Economic externalities in the agricultural use of pesticides and an evaluation of alternative policies

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Economic externalities in the agricultural use of pesticides and an evaluation of alternative policies
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xi, 210 leaves : maps ; 28 cm.
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Edwards, William Franklin, 1938-
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Pesticides -- Environmental aspects   ( lcsh )
Economics thesis Ph. D
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theses   ( marcgt )
non-fiction   ( marcgt )

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Thesis:
Thesis--University of Florida.
Bibliography:
Bibliography: leaves 206-210.
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Also available online.
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Manuscript copy.
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Vita.
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by William Franklin Edwards

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ECONOMIC EXTERNALITIES IN THE AGRICULTURAL

USE OF PESTICIDES AND AN EVALUATION

OF ALTERNATIVE POLICIES











By
WILLIAM FRANKLIN EDWARDS













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














UNIVERSITY OF FLORIDA
1969




































Copyright by
William Franklin Edwards
1969














ACKNOWLEDGMENTS


The author is indebted to the many persons who have both

encouraged and contributed to his graduate training.

Appreciation is first expressed to the Institute of Food and

Agricultural Sciences and particularly to Dr. K. R. Tefertiller,

Chairman of the Department of Agricultural Economics, for the oppor-

tunity to participate on this research grant.

The financial support of Resources for the Future, Incorporated,

Washington, D.C. is gratefully acknowledged. A special word of thanks

must go to Mr. Blair T. Bower, Dr. Michael F. Brewer, and Dr. Allen V.

Kneese of that organization and to Dr. J. C. Headley of the University

of Missouri for their willingness to read and comment on versions of

this paper. At no time, however, did they attempt to influence in any

way the outcome of the research findings.

My greatest debt of gratitude is owed to Dr. Max R. Langham,

Chairman of the Supervisory Committee, for his continuous guidance and

encouragement during the graduate program, and for his indefatigable

efforts throughout this research study.

Special appreciation is also extended to Dr. Elmo L. Jackson and

Dr. John B. McFerrin, members of the Supervisory Committee, for pro-

viding helpful suggestions.

The author would also like to thank the staffs of the Dade

County Agricultural Agents Office and the Hlomestced Experiment Station

for assistance in gathering data on the area studied. Mr. Richard 1M.

Hunt, Assistant Map'kcting Agent, was especially helpful in establishing

iii












contacts with growers and planning the data gathering phase of the

research. The consulting help of Mr. Fred A. Johnson, an entomologist

in Dade County, is also gratefully acknowledged.

To the many growers in Dade County who supplied information for

this project, I would also like to express my appreciation.

Capable secretarial and clerical assistance was provided by

Mrs. Audra White throughout the project, and the author extends to her

his sincere appreciation for her conscientious service.

The services of the University Computing Center are herein

recognized.

Finally, I would like to thank my wife, Emmy, and our children,

Russell and Carroll, for their patience during this somewhat trying

period. To them, and to my parents, I dedicate this dissertation.














TABLE OF CONTENTS

Page

ACKNtO1 I DGMENTS ............................................... iii

LIST OF TABLES............................ .. ...................... viii

LIST OF FIGURES............................................... .xi

CHAPTER I

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

The Problem.......................................... 1
Background of the Problem............................. 2

CHAPTER II

AREA STUDIED............... .............................. ... 7

Choice of an Area......................... ... ............ 7
Agriculture of Dade County............................ 8

CHAPTER III

THE MODEL.......... .............................. ......... .. 19

CHAPTER IV

ESTIMATES OF MODEL COEFFICIENTS.............................. 30

Objective Function....... .......... .................... 30
The Demand Model............................... .30
The Supply Model............................... .34
Shifting the Supply Functions.................. 41
The Externality Functions.......................... 51
Constraints on the Objective Function............... 54
Coefficients of Variables in the Constraints... 55
The Constraint Vector .......................... 56

CHAPTER V

APPLICATION OF AGRICULTURAL PESTICIDES IN DADE COUNTY.... 58

Technology of Pesticide Application in Dade County.. 58
Estimated Quantities of Pesticides Used in Dade
County.......................... ... ................... 60











TABLE OF CONTENTS--Continued.

Page

CHAPTER VI

EXTERNALITIES IN DADE COUNTY................................. 66

The Grower Interview................................... 66
Insurance Claims.................................... .73
Veterinarians....................................... .89
Biologists.......................................... .90
Community Studies on Pesticides......................... 93
Environmental Monitoring................................ 94
Concluding Remarks on Externalities in Dade
County............................................. 100

CHAPTER VII

ANALYTICAL RESULTS, IMPLICATIONS AND F.iC.C 1:i',TIONS..... 104

Analytical Results .................................. 104
Implications........................................ .116
For Policy Makers.............................. .116
For Economic Theory and Methodology............ 117
For Future Research..... ........................ 117

APPENDIX A

NET PROFIT PER ACRE OF SELECTED CROPS IN DADE COUNTY ..... 119

APPENDIX B

ESTIMATES OF PESTICIDE USAGE COMPILED BY A ,.'(;L:T
BIOLOGIST AT THE EVERGLADES NATIONAL PARK.............. 124

APPENDIX C

COTWiOi, CHEMICAL, AND/OR TRADE NAME OF PESTICIDES
IDENTIFIED IN DADE COUNTY .............................. 131

APPENDIX D

ESTiJ''AI.IL) QUANTITIES OF AGRICULTURAL PESTICIDES
USED IN DADE ('i I'U ................................... 139

APPENDIX E

PESTICIDE QUESTIO'N".%JI: !' : I l IN DADE COUNTY .............. 184










TABLE OF CONTENTS--Concluded.

Pag,

APPENDIX F

ENVIROIMCMErTAL MONITORING PROGRAMS............................ 191

Residues in Food and Feed.......................... 193
Pesticides in People.............................. 194
Residues in Fish, Wildlife, and Estuaries...... 195
Pesticides in Water ............................. 196
Pesticides in Soil ............................. 197
Pesticides in Air .............................. 200

APPENDIX G

MATHEMATICAL STATEMENTS OF POLICIES 2A, 2B, AND 2C....... 202

Policy 2A...................................... .203
Policy 2B...................................... .204
Policy 2C....................................... 205

BIBLIOGRAPHY...... ............................................. ... 206













LIST OF. TABLES

Table Page

1 Vegetable, ornamental horticulture, and fruit production,
by commodities, in Dade County, Florida, 1966-67.......... 11

2 Production of livestock and livestock products in Dade
County, Florida, 1967..................................... 12

3 Florida and Dade County population changes for 1950 and 1960 16

4 Empirically estimated relations from which demand functions
were derived.............................................. 35

5 Demand functions used in the model........................... 37

6 Empirically estimated relations from which supply functions
were derived.............................................. 39

7 Recommended insect control measures for tomatoes............ 42

8 Recommended insect control measures for potatoes............ 43

9 Recommended insect control measures for beans............... 44

10 Recommended insect control measures for corn................ 45

11 Estimated average costs of chlorinated hydrocarbons and
organic phosphates used on crops in Dade County, 1966-67.. 48

12 Estimates of average total production costs in Dade County,
1966-67, by crop and pesticide policy...................... 50

13 Supply functions used in the model........................... 52

14 Flexibility constraints used for the empirical model........ 57

15 A summary of grower responses concerning human sickness
from pesticides............ .............................. 68

16 A summary of grower responses concerning damage from
pesticide drift........................................... 74

17 Workmen's compensation claims, State of Florida; work
injuries, days of disability, and cost by industry for
disabling and non-disabling work injuries, 1962, 1963,
1966, and 1967............................................ 79


viii













LIST OF TABLES--Continued.


Table


18 A list of the categories constituting Agency 10, "Poisons
and Infectious Agents .....................................

19 Workmen's compensation claims, State of Florida; work
injuries, days of disability, and cost by agency, for
disabling work injuries, 1962, 1963, 1966, and 1967.......

20 Dollar costs of disabling workmen's compensation claims for
Dade County, Florida, 1966, by kind of payment and agent..

21 Dollar costs of disabling workmen's compensation claims for
Dade County, Florida, 1967, by kind of payment and agent..

22 A summary of data gathered from veterinarians in Dade County

23 A summary of data gathered from the Communities Studies
Program on Pesticides in Miami ............................

24 A summary of the externalities incorporated in the
empirical model, Dade County, 1967 ........................


102


25 Model solution for Policy 1 ................................. 107


26 Model solution for Policy 2A ................................

27 Model solution for Policy 2B ................................

28 Model solution for Policy 2C ................................

29 A comparison of model solutions among policies for z and
g2 coefficients of 0 and -.0301,respectively ..............

30 Net profit per acre for a sample of tomato growers in
Dade County, 1960-61 through 1966-67 ......................

31 Net profit per acre for a sample of potato growers in
Dade County, 1960-61 through 1966-67 ......................

32 Net profit per acre for a sample of pole bean growers in
Dade County, 1960-61 through 1966-67 ......................


108

109

110


113


120


121


122


33 Net profit per acre for a sample of squash growers in
Dade County,1960-61 through 1966-67 ....................... 123

34 Estimates made by Richard Klukas of the quantities of
insecticides used on various crops in Dade County, 1966-67 125


Page










LIST OF TABLES'--Concluded.


Table Page

35 Estimates made by Richard Klukas of the quantities of
fungicides used on various crops in Dade County, 1966-67.. 128

36 Dade County growing seasons and 1967 crop acreages used for
Klukas projections........................................ 130

37 Common names, trade names, and/or chemical names of
pesticides observed in Dade County, Florida, 1966-67...... 132

38 Estimated quantities per acre of certain pesticide
categories used by farmers in Dade County, 1966-67 crop
year, by crop ............................................. 140

39 Estimated quantities per acre of certain pesticide
categories used by farmers in Dade County, 1966-67 crop
year, by crop and month ................................... 142

40 Estimated pesticide usage in Dade County, Florida, by
crop and pesticide, 1966-67 crop year..................... 155

41 Estimated pesticide usage by pesticide, crop, and month
in Dade County, Florida, 1966-67.......................... 163

42 Sample data on pesticide usage, by pesticide and crop,
for growers who used the pesticide, Dade County,
Florida, 1966-67.......................................... 176













LIST OF FIGURES


Figure Page

1 Locational map for Dade County, Florida................... 9

2 Soils of Dade County, Florida............................. 13

3 Population trends in Dade County, Florida................. 17

4 A linear iso-product function for chlorinated hydrocarbons
and organic phosphates.................................. 46

5 A hyperbolic iso-product function for chlorinated
hydrocarbons and organic phosphates..................... 47

6 Point estimate of externalities arising from the
agricultural usage of organic phosphates................ 53

7 A schematic diagram showing data needs in the area of
environmental monitoring................................ 98

8 Hypothesized relations between "welfare," the state of the
arts, and the usage of chlorinated hydrocarbons......... 114

9 U.S. Geological Survey sampling sites for pesticide
residues in aquatic communities of South Florida........ 198

















CHAPTER I


INTRODUCTION


The Problem


This research effort was concerned with the economic external-

ities created by the use of agricultural pesticides and the implications

of such externalities on social welfare. The working hypothesis was

that the externalities could be measured, and the objective was to

measure them for some specified region and time period and to

incorporate them into a model which was developed to aid in the choice

of a pesticide usage policy for the area. The choice criterion was a

measure of social welfare.

The model employed the concept of consumers'-producers' surplus

as a measure of "welfare." For each of two specified policies and

three variations under one of the policies the model maximized this

measure of welfare over production alternatives available to the region.

In each case the objective function explicitly recognized measurable

externalities not accounted for by consumers'-producers' surplus. The

maximization was restrained by flexibility constraints derived from


1This research project is an outgrowth of some earlier work
done by J. C. Headley and J. N. Lewis (23) for Resources for the Future,
Inc.

2YlirtOUl'i. 't the paper this term is to be read as "consumers'
plus producers' surplus."











historical cropping patterns. Finally the policies were ranked by their

maxima.

It was demonstrated that the model could conceptually accommo-

date environmental constraints connecting the levels of pesticide usage

with levels of pesticides monitored in various elements of the environ-

ment. Empirical data to specify such constraints were not available.


Background of the Problem


Environmental modification is not new. In the struggle for

survival, it has been practiced through the ages by most of the

creatures of the Earth. The fox modifies his environment when he digs

a den. Man modifies his environment when he clears a field for culti-

vation, but such environmental modification often has undesirable side

effects. When the farmer clears his land for cultivation, he raises the

probability of soil erosion. The extent of the modification and the

extent of the side effects can become important issues, and herein lie

many of the social problems we face today, one of which concerns the

advantages and disadvantages of using pesticides.

Several factors have played a part in increasing the emphasis

on the pesticide problem. First, world food needs are expanding rapidly.

The world population, estimated at about 500 million in 1650, rose to

1 billion by 1850, and to 1.6 billion by 1900 A.D. Extrapolations of

present growth rates indicate a population of 6.4 billion by 2000 A.D.

(10, pp. 63-71). Present-day nutritionists find that more than half of

the world's inhabitants have a suboptimal diet of less.than 2,200

calories per day (42, pp. 73-80). It is quite possible that thc food

deficit will worsen in future years; the significance of the problem










will depend upon our success at population control and our ability to
3
increase agricultural production.

Second, there is the lurking possibility that the long-run

hazards of persistent pesticides to human health and the Earth's eco-

logical relationships may be more serious than had been imagined

previously. We really know very little about these long-run effects,

but some individuals feel that they may be serious (49, pp. 485, 526,

585, and 684).

Third, it is possible that a nation's values and preferences

change as its standard of living rises. If a large segment of our

population were starving, it is doubtful that the external or side

effects from using pesticides would be given very serious consideration.

It would be worth poisoning a few people if one more than "a few" could

be saved from starving through the use of pesticides. The "pesticide

problem" is a sub-case of the more general "environmental pollution

problem" and a "clean environment" is a luxury public good not demanded

until some minimum quantity of those goods more necessary for sustaining

life is obtained.

Being a public good, an effective demand is not expressed in the

market place. Consequently there are dangers of "social imbalance."

This "social imbalance" was described by J. K. Galbraith more than a

decade ago:

The family which takes its mauve and cerise, air-conditioned,
power-steered, and power-braked automobile out for a tour passes
through cities that are badly paved, made hideous by litter,
blighted buildings, billboards, and posts for wires that
should have long since been put underground. They pass into a


3For two interesting accounts of the world food situation see
Borgstrom (1) and Gunther (22).











countryside that has been rendered largely invisible by commer-
cial art. . They picnic on exquisitely packaged food from a
portable icebox by a polluted stream and go on to spend the night
at a park which is a menace to public health and morals. Just
before dozing off on an air mattress, beneath a nylon tent, amid
the stench of decaying refuse, they may reflect vaguely on the
curious unevenness of their blessings (20, p. 253).

Finally,Rachel Carson's book, Silent Spring (8), did a great

deal to increase public interest in the pesticide issue. Largely as a

result of Miss Carson's book and the interest it created, the United

States Senate, from May, 1963, to July, 1964, conducted extensive

hearings to study the problems created by the increasing use of pesti-

cides and tried to assess their potential hazards (49). The general

conclusion of the Senate investigation was that the benefits from using

pesticides still far outweigh the potential hazards to the environment,

but it was strongly emphasized that additional research is badly needed,

particularly in the area of long-run hazards of pesticide use, where we

have virtually no knowledge.

Stimulated by the Senate Hearings and a growing public concern

about environmental pollution, economists became more interested in the

problem. It is within the purview of the physical sciences to establish

the link between pesticides and various elements of the environment, but

when this is done, the subsequent question, "So what?", still remains to

be answered, and this is left to the social and political sciences. The

benefits must be weighed against the costs; or to put it another way,

relative values must be assessed, and values are the economist's stock

in trade.

The term "pesticides" is commonly used to refer to the whole

family of agricultural chemicals "used to control insects, mites, ticks,

fungi, nematodes, rodents, pest birds, predatory animals, rough fish,










plant diseases, weeds, and also to those which act as regulators of

plant growth, as defoliants, and as desiccants" (49, p. 41). Major

categories have been described as follows (49, p. 41):

1. The chlorinated hydrocarbons containing carbon, hydrogen, and
chlorine are the pesticides used in greatest tonnage. The
most familiar are DDT, dieldrin, aldrin, endrin, toxaphene,
lindane, methoxychlor, chlordane, and heptachlor. Among
those used extensively as herbicides are 2,4-D and 2,4,5-T
for control of broad-leaved weeds in lawns, pastures, cereal
crops, and brush growth along highways and fences.

2. The organic phosphorus compounds, composed of phosphorus,
oxygen, carbon, and hydrogen, are used principally as insec-
ticides and miticides. Parathion, malathion, phosdrin, and
tetraethyl pyrophosphate (TEPP) are examples.

3. Other organic compounds include the carbamates, dinitrophenols,
organic sulfur compounds, organic mercurials, and such natural
products as rotenone, pyrethrum, nicotine, strychnine, and the
anticoagulant rodent poisons.

4. Inorganic substances with a long history of use include copper
sulfate, arsenate of lead, calcium arsenate, compounds of
chlorine and fluorine, zinc phosphide, thallium sulfate, and
sodium fluoroacetate.

Although there is some controversy regarding most of these

groups, the main thrust of the dispute concerns the chlorinated hydro-
4
carbons and the organic phosphates.

The chlorinated hydrocarbons are generally quite persistent in

the environment, and their residues therefore tend to build up in many

elements of the environment, such as soil, water, and plant and animal

tissue. The organic phosphates, on the other hand, decompose very

rapidly into non-harmful substances and thus seldom persist in the

environment for long periods of time. Their detrimental effects tend

to be of an acute rather than chronic nature and have caused many fatal


4Common names, trade names, and chemical names of all pesticides
identified in Dade County are presented in Appendix C ( p. 131).






6


and non-fatal poisonings in man. Education in handling these highly

toxic chemicals can be effective in reducing acute damage.

Current social and legislative trends seem to favor the substi-

tution of non-persistent pesticides for the persistent ones (54, p. 20).

Therefore, alternative policies evaluated in this research project are

oriented toward this objective.

















CHAPTER II


AREA STUDIED


Choice of an Area


The choice of an area for study was initially limited to the

State of Florida because of the location of the researchers. However,

the State as a whole was much too heterogeneous for effective analysis

so a smaller geographic region within the State was chosen. It was

felt that the selected area should be a relatively isolated, heavy

user of pesticides. It was also felt that it would be desirable to have

an area where other scientists had studied the fate and dispersion of

pesticides as well as the effects of pesticides on various elements of

the environment such as wildlife, human health, and domestic animals.

If possible, it should also be an area which would offer the maximum

probability of observing externalities, and it was believed that an area

having a significant interaction of pesticide users with other people

would fulfill this requirement.

The focus rather quickly narrowed to three areas--a small

isolated locale in Northeast Florida (Hastings, Florida) heavily commit-

ted to the production of potatoes and cabbage; an area in Central

Florida (Polk County) which produces primarily citrus; and the winter

vegetable and tropical fruits area of Dade County. The latter area was

the final choice because it had what was believed to be the best work-

able balance of needed characteristics. Since its agriculture is highly










dependent upon pesticides, Dade County uses great quantities of them in

the production of its crops. It is relatively isolated (by the Ever-

glades Swamp, the metropolitan area of Miami, and the Atlantic Ocean),

and there are ample opportunities for urban-rural interaction as the

growing city of Miami encroaches on the agricultural interests in South

Dade County. In addition,several scientists are currently working on

problems related to the use of pesticides in that area.


Agriculture of Dade County


When Dade County was created in 1836, it comprised most of

Southeast Florida. Its first county seat was located at Juno, north of

what is now West Palm Beach. In these early days the industry of the

area consisted of starch production from the native plants, coontie or

zamia. Following the "great freeze" of 1894 (which nearly wiped out the

citrus groves in Central and North Florida) Henry Flagler, seeing Dade

County had escaped all damage, extended the railroad all the way to

Miami and built a hotel there. This was to mark the beginning of the

great development of Dade County.

Today, the county is situated in the southeastern part of the

Florida peninsula, as indicated by Figure 1. It extends about 55 miles

from north to south and 47 miles from east to west. The total area is

2,054 square miles, or 1,314,560 acres. Miami, the county seat, and

other towns are located in the eastern portion of the county near the

coast.

The surface relief is nearly level, and much of the county lies


1I am indebted to the Dade County Agricultural Agents Office
for most of the information contained in this section.



























































DADE
Cu ii;'1 ,


Figure 1.--Locational map for Dade County, Florida.










less than 13 feet above sea level. But a few low ridges in the eastern

portion are slightly higher than 20 feet in elevation. In the western

portion the surface water drains slowly through the peat marshes in a

southerly and southwesterly direction to the Gulf of Mexico. In the

eastern portion the indistinct drainageways of the peat marshes join

many of the canals and ditches which extend through the low sandy and

rocky ridges to the Atlantic Ocean. All major canals in the northeast

and southern part have control-dams for regulating water levels, but

very few pumps are used for controlling water levels in fields except

on several large farm operations. Most of the southwestern part of the

county is included in the Everglades National Park.

Dade County enjoys tropical climatic conditions not found else-

where on the U.S. mainland. It is 500 miles further south than Los

Angeles, but it is not subject to the heat of the tropics due to the

nearness of the Atlantic Ocean, the Gulf of Mexico, and the prevailing

tradewinds. The mean annual temperature at Miami is 75.10 F., and the

mean annual rainfall is 57.8 inches. Most of the rainfall comes during

the summer months. Occasionally disturbances of hurricane type move

across the county during the months from August to November. Killing

frosts may occur from the middle of December to March, but many winters,

sometimes several in succession, pass without damage from freezing

temperatures. Livestock graze the pastures throughout the year and need

little shelter from the weather. Taken together, the climatic con-

ditions of Dade County make it one of the most unusual and productive

farming areas of the nation.

September through March are the months of primary agricultural

activity. Winter vegetables, ornamental horticulture, and tropical










fruit are the three largest activities, representing 44.3, 11.5, and

4.1 million dollars,respectively,as indicated in Table 1. Minor


Table l.--Vegetable, ornamental horticulture, and fruit production, by
commodities, in Dade County, Florida, 1966-67.a



Crop Acres Production Value

.......... Thousands..........
Vegetables
Tomatoes
Fresh 19,000 5,806 crt. $23,050
Processed 1,514 crt. 961
Potatoes 7,660 1,564 cwt. 5,474
Snap beans
Bush 1,470 181 bu. 699
Pole 5,830 1,647 bu. 6,357
Squash 3,080 457 bu. 1,942
Strawberries 670 657 flats 2,214
Corn 1,650 252 crt. 622
Cucumbers 1,290 204 bu. 787
Cabbage 470 189 crt. 312
Okra 400 36 bu. 180
Peas 650 69 bu. 361
Cuban vegetables 1,360 1,102
Other vegetables 390 272
Total vegetables 43,920 $44,333

Ornamental horticulture $11,530
Fruit
Avocados 5,235 220 bu. $ 1,232
Limes 3,585 640 bu. 1,965
Mangos c 1,520 70 bu. 490
Specialty fruit 440 210
Other citrus 365 247
Total fruit 11,145 $ 4,144

aSource of data: Dade County Agricultural Agents Office,
Homestead, Florida.
bIncludes lima beans, cantaloupes, eggplant, escarole, chicory,
lettuce, green peppers, and green onions.
c
Includes lychee, barbados cherries, guava, papyas, and
sapodillas.
dIncludes oranges, grapefruit, tangerines, tangelos, and lemons.










agricultural activities are poultry, dairy, and livestock as shown

in Table 2.


Table 2.--Production of livestock and livestock products in Dade
County, Florida, 1967.a



Value of
Item Unit Quantity production


Dairy (5 farms) $ 1,669,000
Milk Gallons 2,507,112 1,503,000
Cull animals Head 166,000

Poultry (25 farms) $ 3,524,000
Eggs Dozen 5,220,000 2,034,000
Cull birds Head 150,000 40,000
Hatchery chicks Head 10,040,000 1,450,000

Other (including beef cattle,
horses, etc.) Head 5,032,000 $ 796,000

Other farm enterprises 500,000


aSource of data: County Agricultural Agents Office, Homestead,
Florida.


Most of the fruit and vegetable crops in Dade County are grown

on two soil types, marl and rockland, illustrated in Figure 2.

Marl and its various phases comprise a total of about 317,000

acres in Dade County. Surface soil is a light brown or brownish gray

marl of silt loam texture. The subsoil is lighter colored, and the

underlying material is Miami oolite. Native vegetation on these soils

consists of a variable mixture of sawgrass, myrtle, bay, and cypress.

Elevation above sea level of most areas of marl that are suitable for

cultivation ranges from about 8 feet near the rock ridge to 1 to 2 feet

near the shore lines. Soil depth varies from 6 to 60 inches. Marl has






































a
Figure 2.--Soils of Dade County, Florida.a
aReprinted with the permission of the Dade County Agricultural
Agents Office.


poor internal drainage and becomes waterlogged or even flooded during

the rainy season. Occasionally wet conditions occur during the normally

dry winter season; however, this soil may, at times, become too dry

during the winter. Both drainage and irrigation systems are used

extensively for crop production.

Marl is alkaline in reaction,and the application of certain of

the minor elements is imperative for successful crop production. Prior

to discovery of the low availability of manganese, this unknown defi-

ciency limited crop production in the area.

Marl soils are utilized for production of winter vegetables,

including Irish potatoes, tomatoes, bush beans, pole beans, squash,

cucumbers, corn, strawberries, cabbage, beets, lettuce, escarole,










peppers, and eggplant. The total acreage of marl soils cultivated in

these vegetable crops has averaged 18,000 to 20,000 acres annually for

the past 15 years.

There are approximately 279,000 acres of rockland in Dade

County. The rockland area extends roughly from U.S. Highway 1 west to

the Everglades Park boundary and from the Tamiami Trail on the north to

Florida City and Florida Highway 27 leading to the Everglades Park.

The rockland is an oolitic limestone formation with many

solution holes and is relatively soft until it is exposed to the air.

The solution holes below the surface act as storage places for water.

Near the surface these cavities are filled with loam and loamy fine sand

having varying shades of red coloring.

Prior to 1938 farmnning of the rockland was confined to the larger

solution holes and to pockets broken up by dynamiting. Heavy tractors

and scarifying equipment are now used to chisel or "plow" this soil so

that all of the area can be farmed. Vegetable farms are plowed 6 to 8

inches deep over the entire field. Orchards are planted in trenches

that have been cut and crushed to a depth of 20 to 24 inches. The

trenches are back filled after cutting, and young trees are set in the

crushed rock trench that is formed.

Irrigation wells are drilled into the oolitic rock 15 to 40 feet

deep and usually about 300 feet apart. No casing is required. One-half

to an inch of water is applied by overhead irrigation each week. Most

farmers use a single irrigation head on a portable pump, which covers

roughly two acres.

Only vegetables that produce marketable products above the

ground such as tomatoes, corn, squash, beans, cantaloupe, okra, southern










peas, and leaf crops can be grown on rockland. Tree crops in Dade

County, including limes, mangos, and avocados, are grown exclusively on

rockland soils. During the last 15 years, an average of 28,000 acres of

vegetable crops has been produced on rockland soils, and an average of

20,000 acres has been devoted to tree crops.

Both marl and rockland soils are underlaid by Miami oolitic

limestone, which is perforated with numerous vertical solution holes.

The oolite underlies the Atlantic Coastal Ridge south of Boca Raton in

Palm Beach County to an average depth of about 20 feet. Deposits are

thicker near the coast and then fan out to a feather edge in western

Dade and Monroe Counties. It overlies the Tamiami formation, another

porous limestone. These two limestone formations make up the Biscayne

Aquifer, the main source of water for Dade County.

Of the total land area in Dade County (1,314,560 acres) about

117,000 acres or 8.9 percent was apportioned to farms in 1964; in 1959

129,000 acres or 9.8 percent was in farms. Although some growers stated

that suitable farm land is becoming difficult to obtain, it is more

likely that other constraints, such as markets and capital and labor

requirements, are more restrictive than the physical quantity of land.

Metropolitan Dade County is one of the youngest and fastest

growing major metropolitan areas in the United States. Between 1890 and

1962, a span of 72 years, the population increased from 861 to more than

1 million persons. Between 1950 and 1960 the population increased

almost 90 percent as indicated in Table 3.

Currently, the population of the county is estimated at about

1.1 million by the Metropolitan Dade County Planning Department, and

will approach 2.5 million by 1995 if the current growth rates continue.










Table 3.--Florida and Dade County population changes for 1950 and 1960.



Florida Dade County


Total 1950 2,771,305 495,084
Total 1960 4,951,560 935,047
Percent increase 78.7 88.9

Rural 1950 957,415 29,005
Rural 1960 1,290,177 40,705
Percent increase 34.8 40.3

Urban 1950 1,813,930 446,079
Urban 1960 3,661,383 894,392
Percent increase 101.9 91.9


aSource of data: U.S. Bureau of the Census (46).



The Planning Department estimates that 130 additional square miles will

be needed for new homes and apartments to accommodate the additional 1.5

million new residents expected during the next 25 years. The effect of

this growth on the outlying areas is shown in Figure 3.

It is obvious that the projected population growth will have

serious implications for the rural interests in Dade County. Agricul-

tural activities will be driven toward the Everglades Swamp on the west

and southwest. Interdependencies between urban and rural interests will

intensify, and concurrent with this intensification, economic external-

ities arising from such propinquity will increase, giving rise to a

multitude of policy questions such as the one with which this study is

concerned.

Several other characteristics of Dade County and its farmers

should be noted. First, farming in the region requires large amounts of







17






























4


3!






RESLDENT




Figure 3.--Population trends in Dade County, Florida.
a
Reprinted with the permission of the Metropolitan Dade County
Planning Department.


capital relative to other areas of the State. The 1964 Census of

Agriculture indicates that the investment in land and buildings per acre

of farm land is $1,088.12 in Dade County against $285.71 for the State

(47, p. 304-305). For the U.S. as a whole it is $143.81 (48, p. 792).










Second, revenues per acre from farming in Dade County are also very high.

The per acre value of all farm products sold in Dade County is $443.13

against $68.41 for the State (47, p. 332-333). For the U.S. as a whole

this figure is $31.79 (48, p. 802). Third, net returns per acre vary

widely among growers and through time, as indicated in Appendix A

(p. 119). Finally, in the author's opinion, based on observations made

while in the area, the farmers of Dade County are as well educated and

as sophisticated as the entrepreneurs in any vocation. Some of the

larger ones have professional entomologists on their payrolls, and most

of the pesticide firms in the area employ entomologists to sell pesti-

cides and provide consultation to the farmers. The University of

Florida has an experiment station in Homestead, Florida, which is very

active in the area of insect and disease control. In addition, the Dade

County Agricultural Agent's Office, the largest in the State and located

in Homestead, is extremely active in disseminating information to the

farmers.
















CHAPTER III


THE MODEL


The model used for this analysis employed the concept of

consumers'-producers' surplus as a measure of welfare. For each of two

specified pesticide usage policies and three variations under one policy,

the model maximized this measure of welfare over production alternatives

available to the region. In each case the objective function explicitly

recognized measurable externalities not accounted for in the consumers'-

producers' surplus calculation. The maximization was restrained by

flexibility constraints derived from historical cropping patterns.

Finally the policies were ranked by their maxima. The model can be

formally stated as follows:

For a set of subjectively chosen pesticide usage policies, xr,

r = 1, 2A, 2B, 2C, rank the associated estimates of welfare, W where:

n y(t+l)
W = maximum: E f f(y.(t+l)) g(yj (t+l) dy(t+l
r y (t+l) j=l 0

z i(t+l)

m i tl)
Z f [h.(z.(t+l1 dz.(t+i)
i=1 0 L

where the maximization for a given policy r is subject to:

n n n
[I] d(min) E y.(t) Z y.(t-+l) d(max) Z y (t)
j==l j=i j=i











[2] b.(min)y (t) y (t+l) < b (max)y.(t) j=l, .n

n x
r
[3] E a. y.(t+l) z.(t+l) = 0 i=l, .m
j=l 1 1

[4] Cki zi (t+l) < e ki k=l . ..p


[5] y (t+1), zi(t+l) > 0

where:

f (yj (t+l)) = demand function for the jth crop in the (t+l) year.

y.(t+l) = acres of the jth crop in the (t+l) year. For simplifi-

cation the time dimension is omitted in the remaining definitions.
x
r(y.) = supply function for the jth crop under the rth policy
J
alternative.

h.(z.) = a marginal externalityy function," a functional relation-

ship between marginal external effects expressed in dollars, and

the quantity of the ith pesticide.

z. = quantity of the ith pesticide measured in units of 100 percent
1

active material.

d(min) = a minimum flexibility constraint on total farm land.

d(max) = a maximum flexibility constraint on total farm land.

b (min) = a minimum flexibility constraint on the jth crop.

b.(max) = a maximum flexibility constraint on the jth crop.
J

x
r
a.. = the quantity of the ith pesticide used per acre of the jth
Ij

crop under the rth policy.

cki = quantity of the ith pesticide produced in the kth environ-

mental element by 1 unit of the ith pesticide.

eki = an arbitrary upper limit on the ith pesticide in the kth

environmental clement--a parameter to be determined "politically."










There has been a recent revival of interest in the concept of

consumers'-producers' surplus for making policy decisions (37, 45, 55).

Though much criticized (40, pp. 208-209), it is being used in the

literature with increasing frequency to give a rough idea of welfare

aspects of various alternatives. It is defined as the integral of the

demand function minus the integral of the supply function. The integral

of the demand function is, to the user of a product, simply a total

benefits function and the integral of the supply function a total cost

function, so consumers'-producers' surplus as a function of output is a
1
net benefits function.

Buchanan (6, p. 127) argued that the welfare of a group is a

consensus of the preferences of members of the group and that an

observer could introduce a welfare criterion only through his own

estimate of the group's value scales. Hence, the observer can recommend

policy A over policy B given his estimate of the group's preferences,

and the test of his recommendation is to ask whether the given welfare

function reflects the consensus of the group. Even within this frame-

work, however, the question of votes must be resolved before any group

consensus can be revealed. Buchanan (6) further argued that majority

rule does not guarantee consensus or agreement among the parties because

adequate compensation may not be provided for the damaged parties.

At any rate, as a theoretical construct, the social welfare

function is perhaps useful, but for empirical work one generally ends

1The objective function would always be maximized at the
intersection of the demand and supply functions in the absence of the
externality relationship. The introduction of the externalfty relation-
ship will shift the optimum solui ion to the left or to the right
depending on whether the externalities are external economies or external
diseconomies. The externality function will be discussed in later pages.










up using a social welfare function which, to the researcher, represents

a compromise between desired characteristics and empirical feasibility.

Any policy decision concerning pesticide usage is likely to

benefit some parties and hurt others. The model assumes that the
2
income effects of varying prices are small enough to be ignored. Such

an assumption would not seem too unrealistic where the consumer spends

a small proportion of his income on the commodity. The model also

assumes that tastes and preferences remain constant, and that the aggre-

gate demand and supply functions for a crop are independent of those in

other regions, those for other crops, and those in other industries.

The model also assumes that the marginal utility of money is

equal for all parties affected by the policy. Lerner (33, pp. 23-40)

argues that in the absence of knowledge this is the most probable

assumption. Such an argument from ignorance is not very convincing or

comforting.

Nevertheless, the assumption that men are all very much alike is
the foundation of democratic institutions ("We hold these truths
to be self-evident . .") and we certainly do not attempt in
politics to give the voter a number of votes in proportion to
his intelligence and his ability to use them. The one-man-one-
vote principle is forced upon us by the recognition of the
practical impossibility of any other (3, p. 93).

So it is with the issue at hand. We realize that some people probably

derive greater utility than others from an incremental dollar (or

greater disutility from losing a dollar), but it was a practical impos-

sibility to recognize it in the analysis.


A slight variation on this assumption is Hicks' argument that
the marginal utility of money must be assumed constant (25, p. 326).
This assumption seems slightly more restrictive than the one I have
made, but as far as the structure of the model is concerned, it amounts
to the same thing.










The objective function of the model is assumed to be separable.

That is, it is the sum of a number of independent functions in the

variables y. and z.. In the terminology of the problem, this means that

any crop can be grown at any level within the range specified by the

constraints without affecting the demand and supply functions of the

other crops. It also means that the externality functions for chlori-

nated hydrocarbons and organic phosphates are independent. More will be

said about the estimation of these externality functions in later pages.

The concept of a flexibility constraint (see constraints 1 and

2) was used by James M. Henderson (24) in the late 1950's in an attempt

to use linear programming to predict economic behavior of farmers.

Day (11) later applied this idea in his use of recursive programming on

crop production in 11 counties in Mississippi. Considering how a farmer

might allocate his land among various alternative uses, Henderson said

that the farmer's behavior could be explained by two principles. The

first was that the farmer would try to maximize his expected net revenue

and would therefore allocate his land in a manner consistent with

this desire. The second principle said that in allocating his land,

the farmer would also be influenced by the allocation pattern of the

preceding year. This was Henderson's unique contribution. "Speci-

fically, a farmer's acreage plantings for each crop were assumed to be

constrained by maximum and minimum limits which indicated his desire

for diversity and reluctance to depart from an established pattern"

(24, p. 243). There are many factors--economic, technological, insti-

tutional, and sociological--which contribute to this reluctance to

depart from an established pattern. Henderson's problem was to capture

this hypothesis in a uodel without Tmaking the model so complicated that











it could not be used empirically. He did this by placing upper and

lower bounds on allowable acreage changes. The bounds were determined

by observing historical acreage changes and were based on the assumption

that the diverse factors which influenced such changes in the past were

likely to do so in the future with about the same magnitude. He then
3
incorporated these into appropriate linear programming constraints:

bj (min)y (t) J y (t+l) b (max)y (t) j=l, .n

where:

y.(t) = the acreage of the jth crop in period t.

b (min) = one minus the maximum percentage decrease considered

reasonable from observing historical cropping patterns.

b.(max) = one plus the maximum percentage increase considered
J
reasonable from observing historical cropping patterns.

He also had a final restriction on the overall land allocation:

n
Z y.(t+l) -Y
j=l

where:

Y is the total land available for cultivation.

This research effort utilized essentially the same approach.

Maximum and minimum flexibility constraints were applied to the indi-

vidual crops. The overall land restriction differed slightly from

Henderson's. The total quantity of Dade County land which could be

cultivated was not a relevant constraint because it was so much larger

than the quantity that was actually cultivated. Furthermore, the


3Symbols have been changed in order to be consistent with those
of the model.










4
quantity of land allocated to farming appeared to be declining. The

constraint was therefore established to apply to the total land under
5
cultivation, after an adjustment had been made for the other crops.

The form of this constraint was:

n n n
d(min) E y.(t) E- y.(t+l) -< d(max) E yj(t)
j=1 j=l j=l

where:

the d(min) and d(max), as before, represent proportional changes in

total farm land cultivated from one year to the next, as determined
6
from historical cropping patterns.

The third constraint requires little explanation. It was needed

in the model to insure that the quantity of the ith pesticide used on

all crops would coincide with the quantity of the ith pesticide called

for in the solution.

The fourth set of constraints was "environmental." It appears

relatively simple, but it embodies all the physical relationships

between pesticide usage and the environment. When a pesticide is used


4In 1959, 9.8 percent of the land was in farms,while in 1964,
8.9 percent was in farms.

5Other crops were those excluded from the objective function of
the model. These were approximately 20 in number and accounted for
14 percent of planted acreage in 1966-67.
6It has been suggested that the imposition of a new pesticide
policy upon the farmers might disturb the interrelationships which
caused the variations in cropping patterns from which flexibility con-
straints were estimated and that the flexibility constraints would
therefore lose their applicability. Whether or not this is true depends
upon the magnitude of the policy change and the other sources of vari-
ability in cropping patterns. As will be seen in later pages, the
effect of the proposed policy on farmers' costs is relatively small and
is far overshadowed by other exogeneous factors, such as weather, with
which the farmers must contend. It is therefore this writer's opinion
that the flexibility constraints do provide reasonable estimates of
"capacity for change" in the cropping patterns.










on a crop, some of it is taken into the plant, some of it goes into the

soil and water, and some of it into the air. The speed with which a

pesticide breaks down into non-toxic substances varies a great deal

among pesticides. The chlorinated hydrocarbons tend to be very persis-

tent--their residues decompose into non-toxic substances very slowly.

The organic phosphates, on the other hand, tend to break down into non-

toxic substances very rapidly and their residues do not build up

significantly in the environment. Much of the current pesticide

controversy revolves around these physical relations--the residues which

build up and the potential damage which they may represent for the

environment. As will be noted later, large amounts of resources are

being committed to research on these physical relationships, but little

has been done to delineate their role in a decision model for policy

making. For this reason the set of environmental constraints is

included conceptually in the model. It was impossible, however, to

estimate the parameters empirically; therefore, the set is not included

in the succeeding chapter on the estimation of model coefficients. This

was a regrettable compromise because this set of constraints could

possibly be the most important of all in establishing policy. It is

precisely because of pesticides' potential for environmental pollution

that people's emotions become aroused and make them demand policies

regulating the use of pesticides.

The fifth set of constraints simply prevented the occurrence of

negative acreages and pesticide quantities in the solution.

Another aspect of the model which deserves emphasis concerns the

question of property rights, a subject of growing interest in recent

literature on policy issues (9, 12, 41). The model as stated here is











independent of the pattern of legal rights. It is not necessary to

delineate the rights in order to determine the optimal solution, but it

is necessary to delineate the rights in order to evaluate the various

ways of reaching the optimum, a problem with which we are not concerned.

For example, if a policy alternative calling for a tax on chlorinated

hydrocarbons were introduced, then by implication, the farmers do not

have the right to "pollute" the environment and must pay for the privi-

ledge of doing so. On the other hand, if no restraints are placed on

farmers' usage of chlorinated hydrocarbons, then by implication,they do

have the right to "pollute" the environment to some degree. One need

only read a few historical cases in the common law of nuisance to

realize that the question of property rights will vary greatly from time

to time, from place to place, and indeed, from judge to judge (9, p. 22).

Upon reading the following quotation from Prosser, one does not wonder

that the question of property rights is a very elusive concept: a

person is permitted

. to make use of his own property or to conduct his own affairs
at the expense of some harm to his neighbors. He may operate a
factory whose noise and smoke cause some discomfort to others, so
long as he keeps within reasonable bounds. It is only when his
conduct is unreasonable, in the light of its utility and the harm
which results, that it becomes a nuisance . The world must
have factories, smelters, oil refineries, noisy machinery, and
blasting, even at the expense of some inconvenience to those in
the vicinity, and the plaintiff may be required to accept and tol-
lerate some not unreasonable discomfort for the general good (39,
pp. 398, 412).

The phrase in the above quotation, ". . in the light of its

utility and the harm which results . ." (39), illustrates the notion

that property rights are not independent of "the general good" or some

concept of social welfare. To put it another way:

S. The problem which we face in dealing with actions which have
harmful effects is not simply one of restraining those responsible










for them. What has to be decided is whether the gain from pre-
venting the harm is greater than the loss which would be suffered
elsewhere as a result of stopping the action which produces the
harm (9, p. 27).

To summarize, the optimum externality level is independent of

property rights. Property rights become important when we begin trying

to move toward the optimum. The problem of the inter-personal compar-

ison of utility cannot be ignored. The legal profession has been

grappling with it for many years and will continue doing so in the

future. Economists, by training, can bring many valuable tools to bear

on this problem and could be of valuable service to the legal profession

in making such decisions. The fact that policy decisions are difficult

in a world of imperfect knowledge does not justify ignoring them. If

economists refuse to aid in making them, they will be made by others.

Coase (9, pp. 42-43), in his classic article on social cost,

states that in two ways economists have historically fallen short in

helping to make policy decisions. First, they have tended to ignore

considerations which are aesthetic or moral in nature, when,in fact,

these may have a great influence on the final solution. Second, econo-

mists have tended to focus their attention on models that are so simpli-

fied that their solutions are of limited usefulness in the real world.

Conceptually, the model developed here at least partially meets

these criticisms. The environmental constraint allows the introduction

of value judgments through the right-hand-side elements, eki., which

are determined by political compromise. As far as the second criticism

is concerned, it is the writer's opinion that we have not abstracted

from the real world quite as far as most: writers in this area. This is

true because of a slightly different approach. We began with a real







29


problem, for which we needed both a conceptual model and the data to use

it empirically. We retreated or abstracted from reality only to the

point where we felt we could actually satisfy the data requirements.

If we had not been faced with the necessity to use the model empirically,

we might, for example, have developed a dynamic programming formulation

which would have had the advantage of greater realism but for which we

could not obtain the necessary data.

















CHAPTER IV


ESTIMATES OF MODEL COEFFICIENTS


Objective Function


The objective function of the model was based on the concept of

consumers'-producers' surplus, modified to recognize external effects

not included in the surplus calculation. As noted earlier, the area of

consumers'-producers' surplus was defined as the area under the demand

function less the area under the supply function.


The Demand Model

The model which provided the basis for estimating demand

parameters for the objective function was specified as follows:

i] q(t) = T + Tlp(t) + u(t)

subject to:

[2] q(t) q(t-l) = j[q(t) q(t-l 0 < < 2

where:

q(t) = the long-run equilibrium quantity as of period t.

p(t) = price of the commodity in period t.

I(t) = per capital disposable income (deflated) in period t.

q(t) = the quantity demanded in period t.

u(t) = a disturbance term satisfying the classical assumptions,

namely;

E[u(i)] J 0










E[u(i)u(j)] = 0 for i i j, 0a2 for i = j
U
p(t) and I(t) are fixed numbers observed without error.

The matrix of observations on p(t) and I(t) is non-singular.

The constraint, [2], simply describes the adjustment toward the

long-run equilibrium quantity.

Substituting equation [2] into equation [1] generated the
2
following function, which was empirically estimated:

[3] q(t) = T0 + (l-t)q(t-l) + 4Tlp(t) + 21(t) + 4u(t)

The primary purpose of estimating equation [3] was to obtain

estimates of the price elasticity of demand after allowing for the

average effects of I(t) and q(t-l). Data availability necessitated

using State data rather than Dade County data. It was assumed that the

price elasticity for Dade County would be the same as that for the State

since they compete in the same markets and since, in some cases, Dade
3
County represents a relatively high proportion of the State acreages.

Estimates of long-run price elasticity were thus obtained using the

1 rk
If 0 < < 1, q(t) approaches q(t) asymtotically. If = 1,
the entire adjustment is accomplished in one period. If 1 < 4 < 2, q(t)
over-adjusts, but still converges upon 2'(t).
Lagged variables have been increasingly employed in econometric
work in recent years [21, 31, 38].

21n this equation, however, u(t) is no longer independent of
q(t-l), the independent variable (27, p. 212; 35, p. 129). The result
is that the least squares estimates are biased but have the desirable
asymptotic properties of consistency and efficiency.

3Dade County accounted for the following proportions of the
State acreage in 1966-67:
Percent of State acreage
Crop in Dade County

Tomatoes 39
Winter potatoes 64
Beans 12
Corn 3










formula,

E = ip(t) : p
9p(t) 1 -
q q
4
where p and q were the means of the variables from State data. Then,

using this estimate of E and the 1966-67 price and quantity for Dade

County, a linear demand function of the following form was synthesized

for Dade County:

[4] q(t) = 0 + |Ip(t)


41n two cases which will be noted later, the variables were
transformed into logarithms. For these the regression coefficients were
the required estimates of elasticity.

5For each crop the function was derived as follows:
A iA
E = T
q
where:
A
E = estimated long-run price elasticity.
p= average price from State data.
q= average quantity from State data.
T1 = an estimate of T1 in equation [1].
= ^ q_ ^ -^ _qA
[~s1 q*Aj~ q*
LDpjd p* l= p*
where: q
3ql = partial derivative of q with respect to p for Dade County.
|dp
q* = 1966-67 quantity for Dade County.
p* = 1966-67 price for Dade County.
Using the point slope form of a linear function passing through q* and
p* yields:
q*-q(t)= = q*
q p
ap d p*-p(t) 1 P*
q
Sp Tlpq*
q(t) = q* - + p(t) q*(l-E) + j p(t)
q qp* P*
or:
q(t) = 0 + 1P(t)
where: 0 = -qa ,
0 = q*(l-E) and Pi = p*











The average effects of I(t) and q(t-l) on q(t) are, of course,

recognized in these simplified functions since I(t) and q(t-l) were

included as variables in the estimation of equation [3].

Equation [4] was then manipulated algebraically to make q(t) the

independent variable. At the same time the unit of measure on the

quantity variable was converted to acres by multiplying the equation by

the average yield per acre of the past 10 years. This led to demand

equations expressing price as a function of quantity (y) which were used

in the objective function on page 19 as f. (y.), j being the crop desig-

nation.

The conversion of q, units of production, into y, acres, via

the average yield per acre was done in order to express the demand

functions in the same units as the supply functions. Such a conversion

abstracts from the uncertainties of weather on crop yield and rests on

an average expected yield per acre.

Alternative data series (seasonal as well as annual data) were

tried in estimating demand functions. In using seasonal data, the

designated seasons were fall (September through November), winter

(December through February), and spring (March through May). Zero-

one variables were used to allow for the average seasonal effect. If an

observation occurred in the fall, the fall variable was assigned a value

of 1, otherwise 0. If an observation occurred in the spring, the spring
7
variable was assigned a value of 1, otherwise 0. The effect of this

6This, of course, does not necessarily yield the same equation
as one would get had the direction of minimization of the sums of
squares of residuals been in the direction of the p(t) axis. Initially,
p was defined as the independent variable because observations on p were
thought to be more accurate than those on q. Johnston (27, pp. 148-176)
offers a treatment of observational errors in the variables.
7The winter season is recognized in the intercept and therefore
does not have a zero-one variable assigned to it.










dummy variable technique was to shift the intercept of the demand
8
function in an attempt to recognize seasonal shifts in demand. A

logarithmic transformation of the variables was also tried. With the

exception of beans and mangos, these transformations provided little or

no improvement in the fits obtained. Table 4 presents the empirical

results which were selected for derivation of the demand functions

used in the model. Although not of interest for purposes of this study,

short-run price and income elasticities are also presented. Table 5

presents the demand functions which were used for the model.


The Supply Model
x
The second element of the objective function, depicted by g r(y),

was a linear supply function for the jth crop under the rth policy

alternative. The model from which supply parameters for current pesti-

cide usage were estimated was specified as follows:

[5] Y(t) = + E1p(t-l) + u(t)

subject to:

[6] y(t) y(t-l) = 6[y(t) y(t-l) < 6 < 2

where:

y(t) = long-run equilibrium acreage as of period t.

p(t) = price of the commodity in period t.

y(t) = acres planted in period t.

u(t) = a disturbance term satisfying classical assumptions.

As in the demand model, the constraint described the adjustment

toward the long-run equilibrium acreage. Using [6] to eliminate y(t) in


It should be noted that only the intercept changed in this
model. In other words, the demand for fall tomatoes was assumed to
differ from that of winter tomatoes only in level, not in slope.











Table 4.-Empirically estimated relations from which demand functions
were derived.a






Crop Intercept Coefficients of:A
q(t-1) p(t) I(t)/c Sfd


tomatoes


Winter potatoesg

h,i
Beansh


Cornd


Avocados k


Limesm


Mangos,h,i
Mangos


1798.3218


2271.0208


7.3391


969.9456


1130.4805


30.9556


.6427


.3027
(.1394)

.4902
(.2213)

.0930
(.0952)

.2897
(.1674)

.3805
(.1850)

.4280
(.2587)

.0896
(.1805)


- 733.8044
(149.9282)

- 229.7552
(138.8649)

- 1.8514
(.2141)

-1391.9141
(321.5176)

- 44.3303
(13.6422)

- 11.3824
(21.8403)

1.2548
(.2088)


2.8091
(.9227)

-.2824
(.6980)

.2248
(.4506)

1.8613
(.6422)

5.7642
(4.2249)

.1167
(.1344)

1.5275
(1.3004)


-1854.3457
(449.6931)




- .2146
(.0940)

-1141.8203
(332.2517)


sources of data:
1. Tomatoes, potatoes, beans, and corn: Florida Agricultural
Statistics (18).
2. Avocados: Agricultural Statistics (50).
3. Limes:
a. Production and on-tree prices: Florida Agricultural
Statistics (18).
b. Packinghouse prices: Agricultural_ Statisti cs (50).
4. Mangos: Dade County Agricultural Agent's Office, Homestead,
Florida.
Standard errors of the coefficients are shown in parentheses.
CI = per capital income.
S = a "duirmy variable" which equals 1 if the observation

occurred in the fal] and 0 otherwise.
S a "drduiny variable" which equals 1 if the observation occur-
red in the spring and 0 otherwise.
fQuantity measured in thousands of 60 pound crates; price
measured in dollars per crate.











Table 4.--Extended.


se
S


- 797.2398
(335.2292)




.1448
(.0906)

2207.9375
(894.9026)


Number
of
obser-
vations


.8107


.3976


.7492


.9610


.5066


.4832


.9131


gQuantity measured
measured in dollars per ba[
hQuantity measured
dollars per bushel.
iThis function was

JQuantity measured
dollars per crate.
kQuantity measured
mQuantity measured

dollars per box.
nQuantity measured
bushel.


Short-run Long-run Short-run Long-rur


price
elas-
ticity


price
elas-
ti city


income
elas-
ticity


1.4427


- .3094


.2248


1.4705


1.2882


.6272


1.5275


income
elas-
ticity


2.0690


- .6069


.2479


2.0703


2.0795


1.0965


- .9728


- .4856


-1.8514


-1.2703


- .7929


- .1271


-1.2548


-1.3952


- .9525


--2.0410


-1.7881


-1.2800


- .2222


-1.3783


in thousands of 100 pound


Durbin-
Watson
Statistic


1.8095


*}*
1.6151

&
2.3175


1.9689


1.8853


1.8348


1.6778 2.2197



bags; price


in thousands of bushels; price measured in


estimated in natural logs.
in thousands of crates; price measured in


in tons; price measured in dollars per ton.
in thousands of boxes; price measured in


in bushels; price measured in dollars per


Reject the hypothesis that auto-correlation is present at the
95 percent confidence level.
d statisi:ic is inconclusive.


- -4------- 1 i










Table 5.--Demand functions used in the model.a



Crop Function


Tomatoes p = 1521.5660 .0334 y


Potatoes p = 1116.6649 .0747 y2


Beans p = 1152.4799 .0650 y3


Corn p = 764.1282 .1661 Y4


Avocados p = 571.3720 .0479 y5


Limes p = 3285.4824 .7502 Y6


Mangos p = 690.9032 .1911 y7

aFor all functions, price is measured in dollars per acre
and quantity in acres.










[5] yields:

[7] y(t) = 6 0 + (l-6)y(t-l) + 6p(t-l) + 6u(t)

Equation [7] was empirically estimated and the results used to obtain

estimates of the long-run supply elasticity. Results are shown in

Table 6.

As in the development of the demand functions, the estimate of

elasticity and the 1966-67 price and quantity observations for Dade

County were then substituted into the point-slope form of a straight

line to obtain the equations for the supply functions. These equations

were then solved for p in terms of y in order to conform to the require-

ments of the model.

Since equation [5] includes price lagged one time period, the

supply and demand equations constitute a cobweb model which, for

purposes of the study, is assumed to be in equilibrium at the 1966-67

price and quantity. The objective is to determine what happens to this

point of long-run equilibrium when externalities are recognized. There

was no interest in the time path of price-quantity changes but only in

the magnitude of change in the point of long-run equilibrium.9

In the case of avocados, limes, and mangos, data did not exist

to permit the estimation of supply functions, so the acreage for these

crops was constrained at the 1966-67 level. This was probably a


91f the system gets out of equilibrium, cobweb theory (15,
pp. 255-280) states that the time path of price and quantity will be
convergent or divergent depending upon whether the absolute value of the
slope of the supply function is greater or less than that of the demand
function. In the case of potatoes, the relative slope of the supply
function to the demand function would indicate a divergent path. The
negative estimate of the income elasticity for potatoes would aggravate
this problem if income increased. Shifts in the flexibility constraints
might also affect the time path.










Table 6.--Empirically estimated relations from which supply functions
were derived.a


Intercept


y(t-l)


Coefficients of: A


Sf


p(t-l)


Tomatoes


f g
Winter potatoes '


Beansh ,g


CornI


1904.0586


1.9643


3.1023


-4528.8516


.4472
(.1393)

.7367
(.1717)

.6614
(.1636)


1754.8784
(623.8162)


-3727.2373
(1723.1143)


.4033
(.1981)

.2002
(.1659)


.7935 2353.8164
(.1569) (1463.8821)


.0003
(.0708)


1904.8828
(1570.1018)


Limes

Mangos


Acreages for these tree crops were constrained to


aSources of data:
1. Tomatoes, potatoes, beans, and corn: Florida Agricultural
Statistics (18).
2. Avocados: Agricultural Statistics (50).
3. Limes:
a. Production and on-tree prices: Florida Agricultural Statis-
tics (18).
b. Packinghouse prices: Agricultural Statistics (50).
4. Mangos: Dade County Agricultural Agent's Office, Homestead,
Florida.
bStandard errors of the coefficients are shown in parentheses.

CSf = a "dummy variable" which equals 1 if the observation occur-
red in the fall and 0 otherwise.
dS = a "dummy variable which equals 1 if the observation occur-
red in the spring and 0 otherwise.


Crop










Table 6.--Extended.


Number
of Short-run Long-run Durbin-
obser- 2 price price Watson
nations R elasticity elasticity Statistic

S /
s


- 339.7224 33 .6773 .5185 .9379 1.7873
(1392.4358)

17 .5879 .4033 1.5317 1.8363


- .0066 33 .4334 .2002 .5910 2.5493
(.0690)

7843.4375 33 .9648 .3178 1.5390 2.0073
(3283.3203)



be no greater than the 1966-67 levels.


eQuantity measured in acres
per crate
fQuantity measured in acres
per bag.
gVariables were transformed
hQuantity measured in acres
per bushel.
iQuantity measured in acres
per crate.
*
Reject the hypothesis that
95 percent confidence level.


planted; price measured in dollars


planted; price measured in dollars


to natural logs.
planted; price measured in dollars


planted; price measured in dollars


auto-correlation is present at the










somewhat unrealistic restraint for the long-run.


Shifting the Supply Functions

The supply functions developed in the manner described above

pertain to current pesticide usage practices. Those for alternative

pesticide policies could not be observed but had to be synthesized from

information provided by growers, entomologists, and others knowledgeable

about the area.

In order to approximate reality within the data limitations, the

chlorinated hydrocarbons were lumped together as one category and the

organic phosphates as another. This was done on the basis that the

chlorinated hydrocarbons tend to have similar effects on the environ-

ment and that the organic phosphates likewise tend to have similar

effects on the environment. It was also partly a result of political

considerations,since the thrust of the controversy seems to center

mainly around these two categories.

Chlorinated hydrocarbons and organic phosphates are, to a

limited extent, substitutes for each other. This is indicated by Tables

7 through 10 which show alternative insect control measures for the

vegetable crops under consideration. In many cases a given insect can

be controlled by a chlorinated hydrocarbon or an organic phosphate. But

there are also a number of insects for which the only recommended remedy

is a chlorinated hydrocarbon and a number for which the only recommended

remedy is an organic phosphate. Entomologists frequently differ in

their opinions about how to control certain insects. Adding to this

source of variation is the fact that insects' susceptibility to a pesti-

cide seems to vary widely from time to time and place to place.









a
Table 7.--Recommended insect control measures for tomatoes.




Organic Chlorinated Minimum
phosphate hydrocarbon Other days to
Insect Pesticide quantity quantity quantity harvest


Aphids


Armnyworms,
Tomato Fruit-
worms,
Hornworms


Loopers




Leaf Miners


Stinkbugs,
other plant
bugs



Banded Cucumber
Beetle


Dimethoate
Demeton
Parathion
Phosdrin
Thiodan

DDT
Phosdrin
Sevin
TDE (DDD)
Thiodan
**
Dibrom
Parathion
Phosdrin
Thiodan

Diazinon
Dibrom**
Dimethoate
Guthion

Guthion
Parathion
Phosdrin
Sevin
Thiodan

Guthion
Thiodan


aSource of data: Insect Control Guide (17)
with entomologists familiar with the area.


and consultation


bQuantities are expressed in pounds of 100 percent active
material per 100 gallons of water. The gallonage applied per acre will
vary with the size and density of the plants. In the Dade County area
the gallonage usually varies from 100 to 300 gallons.
*
No time limit.
**Dibrom was not observed in Dade County in 1966-67
Dibrom was not observed in Dade County in 1966-~67.


.334
.375
.450
.500


1.000

1.000


1.000
1.000


1.000


.500


2.000
.450
.500


.500
1.000
.334
.500

.500
.450
.250


.500


3
1 *
NTL
1
1

1
3
1
1

1
1
7,*
NTL
*
NTL
3
1
NTL
1
*
NTL
1


1.000


1.000


1.000


1.000










Table 8.-aRecommended insect control measures for potatoes.
Table 8.--Recommended insect control measures for potatoes.


Insect


Aphids


Armyworms,
Loopers, other
caterpillars


Banded Cucumber
Beetle

Leaf-footed
Plant Bug,
Green Stinkbug


Leaf Miners





Wireworms


Pesticide


Demeton
Dimethoate
Meta-Systox-R
Thiodan


Parathion
Phosdrin
Thiodan
Toxaphene

Guthion
Thiodan

Guthion
Parathion
Phosdrin
Thiodan

Diazinon
Dibrom**
Dimethoate
Guthion

Thimet
Parathion


Organic
phosphate
quantity


.375
.500
.375


.300
.500


.500


.500
.300
.250


.500
1.000
.334
.500

3.000
2.000


Chlorinated
hydrocarbon
quantity


1.000


1.000
1.000


1.000


1.000


aSource of data: Insect Control Guide (17) and consultation
with entomologists familiar with the area.

bQuantities are expressed in pounds of 100 percent active
material per 100 gallons of water. The gallonage applied per acre will
vary with the size and density of the plants. In the Dade County area
the gallonage usually varies from 100 to 300 gallons.
A
No time limit.
*Dibrom was not observed in Dade County in 1966-67.
Dibrom was not observed in Dade County in 1966-67.


Soil treatment prior to planting.


Other
quantity


Minimum
days to
harvest


21
7
7
NTL

5
1*
NTL*
NTL

7
NTL

7
5
1
NTL

14 *
NTL
7
7










Table 9.--Recommended insect control measures for beans.a


Organic Chlorinated Minimum
phosphate hydrocarbon Other days to
Insect Pesticide quantity quantity quantity harvest


Aphids


Armyworms,
Corn Earworm


Cowpea Curculio


Bean Leaf-
hopper, Bean
Leafroller





Leaf Miners,
Cucumber
Beetles

Thrips

Stinkbugs


Saltmarsh
Caterpillar

Lima Pod Borer


Demeton
Dimethoate
Parathion
Phosdrin

DDT
Sevin
Toxaphene

Toxaphene
Thiodan

Dimethoate
Guthion
Parathion
Phosdrin
Sevin
Toxaphene

Diazinon
Dimethoate
Guthion

Parathion

Guthion
Parathion
Phosdrin
Sevin

Phosdrin
Toxaphene

Parathion


.334
.500
.300
.500


1.000


1.000


source of data: Insect Control Guide (17) and
with entomologists familiar with the area.


consultation


bQuantities are expressed in pounds of 100 percent active
material per 100 gallons of water. The gallonage applied per acre will
vary with the size and density of the plants. In the Dade County area
the gallonage usually varies from 100 to 300 gallons.

No time limit.


Should not apply Thiodan more than 3 times per season.


.375
.334
.300
.250


1.000


1.000

1.000

1.000
.500


21 *
NTL
3
1

5,
NTL
5

5**
3
*
NTL
7
3
1
NTL
5

7
NTL
7


7
3
1 *
NTL


.500
.334
.500

.225

.500
.300
.250


.500


.300


1.000


1.000










a
Table 10.--Recommended insect control measures for corn.


Organic
phosphate
quantity


Chlorinated
hydrocarbon
quantity


Other
quantity


Minimum
days to
harvest


Aphids, Spider
mites

Fall Armyworms
and Corn Ear-
worm feeding
in bud







Silk-fly

Earworms






Corn Stem
Weevil


Parathion
Phosdrin

DDT
Parathion
Toxaphene
Mixture of
DDT and
Parathion
Mixture of
DDT and
Toxaphene

Parathion

DDT
Sevin
Mixture of
DDT and
Sevin

DDT
Mixture of
DDT and
Toxaphene
Mixture of
DDT and
Toxaphene


aSource of data:


Insect Control Guide (17) and consultation


with entomologists familiar with the area.

bQuantities are expressed in pounds of 100 percent active
material per 100 gallons of water. The gallonage applied per acre will
vary with the size and density of the plants. In the Dade County area
the gallonage usually varies from 100 to 300 gallons.
A*
No specific limitation so long as the usages do not result in
a residue on the edible ears.

tThese amounts should be mixed in 50 gallons of water and
applied to one acre.


Insect


Pesticide


.250


.250
.250


.250


.125


1.000

1.500

1.000


1.000
.750


.250


2.000t


.500


2.000t


2.000


2.000

1.000
1.000

2.000
1.000










Furthermore, if the same pesticide is used constantly for a prolonged

period of time, it raises the probability of immunity on the part of

the insect.

In order to fulfill the data requirements of the model, it was

necessary to synthesize a substitution rate between the chlorinated

hydrocarbons and the organic phosphates. This is not to say that the

consideration is necessarily limited to two dimensions, but these two

categories are so clearly dominant that it is doubtful if the others

could have a significant effect on the average substitution rate.

Entomologists familiar with the problem generally agree that the

substitution rate which maintains equal control is not completely linear,

as shown by the iso-product curve in Figure 4, but is more likely to be

a hyperbolic function somewhat like that of Figure 5.10

Figure 5 merely says that it becomes progressively more

difficult to substitute one for the other, which is in complete agree-

ment with economic theory. It also leads one to the conclusion that

sudden elimination of the chlorinated hydrocarbons may be an unattainable



uo
4J 0~
(0 $




Organic phosphates


Figure 4.--A linear iso-product function for chlorinated
hydrocarbons and organic phosphates.


10Three entomologists provided assistance in developing this
section. Two are located on the University of Florida campus and one in
Dade County.














P 0
0 P




Organic phosphates



Figure 5.--A hyperbolic iso-product function for chlorinated
hydrocarbons and organic phosphates.


policy objective and that reduction is a far more reasonable goal, for

the immediate future, particularly if one is to maintain production

without changing non-pesticide inputs. This doesn't mean that the ulti-

mate goal cannot be elimination or near elimination of the chlorinated

hydrocarbons. It simply means that the first step to be evaluated will

be a reduction policy, and that the ultimate goal, whatever it is, will

be approached asyitotically through a series of policies. It is the

objective of this research effort to trace the welfare implications of

the first step only. The alternative pesticide usage policy for the

analysis was subjectively chosen as a 50 percent reduction in per acre
11
usage of hydrocarbons for each crop.

It was the consensus of entomologists that for policies of 50

percent reduction or less (of chlorinated hydrocarbons) per acre, a one-

pound reduction of chlorinated hydrocarbons would require about .3 to .4


11 There are currently some efforts to make laws banning theuse
of DDT and certain other "hard" pesticides in several states. It is
debatable as to how much effect this would have on reducing the usage of
the "hard" pesticides as a group, for the farmers would be likely to
shift over to some other persistent pesticide not covered by the legis-
lation. Several economists have warreed against the pitfalls of excessive
control in pollution problems (43, pp. 56--58; 30, pp. 17-21).






48


12
additional pounds of organic phosphates. All were careful to specify

that such a statement required gross assumptions about insect infes-

tation, weather conditions, and insect susceptibility. Because of this

great possibility of error, the model was run using rates of .3, .4,

and .5.

Given the substitution rate between chlorinated hydrocarbons and

organic phosphates, it was then necessary to translate this into a shift

in the supply function for the specified policy--a 50 percent reduction

in the per acre usage of chlorinated hydrocarbons for each crop. In

order to do this it was necessary to compute the average costs of chlo-

rinated hydrocarbons and organic phosphates by crops. These are

presented in Table 11. The difference in costs of, say, chlorinated


Table ll.--Estimated average costs of chlorinated hydrocarbons and
organic phosphates used on crops in Dade County, 1966-67.



Chlorinated Organic
Crop hydrocarbons phosphates

... Dollars per pound of 100% active agent.....
Tomatoes 1.09 3.82
Potatoes 2.46 3.01
Beans .99 2.69
Corn .90 1.82
All crops sampled 1.07 2.83



hydrocarbons between tomatoes and potatoes is due to the difference in

mix of chlorinated hydrocarbons between the-two crops. Potatoes tend


12For policies specifying a greater than 50 percent reduction,
none were willing to speculate on the substitution rate,and two indi-
cated that "adequate" control of certain insects would be difficult or
impossible with the current state of the arts.










to use a higher proportion of the more expensive chlorinated hydrocarbons

than do tomatoes, and in computing the average, each chlorinated

hydrocarbon is weighted by its relative importance in the usage mix.

Next, average total costs of chlorinated hydrocarbons plus

organic phosphates for the two alternative policies and the three substi-

tution rates were computed. These are presented in column 3 of Table 12.

In addition to a change in the cost of spray materials, there is

also a possibility that other categories of cost might change under the

alternative pesticide policy. The major ones are those likely to be

affected by a change in the required number of pesticide applications,

since the organic phosphates as a group are less persistent than the

chlorinated hydrocarbons and might require more applications for control.

The entomologists considered this question and came to the conclusion

that there would be very little increase in labor and machinery costs in

Dade County for the crops concerned. The reason for this is that a

fairly rigid fungicide program is required on all crops, and increasing

the number of insecticide applications would merely involve combining

the insecticides with the fungicide applications. Estimates of

increased labor and variable machinery expense were as follows:

Tomatoes 10%

Potatoes 10%

Beans 0

Corn 0

Changes in the cost of spray materials, labor,and variable

machinery cost were then related to the cost per acre of producing the

crops. These are showpin Table 12.

If we assume these changes in average total cost per acre are










Table 12.--Estimates of average total production costs in Dade County,
1966-67, by crop and pesticide policy.


Average
total cost


Cost of chlorinated
hydrocarbons and
organic phosphates


Cost of cultural
labor and variable
cost of machinery
cost of machinery


Tomatoes
Policy
Policy
Policy
Policy

Potatoes
Policy
Policy
Policy
Policy

Beans
Policy
Policy
Policy
Policy

Corn
Policy
Policy
Policy
Policy


repair.


................... Dollars


$866.02e $19.10
881.95 19.27
883.11 20.43
884.28 21.60


$609.24
616.57
616.76
616.95


$754.87c
754.55
755.02
755.48


$399.21'
392.01
395.71
399.42


$20.88
19.88
20.07
20.26


$ 8.27
7.95
8.42
8.88


$48.39
41.19
44.89
48.60


per acre..................


$157.61c
173.37
173.37
173.37


$ 83.32c
91.65
91.65
91.65


$189.80c
189.80
189.80
189.80


$ 47.02c
47.02
47.02
47.02


Includes cultural labor, gas, oil, grease, maintenance, and

bCurrent usage.


CSource of data: Brooke (5).
dA 50 percent reduction in chlorinated hydrocarbons and a sub-
stitution rate of .3 pounds of organic phosphates per pound of chlorinated
hydrocarbons.
eA 50 percent reduction in chlorinated hydrocarbons and a sub-
stitution rate of .4 pounds of organic phosphates per pound of
chlorinated hydrocarbons.
A 50 percent reduction in chlorinated hydrocarbons and a sub-
stitution rate of .5 pounds of organic phosphates per pound of
chlorinated hydrocarbons.










realistic over a wide range of acres, then the change represents a
13
parallel vertical shift in the marginal cost function.13
In summary, it was decided that two categories of cost--(l) cost

of spray materials and (2) cost of labor and variable machinery cost--

were most likely to be affected by the alternative pesticide policy.

The effects of the policy change on average costs per acre were deduced

from cost data currently available and the experience of entomologists

familiar with the region. These cost changes were applied as parallel

shifts in the supply functions used for the first policy. The supply

functions used in the model are presented in Table 13.


The Externality Functions

The explanations for the point estimates of pesticide usage

and externalities are developed in Chapters V and VI respectively. It

is the objective of this section to show how these estimates were used


13Using the functional forms of the model:

given:
TC = A + BY + CY2
then,
MC = B + 2CY
and,
A
AC = A + B + CY
y
where:
TC, MC, and AC are total, marginal, and average cost in dollars
per acre,and Y is the number of acres of output. A parallel vertical
shift, X, in AC gives:
A
AC' = V + (B+A) + CY
for which the corresponding TC and MC become:

TC = A + (B+A)Y + CY2
MC = B + X + 2CY
and the result is a parallel shift in MC.










Table 13.--Supply functions used in the model.a



Function


Tomatoes
Policy 1 p = 83.6862 + .0497 y
Policy 2A dp = 67.6562 + .0497 y
Policy 2B p 66.5962 + .0497 y
Policy 2Ce p = 65.4262 + .0497 yl

Potatoes
Policy 1 p = 189.0995 + .0464 y
Policy 2A p = 196.4295 + .0464 y
Policy 2B p = 196.6195 + .0464 Y2
Policy 2C p = 196.8095 + .0464 Y2

Beans
Policy 1 p = -535.2889 + .2245 Y3
Policy 2A p = -535.6089 + .2245 y3
Policy 2B p = -535.1389 + .2245 y3
Policy 2C p = -534.6789 + .2245 y3

Corn
Policy 1 p = 171.6317 + .1930 Y4
Policy 2A p = 164.4317 + .1930 Y4
Policy 2B p = 168.1317 + .1930 Y4
Policy 2C p = 171.8417 + .1930 y4

Avocados

Limes Acreages on these crops were constrained to be
no greater than the 1966-67 levels.
Mangos


aFor all functions, price is measured in dollars per acre and
quantity in acres.
current usage.

cA 50 percent reduction in chlorinated hydrocarbons and a
substitution rate of .3 pounds of organic phosphates per pound of
chlorinated hydrocarbons.
dA 50 percent reduction in chlorinated hydrocarbons and a
substitution rate of .4 pounds of organic phosphates per pound of
chlorinated hydrocarbons.
e
A 50 percent reduction in chlorinated hydrocarbons and a
substitution rate of .5 pounds of organic phosphates per pound of
chlorinated hydrocarbons.










in the model.

The model required a functional relationship between dollars of

externalities and the quantity of pesticides used. Empirically there

was no way to estimate such a function, so it was necessary to assume

a functional form and rely on sensitivity analysis for some indication

14
about how critical the assumption was to the solution. Figure 6 is

illustrative of the case for organic phosphates. The point estimate of



E
($)


$4,590._____ _





152,348 z2 (organic phosphates)
lbs.


Figure 6.--Point estimate of externalities arising from the
agricultural usage of organic phosphates.


externalities due to the organic phosphates in 1966-67 was $4,590, and

the corresponding usage on the seven crops was 152,348 pounds.15 A

linear functional form passing through the origin was assumed. This

meant that the equation was:

[8] E2 = .0301 z2


14Parametric programming could be employed on both the form of
the function and the parameters of the function. In this paper only the
parameter, or slope, of the linear function was varied.

15Some thought was given to the possibility of measuring the
second-order effects through an input-output technique, but it was later
discovered that the solution was relaLively insensitive to this parameter
and the idea was therefore abandoned.
-x_









The slope of the function was later varied from 0 to 5.0000 in incre-

ments of 1.0 to analyze the criticalness of the assumption.16

The case for chlorinated hydrocarbons presented a more

formidable problem. A point estimate of the chronic externalities could

not be observed, so the function,


E1 = el1z


was used, where e was varied over the interval 0 to 5.0000 in increments

of 1.0.


Constraints on the Objective Function


For ease of illustration, the constraints on the objective

function can be written in matrix notation as:

AB C


where:


[-i3i,n
[1i],n

[Fl]n,n_


rx
_ axillm,n
0
mp,n


J0
1 ,


Ol,m

0l,m

n- 3, m

- - - n,11- -
n-m -


[-l]m,m
[k-1
Lki] mp ,mn


16An interesting point could be raised here. Data from the
Industrial Commission indicate that acute externalities have declined.
This decline was probably in the face of rising organic phosphate usage,
which would imply a negative slope to the function of Figure 6. How is
the positive slope logically justified? The function of Figure 6
represents the relation between externalities and organic phosphate
usage, all other factors held constant. But all other factors have not,
of course, remained constant. Education may be the primary example.
The rural inhabitants of Dade County might be learning how to handle the
dangerous pesticides and this is at least a partial explanation of why
externalities have declined in spite of rising usage.









n
-d(min) Z y (t
j=l i,i
n
d(max) yj(t)

B j ( tL j = l C1


Jm 19
B= C I (In V i h (min)y (to
J j n-3,1
[zil m~ 11[bj (max)y (t) n-3,1


[113,1
Om,l

[eki] I


Coefficients of Variables in the Constraints

Matrix A will be considered first. It is the matrix of

coefficients, which had to be empirically determined. As indicated

above, most of the elements of the matrix are ones or zeros. The only

elements which had to be estimated were the a.. The c,
ij k m ,
I m,n L J mp'm
elements would also have to be estimated if the environmental constraints

were maintained in the model, but, as explained previously, the current

state of the arts did not permit estimation of these coefficients. In

the empirical model there were seven crops and two pesticides, so:

X X X
r r r
'x a ll> a12' a 17
a r
nj x x x
in _mn ar ar ar
a2 1 a 2 2 7

Since two pesticide policies with three variations on the second were

considered, the model was run once for r=l and once for r=2A, 2B, and 2C.
xI
The way in which the elements a.. were estimated is explained in
ij









Chapter V. The resulting coefficients were:

Xl 3 6.0719, 1.2860, 3.4304, 40.7003, .0706, .0484, .0205

ijmn .2669, 5.8825, 1.8114, 6.4624, .2239, .1535, .0650j


The Constraint Vector

Of the elements of the constraint vector, C, the first four sets

were flexibility constraints. The underlying philosophy of flexibility

constraints was explained in the previous chapter.

For the estimation of the maximum and minimum flexibility con-

straints, an approach called "the method of average rates" was used (11,

p. 189). For the jth crop, this took the form:

im y (t)
b (max) = E1 for y (t) > y. (t-1)
t=2 (t-l) J
mI1


M 2 y,^)
b (min) = E2 yJ )
t=2 yj (t-l)

m2

where m's were the number of p

total land constraint, it was:


for y (t) < yj (t-1)


periods involved, respectively. For the


p1
d(max) = I
t=2



P2
d(min) = E
t=2


n y (t)


zy _--
j-1 ji

j=l (t
Pl

n y.(t)
Z
j-l Y (t-1)


n
for E y (t) >
j=l J


n
for EZ y.(t) <
j=l J


n
E y.(t-l)
j=l


n
E y.(t-l)
j=l


where p's were the number of periods involved, respectively, and n the

number of crops.

In words, the maximum flexibility constraint was simply one plus

the average of the percentage increases, and the minimum flexibility









constraint was one minus the average of the percentage decreases.17 The

flexibility constraints so generated are presented in Table 14.


Table 14.--Flexibility constraints used for the empirical model.


Crop


Constraints


Total land



Tomatoes

Potatoes


.9022(44843) -



.8284(19000) J

.8527( 7660)

.9037( 5830)

.6881( 1650) j


Beans

Corn


5
j y
j=l


Yl


Avocados


Limes

Mangos


1.0835(44845)



! 1.1140(19000)

1 3.1654( 7660)

S1.1458( 5830)

S2.2872( 1650)

S5235

- 3585

S1520


aDefined as total land under cultivation after allowance for
minor crops (see page 25).


17At least two other possibilities exist for obtaining flexi-
bility constraints. First, one might choose to use the maximum
percentage changes rather than the average percentage change. Second,
one might use a regression approach, fitting the functions:


y (t) = b (max)y.(t-l)
and y j J (t)
and y.(t) b.(mln)y.(t-l)


yj (t) > y (t-l)

yj (t) J y (t-l)


The "regression approach" generated parameters which were very close to
those presented in Table 14.
















CHAPTER V


APPLICATION OF AGRICULTURAL PESTICIDES IN DADE COUNTY


The information contained in this chapter provides the descrip-

tive background and the pesticide usage estimates employed in Chapter IV

for the development of the externality functions.


Technology of Pesticide Application in Dade County


Pesticide spray programs in Dade County vary widely among

growers depending upon the crop involved, the form. of the pesticide

being used, the method of application, and the insect or disease

infestation. There are four dominant pesticide formulations--(1) wet-

table powder, (2) dust, (3) liquid or emulsifiable concentrate, and

(4) granules--and there is a range of concentrations for each.

Parathion, for example, was observed in three different formulations

and seven different concentrations.

The technology of pesticide application ranges from the

relatively primitive hand methods to aerial application using

helicopters, but most growers use ground rigs which hold one, two, three,

or four hundred gallons of liquid. Aerial application is still slightly

more expensive than ground application, but it is more flexible during

adverse weather conditions when ground rigs cannot get into the fields.

Some growers claim that aerial applications are more effective, through

better coverage, than ground rigs. The ground rigs are usually operated

58










at about four miles per hour and three hundred pounds per square inch

nozzle pressure. The number of nozzles varies from three to nine

depending upon the size of plant and desired coverage.

While recommended pesticide dosages are specified on the label

in terms of "amounts per acre," growers tend to make their calculations

in terms of "amounts per one hundred gallons of water" since their spray

rigs hold even multiples of one hundred gallons. They then try to put

the appropriate amount of liquid (water plus pesticide) on an acre so as

not to exceed the recommended dosage. The spray men are, for the most

part, limited in education, so the calculations and mixing are usually

done by the grower,and then the spray man adjusts the speed of the

tractor slightly so that (1) the plants are well covered and (2) a

minimum quantity of spray drips to the ground. In addition, the spray

men try to adjust the discharge slightly so as to run out of spray about

the time they get to the side of the field where the pesticides and

irrigation well are located. In a word, the uniform application of a

given quantity of pesticide to an acre is considerably imprecise.

Aerial spraying is usually done on a contract basis by pilots

who specialize in crop spraying. The farmer purchases the pesticides

and turns them over to the pilot with a mandate to apply a certain

quantity per acre. The pilot, knowing the size and discharge rate of

his spray rig, then mixes the pesticide with water in an appropriate

ratio to achieve the farmer's desired coverage. Boundary lines are

fairly well marked,and experienced pilots familiar with the area can

often spray a field without assistance from the ground. Others use a

helper on the ground to mark off the field boundaries and the spray

progress. Like spraying with ground rigs, aerial T .,.u'g is relatively











imprecise. Wind conditions can effect the coverage significantly, and

the spray apparatus is difficult to turn off at exactly the right moment

as the pilot turns to make another sweep of the field. A survey of

growers and entomologists in the area, conducted by Mr. Richard Hunt of

the Dade County Agricultural Agents Office, indicated that aerial

spraying on the average has increased about 10 to 20 percent over the

past 5 years but that the increase is mainly a function of weather. It

is becoming common practice for the growers to use airplanes after a

rain when it is difficult to get machinery into the fields.

The vagaries of weather are also very important in determining

the frequency with which growers must spray. Not only are certain

weather conditions more propitious than others for disease and insect

damage, but if a grower sprays his field just before a rain he must

often spray it again.right after the rain because the pesticide was

washed off the plant.

In general, it was observed that growers tend to use a pre-

determined spray schedule for disease and a random or "spray-as--needed"

program for insects.


Estimated Quantities of Pesticides Used in Dade County


One might go about estimating Dade County's agricultural usage

of pesticides in several ways. The quickest would be to assume that

growers follow labeled recommendations completely, and then develop the

estimate based on the recommendations, an assumed frequency of appli-

cation, an assumed length of growing season, and a knowl(. of the

acreage of each crop. One such estimate was made by Mr. RichardW.Klukas,










management biologist for the Everglades National Park, and his estimates

are presented in Appendix B (p. 124).

For this study, the quantity of pesticides used for agriculture

in Dade County was estimated by interviewing the growers and by using

records of the growers' pesticide purchases. The interviews provided

background information on spray programs, and, in some cases, the

growers kept individual field records which were made available.

The growers which were interviewed accounted for the following

proportions of harvested acreage in 1966-67:

Tomatoes 73.5%
Potatoes 72.5
Pole beans 65.0
Corn 96.5
Squash 29.2
Other vegetables 43.8
Total vegetables 59.0
Grovesb 12.6


aIncludes okra, peas, cucumbers, cuban pumpkins.

bIncludes avocados, limes, mangos, papayas.



When a pesticide firm makes a sale to a farmer, an invoice is

prepared in triplicate. The farmer receives one copy and the firm keeps

two. At the end of the month, when statements are sent out, another

copy is sent to the farmer along with his bill. The farmers usually

keep this second one for their records. During the interviews, the

farmers were asked for permission to copy these records or to copy the

originals from their pesticide suppliers. If the farmer preferred that

we go to his pesticide supplier, he was asked to sign a permission


We are indebted to Mr. Kliikas for permission to publish these
estimates.









statement for the benefit of the supplier. After making preliminary

visits to the suppliers to explain the research, they were approached

and asked to cooperate. Of the eight major firms in the area, six

agreed to let us copy invoices of sales to customers who had given us

permission to do so. Two refused to cooperate, so the information was

obtained from the grower's copy of the invoice. As noted previously,

there were many different pesticides used in Dade County, and most of

the pesticides were used in a number of different formulations.

Initially,there were 212 separate pesticides and formulations identified.

For a given pesticide the different formulations were converted to units

of 100 percent concentrated material and added together. This reduced

the number of pesticides to 56. The common name, trade name, and/or

chemical name of these are presented in Appendix C (p. 131).

The basic unit of observation for all of the pesticide usage

tables was either a grower or a field. Where a grower had complete

spray information on his fields, each field was considered a separate

observation. If he did not keep field data, the farmer was asked to

designate what crop every pesticide invoice was used on, and it was

assumed that the pesticide was used uniformally on all his land planted

in that crop. It was further assumed that the pesticide was used in

the month in which it was sold.

It was not particularly difficult for the farmers to match the

pesticide with the crop on which it was used since (1) the crops tend

to differ in their basic spray programs both as to ingredients and

timing and (2) most of the farmers tend to specialize in certain crops.

Many of those interviewed grew only one crop and very few grew more than

three. Justification for the assumption that the pesticide was used










in the month in which it was purchased is found in the fact that many

growers stated they were unwilling to tie up capital and warehouse space

maintaining an inventory of pesticides which were dangerous and might,

in some cases, spoil before they were used. Transportation and labor

costs were also saved since the pesticide supplier would deliver directly

to the field where the pesticide was needed.

The crop year for the pesticide usage estimates extended from

July until the following June. Most of the planting was done in

September, October, November, and December, and most of the harvesting

in January, February, March, and April. For an acre of ground, only one

crop per year was grown.

The tables of pesticide usage estimates are presented in Appendix

D (p. 139). Much of the pesticide data presented in the appendix were

not required for the empirical model but they are nevertheless descrip-

tive of pesticide usage in the region and it was felt that they might

also be of use to other researchers concerned with the pesticide issue.

Table 38 (p. 140) summarizes the data for six broad categories

of pesticides---fungicides, organic phosphate insecticides, chlorinated

hydrocarbon insecticides, carbamate insecticides, other insecticides,

and herbicides. Each is reported by crop. Much of the current pesti-

.cide controversy centers around two of the insecticide categories--

the chlorinated hydrocarbons and the organic phosphates. It is the data

on these two categories which were used in the model of Chapter III.

Data on the trend in usage of these categories through the years were not

obtained for Dade County, but the statistical series recently initiated

by the Economic Research Service of the USDA indicates that since 1964

farmers in the Southeast region (South Carolina, Georgia, Florida,










Alabama) have shifted toward the organic phosphates and away from the

chlorinated hydrocarbons while holding the total usage about the

same (51, 52).

Table 39 (p. 142) utilizes the same breakdown of pesticides and

crops,but the data are reported by months for the 1966-67 crop season.

As indicated by the table, usage of pesticides tends to peak out during

the winter months of November, December, January, and February, and

tends to be very low during other months of the year. One might wonder

if the seasonal pattern of pesticide usage combines with the life cycle

of wildlife in the region to cause any particular damage or threat of

damage. There was, however, no evidence to support or refute such an

hypothesis.

Table 40 (p. 155) presents the estimates of annual per acre

pesticide usage for 1966-67 by crop and pesticide. An estimate is also

made of the total pesticide usage for Dade County by pesticide and crop.

Finally, the trend of usage is specified where possible. In a few

isolated cases it was possible to collect data for earlier years, but

the data were not sufficient for making point estimates of pesticide

usage.

Table 41 (p. 163) shows a breakdown by pesticide, crop, and

month for 1966-67. As in Table 39 (p. 142), the intention is to indicate

the seasonal pattern of pesticide usage.

The wide variation of spray programs among growers was noted

early in this chapter. The question arose as to whether this variation

was caused because the giowiers used different pesticides or mainly

because they all used the same pesticides but in greatly varying quanti-

ties per acre. Table 42 (p. 176) was developed to try to "shed some






65


light" on this question. In general, there was a tendency for the

coefficients of variation to fall slightly in this table as compared with

Table 40 (p. 155), but the differences were not particularly striking,

and one is led to the conclusion that not only do the farmers differ

widely in the pesticides they use, but the users of a particular pesti-

cide differ widely in the quantities per acre that they employ.

















CHAPTER VI


EXTERNALITIES IN DADE COUNTY


The information reported in this chapter represents the basis

for the point estimate of externalities used in Chapter IV to synthesize

the externality functions.

For purposes of empirical measurement an externality was defined

as any "cost" which was created by the agricultural use of pesticides

but was not reflected in the supply functions of the producers. This

definition does not preclude the possibility of handling an external

benefit as a negative cost. It is empirically operational and is not

inconsistent with that presented by Buchanan and Stubblebine (7).

The estimation of externalities in Dade County was based on

information gathered from five sources, and the kinds of data needed to

activate the environmental constraints (constraint set 4, page 20) were

considered from a conceptual point of view.


The Grower Interview


A section of the grower questionnaire was devoted to questions

about human sickness, damages to wildlife and domestic animals, and

damage from drift among the growers. This interview, with additional

comments on certain questions, is presented in Appendix E (p. 184).

Most of the growers in Dade County have had some experience with

pesticide sickness. The responses of the growers indicated that such










incidents were not on the increase and possibly were decreasing in the

face of increasing organic phosphate usage. Growers stated that edu-

cation of spray men to the dangers of pesticides and the safe ways of

handling them has been a key factor in stemming the rise of such

incidents. It should be stressed that "safe ways of handling them" did

not always coincide with "recommended ways." This became very evident

from the responses to certain questions (see items 3.28, 3.29, and 3.30

in Appendix E, p. 190) asked of growers. Only one grower stated that he

required his men to wear protective clothing such as boots, gloves, and

masks while spraying. The rest of the growers indicated that they made

such equipment available but could not get their spray men to wear them.

This equipment is very uncomfortable in the hot climate of South Florida,

and most of the spray men would simply prefer to "take their chances"

with the pesticides. Individual tolerance to pesticides among spray men

seemed to vary a great deal. Some spray men were able to handle pesti-

cides with no ill effects, while other spray men tended to be very

sensitive to them. In general, sensitive spray men did not remain spray

men very long. This "natural selection" process is also a possible

explanation for the fact that pesticide sickness does not appear to be

on the increase. It was also reported that some spray men refuse to

apply the highly toxic organic phosphates such as parathion. This was

not, however, observed to be very widespread. Table 15 summarizes the

responses of the growers to questions concerning human sickness.

Grower responses to questions about damage to domestic animals

and wildlife were somewhat vague as to time of occurrence and extent of

damage. In most cases this was probably due to lack of recollection.

Many of the growers acknowledged fish-kills in the canals. Water was

















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frequently taken out of the canals for irrigation and for mixing with

pesticides. Some of this water eventually seeped back into the surface

and ground waters, carrying with it some pesticide residues. It was also

likely that some residues in the surface waters flowed down from the

farming regions just north of Dade County. Growers also acknowledged

that spray rigs were frequently washed out in the canals, increasing the

pesticide residues. Most of the fish found in the canals and drainage

ditches are what conservationists call "rough fish" (not considered suit-

able for human consumption), but the migratory laborers, nevertheless,

ate them, and they were a part of the ecological system of the area.

Some wildlife could, of course, be classified as "pests" and

were purposely poisoned by the growers. This was frequently the case

with rats, blackbirds, crows, and raccoons. One grower stated that the

robin was a pest to strawberry growers in the area. Seagulls are very

plentiful in the area, and several growers stated that they were

sometimes killed by the pesticides. Three growers stated that they had

one dog each killed from drinking polluted water standing in the fields.

It is customary in the area to use honeybees to facillitate

pollination on some of the blooming crops. Squash is the dominant

example. Hives of honeybees are rented from beekeepers by the growers

and placed around the field. One beekeeper, a past president of the

Beekeeper's Association, was contacted about pesticide damage to the

bees. He stated that such damage was very common and that most bee-

keepers assumed they would lose a few bees when they were rented out.

Bees, however, reproduce very quickly and the economic loss was slight.

He further said that most of the growers were using more caution, and

that damage during the past two or three years was much less than it had










been previously. Generally there was no cash settlement above rental

cost when damage occurred.

Damage among growers from pesticide drift appeared to be a very

minor problem, although it did exist. When it occurred, the growers

usually settled the problem informally among themselves with the

damaging party "paying off" in one form or another. Those payments, we

assumed, entered the damaging party's marginal cost function and were

not construed as "externalities." The trends toward fewer crops and

larger crop fields have caused this problem to decline in recent years.

It is conceivable that external benefits may have existed due

to pesticide drift. One grower, located in the center of a number of

growers who sprayed regularly, might not have had to spray as much as

he would otherwise because of the drift from the other growers and the

protective pesticide barrier around his field. This is, however,

speculation, and we were unable to gather any data to substantiate such

an hypothesis. Table 16 is a summary of the drift problem as reported

by the growers.

As a group, the growers were well insured. Not only did almost

all of them carry workmen's compensation on their employees, but they

also carried general liability to protect them in case they damaged the

property of others. They did not, however, have insurance for cases

where others damaged their property. Whether insurance costs should be

considered as external costs will be discussed in a later section.


Insurance Claims


A Florida State law exists which requires all workmen's

compensation claims to be on file at the Florida Industrial Commission
















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in Tallahassee. The Industrial Commission was contacted and they

readily agreed to cooperate on the research. Data for Dade County for

the years 1966 and 1967 were obtained and summarized.

All of the workmen's compensation claims reported to the

Industrial Commission are classified into two groups--disabling and

non-disabling. These terms are defined as follows:

Disabling injury: a work injury resulting in death, permanent
impairment, or loss of time beyond the day or shift of occurrence.

Non-disabling injury: an injury arising out of and in the
course of employment in which there is no loss of time beyond the
day or shift of occurrence (19, 1966 edition).

Very little data are maintained on the non-disabling category. The

number of injuries in this category is much greater than for the dis-

abling category, but the cost involved is much less. Table 17 shows a

comparison between these categories for the state, the agricultural

industry, and commercial farms.

The Industrial Commission categorizes its disabling injuries in

a number of ways. The one which seemed most important for this research

was "disabling work injuries by agency." The "agency of injury" identi-

fies the object, substance, exposure or bodily motion which directly

produced or inflicted the injury (19, 1966 edition). There are 53

agency categories, one of which is called "poisons and infectious agents."

This agency is further divided into the subgroups shown in Table 18.

For the State as a whole the category "poisons and infectious

agents" is declining in importance, as indicated in Table 19. Number of

injuries, days lost, and cost have all declined percentagewise since

1962. The Dade County cost figures for 1966 and 1967, shown in Tables

20 and 21, are consistent with this finding. In 1966 the total cost for

the agency was $175,497 while in 1967 it was $116,450.
































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Table 18.--A list of the categories constituting Agency 10, "Poisons
and Infectious Agents."a



Code
number Agent


10000 Unknown or unreported
10002 Acids, n.e.ccb
10003 Citrus dermatitis
10004 Alcohol
10006 Ammonia
10008 Aniline, other dyes
10010 Arsenic
10012 Beryllium
10014 Carbon dioxide
10016 Carbon monoxide gas fumes
10018 Carbon tetrachloride
10020 Caustics, n.e.c.
10021 Soap
10022 Chlorate of lime
10024 Chlorine
10026 Chromium
10028 Coal tar distillates (naptha, benzol) carbolic acid, creosote
10029 Ethylene gas (fruit coloring)
10030 Di,tetra or perchloroethylene
10031 Dieldrin
10032 Fertilizer, n.e.c.
10033 Food, etc.
10034 Formaldehyde (formain, embalming fluid)
10036 Hydrochloric acid (muriatic)
10038 Hydrocyanic acid
10040 Hydrogen sulphide
10042 Insecticide, n.e.c.
10043 Lead or paint
10044 Malathion
10046 Mercury
10048 Metal fumes (aluminum, monel zinc, welding fumes)
10050 Nitric acid
10052 Parathion
10054 Petroleum distillates (propane, butane, methane, gasoline,
10056 Phosphorus stoddard solvent, kerosene)
10058 Smoke
10060 Sulphuric acid and battery acid
10062 Tar and pitch fumes
10064 Turpentine
10099 Chemicals and poisons, n.e.c.
10100 T.B.
10111 Anthrax
10122 Fungus infections
10133 Larvae migrans (creeping eruption)










Table 18.--Concluded.


Code
number


Agent


10144 Septic infections
10155 Undulant fever (brucellosis)
10200 Dusts
10211 Organic dust (vegetable matter)
10222 Inorganic dust with free silica
10233 Asbestos
10244 Inorganic dust except silica or asbestos
10300 Poison woods or vegetation
10999 Poisonous and infectious agents, n.e.c.


aSource of data: coding form used by the Florida Industrial
Commission.


bNot elsewhere classified.




















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The Southern Farm Bureau was the dominant agricultural insurer

in the area and readily agreed to cooperate on the project. The

following conclusions were drawn from visits with representatives of the

company.

1. Property damage claims would be negligible relative to

workmen's compensation claims.

2. The number of pesticide claims in Dade County has probably

increased in recent years but not as fast as the increase in use of

organic phosphates. Farmers in Dade County are using more caution than

previously.

3. Dade County probably has a smaller amount of pesticide damage

than other areas of the State because its agricultural industry does not

have such a high turnover of farmers and laborers, and the farmers as a

group are relatively sophisticated.

4. In setting premiums, the fact that a farmer does or does not

use pesticides, is not considered. The Farm Bureau takes a "whole risk"

approach. This provides an interesting hypothesis about the behavior

of insurance companies. With regard to a farmer or a region there might

be a "sensitivity threshold" which must be broken before a response is

generated on the part of the insurance company. This "sensitivity

threshold" could be visualized as follows:





.g __ HSensitivity
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When the dollar claims break the sensitivity threshold, the insurance

company responds by raising rates, cancelling policies, or some other

action. If the dollar claims are defined to be the marginal claims due

to pesticides, then external costs are imposed on the insurance company

by the farmer until the threshold is broken and the situation adjusted.


Veterinarians


In order to get additional information on the effects of pesti-

cides on domestic animals, arrangements were made to sample the records

of three veterinarians in the area. Two were located approximately on

the dividing line between the rural and urban areas, while the third was

located in Homestead, the heart of the rural area. Samples which were

as nearly random as possible were taken from each. The frequency of

pesticide calls was noted along with the diagnosis, the specie, and the

treatment. After sampling, a brief conversation was held with each

veterinarian to see if the sample bore out his a priori notions. The

veterinarians all stated, before seeing the sample results, that they

would expect the frequency of pesticide cases to be a small fraction of


IThe first vet numbered his cases sequentially and had about
10,000 of them. Each case contained an average of 4.5 calls. Using a
table of random numbers to determine the starting point, 909 cases were
examined for a total of 4,090 calls. There was no relation between a
case's sequence number and the dates of the calls contained therein.

The second vet maintained his cases alphabetically, by last name
of owner, and did not know how many he had. There were 10 small file
drawers involved so the first and last 150 calls in each drawer were
arbitrarily examined. This vet also kept a separate file for deceased
cases, and all of these were examined.

The third vet maintained his files alphabetically, by last name
of owner, and they did not lend themselves well to sampling. For this
vet, all of the "D's" were examined--a total of 275 cases with 4 calls
per case.