estimates of the marginal rates of returns to R&D
investments in the fresh-winter tomato industry are reported in
Table 4. These were obtained by adjusting the values in Table 3 by
the U.S. population and units of research expenditures ($10 million
in the case of preharvest and $1 million in the case of postharvest).
Using estimates at the retail level, marginal rates of return to
R&D investments indicate that for every additional dollar of
investment made in preharvest R&D on tomato research, a gross return
of $10.85 was realized by society, of which $3.58 or 33 percent
accrued to distributors and growers, and $7.27 or 67 percent to U.S.
consumers. Similarly, an additional unit of investment made in
postharvest R&D on tomato research yielded a gross return of $12.70
to society (table 4). The percentage distribution of these returns
between producers and consumers was nearly identical to that for
preharvest research. Thus, benefits from both preharvest and
postharvest research investments on fresh tomatoes were estimated to
accrue mostly to consumers as a group. Economic theory would suggest
that to maximize surplus, additional dollars of research should be
added until the present value of marginal return was $1. The
estimates indicate that additional dollars of both preharvest and
postharvest research are needed to reach the social optimum.
The grower and retail level prices were in cents per pound and
the quantities were measured on per capita basis, so the
surpluses were in cents per capita.
3

85
Table 3. Rate of Change of Surplus with Respect to a One Unit Change
in R&D Expenditures at the Retail Level (cents per capita)
Preharvest
R&D (1 unit-
-SI mil.)
Postharvest
R&D (1 unit=$l mil.)
Change
Change
Change
Change
Change
Change
in
in
in
in
in
in
total
producer
consumer
total
producer
consumer
surplus
surplus
surplus
surplus
surplus
surplus
4.52042 1.4926 3.0278 5.2918 1.7611 3.5306

Table 4.
Estimates of Marginal Rates of Returns to R&D Investments
in the Fresh-Winter Tomato Industry at the Retail Level (in dollars)
PVa of returns per $1.00 PV of returns per $1.00
preharvest R&D investments postharvest R&D investments
PVa of
returns to
society
PV of
returns to
producers
PV of
returns to
consumers
PV of
returns to
societv
PV of
returns to
producers
PV of
returns to
consumers
10.85
3.58
CVJ
12.70
4.23
8.47
a. Present Value
oo

87
Estimates of the net present value of average rates of returns to
preharvest and postharvest research investments on fresh winter
tomatoes are presented in table 5. They show higher rates of return
on average to postharvest research than preharvest and indicate that
society is being very well rewarded for its research investments in
both preharvest and postharvest research. These values were obtained
by evaluating equations (5.11) and (5.12) at the means of the
research expenditures and adjusting for population and the units of
research expenditures.
It had been hoped to measure the impact on Mexican producers
(spillover effects) of U.S.-based preharvest and postharvest research
investments, however, the model did not permit separate estimation of
benefits on the Mexican side.

Table 5.
Estimates of NPV*5 Average Rates of Returns to R&D Investments
in the Fresh-Winter Tomato Industry at the Retail Level (in dollars)
Preharvest R&D Investments
Postharvest
R&D Investments
NPV of
average
returns to
societv
NPV of
average
returns to
producers
NPV of
average
returns to
consumers
NPV of
average
returns to
societv
NPV of
average
returns to
producers
NPV of
average
returns to
consumers
348.70
114.70
234.00
2,055.46
677.79
1,377.67
b. Net Present Value
CO
CO

CHAPTER VI
SUMMARY, CONCLUSION AND RECOMMENDATIONS
Summary
Considerable effort has been devoted in the past to evaluating
returns to R&D investments in agriculture and manufacturing. Most of
these studies, however, have been directed at production level R&D
investments, with little emphasis on postharvest and marketing-related
R&D investments. Lately there has been an increased interest in
measuring the returns to postharvest R&D investments.
The studies that have been done thus far have largely ignored the
impact of both preharvest and postharvest R&D expenditures on the
demand side. They tend to focus on the impact on productivity and
cost saving. Producers' and consumers' welfare analyses have been
done assuming shifts in the supply curve and supply elasticities
resulting from the R&D-induced productivity and cost changes. The
supply shift is the net effect of a combination of factors, including
R&D expenditures. Thus, by attributing the shift in supply only to
R&D expenditures leads to overestimation of the benefits of research
investments. The impact of R&D investments needs to be separated from
that of other factors. Also, the returns have often been expressed in
terms of internal rates of return, which does not reveal much about
the distribution of benefits. Impacts on producers and consumers
provide useful information to policymakers.
89

90
This study has attempted to address these problems by specifying
a simultaneous equations model which encompasses a shipping-point
supply equation for tomato producers in Florida, a Mexican export
supply equation, a marketing margin equation and the U.S. retail
demand equation for fresh-winter tomatoes. Both preharvest and
postharvest R&D expenditures entered the retail demand equation, the
marketing margin equation and the Mexican export supply equation.
This specification provides a basis for measuring the impact of
research expenditures on demand. It also enabled estimation of the
separate impact of research expenditures on supply and demand.
Supply and demand elasticities were also estimated as well as the
nature of shift in the supply curve, in the analysis of consumers' and
producers' welfare.
The model was estimated in linear form by 2SLS, by nonlinear 2SLS
in log form and by 3SLS. The 2SLS estimates were used in estimating
the benefits from pre and postharvest R&D. The research expenditures
entered the estimated model as 10- and 11-year Almon polynomial-
distributed lags. The supply price was a combination of current and
lagged prices reflecting the sequential nature of supply decisions,
i.e., the decision to plant and then the decision to harvest and
market. The model was fitted to seasonal (December-June) winter-
tomato data from Florida and Mexico from 1965-1984. The parameter
estimates were then used to define both grower- and retail-level
supply and demand equations. The grower-level supply was the sum of
the Florida and the Mexican export supply equations; the grower-level
demand was obtained by subtracting the marketing margin from the

91
retail demand. The retail supply was obtained by adding the marketing
margin to the grower-level supply.
Results
The estimates of some of the structural parameters were not consistent
with the expected sign, and estimated standard errors of some were
large relative to the size of the coefficient estimated. Most
parameter estimates in the Florida shipping-point supply equation had
the correct signs, but a number of them had large estimated standard
errors, indicating a weak impact on supply. However, important
variables such as current own price, Mexican shipments and the
research variables had quite significant influence on the supply. The
Mexican excess supply equation had several coefficient estimates which
seemed incorrect in sign and had large standard errors. The research
variables had negative signs in the Mexican excess supply equation and
these results reduced the total impact of research variables in the
final equations used in the analysis. Key variables such as the
supply price and the shipments from Florida were very significant,
however. In the marketing margin equation all coefficients had the
correct signs but the per capita quantity and marketing cost index had
very weak impact. The parameter estimates for the research variables
were significant. Overall, the retail demand equation had much
sounder parameter estimates than all the others. Per capita income
and the price of substitutes seemed from the results to have the
weakest influence on retail price. The research variables coefficient
estimates were quite significant.

92
Most of the variables that had either weak or incorrect signs in
their coefficients were, however, held constant at their means and
entered the intercept term in the final equations used for the welfare
analysis. Thus, final results were in large part based on parameter
estimates with correct signs and relatively small standard errors.
The surplus analysis indicated that for every additional dollar
of preharvest research investments societal surplus increased by
$10.85. Growers and distributors received 33 percent of the benefits
and consumers 67 percent. The marginal rate of return to postharvest
research investments was much higher. Society as a whole received an
estimated $12.70 for every additional dollar invested. The
distribution of the benefits among tomato growers, consumers and
marketing agents was essentially in the same proportion as benefits of
preharvest research investments.
The net average rates of return show a similar pattern of
distribution. On average $348.70 were received by society as a whole
from preharvest research investments. For postharvest research
investments the average rates of return were again much higher.
Conclusion
The results suggested high marginal rates of return to both
preharvest and postharvest research investments in the fresh-winter
tomato industry. This meant that research investments in the fresh-
winter tomato industry have been very productive. However, in terms
of economic efficiency Ruttan (1960) argues that funds should be
allocated in such a situation to drive the marginal rate of return
down to the social marginal rate of return. This study has shown that

93
the major beneficiaries of preharvest and postharvest research
investments in the fresh-winter tomato industry were consumers as a
group; however producers and distributors enjoyed an estimated 33
percent of the benefits.
This study did not consider the extension and education costs
incurred in order to get farmers and other agents in the system to
adopt the improved technology developed through research and this
could bias estimates of the returns upwards. Also, the research
expenditures used were largely those for public investments.
Considerable private research in areas of transportation,
refrigeration, packaging, etc. have increased productivity in the
marketing channels. Since our data did not account for such research
this omission too could result in overestimation of the returns. If
the public research investments are highly correlated with private
postharvest research investments, then the marginal rates of return
obtained would be more accurate than estimates of the average rates of
return. Some postharvest research investments on other vegetables
could be applicable to fresh-winter tomatoes and lead to an
overestimation of average rates of return.
Recommendations
The estimated distribution of benefits would suggest that
research expenditures on winter tomatoes should be increased and
shared by producers and consumers. If costs were shared in proportion
to benefits, one dollar of private money should be forthcoming for
each two of public funds.

94
Areas for Future Emphasis
The greatest need for improved estimates of the returns to
research expenditures to society, is for more accurate reporting of
both private and public research monies. Reporting in the public
sector is much more complete than in the private. However, it is
difficult, if not impossible, even in the public sector to allocate
expenditures accurately among the targeted commodities. Future
research could benefit greatly from more complete data.

APPENDICES

APPENDIX A
DATA USED IN THE MODEL

Table A.l Data Set for Fresh-Winter Tomatoes in Florida.
Year
Per capita
quantity
shipped
Real
FOB price
Lagged
real
FOB price
Real
daily
wage rate
Real price
for mixed
fertilizer
Real interest
rate charged
farmers
Days
below
freezing3
lb.
cents Der lb.
$/dav
$/ton
no.
1965
3.28
8.77
9.29
8.12
44.68
5.85
1.00
1966
3.37
9.29
8.77
8.94
48.81
5.90
1.00
1967
3.33
12.63
9.29
9.72
43.94
5.88
0.00
1968
3.02
11.05
12.63
10.33
44.26
5.71
0.00
1969
2.56
12.09
11.05
9.94
40.74
5.66
0.00
1970
1.89
10.11
12.09
9.69
38.43
5.89
0.00
1971
2.35
11.78
10.11
9.73
39.58
5.37
1.00
1972
2.75
11.42
11.78
10.73
37.90
5.09
0.00
1973
2.67
12.40
11.42
11.53
42.41
5.53
0.00
1974
2.75
11.28
12.40
11.76
66.57
5.67
0.00
1975
3.09
10.73
11.28
11.21
60.16
4.92
0.00
1976
3.23
12.73
10.73
11.63
54.20
4.69
0.00
1977
2.38
10.29
12.73
11.61
65.69
4.36
2.00
1978
2.98
11.03
10.29
11.61
61.51
4.21
0.00
1979
3.47
9.91
11.03
11.48
67.34
4.37
0.00
1980
4.14
10.29
9.91
11.14
68.56
4.36
0.00
1981
3.86
7.65
10.29
10.33
64.93
4.43
2.00
1982
4.23
8.52
7.65
11.63
83.55
4.23
0.00
1983
5.33
10.22
8.52
10.87
71.09
3.46
1.00
1984
4.16
9.79
10.22
11.71
72.54
3.31
0.00
a. Homestead, Florida weather station.

Table A. 2
Data Set for Fresh-Winter Tomatoes in Mexico
Year
Per capita
quantity
shipped
Real
FOB price
Lagged
real
FOB price
Real
daily
wage rate
Real
fertilizer
price index
Real interest
rate charged
farmers
Real
per capita
national
income
Average
season
tempera¬
ture
lb.
cents per lb.
$/day
1967=100
$1,000
—F
1965
1.67
14.89
13.86
1.56
1.22
12.46
4.14
63.23
1966
1.99
12.91
14.89
1.61
1.21
11.76
4.30
61.10
1967
2.15
12.37
12.91
1.69
1.00
11.20
4.47
62.99
1968
2.00
15.32
12.37
1.72
1.18
11.17
4.58
63.14
1969
3.14
11.48
15.32
1.87
1.13
10.79
4.82
64.52
1970
3.11
13.36
11.48
1.93
1.15
10.71
4.96
63.68
1971
2.73
15.83
13.36
2.00
1.04
11.90
4.91
64.03
1972
2.64
10.48
15.83
2.04
1.05
10.64
5.81
64.67
1973
3.02
10.79
10.48
2.29
1.18
9.38
5.41
63.49
1974
2.62
11.91
10.79
2.38
1.29
8.00
5.62
63.16
1975
2.44
12.67
11.91
2.27
1.32
7.10
5.51
62.64
1976
2.57
10.72
12.67
1.63
1.00
6.15
4.61
63.11
1977
3.21
15.72
10.72
1.37
1.19
6.33
3.25
62.26
1978
3.15
9.99
15.72
1.36
1.39
6.24
3.36
62.23
1979
2.85
11.46
9.99
1.36
1.42
5.50
4.14
63.41
1980
2.59
7.94
11.46
1.33
1.52
5.30
4.50
62.96
1981
1.51
23.65
7.94
0.88
1.85
5.36
4.36
63.62
1982
2.13
9.49
22.65
0.39
1.18
5.27
2.16
63.25
1983
2.47
9.96
9.49
0.18
1.13
3.43
1.04
62.06
1984
4.16
9.45
9.96
0.15
1.19
2.42
0.68
63.17

99
Table A.3 Data Set for Fresh-Winter Tomatoes in the U.S. Retail Market
Year
Per capita
quantity
at retail
level
Real
retail
price for
tomatoes
Real
retail price
for green
peppers
Real per
capita U.S.
disposable
income
Real
expenditures
on advertise¬
ment
lb.
cents
Der lb.
$1.
000
1965
4.95
39.68
38.33
2.52
0.00
1966
5.36
39.04
39.39
2.64
0.00
1967
5.48
37.47
37.70
2.73
0.00
1968
5.02
40.95
41.03
2.81
0.00
1969
5.70
41.48
37.96
2.79
0.00
1970
5.00
40.70
51.61
2.88
0.00
1971
5.07
40.08
45.88
2.94
0.00
1972
5.39
40.16
42.40
3.00
0.00
1973
5.68
38.63
41.27
3.15
0.00
1974
5.38
40.01
40.27
3.17
0.00
1975
5.52
38.32
38.21
0.31
2.53
1976
5.81
35.08
35.85
3.17
4.94
1977
5.59
40.60
41.98
3.24
2.13
1978
6.13
36.80
35.21
3.34
2.37
1979
6.32
36.92
40.07
3.37
2.15
1980
6.72
28.22
33.03
3.26
1.17
1981
5.36
29.07
37.37
3.23
1.96
1982
6.35
28.10
33.04
3.49
2.13
1983
7.81
27.13
33.65
3.34
12.71
1984
8.33
26.35
28.95
3.47
19.63

100
Table A. 4 Real Research Expenditures on Fresh-Winter Tomatoes
in Florida.
Preharvest
Postharvest
research
research
Year
expenditures
expenditures
S1.000 $1.000
1953
831.45
70.53
1954
833.00
72.79
1955
858.03
104.06
1956
850.52
136.44
1957
921.45
161.22
1958
1074.87
162.53
1959
1027.58
165.77
1960
1033.94
155.81
1961
2127.92
158.35
1962
2003.82
164.42
1963
2330.91
161.97
1964
2463.37
153.79
1965
930.83
157.02
1966
962.42
64.48
1967
1010.77
166.09
1968
1073.34
76.38
1969
1118.54
402.68
1970
1190.03
210.92
1971
1047.76
226.45
1972
1089.20
183.93
1973
992.67
178.02
1974
901.67
157.01
1975
860.77
153.11
1976
827.54
90.78
1977
580.32
126.40
1978
741.75
137.73
1979
702.27
232.87
1980
714.67
42.60
1981
813.37
132.99
1982
796.93
140.07
1983
639.61
64.05
1984
638.28
62.07

APPENDIX B
N2SLS AND 3SLS PARAMETER ESTIMATES OF THE MODEL

102
Table B.l NL2SLS Structural Parameter Estimates3
Equation
Variable
Coeff.
Std.
Elast.
Estim.
Error
Florida Shipping
Point Supply
intercept(-+)
2.764
2.199
(log per capita
quantity)
log deflated'3
wage rate(-)
-1.434
1.109
-1.434
log deflated
fertilizer
0.614
0.317
0.614
price
number of
days below
freezing
point in
Homestead(-)
-0.059
0.059
log deflated
current price(+)
1.187
0.607
1.187
log deflated
lagged price(+)
-0.251
0.404
-0.251
log per capita
quantity from
Mexico(-)
-0.522
0.234
-0.522
log preharvest
R&D expenditure
t(+)
-7.050
5.894
t-l(+)
-12.818
10.717
t-2(+)
-17.304
14.468
t-3(+)
-20.508
17.147
t-4(+)
-22.431
18.755
t-5(+)
-23.072
19.291
t-6(+)
-22.431
18.755
t-7(+)
-20.508
17.147
t-8(+)
-17.304
14.468
t-9(+)
-12.818
10.717
t-10(+)
-7.050
5.894
Mexican Export
intercept(+)
-0.549
7.631
Supply(log
per capita
log deflated
1.082
0.755
quantity)
wage rate(-)
log deflated
1.399
0.889
fertilizer price(-)

Table B.l cont.
103
0.808
log deflated 0.808
current price(+)
average growing 0.028
season tempera¬
ture^)
log per capita -0.692
quantity
from Florida(-)
per capita -0.378
national income(-)
0.303
0.047
0.252
0.189
log preharvest
R&D expenditures
t(+)
t-l(+)
t-2(+)
t-3(+)
t-4(+)
t-5(+)
t-6(+)
t-7(+)
t-8(+)
t-9(+)
t-10(+)
t-ll(+)
log postharvest
R&D expenditures
t( + )
t-l(+)
t-2(+)
t-3(+)
t-4(+)
t-5(+)
t-6(+)
t-7(+)
t-8(+)
t-9(+)
t-10(+)
t-ll(+)
7.598 4.837
13.930 8.869
18.995 12.093
22.795 14.512
25.327 16.125
26.594 16.931
26.594 16.931
25.327 16.125
22.795 14.512
18.995 12.093
13.930 8.869
7.598 4.837
-8.014 12.818
-14.691 23.500
-20.034 32.045
-24.041 38.454
-26.712 42.727
-28.047 44.863
-28.047 44.863
-26.712 42.727
-24.041 38.454
-20.034 32.045
-14.691 23.500
-8.014 12.818
Marketing Margin intercept(- +) -2.204 1.543
Equation
(log marketing log per capita -0.058 0.253
margin) quantity at
retail(-)
-0.692
-0.378
-0.728
log marketing
0.069
0.239

104
Table B.l cont. cost index(+)
log preharvest
R&D expenditures
t(+)
7.711
3.808
t-l(+)
14.136
6.981
t-2(+)
19.277
9.519
t-3(+)
23.132
11.423
t-4(+)
25.703
12.692
t-5(+)
26.988
13.327
t-6(+)
26.988
13.327
t-7(+)
25.703
12.692
t-8(+)
23.132
11.423
t-9(+)
19.277
9.519
t-10(+)
14.136
6.981
t-ll(+)
7.711
3.808
log postharvest
R&D expenditures
t( + )
9.122
2.296
t-l(+)
16.723
4.208
t-2(+)
22.804
5.739
t-3(+)
27.365
6.886
t-4(+)
30.406
7.652
t-5(+)
31.926
8.034
t-6(+)
31.926
8.034
t-7(+)
30.406
7.652
t-8(+)
27.365
6.886
t-9(+)
22.804
5.739
t-10(+)
16.723
4.208
t-ll(+)
9.122
2.296
U.S. Retail
intercept(+-)
-0.282
0.999
Demand(log
average U.S.
log per capita
-0.274
0.229
-3.953
retail price)
quantity at
retail(-)
log price of
green peppers
(substitute)(+)
0.170
0.178
0.135
log per capita
U.S. disposable
income(+)
-0.000
0.027
0.002
expenditures
on advertising
to promote
tomatoes
consumption(-t-)
0.007
0.005

105
Table B.l cont.
log preharvest
R&D expenditures
t(+)
4.427
0.854
t-l(+)
8.116
1.566
t-2(+)
11.067
2.135
t-3(+)
13.280
2.562
t-4(+)
14.756
2.847
t-5(+)
15.493
2.989
t-6(+)
15.493
2.989
t-7(+)
14.756
2.847
t-8(+)
13.280
2.562
t-9(+)
11.067
2.135
t-10(+)
8.116
1.566
t-ll(+)
4.427
0.854
log postharvest
R&D expenditures
t( + )
6.907
1.814
t-l(+)
12.663
3.326
t-2(+)
17.267
4.535
t-3(+)
20.721
5.442
t*4(+)
23.023
6.046
t-5(+)
24.174
6.349
t-6(+)
24.174
6.349
t-7(+)
23.023
6.046
t-8(+)
20.721
5.442
t-9(+)
17.267
4.535
t-10(+)
12.663
3.326
t-ll(+)
6.907
1.814
a. Expected signs of coefficients are indicated by the
variables.
b. Deflated with the interest rate.

106
Table B.2 3SLS
Structural Parameter
Estimates3
Equation
Variable
Coeff.
Std.
Estim.
Error
Florida Shipping
Point Supply
intercept(- +)
0.277
1.334
(per capita
deflated^3
quantity)
wage rate(-)
0.222
1.185
deflated
fertilizer
price(-)
0.154
0.077
number of
days below
freezing
point in
Homestead(-)
-0.020
0.106
deflated
current price(+)
0.917
0.644
deflated
lagged price(+)
-0.058
0.398
per capita
quantity from
Mexico(-)
-1.007
0.214
preharvest
R&D expenditure
t( + )
0.471
0.252
t-l(+)
0.856
0.458
t-2(+)
1.155
0.619
t-3(+)
1.369
0.733
t-4(+)
1.498
0.802
t-5(+)
1.540
0.825
t-6(+)
1.498
0.802
t-7(+)
1.369
0.733
t-8(+)
1.155
0.619
t-9(+)
0.856
0.458
t-10(+)
0.471
0.252
Mexican Export
intercept(+)
14.885
7.990
Supply(per
capita
deflated
2.492
2.905
quantity)
wage rate(-)
deflated
-0.116
0.189
fertilizer price(-)

Table B.2 cont.
107
Marketing Margin
Equation
(marketing
margin)
deflated
1.264
0.276
current price(+)
average growing
-0.112
0.098
season tempera-
ture(+)
per capita quant.
-0.941
0.191
from Florida(-)
per capita
-0.116
0.189
national income(-)
preharvest
R&D expenditures
t(+)
-0.355
0.250
t-l(+)
-0.651
0.459
t-2(+)
-0.888
0.625
t-3(+)
-1.066
0.750
t-4(+)
-1.184
0.834
t-5(+)
-1.243
0.875
t-6(+)
-1.243
0.875
t-7(+)
-1.184
0.834
t-8(+)
-1.066
0.750
t-9(+)
-0.888
0.625
t-10(+)
-0.651
0.459
t-ll(+)
-0.355
0.250
postharvest
R&D expenditures
t<+)
-0.808
0.411
t-l(+)
-1.480
0.753
t-2(+)
-2.018
1.026
t-3(+)
-2.423
1.232
t-4(+)
-2.692
1.369
t-5(+)
-2.826
1.437
t-6(+)
-2.826
1.437
t-7(+)
-2.692
1.369
t-8(+)
-2.423
1.232
t-9(+)
-2.019
1.026
t-10(+)
-1.480
0.753
t-ll(+)
-0.808
0.411
intercept(- +)
9.364
13.535
per capita
0.084
0.841
quantity at
retail(-)
marketing
0.147
0.125
cost index(+)

108
Table B.2 cont.
preharvest
R&D expenditures
t(+)
1.767
1.498
t-l(+)
3.239
2.746
t-2(+)
4.417
3.745
t-3(+)
5.300
4.494
t-4(+)
5.889
4.993
t-5(+)
6.183
5.243
t-6(+)
6.183
5.243
t-7(+)
5.889
4.993
t-8(+)
5.300
4.494
t-9(+)
4.417
3.745
t-10(+)
3.239
2.746
t-ll(+)
1.767
1.498
postharvest
R&D expenditures
t( + )
2.815
1.076
t-l(+)
5.161
1.970
t-2(+)
7.037
2.689
t-3(+)
8.445
3.227
t-4(+)
9.383
3.585
t-5(+)
9.852
3.764
t-6(+)
9.852
3.764
t*7(+)
9.383
3.585
t-8(+)
8.445
3.227
t-9(+)
7.037
2.689
t-10(+)
5.161
1.972
t-ll(+)
2.815
1.076
U.S. Retail
intercept(+-)
12.403
10.027
Demand(average
U.S. retail
per capita
-2.239
0.971
price)
quantity at
at retail(-)
price of
green peppers
(substitute)(+)
0.022
0.067
per capita
U.S. disposable
income(+)
0.127
0.281
expenditures
on advertising
to promote
tomato
consumption(+)
0.305
0.104

109
Table B.2 cont,
preharvest
R&D expenditures
t(+)
4.050
0.626
t-l(+)
7.425
1.147
t-2(+)
10.125
1.564
t-3(+)
12.149
1.877
t-4(+)
13.499
2.086
t-5(+)
14.174
2.190
t-6(+)
14.174
2.190
t-7(+)
13.499
2.086
t-8(+)
12.149
1.877
t-9(+)
10.125
1.564
t-10(+)
7.425
1.147
t-ll(+)
4.050
0.626
postharvest
R&D expenditures
t(+)
4.246
0.829
t-l(+)
7.784
1.519
t-2(+)
10.614
2.072
t-3(+)
12.737
2.486
t-4(+)
14.152
2.762
t-5(+)
14.860
2.900
t-6(+)
14.860
2.900
t-7(+)
14.152
2.762
t-8(+)
12.737
2.486
t-9(+)
10.614
2.072
t-10(+)
7.784
1.519
t-ll(+)
4.246
0.829
a. Expected signs of coefficients are indicated by the
variables.
b. Deflated with the interest rate.

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BIOGRAPHICAL SKETCH
James S. Ansoanuur was born in 1949 in Nambeg, a small farming village
in the northwestern corner of Ghana. After completing his secondary
education at the Navrongo Secondary School, he entered the University
of Ghana in October 1970 to pursue a degree in agriculture. K-
graduated from the University of Ghana in June 1974 with a B.S.
(Honors) in agricultural economics and then went on to do a one-year
national service with the Ghana National Economic Planning Council.
In September 1975, he joined the permanent staff of the Ghana Ministry
of Finance and Economic Planning as an economic planning officer.
He entered the University of Florida in September of 1977 for
studies towards a master's degree in agricultural economics. After
completing his studies at the University of Florida, he returned to
Ghana in August of 1978 and resumed his post in the Ghana Ministry
of Finance and Economic Planning. In January 1979, he was reposted to
the onchocerciasis (river blindness) control project involving seven
countries in West Africa, to work as a member of an interdisciplinary
regional planning group. He proceeded to the University of Michigan,
Ann Arbor, in April 1981 to pursue a master's degree in Public Health.
After wandering about for a while he decided to return to the
field of agricultural economics and thus he entered the University of
Florida in May, 1983 to pursue a doctorate in agricultural economics.
He and his wife Elizabeth have three children, Frieda, Mwitse and
George.
120

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. y*
Max R. Langham, Chair
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
Doctor of Philosophy.
Timothy G. Taylor
Associate Professor of Food and
Resource Economics
/
I certify that I have read
conforms to acceptable standards
adequate, in scope and quality,
Doctor of Philosophy.
this study and that in my opinion it
of scholarly presentation and is fully
Associate 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
Doctor of Philosophy.
ansickle
Sociate Professor of Food and
íésource 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
Doctor of Philosophy.
G. S. Maddala
Graduate Research Professor
of Economics
This dissertation was submitted to the Graduate Faculty of the
College of Agriculture and to the Graduate School and was accepted as
partial fulfillment of the requirements for the degree of Doctor of
Philosophy.
August, 1988
Dean, College of Agriculture
Dean, Graduate School

UNIVERSITY OF FLORIDA
3 1262 08554 1703



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