ANDREW M. NOVAKOVIC

AN INTERREGIONAL ANALYSIS OF U.S.-MEXICO TRADE IN DAIRY PRODUCTS

by

Tom Cox Jean Paul Chavas

Jorge Cornick Brad Barham l

Final Report on USDA National Research Initiative Grant # 92-37400-8143

January, 1994

Associate Professor, Professor, former graduate Research Associate, and Associate Professor respectively, Department of Agricultural Economics, University of Wisconsin-Madison.

We wish to gratefully acknowledge the assistance of the other members of the Wisconsin Interregional Competition Model (IRCM) team (Bob Cropp, Bill Dobson, Brian Gould, and Ed Jesse) in completing this research and providing many insightful comments. Heidi Knapp, Dan Lewis, and Yong Zhu provided additional graduate research assistance. We are, of course, alone responsible for any errors and/or omissions .

TABLE OF CONTENTS

I.- INTRODUCTION . .. . . .... ... .. . . ...... . .. . . .. . . . . . . . . . . . .. . ... . . A. Motivation .. . .. . ... .... .. .. . .. .. . . .. . . ...... . ... . .. ... . . .. . B. Objectives and Procedures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

C. Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1. Key Assumptions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2. Scenario Summaries. . . .. .. .. . ... . .. .. . . .. .. ... .. .. .. . .. .. 4 3. Key Findings .... ... . . ... .. .... . ... . . . .. . .. . ... . .... . ... 5

II. THE MEXICAN DAIRY MARKET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 7

A. An Overview of Previous Studies .... ... .. .. ...... . .. . . . . . . . . . 7 1. U.S. Studies on the Impact of NAFTA on Dairy Trade Between

Mexico and the U.S. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2. Mexican Studies on the Mexican Dairy Sector and NAFT A

Impacts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3. Limitations of Previous Research. . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

B. The Macroeconomic Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

C.

D.

E. F.

1. Economic Reform, Modification of the Trade Regime, and Macroeconomic Stabilization: 1980-1990. . . . . . . . . . . . . . . . . . . . . . 14

2. Population and Income: Recent Trends. . . .. . .. ..... . .. .. . .. . . The Role of Government in the Mexican Dairy Sector .. .. . ... . 1. Price Controls. . . .. . . ... . . . . . . . .. . ..... .. .. .. . .. .. . . .. . . 2. Trade Regulations. . .. . . . .. .. . . ... . .. . ... . . . .. . ... . . . .. . . 3. Land Policies. . ... .. .. . .... .. . .. . ...... ... . .... .. . .. . .. . 4. Restrictions on the Use of Grains as Feed .. ... . . . .. . .. . . . . . . . . . 5. NAFTA ..... ... ... ... .. .. . .. .. ... . . .. . ... . .. . . ....... .

a. Market access conditions. . .. .. .. . ... . . . . . . ... .. .. ... . b. Rules of origin . . . . .. .. ... ... . .. . . . . . . .. .. .. . .. . .. . . c. Sanitary and phytosanitary regulations. . . ..... .. . . .. .. ... .

Mexican Production of Milk and Dairy Products . . .. . .... . .. . . 1. Recent Trends at the National Level. . . .. ...... . . . . . .... . . . . . 2. Regions and Production Systems. . .. . . .. . . . .. ...... . ...... . .

a. Production systems. . .. . ... . . ... . . ... . .. .. ........ . . b. Regional distribution of milk production . .. . ..... . . .. .. ... .

3. Market Structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. A Note on Data Availability .... . . ....... .. . . . . .... .. . .. . .. . Mexican Demand for Milk and Dairy Products . .. ....... . . . .. . Mexican Dairy Products Trade ... .. .. . . ... . . .. . ..... . .. .. . . .

18 20 20 21 22 23 23 24 25 25

25 25 27 27 30 30 31

32

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III. U.S.-MEXICO EXPORTS UNDER ALTERNATIVE NAFTA SCENARIOS ................................ . .... .. ... ......... 41

A. Projecting Mexico's Milk Deficit . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 41 1. Introduction.. .................. , . . . . . . . . . . . . . . . . . . . . .. 41 2. Base Level Production and Commercial Disappearance. .... . . ... 42 3. Growth Rate Assumptions for Mexico Milk Production. . ....... .. 43 4. Growth Rate Assumptions for Mexico Dairy Consumption. . . . . . .. 4S S. Projected Milk Deficit in Mexico. . ...... .. ........ ..... , . . . . 46 6. Detailed Comparison with Previous Studies. ............ . .. ... . 46

B. Projecting U.S. to Mexico Dairy Exports ....... . ............. 48 1. Assumptions and Procedures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 2. The NAFTA Scenarios. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 49

IV. U.S. DAIRY SECTOR INTERREGIONAL COMPETITION MODEL (IRCM) ......... . .. .. .. . . . .... . .. . . , . . . . . . . . . . . . . . . . . .. S9

A. Model Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. S9 1. Introduction.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. S9 2. Transportation and Component Constraints. . . . . . . . . . . . . . . . . . .. 59 3. Price Supports and Marketing Orders. .... ......... ... ....... 60

B. Region and Product Specification . . . . . . . . . . . . . . . . . . . . . . . . . . .. 61

C. Dairy Component Accounting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

D. Wholesale Demand Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 62

E. Farm Level Supply Specification . . ....... ... .. . .. .. ...... .. . 63

F. Transportation Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 63

G. Export Specification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 64

V. SIMULATION RESULTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 79

A. Introduction ... .... .. ..... ........... .. ... . ... . ... .. ..... ' 79

B. Welfare Impacts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 79

C. Farm Level Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 80

D. Wholesale Level Impacts ................ . ..... . ... . .. . .. . .. 81

E. Component Price Impacts .... .. . .... ... ... .. .. .. ... . . . .. . .. 81

F. Trade Flow Impacts .... ..... . ............ . .. . ..... . ... . ... ' 82

G. Commodity Production Profile Impacts ...................... 82

VI. SUMMARY AND CONCLUSIONS, LIMITATIONS OF THE ANALYSIS, AND SOME THOUGHTS FOR FURTHER RESEARCH ............................ . ............... . ... . ... 91

A. Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

B. Limitations of the Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 92

C. Some Thoughts for Further Research ... ..... .. ... . . . ... .. . .. 93

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VII. REFERENCES . . .... . . . . ... .. . .. . . . .. . . . .. . .. . .. . . . . . . . . ... . ... 94

VIII. APPENDIX A: MODELING INTERNATIONAL TRADE WITH HEDONIC PRICING .. .... ... . . .. .. .... ... ...... . ...... . ..... . .. 97

A. A General Model . ..... . .. . ... .. .... . . . .... .... . .. ... . . . .. . 97

B. Spatial Hedonic Prices and Trade . . ... ..... .. ... . .. . .. .. .... 103

C. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

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AN INTERREGIONAL ANALYSIS OF

U.S.-MEXICO TRADE IN DAIRY PRODUCTS

I. INTRODUCTION

A. Motivation

With increasingly competitive global markets, exports of value-added, manufactured dairy

products need to be expanded in ways to help revitalize the rural economies supported by the U.S.

dairy sector. The importance and growth potential of Mexico as an importer of U.S. manufactured

dairy products and the existence of several previous studies dealing with agricultural trade between the

U.S. and Mexico both justify and make feasible Ithe development of a spatial trade model for the U.S.

and Mexico. This final report summarizes the results of such an endeavor.

A unique aspect of this research is the level of commodity and regional disaggregation. The

model includes:

9 dairy products (fluid, soft products, frozen products, american cheese, italian cheese, other

cheese, butter, nonfat dry milk, and other manufactured products);

14 U.S. regions (Northeast, Middle Atlantic, South Atlantic, Southeast, Central, East South

Central, West South Central, East North Central, Wisconsin, West North Central, West

Central, Northwest, Mountain, and California);

Several exogenous sectors (private stocks, government purchases and stocks, U.S. imports,

U.S. exports to Mexico, and U.S. exports to the rest of the world);

• Three milk components (proteins, fat, and carbohydrates (mostly lactose)).

This level of detail facilitates economic policy analysis of the aggregate as well as regional impacts of

changes in manufactured dairy product export~ to Mexico with respect to a variety of factors (e.g.,

input costs, fiscal and trade policies, monetary exchange rates, farm policy, etc.). In the future, the

modeling framework developed here could be expanded to include other countries who may be

potential importers or competitors in manufactl!red dairy products.

The project directly addresses two of the purposes of agricultural research and extension as

established by the National Agricultural Research, Extension and Teaching Policy Act of 1977, and

described under" Applicable Regulations" in the Guidelines for proposal preparation of the National

Research Initiative Competitive Grants Program. In particular, the results from this project will

contribute to I) enhancing the long-term viability and competitiveness of the food production and

agricultural system of the United States within the global economy; and 2), by helping farmers identify

and quantify export opportunities it will also contribute to expanding economic opportunities in rural

America and enhance the quality of life for farmers, rural citizens, and society as a whole.

B. Objectives and Procedures

The project has two main objectives:

To develop and implement an interregional competition model (IRCM) for the analysis of

trade in dairy products between the U.S . and Mexico.

To evaluate the likely impacts on aggregate and regional U.S . dairy markets of trade

liberalization between the U.S. and Mexico using this interregional competition model.

We will accomplish these objectives using the following procedures: I) characterize the Mexican dairy

sector; 2) develop projections of Mexico ' s milk deficit (demand for imports) and associated projections

for U.S. to Mexico dairy exports under several alternative NAFTA scenarios; 3) develop and

implement the spatial competitiveness model for analysis of U.S .-Mexico trade in dairy products; and

4) simulation exercises. In this section we describe each of these steps in more detai I.

1. Characterization of the Mexican Dairy Sector. This step is crucial as it provides the basic

description and data required to build the spatial competitiveness model. By way of background , we

review of the previous literature on the Mexican dairy sector, summarize the macroeconomic context

during the 19980's and 90's, and discuss the role of the Mexican government in the dairy sector.

In this latter section we summarize the U.S.-Mexico dairy portions of North American Free Trade

Agreement (NAFTA). Next, we assess the basic quantitative information concerning milk supply,

dairy product demand and imports in Mexico using Mexican data sources. Section II summarizes

these procedures.

2. Projections of U.S.-Mexico Dairy Exports under Alternative NAFTA Scenarios. Based on

the findings in (I), we then proceed to project Mexico ' s milk deficit (which, in turn, drives the

demand for dairy product imports) under alternative production and consumption scenarios. We

characterize four of the production/consumption growth scenarios in terms of NAFTA. Using the

associated five year growth projections in the Mexican milk deficit, we project the implications of

these scenarios for U.S. to Mexico and total U.S. dairy exports. These procedures are summarized in

Section III.

3. Development and Implementation of the U.S. Dairy Sector IRCM. The results from steps

1 and 2 are incorporated into a mathematical programming model then used to simulate trade between

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the U.S. and Mexico under the different NAFfA scenarios. The U.S. Dairy Sector IRCM is

summarized in Section IV and Appendix A.

4. Policy Simulations. Once the model developed and its performance deemed satisfactory, we

perform a series of simulation exercises. In particular, we model the effects of trade liberalization in

the context of NAFfA under four alternative scenarios. Impacts on aggregate welfare (producer,

consumer and total), aggregate and regional farm level prices, production and revenues, aggregate

wholesale level prices, production and consumption, and regional component prices, trade flows and

regional processing profiles are summarized in Section VI. Lastly, Section VII provides a summary

and conclusions, a discussion of limitations of the study, and some thoughts for further research .

c. Executive Summary

1. Key Assumptions.

a) Changes in U.S.-Mexico dairy trade due to NAFfA are modelled as exogenous shocks to the

U.S. dairy sector. These shocks are compared to a base scenario characterizing 1989-92 average U.S.

total and U.S.-Mexico dairy exports. Four "NAFfA" scenarios are analyzed.

b) An hedonic spatial equilibrium model of the U.S. dairy sector is used for the analysis.' The

model includes 14 U.S . supply/demand regions (including Wisconsin and California) and several

exogenous sectors (private stocks, government purchases and stocks, U.S. imports, and U.S. exports to

Mexico and to rest of the world). The model solves for farm level blend prices and milk production

as well as the price, supply, demand and trade flows for 9 wholesale level dairy products (fluid, soft,

frozen, american cheese, italian cheese, other cheese, butter, nonfat dry milk (NFDM), and a residual

manufacturing category (mostly whey products and evaporated/condensed milk)). A unique feature of

the model is that it also generates regional component prices for milk fat, protein and carbohydrates

(mostly lactose).

c) The model assumes the U.S . will continue to subsidize U.S. dairy exports as experienced in

the past 3-5 years. In the absence of these subsidies, it is questionable whether the U.S. would export

any NFDM or butter to Mexico since domestic prices are above world market prices. Hence, the

analysis assumes that changes in U.S.-Mexico dairy exports are determined mainly by the

supply/demand situation in Mexico. All other exogenous sectors (e.g., U.S. exports to the rest of

world and government removals) are held fixed at base levels. U.S. supply and demand price

elasticity parameters reflect intermediate (3-5 year) responses.

/,

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d) Given the questionable accuracy of the data on Mexican production, consumption and imports ,

the "NAFT A" scenarios are generated as "what if' exercises, not projections based on econometric

analysis. Base Mexican production and imports are I 989-92 averages using Mexican government

data. Total base consumption is taken as production plus imports . Supply scenarios were generated

under high, medium and low annual compounded growth rates for four production regions in Mexico

(North, Central, South-East, and Other) using historical data and field visits. High, medium and low

growth rates in consumption assume 2% annual growth due to population with 2%, I %, and 0%

growth due to income effects. Production and consumption are projected for 5 years at each of these

compound growth rates. For each of these scenarios, total Mexican import demand is computed as the

difference between consumption and production . The U.S. exports to Mexico are then assumed to

grow at the same rate as total Mexican imports. These growth rates are then projected on the 1989-92

average U.S . total and Mexico dairy trade for each of the nine commodities obtained from USDA and

FATUS data sources.

2. Scenario Summaries.

a) NAFTA+: A very optimistic NAFTA scenario where Mexican dairy production is low (1-2%

annual growth rates due to drought conditions and/or a cheap food policy by the Mexican government

(i.e., re-establishing price controls and/or allowing subsidized imports to grow very quickly, not

enforcing NAFf A tariffs or quota on NFDM» and consumption is high (4% annual growth: 2% due

to population, 2% due to NAFfA induced income effects) . To further reflect export growth optimism,

the 3% annual growth in the nonfat dry milk quota (which would be binding under this scenario) is

not imposed. This increases U .S. base level exports to Mexico by 69%. Note that this increase is a

very small share of base level total supply of milk components (0.5 % of total fat, 1.4% of total

protein , 1.9% of total carbohydrates) , hence is a relatively small demand shift to the U.S. dairy sector.

b) NAFTA: A best guess scenario characterized by medium growth in Mexico production (5-7 %

annual growth : average sustainable historical growth rates) and high consumption growth (same as

above) . This increases base level U.S-Mexico dairy trade by 4%, again a relatively small amount of

total U.S . supply (about 0.1 % of total fat , protein, and carbohydrates) .

c) STA TUS QUO: Current neo-liberalist policies in Mexico continue to improve incomes , but

not as strongly as with NAFTA. Hence consumption growth is medium (2% due to population, 1%

due to incomes) and production growth is medium (same as above). This generates a decrease in

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U.S.-Mexico exports of 19% from base levels (0.1 % of total U.S. milkfat supply, 0.4% of total

protein, 0.5% of total carbohydrates) .

d) NO NAFTA: This is a likely scenario if the NAFTA had been defeated in the U.S. Congress.

NAFT A defeat would stifle Mexico's neo-liberalist growth and results in stagnate income growth .

Hence, consumption growth is low (basically expands with population at 2%) while production growth

is medium. This generates a 40% decrease in U.S.-Mexico dairy exports (0.3% of total fat, 0.8% of

total protein, 1.1 % of total carbohydrates).

3. Key Findings.

a) Aggregate U.S. farm level total revenues impacts (changes from base farm level total

revenues of $20,985 million). Aggregate farm level total revenue impacts are quite small under the

NAFTA and STATUS QUO scenario's, +$21 million (+0.1%) and -$104.9 million (-0.5%),

respectively. Under the very optimistic (very pessimistic) NAFTA+ (NO NAFTA) scenarios, farm

level total revenues increase +$399 million, or + 1.9% (-$231 million, -1.1 %). Hence, the most likely

farm level total revenue impacts will likely be somewhat small and bounded between +/- 2% changes .

NAFrA+ NAFTA STATUS QUO NO NAFTA -------- ======= ============ ========= --------

a) % change: +1.9% +0.1% -0.5% -1.1 %.

b) millions: +$398 .7 +$21.0 -$104.9 -$230.8

b) Aggregate U.S. producer, consumer and total surplus impacts (changes from base

scenario) . Aggregate producer, consumer, and total welfare effects are also quite modest for the

NAFrA+ NAFrA STATUS QUO NO NAFrA

======== ======== ------------ ========= ------------a) Producer: +2 .5% +0.1% -0.7% -1.4%

b) Consumer: -0.4% +0.0% +0.1% +0.3%

c) Total: +2.1% +0.1% -0.5% -1.2%

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NAFfA and STATUS QUO scenarios, on the order of 0.1 % and -0.5% to -0.7% respectively. For the

NAFf A+ and NO NAFf A scenarios, these effects range from +2.1 % to +2.5% and -1.4% to -1.2%,

respectively. As expected, consumer welfare generally increases when producer falls, and vice versa.

Total welfare effects are bounded between +2.1 % and -1.2%.

c) BOTTOM LINE.

Aside from the very optimistic (NAFf A+) and very pessimistic (NO NAFf A) scenarios, the

more likeLy NAFTA and STATUS QUO scenarios generate very modest impacts on aggregate and

regionaL U.S. dairy markets. This is mainly due to the fact that while the NAFfA may potentially

generate large impacts on U.S.-Mexico dairy trade, these changes in U.S . exports are likely to remain

a relatively small portion of U.S . total milk supply.

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II. THE MEXICAN DAIRY MARKET

A. An Overview of Previous Studies

1. u.s. Studies on the Impact of NAFTA on Dairy Trade Between Mexico and the U.S.

Numerous studies have attempted to evaluate the impact of the NAFfA on the U.S. economy

in general, and on U.S. agriculture in particular. A few studies have focused specifically on the

impacts on the U .S. dairy sector, and those are briefly reviewed in this section. Most of these studies

include projections of dairy products supply and demand balance in Mexico. The projections are

presented in detail in Section II.

a. National Dairy Promotion and Research Board (1991) . The Mexican Dairy Market:

Prospects for Value-Added U.S. Products.

The report is organized in six sections. Section I is an executive summary , the main points of which are summarized above. Section II is a country overview, Section III presents information on demographics and communications, Section IV presents information on requirements and procedures to get into the Mexican market. Section V is a general characterization of the Mexican Dairy Market, and finally Section VI discussed channels of distribution.

This report focuses on cheese, frozen dairy desserts, chilled yogurt and fresh milk. It notes that market data in Mexico are both incomplete and unreliable, as well as discrepancies between Mexican and U.S. trade data. Main points in the report are as follows:

While overall governmental intervention in the Mexican economy has diminished significantly, in dairy the government continues to playa prominent role, which includes: a) setting maximum retail prices for fluid milk; b) import tariffs on most dairy products ; c) import licenses, health permits and product labeling reviews, all of which change frequently.

The Mexican cheese market is characterized by: a) per capita consumption at 25% of US levels; b) rapid growth in the last few years; c) fresh and white cheese predominate; d) high retail prices relative to income; e) prices close to U.S. prices; f) low packaging quality; g) 20% import tariff and import licenses on most U.S. varieties; h) dominance of the import market by Uruguay and Holland and i) rapid growth of U.S . exports .

The frozen dairy desserts market is characterized by: a) 20% the size of the U.S. market; b) low per capita consumption, but annual growth rate of 9% in recent years; c) in-home consumption low due to low incidence of freezers; d) standards of identity allow the use of vegetable oil; e) dip shops and street vendors are the main distribution channel; f) "light" products have a very small market share; g) U.S . products have 20% tariff, but dominate the import market; and h) interest in joint ventures with U.S . companies.

The yogurt market is characterized by: a) per capita consumption of 6.4 per year, which is higher than the U.S.; b) explosive growth in the last two years; c) wide product variety; d) product dating is required but not enforced; e) no import licenses, 20% import tax; and f) U.S. dominates the import market.

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b. Harris, H., and McClain, E. (1991). A U.S.-Mexico Free Trade Agreement: Potential

Impacts and Implications for the U.S. Dairy Industry.

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This report is organized in seven sections: Executive Summary, Introduction, The Mexican Dairy Industry, Trends in U.S.-Mexican Trade in Dairy Products, Projections of Mexico's Supply-Demand Balance, Potential Impacts on the U.S. Dairy Industry and, Issues Affecting an FT A. In the Introduction, the authors note that a free trade agreement could stimulate Mexico's demand for dairy products directly, through the reduction of tariff and non-tariff barriers, and indirectly, through faster growth and increased incomes in the Mexican economy. They also note that rapid demand growth, fueled by population and income growth, is unlikely to be satisfied by domestic production in Mexico.

The report classifies Mexican dairy farms in three groups : specialized, family, and dual purpose. The first group accounts for only I % of farms, but for 25 % of all milk, with average herd size of 230 cows . They report that only 34% of producers in this group have milking equipment and cooling tanks. Within the family dairies they distinguish two sub-groups: semi-specialized farms averaging 40 cows, and "family" farms , averaging 5 cows, mostly dairy-Zebu crossbreeds. Finally, the dual purpose farms range from 20 to 80 cows, produce 40% of all milk and are milked only 90-150 days a year.

Regarding the processing sector, the authors report high and increasing concentration, and pervasive excess capacity problems. Per capita fluid milk consumption estimates are reported, indicating a decline form 90 liters in 1981 to 6 I liters in 1989. It is noted, however, that if reconstituted milk is included in the calculation the decline is smaller, and consumption is estimated to be close to 2/3 of U.S. levels. Cheese and butter consumption are below 50% of the US levels. While this has long be the situation for cheese and butter, fluid milk consumption was above US levels in 1980 and 1981.

The role of the Mexican government in the dairy markets is briefly described in the report. The government sets prices at different points in the marketing channel, and CONASUPO has a monopoly on the importation of dry milk . LICONSA manages "social programs" that distribute subsidized milk to poor consumers.. Milk safety regulations are stringent, but not enforced. Additional components of the governments intervention in the dairy sector are: (I) Land Tenure Policies that have resulted in tenure insecurity for large farms.

Restrictions on farm size restrict the ability of large dairies to produce forage. The ejido system limits access to credit , as ejidatarios do not own the land they use, and therefore can not use it as collateral.

(2) Restriction on the use of feedgrain. Grains suitable for human consumption can not be legally used as feed .

USDA estimates the aggregate impact of government intervention in terms of producer subsidy equivalents at -24%. The US system, in contrast, subsidizes producers and penalizes consumers.

In the final section the report discusses several issues affecting a free trade agreement, including GAIT negotiations, the possibility of transshipments, the U.S .-Canada Free Trade Agreement, the mixture of public and private trade, and domestic US dairy programs.

c. Schulthies, B ., and Schwart. R. (1991). The U.S.-Mexico Free Trade Agreement: Issues

and Implications for the U.s. and Texas Dairy Industry. T AMRC International Market

Research Report No. 1M-I 0-91, 13 pp. plus appendices.

This report describe$ Mexico's traditional role as a large importer of U.S. dairy products as well as of dairy breeding cattle, dairy plant and milking equipment. It notes that a combination of high population growth and potential increases in per capita income in Mexico, resulting from internal economic reform as well as the potential free trade agreement with the US, are likely to result in continued market expansion for U.S. dairy products in Mexico. Prospects of Mexican exports to the U.S. are considered to be limited, but exist in specialty products.

The authors note that since Mexico has substantially reduced tariff and non-tariff barriers to trade since joining GAIT in 1986, a NAITA may have little additional impact on dairy trade: "The elimination of only licensing requirements and tariffs would probably have only a moderate direct effect on dairy trade between the two countries. U.S. sales of finished milk products and diary equipment would likely increase as Mexican import tariffs are eliminated. Neither the availability of dairy products nor U.S. consumer prices for dairy products would probably be affected since most U.S. dairy products exported to Mexico represent excess USDA stocks. In Mexico, consumer prices for dairy products should decrease somewhat as availability increases" (p. IO). Further, they estimate that "Even with annual mi I k output increasing at S% (the approximate growth rate for the last 30 years), Mexican consumption would still outstrip production . By the year 2000, Mexico would have to import nearly 2S billions of milk and milk products or about 41 % of projected demand" (p. ). These estimates are discussed in detail in Chapter II.

The authors estimate that large producers both in Mexico and the US would benefit from increased demand for high quality milk, while smaller producers might be unable to meet the higher quality and safety standards that might result from a NAIT A, and may be forced out of the market. Manufacturers of dairy equipment and suppliers of genetic material would benefit from the increased demand for their products. Mexican consumers would benefit from lower prices, increased variety and higher quality products . Big raw milk producers on both sides of the border would also benefit in the author's view: "Large producers south of the border would probably be the principal suppliers to the Mexican fluid milk market. Dairy farmers in US border states would likely be the suppliers to Mexican manufacturing markets. Also, milk from U.S . producers would probably be exported to Mexico in the form of finished products by U.S. processors" (p.IO). The author's also indicate that US supplies of breeding stock and genetic material, dairy equipment, technical consultant and dairy nutrition specialist, as well as Mexican dairy processors, are likely to benefit. from a free trade agreement. In contrast "Small Mexican producers would likely be the main losers if an IT A is implemented" (p.ll)

2. Mexican Studies on the Mexican Dairy Sector and NAFTA Impacts ..

a. Agrobiotec S .c. and University of Wisconsin (1992). Estudio de la Cadena de

Comercializacion de la Leche en Polvo en Mexico. Report presented to Secretarfa de

Agricultura y Recursos Hidraulicos , Direcci6n General de Asuntos Internacionales.

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9

Of all the reports we review, this is the only one that was prepared by a team that included both Mexican and US specialists. Its classification as a "Mexican" study is therefore somewhat arbitrary.

The study was requested by the Secretarfa de Agricultura y Recursos Hidniulicos (SARH) and it addresses a policy question, namely , how to design policies and instruments that would achieve, simultaneously, the goals of liberalizing the importation of dry milk , guarantee the supply for Mexican consumption needs, with minimum inflationary effects and stimulating the growth of the Mexican dairy industry.

Needless to say, some of these goals are contradictory. The study is useful not because it comes out with some prescription to achieve all of them, but because it provides a detailed diagnosis of the Mexican dairy industry (in as much as the data allow it) and because it highlights some of the trade-offs between the goals stated above.

The study is organized in five sections. Section A presents a diagnosis of the Mexican dairy sector. Section B discusses the international market for dry milk. Section C discusses the zoosanitary risks for Mexico associated with liberalized trade in dairy products. Section D evaluates the impact that dairy policies in the main exporting countries have on the future prices of non-fat dry milk in the international market. Section E presents policy recommendations.

For the purposes of this report, Section A provides the most useful information, and the reader is referred to it for detailed discussions of milk production in the different regions and production systems in Mexico. The authors note that milk production in Mexico has been growing at a slower pace than population has. There is great regional variation in the cost of production, and in some areas there is extreme seasonality in production, which may result in processing plants being under utilized most of the year, and unable to process all the available milk at the peak of the flush season.

The greatest potential for milk production increases is estimated by the authors to be in the tropical regions, where improved pastures, better management practices and improved sanitary practices could result in large increases at low cost. However, in the short run, the regions using intensive production techniques (mostly in the north and center of the country) have the greatest capacity to respond to price increases and technical support. At any rate, self-sufficiency in milk is likely to prove a prohibitively costly alternative , in the authors OpInIOn.

b. Valle Rivera, M. (1992). Perspectivas de fa Produccion Lechera Mexicana ante el Tratado

de Libre Comercio. Cuadernos Agrarios N. 4. Enero-Abril, Nueva Epoca.

This paper summarizes some of the differences in production technology between Mexico, Canada and the U.S. It notes that, if the three countries are considered a single region, the US has just slightly less than 60 per cent of the dairy herd, but produces more than 80 per cent of the milk. At the other extreme, Mexico has more than 30 per cent of the herd, but produces less than 8 per cent of the milk. While the first country has had a milk surplus for many years, the latter has had a large deficit.

In Mexico, imports of non-fat dry milk account for about a third of the milk availability in any given year. Domestic fluid milk prices increased sharply in 1988 and 1989, which resulted in increases in domestic production in 1989 and 1990. Imports diminished in those two years .

10

On average, milk production per cow per year is about six times as much in the US as in Mexico. However, in Mexico there are large variations between regions and production systems . In the specialized dairy farms characteristic of the northern region, for example, milk production per cow is, on average, two thirds of the US figure .

Valle Ri vera quotas GAIT figures to the effect that about 60 per cent of the price paid for milk in the US is accounted for by subsidies, while in Mexico it is consumption that is subsidized by the government. Under these circumstances it may be very difficult for Mexican producers to compete with US milk producers if trade is liberalized and no duties are applied to compensate for US production subsidies.

The author notes that there is already a differentiation in the Mexican dairy market, where high-value added products have exhibited considerable dynamism (cheese, processed milks, yogurt), while production of pasteurized milk has stagnated. She speculates that domestic production may be satisfying the demand of middle and high income groups, while imports of non fat dry milk satisfy the demand of low-income groups. However, she also notes that as much as 48 per cent of Mexican milk production is consumed as raw or "bronca" milk, and it is doubtful that this milk is being consumed by middle and high-income groups, which presumably are aware of the health risks associated with raw milk.

In her conclusions, the author makes a forecast that points in a somewhat different direction than the Agrobiotec study. She opines that the effects of trade liberalization will be quite different for producers in the intensive systems of production and for those in the extensive systems. Weaker producers will likely be swept away by the increased competition, while producers that are already strong financially and technologically are likely to expand both horizontally and vertically, improving their profitability. The outcome wi II be a more concentrated dairy sector.

c. Chauvet, M., Massiew, Y., Castaneda, Y., and Barajas, R. (1992). "La biotecnologfa aplicada

a la producci6n ganadera en Mexico", en La biotecnologia y sus repercusiones

socioeconomicas y politicas. Depto. de Sociologfa, UAM Azc.; Instituto de Investigaciones

Economics, UNAM, Mexico; Instituto de Investigaciones Sociales, UNAM, Mexico.

While the focus of the paper by Chauvet et al is somewhat narrow, a couple of points are of interest for the purposes of this report . First, the authors note that one of the characteristics of Mexican cattle farming is resistance to innovations. Only ten or fifteen years ago, the authors state, it was rare for a rancher to have an in-house veterinarian or to accept any changes in his practices . This has changed, partly because many ranches are now headed by the sons of the old ranchers, and these sons are themselves veterinarians or agronomists, or, at any rate, by younger people who are more open to change than their parents were .

The above observation fits exactly with the observations made by the authors of the current report on two field trips to Mexico . Many veterinarians complained that the ranchers were unwilling to improve their herd management practices, but were quick to note that the situation had improved a lot in the last ten to fifteen years. US veterinarians were sometimes specially discouraged by the resistance they found in ranchers who thought they were producing milk as well as it could be done, when this was far from being the case according to the US veterinarians.

The second point of interest in the report is the description of several experiences with "biotechnology". In particular, they describe the use of pro-biotics (as opposed to antibiotics)

II

prQduced by the firm APLIGEN, embryo. transplants and the use Qf BST. On this last PQint, it shQuld be nQted that while MQnsantQ recQmmends that up to' 30 per cent Qf a dairy herd CQuid be Qn BST, the authQrs repQrt that their field wQrk indicates that this is widely QPtimistic. Again, the Chauvet et aI' s repQrt cQincides with the QbservatiQns made by the authQrs Qf the current report.

d. Chau vet, M. (1987). Diagn6stico del sistema ganadero bovino: carne y leche en Mexico,

Alternativas de Desarrollo que ofrece la Biotecnologia. Tesis de Maestrfa, Facultad de

ECQnQmfa, UNAM, Mexico..

Chapter I Qf this thesis presents a discussiQn Qf the Marxist theQry Qf the "internatiQnal reprQductiQn Qf capital", which is prQbably Qf little interest fQr researchers who. want to' make quantitative fQrecasts abQut the Mexican dairy industry Qr abQut the U.S .-Mexico. dairy trade. The last two. sectiQns Qf Chapter I are Qf mQre interest fQr empirically Qriented researches, as they present, respectively, a histQrical Qverview Qf the eVQlutiQn Qf the agrarian sectQr in Mexico. , and an Qverview Qf studies abQut the cattle (beef and dairy) sectQr in Mexico..

Chapters IV and V present mQst Qf the empirical results in the thesis. Chapter IV presents in mQre detail the histQrical data that were summarized in Chapter I, and includes a sQmewhat detailed discussiQn Qf changes in productiQn by state and municipality frQm 1950 to' 1986. Chapter V discussed the different prQductiQn systems in the Mexican dairy sectQr, which she labels as cQnfined, semi-cQnfined , and seaSQnal milking. These twO. chapters prQvide a gQQd backgrQund Qn the Mexican dairy sectQr, and are Qf interest fQr researches that may nQt share Chauvet's theQretical perspectives.

3. Limitations of Previous Research.

MQst Qf the US repQrts have relied Qn data available frQm either public Qr private SQurces. On

the Qther hand , the Mexican studies have relied mQre Qn field wQrk undertaken specifically fQr the

purpQses Qf the specific studies. These apprQaches are cQmplementary, and the limitatiQns Qf the

available research are sQmewhat symmetrical Qn each side Qf the bQrder.

Reliance Qn "Qfficial" data, as nQted by all the authQrs, is quite dangerQus, as the data are

unreliable and sQmetimes incQnsistent. The authQrs Qf this repQrt did "SPQt checks" in TQrreQn and

CQhuahuila, and fQund that the data repQrted to' SARH' s Qffices in Mexico. (which in turn shQWS up in

the Qfficial estimates Qf milk prQductiQn, CQW numbers and so. Qn), are Qften based in less than

rigQrous data cQllectiQn prQcedures. And this is indeed an understatement Qf the data cQllectiQn

prQblems we Qbserved. Granted, the authQrs did nQt systematically evaluate data cQllectiQn prQcedures

in all the states Qf Mexico.. But at the minimum, Qur QbservatiQns lead to' the cQnclusiQn that SARH

dQes nQt systematically enfQrce cQnsistent and reliable data cQllectiQn procedures in all its regiQnal

Qffices.

J2

On the other hand, studies that are based on field work on a specific region avoid the pitfalls

of using the official data, but the conclusions of such studies are very hard to generalize. While

researchers with field experience may have a strong feeling that, say, region A and region B are pretty

much the same, and thus the results of a study in region A can be applied with little if any

modification to region B, given the available data there is no way to determine whether the similarity

indeed holds or not.

We did not find any study in which behavioral relationships, namely, the parameters of supply

and demand functions for dairy products, were estimated. To a certain extent this is of course simply

a consequence of the data problems discussed above. In other words, given the quality of the data it

may be futile to try to estimate econometric models in many cases. There are, however, at least two

data sources that should be studied in more detail, as they are large enough, and possible accurate

enough to support the estimation of econometric models.

On the milk supply side, the results of the latest Agrarian Census were being published at the

end of 1993. Researchers at UNAM reported to members of this team that there were very large

discrepancies in the figures for herd size as reported in the Census and as reported by SARH and used

to estimate monthly milk production figures. A thorough examination of the Census figures seems

worthwhile. Such an examination may result in a more reliable "number of cows" figure (by state,

and hopefully by production system) which could in turn be used to improve the milk production

figures. If the Census data are rich enough, it may be possible to go beyond this, and estimate some

basic supply response parameters.

On the dairy products demand side, there are periodic income and expenditure surveys in

Mexico, the latest of which has already been obtained by the authors of this report. Clearly, the data

in the survey could be used to estimate a complete system of demand equations, including demand

functions for several dairy products. From a preliminary examination of these surveys, it seems that

both price and income response parameters could be estimated.

While some data sources may be under utilized at the moment, a more thorough knowledge of

the Mexican dairy sector can not be acquired without extensive additional field work . On the one

hand , there is a need for specific research projects . These projects would allow to find answers to

specifiC questions, regions and production systems . For some time, it may be necessary to generalize

to Mexico as a whole from such projects. But in the long run, the need to improve SARH ' s statistical

system is unavoidable. Researchers from Mexican or foreign universities may be able to provide in

depth analysis of certain questions from time to time. But reliable and periodic estimates of milk

13

production, production of manufactured dairy products, dairy product prices at the farm, wholesale and

retail levels, clearly will not be produced by anybody except a government agency, be it SARH,

INEGI or some other. Until such reliable database is systematically generated by the Mexican

government, academics, policy analyst, and perhaps more importantly, decision makers in the

Mexican dairy sector, will be shooting in the dark.

B. The Macroeconomic Context

1. Economic Reform, Modification of the Trade Regime, and Macroeconomic Stabilization:

1980-1990.

Since the economic crisis of 1982, the Mexican economy has undergone a remarkable

transformation. The economy has become more open, state intervention has been redefined and

generally reduced, many state owned businesses have been sold to the private sector, the external debt

has been renegotiated , and inflation has been brought under control. While the focus of this report is

on dairy trade issues , a brief description of the macroeconomic context is helpful to understand the

transformations that are already taking place in the Mexican dairy economy, and to evaluate how this

sector may respond to further changes brought about by a N AFT A. However, the reader who is

interested only on the dairy issues, may skip this section with little loss of continuity .

To our knowledge, Nora Lustig's book Mexico: The remaking of an economy offers the first

comprehensive overview of the economic reform in Mexico from 1982 to the present, and this section

is essentially a summary of her book. During the post-war years, Mexico, along with many other

Latin American countries, followed what is known as an "import substitution" development strategy.

The strategy relied heavily on trade barriers to promote industrial development, and is generally

viewed as biased against the agricultural sector. The strategy also relied on the internal market (what

has been known as "inward looking" development) rather than on export markets, and entailed a heavy

dose of governmental regulation in the form of price controls, allocation of credit, etc. The change

that took place in Mexico during the 80's was dramatic. In the new model markets are used "to

replace regulation, private ownership to replace public ownership, and competition, including that from

foreign goods and investors, to replace protection" (Lustig, p.I).

As it is well known , the change in economic strategy was precipitated by the debt crisis of

1982. Mexico borrowed heavily in the 70' s, counting on oil revenues to pay for the loans. When oil

prices fell and interest rates rose, a crisis ensued. A schematic chronology follows.

14

The 60's to mid 70's were the golden years of what was called "desarrollo estabilizador": per

capita annual growth rates were in the 3 to 4 percent range, while inflation hovered at about 3 percent.

This came to an end in the late 70's. The fiscal deficit grew from 2.5% of GDP in 1971 to 10% in

1975. Foreign debt grew from 6.7 billion US dollars to 15 .7 billion US dollars. The inflation rate

grew from 3.4% in 1969 to J 7% in the J 973- J 975 period. In August 1976, for the first time in

twenty-two years, the peso was "allowed to float" in the foreign exchange market. An almost 40

percent devaluated of the peso against the dollar followed. Output fell sharply and inflation

accelerated.

The government was just starting to develop an adjustment policy when massive oil

discoveries triggered a new approach. Instead of adjusting to scarcity, the government had to

"administer the abundance". "The "public-expenditure-Ied growth" produced impressive results in

aggregate output, investment and employment during the four years of the oil boom. GDP grew at an

annual average of 8.4 percent, total investment increased by 16.2 percent a year, and urban

employment expanded at 5.7 percent a year between 1978 and 1981 (Lustig, p.20).

However, the currency became increasingly overvalued, and expectations of future public

revenues fomented a large fiscal deficit. As a result of the overvalued peso and the fiscal deficit, the

balance of payments disequilibrium started to grow. Spending accelerated in the second half of 1980

and beginning of 1981. The fiscal deficit reached 14.1 percent of GDP. Foreign debt up to the end of

1980 was not a problem. In 1981, with oil prices declining, the situation deteriorated markedly.

Policymakers could not reach a consensus on whether devaluate the peso or impose import and capital

controls. Capital flight increased. By mid-February 82 maintaining the peso through short term

borrowing was no longer possible. A devaluation of the peso followed . In August the government

froze, and then forced the conversion at below market rates, of dollar denominated accounts. New

devaluations and a 90 day suspension of foreign debt payments triggered a crisis. In September, the

banking system was nationalized.

Miguel de la Madrid was elected President in December 1982. The consensus view was that

the 1982 crisis had resulted from a large fiscal deficit and the overvalued peso. In consequence, the

government expected that by restoring fIscal balance, adjusting the foreign exchange rate and

restructuring external debt payments, capital inflows would be encouraged and that growth would

follow . As it turned out, these "orthodox" adjustment measures were not enough to start the

economic recovery that the government was seeking.

15

A "shock treatment" approach to macroeconomic stabilization was implemented in 1983,

known as the Programa Inmediato de Reordenaci6n Econ6mica. The shock was followed by

gradualist policies in J 984 and 1985. The shock began with large devaluations of the "free" and

"controlled" exchange rates in December 82. The program envisioned an increase in tax and nontax

revenues, and a cut in public expenditures, so that by the end of 1983 the fiscal deficit would be

reduced to half of the record 1982 level (16.9%) . It was expected that both measures would result in a

reduction in the inflation rate and the current account deficit. In August 1983 the first round of debt

renegotiation was completed, and as a result the payments of principal due for 1983-84 were reduced.

However, starting in 1985 the payments would increase again. Net resource transfers remained above

7 percent of GNP 83-85 .

The plan was short lived. As soon as the external environment turned less favorable (declines

in the price of oil and higher interest rates) for the Mexican economy, the balance of payments and

the general climate of expectations deteriorated rapidly. In July 1985 trade liberalization began , with a

substantial reduction in the number of imports subject to license requirements. Fiscal and monetary

policy became contractionary . The government expected to reap benefits in 1986, but as oil prices fell

sharply, this did not happen. "The loss in foreign exchange revenue was U.S. $ 8.5 billion, equivalent

to 6 .7 percent of GDP, 48 percent of total export receipts , and 26.2 percent of public sector revenues"

(Lustig, pp. 39-43).

The peso was depreciated in an effort to protect the balance of payments and avoid depletion

of foreign currency reserves. It was hoped that the depreciation would also help diversify Mexico's

exports. GDP grew by 3.8 percent and the trade surplus was U.S. $ 4.6 billion in spite of the lower

price and volume of oil exports. Moreover, gross foreign currency reserves rose by about U.S . $1

billion, ending the year at U.S. $ 6.8 billion. Perhaps more importantly, oil revenues represented only

39.5 percent of total export revenues, down from 68 .2 percent in 1985.

A second round of debt rescheduling was concluded in 1985. Toward the end of 1986, under

the Baker Plan new funds were made available, and the old debt was rescheduled. Contingency

financing was included, in case the Mexican economy did not grow as anticipated. In J 987, the focus

of macroeconomic policy shifted towards price stability and economic recovery. The rate of

devaluation of the peso was slowed, but fiscal discipline was maintained. As the price of oil

increased, this resulted in a significant increase in the primary surplus of the public sector: from 1.6

percent of GDP in 1986 to 4.7 percent in 1987 (Lustig, p.47) and a current account surplus was

achieved in 1987.

J6

Nevertheless, the stock exchange crashed, and it was followed by a run on the peso. The run

on the peso, in the face of sustained improvement of macroeconomic indicators, convinced the

government that an orthodox adjustment package would not be enough to restart the Mexican economy

on a sustained path of economic growth. Therefore, a new economic plan, known as the Economic

Solidarity Plan, was announced by the government, and signed by the government and representatives

of labor, agricultural producers and the business sectors in December 1987. The Pact had 3

"ingredients":

(1) Increase the fiscal primary surplus and reduce the supply of credit.

(2) Incomes policy (in fact, a price and wages freeze) .

(3) Structural reforms: trade liberalization and divestiture of public enterprises.

"The Pact produced immediate good results . During the second semester of 1988 average

inflation was 1.2 percent a month, far lower than the 9 percent registered during the same period in

1987 . In 1988 real GDP grew at 1.3 percent, non-oil exports at 15 .2 percent, and private investment

at 10.9 percent. The Pact fared better than the more orthodox stabilization program of 1983. The

reduction in inflation was much larger, growth was positive, and real wages fell considerably less than

In 1983 (see Lustig, table 2-4).

As successful as the Pact was in bringing inflation under control, it did not spur an economic

recovery . When Carlos Salinas became President in 1988, he announced a Pact for Economic Stability

and Growth (PECE), underscoring the government's commitment to growth without sacrificing price

stability. To achieve recovery, it was required to reverse the resource transfers to foreign economies.

The government concentrated in three fronts: reduce the burden of debt service, encourage capital

repatriation, and attract new foreign capital.

Debt rescheduling under the Brady Plan was announced on March 1989, The new

negotiations resulted in modest savings but the government hoped that they would boost private sector

confidence in the government. Private investment did not pick up as fast as the government had

hoped, and two additional measures were announced: reprivatization of the banks, which was

followed immediately by capital inflows and a reduction in nominal interest rates, and second, start of

NAFrA negotiations with the U.S.

In Lustig's view, therefore, NAFrA, fulfills two different roles from the Mexican perspective,

On the one hand, it is simply a trade agreement, that will result on more open borders between the

U.S. and Mexico. But, on the other hand, NAFrA plays an important symbolic value as a sign of the

government's commitment to economic reform, From this perspective, the most important effect of

17

NAFT A may not be the tariffication of quantitative trade restrictions or the gradual elimination of

tariffs between the US and Mexico. The most important impact of NAFT A may be in the increased

flow of investment capital into the Mexican economy, which may result if foreign investors are

convinced that economic reform in Mexico is a long term commitment, not a another short lived

attempt at economic transformation.

Economic reform in Mexico goes beyond the fiscal and monetary reforms outlined above. The

government has been divesting itself from public enterprises, and it continues to do so. Attempts have

been made at Fiscal reform, reducing tax evasion that was once commonplace. The land laws have

been changed, and in the near future ejido lands may be converted into privately owned lands. Along

with the elimination of import license requirements and other restrictions on trade, domestic prices

have been freed to a considerable extent. In Lustig's words, "The crisis and adjustment process

provided opportunity to streamline the bureaucracy, improve the public sector's revenue -collection

and expenditure mechanisms, and redefine the nature and extent of state participation in the economy.

Fiscal policy reform, decentralization, divestiture of public enterprises, and the elimination or

relaxation of ownership, price setting, and trade restrictions (that is, deregulation) became the core

ingredients of public sector reform" (Lustig, p.97)

2. Population and Income: Recent Trends.

Mexico's population is young, and growing quickly. The age distribution of the Mexico's

population, as reported by NDPRB (1991), and the annual population and growth rates 1980-90 are

presented in Table 2 . Note that almost 50% of the population is under the age of 20, and nearly 70%

under 30. The annual compounded growth rate is 2.03 percent. Conflicting estimates of Mexico's

18

Table 3.

Age Distribution of Mexico·s Population.

Age Group

0-9 10-19 20-29 30-39 40-49 50·

Percent of Population

2 4 24 20 13 9

11

Source: NDPRB (1991). p.9 o

Population of Mexico and Annual Growth RfIles : 1980-90 .

Annual Growth Year PopUlation ('Yo)

1980 7 0.416.000 1981 72.070.000 2. 3 1982 73.719. 008 2.3 1983 7 5.354.000 2 .2 1984 76.958.000 2 .1 1985 76.524.000 2 .0 1986 60.063.008 2. 0 1987 81.584.000 1 .9 1988 83.098.000 1 .9 1989 84.61 7. 008 1 .8 1990 86.154.000 1 .8

Average 78.414.275 2 .0

Source: World Bank World Tables.

population and its growth rate are available from different sources, but all estimates indicate that the

population is growing at least at 2% annually, and perhaps as fast as 2 .5% annually. In contrast,

population growth in the United States is just 1 % annually (NDPRB , 1991, p.8). The demographic

profile , then, shows considerable potential for consumption of dairy products, even if demand expands

only at the same rate as the population .· Nevertheless, and considering that consumption levels are

well below US levels for most products, there is probably considerable room for expansion above

population growth. Whether this additional expansion materializes or not will depend, to a great

extend , on the evolution of real incomes in Mexico. Table 2 presents miscellaneous indicators of

macroeconomic performance, expressed in terms of rates of change, for the 1981-1991 period. Gross

domestic product contracts by more than 4 percent in 1983, and exhibits somewhat erratic behavior

after that, alternating moderate growth with a large contraction in 1986. Starting in 1989, growth

Table 4. Mexico Macroeconomic Indicators 1981-91 (annual percentage changes).

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 Output and Income

GOP 8.8 (0.01 (4.2) 36 2.6 (3.8) 1.7 U 13 4.4 16 GOP per capita 5.9 (10) (6.3) 15 0.6 (5.5) (0 .1) (0.5) 1 5 2.6 1.9 Disposable Income per capita 3 4 (5.4) (142) (UJ) 1.9 (15.3) 5.0 (5.4) 79 6 3 oa

Inflation CPt annueJ average 279 58.9 101.9 654 577 86 2 131.8 114 2 20.0 267 22.7

Wages Wages quoted by industrial survey 5.0 0.1 (2<tl) (6.8) 1.1 (6.9) (6.5) (0.5) 8.9 06 06

Minimum wege 1.0 (0.1) (21.9) (90) (1.2) (105) (6 .3) (127) (6.6) (9.1) (45) Average real wage in monufacturing 18 0) -22.8 -7.1 -2.8 -5.9 -1.S -13 90 2.9 4.9

a/ Source: 1981 to 1988: Mexico. Sistemnde CUentas Nacionales. 1980 8Ild 1989: World Bank. World Tables . bl GOP deftotor form World Bonk. World Tobles. 1987 - 100. c/ Population dmo from World Bonk. World Tables .

becomes more vigorous: 3 .3 percent in 1989,4.4 percent in 1990, and 3 .6 percent in 1991. In per

capita terms, the gross domestic product decreases in most years after 1981, and grows very modestly

in the others. This trend does not change until 1989. While growth is still modest, three consecutive

years of positive growth are observed (1989, 1990 and 1991). Inflation is a problem for most of this

period. For 1982 to 1988, inflation as measured by changes in the CPI is well above 50% in all years,

and as high as 131 % in 1987. However, just as growth was resumed in 1989, inflation seems to be

under control from that year on, with changes of 20 percent in 1989, 26.7 percent in 1990 and 22.7

percent in 1991 .

Wages, as reported in the industrial survey, decline rather abruptly In 1983 (by 24 percent) and

continue to decrease until 1989, when they finally increase by almost 9 percent. The rate of growth in

the minimum wage is negative for all years for which there is information. To a great extent ,

r

I

19

however, the minimum wage has ceased to be meaningful, as many businesses pay wages above it,

and the self-employed urban poor, the informal sector, are not affected by it. Finally, the average

wage paid in the manufacturing sector increases in 1989, 1990 and 1991, after several years of steady

decline.

The picture, then, is one of economic crisis starting in 1982, and economic recovery , both in

terms of increased growth and reduced inflation, starting in 1989. These changes have been attributed

to the substantial changes in the governments economic strategy, which were schematically described

in the previous section.

c. The Role of Government in the Mexican Dairy Sector

The Mexican government has traditionally exercised a prominent regulatory role of the

Mexican dairy industry, both with sectoral specific policies and indirectly , through land, trade and food

policies. Since the Mexican government started shifting away from interventionism and towards a

more market oriented economic strategy the extent of the intervention in the dairy sector has

diminished, and it is likely to be further reduced with NAFT A approval as is discussed under Trade

Regulations below

1. Price Controls.

Traditionally , prices of milk at the farm level , and fluid milk at the retail level were set by the

government. In contrast with the U.S., where Producer Subsidy Equivalents (PSE) are positive and

Consumer Subsidy Equivalents (CSE) are negative, in Mexico price controls and other policy measures

have been used to generate negative PSE's and positive CSE's. Hallberg et al (1992), analyzing the

period 1982 to 1989, report CSE, as a percentage of the price of milk, ranging from 1.94 percent in

1985 to almost 16 percent in 1989 (Table 1, p. 7). During the same period, PSE range from a rather

exceptional 1.18 percent in 1984 to -6.23 percent in 1989 .

As indicated above, the government has been reducing its price control activities. Currently,

the farm level milk prices is no longer regulated, but the retail price of fluid milk continues to be set

by the government which, farmers claim, in effect sets a cap on the prices that processing plants can

pay to milk producers. Depending on policy decisions, milk producers could be in fact in a very

uncomfortable situation: the price floor on farm milk has been removed, but there is an implicit price

ceiling, via the maximum retail price of milk . Representatives of dairy associations , moreover,

indicated confidence in the removal of retail level price controls in the near future.

20

The price setting mechanism has also changed in recent years. Prices used to be set at the

Federal level and were allowed to lag behind inflation . Now prices are set at the State Level , and

Milk Commissions with representatives of the Federal and State Governments, the milk producers and

the processors, are supposed to set prices to reflect local supply and demand conditions. While some

sources indicated that State level governments are under strong pressure not to set prices that are too

far off Federal level unofficial guidelines, it is at least clear that in the aggregate, the price of fluid

milk was allowed to catch up with inflation in 1989 and 1990 (thus encouraging the recovery in milk

production observed in 1990 and 1991).

2. Trade Regulations.

There are three types of trade regulations concerning dairy products in Mexico: tariffs, import

licenses, and in the case of dry milk, a governmental monopoly on imports through the parastatal

agency CONASUPO. In addition, the government requires health certificates for some products.

Table 5. Import Requirements for Various U.S. Dairy Exports to Mexico.

Product Import license Health Certificate Fluid Milk No Yes Non Fat Dry Milk Yes Yes Evaporated Milk Yes No Condensed Milk No No Yogurt No Yes Whey No Yes Butter No Yes Butteroil No Yes Cheese Varies Varies Lactose No No Ice Cream No Yes

Source: NDPRB (1991), Table IV·A

Table 5 describes the import

requirements for major dairy

products, as reported by NDPRB

(1991). We do not have historical

data on import duties, but

conversations with farmers in Mexico

indicate that either the duties were

lower, import permits were not

generously granted until recently, or

both. Apparently the trade policy

changes that followed Mexico's

accession to GATT did not escape the

dairy sector, which has been facing

increased competition (and, in some

cases, successfully, adapting to it since then). From this point of view, NAFT A approval would bring

only incremental change to the environment in which the Mexican dairy sector operates, not a radical

and sudden change. This is especially true in the first few years of implementation of the accord, as

wi II be made clear in the Section V of this report.

21

r

3. Land Policies.

Mexico's land policies were shaped as one of the major outcomes of the 1910 revolution.

Rural lands were essentially divided in two types of properties: privately owned land, and "ejidos" ,

land owned by the government but given to rural communities for indefinite administration.

Private land ownership was subject to quantitative restrictions under this scheme. Maximum

amounts were set for agricultural and for cattle farms . The limits were larger for cattle farms than for

agricultural farms. Land owned above the legal limits was subject to expropriation if an ejido claimed

it needed the land. Thus, considerable land property ownership insecurity was a key factor in rural

Mexico . Moreover, because the tenure limits were stricter for agricultural farms, cattle ranchers were

weary of developing more intensive exploitations (producing more forages, for example) lest the land

be reclassified as agricultural , thus endangering ownership.

On the flip side of the coin, ejidos assigned land use rights to individual users, who could in

turn transfer those rights to their heirs. However, it should be emphasized that the ejidatarios did not

have a title to the land. As a result, the land could not be used as collateral, and ejidatarios had a

harder time than private owners securing credit. Presumably, the intent was to prevent the

development of a land market that would lead to the high concentration of land ownership that was

one of the causes of the revolution in the first place. The result was farms that were frequently

undersized and undercapitalized with little means for changing that situation .

The framework of land ownership is now completely changed, as Article 27 of the Mexican

constitution was reformed earlier this year. Votes are to be held in the ejidos, and the members will

decide whether to remain organized as an ejido, or whether individual ejidatarios should be issued title

to the land they already use. If the vote goes for the private ownership option, the ejidatarios could

sell or buy land, although some restrictions apply in the beginning of the process. Even if the option

is to remain an ejido, the land could now be used as collateral for loans (presumably the ejido would

be the subject of credit) . Both options , therefore, encourage the formation of a land market.

Outside the ejido lands, the new law dispenses with the limits on land tenure. Besides

encouraging the development of a land market and capitalization of the ejido land, the change in

Article 27 will thus eliminate the land ownership insecurities that were associated with the old law.

While the legal changes brought by the modification in Article 27 are rather dramatic, it is

likely that actual changes in the field will be very gradual. There are two reasons for this: the process

of land titling is likely to proceed very slowly. It will take time for the ejidos to organize the

"referendums" on the preferred ownership form , and if the option is for private ownership, then each

22

parcel will have to be mapped and registered. Informal reports indicate that land ownership transfers,

in the case of land that was private in the first place, can take more than a year. It seems likely that

the time required to transfer land that is now part of an ejido will be longer.

The second reason to expect gradual changes, rather than abrupt ones, is that many ejidatarios

have already made arrangements with private investors interested in using their land, be it renting the

land or, for all practical purposes, selling it. In this context it seems reasonable to expect that the first

few years of operation of the new law will see a formalization of transactions that already have taken

place rather than a flurry of new transactions.

The investment climate, however, will likely change even if there are few new land

transactions in the immediate future. If somebody "bought" land from an ejidatario and was using it

under the old law, the user was always under some risk of having the ejido reclaim the land. This was

especially true if no installations were built on the land (for example, if somebody had bought a piece

of land, but did not have access to capital to start developing it immediately). Tenure can now be

made safer, even though the land transaction had taken place some years ago.

4. Restrictions on the Use of Grains as Feed.

Another restriction that dairy farms have faced in Mexico is that by law they have been

prevented from using human consumption grade as animal feed. In Mexico, corn is commonly a

major component" of the diet. Whatever the merits of the law in terms of providing nutrients to the

population efficiently, its effect has been to constrain the yields in Mexican dairy farms below what

they could be if higher quality grains were used as part of the animal diet.

It was not clear at the time of this writing if the current restrictions were going to be formally

lifted . However, if import restrictions on corn are eliminated as a result of a NAFT A accord, the

restrictions may be hard to enforce, as farmers will have access to large supplies of corn with very few

restrictions.

5. NAFTA.

NAFT A would modify the legal framework for dairy trade between the U.S. and Mexico in

several ways. Market access conditions would be modified and "rules of origin" would be put in

place. Additionally, under NAFTA each country would retain its ability to impose sanitary and

phytosanitary regulations, even if the standard that a country or state chooses is higher than a certain

international standards (see Vitaliano for a more detailed discussion of the topics presented in this

section).

23

a. Market access conditions.

The main change that NAFfA would introduce in dairy trade between the U.S. and Mexico

would be the immediate tariffication of all quantitative restrictions to imports from the other country.

Currently such restrictions are in place through Section 22 quotas on the U.S. side and import license

requirements on the Mexican side. Both Mexico and the U.S. are allowed to impose tariff quotas in

place of the current quantitative restrictions, but those tariff quotas have to be phased out according to

a predetermined schedule. The details vary from product to product, but the phase out period ranges

from 10 to a maximum of 15 years, which applies only to U.S. exports of NFDM into Mexico.

All items subject to Section 22 quotas will become subject to tariff-quotas, with a phase out

period of ten years. Tariffs for in-quota imports from Mexico become zero immediately. Similarly,

U.S. export to Mexico that are currently under import licensing requirements will become subject to

tariffs only, with the exception of non fat dry milk which receives a special treatment.

With respect to U .S. exports of NFDM the rules are as follows. Initially, a tariff-quota of

40,000 metric tons will be set, with an over-quota tariff of 139 percent ad valorem. The over-quota

tariff is phased out over 15 years, and the tariff-quota will be increased by 3 percent each year.

The tariff phase out for NFDM is not linear. Over the first six years of the agreement, a total

of 24 percent of the over-tariff quota will be eliminated. The remainder of the over-quota tariff will

be eliminated linearly over the next eleven years. In other words, the over-tariff quota is reduced by 3

percent (with respect to the original level) each of the first six years of the accord, and by 4 percent

for the last nine years of the accord. (USDA, 1993)

Mexico's import license requirements for evaporated milk and cheeses will be converted into

ordinary tariffs at 20 percent ad valorem, except for fresh cheese, which will be subject to a 40 percent

tariff.

Rules concerning the importation of Mexican products are as follows. Five "quota-baskets"

have been established: (I) all cheese, (2) fluid products, (3) dried lowfat products, (4) butter products

and (5) processed quotas. Mexicos initial tariff-quotas will be set at 5 percent of total current section

22 quotas that make up the quota basket. Within quota imports will not be subject to tariffs, and

above quota imports will be subject to tariffs calculated as "tariff equivalent" of current quota

restrictions. The tariff quota will be increased by 3 percent each year and the over-quota tariffs are

phased out over ten years.

24

b. Rules of origin.

A particular concern for the U.S . in negotiating the NAFfA was to include provisions that

would prevent Mexico from acting as a transshipment point for dairy products originating in the

European Economic Community or in New Zealand, for example. The preferential market access rules

established by NAFf A are supposed to apply only to products originating in the countries signing the

accord.

For most products, if some materials used in production came from non-NAFf A countries, the

agreement requires that the product undergoes a change in tariff classification as a result of production

activities in a NAFf A country. A "de minimis" rule, however, allows for up to seven percent of the

value of any product to be composed of non-originating materials that have not met the required tariff

re-classification.

Most dairy products are exempt from the "de minimis" rule. Products in Chapter 4 of the

Harmonized System of Tariffs will not be considered of NAFf A origin if made with any other

Chapter 4 dairy product from non-NAFfA sources. "Thus, to cite two examples, cheese made in

Mexico will not be considered of Mexican origin if it contains milkfat or nonfat solids imported from

the EC, and condensed milk reconstituted in Mexico from New Zealand butteroil and nonfat dry milk

will not be considered of Mexican origin" (Vitaliano, p. 13). In contrast, Chapter 4 products can

include non-NAFfA products from other H.S. chapter (say, imported fruit in yogurt) and still be

considered of NAFfA origin (but there are some exceptions to this rule).

c. Sanitary and phytosanitary regulations.

"The NAFf A sanitary and phytosanitary provisions essentially require the health-related

sanitary and phytosanitary standards maintained with respect to trade between the three NAFf A

countries to be based on scientific evidence, not be applied in an arbitrary or discriminatory manner,

be based on risk assessment and be applied only to the extent necessary to achieve a

clearly-understood level of protection" (Vitaliano, p. 5).

D. Mexican Production of Milk and Dairy Products

1. Recent Trends at the National Level.

Fairly disaggregated data are available on farm milk production in Mexico. SARH publishes

monthly data by state. However, this abundance of data may be misleading. Field work by the

authors of this report found that rigorous statistical procedures are not necessarily followed by SARH's

25

Figure 1. Mexico Aggregate Milk Production, 1985-1993 (Source: SARH).

billion liters

8.-----------------------------------~

6

4

2

o 1985

Production. 7.2

% change -:

* 1993 is preliminary.

Source: SARH

1986 1987

6.4 6.2

-11.1 , -2.7

1988 1989 1990 1991 1992 1993·

6.2 5.6 6.1 6.7 7.0 7.2

-0.7 -9.4 10.1 9.4 3.8 3.1

%

10

o

(10)

local offices. Annual data are perhaps more reliable than monthly estimates that are likely to be

statistically unsound. The figures presented below should be interpreted with great caution. Data from

the latest agricultural census are being published in Mexico at the date of this writing, and it has been

reported that there are serious discrepancies between the data collected by INEGI (the Mexican

Government agency in charge of the Census) and the data reported by SARH.

As shown in Figure I, production declined rather sharply from 1985 until 1989. but initiated a

quick recovery after that. As was previously noted in Table 2 (page 18), per capita GDP started

growing in 1989 after several years of decline. The increase in milk production is likely in part

associated with increased demand as a result of income growth. However, retail fluid milk prices in

Mexico are set by the government, so that the increase in income would not benefit the producer

unless the government allowed the retail price to increase. Data on real prices of fluid milk are

presented in Table 6 (page 26). Note that from 1986 to 1988 the changes in the retail prices of dairy

products are less than the changes in inflation during those years. In other words, the real prices of

26

Table 6. Annual Percentage Changes in Retail Dairy Product Prices for Mexico, 1986-91.

Product 1900 1987 1988 1989 19!1l 1991

Pasteurized Milk 74 126 97 42 29 17 Unpasteurized Milk 69 115 99 53 19 23 Dry Milk 72 109 91 30 26 16 Dry Milk Formula 69 92 98 28 20 14 Butter 78 106 100 13 20 9 Cream 75 101 126 13 9 10 Avg. Cheese 81 127 118 12 16 13 Yogurt 100 143 100 -5 12 11 lice Cream 97 138 33 27

CPI, Annual Average 86 132 114 20 27 23

Sources: Dairy Prices from SARH; CPl's from Lustig (1992), Table 4-2.

dairy products are decreasing in this period, with the exception of yogurt where the price increases

more than the CPI in 1986 and 1987 . It is not surprising, given this evolution of retail prices, that

production of milk tended to decrease during the same years . The situation changes in 1989. The

prices of pasteurized milk, unpasteurized milk, dry milk and dry milk formula increase faster than

inflation, while the prices of butter, cream, cheese and yogurt lag behind inflation . In 1990, the prices

of pasteurized milk and ice cream increase more than inflation but other prices lag behind. In 1991,

only the price of unpasteurized milk grows faster than inflation.

Data availability makes it very difficult to discuss changes in production of manufactured dairy

products . Most analysts agree that a large proportion of the milk that is used in manufactured

products is processed in artisan type factories. While an industrial survey collects data on "formal

sector" establishments, there is simply no hard data on the" informal sector" establishments, and it is

therefore impossible to evaluate apparent consumption of manufactured dairy products with any degree

of certainty.

2. Regions and Production Systems.

Q. Production systems.

Most authors agree that several distinct production systems can be observed in the Mexican

dairy industry, and that those production systems tend to be concentrated in particular regions . In this

27

section the classifications that have been offered in the literature are discussed, and the distribution of

milk production by state is examined.

As reported by Agrobiotec (1992, p. 11), SARH classifies dairy farms in three groups:

specialized, semi-specialized and non-specialized. Specialized farms have average herd size of 230

cows, with production ranging from 4000 to 6000 liters per lactation, which may last from 100 to 400

days . Cows are generally pure breed Holsteins, and the dominant reproduction method is artificial

insemination. Both forages and balanced feeds are used to feed the cows. Specialized farms have

milking and cooling equipment, and high sanitary standards. These farms tend to be concentrated in

the northern states of Chihuahua, Coahuila and Durango, and in the milk sheds of lalisco, Guanajuato,

Puebla, Queretaro, Mexico and Hidalgo. Specialized farms have 14 percent of the dairy herd, but

produce 55 percent of Mexico's milk (Agrobiotec, 1992, pp. 11-12).

Semi-specialized farms account for 17 percent of milk produced and 26 percent of the dairy

herd. Most cows in this system are in Veracruz, Chiapas, Michoacan, Sinaloa and lalisco . Two

sub-groups are distinguished: semi-confined farms, with an average of 40 cows, 2400 to 4000 liters

per lactation, which last from 100 to 300 days. Cows tend to be Holstein and Swiss, but they are not

pure breeds. Forages, grass and balanced feeds are used to feed the cows. These farms lack cooling

equipment, have low sanitary standards, and are milked by hand (Agrobiotec, 1991, p. 12)

The second subgroup are the grazing (pastoreo) family farms. These farms average 5 cows,

producing 300-700 liters per lactation. Low quality Holstein are the dominant breed. Cows are feed

grass and forages (Agrobiotec, 1991, p. 12).

The third system is the "dual purpose" system, which accounts for 60% of the dairy herd and

25% of milk production. Farms in this system produce both milk and beef, using cows that are

cross-breeds of Holstein, Swiss, "Criollo" and Zebu. The farms have an average of 80 cows, which

produce 750 liters during lactations lasting from 90 to I 50 days. The cows are fed mostly grass, are

milked by hand, and the sanitary standards are very low. These farms are located in the tropical areas

around the Gulf, and in the South-East of Mexico.

Hallberg et al (1992) offer a classiflcation with only slight variations with respect to the one

presented in the Agrobiotec report. Hallberg et al (1992) define a confmed system, which is simply

Agrobiotec's specialized system. However, their assessment of the extent of use of milking equipment

is different: "the majority of these herds are milked by hand due to the low cost of labor in Mexico.

Only 32 percent of confined system dairy farms have milking machines . A limited number of

producers use cooling tanks" (Hallberg et aI, pp. 7-8).

28

A second system discussed by Hallberg et al is the "pastoral system", which corresponds to

Agrobiotec's "semi-confined" system . The note that "These cows are generally maintained on native

or improved pasture, and fed grains fortified with oilseed meals and corn stalk. Nutritional

deficiencies are common and the genetic makeup is not well-managed. Herd in this systems are

widely distributed throughout Mexico's central and northern regions. Facilities for cooling milk are

rare on these farms, and other facilities are generally inadequate for achieving maximum production

efficiency" (Hallberg et ai, p. 8).

The discussion of the "dual purpose" systems closely parallels Agrobiotec's, but their estimates

of the share of this system in Mexico's herd and milk production are slightly different: 63 and 28

percent, respectively.

Finally, a fourth group is noted, the "traspatio" system, which is similar to Agrobiotec's

"grazing family farms". They note that this system "is not very well defined nor well understood.

Herds in this system can be found in and around the large cities in Mexico and range in size form 5 to

30 cows. They produce raw milk for sale directly to consumers, and it is believed that they make a

significant contribution to the overall milk supply of these cities" (Hallberg et al 1991 p. 9).

Harris and McClain (1991) use the same classification as used by Agrobiotec. The

classification system of Schulthies and Schwart (1991) is basically the same as used by Agrobiotec,

although the terminology is slightly different. More importantly, Schulthies and Schwart add some

more detail to the description of each system, and somewhat different productivity estimates

For the "dry lot system" they report herd sizes ranging from 230 to 3000 head or more. The

cattle are improved dairy breeds (95 percent Holstein, 4 percent Swiss) with annual production of

about 5000 liters. Farms in this system account for 14 percent of all dairy farms in Mexico, but they

have 25 percent of the dairy herd and produce from 55 to 60 percent of all milk, and as much as 80

percent of the milk that is pasteurized.

"The major differences between the large Mexican dairies and their southwestern U.S.

counterparts are the level of milking technology and the utilization of feed concentrates. Due to

inexpensive labor, the majority of the farmers in this group milk by hand with only 34 % utilizing

milking equipment and cooling tanks . Cows are usually fed concentrates in the milking parlor only

during milking time. This practice limits the animals' nutritional intake . In contrast, large U.S. milk

producers seldom offer any type of feed to their cows during milking but feed them concentrates

several times throughout the day out of the milking parlor allowing the animals to consume higher

quantities of concentrates" ( Schulthies and Schwart, p.2).

29

The semi-confined farms are described as having an average herd size of 40 cows, with

production of 5,500 to 9,200 lb. per cow annually . Cooling tanks and milking equipment are rare,

animal nutrition is deficient, fertility rates are low and the animals are of poor genetic quality.

Finally, with respect to the dual purpose sys tem, Shulthies and Schwart note the "Animal

nutrition intake among this group is very poor, usually comprised of native grasses as the only feed

source. Annual milk production ranges from 1,240 to 1,720 lb .. per cow. Of the three types of

Mexican dairy operations, the dual purpose system is considered to have the greatest potential for

growth ... The cost of milk production in Mexico is estimated to be 25% to 30% lower among dual

purpose systems than with the other two types of systems" (pp. 2-3).

b. Regional distribution of milk production.

Table 7 and Table 8 (pages 35 and 36) present data on milk production by State in absolute

and relative (state share of aggregate Mexico production) terms, respectively . Note that Jalisco is, by

far, the largest single producer, with its share of total production increasing from 13.8 percent in 1985

to an estimated 17 percent in 1993 . The second largest producer is Veracruz (dual purpose operations

for the most part), with a share of 8.3 percent in 1985 and 9.2 percent in 1993. The Northern states of

Durango, Chihuahua and Coahuila, added together, provided more than 17 percent of Mexico's milk

production in 1985, and 18.4 percent in 1993 . Also note that the contribution of both the State of

Mexico and the Distrito Federal have declined steadily from 1985 to 1993, from almost 10 percent to

less than 6 percent, and from 1.8 to 0.2 percent, respectively.

3. Market Structure.

While there are a large number of dairy farms in Mexico, the dairy sector as a whole may be

less than perfectly competitive. At the farm level, it seems that a distinction can be made between

large farmers who are vertically integrated (i.e ., who are members of a processing plant) and those

farmers who are not vertically integrated. A couple of comments are in order.

Producer-owned processing plants are frequently referred to as "cooperatives", perhaps as an analogy

to the U.S. situation, but not all the plants are indeed cooperatives. Some are, rather, privately owned

companies. As such, they do not offer "open membership" , and it was our impression that probably

the opposite is true: membership is closely guarded. Moreover, while some of these processing plants

have "cow shares", which enable the producer to send to the plant the milk of as many cows as shares

he has, in other cases the plants have straight shares. Different producers will receive different prices

30

for their milk, according to the number of shares they own. The price of milk, in this case, serves as a

mechanism for paying dividends.

Farmers and government officials agreed that those farmers who are affIliated with a dairy

plant (through share ownership or membership in a cooperative) face a more stable economic

environment than those who don't. The later tend to receive lower prices for their milk, and in some

cases may not even have any security of selling their milk at all. SARH officials are looking into the

possibility of helping farmers who are in this situation develop some form of contracts with the

processors, so as to bring more stability to the relationship, and perhaps redress imbalances in

bargaining power between processors and producers .

At the processing level, the overall number of processing plant has been diminishing very

quickly, partly as a response to the low controlled prices of milk and problems of excess capacity.

Within the remaining plants, a few companies seem to control most of the market in pasteurized and

ultra-high pasteurized milk, and the degree of market power may be considerable on a regional basis.

While we don't have enough information at this moment to evaluate market structure issues with any

rigor, but it seems that any future efforts at modeling the Mexican dairy sector should be prepared to

deal with imperfectly competitive market structures.

4. A Note on Data Availability.

This is a caveat that the reader is likely to find in any study of the Mexican dairy sector: the

data on the sector are incomplete and unreliable. Personal communications from SARH officials

indicate that Mexican trade data from before 1989 is not considered reliable by SARH (this is

specially true for value data; volume data may be more reliable). Our field work indicated that local

SARH offices collect data without the use of a uniform and statistically rigorous methodology. The

reliability of the data collected may vary widely form one regional office to the other. As far as we

could tell, there is no "quality control" process centrally administered. In short, it is not possible to

know the reliability of the data on cow numbers and production produced by SARH.

Researchers at UNAM have indicated2 that there are large discrepancies between the

agricultural census data on number of cows that has recently begun to be published and the figures

reported by SARH. If, as is likely, the Census data are more reliable, perhaps some rough adjustments

can be made to improve estimations based on SARH's numbers.

Maria del Carmen del Valle, personal communication.

31

We did not have an opportunity to discuss with Mexican officials the monthly industrial

survey which generates data on manufactured dairy products. However, as was noted earlier, this

survey covers only industrial or "formal sector" establishments. There seems to be consensus among

researchers of the Mexican dairy sector that a large proportion, perhaps as much as 50%, of the

Mexican production of manufactured dairy products comes out of "informal sector" or artisan

establishments. Therefore, even if the industrial survey data are more reliable than the milk production

data, they are certainly incomplete. There is no easy way of solving this deficiency.

E. Mexican Demand for Milk and Dairy Products

The market for dairy products in Mexico seems to have at least three different segments1:

(I) a "formal" sector; (2) an "informal" sector; and, (3) a "government subsidized" or "social" sector.

The formal sector is composed of those sales that take place through regular retail channels

(supermarkets, convenience stores, dip shops and carts for frozen desserts) plus sales to the food and

service industries.

The informal sector, as far as we can tell, is composed mostly of farmers that sell their milk

directly to consumers, be it in fluid or in processed form . Raw or "bronca" milk seems to be preferred

by many consumers, and it fetches a higher price than pasteurized milk (which is price controlled) ,

and, as indicated above, some analysts have estimated that as much as 50% of fluid milk consumption

is of bronca milk. Similarly, artisan cheeses are common. Market data are unavailable on this

segment of the market.

LICONSA's sales of subsidized milk also constitute a distinct market segment. It is debatable

whether LICONSA's sales reach only consumers who otherwise would not buy any milk, or if they

displace private sales. Given that LICONSA has tightened the criteria for eligibility to subsidized milk

which now includes only households making less than two times the minimum salary, it seems

reasonable to guess that displacement is minimal. At any rate, given that LICONSA's coverage goals

(in terms of its target population) are public information, this may be the segment of the market whose

behavior is easiest to forecast (assuming the government's stated goals are credible)

The terminology "market segmentation" is being used loosely to indicate different parts or components. While segmentation in a more rigorous sense may be present, we know of no formal tests of this hypothesis.

32

The data limitations discussed above make estimation of consumption of specific dairy

products particularly difficult, and the task will not be attempted here (see NDPRB for some estimates

for specific product markets). However, it is possible to attempt "milk equivalent" measures (i.e.

measures of the amount of farm milk contained in all dairy products consumed). SARH has published

some estimates. Additionally, production estimates and trade data (expressing the imports in milk

equivalent units) can be used to develop another rough measure of consumption (admittedly an

imperfect one, since it does not account for changes in private or governmental stocks). In Section III

we present commercial disappearance and milk deficit estimates for Mexico based on the latter

procedure.

F. Mexican Dairy Products Trade

As indicated before, officials at SARH do not consider Mexican trade data before 1989 to be

reliable. According to personal communications, as well as some published research (see Barkin)

under and over-invoicing were widely used in that period to avoid taxes. Note, however, that under

and over-invoicing would affect only trade value data, not volume data. Accordingly, we report

volume trade data for 1989-1992.

As indicated in Table 8 (see page 37) and Figure 3 (see page 38), Mexico's imports of most

dairy products grow steadily from 1989 to 1992. The exception is dry milk which shows large

fluctuations. But since a parastatal agency, CONASUPO, has the monopoly of importation of this

product, decisions are not based on purely economic criteria but instead on political considerations as

well. Thus, it is thus not surprising that this product exhibits markedly different behavior. Dry milk

clearly dominates Mexico's dairy imports in quantity terms. Note that dry milk (59%), whey (II %)

and butter (10%) on average account for over 80% of Mexico's dairy product imports over the 1989-

92 period. Growth markets over this time period are fluid milk (+24%), whey (+32%), cheese (+42%)

and other (+77%).

Figure 4 (page 39) summarizes the aggregate value and share of Mexico's dairy imports from

the U.S. With the exception of 1988 and 1989, U.S. dairy exports to Mexico average about $60-70

million. In 1988 and 1989 aggregate U.S. dairy exports to Mexico were 2-3 times higher, $137 and

$205 million, respectively. These are precisely the worst years in terms of Mexican domestic

production (recall Figure I, page 25). In 1990, Mexican milk production recovers substantially, and

imports from the U.S. drop back to 1985-86 levels. In terms of shares, Mexico averages 23% of total

value of U.S. dairy exports from 1985-1990. Again, in 1988 and especially 1989 Mexico had a higher

33

than average share of U.S. dairy exports. With respect to particular commodities, Figure 5 (page 40)

indicates considerable year to year variation in the volume shares U.S.-Mexico dairy exports,

particularly for the major commodities NFDM (on average 46% of U.S. total exports over the J 985-90

period), butter (J 2%), and other (26%). This in part reflects the impacts of CONASUPO in the

purchase of subsidized U.S. dairy exports.

34

Table 7. Mexico Milk Production by State, 1985-93 (1000 liters).

STATE 1935 1986 1987 1988 1989 1900 1991 1992 1993 •

AGUASCAUEIlTES 160,759 205,659 188,726 150,013 152,104 183,106 205,636 217,599 218,000 BAJA CAl...IFO~IIA 133,946 206,094 230,500 205,641 183,749 171,817 176,070 172,525 219,304 BAJA CAl...IFO~IIA SlR 13 ,039 9,827 21,820 13,S36 14,233 14,970 16,412 18,793 17,7'04 CM1PEotE 43,574 20,338 21,263 19,827 16,255 15,500 H,218 11,112 17,21 3 COAHUILA 497,379 292,856 314,068 295,406 294,456 325,724 392,896 407,153 358,611 COLIMA 39 ,((20 38,(02 36,773 34,500 38,679 38,730 33,000 34,596 35,918 CHIAPAS 343,899 200,480 232,828 172,738 187,664 200,469 204,320 217,380 232,886 CHIiUAHUA 388,939 415,183 359,233 340,271 340,271 467,431 545,982 510,370 510,370 DlSTRlTO FEDERAL 131,449 80,El18 42,070 47,939 30,190 26,593 21,700 16,337 16,321 DURAtlGO 345,184 314,509 378,296 370,036 317,103 343,947 347,112 376,140 456,511 GUAtIAJUATO 433,534 434,930 452,315 429,400 416,780 499,390 528,383 543,630 554,000 GUmRERO 96,379 51,664 54,0:14 69,749 35,351 55,810 66,336 59,555 63,551 HIDALGO 177,519 184,840 199,790 257,576 263,306 273,229 278,495 313,732 318,960 JAUSCO 988,348 1,016,000 1,021,628 956,984 1,046,143 1,120,400 1,183,659 1,220,779 1,220,779 MEXICO 687,872 588,658 448,308 365,638 353,372 304,519 410,016 409,250 426,956 MlotOACAII 320,146 203,078 214,024 440,265 193,708 236,618 234,428 259,737 270,036 MORflOS 29,780 51,496 53,612 54,679 45,319 17,751 19,701 19,105 20,464 tlAYARlT 81,327 16,540 21,000 35,789 39,314 43,265 53,718 54,957 53.000 tlUEVO LEOti 56,743 22,0:16 23,605 30,(02 37,650 31,845 25,731 25,000 24,600 OAXACA 136,321 121,123 136,919 125,500 70,440 91,600 121,443 144,178 135,393 PUffiLA 288,345 253,838 237,336 271,083 208,264 260,400 262,046 266,470 299,400 QUERETAA.O 217,724 136,763 125,099 126,040 114,537 127,750 149,650 152,910 154,948 QUIITAM ROO 4,210 4,000 5,015 6,568 1,767 1,771 2,479 2,740 2,740 SAtl LUIS POTOSI 124,239 189,742 213,268 199,220 243,118 247,593 263,501 278,705 291,651 SItIALOA 110,191 195,001 208,914 138,133 106,064 112,303 166,439 181 ,345 159,955 SOtlORA 153,094 103,810 95,030 100,455 75,109 80,075 81,208 92,272 99,463 TABASCO 152,941 89,201 89,810 86,828 86,135 89,495 90,279 87,320 98,335 TAMAUUPAS 136,675 150,762 26,824 60,m3 23,961 22,752 31,275 23,832 41,344 TLAXCALA 115,938 77,038 81,930 85,250 52,054 77,144 67,034 75,390 73,883 VERACRUZ 592,695 545,230 485,303 428,838 465,735 549,468 597,219 644,160 664,000 YUCATAII 32,256 12.000 16,0:10 34,002 9,447 8,237 9,800 20,916 13,900 ZACATECAS 139,490 142,030 165,159 205,382 114,961 101 ,843 11 3,849 116,281 120,667

TOTAL 7,172,955 6,3n,406 6,200,980 6,159,171 5,571,309 6,141,545 6,717,115 6,974,269 7,100,993

SOlRCE: DIRECCIOIi GEIIERAL (l: EST AOISTICA, S.A.R.H. • Preliminary.

35

Table 8. Mexico Milk Production by State, 1985-93 (state share (%) of total production).

STATE 1985 1986 1967 1968 1989 1990 1991 1992 1993 •

AGUASCAlIEIiTES 22 3.2 3.0 2.4 2.7 3.0 3.1 3.1 3.0 BAJA CAlIFOP!UA 1.9 3.2 3.7 3.3 3.3 2.8 2.6 2.5 3.0 BAJA CAlIFOP!IIA SIR 02 02 0.4 02 0.3 02 0.2 0.3 0.2 CM1PEOIE 0.6 0.3 0.3 0.3 0.3 0.3 0.3 0.2 02 COAHUILA 6.9 4.6 5.1 4.8 5.3 5.3 5.8 5.8 5.0 COliMA 0.5 0 .6 0.6 0.6 0.7 0.5 0.5 0.5 0 .5 CHIAPAS 4.8 3.1 3.8 2.8 3.4 3.3 3.0 3.1 3.2 CHHUAIlJA 5.4 6 .5 5.8 5.5 6.1 7.6 8.1 7.3 7.1 DlSTRlTO fEDERAL 1.8 1.3 0.7 0.8 0.5 0.4 0.3 0.2 0.2 DURAIIGO 4.8 4.9 6.1 6.0 5.7 5.6 5.2 5.4 6.3 GUAtIAJUA TO 6.0 6.8 7 .3 7.0 7.5 8 .1 7.9 7.8 7.7 GUERRERO 1.3 0.8 0.9 1.1 0.6 0.9 1.0 0.9 0.9 HIDALGO 2.5 2.9 3.2 42 4.7 4.4 4.1 4.5 4.4 JAUSCO 13.8 15.9 16.5 15.5 18 .8 18.2 17.6 17 .5 17 .0 ME>:ICO 9.6 9.2 7.2 5.9 6.3 5.0 6.1 5.9 5.9 MIOIOACAII 4.5 3.2 3.5 7.1 3.5 3.9 3.5 3.7 3.8 MORELOS 0.4 0 .8 0.9 0.9 0.8 0.3 0 .3 0.3 0.3 IIAYARlT 1.1 0.3 0.4 0.6 0.7 0.7 0.8 0.8 0.7 IIUEVO LEOII 0 .8 0.3 0.4 0.5 0.7 0.5 0.4 0.4 0.3 OAXACA 1.9 1.9 2.2 2.0 1 .3 1.5 1.8 2.1 1.9 PUffiLA 4.0 4.0 3.8 4.4 3.7 4.2 3.9 3.8 4.2 QUERETARO 3.0 2.1 2.0 2.0 2.1 2.1 2.2 22 22 QUIITAIIA ROO 0.1 0.1 0 .1 0.1 0.0 00 00 0.0 0.0 SAil LUIS POTOSI 1.7 3.0 3.4 3.2 4.4 4.0 3.9 4.0 4.1 SIIIALOA 1.5 3.1 3.4 2.2 1.9 1.8 2.5 2.6 2.2 SOIIORA 2:1 1.6 1.5 1.6 1 .3 1 .3 1.2 1.3 1.4 TABASCO 2.1 1.4 '1.4 1.4 1.5 1.5 1.3 1.3 1.4 TAMAUUPAS 1.9 2.4 0.4 1.0 0.4 0.4 0 .5 0.3 0.6 TLAXCALA 1.6 12 1.3 1 .4 0.9 1 .3 1.0 1 .1 1.0 VERACRUZ 8.3 8.6 7.8 7.0 8.4 8.9 8 .9 9.2 9.2 VUCATAII 0.4 0.2 0.3 0.6 0.2 0.1 0.1 0.3 0.2 ZACATECAS 1.9 2.2 2.7 3.3 2 .1 1.7 1 .7 1 .7 1.7

TOTAL 1000 100.0 100.0 100.0 100.0 100.0 1000 100.0 1000

SOIRCE: DlRECCIOIi GEIIERAL II: ESTAlJISTICA, S.A,R.H. • Preliminary.

36

Figure 2. Mexico Dairy Product Imports, 1989-92 (SARH Data).

1989-92 Mexican Dairy Imports 1989-92 Growth (metric tons) 1989 19!1l 1991 1992 Average Rates (%)

Fluid Milk 38581 32,479 53,405 72)15 49,295 215 Dry Milk 239,916 287,837 57,831 213,805 199,847 -3.8 Whey 23,113 36,404 40,958 53,354 38,457 32.2 Butter 30,206 27,11 0 37,940 39,313 33,642 9.2 Cheese 7,898 10,004 14,951 22,433 13,822 41.6 Other 1,620 5,289 6,273 8,948 5,532 76.8 Total 341,335 399,124 211,357 410,569 340,596 6.3

Mexican Dairy Imports 1989-92 (product shares) 1989 19!1l 1991 1992 Average

Fluid Milk 11.3% 8.1% 25.3% 17.7% 14.5% Dry Milk 70.3% 72.1% 27.4% 52.1% 587% Whey 6.8% 9.1% 19.4% 13.0% 11.3% Butter 8.8% 6.8% 18.0% 9.6% 9.9% Cheese 2.3% 2.5% 7.1% 5.5% 4.1% Other 05% 1.3% 3.0% 2.2% 1.6%

Source: SARH

37

Figure 3. Mexico Dairy Product Imports, 1989-92 (SARH Data).

metric tons

250,000

200,000

150,000

100,000

50,000

o Fluid Milk Whey Cheese

Dry Milk Butter Other

[-.m~989 IfII 1990 III 1991 0 1992 II Average-I

Source: SARH

38

Figure 4. U.S.-Mexico Dairy Exports: Value and Share of U.S. Total Dairy Exports, 1985-90.

Millions of $ Percent

250

200 f JII\ l 40

150

100 f ... 111("-. 20 ... 50

0' 0 1985 1986 1987 1988 1989 1990 85-90 AVG

VALUE. 62 75 74 137 205 60 102

SHARE- 14 17 15 24 49 18 23 - - - - ------- -----~ - - - -

Source: McClain and Harris, p. 144.

39

40

Figure 5. U.S.-Mexico Dairy Exports: Commodity Shares of U.S. Total Volume, 1985-90.

market share (%)

80 I

60

40

20

o

Source: McClain and Harris, p. 144.

10 9 7 15

III. U.S.-MEXICO EXPORTS UNDER ALTERNATIVE NAFTA

SCENARIOS

A. Projecting Mexico's Milk Deficit

1. Introduction.

In this section, Mexico's milk production deficit is projected over five years under a variety of

assumptions concerning growth rates of Mexican milk production and consumption of dairy products.

The implicit growth rates in Mexican milk deficits under these alternative scenarios are then used to

project U.S.-Mexico exports as a means to measure potential export shocks likely to result from

NAFT A. Our results indicate that Mexico's milk deficit might be considerably smaller under most

plausible scenarios than found in previous research. To clarify the sources of this discrepancy, we

present a detailed comparison of the data and assumptions used in this study and those used in

previous studies.

Characterization of the Mexican dairy sector under alternative NAFTA scenarios is a

speculative enterprise . Originally, our intent was to model the Mexican dairy sector as another set of

regions to add to the 14 region U.S . Dairy Sector Interregional Competition Model (IRCM) of Chavas,

Cox and Jesse. This requires considerable data on regional production and supply response for farm

level milk, regional production and consumption response at the processing and retail sector, as well as

reasonable estimates of transportation costs between supply demand regions in Mexico with U.S .

regional markets. While regional milk production and supply response information is available from

government sources, regional wholesale production and consumption data is lacking.

Several additional factors raise serious data limitations with respect to modelling this sector.

There is evidence that a considerable amount of farm level milk moves through informal markets and

hence is difficult to trace. As well, there is considerable substitution of vegetable oils for dairy fat in

the processing sector which clouds the standards of identity for dairy products . This is particularly

troublesome for component pricing regional milk markets. Hence, a particular strength of the U.S.

Dairy Sector IRCM used for the analysis (the model is discussed in detail in Section IV) is somewhat

of a liability in this respect when trying to model the Mexican dairy processing sector.

Given these concerns, scenarios were developed that would allow use of those data which

appeared to be most reliable while minimizing the use of those which were suspect. To that end ,

1989-92 regional milk production and dairy import data provided by the Secretary of Agriculture and

Water Resources (SARH) were used as the basis for projecting the dairy deficit (excess demand) in

41

Mexico under alternative supply and demand growth scenarios. This excess dairy demand is then

taken as the demand for dairy imports by Mexico . Basically we consider high, medium and low

growth rates for both milk supply and dairy product demand. We assume that the U.S . quantity share

of this excess demand reflects the observed 1989-92 average.

2. Base Level Production and Commercial Disappearance.

Table 9 (page 51) summarizes the regional farm milk production (thousands of liters) and

dairy product imports (metric tons) for 1989-92 provide by SARH. These data are used to compute

average annual growth rates for regional milk production and imports. Growth rates range from 9%

(northern region and rest of country) to 0.4% (southeast) with an aggregate average of 7.7% over the

1989-92 period. Note that southeast produces a relatively small part of total Mexico milk supply. Dry

milk, and to a lesser extent fluid milk, whey and butter dominate Mexico's dairy imports in volume

terms over this time period. As evidenced from these data, growth markets over this time period are

other (+77%), cheese (+42%), whey (+32%) and fluid milk (+24%). Despite large changes in the

imports of particular commodities, the average aggregate volume of imports grew only 6.3% over the

1989-92 period.

Both farm level milk production and

imports are converted to a milk equivalent basis

taken as the share weighted average of the fat and

non-fat solids associated with each product. In the

absence of more reliable information, the milk

composition of Mexico 's imports are taken from

Selinsky, Cox and Jesse . These conversion factors

are summarized to the right. This procedure

assumes that dairy products imported by Mexico

have a similar composition as those products in

U.S. markets . This is not unrealistic. Note in

Pounds of Fat and Non-fat Solids Per Pound of Product

Farm Milk Fluid Milk Dry Milk Whey Butter Cheese Other

FB.t 00367 00210 0.0077 0.0081 0.8111 0.2858 00540

Non-FB.t Solids 0.0870 011 03 0.9607 0.9585 0.0302 0.2457 0.4687

Source: Selinsky_ Cox and Jesse

particular that this assumption avoids the problem of assigning milk components to Mexican dairy

consumption directly, hence avoids the problems with Mexican standards of identity and the prevalent

use of filled milk products.

Aggregate commercial disappearance is then computed as the sum of production and imports

over this time period. These figures and the associated import shares by product are summarized in

42

Table 10 (page 52) . Note that on a milk equivalent (average non-fat and fat) basis, dry milk, whey

and butter account for over 90% of Mexican dairy imports on average over the 1989-92 period. These

are clearly the volume market for U.S. dairy exports.

In summary, Mexico's milk consumption is estimated, for the purposes of this report, as the

sum of Mexico's milk production and all dairy imports, expressed in total solids milk equivalent units.

This is not a very satisfactory estimate, because it does not take into account changes in public or

private inventories. Perhaps the estimate could be improved by computing consumption as the sum of

Mexico's milk production and CONASUPO' s sales (expressed in milk equivalent terms) . This way,

changes in CONSASUPO's inventories would not have an impact on the estimated consumption

figure. However, the needed data were not available to us nor were data on private inventories

available. Hence, more precise estimates of Mexican milk consumption might simply be unattainable

gi ven these data constraints.

3. Growth Rate Assumptions for Mexico Milk Production.

Table I I (page 53) summarizes the regional production and aggregate consumption growth

rate assumptions used to generate the NAFT A scenarios . Several comments are in order. First, we

gave special treatment only to the southeast region . The reason is that this region , while exhibiting

very low productivity (average of 2 liter per cow per day, with lactations of 200 days)4 is also the one

in which the greatest increases in productivity could be achieved at the least cost. According to

extension agents in the region, productivity levels of 6 liters per day, and lactations of 300 days , could

be achieved with modest investments and changes in herd management practices. On the other hand,

farmers in this region tend to be less educated and have less access to capital than the large farmers in

the northern states . . It is therefore an open question whether or not the productivity potential of the

zone will be realized or not in the near future.

The northern states tend to have the largest dairy farms, with intensive use of technology and

access to capitaL Working against expansion of production in this area is limited access to water.

While this has not been a binding constraint, some analysts note that the precipitation levels of the last

few years are abnormally high, and that as precipitation goes back to normal levels, water will become

a binding constraint once again.

Personal communication, SARH regional office

43

Farmers in the central region occupy an intermediate position between the northern and

southeastern regions. Dairy farms are of moderate size, water availability is less of a problem, but

farmers tend to be more conservative and reluctant to adopt technological innovations.

In sum, it seemed that until more information is available, only the Southeast justified a

separate treatment, and we assume that it grows faster than all other regions in all the scenarios we

developed.

Based on this discussion, we derived the following high, medium, and low production growth

assumptions . Basically, the medium production assumptions reflect sustainable growth in regional

milk production based on the historical "long term" growth rates observed over the last twenty years

with the exception of the south-east (see Agrobiotec, 1992). The high production scenario could

materialize under very favorable economic and environmental conditions (e.g., ample rainfall) and are

not likely to be sustainable for very long. These high rates stand somewhere between the historical

rates and the most optimistic forecasts that were made by regional dairy sector experts , both in the

public and private sector. The low production scenario could materialize if price controls are re­

established, and/or the Mexican government allows subsidized imports to grow very quickly, and/or

drought conditions prevail. These low growth rates are more or less arbitrary , and correspond to un

almost stagnating dairy sector in Mexico.

Weather conditions aside, the growth rates of Mexican milk production will be affected mostly

by internal policy decisions and only marginally related to NAFTA. Under the current import system,

with CONASUPO having the legal monopoly on importation of dry milk , the government could

stimulate the growth of the Mexican dairy sector by maintaining importation at low levels and by

allowing the retail price of fluid milk to increase (via deregulation or increases in the maximum price

set by the government). If the Mexican government decides, on the contrary, that providing cheap

milk to the urban population is a top priority, then it could attain this by importing large quantities of

subsidized milk and keeping retail prices low.

The only thing that NAFTA changes is how the government can use trade policy decisions to

either stimulate domestic production or discourage it. As increasing quantities of non fat dry milk

from the U .S. enter Mexico without duties or import permits, the government could protect the

domestic dairy industry by using countervailing duties , thus compensating for U.S. subsidies, or could

decide to take advantage of the cheap milk it is offered.

44

4. Growth Rate Assumptions for Mexico Dairy Consumption.

With respect to aggregate consumption growth, all scenarios assume a 2% annual growth due

to population change. The low consumption scenarios assume that income will remain flat; hence all

growth in consumption is due to population change. The moderate consumption scenario assume that

moderate income growth will induce an additional I % annual consumption growth. The high

consumption scenario assumes rapid income growth induces a 2% annual consumption growth in

addition to that due to population .

NAFf A defeat would likely be associated with the low consumption growth rate. In this

scenario, investment is likely reduced as the failure of NAFfA undermines investor' s confidence, but

there is no dramatic change in economic policy (as there could be if Cuahutemoc Cardenas became

President) and per capita consumption at least does not diminish. The low consumption growth rate is

roughly the population growth rate in Mexico. This rate is equivalent to assuming that per-capita

consumption remains at the average 1989-92 levels for the next five years. While this is a pessimistic

hypothesis (because those levels are still below the pre-crisis consumption level), it is not quite a worst

case scenario . During the economic crisis in Mexico in the early 80's per capita consumption

frequently declined from year to year.

The medium and high consumption growth rates assume that per-capita income grows in real

terms, and that demand for dairy products is highly income elastic.s If one is willing to assume

unitary income elasticity, the hypothesis correspond then to I and 2 percent income growth rates ,

neither of which is beyond what the Mexican economy could reasonably achieve in the next five

years.

NAFf A is more likely to have an impact on the observed consumption growth rates than in

production growth rates. With NAFf A approval the high consumption growth rate is more likely to

be observed as foreign investment continues flow to Mexico, economic reform is consolidated, and the

expectations of a favorable economic environment are strengthened . The medium consumption growth

scenario could correspond to a protracted NAFf A negotiations case. This scenario that might have

arisen if the U.S. Congress failed to approve the treaty according to fast track procedures and instead

recommended modifications requiring renegotiation of NAFfA.

Some authors have estimated the income elasticity of dairy products may be higher than unitary, on the order of 1.5 or 2 in countries with income levels similar to Mexico.

45

Note that in all cases we are incorporating the effects of NAFTA via changes in per capita

income in Mexico, and the overall changes in demand that ensues . We are not modeling the price

effects from NAFT A; i.e. the impact of reduced duties on imports of U.S. dairy products. In the first

few years of NAFT A implementation, however, the price effects likely will be very modest due to the

phasing out of the current tariffs on most products.

5. Projected Milk Deficit in Mexico.

All combinations of the growth rates associated with these regional production and aggregate

consumption scenarios are then projected out five years from the base, 1989-92 milk production and

commercial disappearance. These projections are then used to generate compound and total growth

rates in the aggregate milk deficit (i.e., excess demand or the demand for imports) in Mexico over thi s

five year period with respect to the 1989-92 base. As indicated in Table I J (page 53), the projected

Mexican dairy deficit (import demand) tends to decrease rather quickly under all high production

growth scenarios with decreases ranging from -33% to -77%. Under medium production growth

scenarios, imports remain stable with high consumption and decrease at a moderate to fast rate

otherwise. Demand for imports grows at a moderate to rapid rate only under the low production

growth scenarios. Note that, generally speaking, the milk deficit is larger the lower the production

growth rate. Within each production growth rate scenario, the deficit is larger the higher the

consumption growth rate .

6. Detailed Comparison with Previous Studies.

In thi s section , we compare the procedure used in the previous section to estimate Mexico ' s

milk deficit with those of other recent studies. We focus on the data and assumptions used in each

case . Some authors have used a more elaborate approach, first projecting population, then per capita

demand, and finally deriving total demand. To make meaningful comparisons possible, we express the

assumptions of the studies reviewed in terms of the rates or growth of Mexican milk production and

the (sometimes implicit) rates of growth of milk equivalent consumption.

The basic assumptions of this study and of previous ones are presented in Table J 2 (page 54).

Table 13 (page 55), in turn, presents the resulting milk deficit in Mexico in the fifth year of each

projection (except in the Agrobiotec case, in which the deficit reported in Table 13 corresponds to

1995). While there is a wide range in the predicted milk deficit, according to the scenario chosen , the

study that stands out is Schulthies and Schwart, which predicts by far the largest deficit in Mexican

46

milk production . This result is clearly driven by the very high consumption growth rate assumed in

that study, which in our opinion is unduly optimistic. Leaving aside the Schulthies and Schwart

scenario, and the High Production Low Consumption scenario in this study, the projected deficit

ranges from roughly I to 4 .5 billion pounds (one million metric tons is ' equivalent to approximately

2.2 billion pounds). Both extremes seem unlikely to the authors of this report. A subjective best

forecast would place the deficit in year 5 around 2 billion pounds.6

Note the sources of the different results among studies . The earlier studies by McClain and

Harris and Schulthies and Schwart estimate production in the base year to be above 9 million metric

tons . Agrobiotec and this study use more modest estimates: 7 .2 and 6.4 million metric tons

respectively . For this report, the latest data available from the Mexican government were used.

While we have noted that there may be serious problems with such data, it is questionable whether

more accurate estimates could be obtained from other sources.

With regards to production growth rates,S percent seems to be the consensus figure, and it is

based on the historical performance of the Mexican dairy sector. Higher growth rates could be

observed, but they would probably require a combination of favorable precipitation and accelerated

technical change in the sector. The very low growth rates , say , of 2 percent, are unlikely to be

observed unless economic policy takes a very sharp turn against the Mexican dairy sector.

Finally, a major source of differences between our study and the other studies reported in

Table 18 is that we tend to make more cautious assumptions about the growth of demand for dairy

products. The implicit assumption of a 12.6 percent growth rate in the Schulthies and Schwart study

seems overly optimistic, and it is explained only by their explicit assumption that Mexican per capita

consumption of dairy products would reach current US levels by the end of the century . Given the

available data on population growth, and the prevailing expectations concerning the growth of the

Mexican economy in the next five years, consumption growth rates in the 3 to 4 percent range seem

reasonable.

6 To keep an idea of relative magnitudes in mind, it may help to remember that current U.S. production is about 150 billion pounds per year. If Mexico were to import dairy products exclusively from the U .S. , the 2 billion deficit would represent only 1.33 percent of U .S . production, and likely an even smaller percent of U.S . production in five years.

47

B. Projecting U.S. to Mexico Dairy Exports

1. Assumptions and Procedures.

To estimate U.S . exports to Mexico we combine the results from the previous section (based,

the reader will recall, on SARH data) with U.S. export data that are presented in this section. Briefly,

our procedure is as follows. We estimate the percent change in Mexico's milk deficit from the base

year to the 5th year projection under four selected scenarios . We assume that U.S. exports to Mexico

change at this base to year 5 growth rate and that U.S. market share of Mexican imports reflects the

1989-92 average. We then take FATUS data to estimate U.S. exports in a base period using 1989-92

average exports. Finally, we perturb the base period exports by the percent change in Mexico's milk

deficit under the four selected scenarios to obtain U.S.-Mexico and U.S. total dairy product exports.

The procedure is a bit rough and ready, but given the limitations on data availability it is

questionable that a more sophisticated approach would yield much improved estimates. As noted

above, this procedure does not take into account the price effects associated with NAFfA (tariffs on

US imports would be reduced under the accord). But these price effects are likely to be negligible in

the first few years of the agreement, as the phase out period for those products subject to tariffs is

rather long. The income effect is therefore likely to dominate any price effects. As well , we were

unable to locate dairy products demand function estimates for the Mexican market. Hence, additional

work on estimating dairy product demand function for Mexico will be required to incorporate the

potential price effects.

Additional limitations of our procedure are that we do not take into account that exports of

different products might grow at quite different rates nor that U.S. market share of Mexico imports

might increase under NAFfA. Concerning the former, we proceeded in this fashion because no

reliable estimates of commercial disappearance of dairy products are available from Mexico. The

government keeps track of industrial production of cheese, butter, etc., but most analysts agree that as

much as 50 percent of the Mexican milk is processed through "informal sector" channels. There is

also the standards of identity and filled milk product issues discussed above. This being the case, it

seemed preferable to use rough estimates of "milk equivalent" commercial disappearance, rather than

attempting to generate product by product estimates, when good data are not available to support the

estimates. As to the latter, there is considerable uncertainty with respect to world dairy prices and

regional comparative advantage in dairy exports , particularly with the immanent GAIT liberalizations.

In this context, recent historical average export shares were judged to be appropriate.

48

2. The NAFT A Scenarios.

From the 9 production and consumption growth alternatives elaborated above, four scenarios

are selected for analysis. These scenarios are characterized as portraying the following conditions:

1) NAFTA+: A very optimistic NAFTA scenario where Mexican dairy production is low (1-2% annual growth rates due to drought conditions and/or a cheap food policy by the Mexican government (i.e., re-establishing price controls and/or allowing subsidized imports to grow very quickly, not enforcing NAFTA tariffs or quota on NFDM)) and consumption is high (4% annual growth: 2% due to population, 2% due to NAFTA induced income effects). Note that the 3% annual growth on the nonfat dry milk quota would be binding under this scenario, but is not imposed.

2) NAFT A: A best guess scenario characterized by medium growth in Mexico production (5-7% annual growth: average sustainable historical growth rates) and high consumption growth (same as above).

3) STATUS QUO: Current neo-liberalist policies in Mexico continue to improve incomes, but not as strongly as with NAFTA. Hence consumption growth is medium (2% due to population, I % due to incomes) and production growth is medium (same as above).

4) NO NAFTA: This is a likely scenario if the NAFTA had been defeated in the U.S. Congress. NAFTA defeat would stifle Mexico's neo-Iiberalist growth and results in stagnate income growth. Hence, consumption growth is low (expands with population at 2%) while production growth is medium.

The import demand growth rates associated with these scenarios (recall Table 11, page 53) are

then used to shock the base, 1989-92 average U.S.-Mexico dairy exports obtained from FATUS. This

assumes that the U.S. will maintain its 1989-92 share of Mexican dairy imports and that U.S. to the

rest of the world exports and U.S . imports remain at base, 1989-92 levels. Figure 6 and Figure 7

(page 56) illustrate the impact of these four scenarios on U.S.-Mexico dairy exports. Figure 8 and

Figure 9 (page 57) summarize their impacts on total U.S. dairy exports. Figure 10 (page 58) illustrates

their impact on Mexico's share of total U.S. dairy exports. Last, Figure II (page 58) summarizes the

impacts of the export induced, exogenous demand shocks in terms of the total U.S. supply of milk

components in the base, 1989-92 average scenario.

Several general impacts are suggested by these projections. Noteworthy among these are:

1) The NAFTA scenarios generate large changes in U.S. to Mexico dairy exports relative to the 1989-92 base (Figure 6 and Figure 7, page 56).

2) The NAFTA scenarios generate some what smaller changes in total U.S. dairy exports relative to the 1989-92 base (Figure 8 and Figure 9, page 57).

49

3) Mexico remains a major U.S. export market for dairy products under all scenarios, particularly for nonfat dry milk, and fluid and soft products (Figure 10, page 58) .

4) Base U.S. total dairy exports account for 2.1 % of total supply of milk fat, 3.8% of milk proteins, and 5.6% of lactose supplies (Figure I I, page 58). Hence, U.S. dairy exports are a small part of total U.S. dairy demand .

5) The incremental demand shifts to the aggregate U.S. dairy sector generated by these NAFT A scenarios are relatively small, on the order of + 1.9% to -1.1 % of total component supplies. Hence, particularly for the NAFTA and STATUS QUO scenarios, aggregate and regional impacts on the U.S. dairy sector are likely to be correspondingly small.

The particular impacts of the NAFTA+, NAFTA, STATUS QUO, and NO NAFTA scenarios

are summarized as:

1) NAFTA+: This increases U.S. base level exports to Mexico by 69%. Note that this increase is a very small share of base level total supply of milk components (0.5% of total fat, 1.4% of total protein, 1.9% of total carbohydrates). Hence, even this relatively optimistic scenarios suggests a relatively small aggregate demand shift to the U.S. dairy sector. Regional impacts, however, could be more substantial. Note that the 3% annual growth on the nonfat dry milk quota would be binding under this scenario, but is not imposed.

2) NAFTA: This increases base level U .S-Mexico dairy trade by 4%, again a relatively small amount of total U.S . supply (about 0.1 % of total fat, protein, and carbohydrates). Impacts of this magnitude are not likely to be numerically significant given the accuracy of the model.

3) STA TUS QUO: This generates a decrease in U.S.-Mexico exports of 19% from base levels (0.1 % of total U.S. milkfat supply, 0.4% of total protein, 0 .5% of total carbohydrates). Hence, this scenario suggests a very small decrease in aggregate U.S . dairy demand.

4) NO NAFTA: This generates a 40% decrease in U.S.-Mexico dairy exports (0.3% of total fat, 0.8% of total protein, 1.1 % of total carbohydrates), a relatively small decrease in aggregate demand. Regional impacts, however, could be more substantial.

The simulated impacts of the exogenous demand shifts implied by these four scenarios relative to the

1989-92 base scenario are presented in Section V.

50

Table 9. Summary of Mexican Regional Milk Production and Dairy Product Imports, 1989-92.

Mexican Milk Production Growth (thousands of liters) 1989 19!:x) 1991 1992 Average Rates (%)

Southeast 114.%1 101.843 11 J849 116.281 111)34 0.4 Northem Region 1.210.688 1.388.994 1.543.268 1.558.460 1.425.353 8.8 Central Region 1.959.652 2.035.597 2.249.156 2.330.607 2.1 43. 753 5.9 Rest of the Country 2)92.008 2.615.1 11 2.810.842 2.968.921 2.671)21 9.0 Total 5.577.309 6.141 .545 6.717.115 6.974.269 6.352.560 7.7

Mexican Dairy Product Growth Imports: (Metric Tons) 1989 19!:x) 1991 1992 Average Rates (%)

Fluid Milk 38.581 32.479 53.405 72)15 49.295 23.5 Dry Milk 239.916 287.837 57.831 21 J805 199.847 -3.8 Whey 23.113 36.404 40.958 53.354 38.457 32.2 Butter 30.206 27.11 ° 37.940 39.313 33.642 9.2 Cheese 7.898 10.004 14.951 22.433 13.822 41 .6 Other 1.620 5.289 6.273 8.948 5.532 76.8 Total 341.335 399.124 211.357 410.569 340.596 6.3

Source: SARH

51

Table 10. Summary of Computations for Aggregate Commercial Disappearance in Mexican Dairy Sector, 1989-92.

1989-92 Growth

1989 19!1l 1991 1992 Average Rates (%) Milk Production: (thousands of liters) 5,577J09 GJ 4L545 6,717J 15 6,97{269 6J52,560 7.7

Milk E(IUivalent Impons (metric tons): Fluid Milk 38581 32,479 51405 72)15 4n95 235 Dry Milk 1,869,556 2,242,982 450,647 1,666,085 L557,3 17 -38 Whey 179,778 281158 318,573 41{993 2n125 32.2 Butter 201615 186J32 260,771 270,211 23t233 9.2 Cheese H067 41148 64,485 96}56 5%14 41.6 Other 6,822 22,280 26,423 37,692 2}304 76.8 Total 2,336,419 2,810,379 U7{304 2,558,452 2,219,889 31

Commercial DisaplJearance: (thousands of liters) r892)00 8,926,631 7,880,850 9,509,695 8,552,469 6.4

Dairy Import Market Profile (shares): Fluid Milk 2% 1% 5% 3% 3% Dry Milk 80% 80% 38% 65% 66% Whey 8% 10% 27% 16% 15% Butter 9% 7% 22% 11 % 12% Cheese 1% 2% 5% 4% 3% Other 0% 1% 2% 1% 1% Total 100% 100% 100% 100% 100%

SOURCE: SARH and computations hy the authors.

52

Table 11. Summary of Production and Consumption Annual Growth Rate Assumptions with Projected Compound and Total Growth Rates in Mexican Import Demand (Milk Deficit) from Base to Year 5.

High Medium Low Annual Production Growth Rates: Southeast 10 7 2 Northern Region 7 5 1 Central Region 7 5 1 Rest of the Country 7 5 1

Annual Consumption Growth Rates: 4 ') 2 .J

Import Demand Growth Rates From Base to Year 5:

Compound Total Production: High Consumption: High -7.7 -29.0 Consumption: Medium -14.9 -50.5 Consumption: Low -25.4 -73.2

Production: Medium Consumption High 0.7 3.8 Consumption: Medium -4.0 -15.2 Consumption: Low -9.7 -34.9

Prod uction: Low Consumption: High 11 .1 51.8 Consumption: Medium 8.0 35.5 Consumption: Low 4.5 20.9

53

54

Table 12. Estimating Mexico's Milk Deficit: A Comparison of Recent Studies.

P d . . Pd· Consumption C . ro udlOn In ro udlOn . b onsumptlon

Study base year HI growth rate bl In as:; year growth rate bl

McClain and Harris 9,300 5.3 9,300 77

Schulthies and Schwert 9.180 4

Agrobiotec c/ un 7.5 5

3.5

Current Study dl 6,418 10 and 7 7 and 5 2 and 1

12,000 12242

12570

not reported

8,647

4.3 6.3

12.6

8.6 4.3 3.9

4.0 3.0 2.0

==================================================================

a/ Thousands of metric tons.

b/ Average annual compounded .

c/ The report indicates that average consumption per capita in 1988-92 was used as the basis for projections, butthe actual figure is not reported. It assumes population growth of 2.1 percent annual, and per capita increases in consumption of 1.76,4.34 and 860 percent The consumption growth rates reported in the table are the sum of the per capita and lhe popUlation growth rates, but due to compounding actual consumption growth rate using Agrobitec's procedure vvould be higher. Unfortunately since they did not report lheir base consumption figure, the implicit consumption growth rates can notbe computed.

dl I\jote that figures reported in previous tables are corrverted to metric tons.

Table 13. Comparison of Alternative Projections of Mexico's Milk Deficit.

Study

Harris and McClain

SdlUlthies and Schwart

Agrobiotec

Current Study

Production/Consum ption Scenario

High/High High/Law Low/High Low/Low

Low/Low Medium/Medium

High/High High/Law Low/High

High/High High/Medium

High/Law Medium/High

Medium/Medium Medium/Low

Low/High Low/Medium

Low/Low

Milk deficit in 5th year projection aJ

J149 1.315 {589 2.755

6,050

2,281 1.988 l037 914

4,404

1.488 992 515

n07 1.812 1.335 J762 1266 2,789

55

Figure 7.

Figure 6.

56

u.s. to Mexico Dairy Exports Under Alternative NAFTA Scenarios.

million pounds

200

150 i- ----- - ----------fl----i

100 r4~----------------------~--~~

50

o flrn* I sof ach ich och but fzn mfg i nEt

89-92 AVG • 80 I 6 4 2 0 17 6 91 I 83

NAFfA + all 136 I 11 7 3 1 28 10 154 I 140

NAFfA [II 83 1 7 4 2 0 17 6 94 1 86

STATUSQUO fl] 65l 5 3 2 0 13 5 741 68

NONAFfA fIl 48J 4 2 1 0 10 4 55J 50

* FLM is 10 million pounds.

SOURCE: Computations by the authors.

Growth in U.S. to Mexico Dairy Exports Under Alternative NAFTA Scenarios Relative to 1989-92 Base Level Exports.

Growth Rates

2~-------------------------~

1.5 .-.--... ---11---1 __ --11--__ ----11---_______ -1

SOURCE: Computations by the authors.

Figure 8.

Figure 9.

u.s. Total Dairy Exports Under Alternative NAFTA Scenarios.

million polUlds

400 ,---------------------------------

300 ~---------------------

200 r---- ---- --,--------

100 f----1l!1------ - ---

o Om* sof ach ich och but (zn mfl! nft

89-92 AVG • 84 U 18 I 8 2 107 47 307 150

NAFfA + II 140 16 20 9 2 118 51 370 207

NAFfA II 87 12 18 8 I 2 107 47 311 153

STATUS OUO OOTI 70 11 17 7 2 104 46 290 135

NO NAFfA III 52 9 16 7 2 100 45 271 117

* FLM is 10 million pounds.

SOURCE: Computations by the authors.

Growth in U.S. Total Dairy Exports Under Alternative NAFTA Scenarios Relative to 1989-92 Base Level Exports.

Growth Rates

2r-------------------~

1.5 ~I--------------------------___l

1

0.5

fun sof ach ich och but fzn mig nft

NAFfA + • 1.66 1.37 1.16 1.17 1.17 1.11 1.09 1.20 1.38

NAFfA !II 1.04 1.02 1.01 1.01 1.01 1.01 1.00 1.01 1.02

STATUS QUO II 0.82 0.90 0.96 0.95 0.95 0.97 0.98 0.95 0 .90

NO NAFfA !iID .~ 0.62 0 .79 0.91 0.90 0 .90 0.94 0.95 0 .88 0.78

57

Figure 10.

Figure 11.

58

Mexico's Shares of Total U.S. Exports Under Alternative NAFTA Scenarios.

1989-92

NAFrA +

NAFrA

1.2 r------------ - - - - - ---,

l ~~-----------------------------l

0.8

0.6

0.4

0.2

o

• III ,. flm

0.95

0.97

0.95

sof aeh ieh

0.53 0.22 0.22

0.66 0.33 0.36

0.54 0.23 0.26

oeh but I fzn mfg nft

0.22 0.19 0.13 0.30 0.47

0.36 0.24 0.20 0.42 0.68

0.26 0.16 0.13 0.30 0.56

STATUS QUO 0 0.94 0.48 0.19 0.21 0.21 0.13 0.10 0.26 0.50

NO NAFrA III 0.92 · 0.40 0.15 0.17 0.17 0.10 0.08 0.20 0.43

SOURCE: Computations by the authors.

Net Demand Shifts Due to Alternative NAFTA Scenarios: Percentage Changes to Total Component Supplies.

o.~ r---------------------------,

0.04 1------------ - -

FAT PROTEIN CARBO's

2.1% 3.8% 5.6%

cb 0.5% lA% 1.9%

cb 0.0% 0.1% 0.1%

cb ~.1% -OA% ~.S%

cb NO NAFTA ~.3% ~.8% -1.1 %

SOURCE: Computations by the authors.

IV. U.S. DAIRY SECTOR INTERREGIONAL COMPETITION MODEL

(IRCM)

A. Model Overview

1. Introduction.

An hedonic spatial equilibrium model of the U.S. dairy sector is used to measure the regional

effects of U.S.-Mexico dairy trade under alternative NAFT A scenarios. As developed by Chavas, Cox

and Jesse, this conceptual framework combines the hedonic pricing of implicit markets for milk

components at the regional level with the spatial equilibrium model of Takayama and Judge. The

model maximizes the sum of regional consumer and producer surplus minus transportation costs

subject to regional milk component supply and demand balances in fat, protein and carbohydrates

(lactose). Analytical details of modeling interregional trade with hedonic pricing are presented in

Appendix A.

Our objective in building a U.S . dairy sector hedonic spatial equilibrium model is to simulate

competitive market conditions as the outcome of a Pareto optimal resource allocation associated with

the maximization of social welfare. Social welfare involves consumer benefits, the costs of milk

production, and transportation costs. The consumer benefits are measured by consumer surplus as the

area under the corresponding demand curves . To simplify the analysis, we work with the farm level

derived demand curves for the various dairy products. Production cost is measured as the area under

the milk supply curve. In the absence of strong prior information on their functional form, the price

dependent supply and demand functions are assumed to be linear. More specifically, their intercept

and slope are set consistent with dairy market conditions (i.e. price and quantity) prevalent in 1990.

The supply and demand parameters used in the model are summarized in sections C and D below ..

2. Transportation and Component Constraints.

The assumed transportation cost for farm milk and fluid milk is $ .35/cwt/100 miles. The

transportation costs for other dairy products is estimated from actual transportation costs prevalent in

1990 for refrigerated products (soft dairy products, cheeses, butter, frozen products, and manufactured

products) as well as non-refrigerated products (nonfat dry milk) . The use of actual transportation rates

allows for asymmetric rates, where the unit transportation costs of a given commodity between two

regions can differ for imports versus exports (e.g. because of backhauling opportunities) . These costs r::

are discussed further in section ~ below .

59

The model required some adjustments for components that never reach consumers. For

example, whey is a byproduct of cheese production. Although some of this byproduct is recovered

and sold as dry whey, a significant proportion of whey is typically discarded in cheese plants. Also, a

small percentage of farm milk production is consumed on farm and therefore never reaches the market

place. Appropriate adjustments were made to reflect these characteristics of the dairy industry.

As well, there are technological constraints that prevent perfect substitution of components

across commodities in the U.S. dairy industry. Such constraints are typically associated with

specialized plants that can use components in the production of only selected dairy commodities.

First, because of the difference in fat composition, the production of fluid milk out of raw milk

generates fat byproduct that is typically used only in the production of soft products, frozen products,

or butter. Second , butter is a residual commodity using fat surpluses generated by two factors: lithe

fat in whey associated with cheese production; and 21 the fat surpluses due to induced production of

butter and nonfat dry milk from "reserve fluid milk" that is needed to account for seasonality and

uneven weakly bottling schedules in the fluid milk market. Appropriate constraints further restricting

the allocation of components across commodities have been added to model to incorporate these

specific characteristics .

3. Price Supports and Marketing Orders.

This generated the basic "competitive scenario" of the U.S. dairy industry which is then

modified to account for federal government purchases of dairy commodities. Such purchases are part

of the federal milk price support program, designed to stimulate aggregate demand for milk and

maintain the price received by dairy farmers above a minimum level set by government. Those

purchases are limited to the most storable dairy products. In 1990, government purchased 44 million

Ibs of american cheese, 404 million lbs of butter, and 100 million Ibs of nonfat dry milk. In an

attempt to include government purchases in the model, an "additional region" was created to account

for government demand. The quantity demanded by government was treated as exogenous and set at

the 1990 level (as reported above) as were U.S . dairy imports and private stocks.

In addition to government purchases, we also incorporated milk marketing orders in the model,

including the California order as well as the federal milk marketing orders. The federal milk

marketing order programs involve blend pricing of farm milk, as well as classified pricing rules based

on the Minnesota-Wisconsin (MW) prices. We assumed that MW prices were the same as Wisconsin

prices in our model. Blend pricing consists in paying dairy farmers the weighted average value of the

dairy commodities produced from farm milk in each region . This allows for possible price

60

discrimination across dairy markets, which can raise farm price and benefit farmers. Blend price

equations, defining the price received by farmers in each region, are added in the model.

The current federal milk marketing orders also place restrictions on the pricing of fluid milk .

First, lower bounds on regional fluid milk prices are imposed, based on the fluid milk price in

Wisconsin . More specifically, the fluid milk price in any region is restricted to be at least as large as

the Wisconsin fluid milk price, plus a differential of $.21/cwt/100 miles distance from Wisconsin.

This constraint was added to the model. Second, the federal milk marketing orders impose a base

"class-I differential" between the price of fluid milk and the value of non-fluid uses of milk in

Wisconsin . In 1990, this class-I differential was $1.25/cwt milk. This differential allows for price

discrimination between the fluid milk market and the nonfluid dairy markets . It was included in the

model as an additional constraint. Finally, the blend price equation in California was specified to

reflect the functioning of California milk marketing order. In particular, it incorporates "make

allowances" that reduce the value of cheese, butter, and nonfat dry milk used in the calculation of the

price paid to California dairy farmers .

The addition of the above pricing constraints reflecting current milk marketing orders created a

problem in the solution of the resulting model. It distorted transportation incentives across regions and

no longer guaranteed the efficiency of trade. As a result, competitive conditions for trade arbitrage

across regions had to be explicitly added as constraints in the model in order to obtain an efficient

trade allocation . As these marketing order constraint were found to induce considerable regional price

and trade distortions, they were dropped from the base scenario art all subsequent analysis. ~

B. Region and Product Specification

The model is composed of 14 U.S. supply/demand regions (including Wisconsin and

California as separate regions) and several exogenous sectors (private stocks, government purchases

and stocks, U.S. imports, and u.s. exports to Mexico and U.S. exports to the rest of the world).

These regions are summarized in Table 14 (see page 66). The model endogenously solves for farm

level blend prices and milk production as well as the price, supply, demand and trade flows for 9

wholesale level dairy products (fluid (FLM), soft (SOF), frozen (FZN), american cheese (ACH), italian

cheese (ICH) , other cheese (OCH), butter (BUT), nonfat dry milk (Nfl), and a residual manufacturing

category (MFG) that is mostly whey products and evaporated/condensed milk)). The individual dairy

products comprising these wholesale product categories are defined in Table 15 (see page 67).

61

C. Dairy Component Accounting

A unique feature of the model is that it also generates regional component prices for milk fat ,

protein and carbohydrates (mostly lactose). Dairy product component yield s and component supply­

balance computations (i .e ., the amount of fat, protein, and carbohydrate per pound of dairy product)

summarized in Table 16 through Table 19 (pages 68 through 71). These component yields are derived

from USDA conversion factors and a component supply/demand balance from the USDA 1990 dairy

sector statistics (farm milk production and wholesale production of dairy products). For a more

detailed discussion on these component accounting computations, see Selinsky , Cox and Jesse .

Regional component allocation constraints are used to generate the hedonic spatial equilibrium and

yield regional "shadow prices" for proteins, carbohydrates and three fat utilizations: fat used in butter

(a residual claimant), fat used on soft and frozen products, and fat used in all other products . For

more details on this specification, see Chavas, Cox, and Jesse .

D. Wholesale Demand Specification

The model is calibrated to 1990 farm and wholesale level production and aggregate

disappearance data from USDA. Regional consumption is projected from aggregate wholesale demand

functions with adjustments for regional population, demographic profile and per capita incomes

aggregated up from state level data. These demand functions, summarized in Table 20 (see page 72),

are updated estimates that deri ve from Selinsky. The associated short and "long run" (3-5 year)

elasticities are compared to alternative estimates in Table 21 (see page 73). Note that, in general,

dairy demand appears to be more inelastic over time; some products are quite inelastic (e.g., fluid

milk, american cheese, other cheese, butter, and the residual manufactured category). The associated

regional projections an aggregate supply demand balance for 1990 are summarized in Table 22 (see

page 74). Note that the percentage errors in the aggregate commercial disappearance implied by these

regional projections are quite modest with the exceptions of italian cheese (-5 .8%) , other cheese

(+5.7 %), residual manufacturing (-6%), and nonfat dry milk (+2.4%). By construction, these regional

projections are adjusted so as to yield an aggregate dairy sector supply-demand balance with 1990

USDA data. For more details on these procedure, see Selinsky.

62

E. Farm Level Supply Specification

The intermediate run (3-5 year) regional and aggregate farm level supply response elasticities,

1990 regional blend prices, production, total revenues, production and revenue shares used in the

model are summarized in Table 23 (page 75). This table also summarizes the wholesale level demand

elasticities, and 1990 commodity prices, production, and consumption used in the model. Note that

aggregate U.S. supply response used in the analysis is 0 .56 with considerable regional differences. In

particular, C (Central), ENC (East North Central), and WC (West Central) indicate relatively elastic,

intermediate run supply responses; not surprisingly, these assumptions will be manifest in the

simulation results. Also note that the highlighted regions (Middle Atlantic (MA), West South Central

(WSC), East North Central (ENC), Wisconsin (WIS), West North Central (WNC), and California

(CA)) account for roughly 70% of aggregate (AGGUS) production and farm revenues in 1990.

Discussion in the results section will focus on these major U.S. dairy production regions.

F. Transportation Costs

Asymmetric transportation rates for refrigerated and non-refrigerated products (derived from

negotiated rates for 1990 obtained from the Interstate Commerce Commission (ICC) are used to reflect

the presence of backhauls in the transportation of dairy products. Average transportation rates for

refrigerated and general (not refrigerated) commodities were calculated using selected published (ICC)

tariff rates . Refrigerated dairy commodities include cheese, butter, packaged fluid products, soft

products and frozen products. Non-fat dry milk need not be refrigerated, and therefore may be hauled

at the general commodity rates . These asymmetric transportation costs are summarized in Table 24

(page 76).

These ICC tariff rates are set in an ICC hearing based on evidence presented by "carriers" and

by "customers". These rates represent the maximum rate which carriers may charge per truckload.

Loaded weights are specified in the tariff, and usually vary between 40,000 and 48,000 pounds. The

rate which a carrier actually charges any given customer will be negotiated on a customer by customer

basis . Conversation with several carriers and customers indicates that the actual rate charges will be a

function of the number of unloading and loading stops the truck is required to make, the shipment

weight, loading and unloading services provided by the carrier, the number of carriers running a

particular route, availability of back hauling opportunities, and the frequency with which a given

customer employs a particular carrier.

63

The sample of published rates were selected by Edwards and Associates , a consulting firm

which provides tariff information as a service to carriers and customers. Carriers were selected if

Edwards and Associates knew that the carrier delivered to location(s) which were included in the

regional dairy market model. Average tariff rates are based on various samples sizes, with minimum

sample size of 1 and maximum samples size of 10.

Note that ICC "issue" and "effective" dates vary across observations, with all effective dates

ranging between 1989 and 1990. These dates were chosen in an attempt to avoid possible "Gulf War"

effects on transportation rates .

Original data is published in "cents per loaded mile" for a variety of "maximum truckload

weights" . These were converted to "cents per hundredweight per mile" via the following formula :

cents per cwt per mile = cents per loaded mile * (max truckload weighU 100 pounds)

Transportation to Mexico from each region is taken as the additional cost to ship product from

Dallas (the transportation node of the West South Central region) to Laredo, Texas.

G. Export Specification

Analysis of recent U.S . dairy export trends using FATUS data indicates that 1990 is somewhat

of an odd year, particularly with respect to nonfat dry milk exports, a key U.S. export to Mexico.

Note that U.S. total dairy exports (Figure 12, page 77) and U.S. to Mexico dairy exports (Figure 13,

page 77) are quite variable over the 1989-92 period . Given this, the 1989-92 average dairy export

profile was judged to be a more reliable base scenario for evaluating the potential impacts of increased

U.S.-Mexico dairy trade under NAFTA. Hence, in the base scenario U.S. dairy exports to the rest of

the world and to Mexico are set at 1989-92 average levels as discussed in Section III. To accomplish

this in the base scenario, 1990 total dairy exports and the share of U.S . dairy exports to Mexico are

exogenously shocked by the growth rates required to attain the 1989-92 average exports and export

shares. These rates are summarized in Figure 14 (see page 78). Note that these shocks from 1990 to

1989-92 averages are relatively small for U.S . cheese and butter exports (-15% for butter to +6% for

american cheese) while all other U.S . exports increase about 40% (except for nonfat dry milk (NFT)

which grows six-fold). Likewise, U .S.-Mexico dairy exports increase some 40% to 60% with the

exception of the residual manufacturing category (mostly evaporated/condensed milk and whey

products) and nonfat dry milk which roughly double and increase 10-fold, respectively.

64

For each NAFT A scenario, the growth rates required to adjust 1989-92 base exports to the

level of U.S. total and U.S.-Mexico dairy exports and Mexico's export shares under the alternative

scenarios are employed. These growth rates and export market shares were summarized in Section Ill,

Figure 9 (page 57) and Figure 10 (page 58). US-ROW exports, ROW-US imports, private stocks, and

government stocks and purchases are held at 1990 levels in all simulations.

65

Table 14. Producing and Consuming Regions of Fluid and Manufactured Dairy Products in the U.S. Dairy Sector IRCM.

66

1. California (CA)

2. Central (C): Kentucky , Tennessee

3. East North Central (ENC): Illinois, Indiana, Michigan, Ohio

4. East South Central (ESC): Alabama, Arkansas, Louisiana, Mississippi

5. Middle-Atlantic (MA): New York, New Jersey, Pennsylvania

6. Mountain (MOU): Arizona, Colorado, Montana, Nevada, Utah, Wyoming

7. North East (NE): Connecticut, Massachusetts, Maine, New Hampshire, Rhode Island , Vermont

8. North West (NW): Idaho, Oregon, Washington

9. Other (OTH): Alaska, Hawaii

10. South Atlantic (SA): District of Columbia, Delaware, Maryland, Virginia, West Virginia

11. South East (SE): Florida, Georgia, North Carolina, South Carolina

12. West South Central (WSC): New Mexico, Oklahoma, Texas

13. West Central (WC): Iowa, Kansas, Missouri, Nebraska

14. Wisconsin (WIS)

15. West North Central (WNC): Minnesota, North Dakota, South Dakota

16. Government (GOV): Net Government Stocks and Disposals.

17. Private (PRI): Net Private Stocks.

18. Exports (Mexico (US-MEX).

19. Exports to Rest of World (US-ROW).

20. Imports (ROW-US).

Table 15. Nine Categories of Fluid and Manufactured Dairy Products Used in the U.S. Dairy Sector IRCM.

Fluid (FLM):

Soft Products (SOF):

Frozen Products (FZN):

CONSUMER DAIRY PRODUCTS

Beverage fluid milk including regular and flavored milk (whole, 2%, I %, skim) and buttermilk .

Cream (Half and Half, heavy and light), sour cream, yogurt, eggnog, cottage cheese.

Ice-cream, ice-milk, sherbet, frozen dairy mix and mellorine.

INDUSTRIAL DAIRY PRODUCTS

Butter (BUT):

American Cheese (ACH):

Italian Cheese (ICH):

Other Cheese (OCH):

Nonfat Dry Milk (NFT):

All Other Mfg (MFG):

Butter.

American, Cheddar, Colby, Monterey and processed American cheese .

Mozzarella, Provolone, Parmesan, Romano and Ricotta .

Swiss, Edam, Gouda, Brick, Muenster, Gruyere, cream cheese and all other cheeses.

Nonfat dry milk.

Canned and bulk whole milk and skim milk, dry whole milk and buttermilk, and dry whey products.

67

Table 16. 1990 U.S. Fluid and Manufactured Dairy Product "Component Accounting" Worksheet from Selinsky, Cox and Jesse.

1990 1990 Total Component Usage Per Capita 1990 Component Yields 1990 ----------- - ---------------------------Sales or Category ---------------- - - - -------------------- Prod / Sales Protein Carbohyd. Fat SNF

Production %Share PROT / 100g CARB / 100g FA'I'/100g SNFIlOOg (mill#) (mill#) (mill#) (mill#) (millff )

FLUID: vltd category average- - > 3.32 4 . 73 2.21 8.78 54,736 1 , 817 2,587 1,208 4,807 share of components-- > 0.47 revised %components--> 9.11 + / - error - - > 1,817 2,587 1,208 4,985

Whole 0.40 3.29 4.66 3.27 8.67 22,011 724 1,026 719 1,908 2%/1 0.40 3.33 4.80 l. 98 8 . 87 21,754 724 L 044 431 1,930 1% 11 0.07 3.29 4.78 0.95 8.81 4,076 134 195 39 359 Skim/1 0.11 3.41 4.65 0.20 8.82 6,016 205 28 0 12 531 Buttermilk 0.02 3.31 4.79 0.96 8.99 879 29 42 8 79

Sum: 54,736 1,817 2,587 1, 20 8 4,807

SOFT MFG: wt category average-- > 5.56 4.65 9.20 11.03 3 ,760 209 175 346 415 share of components-- > 0.13 0.14 0.04 revised %components--> 5.60 9.78 11.44 + / - error-- > 211 175 368 430

HalE&Half 0.20 2.96 4.30 10.82 7. 8 7 739 22 32 80 58 Heavy Cream 0.06 2.47 3.48 35.72 6.50 227 6 8 81 15 Light Cream 0.05 2.70 3.66 17.87 6.94 185 5 7 33 13 Sour Cream 0.17 3. 16 4.27 16.82 8.09 626 2 0 27 105 51 Egg Nog 0.03 3.81 13.54 7.48 18.15 123 5 17 9 22 Yogurt 0.27 4.36 5.85 1 . 60 11.12 1,027 45 60 16 114 Cottage Cheese 3.40 0.22 12.87 3.01 2 . 49 17.04 833 107 25 21 142

Sum: 3,760 209 175 346 415

FROZEN: wtd category average- - > 3.46 4.86 9.20 10.44 7,188 249 349 661 750 share of components-- > 0.14 III 0.20 0.07 revised %components--> 3.49 9.64 10.83 + / - error-- > 251 349 693 778

Ice-Cream 15.70 0.54 3.20 4.48 13.39 8.54 Sherbet l. 20 0.04 l. 12 1. 57 1. 98 31.95 Ice - Milk 7.70 0.27 4.27 5.98 3.47 11.36 Other 4.20 0.15 3.66 5.13 6.25 9.73 Mellorine / 2 0.10 0.00 3 . 33 4.67 3.46 8.87

68 ,

Table 17. 1990 U.S. Fluid and Manufactured Dairy Product "Component Accounting" Worksheet from Selinsky, Cox and Jesse (continued).

1990 1990 Total Component Usage Per Capita 1990 Component Yields 1990 ----------------------- -- --------------Sales or Category ------------------ - -------------------- Prod/ Sales Protein Carbohyd. Fat SNF

Production %Share PROT / I00g CARB / I00g FATIlOOg SNFIlOOg (mill#) (mill#) (mill# ) (mill#) (mill#)

AM-CH: wtd category average-- > 24.65 1. 34 32.29 29.91 2.891 713 39 933 865 share of components--> 0.24 0.25 0.08 revised %components--> 24.76 33.67 31.03 + / - error--> 716 39 973 897

Cheddar 9 .15 0.82 24 . 90 1. 28 33.14 30.11 Other/3 1 . 97 0 .1 8 23.46 1. 62 28.32 29.01

IT-CH: wtd category average--> 21.18 2 . 49 22.68 26.70 2.209 468 55 501 590 share of components--> 0.19 0.17 0.06 revised %components--> 21.30 23.90 27.69 +/- error--> 471 55 528 612

Provolone 0.63 0.07 25.58 2.14 26.62 32.43 Romano 0.21 0.02 31. 80 3.63 26.94 42.15 Parmesan 0.43 0.05 38.66 3.48 27 . 93 48.67 Mozzarella 6.93 0.76 20 .51 2.35 23.12 25.62 Ricotta 0.79 0.09 11.26 3 . 04 12 .98 15.39 Other 0.11 0.01 21.04 3.68 13.65 27.43

OTH-CH: wtd category a v erage-- > 19.14 2.64 28 . 58 24.57 961 184 25 275 236 share of components - -> 0.12 0.13 0.02 revised %components - - > 19.32 30.65 25.48 + / - error--> 186 25 295 245

S",iss / Gr 1. 35 0.30 28.43 3.38 27.45 35.34 Brick 0.07 0.02 23.24 2 . 79 29.68 29.21 Muenster 0.40 0.09 23.41 1.12 30.04 28.19 Cream/Neuf. 1. 72 0.39 8.76 2.80 29.15 12.87 Blue 0.17 0.04 21.47 2.17 29.69 29.42 Edam / Gouda 0.21 0.05 24.97 1. 83 27.62 30.87 Other 0.52 0.12 22.46 1. 80 28.36 27.73

69

Table 18. 1990 U.S. Fluid and Manufactured Dairy Product "Component Accounting" Worksheet from SeJinsky, Cox and Jesse (continued).

70

1990 Total Component Usage 1990 Per Capita Sa les or

Production

1990 Component Yields 1990 --------- -- ------------- - - -------------Protein Carbohyd. Fat SNF Category - - ----- - - - -- ---- --------- - - ------------ Prod/Sales

%Share PROT /100g CARB/100g FAT/100g SNF/100g (mill#) (m illff ) (mill#) (mill#) (mill#)

BUTTER: 4.40 1.00 share of components--> revised %components-->

DRY-NFAT: 2.60 1. 00 share of components--> revised %components-->

MFG-PR: wtd category average -- > share of components--> revised %components-- >

C!\.N-'lJM 2.10 BULK-111M 1. 00 CB-SKIM 4.80 DRY I'JM 0.60 DRY BM 0.20 DR-WHEY 3.00

Total Products (mill#): On-Farm Consumption:/4 Residual Whey:/5

0.18 0.09 0.41 0.05 0.02 0 .2 6

0.85

36.16

11.67 0 . 18

1l. 73

6.8 1 26.32 7.55

26.32 34.30 12.33

Sum of Calculated Components (1990):

Actual Milk Production (mill#)/6:

0.06

51.98

30 . 98 0.23

37.91

9.54 36.89 10.58 38.42 49.00 73 . 96

8 1.11

0.77

5.40 0.11 5.88

7.56 26.71 0.20

26.71 5.78 0 . 81

3.02 0.00 3.13 +/-

96.07 . 0.08 99 . 64 +/-

46.87 0.17

48 .62 +/-

18.40 70.82 18.89 70.82 91. 25 95.85

Calculated:

Actual:

1, 302

error-->

877

error-->

3,652

error-->

77,576 2,235 2,159

148 ,28 4

Sum of Difference: /5

Percent Difference:

Revised Component Total:

Sum of Difference - ->

Percent Di Eference-->

11 1,056 39

11 1 ,0 56 41

317 456 7 843

317 456 7 874

426 1 , 131 1 97 1,712

42 9 1,384 2 1 5 1,775

4,394 4, 818 5,184 10,256 72 104 82 194

266 1 ,596 17 2,069

4,731 6,518 5,284 12,519

4,745 6,895 5,442 12,90 1

114 ) (377 ) ( 15 8) (381 )

-0.29% -5 . 47% -2.91% - 2.96%

4 ,7 45 6,895 5,442 12,901

0 0 0 ( 0)

0.00% 0.00% 0 . 00% -0.00%

Table 19. 1990 U.S. Fluid and Manufactured Dairy Product "Component Accounting" Worksheet from Selinsky, Cox and Jesse (continued).

Summary Table of U.S. Dairy Products Component Yields, 1990.

Original------> Revised with Error--------> Protein Carb Fat SNF Protein Carb Fat SNF

FLUID ].]2 4.7] 2.21 8.78 3.32 4.73 2.21 9.11 SOFT 5.56 4.65 9.20 11. 03 5.60 4.65 9.78 ll. 44 AM-CH 24.65 1. ]4 32.29 29.91 24 . 76 1. ]4 33.67 31.0] IT-CH 21. 18 2.49 22.68 26 . 70 2l. ]0 2.49 23.90 27.69 OTH-CH 19.14 2.64 28.58 24.57 19 . 32 2.64 ]0.65 25.48 BUTTER 0 . 85 0 . 06 81.11 ].02 0.85 0.06 81.11 3.13 FROZEN ].46 4.86 9.20 10.44 3 . 49 4.86 9 . 64 10.83 RESIDUAL 11.67 30.98 5.40 46.87 11.73 37.91 5.88 48.62 NON-FAT 36.16 51.98 0.77 96.07 36.16 51.98 0.77 99. 64

NOTES: 11 2% and 1% is weighted by production percents in major producing regions. Includes flavored milks. 12 Based on category "Filled milk" containing other oils. 1 3 Includes processed American, Colby and Monterey. 14 Assessed on average fat, SNF, carbohydrates & protein: 3.67%, 8.7%, 4.65% and 3.2%. 1 5 Residual Whey = Sum(Tot Ch Prodt)/10.1j*5.6-Sum(Whey Prodt) using 1990 whey prod't (milln)----- > 1202

For carboh., residual whey includes the weighted average of residual error of the total all other dairy manufactured goods----- - -> -124

16 Calculated error redistributed across product categories: residual error for, respectively, protein, carb, fat and SNF---> (14) (377) (158) (381) For protein and fat, share error is distributed among 6 products excluding: butter--> 0.00 0.00 0.20 0.00 nonfat dry--> 0.07 0.09 0.00 0.08 fluid--> 0.41 0.54 0.23 0.47 Error for carbohydrate is allocated between residual manufacturing and residual whey on a weighted basis. Error for SNF is allocated between all product categories.

17 For fluid and soft (except egg nog and cottage cheese), components are computed according to the implicit average protein and carbohydrates--> 3.228 4.524 Factor--> 1.4015 This implicit average is used for all frozen and can l bulk dry milk in order to account for added sugar in processing.

71

72

Table 20, Single Equation, Double-Log Demand Functions for the Nine Categories of Fluid and Manufactured Dairy Products Used in the Dairy Sector IRCM,

Per Capita Consumption Own-Price Income

Lagged Output

Percent Under-IS

Percent Over-6S

School Years

Cross Price*

--- -

I Fluid -0. 144" 0.009 0.794' 0.175" -0.210" 0.14 ---Milk (0.048) (0.033) (0098) (0.070) (0.090) (0.260)

I -0.420" -0.121'" 0.596 ' -1.12'" I Soft Mfg. 0.153 --- ---

Products (0.159) (0.062) (0.150) (0.104) (0.856)

I Frozen -0.327' 0.071 0.225 0.491' --- 0.05 ---

Products (0 . 103) (0.090) (0.184) (0 134) (0.217)

I American -0. 160 -0.035 0.435" -0.735' --- 1.52' ---

Cheese (0.112) (0057) (0.171 ) (0.260) (0.533 )

Italian -0.261" 0.243' 0.801' -0.590" --- 1.40' ---

Cheese (0.119) (0.075) (0.073) (0 .242) (0.464 )

Other -0.155'" 0. 129" 0.827' -0.663' -0.638" 1.13' ---

Cheese (0.087) (0.060) (0.123) (0.206) (0 .302) (0. J 62)

Butter -0.094'" 0.116" 0.585' 0.218" --- -4.08'" -0.07

I (0.048) (0.055) (0 .117) (0.087) (2.754) (0.065)

Non-Fat -0.449' -0.050 0.740' --- --- 1.74 ---

Dry Milk (0.162) (0.191 ) (0 . 105) ( 1358)

Residual -0.018 0.164'" 0.834' --- -0.431 -1.99" ---

I Manufacturing (0.109) (0092) (0.105) (0.415) (0.928)

NOTE:

*

Where left blank, the respective variable was excluded from estimation . Estimated standard errors are shown in parentheses. For a I-tail test, " indicates statistical significance at the 0.0 I level , ** at the 0.05 level , and '-'H at the 0.10 level.

Butter cross-price is with respect to margarine.

Table 21. Comparison of Alternative Dairy Product Long and Short Run Wholesale Elasticities, and Retail Elasticities.

Cox, et al. Cox, et al. Cornick Cornell Huang GeorgelKing Lon~ Run Short Run Short Run Short Run Short Run Short Run Who esale Wholesale Wholesale Retail Retail Retail

Product Elasticity Elasticity Elasticity Elasticity Elasticity Elasticity

I) Fluid milk - .420 -.144 -.012 -.036 -.26 -.35

2) Soft products -.907 -.420

3) American cheese -.274 -.160 -.1362 -.2002 -.332 _.45 2

4) Italian cheese -.741 -.251

5) Other cheese -.477 -.155

6) Butter -. 199 -.093 -.451 -.077 -. 17 - .65

7) Frozen products -.421 -.327 -. 172 -.364 -.12 -.53

8) Residual manufactured -.055 -.018 1 -3.274 -.83 - .32

9) Non-fat dry milk -1.208 -.449 -2.656

I) This elasticity is implausib1r small. In the results presented, an elasticity of -0.413 is used. This is one half the value of the "evaporated, condensed an dehydrated milk" retai elasticity from Huang.

2) This elasticity is for an aggregate cheese category.

73

Table 22. Summary of 1990 Projections of Regional Dairy Product Consumption with Supply-Demand Balance Used in the Diary Sector IRCM.

REGONS FLUO sen AM-CH IT-Gl OTH-CH BUTTER FROZEN EVAPICONO RESMFG NFORY NE 2,821 201 159 133 68 48 370 40 180 38 MA 7,984 571 444 368 182 137 1,054 114 506 107 SA 3,116 214 161 130 78 52 395 46 204 40 SE 6,245 459 339 262 129 108 818 86 384 84 C 1,869 134 94 70 39 32 239 26 117 24 ESC 2,927 203 137 98 54 49 384 40 177 38 wsc 5,014 325 223 168 106 80 650 71 315 62 ENC 8,190 561 405 319 1 74 137 1,078 115 513 106 IMS 1,069 74 53 41 21 18 143 15 65 14 WNC 1,262 85 61 48 25 21 171 17 76 17 WC 2,568 181 131 101 48 44 349 35 154 34 NVV 1,932 131 96 75 40 31 258 26 118 25

MOU 2,557 167 117 90 53 40 339 35 156 33 CA 6,833 441 329 270 169 109 886 99 442 86 OTH 398 24 18 14 11 6 51 6 27 5

SLIvIMARI': TOTAL us 54,784 3,77"1 2,767 2,187 1,194 911 7,185 772 3,432 715 ACTUAL 90 54,770 3,759 2,784 2,274 1,152 915 7,188 772 3,652 698 # DlFF: 14 12 ("18) (87) 42 (4) (3) (0) (220) 17 % DlFF: 0.0% 0.3% -0 .6% -3.8% 3.7% -0.4% -0.0% -0.0% -6 .0% 2.4%

SUllIlly-Oeman:1 Balance Using Revised Rl!{Jionai Coroorcial Disa(I/IBarance Projections 11.

UTLIZAnON (CONSLlvlpnON) BREAKOUT: FLUO SOFT AM-CH IT-Gl OTH-CH BUTTER FROZEN EVAP/CON RESMFG NFORY

1990 COMM DlSP 54,784 3,771 2,767 2,187 1,194 911 7,185 772 3,432 715 TOTAL /lET GO\! 0 0 23 0 0 332 0 (1 ) (1 ) 61 EXPORTSf3 611 8 16 36 9 72 32 57 213 39 CHG PVT STOCKS 0 0 110 12 5 (8) 0 32 32 65 TOTAL UTILIZATION 55,395 3,779 2,915 2,235 1,209 1,307 7,217 860 3,676 880

AVAILABlLllY (SUPPLY) mEAKOUT: 1990 PROON 12 55,381 3,767 2,894 2,207 960 1,302 7,188 853 3,652 879 IMPORTSf3 14 12 21 29 248 5 29 7 24 1 TOTAL AVAILABILITY 55,395 3,779 2,915 2,235 1,209 1,307 7,217 860 3,676 880

UTLIZA nON - SUPPLY (0) 0 0 0 0 (0) (0) (0) 0 (0) % DIFFERENCE -0.0% 0.0% 0.0% 0.0% 0.0% -0 .0% -0.0% -0.0% 0.0% -0.0%

--------------11 Source OS-431(T11) (SUpercellell hy 05-436: 8193) )2 Fluid, Soft, <suI Frozen Prolluction = Cornnercial Oisappearance.Exllorts..lrnporls. f3 Imllort allll export data fron USDA OS-436 where av-allal~e, else from FA TUS lllia.

74

---~

Table 23.

NE MA SA SE C ESC WSC ENC WIS WNC WC NW MOU CA AGGUS

Summary of Farm and Wholesale Level Elasticities and 1990 Starting Values Used in the Dairy Sector IRCM.

FARM 1990 1990 1990 1990 1990 SUPPLY BLEND MILK FARM REGIONAL REGION

ElASllCITY PRICES PROD'N REVENUE PROD'N REVENUE (CHAVAS) (S/cwt) (millbs) (mil S» SHARE SHARE

0.28 14.62 4.235 619 0.029 0.031 0.61 14.79 21.089 3.119 0.144 0.156 0.11 14.91 3.709 553 0.025 0.028 0.65 16.20 5.853 948 0.040 0.047 1.44 14.50 4.390 637 0.030 0.032 0.57 15.30 2.990 457 0.020 0.023 0.68 14.40 8.192 1.180 0.056 0.059 0.99 13.81 14.617 2.019 0.100 0.101 0.15 13.47 24.059 3.241 0.165 0.162 0.25 13.13 12.646 1.660 0.087 0.083 1.44 13.36 9.821 1.312 0.067 0.065 0.52 12.92 8.833 1.141 0.060 0.057 0.46 13.78 4.953 683 0.034 0.034 0.34 12.02 20.660 2.483 0.141 0.124

0.555 13.81 146.048 20.052 1.000 1.000

WHOLESALE LEVELASSUMPTIONS.

DEMAND ElASllCIllES 1990 1990 1990 =================================NHOLESAl...5'VHOLESAl...5'VHOLESALE WHOLESALE RETAIL RETAIL PRICES PROD'N CONSUMP

Cox. eta!. Huang George&King (S/cwt) (millbs) (millbs) FLM. -0 .144 -0.26 -0.35 14.09 55.387 5{387 SOF -0.420 29.20 3.755 3.747 ACH -0.160 -0.33 -0.45 112.00 2.894 2.749 ICH -0.251 121.00 2.207 2.173 oa; -0.155 122.00 960 1.186 BLIT -0 .093 -0.17 -0.65 83.10 1.302 906 FZN -0.327 -0.12 -0 .53 24.05 7.220 7.134 MFG -0.413 -0 .83 -0.32 40.03 3.652 3.407 NFT -0.449 90.00 879 708

75

Table 24. 1990 Asymmetric Transportation Costs Used in the Dairy Sector IRCM. -- - - - - - - - - - _ . _--_. _ . _ ._-

AVERAGE TRANSPORTATION RATES FOR REFRIGERATED COMMODITIES (CENTS PER CWT PER 100 MILES) .

REGION FROM: TO: NE MA SA SE C ESC WSC ENC WIS WNC WC NW MOU CA

NE 0 . 00 48.39 48.39 30.00 35 . 74 35 .56 31.74 39 . 38 38 . 33 32.92 32.92 26.77 27 . 92 26.35 MA 45.23 0.00 48.39 30.00 35.7 4 34.44 31.74 39.38 38.33 32.92 32.92 26.15 27.29 25.73 SA 45.66 48.39 0.00 30.00 35.74 34.44 3l. 74 40.63 38.33 32.92 32.92 26.15 27.29 25.73 SE 26.56 26.41 26 .41 0.00 37.99 33.33 30 . 65 35 . 00 35.00 31 . 88 33.96 26.15 26 . 98 25.73 C 24.59 25.57 25.57 27.78 0.00 32.22 30.65 46.04 38.33 35.00 40.63 25.52 26.25 25.10 ESC 26.29 26.61 26.61 27.78 52.11 0.00 32.83 35 . 00 35.00 31.88 37.29 25.94 26.67 25.52 WSC 25.42 26.61 26.6 1 27.78 32.73 40.00 0 . 00 35.00 36.25 33.96 39.38 25.31 26.46 24 . 69 ENC 28.59 30.57 30.57 24.44 33.50 25.56 30.65 0.00 54.79 38.33 43.75 25.10 26.98 24.48 WIS 24.59 25.57 25.57 23.33 27 . 87 27 . 78 30 . 65 49.38 0.00 54.79 40.63 25.10 26.98 24.48 WNC 24 . 59 25.57 25.57 23.33 28.26 25 . 56 30.65 39.38 54.79 0 . 00 41 . 67 25. 1 0 26 . 98 24.48 I~C 2 4 .59 25.57 25.57 23.33 34.58 25.56 30.65 39.38 36.25 37.29 0 . 00 25.10 26 . 98 24 . 48 NW 26 . 13 26.41 26.41 25 . 56 26.34 27.78 28.26 28 . 13 28. 54 27.50 28.5 4 0.00 30.83 28.44 NOU 25.71 25.99 25.99 28.89 29.38 30.00 32.83 28.54 30.63 28.54 29.58 31.56 0.00 29 .4 8 CA 25.79 25.99 19.79 25.56 26.46 26.67 28.26 28.13 28.54 27 .5 0 28.54 27.40 27.71 0.00

AVERAGE TRANSPORTATION RATES FOR GENERAL COMMODITIES, NO REFRIGERATION (CENTS PER CWT PER 100 MILES).

REGION FROM: TO: NE MA SA SE C ESC WSC ENC IHS WNC WC NW MOU CA

NE 0 . 00 31.07 31. 86 22.80 27.94 27.81 22.82 28.07 27.77 25.38 25.97 12.90 15.69 16.10 MA 3 4 .15 0.00 34.03 24.05 29.35 29 . 23 24 .15 29.36 28.52 26.88 27 . 22 12.90 15.69 15.89 SA 31.55 3l. 59 0.00 22.51 27.52 2 8 . 03 22.07 27 . 78 27.40 24.88 25.3 4 12.90 15.69 1 5.89 SE 19.73 21 .29 22.3 0 0.00 27.59 27.46 23.10 25.20 24.70 24.03 24.91 12.58 16.67 15.82 C 15.63 16.46 17.02 17.60 0.00 25 . 02 19.36 22.52 22.31 22 . 23 23.90 11 .72 14.20 14 . 33 ESC 1 5.94 16.92 17 .47 1 7.59 24.99 0.00 21.28 22.35 22.69 22.35 24.56 11 .61 13.99 14 .40 WSC 19 . 20 19.52 19.48 1 9.99 24.88 25.72 0 . 00 23.07 23.24 23. 4 0 26.24 12.15 15.13 15.04 ENC 1 5 . 26 16.04 16.81 18.06 22 .1 0 21 . 69 18.6 1 0 . 00 27 . 92 21 . 64 21.99 11.40 14.20 14.33 WIS 15 .10 16.00 1 6.73 17.43 21.51 21.69 18.57 24.98 0.00 23.86 21. 35 11 .40 13 .9 9 14.12 Itmc 17.29 17.67 18.27 19.52 23.60 24.06 21. 28 24.98 26.72 0.00 23.60 11.40 14.27 14.33 WC 17. 03 17.50 18.27 18.68 24 . 88 24.28 2 1 .28 24 . 77 24.27 24.60 0.00 11.40 13.99 14 .12 NW 13 .12 1 5.27 15.62 16.73 18.37 1 8.44 17.25 18.16 18.16 18.16 17. 81 0.00 16.21 15.34 MOU 12.90 17 . 42 18.05 17.08 21.43 21.86 20.86 21 . 35 22.21 21.37 22.21 14.84 0.00 17.57 CA 13.12 15.76 1 6 . 31 1 6.73 18.37 18.44 17.46 18.23 18.23 18.23 17.95 13 .66 16.77 0.00

76

Figure 12.

Figure 13.

Summary of U.S. Dairy Exports (million pounds), 1989-92.

million pounds

500 ,-----------------------------------.

400

300

200

100

o flm- sof

1989 • 73 4

1990 III 61 8

1991 III 87 13

1992 r&1I 117 22

89-92 A VG III 84 12

* FLM is 10 million pounds.

Source: FA TUS

8eh

17

17

17

20

18

ieh Deb but I fzn mfl! nft

4 1 68 18 296 280

8 2 125 32 213 24

8 2 50 54 318 lOS 11 3 183 84 402 188

8 2 107 47 307 150

Summary of U.S.-Mexico Dairy Exports (million p~)Unds), 1989-92.

million pounds

250 ~------------------~----------~

200 ~---------------------------.~

150

* FLM is 10 million pounds.

Source: FATUS

77

Figure 14. Summary of Growth Rates From 1990 to 1989-92 Average U.S. and U.S.-Mexico Dairy Exports.

Growth Rates

12

10 r---------------------------------

8r-------------------------------------

6 ~---------------------------------

4 1--------------------------------------

2~------------------------~~----~,--

o fun sof ach ich och but I fzo I mfg oft

US Expons • 1.38 1.44 1.06 0.99 0.99 0,85 1.47 1.44 6.30

~I I

US-Mex Expons 1.38 1.44 1.57 1.62 1.62 I 1.42 1.47 1.96 10.48

Mexico Share • 1.00 1.00 1.42 I 1.42 1.42 2,01 1.00 1.37 1.41

SOURCE: FATUS

78

v. SIMULATION RESULTS

A. Introduction

This discussion will focus on summarizing the farm and wholesale level impacts for 6 regions

which account for over 70% of aggregate U.S. (AGGUS) total milk production and revenues (see

Table 3): Middle Atlantic (MA); West South Central (WSC); East North Central (ENC); Wisconsin

(WIS); West North Central (WNC); and California (CA). Aggregate producer, consumer and total

surplus impacts are presented first. Not surprisingly, given the relatively small demand shifts implied

by these four scenarios, aggregate welfare impacts tend to be quite small. Regional farm level blend

price and production impacts are presented next followed by regional farm level total revenue and

producer surplus impacts. Some significant regional differences are evident. Aggregate wholesale

level price, production and consumption impacts are rather minor (with the exception of nonfat dry

milk and butter) and are briefly summarized next. The associated NAFf A scenario impacts on

regional component prices for fat in all products except butter, fat used in butter, proteins and

carbohydrates are then presented. The results section concludes with a summary of the regional U.S.­

Mexico dairy product trade flows .

Recall from Section III that NAFTA+ is a very optimistic scenario where Mexican demand is

high (4% annual growth , 2% due to improved incomes) and production is low. All other scenarios

assume medium production growth in Mexican dairy production, roughly at sustainable rates evidenced

from the recent past. What distinguishes the NAFfA, STATUS QUO, and NO NAFfA scenarios is

basically different income induced consumption growth rates of 4% (2% due to incomes), 3% (I % due

to incomes), and flat income growth (2% due to population). All scenarios were shown to imply

relatively small demand shocks to the U.S . dairy sector relative to the total supply of dairy

components. As indicated in Section III, the NAFf A and STATUS QUO scenarios are the most

likely .

B. Welfare Impacts

As indicated in Table 25 ( page 80 below), aggregate welfare impacts are pretty much as one

might expect. The N AFf A scenario has a 0 .1 % impact on producer and total surplus and a negligible

impact on consumers. Since low or no income induced consumption growth in Mexican dairy

consumption are projected to decrease Mexico's dairy deficit (i.e., the STATUS QUO or NO NAFf A

scenarios), U.S .-Mexico exports and U.S. total demand for dairy products decline. Hence, farm level

79

prices and producer surplus falls, while consumers gain from the lower prices and consumer surplus

rises. Since consumer gains are insufficient to offset producer losses, total surplus falls but by less

than producer surplus falls. With the almost 70% increase in U.S. to Mexico exports projected by the

NAFf A+ scenario, producers gain from the demand expansion, prices rise and consumers are worse

off but by considerably less than farmers gain . Hence, producer and total surplus increase +2.5% and

+2.1 %, respectively.

Table 25.

Producer:

Consumer:

Total:

Aggregate U.S. Producer, Consumer and Total Surplus Impacts (Changes From Base Scenario).

NAFfA+ NAFfA STATUS QUO NO NAFfA -------- -------- ============ ----------------- -------- ---------

+2.5 % +0.1 % -0.7% -1.4%

-0.4% +0.0% +0.1% +0.3%

+2.1% +0.1% -0.5% -1.2%

c. Farm Level Impacts

Next, regional farm level impacts are discussed . As indicated in Table 26 (see page 84), the

impacts of the scenarios on farm level blend prices are fairly flat across regions and roughly parallel

the magnitude of the associated demand shift implied by the scenarios. In contrast, the regional

impacts on farm level production reflect larger percentage changes as generally expected given

somewhat inelastic supplies. Part of the regional milk production differences reflect the fact that many

regions have a less inelastic supply response given the 5 year adjustment horizon (see Section IV,

Table 23 , page 75). In particular, those regions with largest (smallest) production response tend to

have the largest (smallest) supply elasticities . Note in particular that SA, WIS and WNC are projected

to have the smallest supply response across all regions for all scenarios while C, ENC, and WC are

projected to have the largest.

Table 27 (see page 85) summarizes regional farm level total revenue and producer surplus

impacts. As one might expect, those regions comprising the bulk of the U.S . to Mexico exports (see

Table 31, page 89), the West South Central (WSC: Texas, New Mexico and Oklahoma) and California

(CA), in general have larger impacts as do other regions with relatively elastic supply response (e.g.,

C, ENC, and We). However, these results also support the "rising tide" arguments since regions not

80

directly exporting to Mexico do experience revenue and welfare gains/losses. This result should not

be too surprising in the context of an hedonic spatial equilibrium model of the U.S . dairy sector in

which considerable interregional exports occurs and regions have differential supply responses. Hence,

regional demand shifts equilibrate throughout the U.S . dairy sector and affect all regions. The

magnitude of these impacts reflects the size of the region's milk supply, "domestic" demand , its

processing sector, and price responsiveness .

D. Wholesale Level Impacts

Table 28 (see page 86) summarizes wholesale price, production, and consumption impacts.

Wholesale production and consumption shifts generally move together and opposite to wholesale price

shifts. These impacts tend to be small except for nonfat dry milk (NFr) and butter (BUT). Recall

that NFr and BUT accounted for over 80% of total Mexican dairy imports (milk equivalent basis, see

Section III, Table 10, page 52) during the 1989-92 period. Thus, the nonfat dry milk and associated

butter markets absorb the major portion of the export demand shifts. As demand for NFr increases

(decreases) prices and production rise (fall). Note that NFr demand is basically a demand for dairy

proteins. Increases in NFr demand induce a relative surplus in the associated butter markets which

then must clear; hence butter prices move opposite to NFr. Given the prevalence of joint NFr/butter

production, these results are not unexpected.

E. Component Price Impacts

Similar impacts are evident in the regional component prices for fat in butter (Table 29, page

87) and proteins (Table 30, page 88). Note that values of fat used in all products except butter varies

considerably by region while the value of fat used in butter does not. This reflects different regional

utilization of milk fat in non-butter products (e .g., higher valued class I and/or class II utilizations).

On average (see AGGUS prices in Table 29) the fat used in butter is generally worth 10-13 cents per

pound less than the value of fat used in all other products . This reflects the somewhat irreversible

nature of fat in butter (i.e. , it is difficult to substitute butter as fat in the manufacture of other dairy

products) and it's residual claimant status. Note that the percentage changes in both of these regional

component price profiles are of very similar magnitudes in each scenario (i.e., 27-30%, 1-2%,7-8%

and 16-17% for NAFrA+, NAFfA, STATUS QUO and NO NAFrA, respectively). The protein

component prices summarized in Table 30 reflect similar results: considerable variation across

81

•

scenarios but relatively similar percentage changes across regions by scenario. As with the wholesale

price impacts, prices in the implicit fat and protein markets move inversely, as expected .

F. Trade Flow Impacts

Table 3 I (page 89) summarizes the U .S.-Mexico trade flows by region. In all scenarios, WSC

(West South Central) and CA (California) dominate U.S. dairy exports to Mexico. California supplies

all of the U.S .-Mexico cheese exports in all scenarios. California is also a major supplier of the

residual manufacturing category (MFG: whey and evaporated/condensed milk) accounting for all MFG

exports to Mexico in the NAFTA+ scenario, and over half of the MFG exports in the BASE and

NAFTA scenarios. WSC is the major supplier of nonfat dry milk (NFT) and butter exports to Mexico,

and the second largest (if not the largest) supplier of MFG exports under all scenarios. WSC is

consistently found to be one of the lowest cost regions with respect to NFT, an indication that NFT is

in relative surplus in the WSC compared to the rest of the U.S . in these simulations. The WSC also

supplies all of the U.S. fluid (FLM), soft (SOF), and frozen (FZN) exports to Mexico under all

scenarios . Note that under the two export growth scenarios, NAFTA+ and NAFTA, regions such as

SE (South East), ESC (East South Central), ENC (East North Central), NW (North West), and MOU

(Mountain) also export nonfat dry milk to Mexico. This partly reflects differences in supply

elasticities, regional component utilization, and the hedonic spatial equilibrium distributing these

demand shocks across the U.S . dairy sector.

G. Commodity Production Profile Impacts

Table 32 (page 90) summarizes the regional wholesale commodity production profiles under

the base scenario (i.e., 1989-92 average U.S . dairy exports with all other starting values at 1990 levels)

and compares this profile with actual 1990 production levels (obtained from USDA state level

commodity production aggregated into the U .S. dairy Sector IRCM regions) . This comparison

indicates how the IRCM optimal regional commodity production differs from actual 1990. While

these profiles are not unique, sensitivity analysis indicates that they are fairly robust. These solutions

reflect the IRCM ' s optimal allocation of regional milk components across regional commodity markets

so as to maximize social welfare as well as all the assumptions contained in the model specifications .

Whether the "real world" is satisfactorily modeled by these assumptions is always an open question.

Given these caveats, the results do provide some suggestions how regions would optimally adjust their

82

wholesale commodity production to the fairly large 1989-92 average export shocks (recall that these

are relatively smaller total demand shocks).

For example, Wisconsin is projected to have large increases in italian cheese (ICH, + I 0%),

other cheese (OCH, +216%), and frozen products (FZN, +205%) and sharply reduce production of

butter (BUT, -60%), and produces no non-fat dry milk (NFT) compared to actual 1990 levels.

Similarly, California is projected to have large increases in american cheese (ACH, +76%) and other

cheese (OCH, +341 %), while sharply reducing its production of butter (BUT, -39%), residual

manufacturing (MFG, -14%), and non-fat dry milk (NFT, -78%). Large projected regional changes

from a commodity perspective include :

american cheese (ACH): west central (WC, -51 %), northwest (NW, +75%), mountain (MOU,

-45%), and California (CA, +76%);

italian cheese (ICH): east northcentral (ENC, -37%), west northcentral (WNC, + 159%), and

northwest (+90%);

other cheese (OCH): west northcentral (WNC, +229%), northwest (NW, +66%), and

California (+341%).

butter (BUT): all regions;

residu.al manufacturing (MFG): all regions except southeast (SE), Wisconsin (WIS), west

northcentral (WNC), and California (CA);

nonfat dry milk (NFT): all regions except southeast (SE).

Again, within the caveats implied by the limitations and assumptions of the model, these results

suggest considerable changes in regional commodity production profiles compared to actual 1990

production and provide an indication of the potential marketing/policy usefulness of the U.S. Dairy

IRCM.7

Note, in particular, that the southeast is projected to radically increase its butter production in percentage terms. Partly this reflects relatively small 1990 production levels . It also reflects a structural characteristic of the model where the fat associated with fluid reserve requirements (imposed on those regions with greater than 60% class I utilization) is forced to be used in butter, soft and/or frozen "final demand" products. Hence, intermediate products such as skimmed milk and/or fat are not allowed to move between regions except as "final demand" products. This does not accurately reflect the U.S. dairy component markets and is a structural deficiency that should be addressed in further model refinements.

83

84

Table 26. Summary of Regional and Aggregate Farm Level Blend Prices and Milk Production Impacts Under Alternative NAFT A Scenarios.

FARM LEVEL PRICES ($/cwt). % OlANGE ACROSS SCENARIOS.

LOW PROD MED PROD MED PROD MED PROD 89~2 AVG HIGH CON HIGH CON MED CONS LOW CONS Base: BASE BASE BASE BASE

BASI: IlAFTA+ IlAFTA STATUS QUO 1l0llAfTA Scenario: IlAFTA+ IlAFTA STATUS QUO tlO/lAfTA NE 14.74 14.90 14.75 14.70 14.65 NE 1.1 0/0 0.00/0 -OJ% -0.60/0 MA 14.34 14.50 14.34 14.29 1424 MA 1.10/0 0.00/0 -OJ% -0.70/0 SA 14.85 15.02 14.86 14.80 14.74 SA 1.20/0 0.1 0/0 -OJ% -0.70/0 SE 16.1 4 16.32 16.15 1609 16.03 SE 1.10/0 0.10/0 -0.30/0 -O}% C 14 .68 14.84 14.68 14.63 14.58 C 1.1 0/0 0.00/0 -030/0 -0 .70/0 ESC 15.68 15 .87 15.69 1563 1557 ESC 1.20/0 0.10/0 -OJ% -0.70/0 WSC 14.74 14 .92 14.76 14.70 14.64 WSC 1.20/0 0.1% -0 .3% -0.70/0 ENC 14.08 14.24 14.09 1404 13.99 ENC 1.1% 0.0% -OJ% -O}% WIS 13.84 14.00 13.85 13.80 13.75 WIS 1.10/0 0.0% -030/0 -O}% WNC 1376 13.93 1377 13.72 1367 WNC 1.20/0 0.00/0 -03% -0.70/0 WC 13.95 14.12 13.96 13.91 1386 WC 1.20/0 0.0% -OJ% -0.70/0 NW 13.62 13.80 13.64 13.57 1352 NW 1.30/0 0.10/0 -0.3% -0.70/0 MOU 13.97 14.15 13.99 13.93 1388 MOU 13% 0.1% -OJ% -0.7% CA 13.64 13.82 13.65 13.59 13.54 CA 1.3% 0.1% -0.3% -O}% AGGUS 14.15 14.32 14.16 14.11 14.06 AGGUS 1.20/0 0.1% -OJ% -O}%

FARM LEVEL PRODUCTION (million 1IOIIIHIs). % OlANGE ACROSS SCENARIOS.

LOW PROD MED PROD MED PROD MED PROD 89~2 AVG HIGH CON HIGH CON MED CONS LOW CONS Base: BASE BASE BASE BASE

BASI: IIAFTA+ IIAFTA STATUS QUO 1l0llAfTA Scenario: IlAFTA+ IlAFTA STATUS QUO 1I0ilAfTA NE 4.245 4.258 4.245 4.241 4.237 NE 0.30/0 0.0% -0.1% -0.20/0 MA 20.694 20m5 20.700 20.655 20.612 MA 0.7"/0 0.00/0 -0.2% -0.40/0 SA 3.708 3. 712 3.708 3. 706 3.705 SA 0.10/0 0.00/0 -0.00/0 -0 .1% SE 5.838 5.882 5.841 5.827 5.813 SE O}% 0.0% -0 .2% -0.40/0 C 4.467 4.540 4.470 4.448 4.426 C 1.60/0 0.10/0 -040/0 -0.9% ESC 3.032 3.053 3.033 3.027 3.019 ESC O}% 0.00/0 -0.2% -0.4% WSC 8.325 8.393 8.331 8.308 8.283 WSC 0.80/0 0.10/0 -0.2% -0.50/0 ENC 14.903 15.065 14.909 14.856 14.804 ENC 1.10/0 0.0% -0.3% -0.7% WIS 24.159 24.201 24.161 24.147 24.133 WIS 0.20/0 0.0% -0.00/0 -0.10/0 WNC 12.799 12.838 12.800 12.788 12.775 WNC 0.3% 0.00/0 -01% -0.2% WC 10.450 10.62 1 10.456 10.403 10.349 WC 1.60/0 0.10/0 -0 .4% -1.00/0 NW 9.081 9.145 9.087 9.066 9.046 NW 0.7% 0.10/0 -0 .20/0 -0 .40/0 MOU 4.985 5.014 4.987 4.977 4.969 MOU 0.60/0 0.10/0 -0.1% -030/0 CA 21.605 21.710 21.615 21.579 21.551 CA 0.50/0 0.0% -0.1% -0.20/0 AGGUS 148.290 149.267 148.345 148.029 147.724 AGGUS 0.7"/0 0.00/0 -0.20/0 -0.4%

I

I

Table 27. Summary of Regional and Aggregate Farm Level Total Revenue and Producer Surplus Impacts Under Alternative NAFT A Scenarios.

FARM LEVEL TOTAL REVENUES (million $) . PERCENTAGE CHANGES ACROSS SCENARIOS.

LOW PROD MED PROD MED PROD MED PROD 89.92 AVG HIGH CON HIGH CON MED CONS LOW CONS Base: BASE BASE BASE BASE

BASE I/AFTA. I/AFTA STATUS QUO IIOI/AHA Scenario: IIAFTA< I/AFTA STATUS QUO IIOI/AHA NE 626 635 626 623 621 NE 1.4% 0.0% -0.4% -0.8% MA 2.967 3.020 2.969 2.952 2.936 MA 1.8% 0.1% -0.5% -1.0% SA 550 557 551 548 546 SA 1.3% 0.1% -0.4% -0.8% SE 942 960 943 938 932 SE 1.9% 0.1% -0.5% -1.1% C 656 674 656 651 645 C 2.8% 0.1% -0.7% -1.6% ESC 475 484 476 473 470 ESC 1.9% 0.1% -0.5% -1.1% WSC 1.227 1.252 1.230 1.221 1.212 WSC 2.0% 0.2% -0.5% -1.2% ENC 2.099 2.145 2.101 2.086 2.071 ENC 2.2% 0.1% -06% -1.3% WIS 3.344 3.387 3.346 3.332 3.318 WIS 1.3% 0.1% -0.4% -0.8% WNC 1.762 1.788 1.762 1.754 1.746 WNC 1.5% 0.0% -0.4% -0.9% WC 1.458 1.499 1.460 1.447 1.434 WC 2.8% 0.1% -0.8% -1.6% NW 1.237 1.262 1.239 1.231 1.223 NW 2.0% 0.2% -0.5% -1.1% MOU 697 710 698 693 690 MOU 1.9% 0.2% -0.5% -1.0% CA 2.946 2.999 2.951 2.933 2.919 CA 1.8% 0.2% -0.4% -0.9% AGGUS 20.985 21.374 21.007 20.882 20.763 AGGUS 1.9% 0.1% -0.5% -1.1%

FARM LEVEL PRODUCER SURPLUS ($1,000). PERCENTAGE CHANGES ACROSS SCENARIOS.

LOW PROD MED PROD MED PROD MED PROD 89..92 AVG HIGH CON HIGH CON MED CONS LOW CONS Base: BASE BASE BASE . BASE

BASE I/AFTA+ tlAFTA STATUS QUO IIO/IAFTA Scenario: I/AFTA+ tlAFTA STATUS QUO I/Ol/AHA NE 111.076 111)66 111.101 110.888 110.676 NE 0.6% 0.0% -0.2% -0.4% MA 246.171 249.525 246.304 245.246 244.230 MA 1.4% 0.1% -0.4% -0.8% SA 251.159 251.800 251.199 250.984 250.758 SA 0.3% 0.0% -0.1% -0.2% SE 72.570 73.656 72.635 72.290 71.941 SE 1.5% 0.1% -0.4% -0.9%

, ,

C 22.889 23.635 22.914 22.689 22.463 C 33% 0.1% -0.9% -1.9% ESC 41.270 41.836 41.302 41.125 40.926 ESC 1.4% 0.1% -0.4% -0.8% WSC 89.582 91.047 89.714 89.216 88.678 WSC 1.6% 0.1% -0.4% -1.0% ENC 105.972 108.301 106.071 105.316 104.578 ENC 2.2% 0.1% -0.6% -1.3% WIS 1.089.226 1.092.983 1.089.376 1.088.150 1.086.935 WIS 03% 0.0% -0.1% -0.2% WNC 340.150 342.235 340.215 339.570 338.920 WNC 0.6% 0.0% -0.2% -0.4% WC 51.577 53.282 51.642 51.118 50.591 WC 33% 0.1% -0.9% -1.9% NW 115.979 117.621 116.137 115.588 115.098 NW 1.4% 0.1% -0.3% -0.8% MOU 75.142 76.041 75.225 74.921 74.681 MOU 1.2% 0.1% -03% -0.6% CA 399.345 403.258 399)29 398.395 397.358 CA 1.0% 0.1% -0.2% -0.5% AGGUS 31.118.070 31.903.767 31.162.913 30.912.208 30.671.404 AGGUS 2.5% 0.1% -0.7% -1.4%

85

86

Table 28. Summary of Aggregate Wholesale Level Commodity Price, Production and Consumption Impacts Under Alternative NAFTA Scenarios.

WHOLESALE LEVEL PRICES ($/cwt). % OiANGE ACROSS SCENARIOS.

LOW PROD MED PROD MED PROD MED PROD 69~2 AVG HIGH CON HIGH CON MED CONS LOW CONS Base : BASE BASE BASE BASE

BASE IIAFTA+ IIAFTA STATUS QUO IIOIlAFTA Scenario: IIAFTA+ IlAFTA STATUS QUO tlOtlAFTA FLM 14.19 14.71 14.21 14.05 13.89 FLM 3.6% 0.2% -1.0% -2.1% SOF 27.82 27.38 27.78 27 .93 28.07 SOF -1 .6% -0.1% 0.4% 0.9% AOi 128.51 129.85 128.57 128.14 127.74 AOi 1.0% 0.0% -0.3% -0.6% ICH 109.74 11206 109.83 109.11 108.42 ICH 2.1% 0.1% -0 .6% -12% 001 106.99 107.43 107.01 106.87 106.73 001 0.4% 0.0% -0.1% -0.2% BUT 56.77 4102 56.14 61.06 65 .63 BUT -27.7% -1 .1% 7.6% 15.6% FZN 20.50 19.47 20 .43 20.77 21.10 FZN -50% -0.3% 1.3% 30% MFG 47.51 49.72 47 .65 46 .92 46.23 MFG 47% 0.3% -12% -2.7% NFT 126.97 13802 127.74 124.16 120.61 NFT 8.7% 0.6% -2.2% -50%

WHOLESALE LEVEL PRODUCTION (million pounds). % OiANGE ACROSS SCENARIOS.

LOW PROD MED PROD MED PROD MED PROD 69~2 AVG HIGH CON HIGH CON MED CONS LOW CONS Base: BASE BASE BASE BASE

BASE tIAFTA+ UAFTA STATUS QUO UOtlAFTA Scenario: tlAFTA+ UAFTA STATUS QUO tlO UAFTA

FLM 55.564 55.840 55.581 55.491 55.402 FLM 0.5% 0.0% -0.1% -0.3% SOF 3.844 3.872 3.846 3.837 3.82 7 SOF 0.7% 0.1% -0.2% -0.4% AOi 2.831 2.828 2.831 2.831 2.832 AOi -0 .1% -0 .0% 0.0% 0.1% ICH 2.25 7 2.252 2.257 2.258 2.259 ICH -0.2% -0.0% 0.1% 0.1% 001 986 987 986 986 986 001 0.1% 0.0% -0.0% -0.1% BlIT 1.320 1.343 1.321 1.313 1.307 BUT 1.8% 0.1% -0.5% -1 .0% FZN 7.545 7.649 7.552 7.518 7.484 FZN 1.4% 0.1% -0.4% -0.8% MFG 3.485 3.471 3.483 3.488 3.493 MFG -0 .4% -0.0% 0.1% 0.2% NFT 952 1.008 955 937 920 NFT 5.8% 0.3% -1 .6% -3.3%

WHOLESALE LEVEL CONSUMPTION (million IIOUIHls!. % OiANGE ACROSS SCENARIOS.

LOW PROD MED PROD MED PROD MED PROD 69~2 AVG HIGH CON HIGH CON MEO CONS LOW CONS Base : BASE BASE BASE BASE

BASE tIAFTA. tlAFTA STATUS QUO 110 tlAFTA Scenario: IlAFTA+ IIAFTA STATUS QUO 1IOIlAFTA

FLM 55.142 55.418 55.159 55.069 54.980 FLM 0.5% 0.0% -0.1% -0.3% SOF 3.826 3.854 3.828 3.819 3.810 SOF 0.7% 0.1% -0.2% -0.4% AOi 2.688 2.685 2.688 2.689 2.689 AOi -0 .1% 0.0% 0.0% 0.1% ICH 2.232 2.229 2.233 2.234 2.235 ICH -0.1% 00% 0.1% 0.2% 001 1.211 1.212 1.211 1.211 1.210 001 0.1% 0.0% 0.0% -0.0% BUT 944 965 943 936 929 BlIT 2.2% -0.1% -0.9% -16% FZN 7.482 7.586 7.490 7.455 7.421 FZN 1.4% 0.1% -0.4% -0.8% MFG 3.237 3.222 3. 235 3. 240 3.244 MFG -0 .5% -0.1% 0.1% 0.2% NFT 693 769 716 698 682 NFT 11.0% 3.3% 0.8% -1 .7%

Table 29. Summary of Impacts on Regional Component Prices for Dairy Fat Under Alternative NAFTA Scenarios.

REGIONAL COMPONENT PRICES: Value of Fat In All Products Except Butter. Sof~ ann Frozen .

Price levels: $/CWf Percentage Olanges

lOW PROD MED PROD MED PROD MED PROD 89.92 AVG HIGH CON HIGH CON MED CONS lOW CONS Base: BASE BASE BASE BASE

BASE tiAfTA+ tlAFTA STATUS QUO II0tiAfTA Scenario: IIAFTA+ IIAFTA STATUS QUO /lOtlAHA NE 98.99 77.62 97 .45 104.74 112.07 NE -21.6'l'o -1 .5'l'o 5.8'l'o 13.2'l'o MA 87.74 66.34 86.21 93.50 100.83 MA -244'l'o -1.7'l'o 6.6'l'o 14.9'l'o SA 101.97 80.89 100.58 107.65 114.67 SA -20.7'l'o -1.4'l'o 5.6'l'o 12.5'l'o SE 138.03 115.81 136.64 143.70 150.72 SE -16.1 'l'o -1.0'l'o 4. 1 'l'o 9.2'l'o C 80.86 58 .84 79.30 86.61 93.73 C -27.2'l'o ,1.9'l'o 7. 1 'l'o 15.9'l'o ESC 124.72 102.50 123.31 130.39 137.88 ESC -17.8'l'o -1.1'l'o 4.5'l'o 10.6'l'o WSC 96.03 73.01 94.25 101.79 109.36 WSC -24.0'l'o -1.9'l'o 60'l'o 13.9'l'o ENC 8078 59.20 79.24 86.53 93.85 ENC -26.7'l'o -1 .9'l'o 7.1'l'o 16.2'l'o WIS 69.42 48.03 67.90 75 .06 81.75 WIS -30.8'l'o -2.2'l'o 8.1'l'o 17.8'l'o WNC 71.98 50.64 70.45 77.73 84.30 WNC -29.7'l'o -2.1 'l'o 8.0'l'o 17.1 'l'o WC 77.20 54.41 75.65 82.96 90.27 WC -29.5'l'o -2.0'l'o 7.5'l'o 16.9'l'o NW 6603 46.32 65.25 71.41 77.13 NW -29.9'l'o -1.2'l'o 8. 1 'l'o 16.8'l'o MOU 74.52 54.95 73.29 79.17 84.88 MOU -26.3'l'o -1.7'l'o 6.2'l'o 13.9'l'o CA 66.02 46.31 65.24 7140 77.12 CA -29.9'l'o -1.2'l'o 8.2'l'o 16.8'l'o AGGUS 78.91 57.70 77.54 84.52 91.25 AGGUS -26.9'l'o -1.7'l'o 7. 1 'l'o 15.6'l'o

REGIONAL COMPONENT PRICES: Value of Fat in Butter.

Price levels: $/CWf Percentage Olanges

lOW PROD MED PROD MED PROD MED PROD 89.92 AVG HIGH CON HIGH CON MED CONS lOW CONS Base: BASE BASE BASE BASE

BASE tlAFTA+ tlAFTA STATUS QUO tlOllAFTA Scenario:: IlAFTA+ tlAFTA STATUS QUO tlOtlAFTA

NE 66.26 46.53 65.47 71.64 77.38 NE -29.8'l'o -1 .2'l'o 8. 1 'l'o 16.8'l'o MA 66.22 4649 6543 71.61 77.35 MA -29.8'l'o -1.2'l'o 8.1'l'o 16.8'l'o SA 66.17 46.44 65.38 71.56 77.30 SA -29.8'l'o -1.2'l'o 8.2'l'o 16.8'l'o SE 66.10 46.34 65.31 71.48 77.22 SE -29.9'l'o -1.2'l'o 8.1'l'o 16.8'l'o C 66.35 46 .62 65.57 71.74 77.48 C -29.7'l'o -1.2'l'o 8.1'l'o 16.8'l'o ESC 66.07 46.32 65.28 71.46 77.21 ESC -29.9'l'o -1.2'l'o 8.2'l'o 16.9'l'o WSC 66.11 46.35 65.31 71.50 77.25 WSC -29.9'l'o -1.2'l'o 8.2'l'o 16.8'l'o ENC 67.81 48.08 6701 73.19 78.94 ENC -29.1 'l'o -1.2'l'o 7.9'l'o 16.4'l'o WIS 65.99 46.27 65.21 71.37 77.11 WIS -29.9'l'o -1.2'l'o 8.2'l'o 16.9'l'o WNC 66.05 46.33 65.26 71 .43 77.16 WNC -29. 9 'l'o -1.2'l'o B.2'l'o 16.8'l'o WC 66.11 46.37 65.33 71.50 77.24 WC -29.9'l'o -1 .2'l'o 8.1'l'o 16.8'l'o NW 66.03 46.32 65.25 71.41 77.13 NW -299'l'o -1.2'l'o B.1'l'o 16.8'l'o MOU 66.30 46.58 65.56 71.71 77.43 MOU -29.7'l'o -1.1'l'o B.2'l'o 16.8'l'o CA 66.02 46.31 65.24 71.40 77.12 CA -29.9'l'o -1.2'l'o 8.2'l'o 16.8'l'o

87

88

Table 30. Summary of Impacts on Regional Component Prices for Milk Proteins and Carbohydrates (Lactose) Under Alternative NAFT A Scenarios.

REGIONAL COMPONENT PRJCES: Proteins.

Price Levels: $/CWT Percenta!Je Olan!Jes

LOW PROD MED PROD MED PROD MED PROD 89-92 AVG HIGH CON HIGH CON MED CONS LOW CONS Base: BASE BASE BASE BASE

BASE tlAFTA+ tlAFTA STATUS QUO IIOIiAFTA Scenario: IIAFTA+ !lAFTA STATUS QUO 110 !lAFTA NE 326.87 356.61 328.80 318.84 30860 NE 9.1% 06% -25% -5.6% MA 330.11 359.84 332.04 322.08 311.83 MA 9.0% 0.6% -2.4% -5.5% SA 335.60 365.44 337.59 327.54 317.19 SA 8.9% 0.6% -2.4% -5.5% SE 34309 375.55 345.07 335.03 324.67 SE 9.5% 0.6% -2.3% -5.4% C 366.19 396.55 368.17 358.22 348.49 C 8.3% 0.5% -2.2% -4.8% ESC 345.67 377.95 347.69 337.60 326.20 ESC 9.3% 0.6% -2.3% -5 .6% WSC 341.23 374.24 344.14 333.00 321.73 WSC 9.7% 0.9% -2.4% -5.7% ENC 337.69 367.35 339.62 329.66 319.41 ENC 8.8% 0.6% -2.4% -5 .4% WIS 353.18 382.54 35509 345.32 336.08 WIS 8.3% 0.5% -2.2% -4.8% WNC 347.78 377.30 349.71 339.75 330.71 WNC 8.5% 0.6% -2.3% -4.9% WC 341.13 372.99 343.10 333.09 322.85 WC 9.3% 0.6% -2.4% -5.4% NW 349.62 377.73 350.99 342.08 33430 NW 8.0% 0 .4% -2 .2% -4.4% MOU 340.16 368.04 342.24 333.77 325.67 MOU 8.2% 0.6% -1 .9% -4.3% CA 350.67 378.78 352.01 343.08 33497 CA 8.0% 0.4% -2.2% -4.5% AGGUS 34383 373.62 345.71 335.96 326.42 AGGUS 8.7% 0.5% -2 .3% -5.1%

REGIONAL COMPONENT PRJCES: Value of Carhohydrates (lactose).

Price Levels: $/CWT Percenta!Je Olanges

LOW PROD MED PROD MED PROD MED PROD 89-92 AVG HIGH CON HIGH CON MED CONS LOW CONS Base: BASE BASE BASE BASE

BASE IrAFTA+ !lAFTA STATUS QUO IIOIiAfTA Scenario: t~AFfA" IIAFTA STATUS QUO !lOIIAfTA NE 14.15 13.99 14.15 1419 14.40 NE · -11% 0.0% 0.3% 1.8% MA 12.06 11.91 1206 12.10 12.31 MA -1.2% 0.0% 0.4% 2.1% SA 8.03 7.80 7.99 8.09 8.38 SA -2.9% -0.5% 0.8% 4.4% SE 2.29 1.42 2.25 2.35 2.64 SE -37.9% -1.5% 2.9% 15.6% C 0.00 000 000 0.00 000 C ESC 1.16 0.44 1.11 1.23 1.77 ESC -62.1 % -3.8% 58% 52.5% WSC 6.67 5.85 6.41 6.85 7.26 WSC -1 2.3% -3 .9% 2.7% 8.9% ENC 6.89 6.79 6.90 6.94 7.15 ENC -1.4% 0.1% 0.7% 3.7% WIS -0 .01 -0 .04 000 000 -000 WIS -64.6% WNC EPS 0.00 0.00 0.04 000 WNC WC 4.55 4.05 4.55 4.60 4.81 WC -11.0% -0.1% 1.1% 5.6% NW 0.28 0.33 0.34 0.31 0.00 NW -98.9% MOU 7.73 7.83 7.65 7.52 7.56 MOU 1.2% -1.1 % -2.8% -2.2% CA -0 .04 000 0.03 -000 0.04 CA -199.1% AGGUS 4.86 4.66 4.85 4.91 5.04 AGGUS -4.1% -0.2% 0.9% 3.7%

Table 31.

BASE: WSC.MEX CA .MEX GOV.MEX PRI.MEX

NAFTA(t): SE .MEX ESC.MEX WSC.MEX ENC.MEX WC .MEX NW .MEX MOU.MEX CUlEX GOV.MEX PRI.MEX

NAFTA: SE .MEX ESC.MEX WSC.MEX CA .MEX GOV.MEX PRJ.MEX

STATUS QUO: WSC.MEX CA.MEX GOV.MEX PRJ.MEX

NO NAFTA: WSC.MEX CA .MEX GOV.MEX PRJ.MEX

Summary of U.S. to Mexico Diary Commodity Trade Flows Under Alternative NAFTA Scenarios.

FlM SOF AOI ICH 001 BUT FZN MFG IlFT 800 6 8 6 44 115

47 1

FLM SOF AOI ICH 001 BUT FZN MFG /lFT 15 12

1.354 10 14 10 112 30 42 17 2

15 153

FLM SOF AOI ICH 001 BUT FlN MFG /lFT 5 2

830 40 134 53

FlM SOF AOl ICH 001 BUT FlN MFG NFT 652 5 3 5 44 11 0

28 1

FLM SOF AOI ICH OOl BUT FlN MFG /lFT 480 4 4 53 81

89

Table 32. Base Scenario Regional Commodity Production Profile With Percentage Changes From 1990 Actual Production.

BASE: EXOGENOUS 1989-92 AVERAGE US AND US TO MEXICO EXPORTS, CHAVAS SUP~'y ELI\STICITIES.

REGIONAL COMMODITY mODUCTION (million pounds).

FLM SOF ACH ICH OCH BUT FZN MFG NFf NE 2,812 203 73 109 53 57 MA 8,315 799 136 375 138 157 1,647 4ro 158 SA 2,001 73 137 20 50 SE 4,411 115 216 31 78 C 3,932 57 58 250 ESC 2,291 60 112 16 41 WSC 5,798 6 151 519 45 135 ENC 6,732 1,098 76 107 1,72 6 274 218 WlS 2,590 77 988 764 318 129 436 904 "VNC 1,273 88 662 362 81 70 183 329 32 WC 2,000 717 128 170 157 56 530 339 67 NW 2,388 166 318 131 64 59 369 336 20 MOU 2,575 173 54 25 32 41 300 103 26 CA 6,844 461 544 353 197 170 951 556 70 AGGUS 55,563 3,844 2,831 2,257 986 1,320 7,545 3,485 952

CHANGE IN BASE COMMODITY PRODUCTION RElATIVE TO 1990 ACTUAL PRODUCTION.

FLM SOF ACH ICH OCH BUT FZN MFG NFf NE ·0.31 0.75 -100 .00 -100.00 '100 .00 25809 -70.58 -70.35 188.05 l\.-tA 415 39.92 -1.91 -22.05 -2411 63.24 56.28 -17.19 414.20 SA -10.10 -100.00 -100.00 99.72 ·65.27 -81.05 49.83 SE -29.37 -100 .00 -100.00 3286 .18 -73 .59 C 110.38 ·57.53 '100.00 -100.00 ' 100.00 23.5 2 4.70 ·100 .00 -10000 ESC -21.7 4 -100.00 -100.00 ·100.00 -70.78 402.29 44709 WSC 15.64 -98.13 -100.00 168.89 -2016 -6516 224.42 ENC 0.63 95.74 -100.00 -37.48 -100.00 25 .67 60.15 -19.82 683 .53 \>VIS 0 .81 3.75 3.53 10.22 20.65 -59.76 204.68 -0 .12 -100 .00 WNC 0 .91 3.82 -4.27 159.39 228.70 -41.77 6.74 ·11.82 -42.85 WC 9.04 295 .87 -50.98 -27.93 -11.95 ·31.95 5113 62.04 -10.04 NW 23.60 26.86 74.71 90.26 65 .50 -55 .88 42.95 199.55 -89.64 MOU 0.71 3.56 -44.46 0.78 22.24 104.72 6.26 163 .14 70.51 CA 0 .16 4 .44 76.49 2.18 340.79 -39.30 7 .39 ·13.63 -7829

90

VI. SUMMARY AND CONCLUSIONS, LIMITATIONS OF THE

ANALYSIS, AND SOME THOUGHTS FOR FURTHER RESEARCH

A. Summary and Conclusions

The two main objectives of this research were: I) to develop and implement an interregional

competition model (IRCM) for the analysis of trade in dairy products between the U.S . and Mexico;

and 2), to evaluate the likely impacts on aggregate and regional U.S. dairy markets of trade

liberalization between the U.S. and Mexico using this interregional competition model. These

objectives were accomplished by the following four procedures:

1) Characterization of the Mexican dairy sector (Section II);

2) Development of projections of Mexico's milk deficit (demand for imports) and associated

projections for U.S. to Mexico dairy exports under several alternative NAFT A scenarios

(Section III);

3) Development and implementation of an hedonic spatial equilibrium model for analysis of U.S.-

Mexico trade in dairy products (Section IV);

4) Simulation exercises to model the effects of U.S.-Mexico dairy trade liberalization V).

The analysis focussed on measuring the regional effects of U.S.-Mexico dairy trade under alternative

"NAFrA" scenarios. Given data limitations, projections of Mexico's milk deficit (demand for dairy

imports) are generated under alternative supply and demand growth scenarios. Four scenarios in

particular are considered. All of these scenarios generate large changes in U.S. to Mexico dairy

exports relative to the 1989-92 base exports . These changes, in turn, generate somewhat smaller

changes in total U.S. dairy exports relative to the 1989-92 base. Since U.S. dairy exports are a

relatively small part of U.S. total supply, the incremental changes in U.S.-Mexico exports under

alternative NAFTA scenarios are relatively small -- on the order of +1.9% to -1.1% of total U.S.

component supply. Hence, particularly for the more likely NAFrA and STATUS QUO scenarios,

aggregate and regional impacts on the U.S. dairy sector are correspondingly small. Even under the

very optimistic NAFr A scenarios (not all of which were analyzed here), the 3% annual growth in the

import quota on nonfat dry milk, the largest U.S. dairy export to Mexico, will limit the magnitude of

export induced demand shocks. As expected, regions exporting products directly to Mexico are

affected the most; other regions are indirectly affected as the demand shocks of changes in exports

equilibrate through the spatially linked dairy product and component markets .

91

The spatial hedonic pricing framework and the U.S. dairy sector IRCM used for the analysi s

provide fairly comprehensive aggregate and regional effects for farm, wholesale and component

markets. Interregional comparative advantage can be assessed from the regional production,

consumption, and trade flows. Regional component prices indicate that the value of milk fat , protein

and carbohydrates can vary significantly between regions and are likely to shift due with changes in

the magnitude and types of dairy product exports. This type of information could facilitate better milk

pricing and policy analysis in the U.S. dairy sector. The sizable use of filled milk products in Mexico

(i.e ., substituting vegetable fat for milk fat) suggests that the deri ved demand for milk components

ultimately will reflect component supply/demand balance from both animal and plant sources. Thus,

for example, the world oi Iseeds market should eventually impact the value of dairy components.

Analysis of these more broadly defined interregional component markets will likely become more

important for understanding the derived demand for and marketing of milk components. The spatial

hedonics equilibrium framework provides an ideal way to address these issues.

B. Limitations of the Analysis

As with all analyses, several limitations are worth noting. The analysis is not fully dynamic

but rather reflects a static, intermediate run with 3-5 year supply and demand responses . The model

reflects partial rather than general hedonic spatial equilibrium in that it focusses on the dairy sector

and not the broader agricultural sector nor the total economy. This allows considerable regional and

commodity disaggregation at the cost of measuring more general economy wide impacts. Given data

limitations, the impacts of the NAFfA are modeled as exogenous demand shocks to the U.S. dairy

sector via changes in Mexico's excess demand for imports. Hence, NAFfA specifics concerning the

reduction of specific tariff and non-tariff barriers, rules of origin, phytosanitary rules and other

NAFfA dimensions (environmental , labor, etc.) are not explicitly modeled .

World dairy markets are largely ignored; in particular, world market prices and trade

distortions are not explicitly modeled. Ideally, world market dairy prices , production and trade flows

would be solved endogenously. This is a considerably larger undertaking than the current research. In

the final results presented, federal milk marketing order (FMMO) constraints were dropped from all

specifications. These constraints on domestic spatial price surfaces induce considerable interregional

price and trade distortions (see Chavas, Cox and Jesse for a summary of these impacts) . For the

purposes of the current analysis , these FMMO induced spatial price and trade distortions were

dropped.

92

Another structural shortcoming of the current model is that intermediate products such as

skim milk and cream (butter fat) are not explicitly incorporated. This means that interregional

component trade flows occur in terms of final demand products. This structure does not fully reflect

the characteristics of the U.S. dairy sector and future refinements of the model should address this

limitation. Last, aside from the FATUS exports data, the model reflects data up to 1990; it is now

possible to update the model using data up through 1992.

c. Some Thoughts for Further Research

Despite the above limitations, the current analysis has made considerable improvements over

extant regional U.S. dairy models. II) particular, the relatively disaggregate dairy product specification

(9 products versus the more traditional milk for fluid versus manufacturing products) and the hedonic

pricing of the implicit markets for dairy fat, proteins, and carbohydrates (versus the use of whole milk

equivalents) are significant improvements. As well, the current model solves in less than an hour on a

486-66 class PC-clone microcomputer making it usable for short term policy analyses.

Our thoughts on how to further proceed to generate better analysis of the regional effects of

agricultural and trade policies on the U.S. dairy sector can be summarized as follows. First, it is

crucial to endogenize the ROW dairy sector, especially Canada, the European Community, New

Zealand, Australia, Japan, Pacific Rim, South America (Brazil, Argentina, Uruguay as well as Mexico).

These ROW sectors can be sequentially added as additional regions to the current model and/or added

to an aggregate counterpart to the current model that treats the U.S. as an aggregate region (this will

greatly facilitate incorporating endogenous U.S. price supports and government removals). In

particular, these developments will greatly enhance the model's ability to analyze GATT and other

trade liberalization policies. Other improvements include: the addition of intermediate products (whey

proteins, skim milk, and cream); updating the base data using 1992 (versus 1990) data; and, further

disaggregation of the residual manufacturing category to isolate whey products, casein, and

evaporated/condensed milks.

In conclusion, Mexico likely will remain a major U.S. export market for dairy products,

particularly for nonfat dry milk, and fluid and soft products. While exports have historically been a

small part of total U.S . milk supply and utilization, dairy exports are likely to increase in importance

as the U.S. and rest of the world move toward freer trade. It is imperative that the U.S. learn how to

better market and compete in these world markets . Hopefully the model developed and used for this

analysis will prove to be a useful tool for evaluating how best to accomplish these objectives.

93

VII. REFERENCES

Agrobiotec and University of Wisconsin (1992). Informe Final. Estudio de la Cadena de Comercializaci6n de la Leche en Polvo en Mexico. Sometido a la consideracion de la Secretaria de Agricultura y Recursos Hidraulicos de Mexico, Direccion General de Asuntos Internacionales.

American Farm Bureau Federation (1991). Farm Bureau News, Special Issue on NAFT A Effects.

Barichello, R. et.a!. (1991). The Implications of a North American Free Trade Area for Agriculture. Report of the International Agricultural Trade Research Consortium (IATRC).

Brown, Drusilla, Robert Stern (1991). Some Conceptual Issues in the Modeling and Computational Analysis of the Canada-U.S. Free Trade Agreement. Working paper presented at American Agricultural Economics Association Annual Meetings.

Chau vet, M. (1987). Diagnostico del sistema ganadero bovino: carne y leche en Mexico, Alternativas de Desarrollo que ofrece la Biotecnologia. Tesis de Maestrfa, Facultad de Economfa, UNAM, Mexico.

Chauvet, M., Massiew, Y., Castaneda, Y., and Barajas, R. (1992) . "La biotecnologfa aplicada a la producci6n ganadera en Mexico" , en La biotecnologia y sus repercusiones socioeconomicas y politicas. Depto. de SocioJogfa, UAM Azc.; Instituto de Investigaciones Econ6micas, UNAM; Instituto de Investigaciones Sociales, UNAM; Mexico

Chavas, J.P., T.L.Cox, and E.V.Jesse (1993). "Spatial Hedonic Pricing and Trade." University of Wisconsin-Madison, Department of Agricultural Economics Staff Paper No. 367, August, 1993.

Congressional Digest (1992) . "U-S Mexico Free Trade Agreement." Congressional Digest, February 1992, pp . 34-64.

Deaton, Larry, et.a!. (1990). GATT Trade Liberalization: The U.S. Proposal. U .S.D.A., ERS Agriculture Information Bulletin # 596.

Fisher, Robert (1992). "Nafta: A U.S . Perspective." SAIS Review, 12:43-55.

Grennes, Thomas, Barry Krissoff, Jerry Sharples, Julio Estrada, Jaime Gardea, Valdes (1992) . An Analysis of a United States-Canada-Mexico Free Trade Agreement. IATRC Task Force Commissioned Report .

Halberg, Milton , James Cranney, Stephen Smith, and Constanza Valdes (1992) . The Mexican Dairy Economy and Potentials of Liberalized Trade for the U.S. Dairy Industry. Working Paper, Pennsylvania State University, University Park.

Haidacher, R c., J. Blaylock, and L.H. Myers (1988) . Consumer Demand for Dairy Products. U.S. Department of Agriculture, Economic Research Service, Agricultural Economic Report No. 586.

94

Harris, H, and E. McClain (1991). A U.S.-Mexico Free Trade Agreement: Potential Impacts and Implications for the U.S. Dairy Industry. Report to the American Farm Bureau Federation .

Huang, K.S . (1985). U.S. Demand for Food: A Complete System of Price and Income Effects. U.S. Department of Agriculture, Economic Research Service, Technical Bulletin No. 1714.

Kondracke, Morton (1991) . "Mexico and the Politics of Free Trade." National Interest. 25:36-43.

Lustig, Nora (1992). "NAFTA: A Mexican Perspective." SA IS Review. 12:57-67.

Lustig, N. (1993) . Mexico: The Remaking of an Economy. Brookings Institution, Washington.

McDowell, H., A. M. Fleming and R. F. Fallert (1988) . Federal Milk Marketing Orders: An Analysis of Alternative Policies. USDA, ERS, Agricultural Economic Report No. 598.

McDowell, H. , A. M. Fleming and F. Spinelli (1990) . U.S. Milk Markets Under Alternative Federal Order Pricing Policies . USDA, ERS, Staff Report No . AGES 9068.

Mclain, E., and Harris, H. (?). A U.S.-Mexico Free Trade Agreement: Potential Impacts and Implications for the U.S. Dairy Industry. Report prepared for the American Farm Bureau Federation. This version seems to be part of a book, as it starts in page 99.

Morici, Peter (1992). "Free Trade With Mexico." Foriegn Policy. 87:88-104.

National Dairy Promotion and Research Board (1991). The Mexican Dairy Market: Prospects for Value-Added U.S. Products. Arlington, Virginia, 32 pp. plus Appendices.

Sanders, Larry, and Parr Rosson, (1992). "International Trade Policy: Challenges and Opportunities for U.S. Agriculture." Regional Journal. pp. 184-194. Schulthies, B., and Schwart. R. (1991). The U.S.-Mexico Free Trade Agreement: Issues and Implications for the U.S. and Texas Dairy Industry . TAMRC International Market Research Report No. IM-IO-91, 13 pp. plus tables.

Selinski, R. (1992). Measuring Interregional Competition in the U.S. Dairy Sector. M.S. Thesis, Department of Agricultural Economics. University of Wisconsin-Madison.

Selinsky, R., T.L.Cox" and E.V.Jesse (1992). "Estimation of U.S. Dairy Product Component Yields." University of Wisconsin-Madison, Department of Agricultural Economics Staff Paper No. 355, September, 1992.

Schulties, B. and R. Schwart (1991). The U,S,-Mexico Free Trade Agreement: Issues and Implications for the U.S. and Texas Dairy Industry. TAMRC International Market Research Report No. IM-1O-91.

Sumner, Daniel A. (1992). "How NAIT A will affect Agriculture in the United States: Regional Impacts." Regional Journal. pp. 173-181.

95

Takayama, Y. and G. G. Judge (1971). Spatial and Temporal Price and Allocation Models. North Holland Pub!. Co., Amsterdam.

USDA, Foreign Agricultural Service (1993) . World Dairy Situation. November 1992.

USDA, Economic Research Service (1991). FATUS (1990) Foreign Agricultural Trade of the United States. Jan/Feb 1991 .

USDA, Economic Research Service (1992). FATUS (1991) Foreign Agricultural Trade of the United States . JanlFeb 1992.

USDA, Economic Research Service (1993) . FATUS (1992) Foreign Agricultural Trade of the United States. Jan/Feb 1993.

USDA (1993). NAFTA Agriculture Fact Sheets: Commodities and Other Topics. USDA, Washington D.C.

USDA, (1992) . Preliminary Analysis of the Effects of the North American Free Trade Agreement on U. S. Agricultural Commodities. Office of Economics.

Valle Rivera, M. (1992) . Perspectivas de la Produccion Lechera Mexicana ante el Tratado de Libre Comercio. Cuadernos Agrarios NA, Enero-Abril, Nueva Epoca . Mexico .

Vitaliano, P. (nd): Explanation of the Key Provisions Relevant to Dairy in the Text of the North American Free Trade Agreement (NAFTA). Milk Producers Federation.

Villa-Issa, Manuel (1990). "Performance of Mexican Agriculture: the Effects of Economic and Agricultural Policies ." American Journal of Agricultural Economics. 72(3) :744-748.

Williams, Gary and C. Parr Rosson III (1992). "Agriculture and the North American Free Trade Agreement." Choices:The Magazine of Food, Farm and Resource Issues . Fourth Quarter 1992, pp.16-25.

96

VIII. APPENDIX A: MODELING INTERNATIONAL TRADE WITH

HEDONIC PRICING

A. A General Model

Consider the spatial allocation of resources among J regions. The resources consist in a set of

primary commodities and a set of secondary commodities. The primary commodities are not

consumer goods: they are used exclusively as inputs in the production of the secondary commodities.

Each region is a potential producer of the primary commodities, and a potential producer as well as

potential consumer of the secondary commodities. Also, each region can trade both primary and

secondary commodities with any other region . The question, then, is how to analyze the

corresponding competitive spatial market equilibrium. This can be done by developing a market

equilibrium model of resource allocation and trade over the J regions. In this section, we propose a

simple formulation of spatial competitive market equilibrium and set up the notation for the rest of the

paper.

Let N be the number of primary commodities, win denoting the quantity produced of the n-th

primary commodity in region i, and xin being the quantity of the n-th primary commodity used as an

input in the production of the secondary commodities in region i, n = I, ... , N, i = I, ... , J. Let K be

the number of secondary commodities. Denote by Yik the production level of the k-th secondary

commodity in region i, k = I, ... , K, i = I, ... , J. And denote by Zik the consumption level of the k-th

commodity in region i, k = I, ... , K, i = I, .. . , J.

The production of the secondary commodities y will be influenced by the interregional trade in

the primary commodities x and by the production technology transforming the primary commodities

into the secondary commodities y. And the consumption of the secondary commodities z will be

influenced by their corresponding production y and by the interregional trade in the secondary

commodities. Denote by Tijn ~ 0 the export of the n-th primary commodity from region i to region j,

and by Tjin ~ 0 the import of the n-th primary commodity from region i to region j. Similarly, denote

by tijk ~ 0 the export of the k-th secondary commodity from region i to region j, and by tjik ~ 0 the

import of the k-th secondary commodity from region i to region j. Using this notation, T iin ~ 0 will be

interpreted as the quantity of the n-th primary commodity that is both produced and used in the

production of the secondary commodities within the i-th region (i.e. not exported to other regions).

And tiik ~ 0 will be interpreted as the quantity of the k-th secondary commodity that is both produced

and consumed in the i-th region .

97

The production of the secondary commodities y involves two kinds of inputs: the primary

commodities x, and other inputs denoted by the vector v. The technology involved in the

transformation of the primary inputs x into the secondary inputs y in region i is given by the

production possibility set Fi:

( [ )

where Xj = {xin: n = 1, ... , N} is the vector of primary inputs, Yj = {Yik: k = I, ... , K} is the vector of

secondary outputs, and Vj is the vector of other inputs (besides xJ used in the production of Yi' i = I ,

... , J. Equation (I) simply establishes the technological relationship between inputs (vj, xJ and feasible

secondary outputs Yi in each region. We assume that the production possibility set Fj is nonempty,

closed, and convex .

Under competition, let ri denote the vector of market prices for the inputs Vi' i = I, ... , J. Then

efficient use of the inputs Vi requires that they are chosen in a cost minimizing way as follows:

(2)

where G;CXi' y) is a (restricted) cost function measuring the cost of optimal input use Vi' conditional on

primary inputs Xi and on output levels Yi, i = I, ... , J. We will assume throughout the paper that the

cost function G;Cx i, yJ is a decreasing function of Xi' and an increasing function of Yi.

The trade tlow constraints across regions take the form:

Win ~ LTijn, j=1

J

LTjin ~ Xin' j=1

)

Yik ~ L tijk , j= I

J

L tjik ~ Zik· j= [

Equation (3a) states that the quantities of the primary commodities domestically produced and

non -exported (Tiin) as well as exported from region i (Li"j Tijn) cannot be larger than its domestic

98

(3a)

(3b)

(3c)

(3d)

production win. Equation (3b) says that, in region i, the primary inputs xin cannot be greater than the

non-exported domestic production (Tiin) plus the total imports from other regions (Ij>'i Tjin). Equation

(3c) states that the quantities of the secondary commodities domestically produced and nonexported

(tiik) as well as exported from the i-th region (Ij>'i tijk) cannot be greater than the quantity produced in

that region Yik. Finally, equation (3d) states that the quantity of the secondary commodity consumed in

the i-th region Zik cannot be larger than the sum of its nonexported domestic production (tiik) plus its

imports from other regions (Ij>'i tjik).

A market equilibrium must satisfy the technology constraints (I) and the trade flow constraints

(2). It must also allocate resources in an efficient manner both across commodities and across space.

One way of capturing this efficiency is to consider the following quasi-welfare function :

Yew, x, y, z) = L {Di(z) - Si(W) - Gi(x;, y)}, (4) i=l

where w = {win: i = 1, .. . , J, n = I, ... , N}, x = {xin: i = I, ... , J, n = I, ... , N}, y = {Yik: i = I, ... , J, k = I, .. . , K}, z = {Zik: i = I, ... , J, k = I, ... , K}, and G;(xi, y;) is the cost function defined in equation (2) .

The quasi-welfare function Y defined in (4) involves three sets of terms: D, Sand G. The terms D is

interpreted as a measure of the total benefits to the consumers purchasing the secondary goods z. And

the terms S is interpreted as the cost of producing the primary commodities w. Given the cost

function G defined in (2), it follows (S + G) is the total cost of production of the secondary goods z in

the absence of trade. Then, the quasi-welfare function Y in (4) is a measure of net social benefits (i.e.

consumer benefits (D) minus total production cost (S + G» in the absence of trade.

We will make the following assumption:

Assumption A: The function Yew, x, y, z) is differentiable and concave in (w, x, y, z), and satisfies:

as/awin = Pin' :2' 0, n = I, .. . , N,

aD/aZik = Pikd :2' 0, k = I, ... , K,

where Pin' is the price received by the producers of the n-th primary commodity in region i, and Pikd is

the price paid by the consumers of the k-th secondary commodity in region i , i = I, ... , J.

Assumption A states that the quasi-welfare function is well-behaved, that the market prices of the

primary commodities are equal to their marginal cost of production, and that the market prices of the

secondary commodities are equal to their marginal consumer benefit. These conditions are consistent

99

Ii

with competitive market equilibrium, where prices reflect the marginal valuation of the corresponding

goods.

Let Cijn 2: 0 be the unit cost of transportation of the n-th primary commodity from region i to

region j. Similarly, let Cijk 2: 0 be the unit cost of transportation of the k-th secondary commodity from

region i to region j . We will assume throughout the paper that Ciin = 0 and Ciik = 0, i.e. that

transportation costs are zero in the absence of trade. Then, consider the following optimization model:

max T {V(w, x ,y, z) - "T C. -" t ·kC ··k W.X.y.l .. 1 ~ IJn IJn L IJ ' IJ' i.j.n i.j.k (5)

equations (3), w2:0, x2:0, y2:0, z2:0, T2:0, t2:0).

Equation (5) maximizes the quasi-welfare function V(w, x, y, z) net of transportation cost, subject to

the trade flow constraints (2).

We will now show that, under assumption A, the optimization problem (5) generates the

competitive spatial market equilibrium. Under assumption A, problem (5) is standard concave

programming problem, subject to linear constraints. Provided that it has a bounded solution, it can be

alternatively characterized as the saddle point of the following Lagrangean:

L = V(w, x, y, z)-L TijnCijn-L tijkCijk i.j.n i ,j.k

+ L a ill [win -L TijnJ i.n

+ L ~ ill [L TJin -\nJ i ,n j

+ EY ik [Yik -E tijkJ iJ j

+ L 6 ik [L tjik -ZikJ, i .k

where a 2: 0, ~ 2: 0, Y 2: 0 and 6 2: 0 are Lagrange multipliers corresponding to the constraints (2).

Under assumption A, the Kuhn-Tucker conditions associated with the optimization problem (5) provide

necessary and sufficient conditions for the solution to (5). They are:

dL dS

dW -r+ain S; 0, w. 0, (6a) III w.

In ,n = 0, w. > 0,

III

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aG - I _A < 0 X - 0 ax. fJ in - , in - ,

In (6b)

= 0, Xin > 0,

(6c)

(6d)

(6e)

= 0, T ijn > 0,

(6f)

= 0, tijk > 0,

aL = w. -" T ~ 0, U. = 0 aU. In ~ IJn In

In J (6g)

= 0, Uin > 0,

;AL = LTjin -X in ~ 0, ~in = 0

Pin J (6h)

= 0, ~in > 0,

aL ay = Yik -L tijk ~. 0, Yik = 0

ik 1 (6i)

= 0, Y ik > 0,

aL -as: =L tjik -Zik ~ 0, b ik = 0 U ik j (6j)

= 0, b ik > o.

From assumption A and equation (6a), it follows that u in can be interpreted as the market price

for the primary commodity win in region i. Indeed, given win> 0, (6a) and assumption A imply that

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a in = Pin'· Similarly, it follows from equation (6d) that bik can be interpreted as the market price for

the secondary commodities Zik in region i. To see this, note that given Zik > 0, (6d) and assumption A

imply that bik = Pikd

.

Equations (6e) and (6f) characterize the transportation arbitrage conditions expressed in terms

of spatial prices. Note that, given C iin = Ciik = 0, it follows from (6e) and (6f) that ~in = a in (or Yik = bik) whenever T iin > 0 (t iik > 0). In this case, prices paid by producers as well as consumers for a

pmticular commodity in a particular region are necessarily equal. This implies that ~in can be

interpreted as the market price of the primary commodity Xi1\" Similarly, Yik can be interpreted as the

market price of the secondary commodity Yik' Equations (6e) and (6f) state that commodity prices

between any two regions cannot differ by more than the corresponding unit transportation cost. And

in the case where trade takes place (i.e., T ijn > 0, t ijk > 0, for i 1: j), then the spatial price difference

between the importing region and the exporting region must be exactly equal to the unit transportation

cost. Note that an implication of (6e) and (6f) is

(7a)

and

(7b)

Equations (7a) and (7b) mean that the equilibrium conditions for trade necessarily imply zero profit

from transportation activities. Thus, any departure from (6e) and (6f) cannot correspond to an

equilibrium situation since it would provide incentives for transportation firms to alter trade patterns.

In this sense, equations (6e) and (6f) characterize trade efficiency.

The Lagrange multipliers ~ and Y measure the shadow price of the trade constraints (3b) and

(3c). More specifically, ~in measures the marginal social cost of one unit of the primary commodity

xin' i = I , ... , J, n = I, ... , N. Then, equation (6b) simply states that, at the optimum, the marginal cost

of the commodity Win 2': 0) is equal to its marginal value (-oG/OXin 2': 0) whenever xin is positive. But

we have seen that ~in can be interpreted as the market price of Xin in region i. It follows that our

model is consistent with a competitive market equilibrium, where market price is equal to the marginal

cost of each commodity at the optimum.

Similarly, Yik measures the marginal social value of one unit of the secondary commodity Yik' i

= I, ... , J, k = I, ... , K. Then, equation (6c) states that, at the optimum, the marginal value of the

commodity (Yik 2': 0) is equal to its marginal cost (OG/OYik 2': 0) whenever Yik is positive. We have

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seen that Yik can be interpreted as the market price of Yik' Thus, our model is consistent with a

competitive market equilibrium, where market price is equal to the marginal value of each commodity

at the optimum.

Finally, equations (6g), (6h), (6i) and (6j), together with the complementary slackness

conditions with respect to the corresponding Lagrange multipliers, are simply the trade flow constraints

corresponding to (2). They represent the feasibility conditions for interregional trade.

These results indicate that the optimization problem (5) provides a representation of a

competitive market equilibrium both across commodities and over space. By considering both trade

and the transformation of primary commodities into secondary commodities, they provide a

generalization of the Samuelson-Judge-Takayama approach to spatial market equilibrium (see

Samuelson; Takayama and Judge, pp. 107-121). As such, they should be useful in analyzing the

spatial implications of resource allocation in a marketing channel.

B. Spatial Hedonic Prices and Trade

The model just developed can be refined when the production of the secondary commodities

from the primary commodities involves well identified characteristics. In this case, the allocation of

the primary characteristics both among secondary commodities and across space is of interest. This

issue can be explored in the context of a Lancasterian model with trade. This section examines the

implications of our analysis for spatial hedonic prices of characteristics under competitive markets and

trade.

Assume that the N primary commodities involve S characteristics, the s-th characteristics being

denoted by rs, s = I, ... , S. Each primary as well as secondary commodity in each region has a given

composition in terms of its underlying characteristics . In region i, let ains ~ 0 denote the quantity of

the n-th characteristic per unit of n-th primary commodity xin · And let biks ~ 0 denote the quantity of

the n-th characteristic per unit of the k-th secondary commodity. Assume that the characteristic

composition of each commodity is constant, i.e. that ains and biks are constant. Under this assumption

of constant proportions, consider that the production technology Fi in region i (as given in equation

(I)) takes the specific form:

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mIn N a

{ ~ inl ~ Xink --, n~1 bik,

(8)

where Xink is the quantity of the n-th primary input used in the production of the k-th secondary output

in region i, which satisfies the identity: xin = 2.k Xink . The production technology (8) assumes fixed

proportions with respect to each of the characteristics used in the production of the secondary output

Yik' 2.n Xink ain" s = I, ... , S. However, given the general function fik(v ik , Xik), it imposes no a priori

restriction on the elasticities of substitution among the various inputs (Vik ' Xik). Under the technology

(8), the cost function given in equation (2) becomes:

(9a)

subject to

K N

L Y ik bib = L \n ain" (9b)

k.1 n~1

1 = I, ... , J, s = I, ... , S. Equation (9b) ensures the balance in the allocation of component s in region

i. It corresponds to a linear Lancasterian model where each commodity exhibits fixed proportions, but

where the components are perfect substitutes in their allocation among commodities (see Lancaster).

Then, the optimization problem (5) becomes:

with corresponding Lagrangean:

maxw.x y.z.T .1 {L [Di(z)-Si(W)-gi(\' y)J i

- L Tijn Cijn - L tijk Cijk i.j.n i.j.k

: equations (3) and (9b),

w~o, X~O, y~O, z~o, T~O, t~O},

(10)

where Ai, ~ 0 is the Lagrange multiplier for the s-th characteristic constraint (9b) in region i. At the

optimum, the A'S provide a measure of the shadow price, or implicit market price, of the S

components. This will give a convenient basis for evaluating component pricing in a spatial market

equilibrium framework.

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L = ~ [D(z.)-S.(w.)-g.(x, y.)] L I I I I "-'I I I

-LTijnCijn -L tijkCijk i.j.n i.j.k

+ L \s [L \n ains -L Yik bikJ i.s n k

+ ~ a [w. -~ T. ] ~ 10 In ~ Iln I,n J

+ L ~ in [L Tjin -\n] I.n J

+ LY ik [Yik -L tijk] i.k j

+ L 0ik [L tjik -Zik], i.k j

Except for (6b) and (6c), the Kuhn-Tucker conditions associated with the above Laoranoean to b

satisfy equations (6). Equations (6b) and (6c) take the form:

ago LS .

___ I + A. a. _R.. < 0 x a IS lOS tJ an - , in

X in s=1

= 0, (I I a)

= 0, Xin > 0,

( lib)

Interpreting tv.s as the shadow price of the s-th component in region i, expressions (1Ia) and (lIb)

indicate how the shadow valuation of components affects market equilibrium. Equation (II a) involves

the marginal value of the n-th primary input xin ' which is equal to the marginal value associated with

inputs Vi (-ag/axin 2: 0), plus the marginal value of the S components (Is Ais Ci;ns 2: 0). It simply states

that, at the optimum, marginal value is equal to the price of the primary input ~in' as found in a

competitive market equilibrium. Equation (II b) involves the marginal cost of the k-the secondary

product Yik' which is equal to the marginal cost (ag/aYik 2: 0), plus the marginal cost of the S

components (Ik Aik bib 2: 0). It shows that, at the optimum, marginal cost is equal to the market price

Yik' Again, these results are consistent with resource allocation under competitive equilibrium.

Finally, the following additional Kuhn-Tucker condition must be satisfied:

which simply represents the component balance constraint for component s in region i, i = I, "., J, s =

I, .'" S. This set of equations provides a convenient characterization of spatial competitive

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