9 c ' u.s. milk production

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^¡9^ United States Department of Agriculture 9

C ' Economic Research Service

Agricultural Economic Report Number 527

Factors Affecting U.S. Milk Production —tm

Boyd M. Buxton u.yi.F

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Factors Affecting U.S. Milk Production. By Boyd M. Buxton. National Economics Division, Economic Research Service, U.S. Department of Agriculture. Agricultural Economic Report No. 527.

Abstract

A 1-percent increase or decrease in milk prices Increases or decreases U.S. milk production a total of about half of 1 percent over a 4-year period. This study measures the effects of changes in major economic factors of milk production on the amount of milk dairy farmers produce. The major factors include prices farmers receive for milk; input costs of running a dairy farm (measured here by prices of alfalfa hay and corn); profits farmers would receive in alternative farm enterprises {measured by beef prices and, again, feed prices); and general economic conditions (measured by the unemployment rate).

Keywords: Milk production, milk supply response, elasticity of milk production

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Contents

Page

Summary iv

Introduction 1

Factors Affecting Milk Supply .. 1 Milk Prices 1 Competing Enterprises 1 Input Costs 2 General Economic Conditions 2

Lagged Adjustments 3 Milk Price Reversibility 3

Procedures 3

Results 5 Estimated Supply Equations 5 Supply Elasticities .10 Price Increases and Decreases 14

Conclusions 15

Bibliography 20

Appendix—Literature Review 21

III

Summary

This study measures the supply elasticity of milk production—that is, how a change in major economic factors affecting milk production influences the amount of milk dairy farmers produce. The major factors include:

• Milk prices. A 1-percent change in the price farmers receive for milk changes national milk production about half of 1 percent in the same direction over a 4-year period. The greatest impact occurs in the first year (0.175) and second year (0.182) after a price change. The impact in the year of the price change is relatively small (0.036).

• Input costs. Input costs of running a dairy farm are represented here by feed (alfaifa hay and corn) prices. A 1-percent movement in alfaifa hay price per ton changes national miik production an estimated -0.164 percent over a 4-year period, and a change in corn price per bushel changes production -0.075 percent. Alfalfa hay prices significantly affect milk production in 28 of 48 States. Corn prices significantly affect miik supply in 14 of 48 States, especially in the North.

• Profits in an alternative farm enterprise. This factor is measured by feed and beef prices. A 1-percent change in beef price changes national milk production -0.056 percent over a 4-year period. Beef price helps determine milk production in the Northeast and eastern Corn Beit but not in the rest of the United States.

• General economic conditions. These conditions are measured by the unemployment rate. A 1-percent change in unemployment affects national milk production 0.085 percent. The effect of the unemployment rate on milk production is significant in only 16 of 48 Slates, mostly those located in the North Central and South Central areas.

In examining each of these factors, this study determines:

• The relationship of a change in these factors to the amount of milk produced over a 4-year period for 48 contiguous States.

• In which years over the 4-year period the change in production factors has the greatest effect.

• The difference between milk production's response to milk price increases and its response to decreases.

IV

Factors Affecting U.S. Milk Production

Boyd M. Buxton'

Introduction

How changes in milk prices might affect the amount of milk produced Is a central question in many dairy policy decisions. This study examines the supply elasticity of milk (that is, the percentage change in the amount of milk produced due to a percentage change in major factors of milk production such as milk price). Individual States in the continental United States are analyzed to identify possible regional differences (fig. 1).

This study (1) analyzes the time required (lag) between changes in factors affecting milk production and the resulting change in milk production and (2) determines if the response of milk production is different for milk price increases than for decreases.

Fßctors Affecting IVIilk Supply

For specific farmers, many noneconomic factors, including individual preferences for dairy farming, can be important determinants of milk production. However, this analysis focuses on four major factors that economic theory suggests affect the decision to produce milk:

• The prices farmers receive for milk; • The profitability of competing onfarm enterprises; • The input costs of milk production; and • The condition of the general economy.

Milk Prices

The price farmers receive for milk directly affects the profitability of milk production and, therefore, the decision of whether or not to produce it. The

*Agricultural economist with the Economic Research Serv- ice, U.S. Department of Agriculture, stationed at the University of Minnesota.

''all milk" price received by farmers reflects both the price received for milk eligible to use as a fluid beverage (Grade A milk) and the price received for milk that can only be used for nonfluid products (Grade B or manufacturing milk). Therefore, the all milk price reflects policies under both the price support and Federal milk order programs. The national average all milk price exceeds the U.S. manufacturing milk price by about $1 per hundredweight, primarily reflecting higher prices for milk used as a fluid beverage than prices for milk used to make manufactured products. Dairy farmers producing Grade B milk (about 17 percent of all milk produced in 1982) receive only the manufacturing milk price. Most of these farmers are in the upper Midwest.

Using the changes in the all milk price which result from price support decisions is not a serious problem because both the all milk price and the manufacturing milk price change by about the same absolute amount. However, a price change due to milk order pricing decisions mostly affects farmers selling Grade A milk. The infrequency of pricing policy changes under Fed- eral milk orders should allow the all milk price to be a good estimate of the changes in price received by both Grade A and B dairy farmers.

Competing Enterprises

The profitability of dairy farming compared with other types of farming directly influences resource adjustments in dairy. The price of beef cattle is used to reflect changes in the profitability of alternative livestock enterprises. Corn and feed prices also reflect alternative uses for resources. A decrease in the profitability of either livestock or crops relative to dairy would be expected to at- tract resources to milk production.

Boyd M. Buxton

The price of beef cattle also affects a farmer's decision to cull low producing cows from the herd. Therefore, beef cattle prices can influence the amount of milk produced by (1) changing the relative profitability of dairy and beef and (2) affecting the culling decision.

Input Costs

In the short run, many factors of production are fixed on dairy farms. Whether a resource is fixed or not depends on the dairy operation and the time period considered. For dairies that purchase most of their feeds from off the farm, feed costs vary. On dairies that produce most of their own feed, much of the feed cost may be fixed in ma- chine and land investments. In this study, hay and corn (or dairy ration) prices are used to reflect changes in input costs. In the short run, as feed prices vary, a farmer may vary the quan-

tities of feed fed, affecting the amount of milk produced. In the long run, farm operators would go in or out of milk production, depending on the value of their resources in alternative uses.

General Economic Conditions

Off-farm employment opportunities, credit avail- ability, and other general economic conditions can affect the amount of milk produced. High unemployment not only affects a dairy owner's off- farm opportunities but, probably more important- ly, affects the number of family members desiring to start dairying. The expected high (low) levels of unemployment would be expected to increase (decrease) aggregate milk production. This study uses the unemployment rate as an indication of whether general economic conditions are favor- able or unfavorable for expansion of milk production.

Figure 1

Dairy Production Regions


Lagged Adjustments Procedures

Milk production is not quickly adjustable to changes in most production factors. The time required to make and implement production decisions in response to a change in milk price or other supply factor is very important to under- standing milk supply. Increasing the size of the national dairy herd is limited by the time required to raise dairy cow replacements. Feeding decisions may be adjusted more rapidly than the 2 years needed to raise a replacement cow. How- ever, keeping older cows longer than normal or culling them sooner than normal can cause adjustments in milk production much quicker than the time needed to raise a replacement.

A major objective of this study is to determine the historical lag between adjustments in aggregate milk production and supply factors, especially changes in milk price. The procedure used to determine the lags is described later in this report.

IViiilc Price Reversibility

A question of significant policy implication is whether an equivalent change in milk production would result from an increase or a decrease in the milk price (that is, is the supply elasticity for milk price reversible?). An economic argument can be made that the percentage change in aggregate milk production is different for a 1-percent increase in milk price than for a 1-percent decrease in milk price. The economic argument for nonreversibility is based on asset fixity. An asset becomes fixed when its value in milk production is less than its acquisition cost but more than its salvage value. The divergence between the acquisition cost and salvage value then results in a range of milk prices (value marginal products) over which the use of the asset will neither be expanded (acquisition cost exceeds its value) or contracted (salvage value is less than its value).^

For analytical purposes, the following annual variables were obtained for each of the 48 contiguous States:

QM = Quantity of milk sold to plants and dealers (million pounds)

DPIVI = Deflated all milk price per hundredweight (dollars)

DCP = Deflated corn price per bushel (dollars)

DPB = Deflated beef cattle price per hundredweight (dollars)

DHAYP = Deflated alfalfa hay price per ton (dollars)

DDR = Deflated 16-percent protein dairy ration price per ton (dollars)

UR = Unemployment rate (percent)

All prices were deflated with the U.S. consumer price index (1967 = 100).

The 16-percent dairy ration price was used in only six Northeastern States and in Nevada where no corn price was reported. Time trends (1947 = 1) and trend squared (designated TR and TRSQ, respectively) were also considered in the analysis.

A weighting scheme was used to construct variables with alternative lags. For example, new milk price variables were calculated by using alternative weights for the milk price:

DPIVILt = W^DPMt -h WgDPMti + WgDPiVltg + W4DPM, 4-3

where:

DPML^ = weighted average price in year t;

Wj = weight given price in year indicated;

DPM^ = deflated milk prices in year t; and

^For more detail on fixed asset theory, see (Ö). Italicized numbers in parentheses refer to references in the Bibliography.

E w¡ = 1 i = 1

ioyd M. Buxton

Variables constructed using the weighting scheme (lagged) are designated by using an "L'* as the last letter in the variable name as above (DPML).

Thirteen separate milk price variables were calculated based on 13 preselected sets of weights (table 1). The weights used represent a range in the assumed lag. The lag structure 0100 in table 1 is equivalent to using a variable lagged one period. It is the special case where W2 is equal to 1 and W^, W3, and W4 are zero. Lag structure 2620 gives more weight to early years (shorter lag im- plied) than lag structure 0011 (longer Jag impiied). Thirteen separate corn, hay, dairy ration, beef price, and unemployment rate variables were calculated using the same 13 sets of weights (lag assumptions) used for milk prices.

The first step in the analysis was to estimate a regression for each of the 13 lags. The same lag was assumed for each supply factor.

The second step was to select from step one the alternative lag formulation for each variable that had the theoretically correct sign and was the most statistically significant. A new equation was then estimated using the set of variables with specific lag assumptions judged most appropriate for each variable.

In most equations where serial correlation was conclusively indicated, based on the Durbin- Watson statistic, a Cochrane-Orcutt Iterative procedure was used to estimate a single-order autocorrelation. The above procedure resulted in one estimated equation for each of the 48 States,

Step three involved the reversibility test to determine if the elasticity of milk production was different for a price increase than for a price decrease. The milk price variable was split Into price increase and decrease components using the Wolff ram procedure (73). The reversibility test was based on the following specification:

(1) QlVft = ao H- a^DPML^ -h agDPBLt...

where:

ag, a^, a2, etc. = equation parameters.

Table 1—Weights used to construct lagged prices for Individual States

Weights

Lag structure Current year (t)

Previous years

t-1 t-2 t-3 (Wi) (W2) (W3) (W4)

0000 1 0 0 0 0100 0 1 0 0 0010 0 0 1 0 0001 0 0 0 1 2620 .2 .6 .2 0

1441 .1 .4 .4 .1 1342 .1 .3 .4 .2 1252 .1 .2 .5 .2 1243 .1 .2 .4 .3 0011 0 0 .5 .5

0111 0 .333 .333 .333 0154 0 .1 .5 .4 6121 .6 .1 .2 .1

The lagged, deflated price in time t (DPML^) is equal to the initial period price plus the sum of the changes from the initial price to the ith period:

t

DPMLt = DPMLo + ^ ADPMLj. i = 1

The change in price can be split into price increases and decreases as follows:

t

(2)DPMLt = DPMLo + ü ADPML: i = 1

t t

+ E ADPMLj i=1 i=1

where:

2^ ADPMLJ = the sum of all price increases, i = 1

and

t

2^ ADPMLJ = the sum of all price decreases. 1 = 1

Substituting (2) into (1) and simplifying gives the following equation:


QM^ = ao + a^(DPMLo) + a:, (£ ADPMLj) i = 1

+ a^'(É ADPML[) + agDPBLt i = 1

Both a!, and a!{ are expected to be positive. The test of significant difference between a!, and a" is equivalent to a test of significant difference between a mill^ price increase and a decrease.

Results

Resuits are presented in three parts. First, estimated equations for individual States are presented and discussed by region. Second, elasticity of mill< production is summarized with respect to changes in milk, corn, beef, and hay prices, and unemployment rate. The nature of the lag is also discussed. Third, results of tests are presented for the difference in milk production response to a milk price increase over its response to a decrease.

Estimated Supply Equations

The estimated equations judged best for each State are presented in this section. The criteria used to select the best equation included:

• Correct signs of the regression coefficient, especially for milk price;

• The highest coefficient of determination (R^) and t value for included variables; and

• A Durbin-Watson statistic for serial correlation.

The t values are reported in parentheses in the following tables, and the weights from which the lag variable was calculated are reported above each estimated coefficient. For example, in table 2, the deflated milk price (DPML) variable for Con- necticut was calculated with 0, 0.1, 0.5, and 0.4 weights (0154) on the milk prices for the current year, last year, 2 years ago, and 3 years ago, respectively. Time trend was generally significant in the estimated supply equations.^

^Significance refers to at least tlie 80-percent level of confidence unless otherwise designated.

Northeast. The milk price coefficient was significant in all except 4 of the 14 States in this region: Connecticut, Massachusetts, New Hamp- shire, and Ohio (table 2). Hay and/or beef prices were significant in all States except Vermont and Ohio, corn (or dairy ration) price in 6 of the 14 States, and unemployment in only 3 States. Trend was significant in most States.

The ordinary least squares method was used, except in New York where a single-order autocorrelation was used. Serial correlation was either in- determinant or nonexistent in all States except Maine. The adjusted R^ exceeded 0.812 in all States except Maine (0.270), Maryland (0.767), New Hampshire (0.650), and Ohio (0.116).

Corn Belt. The milk price coefficient was significant in all States in the region except Iowa (table 3). The hay price and unemployment rate were significant in four of the six States, while the beef price was significant in three States. In all States, trend was significant, the R^ exceeded 0.789, and serial correlation was either inconclusive or nonexistent. A single-order autocorrelation procedure was used for Kentucky and Missouri.

Lake States. The statistical results for Wiscon- sin were the best among all 48 States, but Min- nesota was among the worst (table 4). The adjusted R2 was 0.976 in Wisconsin but only 0.258 in Minnesota. Beef and hay prices were significant in Wisconsin but not in Minnesota. All regression coefficients for Wisconsin were significant at the 99-percent level of confidence except that for beef price, which was significant at the 80-percent level. In addition, milk, corn, hay, and beef prices and the unemployment rate were also significant. No serial correlation was conclusively identified based on the Durbin-Watson statistic.

Southeast. The milk price coefficient was of the correct sign and was significant at the 95-percent level of confidence in all four States (table 5). The hay price was also significant in three of the four States, but the corn price was significant in only one State. Beef prices and the unemployment rate were nonsignificant in all four States. The R^ exceeded 0.912 in all States. No conclusive serial correlation was identified. However, a single- order autocorrelation procedure was used in

Table2- Estimated supply equations lor Individual States, Northeast^

state "Time ,,^^ period '^^"^ Intercept DPML DCPL DDRL DHAYPL DPBL URL TR TRSQ R2 DW

Connecticut 1968-82 Lag OLS Coef

t Elas

1,199.0 (11.918)**^

0154 -17.85

^ (-1.302)

6121 -5.487 (-8.794r*

-.320

6121 -6.957

" (-6.373)*** -.203

-4.376 (-8.675)**^

0.930 2.03##

Delaware 1968-82 Ug OLS Coef

t Elas

122.5 (5.001)***

0001 9.668 (2.022)*

.382

0001 1.713

(4.319)***

0001 5.34 (8.324)**^

.197

-3.787 ' (-17.405)***

.936 1.99##

Maine 1960-62 Ug OLS Coef

t Elas

358.88 (2.169)**

0001 56.91 (2.093)*

.523

0001 -3.017

e-1.841)* -.139

0001 10.75 (2.541)**

.110

-1.182 (-1.139)

.270 .67

Maryland 1968-82 Ug OLS Coef

t Elas

1,627.1 (10.737)***

0001 31.72 (1.430)*

.119

6121 -48.25 (-2.769)**

-.042

6121 -9.86 (-4566)***

-.131

.767 1.83##

Massachusetts 1968-82 Ug OLS Coef

t Elas

739.0 (2.6^)**

0111 39.05

(.901) .380

6121 -3.607 (-2.^5)**

-.215

1441 .116

(.045) 004

-7.92 (-6.306)***

.812 1.15##

NewHampshI re 1968-82 Ug OLS Coef

t Elas

395.5 (4.614)***

0154 7.671 (.575) .131

1441 -1.997 (-5.166)***

-.199

1441 -1.395

(-1.448)* -.073

.650 1.55#

NewJersey 1960-82 Ug OLS Coef

t Elas

1,110.0 (3.652)***

1441 101.35

(1.718)* ,961

1441 -29.27 (-4.^1)***

-.912

1441 -50.1 (-4.278)***

-.581

-6.91 (-1-///)*

.944 1.43#

New York 1968-82 Ug AUTO Coef

Î Elas

6,586.4 (5.310)***

2620 1,226.4

(4.341)*** .629

2620 -1,015.6

(-6.151)*** -.132

2620 -99.39 (-6.694)***

-.256

2620 -47.23 (-2.856)**

-.083

74.73 (10.988)***

.922 2.40#

Ohio 1968-82 Ug OLS Coef

t Elas

6,815.0 (4.500)***

0001 124.0

(.710) .151

0100 1.45 (.150) .088

0100 -8.38 (-.616) -.044

-192.2 (-1.310)

3.078 (1.273)

,116 1.67#

Pennsylvania 1968-82 Ug - OLS Coef 16.846.0

t (18.58)*** Elas -

0111 789.41

(4.667)*** .588

0100 -178.1

(-2.613)** -.032

0111 -42.90 (-6.904)***

-.173

6121 -26.5 (-2.486)**

-.075

-977.5 (-10.73)***

19.50 (12.%^)**^

.995 2.40#

Rhode Island 1968-82 Ug OLS Coef

t Elas

115.30 (7.824)***

0011 3.376

(1.455)* .327

-2.545 (-25.823)***

.981 1.66##

Vemnont 1968-82 Ug OLS Coef

t Elas

858.5 (2.319)**

2620 170.01

(2.262)** .518

6121 -8.871 (-4.077)***

-.368

24.74 (7,627)***

.815 1.65#

Virginia 1968-82 Ug OLS Coef

t Elas

311.71 (.498)

2620 381.8

(2.669)** 1.164

2620 -407.3

(-3.426)*** -.295

2620 -17.15 (-2.441)**

-289

2620 -13.07 -2.094)*

->140

24.94 (7.648)**

.830 1.52#

West Virginia 1968-82 Ug - OLS Coef 1.313.8

t (9.507) Elas -

6121 ia.85 (1.424)*

.160

6121 -2.335

(-2:420)** -.124

-63.74 (-6.589)***

.857 1.56#

nu7"n 2Î. FA? I . V =^^^^^f^^^^ ^^ determination. t = t statistic. OLS = Ordinary least squares estlmatroa AüTO = Cochrane^rcutt Iterate estimation. DW = Durbm-Watson statistic. Coef = Regression coefficient. Elas=Elasticity of milk production. Ug=Weights used to construct variable.

= öQ-percent level of confidence. ** =^percênt level of confidence. ***^99^percent level of confidence. #=I nconciusive serial correlation at 5-percent level of confidence. ## = No serial correlation at 5-percent level of confidence, ^Empty data fields mean that the variable was not in estimating equation. See Procedures section for variable definitions.


Florida and South Carolina. The time period for which the estimates were made was 1968-82 for all States, except Florida where a 1960-82 period provided the best statistical results.

South Central. The milk price coefficient was significant in all seven States, the hay price in all States except Oklahoma, and the corn price and unemployment rate in three of the seven States (table 6). The R^ exceeded 0.857 in all States, ex-

cept Tennessee where it was 0.648. Serial correlation was inconclusive or nonexistent in all States.

Plains. The milk price coefficient was significant in all four States (table 7). Corn and hay prices and the unemployment rate were each significant in two of the four States. The R^ exceeded 0.910 in all States, except South Dakota where it was 0.660. Serial correlation was inconclusive or nonexistent in all of the States.

Table 3—Estimated supply equations for individual States, Corn Belt^

state Time period

Item Intercept DPML DCPL DHAYPL DPBL URL TR TRSQ R2 DW

Illinois 1968-82 OLS

Lag Coef t Bias

8,898.2 (9.250)***

0010 296.3

(1.608)* .558

0010 -33.54 (-2.511)**

-.309

0010 -8.88 (-1.532)*

-.080

0010 31.39 (1.532)*

.059

-426.95 (-7.195)***

6.437 (6.213)***

0.931 1.95

Indiana 1968-82 OLS

Lag Coef t Elas

2,122.0 (4.898)***

0111 336.97

(2.399)** .771

0111 -31.66 (-4.304)***

-.362

0111 62.78 (2.962)**

.146

-37.81 (-3.761)***

.816 2.14##

Iowa 1968-82 AUTO

Lag Coef t Elas

17,253.0 (13.826)**

0154 201.25

(1.036) .220

0154 -173.65

(-1.385)* -.049

-887.53 (-11.610)***

14.0 (10.116)***

.963 1.69#

Kentucky 1968-82 AUTO

Lag Coef t Elas

2.27 (3.166)**

0111 550.35

(3.626)*** 1.161

0111 -148.47

(-4.501)*** -1.612

0111 52.40 (2.452)**

.119

-193.08 (-4.018)***

2.27 (3.166)**

.789 2.15#.

Michigan 1968-82 OLS

Lag Coef t Elas

4,305.0 (6.865)***

0011 342.13

(2.216)** .377

0011 -47.44 (-5.796)***

-.230

0011 -54.51 (-5.364)***

-.251

32.52 (3.661)***

.850 1.72#

Missouri 1951-82 AUTO

Lag Coef t Elas

3,817.6 (14.302)***

0011 120.72

(2.432)** .172

6121 -11.44 (-1.913)*

-.084

6121 71.92 (2.803)***

.100

-54.39 (-13.397)***

.970 1.65#

See footnotes at the bottom of table 2.

Table 4—Estimated supply equations for individual States, Lake States^

state Time period Item Intercept DPML DCPL DHAYPL DPBL URL

0154 0154 -637.6 -264.9

(-1.660)* (-1.866)- -.078 -.123

1441 1441 1441 1441 1,731.3 -106.0 -125.9 786.2

(-3.790)*** (-3.543)*** (-2.239)* (6.988)- -.107 -.117 -.123 .193

TR TRSQ R2 DW

Minnesota 1968-82 Lag — 0154 OLS Coef 9,463.7 443.7

t (9.725)*** (1.555)* Elas — .210

Wisconsin 1968-82 Lag — 1441 OLS Coef 8.156.0 3,115.3

t (5.072)*** (9.159)* Elas — .748

0.258 1.22#

.976 2.17#


Boyd M. Buxton

Table 5—Estimated supply equations for individual States, Southeast^

state Time period Item Intereept DPML DGPL DHÂYPL DPBL URL TR TRSQ R2 DW

Florida 1960-82 AUTO

Lag Coef t Etas

-1,269.3 (-2.325)**

1252 247.79

(3.551)*** 1.003

1252 -25.25 (-3.149)***

-.474

125.6 (5.629)***

-1.594 (-3.767)***

0.981 1.61#

Georgia 1968-82 OLS

Lag Coef t Elas

-231.96 (-.777)

0011 141.54

(2.963)** .691

0011 -110.04

(-2.739)** -.122

26.54 (12.123)*** .939 1.64#

North Carolina 1968-82 OtS

Lag Coef t Bas

1.727.0 (5.915)***

0011 109.85

(3.431)*** .439

0111 -8.644 (-2.626)**

-.176

0111 -3.94 (-.981) -.050

-53.27 (-2.035)*

1.2Cfö (2.655)**

.912 2.03##

South Carolina 1968-82 AUTO

Lag Coef t Elas

912.14 (24.472)***

0111 52.73 (4.470)***

.639

0111 -5.71 (-6.661)***

-.319

26^ -1.02 (-.702) -.011

-43.09 (-10.013)***

.8116 (10,209)***

.956 2.44#

i footnotes at the bottom of table 2.

Table 6—Estimated supply equations for IndividualStates, South Central^

State Time period Item Intercept DPML DCPL DHAYPL DPBL URL TR TRSQ R2 DW

Alabama 1960-82 OLS

Lag Coef t Elas

1,223.4 (5.995)***

0111 74.94 (2.196)**

.609

0111 -66.83

(-2.208)* -.120

0111 -16.12 (-5.258)***

-.563

-16.16 (-16.738)***

0.963 1.43#

Arkarisas 1960-82 OLS

Lag Coef t Elas

8.81 (.046)***

1252 137.4

(3.968)*** .960

1252 -11.«) (-5.033)***

-.367

1242 14.10 (4.843)***

.394

1252 62.79 (11.256)***

.493

-14.78 (-8.073)***

.889 1.64#

Louisiana 1960-82 OLS

Lag Coef t Elas

-406.8 (-2.881)***

0011 86.12 (3.515)***

.523

0011 -4.26 (-1.806)*

-.102

78.63 (16.500)***

-1.447 (-15.335)***

.939 1.75#

Mississippi 1968-82 AUTO

Lag Coef t Elas

1,703.6 (11.206)***

1252 83.81 (2.241)**

.517

1252 -26.38 (-6.676)***

-.689

1252 17.73 (2.824)**

.106

-24.7 (-11,815)***

.978 1,41#

Oklahoma 1951-82 OLS

Lag Coef t Elas

1,210.3 (5.8^)***

0154 134.03

(3.137)*** .544

1252 80.34 (-1.184)

-.086

-22.79 (-10,25)***

.962 1.79##

Tennessee 1968-82 OLS

Lag Coef t Elas

2.518.8 (6.370)***

1441 319.17

(2.149)* .790

1441 -235.1

(-3.323)*** -.153

1441 -49.44 (-2.638)**

-.574

-17.96 (-1.469)*

.648 2.06##

Texas 1951-82 AUTO

Lag Coef t Elas

31.62 (7.649)***

0111 355.77

(4.687)*** .710

0111 -27.03 (-2.492)**

.236

0111 93.98 (2.3^)**

.129

31.62 (7.649)***

.857 1.68#



Mountain. Poorer t values were obtained for Mountain States tlian any other region. Tlie milk price coefficient was significant in all States except Wyoming (table 8). Tlie hay price was significant in three of the five States, while the beef price was significant in two of the five States. The R2 exceeded 0.940 in all States, except Col- orado where it was 0.599. No conclusive serial

correlation was identified. A single-order autocorrelation procedure was used for Montana and Wyoming.

Southwest. Results for California were quite different than for either New Mexico or Arizona. Although milk price was significant In all three States, the milk price elasticity of supply was

Table 7—Estimated supply equations for individual States, Plains^

state Time period

Item Intercept DPML DCPL DHAYPL DPBL URL TR TRSQ R2 DW

Kansas 1968-82 Lag 0011 0100 0001 1000 . OLS Goef 1,446.8 338.4 -118.7 -9.09 12.7 -47.3 0.951 2.17#

t (3.959)*** (2.764)** (-4.005)*** (-1.968)* (.972) (-8.021)*** Elas — 1.159 -.099 -.145 .035 —

Nebraska 1960-82 Lag _ 0001 1243 0001 — .973 1.61# OLS Coef 2,403.2 77.88 -59.49 -11.35 -34.23

t (22.706)*** (3.311)*** (-1.122) (-4.678)*** (-18.873)*** Elas — .225 -.045 -.141 —

North Dakota 1960-82 Lag __ 0011 6121 6121 OLS Coef

t 1,195.9

(4.794)*** 148.54

(2.308)** -204.5

(-2.086)* 157.1

(3.882)*** -46.2 (-9.769)***

.910 1.31#

Elas — .528 -.194 .631 —

South Dakota 1968-82 Lag _ 2620 2620 OLS Coef

t Elas

836.7 (3.339)**

175.1 (1.883)*

.496

195.9 (3.745)***

.432

-24.80 (-2.020)*

.660 2.17#


Table 8—Estimated supply equations for Individual States, Mountain region^

state Time period Item Intercept DPML DCPL DHAYPL DPBL URL TR TRSQ R2 DW

Colorado 1968-82 OLS

Lag Coef t Elas

461.66 (1,800)*

0011 81.115 (1.513)*

.527

0011 -8.14 (-4.165)***

-.^4

0011 3.46 (1.599)*

3.18 (1.657)*

0.599 1.70#

Montana 1960-82 AUTO

Lag Coef t Elas

423.9 (4.447)***

0001 21.78 (1.573)*

.320

0001 -.658

(-1.336)* -.047

-5.63 (-2.535)**

.940 1.32#

Nevada 1968-82 OLS

Lag Coef t Elas

-130.25 (-2.122)*

1252 21.62 (2.177)*

.657

1000 -.580

(-1.133) -.074

7.006 (15.353)***

.952 1.84##

Utah 1968-82 OLS

Ug Coef t Elas

-310.84 (-1.165)

2620 93.39 (1.508)*

.510

2620 -12.26 (-2.974)**

-.354

2620 5.31 (2.897)**

.126

2620 54.29 (3.965)***

.334

22.93 (13.437)***

.969 1.94#

Wyoming 1951-82 AUTO

Lag Coef t Elas

230.0 (4.913)***

0111 7.18 (.885) .218

1342 -15.31 (-1.927)*

-.139

-3.515 (-5.177)***

.973 1.70#


Boyd M. Buxton

considerably higher in New Mexico and Arizona than in Galifornia (table 9). Milk price and trend generally were the only two significant variables In all States. Although the R2 exceeded 0.758, conclusive serial correlation existed in New Mex- ico and Arizona.

Northwest. The milk price coefficient was highly significant (99-percent level of confidence) in all three States (table 10). Unlike that for the South- west, hay price was also significant in all three States, and the unemployment rate was significant in Idaho and Washington. The R^ exceeded 0.816 in all States, and no conclusive serial correlation was identified in any of the States.

Supply Elasticities

The supply elasticities calculated from the estin^ted equations in the previous section are reported below. The supply elasticities are inter- preted as the 4-year impact (percentage change in milk produced), resulting from a 1-percent change over the 4-year period in the milk price or other supply factor. Based on the procedures of weighting previous prices into a predetermined lag structure, the 4-year impact is divided into the impact in the current year (year of the change) and each of the following 3 years. The results summarizing the elasticity of milk production with respect to a change in milk price are dis-

Tabie 9—Estimated supply equations for individual States, Southwest^

state Time period Item Intercept DPIVIL DGPL DHAYPL DPBL URL TR TRSQ R2 DW

Arizona 1^1-82 OLS

Lag Coef t Elas

-520.15 (-2.444)***

1252 76.72 (2.468)**

.750

1000 17.81 (3.477)***

.164

27.91 (15.586)***

0.949 .50

California 1951-82 AUTO

Ug Coef t Elas

1,915.3 (.970).

0111 403.37

(1.355)* .222

279.18 • (8.194)***

.986 1.67##

New Mexico 1961-82 OLS

Lag Coef t Elas

-330.2 (-2.111)**

1342 70.38 (3.115)*** 1.329

1342 -5.600

(-1.328)* -.4{fô

17.35 (8.635)***

.758 .27


Table 10—Estimated supply equations for individual States, Northwest^

state Time period Item intercept DPML DCPL DHAYPL DPBL URL TR TRSQ R2 DW

Idaho 1951-82 OLS

Lag Coef t Elas

1,043.8 (4.630)***

CX)11 91.52 (1.457)*

.243

1342 -209.80

(-1.178) -.184

0111 -17.21 (-1.382)*

-.249

6121 158.18

(6.360)*** .552

0.833 1.61#

Oregon 1951-82 AUTO

Lag Coef t Elas

851.5 (3.682)***

0111 74.1 (2.173)**

.377

0111 -4.80 (-1.747)*

-.127

0111 8.54 (.841) .049

.891 1.16#

Washington 1961-82 OLS

Lag Coef t Elas

1.094.8 (2.775)***

0011 180.6

(2.010)* .448

6121 -1,202.2

(-7.084)*** -.909

6121 50.55 (4.58 )***

.668

6121 86.84 (2.936)***

.284

.816 1.15#


10


cussed first, followed by a discussion of the elasticity of milk production witli respect to feed and beef prices and the unemployment rate.

IVtiilc price. Table 11 summarizes the results for individual States in each region and the regional elasticities calculated by weighting the individual State elasticities. The elasticity was assumed to be zero in States where the price coefficient was not significantly different from zero at the 80-percent level of confidence.

In the Northeast, the elasticity of milk production with respect to price varied from less than 0.2 each in Maryland and West Virginia to about 1 each in New Jersey and Virginia. The estimated elasticities in the other States were much closer to the regional average of 0.495. For the region, the greatest impact of a milk price change oc- curred 1 year (0.242) and 2 years (0.119) after the change.

In the Corn Beit, State estimates varied from over 1 in Kentucky to 0.172 in Missouri. The weighted 4-year response for the region was 0.425, with the impact about equally distributed over the first, second, and third year after a change in milk price.

Minnesota and Wisconsin (Lake States) gave quite different results. The weighted 4-year elasticity was 0.575 in the region, with the greatest impact in the first year (0.209) and the second year (0.237) following a change in milk price.

The weighted average elasticity in the Southeast was 0.714, with the greatest impact expected in the second year (0.346) and third year (0.244) after the price change. Little impact was observed in the year of the price change.

The variation in estimated eiasticity was small among the seven States in the South Central region. The estimates varied from 0.517 in Missis- sippi to 0.960 in Arkansas. The weighted average 4-year elasticity for the region was 0.679, with the greatest impact in the second year (0.278) after the change in milk price.

The estimated 4-year elasticity of milk production in the Plains region was 0.596, with the greatest

impact 2 years (0.227) and 3 years (0.260) after the change in milk price.

The greatest impact in the Mountain region would be expected in the first year (0.123), the second year (0.154), and the third year (0.260) after the change in milk price.

The estimated elasticities in the Southwest were substantially higher in Arizona and New Mexico than in California. Because the amount of milk produced in California greatly exceeds that produced in the other two States, the weighted regional elasticity was only slightly higher (0.289) than that in California (0.222).

The estimated elasticities with respect to milk price were very close in ail three States in the Northwest. The weighted average elasticity for the region was 0.384, with the greatest impact in the second year (0.178) and third year (0.178) after a price change.

Table 12 summarizes the weighted average elasticity of milk production with respect to a change in the milk price for each of the nine regions just discussed. The following pattern emerges based on the weighted regional elasticities. The 4-year response to a 1-percent change in milk price is similar across the Plains, Lake States, Corn Belt, and Northeast. The 4-year response is relatively more elastic in the Southeast and South Central regions but generally less elastic in the Mountain States, Southwest, and Northwest.

The weighted national average 4-year elasticity to a 1-percent change in milk price is 0.510 percent. The greatest impact occurs in the first year (0.175) and second year (0.182) after a price change. The impact in the year of the price change is relatively small (0.036).

Feed prices. Alfalfa hay and corn prices were used to reflect changes in feed costs faced by dairy producers.^ Changes in the alfalfa hay price were significantly related to changes in milk production in most States located east of a line from

^No corn price is reported in six Northeast States and Nevada. For these States, the U.S. average price paid for the 16-percent protein dairy ration was used to reflect the prices dairy producers pay for concentrates.

11

Table 11—Estimated supply elasticity of milk production with respect to milk price, aggregate regions and individual States

Region and State

Estimated supply elasticity

4-year Current- First- Second- Third- total year year year year

Northeast 0.495 0.065 0.242 0.119 0.069 Ohio * * * * * Pennsylvania .588 0 .196 .196 .196 Rhode Island .327 0 0 .163 .163 Vermont .518 .104 .310 .104 0 Virginia 1.164 .233 .698 .233 0 West Virginia .160 .097 .016 .032 .016 Connecticut * • * •k * Delaware .382 0 0 0 .382 Maine .523 0 0 0 .523 Maryland .119 0 0 0 .115 Massachusetts * * * * * New Hampshire * * * * * New Jersey .961 .096 .384 .384 .096 New York .629 .126 .377 .126 0

Corn Belt .425 0 .152 .136 .137 Illinois .558 0 .558 0 0 Indiana .771 0 .257 .257 .257 Iowa * * * * * Kentucky 1.161 0 .387 .387 .387 Michigan .277 0 0 .188 .189 Missouri .172 0 0 .086 .086

Lake States .575 .051 .209 .237 .078 Minnesota .210 0 .021 .105 .084 Wisconsin .748 .075 .299 .299 .075

Southeast .714 .034 .090 .346 .244 Florida 1.003 .100 .201 .501 .201 Georgia .691 0 0 .345 .345 North Carolina .439 0 0 .219 .220 South Carolina .639 0 .213 .213 .213

South Central .679 .029 .187 .278 .185 Alabtama .609 0 .203 .203 .203 Arkansas .960 .096 .192 .480 .192 Louisiana .523 0 0 .262 .261 Mississippi .517 .052 .103 .259 .103 Oklahoma .544 0 .054 .272 .218 Tennessee .790 .079 .ai6 .316 .079 Texas .710 0 .236 .237 .237

Plains 0.596 0.027 0.082 0.227 0.260 Kansas 1.159 0 0 .580 .580 Nebraska .225 0 0 0 .225 South Dakota .496 .099 .298 .099 0 North Dakota .528 0 0 .264 .264

Mountain States .466 .043 .123 .154 .146 Colorado .527 0 0 .263 .264 Montana .320 0 0 0 .320 Nevada .657 .066 .131 .329 .131 Utah .510 .102 .306 .102 0 Wyoming * * * * *

Southwest .289 .009 .089 .107 .084 Arizona .752 .075 .150 .376 .151 California .222 0 .074 .074 .074 New Mexico 1.329 .133 .399 .523 .266

Northwest .384 0 .028 .178 .178 Idaho .304 0 0 .152 .152 Oregon ,Z77 0 .126 .126 .126 Washington .448 0 0 .224 .244

* = Nonsignificant at tlie 80-percent levei of confidence.

12


Minnesota to Louisiana (fig. 2). Hay prices were also significant in about half of the States in the Plains, Mountain, and Northwest regions. The hay price was a significant explainer of milk production in 28 of 48 States.

The estimated elasticities are shown in table 13. A 1-percent change in hay prices would be expected to change national milk production -0.164 percent over 4 years.^ The greatest impact occurs 1 year (-0.056) and 2 years (-0.059) after the change in hay price.

Changes in corn (or dairy ration) prices were significantly related to milk production in 15 of 48 States (fig. 3). Most of these States were located in northern and far eastern areas of the United States. Corn price was not significant in the seven-State Southwest.

The estimated elasticities are shown in table 14. For the United States, a 1-percent change in the corn price would be expected to change national milk production -0.076 percent over 4 years. The greatest impacts would occur in the year of the

^Thls calculation assumes a change In hay price has no Im- pact on milk production in States where the hay coefficient was nonsignificant.

change (-0.020) and in the first year (-0.025) and second year (-0.022) after the change.

Beef price. Beef prices can affect the amount of milk produced through culling decisions or by changing the relative profitability of beef and dairy enterprises. Most of the States where beef price was a significant determinant of milk production were located in the Northeast and eastern Corn Belt (fig. 4). The beef price had the expected negative coefficient in only 13 of 48 States. For the aggregate United States, a 1-percent change in beef price across the United States would be expected to change national milk production -0.059 percent over 4 years (table 15). The greatest impact occurs 2 years (-0.021) after the change in beef price, but the impacts occurring in the year of change (-0.01) and 1 year (-0.016) and 3 years (-0.009) later are also important.

The length of the lag plus the regional pattern suggests that beef may be relatively more important as a competing enterprise than as a market for cull cows. However, both appear to be important.

Unemployment rate. The unemployment rate was a significant determinant of milk production in 16 of 48 States (fig. 5). No geographic pattern emerged, except most of the 16 States were lo-

Table 12—Estimated supply elasticity of milk production with respect to milk price, aggregate United States and individual regions

Milk production^

Estimated supply elasticity Region 4-year

tota|2 Current-

year First- year

Second- year

Third- year

Million pounds

Northeast Corn Belt Lake States Southeast South Central

31,034 19,714 26,668

5,029 10,117

0.495 .425 .575 .714 .679

0.065 0

.051

.034

.029

0.242 .152 .209 .090 .187

0.119 .136 .237 .346 .278

0.069 .137 .078 .244 .185

Plains Mountain Southwest Northwest

5,931 2,484

10,330 4,870

.596

.466

.289

.384

.027

.043

.009 0

.082

.123

.089

.028

.227

.154

.107

.178

.260

.146

.084

.178

United States^ 116,177 .510 .036 .175 .182 .117

""Average milk production over the period from which elasticity estimates were made. ^Based on States with significant coefficients at 80-percent level of confidence.

13

Boyd M, Buxtoii

catedtn the North Central and South Central regions.

The estimated elasticities with respect to the unemployment rate are shown In table 16. A 1-percent change In unemployment rate across the United States would be expected to change national milk production 0.085 percent over 4 years. The greatest Impacts would occur in the first year (a026) and second year (0.028) following the change in unemployment rate.

Price Increases and Decreases

The supply equation judged best for each State, presented earlier in this report, was used to test whether the elasticity of milk production is the same for an increase as for a decrease in milk price. The exact independent variables (specification) were used, except the milk price variable was split into increasing end decreasing com-

Figures

States Where Alfalfa Hay Price was Significantly Related to Milk Production

ponents as described in the procedures section. An F statistic was calcuiated to test whether the Goefficients on the price increase and price decrease variable were significantly different. A summary of regionai estimates, obtained from the weighted individual State estimates is shown in this section. Keep in mind when interpreting the results that statistical results on the price rever- sibiiity tests were generally poor.

The 42 States whose milk price coefficient was significantly different from zero at least at the aO-percent ievel of confidence were estimated for the reversibility test. In only 8 of the 42 States was the coefficient on the price increase variable significantly different from the coefficient on the price decrease variable at the 95-percent level of confidence, in only half of these eight States were both the coefficient for an increase and for a decrease in milk price positive as would be expected.

14


Disregarding the level of confidence of the estimates and assuming all negative coefficients to be equal to zero (price has no impact rather than a negative relationship with milk produced), individual States were weighted into regional estimates. In seven of nine regions, the 4-year response of milk production to a price change was more elastic for a price increase than for a price decrease (table 17). In the South Central and Plains regions, the opposite was observed. The results for the aggregate United States suggest that the elasticity of milk supply is 0.571 for a price increase and 0.374 for a price decrease, compared with the estimated reversible supply elasticity of 0.510.

Conclusions

Supply equations were estimated for the 48 contiguous States. Major supply factors, such as milk prices, input prices, profitability of alternative enterprises, and general economic conditions, were examined. The lag between changes in milk prices and the resulting change in amount of milk produced within each State were also analyzed. Results for individual States were summarized by regions.

The milk price was significant in explaining changes in milk production in 42 of 48 States and was of the correct sign but not significant in 5

Table 13—Estimated supply elasticity of milk production with respect to alfalfa hay price, individual States and weighted United States

Estimated supply elasticity state 4-year Current- First- Second- Third-

total year year year year

Oregon -0.127 0 -0.042 -0.043 -0.042 Idaho -.249 0 -.083 -.083 -.083 Montana -.047 0 0 0 -.047 Utah -.354 -0.071 -.212 -.071 0 Colorado -.264 0 0 -.132 -.132

Nebraska -.141 0 0 0 -.141 Kansas -.145 0 0 0 -.145 Texas -.236 0 -.079 -.079 -.078 South Carolina -.319 0 -.106 -.107 -.106 Alabama -.563 0 -.188 -.188 -.187

Florida -.474 -.047 -.095 -.237 -.095 Arkansas -.367 -.037 -.073 -.184 -.073 Louisiana -.102 0 0 -.051 -.051 Tennessee -.574 -.057 -.230 -.230 -.057 North Carolina -.176 0 -.059 -.059 -.05

Maine -.139 0 0 0 -.139 New Hampshire -.199 -.020 -.080 -.080 -.019 Massachusetts -.215 -.129 -.022 -.043 -.021 Connecticut -.320 -.192 -.032 -.064 -.032 New York -.256 -.051 -.154 -.051 0

Pennsylvania -.173 0 -.058 -.058 -.057 Virginia -.289 -.058 -.173 -.058 0 Kentucky -1.612 0 -.537 -.538 -.537 Illinois -.309 0 0 -.309 0 Indiana -.362 0 -.121 -.121 -.120

Michigan -.230 0 0 -.115 -.115 Wisconsin -.117 -.12 -.047 -.047 -.011 Mississippi -.689 -.069 -.138 -.345 -.138

United States -.164 -.012 -.056 -.059 -.037

15

Boyd M. Buxton

other States. The 4-year response to a 1-percent change in milk price is similar across the Plains, Lake States, Corn Belt, and Northeast. The elasticity of supply is less in the Mountain States, Southwest, and Northwest than in the Southeast and South Central. A 1-percent increase (decrease) in milk price increases (decreases) U.S. milk production 0.510 percent over 4 years.

The alfalfa hay price was more significant in explaining changes in milk production than corn price, especially in the eastern third of the United States. Al-percent change in the deflated alfalfa price per ton was estimated to have a -0.16-percent change in national milk production over a 4-year period. The greatest Impact was estimated to occur in the first and second years after the change in alfalfa hay price.

Corn (or 16-percent dairy ration) price, reflecting both feed costs and profitability of crop alter- natives, was an important milk supply determinant in ttre North but was less significant in the South, especially in the Southwest. Al-percent change i^n the deflated corn price per bushel was estimated to change national milk production - 0.076 percent over a 4-year period. The greatest impact was estimated to occur in the current year and the first and second years after the change in corn price.

Beef price was a significant determinant of milk production in the Northeast region and eastern Corn Belt but not in the rest of the United States. A 1-percent increase (decrease) in beef price would be expected to result in a 0.059-percent decrease (increase) in national milk production

Figure 3

States Where Corn Price was Significantly Related te Milk Production

Significant Not Significant

16


Table 14—Estimated supply elasticity of milk production with respect to corn price, individual States and weigtited United States

Estimated supply elasticity state 4-year Current- First- Second- Third-

totai year year year

-0.182

year Washington -0.909 0.545 -0.091 -0.091 Wyoming -.139 -.014 -.042 -.056 -.027 Minnesota -.078 0 -.008 -.039 -.031 Wisconsin -.107 -.011 -.043 -.043 -.011 Iowa -.049 0 -.005 -.024 -.020

Kansas -.099 0 -.099 0 0 Vermont (DR) -.3 -.221 -.037 -.074 -.036 New Yorl< -.132 -.026 -.080 -.026 0 Pennsylvania -.032 0 -.032 0 0 JVIaryland -.042 -.025 -.004 -.009 -.004

Virginia -.295 -.059 -.177 -.059 0 Tennessee -.153 -.015 -.061 -.061 -.015 Georgia -.122 0 0 -.061 -.061 North Dakota -.194 -.116 -.019 -.040 -.019 Alabama -.120 0 -.040 -.040 -.040

United States -.076 -.020 -.026 -.022 ' -.009

DR = 16-percent dairy ration price, which was used In Vermont because no corn price was reported

Figure 4

states Where Beef Price was Significantly Reiated to IVIiilc Production

I Significant Not Significant

17

Boyd M. Biixton

Table 15—Estimated supply elasticity of milk production with respect to beef price. Individual States and weighted United States

Estimated supply elasticity State 4-year Current- First- Second- Third-

total year year year year

New Mexico -0.405 -0.041 -0.122 -0.161 -0.081 Wisconsin -.123 -.012 -.049 -.048 -.012 Michigan -.251 0 0 -.126 -.125 minois -.080 0 0 -.080 0 New Hannpshlre -.073 -.007 -.029 -.029 -.007

New York -.083 -.017 -.049 -.017 0 Pennsylvanla -.075 -.045 -.008 -.015 -.008 Connecticut -.203 -.122 -.020 -.041 -.020 New Jersey -.912 -.091 -.365 -.365 -.091 Maryland -.131 -.079 -.013 -.026 -.013

West Virginia -.124 -.074 -.012 -.025 -.012 Virginia -.104 -.021 -,062 -.021 0 Missouri -.084 -.050 -.008 -.017 -.008

United States -.059 -.012 -.016 -.022 -.009

Figures

states Where Unemployrhent Rate was Significantly Retated to Miil( Production

t8


Table 16—Estimated supply eiastlcfty of mille production with respect to unemployment rate, Individual States and weighted United States

Estimated supply elasticity State 4-year Current- First- Second- Third-

total year year year

0.077

year

Wisconsin 0.193 0.019 0.077 0.019 Idaho .552 .331 .055 .111 .055 Washington .284 .170 .028 .057 .028 North Dakota .631 .379 .063 .126 .063 South Dakota .432 .086 .259 .086 0

Arkansas .493 .049 .099 .246 .099 Mississippi .106 .011 .021 .053 .021 Texas .129 0 .043 .043 .043 Utah .334 .067 .200 .067 0 Illinois .059 0 0 .059 0

Indiana .146 0 .049 .049 .048 Kentucky .119 0 .040 .040 .039 Missouri .100 .060 .010 .020 .010 Delaware .197 0 0 0 .197 Arizona .164 .164 0 0 0 Maine .110 0 0 0 0

United States .085 .019 .026 .028 .012

Table 17—Estimated supply elasticity for a milk price increase and decrease, regions weighted from individual States and weighted United States

Estimated supply elasticity Region

Reversible Nonreversible

Increase Decrease

Northeast Corn Belt Lake States Southeast South Central

0.494 .425 .575 .714 .679

0.612 .425 .580 .921 .506

0.388 .372 .239 .839 .652

Plains Mountain Southwest Northwest

.596

.466

.289

.384

.546

.525

.513

.787

.675

.354

.116

.255

United States .510 .571 .374

over a 4-year period (most adjustment would occur in the Northeast).

Tests of whether the elasticity of milk production was different for a price increase than for a price decrease were generally insignificant. However, the signs of the estimated coefficients were usually correct. The statistically nonsignificant results show that the elasticity of milk production is more elastic (0.571) for a milk price Increase than for a milk price decrease (0.374), The results suggest that more work needs to be done on the nonreverslbllity question. One area that might be considered is to use a modified Wolffram method to construct a price increasing variable only when the milk price increases above the previous max- imum price.

19

Boyd n/k Buxton

BibHography

(1) Carley, D. H. Factors Related to Production Atfecting CCC Purchases. AgrlGultural Ex- periment Station Reseârehi Report 316, Univ. of Georgia, June 1979:

(2) Chen, Dean, Richard Gourtney, and Andrew Sehmitz. "A Polynomial tag Formulation of Milk Production Response," American Jour- nal of Agricultural Economics, Vol. 54, No. 1 (Feb. 1972), 77^83.

(3) Cromarty, William A. '-An Econometric Model for United States Agriculture," Joi/ma/ of American Statistical Association, Vol. 54, No. 287 {Sept. 1959), 556-74.

(4) Elterich, G. Joachim, and Sharif Masud. **Mill< Supply Response in Delaware," Jot//7?a/ of the Northeastern Agricultural Economics Council, Vol. IX, No. 1 (Apr. 1980), 41-5.

(5) Halvorson, Harlow W. *The Supply Elasticity of Milk in theShort-Run," Joi/ma/ 0/ Farm Economics, Vol. 39 (Dec. 1955), 1186-97.

(6) "The Response of Milk Production to Price," Journal of Farm Economics, Volv 40 (Dec. 1958), 1101-13.

(7) Hammonú, d.W. Regional Milk Supply Analysis. Staff Paper No. 74-12. Dept. of Agricultural and Applied Economics, Univ/of Minnesota, July 1974.

(8) Johnson, Glen L, and Lowell Hardin. Economics of Forage Evaluation. Experiment Station BuHetin623. PurdueUniv., Apr. 1955.

(9) Kadlec, J. E., H. R. Jensen, and E. W. Kehrberg. Estimating Supply Functions for Milk in the Louisville MHkshed with Farm Cost Data. Agricultural Experiment Station

Research Bulletin No. 270. Purdue Univ., May 1961.

(10) Kelley, Paul L, and Dale A. Knight. "Short- Run Elasticities of Supply for Milk," Journal of Farm Economics, Vol. 47 (Feb. 1961), 113-22.

(11) Ladd, George W., and George R. Winter. "Sujjply of Dairy Products by Iowa Farmers," Journai of Farm Economics, Vol. XLIII, No. 1 (Feb. 1961), 113-22.

(12) Stindquist, W. B., and others. Equilibrium Analysis of income-improving Adjustments on Farms In the Lake States Dairy Region, Í965. Technical Bulletin 246. Minnesota Agricultural Experiment Station, Oct. 1968.

(13) Wilson, Robert R., and Russell G. Thompson. "Denmnd, Supply, and Price Relationships for the Dairy Sector: Post World War II Period," Journal of Farm Economics, Vol. 49 (May 1967), 360-71.

(14) Wlpf;L. J., and J. P. Houck. Milk Supply Response in ttie United States-^ Arn Aggre- gate J^^nalysls^ Report No. 532. Dept. of Agri- cultural Economics, Univ. of Minnesota, 1967,

(15) Wolff ram, R. **Positivistic Measures of Ag- gregate Supply Elasticities: Some New Ap- proaches — Some Critical Notes," American Journal of Agricultural Economics, Vol. 53 (1571), 356-9.

(16) Zepp¿ Glenn A., and Robert H. McAlexander. "Predicting Aggregate Milk Production: An Empirical Study," American Journai of Agricultural Economics, Vol. 51 (Aug. 1969), 642-9.

20


Appendix—Literature Review

Considerable research on the elasticity of milk production has been completed during the last three decades. The research represents a wide range of methodologies employed, time periods used, and geographic areas considered.

Methodologies have mostly focused on the analysis of time-series data. One major exception was a series of studies, primarily in the sixties, that used linear programming techniques. In these studies, individual farms were programmed to determine likely adjustments to changes in milk prices relative to input and output prices. The individual farm results were aggregated to derive normative supply functions for milk (10, 12). Zepp and McAlexander concluded that regression analysis was a more appropriate procedure than linear programming (í6). This conclusion, combined with a need to estimate positive rather than normative supply functions, largely marked the end of this approach.

The evolution of time-series studies shows a clear concern about the nature of the lag between changes in the milk price and the resulting Impact on milk production. Halvorson first estimated a single equation using the milk price lagged one period (5). He used a Nerlovian distributed lag 3 years later to estimate both shortrun and longrun elasticities of milk production with respect to milk prices (6). This procedure imposed a

strict assumption about the lag, wherein the first year's impact of a change in milk price was the largest and subsequent years' impact declined geometrically over time. Similar procedures were used by Wipf and Houck (14) in 1967 and Hammond (7) in 1974.

Other studies, recognizing the strict assumptions imposed by the Nerlovian lag, attempted a more flexible procedure. Chen and others used the polynomial lag in 1972 for milk production in California (2). Hammond estimated a polynomial lag for the United States and several regions but was disappointed in the results (7), and Elterich and IVIasud used a polynomial lag in 1980 for milk production in Delaware (4), Studies using the more flexible polynomial lag generally have not had encouraging results even though the concept of a flexible lag structure is appealing.

Other studies have considered disaggregating total milk production into its two components: cow numbers and milk production per cow (1, 2). Separate equations and specifications are then used to estimate the two components.

Estimated elasticities of milk production from several well-known studies are presented in the app. table. The estimated shortrun elasticities range from as low as 0.027 (14) to 2.71 (12). Estimated longrun elasticities range from 0.041 (14) to 4.66 (12).

21

Boyd M. Buxton

Appendix table—Selected estimates of supply elasticities^

Time Region Elasticities ^^^^^^ period or Slate Shortrun Longrun

Chen, Courtney, and Sclinnltz (2)

Wipf and Houck (14)

Kadlec, Jensen, Kehrberg (9)

Halvorson (5)

Wilson and Thompson (13)

Ladd and Winter (11)

Kelley and Knight (10)

Cromarty (3)

Elterich and Masud (4)

Hammond (7)

Sundquist and others (12)

1953-68 quarterly

California

1954-64 annual

United States

1957 annual

Louisville Milk Market

1927-58 1944-57 annual

United States

1947-63 annual

United States

1926-55 annual

Iowa

1960 Topeka Market

1929 annual

United States

1966-78 quarterly

Delaware

1947-72 annual

U.S. aggregate, nine regions

1959 Upper Midwest

0.29

.027-.140

.58-.82

.128-.185

.180-.312

Immediate .003

.065

Price increase Price decrease .04 .187

.212

.921 (4 quarters)

.089

1.14-2.71

2.52

.041-.192

.398-.439

.154-.886

.521

2.53

2.83 (11 quarters)

.145

2.31-4.66

— = Not applicable. "• In part, this summary was reported by (7).

*U.S, GOVERNMENT PRINTING OFFICES 1985-460-941 î20047-ERS

22

Advances Made in the Poultry Industry

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This report looks at changes in the U.S. poultry industry over the last 25 years. While per capita consumption of

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