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1 ECONOMIC ANALYSES ON FLORIDA FARM LABOR MARKET ISSUES By JAMILLE PALACIOS A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 201 3
2 2013 Jamille Palacios
3 To my family, especially to my husband A. Noel Lasanta, my daughter Janelly Lasanta Palacios, and the memory of my mother Rev. Carmen I. Rivera, Ph.D.
4 ACKNOWLEDGMENTS I thank God for helping me get through this process. There are several people and institutions I would like to thank because all that I learned and accomplished with this dissertation is in great part due to them. I especially thank Dr. Emerson, my dissertation committee chair, for all his help; he is knowledgeable and professional, and I learned a lot from his mentoring. My gratitude is extended to the rest of my dissertation committee members, Dr. Roka, Dr. Kenney, and Dr. VanSickle, for their valuable contrib utions to this dissertation. Special thanks goes to Dr. VanSickle, Director of the Agricultural Trade and Policy Center at the University of Florida, and Carmen I. Alamo, at the University of Puerto Rico (Mayaguez Campus Agricultural Extension, for gran ting me a Ph.D. research assistantship I also express my gratitude to three institutions which financially helped support this dissertation. First, I thank the American Society for Hispanic Economists (ASHE) for the ASHE Summer Dissertation Fellowship Award (SDF) ; secondly, RMA for their funding support; and Ivy Tech Community College in Lafayette for development support. Thank you to the Food and Resource Economics Department (FRED) at the University of Florida for having a very talented group of profe ssors admitting a very competitive group of students and hiring excellent staff. One of the department staff I would like specially thank is Carol Fountain who helped me edit the dissertation. I am glad I was given the opportunity of pursuing and achie ving my dream of becoming a doctor in Economics while surrounded by excellent people in the field. These people include my fellow students who became close friends and motivated me while I pursued a Ph.D.
5 Thank you also to all the individuals who helped me get the data required for this dissertation. These individuals are Angela Perry, Manager of the IT Operations Information Technology in the Warrington College of Business at the University of Florida; John Simanski, Office of Immigration Statistics at the Department of Homeland Security (DHS); Mark Aitken in the Statistics Division at the National Agricultural Statistics Service; and Eric Wilhelm at the Bureau of Labor Statistics (BLS). In the personal aspect, I would like to thank my husband Noel Lasan ta for his help, patience, understanding, companionship, love, and faith; I love you. I have to thank my daughter, Janelly, for being so good understanding that I had to take time for this; you gave me the strength to continue. I would like to especially thank my mother, Carmen I. Rivera, and my father, Jose J. Palacios, for all their support and help, and for being great role model s for me; I will always love you and remember all you did for me. Finally, for their emotional support and motivation, I tha nk my siblings, Josy, Ito, Jahny, Jami, and Beba; my aunt Hayi, uncle Teddy, grandmother Monica and extended family ; I love you all very much.
6 TABLE OF CONTENTS P age ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 8 LIST OF FIGURES ................................ ................................ ................................ ........ 10 ABSTRACT ................................ ................................ ................................ ................... 11 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 13 Problem Overview ................................ ................................ ................................ .. 13 Dissertation Format ................................ ................................ ................................ 18 2 MULTI LEVEL MARKET MOD EL APPLICATION TO TH E CIW BOYCOTT AND RESULTING AGREEMENT WITH FOOD RETAILERS ................................ ......... 23 Opening Remarks ................................ ................................ ................................ ... 23 Multi level Market Model ................................ ................................ ......................... 26 ..................... 28 Boycott Multi level Market Model development ................................ .......... 29 Parameter values ................................ ................................ ....................... 32 Boycott model results ................................ ................................ ................. 34 Sensitivity analysis of Boycott Multi level M arket Model ............................ 37 Wage Supplement Agreement Analysis ................................ ........................... 38 Agreement Multi level Market Model development ................................ .... 40 Agreement Multi level Market Model results ................................ .............. 41 Sensitivity analysis ................................ ................................ ..................... 43 Welfare analysis ................................ ................................ ......................... 45 Coalition of Immokalee Workers Agreements ................................ .................. 45 Market Segmentation Analysis ................................ ................................ ......... 47 Se gmented Multi level Market Model development ................................ ... 50 Segmented Multi level Market Model results ................................ ............. 51 Concluding Remarks ................................ ................................ ............................... 52 3 AGE BARGAINING STRAT EGY AGAINST AGRICULTURALLY RELAT ED CORPORATIONS AND SUPPLEMENT AGREEMENTS ................................ ................................ ................................ ....... 61 Overview ................................ ................................ ................................ ................. 61 Theoretical Framework ................................ ................................ ........................... 63 Dynamic Game of Complete Information ................................ ................................ 66
7 Game Assumptions and Methods ................................ ................................ .... 66 Game Development ................................ ................................ ......................... 67 Fourth stage ................................ ................................ ............................... 67 Third stage ................................ ................................ ................................ 68 Second stage ................................ ................................ ............................. 70 First stage ................................ ................................ ................................ .. 70 Game Remarks ................................ ................................ ................................ 71 Strategy Decision Empirical Analysis ................................ ................................ ...... 72 Event S tudy M ethodology ................................ ................................ ................. 73 Probit M odel M ethodology ................................ ................................ ................ 77 Event S tudy and P robit R esults ................................ ................................ ........ 78 Chapter Summary ................................ ................................ ................................ ... 80 4 EMPIRICAL STUDY ON FARM LABOR MARKETS ................................ ............... 88 Overview ................................ ................................ ................................ ................. 88 Recent Immigration Related Policies ................................ ................................ ...... 89 Policy, Worker Efforts, and Farm Labor Markets: Theoretical Framework .............. 94 Model and Data ................................ ................................ ................................ ...... 99 Structural Model Results ................................ ................................ ................ 106 The Reduced Form ................................ ................................ ........................ 109 Implications for Immigration Reform ................................ ............................... 111 Summary of Findi ngs ................................ ................................ ............................ 113 5 CONCLUSIONS ................................ ................................ ................................ ... 129 APPENDIX A DERIVATION OF RETAIL LEVEL MARKET MODEL ....................... 133 B DERIVATION OF GROWER LEVEL MARKET MODEL ........................ 138 C DERIVATION OF SEGMEN TED MULTI LEVEL MARKET MODEL ..................... 143 D CORRELATION MATRIX F OR VARIABLES IN THE FARM LABOR MODEL ..... 149 REFERENCE LIST ................................ ................................ ................................ ...... 150 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 158
8 LIST OF TABLES Table page 1 1 Main farm labor group efforts ................................ ................................ .............. 20 1 2 Recent Florida bills to require the use of E Verify ................................ ............... 21 2 1 Parameter values ................................ ................................ ............................... 55 2 2 Boycott MMM results ................................ ................................ .......................... 55 2 3 Boycott results on tomato and labor market ................................ ....................... 55 2 4 BMMM sensitivity analysis ................................ ................................ .................. 56 2 5 AMMM results ................................ ................................ ................................ ..... 56 2 6 AMMM sensitivity a nalysis ................................ ................................ .................. 56 2 7 Segmented agreement effect on endogenous variables ................................ .... 57 2 8 Segmented agreement results on tomato market ................................ ............... 57 2 9 Segmented agreement results on labor market ................................ .................. 57 2 10 Segmented labor market results ................................ ................................ ......... 57 3 1 CIW Consumer boycott efforts against agriculturally related corporations ......... 83 3 2 Seemingly unrelated regression results ................................ .............................. 84 3 3 Linear hypothesis test ing of the parameters ................................ ....................... 86 3 4 Probit estimates: agreement decision ................................ ................................ 86 4 1 Number and percentage of Braceros by main user states, 1953 64 ................. 116 4 2 Foreign agricultural guest workers admitted into United States, 1942 76 ......... 117 4 3 H 2 and H 2A certifications, 1971 2006 ................................ ............................ 118 4 4 Number and percentage of H 2 certifi cations, by main user states, 1976 86 ... 119 4 5 H 2A certifications, by main user states, 1991 2004 ................................ ........ 119 4 6 H 2A certifications, by leading sender country, 1991 2004 .............................. 120 4 7 Endogenous variables and data source ................................ ........................... 121
9 4 8 Exogenous variables and data source ................................ .............................. 122 4 9 Dummy variables ................................ ................................ .............................. 123 4 10 Demand and supply equation estimates ................................ ........................... 123 4 11 Single equation estimates of farm labor model ................................ ................. 124 4 12 System of Florida farm labor model: 3SLS estimates ................................ ....... 125 4 13 Reduced form estimates ................................ ................................ ................... 126 D 1 Correlation matrix for all variables in the labor market model ........................... 149
10 LIST OF FIGURES Figure page 1 1 Hourly wage rate ratio: agricultural to nonagricult ural. ................................ ........ 22 2 1 Market level boycott and supplement agreement effects ................................ ... 58 2 2 ................................ ......... 59 2 3 ................................ .......... 59 2 4 Segmented tomato market effects ................................ ................................ ...... 60 2 5 Segmented labor market effects ................................ ................................ ......... 60 3 1 Dynamic game of complete information tree ................................ ...................... 87 4 1 Deportable aliens, 1967 2008 ................................ ................................ ........... 127 4 2 Percent of unauthorized farm workers, 1989 2006 ................................ ........... 127 4 3 2007 ................................ ....... 128
11 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy E CONOMIC ANALYSES ON FLORIDA FARM LABOR MARKET ISSUES By Jamille Palacios May 201 3 Chair: Robert D. Emerson Major: Food and Resource Economics The objective of this dissertation is to analyze selected public policies dealing with farm labor availabil ity (i.e., guest worker programs and immigration enforcement activities), and farm worker efforts (i.e., strikes and boycotts) regarding low wage rates. Public policies of interest are those affecting the labor supply from foreign sources. Farm worker ef forts to be evaluated are boycotts and subsequent agreements between farm labor groups and food retailers stipulating harvest worker wage supplements. Three distinct essays tackle the objective. In the first essay, a multi level market comparative statics model is used to evaluate the economic effects of consumer boycotts and resulting wage supplement agreements, as exogenous shocks, on related market levels, specifically retail, grower, and farm labor. The analysis considers the fact that the wage supple ment agreement covers a small segment of the labor force. Measurement of net welfare gains and evaluation of the results with international trade in tomatoes are also incorporated. Results show negative long run effects on worker wage rates due to a redu ction in the demand for the farm product tied to the labor group.
12 In the second essay, this dissertation evaluates the effectiveness of the farm worker strategy of boycotts to secure wage supplements. A dynamic game of complete but imperfect information is developed. The game theory application explains the strategic decisions by the two players: farm labor groups and target agriculturally related retailers or firms. Event study results show no negative financial effects o n retailers from boycott events and subsequent agreements between farm labor groups and target companies. This might be due to counterstrategies taken by target retailers. Probit model estimates show predicting factors for the agreement. A third essay is devoted to the analysis of te mporary work force programs and immigration enforcement initiatives. An econometric model of aggregate supply and derived demand for farm workers is used for the analysis. Results show how as immigration policies become more or less restrictive, the supp ly of foreign farm labor decreases or increases, resulting in new equilibrium wage rates.
13 CHAPTER 1 INTRODUCTION Problem Overview Seasonal farm labor is highly dependent on foreign workers For the most part, growers hire documented foreign labor, includ programs established by the government) and undocumented farm workers. The National Agricultural Workers Survey data from 1993 to 2007 indicate that the ratio of undocumented farm workers to documented workers has been between 41 and 52 percent (Carrol l Saltz, and Gabbard 2009). In the economics literature, there is considerable controversy about how the hiring of foreign workers affects domestic wage rates. While there are arguments that immigrant workers reduce the earnings of domestic workers (Borjas 2003), there are also studies that suggest the complementarity between domestic and foreign workers minimizes the effect of the increased supply of labor (Ottaviano 2007; Hanson 2009; Card 2005). An extension of t he latter view is that additional migrants lower migrant wage rates while increasing real incomes of host country labor and capital. Since at least the 1950s, the ratio of hired agricultural worker average wage rates to that of non agricultural workers has continuously been below 0.6 (Figure 1.1) despite the numerous efforts led by farm workers to improve their wage rates and working conditions through strikes and boycotts (Table 1 1). Most worker efforts are boycott campaigns launched against agricultural ly related firms to gain representation for workers at the grower level; some of the agriculturally related firms are public
14 corporations. 1 Boycotts seem to be an effective tool for farm workers because some of them end with signed labor contracts that in clude wage increases or supplements and stipulations regarding working conditions (i.e., limiting certain pesticide exposure, providing better housing, eliminating slavery and child labor, and providing health insurance). It is important to note that many of the farm labor strikes and boycotts have not been economically evaluated. efforts. Wyeth (1974) conducted an analysis on the table grape boycott in California and found that si gned labor contracts resulting from boycott efforts were not long lived. Palacios, Emerson, and VanSickle (2006) evaluated the boycott efforts launched by the Coalition of Immokalee Workers (CIW) against Taco Bell and the boycott termination agreement sti pulating a wage supplement for a segment of the tomato labor market. These signed agreements stipulated that an extra penny per pound of fresh Florida tomatoes purchased by the food chains would go directly to the pickers as a wage increase. 2 The payment is a supplement by a third party in addition to what the employer pays the worker. Findings showed negative earning effects for workers not covered by the agreement and positive effects on earnings of workers covered by the agreement but with lower leve ls of employment. minimum wage legislation, such as the 1978 amendment to the Fair Labor Standards 1 objective is to protect employees and employers, encourage collective bargaining, and restrict inappropriate labor management practices Some states have labor relation laws that do not exclude agricultural workers; among them are the Wisconsin Employment Peace Act of 1939, the Oregon Employment Relations Board established in 1977, the California Agricultural Labor Relations Board (ALRB) created in 1975. 2 The employer serves as an intermediary in the payment from the retailer to the worker by issuing the payment to the designated workers
15 Act (FLSA). The FLSA amendment eliminated the difference between the federal minimum wage rate for agriculture and nearly the entire labor market. Gardner (1981) found that it increased farm worker wage rates, but at the cost of reducing employment. Perhaps the positive wage rate outcomes from farm worker efforts and minimum wage legislation are offset by market impacts from other efforts dealing with farm labor availability. For instance, the establishment/restrictions of guest worker programs or relaxation/tightening of immigration enforcement initiatives may, if effective, inc rease/decrease farm labor supply and demand. Theoretically, changes in the number of available workers shift the farm labor supply, resulting in new equilibrium wage rates and levels of employment. Farm labor market effects of policies with mismatched ou tcomes regarding farm labor availability or incorporating price floors may be negligible. The Immigration and Nationality Act of 1952 established the H 2 program to admit foreign contract workers to the U.S. labor force for a short period of time (120 da ys), especially to cover peak seasonal needs. This program was later divided into two different worker programs, the H 2A and H 2B, under the provisions of the 1986 Immigration Reform and Control Act ( IRCA ) (Heppel and Papademetriou 1999). To avert any n egative implications on domestic farm wage rates, employers requesting the H 2 or H 2A certifications are required to provide housing, transportation, and insurance, and to pay certain wage rates. Farm workers under these programs must be t of the Adverse Effect Wage Rate (AEWR) in effect at the time the job order is placed, the prevailing hourly or piece rate, the agreed upon collective bargaining rate (CBA), or the f ederal or s
1 6 Labor 2010). Cornelius (1986) evaluated the AEWR contribution to wage differentials between domestic and foreign workers. He found that the presence of the AEWR reduce d any impact of farm foreign workers on the Florida citrus wage bill. IRCA attempt ed to establish s trict sanct ions on employers to prevent hiring of illegal immigrants and reduce the large number of illegal immigrants in the United States. I authorized farm workers was low (see Figure 4 2) This proportion naturally fell after proportion of unauthorized to authorized worker s steadily increase d between 1996 and 2006. The large number of undocumented immigrants in the United States gave rise to other state and federal legislations. One of these is the addition of Section 287(g) to the Immigration and Nationality Act (INA) i n 1996. This section allows agreements between the Secretary of the Department of Homeland Security (DHS) and state and local law enforcement agencies that permit selected appropriately trained officers to perform immigration law enforcement functions und er the supervision of the Immigration and Customs Enforcement investigative agency (ICE) Nowa day s about 25 states have enforcement agencies under this program. In 1999, the Immigration and Naturalization Service ( INS ) and the Social Security Administr ation (SSA) implemented a pilot program to allow employers to confirm work eligibility of newly hired employees. Other important immigration control initiatives have been implemented, particularly after the 9/11 terrorist attack. These include the Secure
17 Border Initiative (SBI), implemented in 2005; the Secure Fence Act, enacted in 2006; and the IMAGE program, initiated in 2007. In 2007, this program was renamed E Verify. The use of E Verify is voluntary except for certain employers with federal contra cts or subcontracts and employers in certain states who are required to participate. Recently, a bipartisan effort to approve an immigration reform bill in c ludes E V erify. I n the s tate of Florida, several bills to require every employer to use E Verify h ave died after consideration (Table 1 2 ) Currently, Florida does not require E Verify (farm workers only have to show a valid agriculture is a way to reduce this mar ket labor supply, particularly the number of unauthorized workers. At the state level, several legislati ve bills have recently been proposed or passed that could potentially affect farm labor markets. These include Oklahoma SB 908, Arizona S.B. 1070, Utah S.B. 47 and S.B 466, Georgia H.B. 87 (252 Act), Alabama H.B. 56, Florida S.B. 2040 and H.B. 7089. 3 At the federal level, relevant legislati ve bills have been the Agricultural Job Opportunities, Benefits, and Security Act of 200 9 ( AgJOBS ) (H.R. 241 4 and S 1038 ); Comprehensive Immigration Reform Act of 2007 (S. 1639); Comprehensive Immigration Reform for America s Security and Prosperity Act of 2009 (H.R. 4321); SAVE Act of 2011 (H.R. 2000); and the H 2A Improvement Act of 2011 (H.R. 1720 and S. 852). Som e of the objectives of these are to improve temporary 3 the President Obama administration; the U.S. District Co urt ruled in favor of the Enforcement Act of 2011.
18 worker programs, increase border security, tighten enforcement, and to both help and integrate recent immigrants into U.S. society The purpose of this research is to evaluate the wage and employment e ffects on the farm labor market resulting from recent public policies and farm worker efforts directed toward a selected group of workers rather than all farm workers. The public policies are toward foreign workers in agriculture. The worker efforts are collective actions involving boycotts of third parties ending with agreements stipulating a wage supplement to a segment of the labor market. More specifically, the objectives of this dissertation are as follows : Analyze the economic effects of farm labor supplemental agreements on three agriculturally related markets: final food retailers, tomato growers, and farm labor. Evaluate redistribution effects Economically explain the strategies used by the farm worker groups leadin g boycott efforts and by retailers and target firms in reaching agreements with farm worker groups. Evaluate the effect of recent farm worker efforts, foreign worker programs and immigratio n enforcement initiatives on Florida labor market. Dissertation Format The proposed dissertation analyses are presented in three essays. The first essay is devoted to analyzing the economic effects of consumer boycotts launched by farm labor groups against agriculturally related corporations and the resulting agreemen ts on related markets. For this analysis, a multi level market economic model is applied. The analysis includes an evaluation of welfare effects, including the redistribution of gains
19 and losses across those affected markets. The boycott efforts and sup plement agreement are treated as exogenous shocks to the multi level market equilibrium. Results show how boycott efforts and agreements negatively affect the input markets at the retail and farm level s although wages increase for workers covered in the wage supplement agreement. In the second essay, an event analysis is conducted to look at the impact that boycotts and their resulting agreements have on the financial value of the target firms. Game theory and the event study results for the events are used to explain the effectiveness of the farm worker strategies for securing the agreements and the decision process of the target firms. The third essay is an empirical study showing Florida labor market effects from temporary foreign worker programs and enforcement activities
20 Table 1 1. Main farm labor group efforts Targets Strategy Farm group a Farm product State Boycott d ates s tart e nd b Schenley Vineyards Strike/boycott UFW Grapes CA 1966 1966 DiGiorgio Fruit Strik e/boycott UFW Fruit/ v eg etables CA 1966 1966 Perelli Minetti Winery, Christian Bro & Almaden Strike/boycott UFW Grapes CA 1966 1970 Giumarra Vineyards Strike/boycott UFW Grapes CA 1967 1970 Bud Antle Lettuce Boycott UFW Lettuce CA 1 970 n/a Coca Cola Strike/boycott UFW Citrus FL 1972 1972 Coca Cola Strike/boycott UFW Citrus FL 1975 n/a G rapes, lettuce, & Gallo Wine Boycott UFW Grapes/lettuce CA 1973 1978 Campbell's Soup/Libby Strike FLOC Tomato/ c ucumber OH/MI 1978 1985 Sun Harve st Strike/boycott UFW Lettuce CA 1979 1979 Bruce Church Inc. Boycott UFW Lettuce CA 1979 n/a Lucky Stores Boycott UFW Lettuce CA 1980 n/a Grapes Boycott UFW Grapes CA 1983 1992 Campbell's Vlasic Boycott FLOC Cucumber MI 1983 1986 Kraemer Farms Strike PCUN Cucumber OR 1991 n/a NORPAC Foods, Inc Boycott PCUN Fruit/ v eg etables OR 1992 2002 Strawberry industry Strike PCUN Strawberry OR 1995 1995 Nature's Fountain Strike PCUN OR n/a 1998 Mount Olive Pickle Co Boycott FLOC Cucumber NC 1997 2004 Taco Bell (only) Boycott CIW Tomato FL 2001 2005 E&J Gallo Winery Boycott UFW Grapes CA 2005 2005 Rest of Yum! Brands Boycott CIW Tomato FL 2005 2007 McDonald's & Chipotle Boycott CIW Tomato FL 2005 2007 SIC 5812 fast food Boycott CIW Tomato FL 2005 n/a B urger King Boycott CIW Tomato FL 2005 2008 Wendy's Boycott CIW Tomato FL 2005 n/a Subway Boycott CIW Tomato FL 2005 2008 Chipotle (after IPO) Boycott CIW Tomato FL 2006 20 12 Whole Foods Boycott CIW Tomato FL 2008 2008 Walmart Boycott CIW Tomato FL 200 8 n/a Winn Dixie Boycott CIW Tomato FL 2008 n/a Kroger Boycott CIW Tomato FL 2008 n/a Safeway Boycott CIW Tomato FL 2008 n/a Supervalu Boycott CIW Tomato FL 2008 n/a Publix Boycott CIW Tomato FL 2008 n/a Sysco Boycott CIW Tomato FL 2008 n/a US Food s ervice Boycott CIW Tomato FL 2008 n/a Aramark Boycott CIW Tomato FL 2009 20 10 Chartwells Boycott CIW Tomato FL 2009 2009 Sodexo Boycott CIW Tomato FL 2009 2010 Ahold (USA) Boycott CIW Tomato FL 2009 n/a RJ Reynolds Boycott FLOC Tobacco NC 2007 n/a a UFW (United Farm Workers), CIW (Coalition of Immokalee Workers), FLOC (Farm Labor Organizing Committee), and PCUN (Pineros y Campesinos Unidos). b End dates as of December 2012.
21 Table 1 2 Recent Florida bills to require the use of E V erify Bill # Title Introducer Last action S 1638 Verification of Employment Eligibility Altman 3/9/2012 S Died in Judiciary H 1315 Verification of Employment Eligibility Harrell 3/9/2012 H Died in Government Operations Subcommittee S 7066 Unauthorized Aliens Judiciary 3/16/2011 S Submit as committee bill by Judiciary (SB 2040) S 0518 Verification of Employment Eligibility Hays 5/7/2011 S Died in Judiciary H 0691 Verification of Employment Eligibility Harrell 5/7/2011 H Died in Government Operations Subcommittee S 1896 Enforcement of Laws Evers 5/7/2011 S Died in Judiciary S 2040 Enforcement of Immigration Laws Judiciary 5/7/2011 H Died in Messages H 7089 Enforcement of Immigration Laws Snyder 5/7/2011 H Died pending review of CS Source: http://www.flsenate.gov/Session/Bills.cfm
22 Figure 1 1. Hourly wage rate ratio: agricultural to nonagricultural. (Note: The ratio for the years 1950 to 2003 was obtained using the farm labor average wage ra te from the Farm Labor Reports ( USDA NASS ) and the hours and earnings in private nonagricultural industries reported in the 1991 and 2003 Economic Report of the President (Tables B 44 and B 47, respectively) ( EOP 1991 and 2003) ; data are based on the Stan dard Industrial Classification (SIC). The 1964 to 2008 ratio uses the farm labor average wage rate from the Labor Reports (USDA NASS ) and the hours and earnings in private nonagricultural industries reported in the 2009 Economic Report of the President ( Table B 47) (EOP 2009) ; data are based on the North American Industry Classification System (NAICS)).
23 CHAPTER 2 MULTI LEVEL MARKET MODEL A PPLICATION TO THE CI W BOYCOTT AND RESULTING AGREEMENT WITH FOOD RETAILERS Opening Remarks The recent boycott act ivity initiated by the Coalition of Immokalee Workers (CIW) in Florida against food related firms is examined in this essay. The purpose of the boycott was to increase wages for Florida tomato harvest workers. Some of the target food related firm s Yum! reached agreements with the CIW to pay a wage supplement. 1 The wage supplement would go to workers ; it consists of an additional penny per pound of tomatoes being sold to retailers (food companies). Although these CIW agreements are unique, there have been other boycott efforts conditions, such as the well known United F arm Workers (UFW) boycott of California wine and table grapes in the 1960s. After the UFW target ed sensitive branded products, labor contracts were negotiated with a number of wineries. In spite of the California grape boycott, the Wyeth (1974) analysis suggests that it had little effect on overall consumption. Distributors used the boycott to extract a lower price from the growers, leading some larger growers to sign labor contracts that were short lived. Another well known labor group effort was the UF W strike against California lettuce growers in 1979. Based on the findings of Carter et al. (1981), this strike 1 Up through 2013 there h ave been o ther food retailers targeted by the CIW The list includes Chipotle, Sysco, US Food Service, Aramark, Compass, Sodexo, and Walmart. ark, Compas, and
24 resulted in increased, rather than reduced, profits for the industry This suggest s that this strategy might not be the best when the number o f firms in the industry is large and there are many farm workers available. Under this circumstance, boycotts seem to be more effective than strikes because into t hose final products are not readily identifiable. The objective of consumer boycotts information content consumers receive and how they value it. 2 T arget firms may agree with the activists because of an actual boycott economic impact or simply because of a fear of potential effects. The intent of recent CIW boycott efforts is to suffici ently affect the demand so that retailers agree to their requests. In the tomato boycott, the CIW is requesting a wage supplement agreement under which retailers pay an extra penny per pound of tomatoes as additional income to workers harvesting the tomat oes. The challenge has been to segment the tomato labor market into labor inside and outside the agreements between the CIW and retailers involved in the boycott. The Florida Tomato Growers Exchange (FTGE) argued that the established agreements between the CIW and the food corporations might be violating anti trust laws (Wides Munoz 2007). According to Wides Munoz (November 24, 2007), the F T GE prohibited its members from supplying tomatoes to retailers participating in agreements with the CIW, along wit h a $100,000 fine for members in violation. This led to a counterargument that the F T GE prohibitions violated anti trust laws. After much debate, 2 For more on the information role in consumer boycotts, see Innes (2006).
25 the FTGE argument was dropped and the money collected went into an escrow account to be distributed to the w orkers. 3 The anti trust argument was based on a unique characteristic of the CIW tomato boycott termination agreements. These agreements stipulate a supplemental wage by the retailers. Under the terms of the agreements, the employers (Florida tomato growers) of the harvest labor are not a party to the agreement because bargaining is only between the buyers of their product and the labor group. These agreements are unique because previous boycott termination agreements have not included wage supplemental payments directly from the retailers to the farm workers. The questions addressed in this essay focus on the economics of the CIW tomato boycott efforts and the potentia l impacts of wage supplemental agreements on the farm labor and industries involved. Analogous to public policy instruments, CIW boycotts and wage supplement agreements introduce exogenous shocks to the market system. level market m odel is commonly used to measure policy effects on firms at one industry level resulting from an exogenous shock at another level of the level markets is used to address the q uestions here. 4 This model allows for linkages between food related corporations, growers, and farm labor groups involved in boycott efforts and wage supplement agreements. 3 While the money was held in escrow, workers picking tomatoes going to retailers in agreement did not rec eive the supplement wage. In 2011, sixteen tomato pickers filed a lawsuit in the Miami Dade Circuit Court against Burger King and Subway for two years of unpaid supplement wages from the Fair Food Agreement. 4 ts to shocks for competitive firms follows from Hicks (1932), Muth (1965), and Floyd (1965).
26 The model is first applied to the CIW tomato boycott efforts and then applied to the wage supplement agreement. The wage supplement analysis includes welfare gains, agreement stipulations, and segmented labor issues. Multi level Market Model The structural model is constructed based on four basic assumptions : (1) all Florida tomato es go to retailers that are either boycott targets or under a wage supplement agreement; (2) all retailers use fresh tomatoes as inputs; (3) there is no other source of fresh tomatoes; and (4) the supplement is directly transferred to workers as additional earnings. The assumptions can be relaxed to consider less restrictive scenarios; the first assumption is relaxed in a later section considering a segmented model. Other important assumptions of the model are that the industries at the retail and grower levels are competitive and optimizing agents characterized by only two inputs and one output. 5 T he retail and grower level agents are assumed to maximize their respective profit functions, identified by the superscripts ( r ) and ( g ) : Max. (2 0a) Max. (2 0b) In the retail level maximization problem, (2 0a) the output, final product for the final consumer, is represented by ( Y ); the two inputs are tomatoes, represented by ( X ), and an aggregate of other neces sary inputs for the final product, ( c ) Corresponding prices of the inputs c and X and the output Y are P c P x and P Y In the grower level maximization problem, (2.0b), the output is the retailer input, tomatoes ( X ) and the two 5 The assumption on the number of inputs and outputs is for convenience to simplify the model development. This can be relaxed to consider other interesting situa tions.
27 inputs are farm labor ( L ) and the remaining necessary farm inputs aggregated as ( z ) P L P z and P x are the prices of inputs L and z and the output X (used as an input at the retail level). The i are the Lagrange multipliers, which are variables in the optimization problem fr om the constraint associated with well behaved production functions at both levels, and 6 Equations 2 1a t hrough 2 5a and 2 1b t hrough 2 5b represent the structural model of food retailer and tomato grower u nder profit maximization, respectively. (2.1a) ( 2 2a) ( 2 3a) ( 2 4a) ( 2 5a) ( 2 1b) ( 2 2b) ( 2 3b) ( 2 4b) ( 2 5b) product sold by the target food retailers. The presence of a boycott changes the final produc t demand equation (Equation 2 5a) to In this equation, B is the 6 This is from the final assumption that the technology for both industries is linear homogeneous.
28 boycotted quantity of the final product. Any changes in the retail market from boycott efforts impact input demands since they are determined at the retail level. Likewise, changes in these input markets affect any input derived demands. The following section graphically explains the boycott effects on Florida tomato markets. Analysis of the C oalition of I mmokalee W Boycott Efforts The Florida fresh tomat o input market is of particular interest because of its relationship with the labor group leading the boycott efforts. Recall that it is presently assumed that all tomatoes going to the final product at the retail level come from Florida and that there is no other source. A boycott shifts the demand curve for the retail level final product to the left from D 1 to D 2 in Figure 2 1A. The new demand intersects with the supply at a lower market equilibrium point. At the new retail market equilibrium point the quantity, Y b and price, P Y b are lower than Y e and P Y e which were established before the boycott efforts. As a consequence of this demand shift at the retail level, the input markets are negatively affected. 7 Tomato growers in this market are get ting a secondary impact from the boycott presence. Lower final product quantities and prices from the boycott presence cause a shift in the demand curve for tomatoes from D 1 to D 2 in Figure 2 1B. Consequently, the price per unit and units demanded of tom atoes decrease in Figure 2 1B from P x e to P x b and X e to X b respectively. These boycott effects on retail level input markets carry over into the grower level input markets. Specifically, farm input demand shifts the demand 7 The demand effects at the grower level come from the first and second assumptions, namely all Florida tomatoes go to retailers and all retailers u se fresh tomatoes. Considering these assumptions, the demand is determined at the retail level. The demand shift corresponding to the final product causes a reduction in the quantity demanded for the inputs, including tomatoes.
29 for tomato labor to the left, from D 1 to D 2 in Figure 2 1C. The new hourly wage rate for tomato labor is P L b which is lower than the pre boycott wage of P L e Also, the tomato market employment level is negatively affected. Before the boycott efforts, employment was at L e ; in the presence of successful efforts, the employment level is reduced to L b (Figure 2 1C). Recall that workers in this market are the ones leading the boycott efforts; nevertheless, they are negatively impacted while leading the efforts. However, the idea beh ind leading these boycott efforts is to pressure target companies to sign a wage supplement agreement in exchange for termination of the efforts that would improve their earnings. Until target firms sign these wage supplement agreements, boycott efforts c ontinue to negatively affect tomato labor markets. The long run boycott effects are obtained as percentage changes by mathematically solving a system of equations corresponding to the two profit maximization problems in Equations 2 0a and 2 0b. The follo wing section develops the Boycott Multi level Market Model (BMMM) system of equations which is later solved to get long run boycott effects. Boycott Multi level Market Model development To develop the BMMM, the long run effects of the boycotts and agreem ents are examined as percentage changes so that the variables are dimension free (see Appendi ces A and B for the derivations ). The resulting percentage change equations determine percentage changes ( E ) in the endogenous variables of the system (the j th ou tput and i th input quantities and prices at both levels [ Y, X, c, L, z, P Y P X P c P L and P z ]) given any exogenous shock to the system. ( 2 6a) ( 2 7a)
30 ( 2 8a) ( 2 9a) ( 2 10a) ( 2 6b) ( 2 7b) ( 2 8b) ( 2 9b) ( 2 10b) Note that the system of equations includes factor shares, K i supply elasticities, e i ; final product demand elasticity, ; and Allen elasticities of substitution (Allen elasticities), The K i in Equations 2 6a t hrough 2 8a and 2 6b t hrough 2 8b are the relative factor sh ares of the inputs at each level. With linear homogeneity, the sum of the relative input shares within a level must equal one, and These boycott effects on the equilibrium quantities and prices are obtained by modifying the system of equations to include successful boycott efforts as exogenous shocks. The new set of equations is divided by the percentage change in the boycott exogenous shock, EB=dB/Y By doing this, Equations 2 5a t hrough 2 10a and 2 5b t hr ough 2 10b become:
31 ( 2 6a) ( 2 7a) ( 2 8a) ( 2 9a) ( 2 10a) ( 2 6b) ( 2 7b) ( 2 8b) ( 2 9b) ( 2 10b) The numeric solution to this system is the percentage change in the endogenous variables given the exogenous shock which here is a percentage change in boycott efforts. The parameter value s in the system are assumed based on knowledge of the industry. The following section provides industry information used for establishing parameter values.
32 Parameter v alues The assumed parameter values to solve the BMMM are in Table 2 1. The demand elast icity for the retail level products is assumed to be fairly elastic. This assumption is based on the market structure in which many retailers operate. Typically, food markets are highly competitive; the food industry has relatively no barriers to entry. 8 Supply elasticities of the factors of production are also assumed to be fairly elastic based on abundant availability. In the case of farm labor, for instance, there is a continuing inflow of immigrant workers. The Allen elasticities of substitution ar e assumed to be equal to one. The share of tomatoes to the total cost at the retail level is assumed to be rather According to the Taco Bell website ( www.tacobell.com our company: interesting facts: cheese, and pinto beans (3,855 million total pounds in 2006). In addition, they buy roughly 10 million pounds of Florida fresh tomatoes annually ($4.4 million, or $0.437 per pound, in 2006). 9 Utilizing this annual tomato spending fact and revenues reported by 0 0 46 percent There are no specific data on fresh tomato annual purchases Whole Foods. 8 According to the U.S Census, there are 132,364 firms (under the limited service restaurant NAICS 722211) with 204,311 establishments. The number of incorporated firms under the 722211 NAICS is fairly large; according to Compustat data there are 937 of these (retrieved from http://wrds.wharton.upenn.edu/). 9 In 2006, Yum! Brands reported revenues totaling $9,561 million (Yum! Brands, Inc. 2006). The tomato price used for the cost share calculation was retrieved from the USDA webpage: http://www.nass.usda.gov/QuickStats/index2.jsp
33 million p ounds of fresh tomatoes annually ; for a total estimated cost of $8.7 million Applying that estimate to revenues reported ($21.6 billion) share is rather small (approximately 0 0000 4 percent indicates 10 t of Yum! Brands (around 30%). 11 spokesperson repo rted an agreement cost of around $300,000 (Weddekind 2008). That is about 0 0 45 percent of their 2008 revenues ($ 666 1 M) in the United States, as reported in the Burger King Holdings, Inc. Annual Report filed on August 27 2009. There is no specific to mato cost share for Subway or Whole Foods; however, some information may indicate a relatively low cost share. For instance, at Subway, tomatoes are listed as one of the nine different vegetables served ; Whole Foods sells tomatoes as a fresh perishable fo od and as an ingredient in some of its prepared food products. 12 Given this information, the value for tomato cost share is assumed to be a 10 http://www.m cdonalds.com/us/en/food/food_quality/nutrition_choices.html 11 This menu is electronically available at http://www.bk.com/cms/en/us/cms_out/digital_assets/files/pages/MenuNutritionInformation_April2013_1.p df 12 This information is available at http://www.subway.com/nutrition/nutritionlist.aspx
34 The system of ten equations corresponding to the BMMM is solved numerically us ing the assumed parameter values. 13 The numeric results from exogenous shocks associated with boycott efforts are examined in the next section. Boycott m odel r esults The percentage changes in each of the endogenous variables with respect to a percentage change in the boycotted quantities are given in Table 2 2. Note that all of the boycott effects on the endogenous variables are negative. The presence of a the BMMM results, the long run equilibrium is at a lower final product price, P Y b and quantity, Y b To be more specific, a one percent increase in boycotted quantities of Y P Y e by 10 percent and quantities, Y e by 0 .50 p ercent As the BMMM results indicate, increases in boycotted quantities of Y also affect input market levels. The tomato market is of particular interest because tomato workers are the ones leading the boycott efforts. The tomato market effects, from a one percent increase in boycotted quantities of Y are represented by and in Table 2 2. Assuming a one percent change in boycott quantities where EB = 1, the corresponding price and quantity effects are and By using these results as approximations for their discrete counterparts, for example, the 13 The solutions are readily calculated from X=A 1 (b), where A is the n x n parameter values matrix, X is a column vector of percentage changes for the endogenous variables, and b is the column vector of n 1 zeros and a 1 corresponding to the exogenous sh ock in question, using matrix software such as Gauss.
35 changes in the tomato market variables are determined usi ng pre boycott prices and quantities ( Table 2 3). The CIW started leading boycott efforts in 2001. According to the Florida Agriculture Statistics Service, in 2000, Florida growers produced 1,594,650,000 pounds; and the average price was $0.3688 per pound. T hese numbers are used as the tom ato quantities and prices prevalent before the boycott to demonstrate the changes in the tomato market variables. According to the BMMM results, a one percent increase in boycott efforts, ceteris paribus reduced the quantity sold to 1,586,676,750 and the per pound price to $0.3684. From these boycott effects, total revenues were reduced by 0 .5995 percent Likewise, a one percent increase in boycott efforts, everything else being constant, ccording to the BMMM results in Table 2 2, and ; that is, a one percent increase in by 0 .10 percent and 0 .50 percent respectively. The BMMM r esults and agricultural wage rate and employment data were used to approximate a post boycott wage rate and employment. Again, the percentage change in boycott, EB is 1; therefore, EP L = 0 .10. Using these to approximate discrete changes as above, P L b a nd the remaining variables are approximated as ( 2 11) The results in Table 2 3 show post boycott wage rates, P L b obtained from Equation 2 11, assuming reasonable values of P L
36 wage rate was esti mated to be $8.53 at the time that the boycott efforts began; this wage rate is reduced to $8.52 because of a one percent increase in the boycott 14 The BMMM result for employment, is likewise used for the post boycott employment r esults in Table 2 3. For a one percent change in boycott success, EB equals one. Given that EL= 0 .50, it represents the change in the units of labor employed from a one percent increase in boycott success. Therefore, the post boycott units of labor, L b c an be approximated by expressing this change in discrete form as in Equation 2 12. ( 2 12) According to 2000 NASS labor expenses data, around 150,574 farm labor hours were paid in Florida that year ; that is the assumed value for L e in Equation 2 12. After a successful boycott effort of increasing by one percent the boycotted quantities of Y the number of hours employed would decline approximately to 149,821. With the approximations of labor units and per unit cost, it is possible to calculate total wage bill effects from an increase in successful boycotts under ceteris paribus conditions. The total post boycott wage bill, P L b L b is $1,276,696 (Table 2 3). This is down from $1,284,396, which translates to 0 .5995 percent l ower labo r costs for income; a wage supplement payment from target retailers would be expected to offset 14 Using boycotting quantities of target food products, Y The result is calculated as : The pre boycott wage was estimated using USDA, NASS, Farm Labor Reports.
37 t his loss. The effects on Florida farm labor markets from wage supplement agreements are analyzed later, but before that, the next section discusses the BMMM solutions from assuming different parameter values. Sensitivity a nalysis of Boycott M ulti level M arket M odel Solving the BMMM using different parameter values yields different results. Changing the demand elasticity for the final product only changes the magnitude of the impact. The original values for the supply elasticities of the two inputs at t he grower level and the input other than tomatoes at the retail level are all equal. Therefore, a change in any one of these supply elasticities will result in different effects on the endogenous variables of interest. An interesting exercise is to consi der lower elasticity values for farm inputs. A reasonable change in one of these parameters is the supply elasticity associated with the non labor input at the grower level, e z The input other than labor at the grower level may be assumed to be less el astic in supply because it includes land, which tends to be an inelastic factor of production. The value for e z is reduced from 5 to 2 to examine its effect on the solution. Notice that it is still assumed elastic; this is due to the fact that this param eter includes many other inputs that tend to be elastic in supply. Likewise, the labor supply elasticity is reduced from 5 to 2 because arguments about labor availability are contentious. Studies on agricultural labor markets have found supply elasticity coefficients ranging from 0 .35 to 6.14 (Emerson, Walker, and Andrew 1976; Morgan and Gardner 1982; Mehra 1984). The BMMM results from a sensitivity exercise are shown in Table 2 4. Note that the less elastic the grower level input supply, the more ne gative the boycott effects on tomato price and the less negative on quantity.
38 Wage Supplement Agreement Analysis T he wage supplement agreement is paid by retailers to tomato workers Under this agreement, retailers pay a wage supplement over the price p aid per pound of Florida tomatoes where V equals 1+v Lowercase v represents a monetary wage supplement over the per unit price of tomatoes, s/P X paid to workers under the agreement. Whenever there is no supplement agreement, v is zero, V equals one, and T his is incorporated in the multi level market model through the tomato demand equation (Equation 2 2a) which becomes By definition, the demand price for tomatoes is determined at the retail level. In Figure 2 1B, this price is represented by P x d It includes a portion that goes to Florida tomato growers, named the supply price, P x s and another portion, if any, paid to workers, P x s v; this is mathematically expressed in Equation 2 13. ( 2 13) The difference between the demand and supply price per pound of tomato is the wage supplement paid from retailers to workers, Clearly, the demand price per pound of tomatoes equals the supply pri ce when there is no supplement agreement, v=0 In contrast, the presence of a supplement reduces the per pound price of tomatoes received by growers, P X s in Figure 2 1B. The additional payment to tomato workers from retailers becomes an input subsidy at the grower level, creat ing a rightward shift i in Figure 2 1B. The grower level market changes from the presence of the supplement agreement affect the demand for tomato labor. Specifically, the reduced tomato pri ce received by
39 growers in the presence of a wage supplement agreement shifts the demand for tomato labor from D 1 to D 2 (in Figure 2 1C). This downward demand shift is not the only labor market effect from the supplement; in addition, there is a change in the supply coming from the last term in Equation 2 13. The last term in Equation 2 13 multiplied by workers, This supplement is only part of the ove earnings. As shown in Equation 2 14, total earnings for tomato workers, w is the hourly market wage rate paid by grower s P L d plus the per hour wage supplement from retailers, ( 2 14) Without the wage supplement agreement, v=0 and the second term in Equation 2 14 drops out In that case, the supply wage per unit of labor, w equals the demand price per hour of labor, P L d From this fact, the labor supply (Equation 2 4) is modified to include the wage supplement paid by food retailers, L=£(w). 1C, is now a function of total earnings, w The equilibrium wage rate, P L d decreases, but w and level of employment, L increase. The effects o f these changes in input supply and demand at the different market levels are illustrated graphically in Figure 2 1C. These results are validated by the multi level market model which is modified in the next section to examine the effects of a restricted supplemental agreement on equilibrium quantities and prices at the retail, grower, and labor market levels.
40 Agreement Multi level Market Model development In this section, the system of equations in the MMM is modified to include the new exogenous shock at the retail level from the supplement paid per pound of Florida ication is the substitution of E quations 2 2a and 2 4b to and L=£(w), respectively. E quations 2 1a t hroug h 2 5a and 2 1b t hrough 2 5b are totally differentiated and converted to percentage changes (see Appendi ces A and B) to become the Supplemental Agreement Multi level Market Model (AMMM) below. ( 2 6a) ( 2 7a) ( 2 8a) ( 2 9a) ( 2 10a) ( 2 6b) ( 2 7b) ( 2 8b) ( 2 9b)
41 ( 2 10b) As previously d efined, the letters e c e L and e z in Equations 2 9a, 2 9b, and 2 10b represent supply elasticities of the inputs c, L, and z, respectively. The in Equation 2 10a is the demand elasticity for Y the retailer output. Finally, the and in Equations 2 7a, 2 8a, 2 7b, and 2 8b represent the elasticities of substitution between the inputs while producing the corresponding market level outputs, Y and X The system is numerically solved using the previously established assumptions and values of the system parameters in Table 2 1. The results of the AMMM, displayed in Table 2 5, confirm the graphical analysis; this is discussed in the following section. A greement Multi level Market M odel r esults The results of the AMMM are for a scenario in which all Florida tomato buyers are in agreement with the CIW given the established basic assumptions and parameter values T h e results of this restricted model show no market impact for the final product at the retail level (Table 2 5). 15 According to the AMMM results, retailers pay the same price per pound of tomatoes, but the amount given to growers is reduced and directly transferred to workers. AMMM results for and in Table 2 5 show that a one percent increase in the unit cost of tomatoes to farm workers results in a proportional decrease in the price for tomatoes. T he AMMM result for wage rate is negative, while t he one for employm ent is positive, The interpretation of th e s e result s is the following: a 15 An assumption not established here that could change this result is that a wage supplement agreement sold.
42 one percent increase in supplement agreement would decrease wage rate by 2.69 percent and increase employment by 1.69 percent In addition to the wage rate, workers receive a supplement (Equation 2 14). T hese total earnings are subject to change as the supplement changes. This is not an explicit AMMM result, but it is easy to elicit by expressing earnings as percentage change. The first step to achieve this is to totally differentiate Equation 2 14; subsequently perform some algebraic manipulations; and finally divide it through by EV. Evaluate at v =0; by definition, dv = dV ( 2 17) V =1 when v =0; the second term i s the inverse of the labor share, K L 1 The final step is to divide Equation 2 17 by EV and plug in the corresponding parameter and AMMM values. In other words, 0 .34 is the percentage from a percentage change in a restricted agreement. Note that it is positive; therefore,
43 true, even though the post agreement e quilibrium wage rate paid by growers is lower than before the agreement. AMMM results clearly show negative effects for the growers and positive effects for the workers from restricted wage supplement agreements ending boycott efforts, especially in the case in which all Florida tomato buyers pay a wage supplement to workers. Changing parameter values or relaxing assumptions of the model produces new results, with different welfare gains and losses generated for retailers, growers, and workers. The foll owing section examines a sensitivity analysis. Sensitivity analysis In the presence of a supplement payment, the supply of labor responds to both wage rate changes and to per unit supplement payment changes. By construction, labor responsiveness to wage rate and supplement changes are the same. Table 2 6 shows the implications of the supplement on the endogenous variables using the original parameter values and new values for e z and e L each change under ceteris paribu s conditions. With a less elastic supply of aggregated inputs e z a percentage change in the supplement will change the price and quantity of tomatoes by 1.11 percent and 0 .12 percent respectively, a slightly larger effect on tomato price than quantity of tomatoes. In the case of labor a percentage change in the supplement will leave the effect on grower level wage rate and employment unchanged. A less elastic supply for labor creates an impact at the retail market level. The effects on the final product price and quantity are small, although in opposite directions. Inputs, other than tomatoes, at the retail level are now more affected. The effect on the price for tomatoes is slightly less
44 affec ted. Because tomato harvesting is a small component of the total labor market in the presence of a continuing supply of foreign workers suggests that a relevant case to consider is a perfectly elastic labor supply. In this case, e L = and P L is taken as given, so =0 The result of a perfectly elastic supply of labor at the grower level include s an increase in employment for tomato workers (15.152%). Under this scenario a percentage change in the supplement increase s the equilibrium quantity of t omatoes by 5.125 percent, but decrease s the price of tomatoes by 5.566 percent Because the percentage reduction in price is greater than the resulting percentage increase in quantity, growers are worse off. Results a t the retail level indicate a signif icantly lower cost per unit of tomatoes, with an associated substantial increase in equilibrium quantity of tomatoes purchased. by 0.14 percent while quantities incr change is greater than its price change; thus a percentage change in the wage supplement, assuming perfectly elastic labor supply, benefits retailers. Altering the demand elasticity for the final product at the retail level, ceteris paribus does not greatly affect the results. Inequality of the input supply elasticities at the grower level results in only minor quantitative differences rather than qualitative differences. The perfectly elastic labor supply does make a large quantitative difference and it is rather relevant in Florida agricultural labor markets.
45 The next section examines gains and losses in the context of economic welfare based on the parameter assumptions in the first row of Table 2 6 since they are believed to be the most defensible. Welfare analysis The welfare effects at the tomato grower market level are illustrated in Figure 2 2. Area (a)+(b) in Figure 2 2 i s the consumer surplus before the supplement agreement; note that the consumer here is the producer at the retail level. After the restricted supplement agreement is applied, the consumer surplus is still (a)+ (b). However, area (c)+(d)+(f)+(g) in Figure 2 2 is paid to workers, not growers. The growers surplus before the agreement is area (c)+ (h)+ (d) in Figure 2 2. After the agreement is applied, grower level surplus change in this market level is (i) (d) ( c ). Areas (a)+(b)+(c)+(d) in Figure 2 3 represent the pre agreement grower surplus in the tomato labor market; note that the growers are the consumers in this market. The post su rplus gain from the supplement agreement is (f)+(j) (a) (d). F now includes the extra rent from the supplement paid by the retailers. This rent is represented by the area (c)+(d)+(e)+(f)+(g)+(h)+(i)+(j)+(k) This area is equivale nt to area (c)+(d)+(f)+(g) in Figure 2 2. Their net gains in Figure 2 3 are area (c)+(d)+(e)+(h)+(i)+(j)+(k)+(m). Coalition of Immokalee Workers Agreements The CIW agreements with food retailers stipulate that retailers will pay a cent per pound of tomato es to Florida tomato workers. T hat is 2. 71 percent of the pre
46 agreement equilibrium price per pound of tomato, which was $ 0 .369 In the context of the model here, v =2. 71 With the base parameter values and assumptions the demand for tomatoes is unaffect ed, but growers in the agreement receive $0.359 (from $0 .3 69 (1 .01*2.71)) per pound of tomatoes. The wage supplement agreement hurts growers, but aids tomato workers Tomato workers receive a market wage below the pre agreement wage, but also an extra cen t per pound of tomatoes purchased by retailers. The post CIW agreement hourly wage rate paid by growers declines to =$7.91= ( 1 0 .0 269 x 2.71) $ 8.53 T otal earnings include the wage supplement in addition to the market wage, ; ; The new value for was just calculated, and the supplement amount will depend on the average product of labor (X/L) in the presence of the supplement. Assuming the average product of labor to be 665.25, the supplement becomes 0.01*(1 0.0169*2.71)*665.25 = $ 6.36. Combined, the worker hourly earnings are now $ 7.91 + $ 6.36 = $14.27, a substantial increase above the original $8.53 (Table 2 9 ). T rate are positive, while effects from boycott eff orts are negative. Depending on the may be offset by the positive effects of restricted wage supplement agreements. Grower s ed agreement than in the presence of boycott efforts.
47 The results above are for retailers in the agreement. However, not all buyers of Florida tomatoes have signed wage supplement agreements with the CIW. The evaluation of the wage supplement agreement in the presence of labor market segmentation is the task for the next section. Market S egmentation A nalysis This section includes an analysis of a wage supplement agreement in which assumption number one (all Florida tomatoes go to retailers in a wage su pplement agreement) is relaxed. Instead, only some of the Florida tomatoes are purchased by retailers under a wage supplement agreement. There are two tomato markets: agreement and non agreement. This is a situation more in tune with the CIW agreements The agreements include specific provisions on the use of the additional cent per pound of tomatoes, such that only tomato workers who pick tomatoes supplied to agreement retailers receive the wage increase. Published information states that the signed agreements between the B urger King, Subway, and Whole Foods) establish a procedure to safeguard the extra income transfer to workers. 16 The effect of the stipulations is to segment the tomato and labor marke t into agreement and outside the agreement. The tomato and labor market segments are illustrated in Figure s 2 4 and 2 5, respectively. 16 According to the Hundley (2006), workers are receiving $10 more per week, thanks to the Taco Bell agreement with Florida tomato growers. However, t he F T GE threatened their members, which total about 90% of all tomato growers in Florida, with a $ 100,000 fine for participating in these agreements. Black (2009) indicate d that because of the FGE threat to growers, the supplement payments, estimated by the CIW to be $1.5 million, we re being held in escrow until a resolution between the F T GE and the C IW wa s reached. In November 2011, the FTGE eliminated fines and agreed to pass on to workers the extra http://www.ciw online.org/FTGE_CIW_joint_release.html )
48 In both figures, panel A represents the market whereas panel B represents the agreement segment. The lower case letter s on the graphs represent the analytic sequence of market events, although all occur simultaneously. T determining the market price of tomatoes, P x d and the market wage rate prior to the wage subsidy, P L e The market price of tomat oes is the same for both markets, so demand for tomatoes by retailers in the agreement (where V =1), initially at the market determined price. The same is true in Figure 2 5 for the labor market: the wage rate is determined by supply and demand for labor at the market level yielding the market and out the agreement tomato segment. Now suppose, the supplement agreement changes and then V Figure 2 4, retailers pay P x V workers receive w in Figure 2 5, as their total earnings, which includes the supplement and the market wage rate. At P x V a greement retailers are only interested 4. The demand for agreement tomatoes shifts to the left, from D a to D a Agreement tomatoes are a part of the total tomato market; therefore, the total d emand curve also shifts to the left, from D 1 to D 2 and the new (lower) price inclu sive of the wage subsidy, P x With a lower tomato price (for all tomatoes), the total demand for labor in Figure 2 5 shifts to the left, from D 1 to D 2 The result of this labor demand shift is a new equilibrium market reflected by
49 A t the ne w market equilibrium, farm wage s decrease, now at P L d 1 the subsidized wage falls from w to w 1 ; and employment level increases from L a to L a The supplement also alters the total labor supply curve in Figure 2 5 With potential earnings (including the s upplement) considerably above the market wage, workers are willing to supply more labor at a given market wage T he labor supply curve shifts to the right This establishes a lower equilibrium market wage arries over to the agreement portion of the labor market. Finally, the labor supply shift results in a rightward shift in the total supply curve in Figure 2 4 with yet a lower market price for tomatoes. Even though the equilibrium price for tomatoes in both segments is lower, the equilibrium quantity in the non agreement market is greater. Under the original parameter assumptions, the increase in the quantity of non agreement tomatoes will offset the d ecrease in the agreement market, leaving the market quantities unchanged. 17 The equilibrium quantity of workers after the agreement is greater than before the agreement. T omatoes going to other retailers are picked by the rest of the workers in the Florid a tomato labor force. These workers, in the non agreement market segment, are only receiv ing a per unit market wage lower than the one previous to the agreement. The earnings differ for the two groups, but even so, t he overall to mato wage rate effect is positive T he magnitude of the effect depends on the number of units in each segment. 17 This does not happen in cases with differing input supply elasticities for L and z.
50 The following section develops the Segmented Multi level Market Model (SMMM) to analyze the wage supplement agreement effects in presence of m arket segmentation. The solution of the SMMM shows, in one step, the previous analytical discussion on market adjustments from exogenous shocks from the supplement agreement and provides the final result. Segmented Multi level Market Model development T o analyze the wage supplement agreement effects in the presence of market segmentation it is necessary to modify t he previously developed AMMM The first step to modify the AMMM for market segmentation is to define the market segments. The proportion of workers in each segment is determined at the grower market level, with agreement and non agreement output as expressed in Equation 2 19, ( 2 19) The proportion of output in each segment is given by dividing Equation 2 18 by X resulti ng in and for Correspondingly, the labor segmentation may be expressed as: ( 2 18) Total labor hours available in the Florida tomato market are represented in Equation 2 18 by letter L From L some are randomly selected for the agreement sector, L a ; these units earn w inclusive of the subsidy, as defined in Equation 2 14. The rest of the workforce is the non agreement segment, L n These units of labor would work in the secondary sector getting the equilibrium wage rate paid by growers, P L d T he proportion of labor in the agreement segment is The remaining labor in the labor
51 force is Under the maintained assumption of constant returns to scale, and maximization problem, Equation 2 0a, includes the two segments : The correspondin g the following: Based on these maximization problems two changes are made to the AMMM (see Appendi x C ) to form the SMMM The changes are to Equations 2 7a and 2 9b: ( 2 7a) ( 2 9b) As in the previous section, the labor supply is a function of w Previously all working units were under a supplement agreement obtaining w ; now, only some labor units are selected to get w This system is numerically solved assuming different values for 0 .50 and 0 .25. Results for this segmented model are explained in the following section. Segmented Multi level Market Model r esults The results from the SMMM are in Table 2 7; the previous AMMM results are reduced in p roportion to the portion of tomatoes under agreement, If 25 percent of
52 tomatoes are under the agreement with the CIW, then a one percent increase in the wage subsidy reduces the price for tomatoes by .25 percent The effect on tomato labor markets is now which is 25 percent of previous AMMM results. This is the change in employment due to a segmented agreement increase. The result for is negative; it represents the change in the wage rate paid by all grow ers. The reduction in this wage rate partly offsets the negative impact of the segmented agreement on tomato grower revenues (Table 2 8). The average earnings (across agreement and non agreement employers) after the agreement is given by th e weighted average in Equation 2 21, ( 2 21) Table 2 10 shows weighted average earnings, simulated using this equation and alternative values for As the proportion of labor units in the agreement decreases, representing a lower portion of tomatoes under the agreement, the positive effect on trast, an increase in the proportion of labor units in the agreement increases average earnings Note that assigning one to means all labor units are paid the supplement; just as shown in the previous section, the agreement negatively affects the equili brium wage rate received by all labor units, but all receive a wage supplement. Concluding Remarks The problem addressed in this essay is an important social issue regarding the wages and working conditions of seasonal farm workers, and is an importan t concern of labor intensive specialty crop producers. The analytical method provides an interesting
53 way of bringing the structure of the product and factor markets and the production technologies directly to bear on the implications for the farm factor, grower, and final product markets involved. In particular, it provides an analytical means of addressing changes in distribution resulting from labor agreements under alternative market conditions as addressed in the paper. This approach uses standard ec onomic analysis in a novel way to address an interesting labor issue across different levels of the marketplace. supplement agreements for the workers. The analysis shows that increasi ng boycott efforts successful ly affecting the demand for the final product, reduce retailers, growers, and even The magnitude of the impact depends on the cost shares and input and output elasticities. s rep resent a different approach for negotiating farm the buyer of the farm product and the farm workers. The results of the analyses show the importance of wage supplement agree ment stipulations the proportion of workers in and out of the agreement and elasticity values. Under this type of agreement and highly elastic output and input supplies (all equal), t omato workers benefit from the wage supplement agreement s directly pa id to workers as oppose d to growers. R etailers are not economically affected; instead, they redistribute income from growers to workers participating in the agreement In other words, the wage supplement incidence is on growers not on retailers The ov erall
54 supplement agreement; this means all tomato growers receive lower prices per unit. th is impact depends on the proportion of the agreement to non agreement segments. In both segments, workers receive lower wage rates, but workers in the agreement income increases as the number of retailers in the agreement increases. Changing the value of the elasticities, for instance assuming perfectly elastic supply of labor improves retailers and workers. Under perfectly elastic supply of labor, agreement shocks i ncrease employment and earnings for tomato workers Because the percentage reduction in price is greater than the resulting percentage increase in quantity, growers are still worse off under this scenario. This is positive for the retailer; it faces sign ificantly lower costs per unit of tomatoes, with an associated substantial increase in equilibrium quantity of tomatoes purchased. It also benefits from an increase in final product quantities sold greater than a reduction in prices.
55 Table 2 1. Para meter values Parameter Value e c 5.00 e L 5.00 e z 5.00 e S 5.00 X 1.00 Y 1.00 Y 5.00 K c 0.95 K L 0.33 K z 0.67 K X 0.05 Table 2 2. Boycott MMM results EY/EB 0.50 EP y /EB 0.10 Ec/EB 0.50 EP c /EB 0.10 EX/EB 0.50 EP X /EB 0.10 EL/EB 0.50 EP L /EB 0.10 Ez/EB 0.50 EP z /EB 0.10 Table 2 3. Boycott results on tomato and labor market Year B P X d EP X /EB P X s X e EX/EB X' 2000 0.01 $0.369 0.10% $0.3686 1,594,650,000 0.50% 1,586,676, 750 TR= $588,425,850 TR' TR= $(3,527,613) Year V P Lx e EP Lx /EV P Lx d L x e EL/EB L x 2000 0.01 $8.53 0.10% $8.52 2,397,069 0.50% 2,385,083 LC=$20,446,997 LC'= $20,324,417 LC' LC= $(122,580) Note: P X e is the average per pound price of tomatoes and X e is yield pounds from Florida Agricultural Statistics Service Vegetable Acreage, Production, and Value available at: http://www.nass.usda.go v/Statistics_by_State/Florida/Publications/Vegetables/ ; P L x e is the weighted average wage rate in Florida from NASS Farm Labor Reports and L x e is the average number of hours paid estimated using P L x e and the Florida Farm Production Expenditures Annual Sum mary at: http://usda.mannlib.cornell.edu/MannUsda/viewTaxonomy.do?taxonomyID=4
56 Table 2 4. BMMM sensitivity analysis e z e L EY/ EB EP Y / EB Ec/ EB EP c / EB EX/ EB EP X / EB EL/ EB EP L / EB Ez/ EB EP z / EB 5.0 5.0 0.50 0.10 0.50 0.10 0.50 0.10 0.50 0.10 0.50 0.10 2.0 5.0 0.49 0.10 0.49 0.10 0.43 0.16 0.49 0.10 0.39 0.20 5.0 2.0 0.50 0.10 0.50 0.10 0.46 0.13 0.40 0.20 0.50 0.10 Table 2 5. AMMM result s EY/EV EP Y /EV Ec/EV EP c /EV EX/EV EP x /EV 1.000 EL/EV 1.692 EP L /EV 2.692 Ez/EV 0.833 EP z /V 0.167 Table 2 6. AMMM sensitivity analysis e z =5 e L =5 e z =2 e L =5 e z =5 e L =2 e z =5 e L EY/ EV 0.017 0.017 0 .699 EPy/ EV 0.003 0.003 0.14 EC/ EV 0.011 0.011 0.466 EPc/ EV 0.002 0.002 0.093 EX/ EV 0.121 0.122 5.125 EPx/ EV 1.000 1.108 0.891 5.566 EL/ EV 1.692 1.703 1.345 15.152 EPL / EV 2.692 2.69 2.358 EZ/ EV 0.833 0.658 0.844 0.187 EPz/ EV 0.167 0.329 0.169 0.566
57 Table 2 7 Segmented agreement effect on endogenous variables Table 2 8. Segmented agreement results on tomato market v P X d (EP X /EV) P x s X e X' 1.00 0.0100 $0.369 1.00% $0.365 1,594,650,000 1 ,594,650,000 1.00 0.0271 $0.369 2.71% $0.359 1,594,650,000 1,594,650,000 0.50 0.0271 $0.369 1.36% $0.364 1,594,650,000 1,594,650,000 0.25 0.0271 $0.369 0.68% $0.367 1,594,650,000 1,594,650,000 0.25 TR' TR= $(3,986,585) TR= $588,425,850 TR'= $584,439,265 Note: P X e is the average per pound price of tomatoes and X e is yield pounds from Florida Agr icultural Statistics Service Vegetable Acreage, Production, and Value available at: http://www.nass.usda.gov/Statistics_by_State/Florida/Publications/Vegetables/ Table 2 9 Segmented agreement results on labor market V P Lx e (EP Lx /EV) P Lx d L X e L X 1.00 0.0100 $8.53 2.690% $8.30 2,397,069 2,437,579 1.00 0.0271 $8.53 7.290% $7.91 2,397,069 2,506,852 0.50 0.0271 $8.53 3.645% $8.22 2,397,069 2,451,960 0.25 0.0271 $8.53 1.822% $8.37 2,397,069 2,424,515 0.25 LC' LC= $(142,795) LC= $20,446,997 LC'= $20,304,202 Note: PL e is the weighted average wage rate in Florida from NASS Farm Labor Reports and L e is the average number of hours paid estimated u sing P L e and the Florida Farm Production Expenditures Annual Summary at: http://usda.mannlib.cornell.edu/MannUsda/viewTaxonomy.do?taxonomyID=4 Table 2 10 Segmented l abor market results v s(X/L) PL d W 1.00 0.010 $2.39 $8.30 $10.69 $10.69 1.00 0.027 $6.36 $7.91 $14.27 $14.27 0.50 0.027 $6.50 $8.22 $14.72 $11.47 0.25 0.027 $6.58 $8.37 $14.95 $10.02 = 1.00 = .50 = .25 EY/EV EP Y /EV EC/EV EP c /EV EX/EV EP X /EV 1.000 0.5000 0.2500 EL/EV 1.692 0.8460 0.4230 EP L /EV 2.692 1.3460 0.6730 EZ/EV 0.833 0.4167 0.2083 EP z /EV 0.167 0.0833 0.0417
58 Figure 2 1. Market level boycott and supplement agreement effects A) Final product L b L e X b X e Boycott Supplement B P x e P x b S P x D 1 D 2 X A Y D 1 D 1 C D 1 D 2 D 1 S D 2 Y b Y e P y b P y e P y L P L e P L b S P L P y S D 1 Y e Y P y e L S D 2 P L e L s P L d L e S P L D 1 w P x d P x s X e X P x D 1 D 2
59 Figure 2 Figure 2 X s f g P x e P x d X e S D 1 e c a b d h i D 2 ( P x P x v ) L D 1 e c a b d j h D 2 P L e L a P L d L e S P L i g k w f l m
60 Figure 2 4. Segmented tomato market effects A) Tomat o market. B) Agreement market segment. Figure 2 5. Segmented labor market effects A) Labor market. B) Agreement labor market segment. d P x s X a X a P x D a D a P x V S P x d P x s X e X P x D 1 D 2 X a X a A B a h b c d Px d X a PxV L D 2 P L e P L d 2 L e S P L D 1 a g L D a L L a P L D a w b g c w 2 e P L d 1 w 1 f L a A B
61 CHAPTER 3 AGE BARGAINING STRAT EGY AGAINST AGRICULTURALLY RELAT ED CORPORATIONS AND SUPPLEMENT AGREEMENT S Overview Hired farm workers are among the lowest paid workers in the United States. In 20 10 95 per hour, 5 7 1 percent of the average hourly wage ra te of workers in private nonagricultural ind ustries Agricultural workers are mainly of foreign origin (73%), many of them Hispanic (83%) (National Agricultural Workers Survey [NAWS]). There are two oppos i ng arguments regarding the effect of immigrant workers on earnings of domestic workers. Borj as (2003) find s immigrant workers reduce t he earnings of domestic workers Other studies suggest that the complementarity between domestic and foreign workers minimizes the effect of the increased supply of labor (Ottaviano 2007; Hanson 2009; Card 2005). There have been public interventions to try to reduce the wage differential between agricultural and non agricultural wage rates (e.g., the 1978 amendment to the Fair Labor Standards Act [FLSA]). The 1978 FLSA amendment eliminated the difference bet ween the federal minimum wage rate for agriculture and nearly the entire labor market. 1 Gardner (1981) f ou nd that a higher minimum wage increases the farm worker wage rates but at the cost of decreasing employment. Farm workers and farm worker interest gr oups have also attempted to improve wage rates and working conditions in this labor market One strategy has been consumer boycott efforts against corporations relative to a particular farm commodity (Table 1 1). The majority of these boycott efforts are conducted without any regulatory 1 Although FLSA made minimum wage in agricultural and nonagricultural work, there are still differences such as with overtime payment requirements.
62 system governing labor relations in agriculture since the National Labor Relations Act (NLRA) excludes agriculture and only a few states (e.g., California and Maine) have authorizing state legislation 2 Even though some of the boycott target corporations are not the direct employers of the farm workers, they choose to end boycott efforts by signing third party include establishing conduct codes for the agricultural product suppliers and increasing wage rates. Because of the motives of these agreements, stipulations deal with farm level working conditions and wage rates involving by default the direct employer, the growers. One objective of th is essay is to analyze farm labor group boycott strategies against agriculturally related firms for better wage rates and working conditions. Another objective is to look at counter boycott strategies by target firms and boycott termination decisions. Th e focus is on the most recent farm worker boycott efforts launched by the Coalition of Immokalee Workers (CIW) against food related corporations and boycott termination agreements between the two groups The boycott termination agreements stipulate that a n extra penny per pound of fresh Florida tomatoes purchased by the food chains should go directly to the pickers as a wage increase. This payment is a supplement paid by a third party in addition to what the employer pays the worker. 3 2 California and Maine have autho rizing state legislation restricting boycott activities. 3 Media sources indicate that part of the wage supplement payments made by target firms in agreement with the CIW we re in an escrow account until tomato growers (employers of farm workers in the agre ement) agreed to pass the wage supplement on to the farm workers
63 T he objective s of th is essay are accomplished by applying game theory, conducting an event study and estimating a probit model for the agreement decision These show the strategic decision making processes of the farm workers and target firms The next section reviews prev ious work related to the issues addressed here. This is followed by game theory event study and probit analyses, starting each with assumptions and methodology. The results section discusses the main findings from the game theory application and the ev ent study. The conclusion s section summarizes the results of both methodologies and discusses the need for further research. Theoretical Framework Boycott efforts are meant to alter the preferences of consumers so as to pressure the target firms to acce de to The pressure exerted against target firms could be on reputation. King (2008), who recently studied influences on the probability of boycott success, finds that damage to corporate reputation is the most critical tool for bo that ot her statistically significant factors are media attention, number of targets, and w h ether the corporat ion is a subsidiary of a large corporation. Fedderson and Gilligan (2001, p. 149) used game theory to analyze the effects of information disseminated by a ctivists on the performance of a market for credence
64 goods 4 They supplying] activist can alter the decisions of 5 An event study application by Godfrey, M errill, and Hansen ( 2008 ) shows that r eductions i n products sold from negative events faced by firms likely impact its profits or raise its risk level may create greater levels of financial distress to the extent that they cause stakeholders to react in ways that destroy (Godfrey, Merrill, and Hansen 2008). The s e theoretical argument s explain the relationship between management decisions and the financial value of the firm when facing negative events such as boycott efforts or boycott termination agreements Considering target firm s, manage rs, and stockholders are optimizing agents, the strategy decision is based on the economics of each strategy. 6 Game theory an event stud y, and a probit model are t he t ools used here to increase wages and imp rove working financial effects. Game theory has been successfully applied to analyze wage bargaining between firms and labor unions. These applications include the static bargaining model by Hall and Lilien (1979), the efficient repeated wage bargaining game by Espinosa and Rhee 4 Credence goods are those for which purchasing decisions by consumers are based on operating methods 5 Their game theory analysis assumed no cooperation and incomplete infor mation. 6 In Baron (2001), the target firm decision could be one that is not profit maximizing, in which case it is not strategic Corporate Social Responsibility (CSR), but is motivated by altruistic behavior.
65 (1989), and the union and strike model with asymmetric information by Hayes (1984). 7 It also has been applied to analyze boycott strategies used by activists to get their demands from the target firms; the list of studies includes those by Baron (2001); Innes (2006); and Glazer, Kanniainen, and Poutvaara (2008). target firms, specifically on their financial value as measured by stock market returns. 8 Pruitt and Friedman (1986) find empirical evidence that media announcements of aggregated consumer boycott actions are interpreted as having a negative effect on th e wealth of the owners of the target firms. In conducting a study to investigate the effectiveness of union sponsored boycotts, Pruitt, Wei, and White (1988) find that investors usually react negatively in the short term after the first public announcemen t of a boycott. In a similar investigation, Koku Akhigbe, and Springer (1997) analyze the short term negative effects on the value of the target firms from both the threat of boycotts and actual boycotts, and whether the effects are independent of the sp onsoring organization. They find that both the threat of a boycott and an actual boycott affect the financial value of the target firms and that there are no significant differences between union and non union sponsored boycotts. There is a significant bo dy of literature examining the impact of corporate social responsibility (CSR) on corporate financial performance (CFP), showing a positive correlation between them. 9 It is reasonable to evaluate the financial impact of the farm labor wage supplement agre ement event as if it were CSR and to expect a positive 7 There are many other important wage bargaini ng game theory models in the literature, including Rubinstein (1982), Sobel and Takahashi (1983), and Fernandez and Glazer (1991). 8 success. 9 For a discu ssion on CSR and CFP studies, see Griffin and Mahon (1997); Roman, Hayibor, and Agle (1999); Ullman (1985); and Ilinitch, Soderstrom, and Thomas (1998).
66 relationship. The expected relationship might not be significant since agreements are costly, sending a negative message to stockholders and investors. Mixed signals from the agreement could offset t he financial effects. It is important to note that none of the game theoretical boycott models mentioned above addresses the issues of the activists being farm labor groups instead of unions; the targets being buyers of farm products instead of direct emp loyers of farm labor groups; and the wage bargaining strategy being a boycott instead of a strike. Also, there have been no event studies to evaluate the financial value impact on the target firms from farm labor group boycott efforts and boycott terminat ion events. This chapter attempts to cover these gaps. The next section s discuss the assumptions and methods for game theory event study and probit application Dynamic Game of Complete Information Game Assumptions and Methods There are two players: Player 1 is a farm worker labor group (FLG) and Player 2 is a retailer, also known as the target firm (TF). For this case, Player 1 is the CIW, a community based organization of agricultural workers in southwest Florida (CIW founded in 1996). Its main ob jective is to promote fair treatment for farm workers according to international labor standards. It has about 4,000 members, mostly Latinos, Mayan, and Haitian immigrants who work for the citrus and tomato industries in Florida. Player 2 is a n importan t retailer, a buyer of Florida fresh tomatoes, such as Yum! Mart, or Sysco (the complete list is shown in Table 3 1). The game starts with the farm labor group (FLG) threatenin g or actually launching a consumer boycott against a target firm (TF) and ends when the TF agrees to a wage
67 supplement payment. Between these two stages lies a sequence of actions chosen under the assumption of perfect information. Some of the decisions are made under uncertainty about the strategy success (i.e., the effect of boycott efforts on TF sales), with all choices being optimal. Where uncertainty is present, the utility or profit maximization functions are of the Von Neumann Morgenstern type A lso, to ensure optimal behavior throughout all the game stages, the final stage is solved first; in game theory, this is known as backward induction. Given these characteristics, the game is a dynamic finite game of perfect but incomplete information with a perfect Nash corresponding equilibrium concept with four stages. The four game stages are display ed in a game tree (Figure 3 .1) and explained next, starting with the last stage. Game Development Fourth stage In the final stage of the game, the TF max imizes its profits (Equation 3 1) given the success or failure of boycott efforts. ( 3 1) Letters Y and X P i represents the respective prices, and S represents a wage supplement payment demande d by the labor group. The farm labor wage supplement is to be paid per unit of input X while the TF faces boycott efforts, S=0 The demand for Y can be expressed as a function of its price and other parameters, including the one related to the FLG boyc ott. In fact, Equation 3 1
68 could be expressed using the inverse demand function, 10 T he marginal cost of production, ; and supplement wage agreement, ( 3 2) The bo ycott parameter, B demand for Y at which the TF prefers to pay the supplement. After a wage supplement agreement is signed, and B=0 I f the boycott strategy is unsuccessful, the game ends with and B=0 The optimal quantity of output under a boycott, Y (B) or supplement agreement, Y is obtained by partially differentiating the profit function in terms of Y The asterisk s uperscript on X and Y indicate s optimal output quantities. ( 3 3a) ( 3 3b) Respective maximized profits are ( 3 4) ( 3 5) The TF agrees with the FLG when Under a successful boycott, the TF agrees with the FLG and pays a wage supplement. Third stage At this stage, the FLG, as an optimizing agent, choo ses the boycott effort, b at a unit cost which maximizes its expected utility. The FLG utility functions ( Von 10 Similar to the one in Baron (2001),
69 Neumann Morgenstern type ) success in getting a wage rate increase and reduc ing employment due to effects on the final product demand and supply ( 3 6 ) With the boycott strategy, the FLG hopes to get an agreement that will increase its initial w ealth WL by SX but a t the cost of risking employment. In Equation 3 6 the p(b) is the probability of a certai n level of boycott effort success; the wl is the reduction of farm workers wealth from the effects on the input market at the retail level. The earnings for the farm workers are in which a portion of the marginal cost of X paid to farm workers The expec ted farm is represented by The expected wealth loss from the farm employment effect is represented by 11 could be changed depending on and the cost of the boycott efforts, Given this FLG utility function, it i s possible to characterize the optimal boycott strategy, b by partially differentiating the expected utility function with respect to b and making it equal to zero; that is ( 3 7 ) 11 The additional cost from boycott effort s generates TF adjustments affecting the farm input market associated with the boycott. These TF adjustments are given by the marginal cost of X A portion of this per unit of output cost, is the cost on the farm level labor market
70 Since the probability function is not specified, it is not possible to get b directly from Equa tion 3 7 However, it is obvious that the FLG chooses b such that the marginal probab i l ity of effort success equals the marginal cost of boycott effort s Second stage The TF must decide whether to agree or disagree with the FLG when facing a boycott th reat. If the TF signs a wage supplement agreement paying SX no boycott efforts are launched. In contrast, if the TF does not sign, it should expect boycott efforts against it. Under a boycott, the TF risks revenues from potential reductions in final pr oduct sales. Therefore, at this stage, the TF chooses either with certainty or (B) with uncertainty. ( 3 8 ) Note that this expression accounts for the uncertainty about boycott success and its effect on profits. Recall that under unsuccessful boycott is not a func tion of B; therefore, the second term in Equation 3 8 representing the probability of an unsuccessful boycott ha s B=0. An equivalent expression of this relationship is obtained by plugging in Equa tions 3 4 and 3 5, respectively, and simplifying : ( 3 9 ) The TF signs the agreement at this stage if the cost of the supplement at is less than or equal to expected cost of the boycott at First stage The FLG decides whether to use a boycott threat or to launch a boycott against the TF in exchange for a wage supplement payment. Assume a consumer boycott
71 threat conveys zero effort, represented by subscript m, and an actual boycott, represented by subscript g conveys some effort The utility of a boycott threat is shown in Equation 3 1 0 and of an actual boycott is shown in Equation 3 1 1 ( 3 1 0 ) ( 3 1 1 ) The FLG threatens if U m U g Assuming that the probability of a successful boycott threat or an actual boycott are equal: Since there are boycott costs associated with an actual boycott; g is greater than zero. Therefore, as the first step, t he FLG thr eatens In contrast, the FLG boycotts if U g U m true under several conditions One of these is that boycott effort costs are negligible Another condition is that the probabilit y of boycott success is higher than that of a threat Al so, a condition for a boycott is that the expected wealth loss from it be smaller than that of a threat Finally, the expected wage increase from a wage supplement agreement is higher than that of a threat. This is illustrated in the following expression : Game Remarks In the first stage and third stage the FLG, as an optimizing agent, chooses the boycott effort which maximizes its expected utility. The FLG decides level of boycott effort, threat or actual, against the TF Thi s decision is based on the probability of
72 success in gett in g a wage sup plement payment This strategy is clear, but there is a remaining question: what influences the probabilities of boycott success ? The success of the FLG boycott strategy in obtaining wage supplemental agreements with the TF depends on the ability to sufficiently affect the demand A successful boycott negatively affects consequently reduc ing cash inflows from sales To ct demand and offset the revenue effect demands also affecting cash flows. In this case, the TF agrees to pay a supplemental wage agreement potentially increasing cash outlays Both effects on cash flows ma y be offset by optimal remedial managerial measures It is clear from stage four of the game that the TFs will choose the optimal level of input and output to maximize profits and that the agreement would be made only if the benefit of that decision is gr eater than the cost of facing the boycott. It is important to point out that TFs are publically owned firms. Cash flows are linked to financial value, therefore, managerial decisions extend to stock markets If optimal decisions regarding risk ing a boy cott or signing wage supplement agreements with the FLGs are made, then there should not be negative e ffect s on the financial value This and the factors influencing the probability of boycott success are empirically tested in the following sections. Strategy Decision Empirical Analys i s This section details the assumptions and methods to empirically analyze t he strategy decisions by FLG and TFs. The approach has two steps. The first step i s to conduct a firm specific event study The second step o f the analysis is to estimate a probit model for the boycott and agreement decisions.
73 The event study provides for the following: 1) identify ing boycott and agreement event dates through media announcements in the Dow Jones and other major news and busin ess publications ; 2) measured by abnormal stock returns ; 3) and finding firm specific characteristics determining the presence of abnormal returns The probit model results expose the factors influencing the probability of boycott success and explain strategic decisions. The following sections are to explain the assumptions and methods applied i n these two steps. Event Study M ethodology Media announcements of boycott and agreement events we re published in Dow Jones and other major news magazines and business publications The first CIW boycott announcement was against Taco Bell, a subsidiary of Yum! Brands, on February 2, 2001. After boycott efforts against Taco Bell ended with an agreemen t on March 8, 2005, the CIW announced that it was turning its boycott efforts to the rest of the fast food industry. On March 7, 2006, the CIW announced the creation of the Alliance of Fair Food, a campaign targeting food corporations (SIC codes 5141, 514 8, 5331, 5339, and 5411) to buy from sellers who guaranteed higher labor wages and employment rights. Other specific corporations were targeted i n later news announcements Table 3 1). T he panel data set h as 56 observations for 45 firms in five four digi t SIC industries. Once the event announcement dates are identified, the pre event, event, and post event windows are defined. The boycott pre event period where t=T 0 +1 to T 1 known also as the estimation window, is of length L 1 =T 1 (T 0 +1). The boycott e vent window includes the event dates when the initial event announcement was published. In the
74 event window, =0, its length is L 2 =T 2 (T 1 +1). In this case it is a one day event window for each TF. The post event period is t=T 2 +1 to T 3 ; its length is L 3 = T 3 (T 2 +1). Th e s e data are included in the estimation window; therefore the estimation window length is L 1 +L 2 The agreement estimation window is the same as the boycott estimation window. The agreement event window includes the initial agreement announc ement date in the Dow Jones and other major news and business publications. The value of is 0 and it is of length L 4 =T 4 (T 3 +1). For each firm, t he size of the boycott and agreement event windows are one; the size of the estimation windows are at least 55. Note the size of the estimation window is larger than the number of firms included. The financial data for analysis are the daily market return s for each firm R it and the stock market portfolio index R mt This is available from the Center for Research in Securities Prices (CRSP) and Compustat. 12 Then, the expected return ( ER it ) is est imated for the pre event window by the following linear model: ( 3 1 2 ) ( 3 1 3 ) where index i t represents a particular day in the estimation window This is one of the most common specifications of event study models It is known as the Single Factor Market Model (SFMM), in which the only explanatory variable is a portfolio of traded securities. Basic assumptions for this model are E( it =0) and Var( it )= i 2 12 The data do exclude dates in which each corporation s splits, acquisitions new product introduction s share repurchases, sales, earnings, or dividend change s are announced since they have been found to affect the financial value of firms.
75 Event an alysis hypotheses are typically based on the cumulative abnormal return (CAR). CAR is a n aggregate of the firm specific abnormal return, the difference between the realized return corresponding to the event announcement and an estimate of the expected ret urn in the absence of the announcement ( 3 1 4 ) ( 3 1 5 ) The null hypothesis of the boycott/agreement event is that there is zero abnormal return. The alternative hypothesis is that there is either a negative o r positive impact on the value of the firm The rejection of the null hypothesis in favor of the alternative implies that the event announcement affects the value of target corporations (measured by stock returns) The first step to get the abnormal retur ns in Equation 3 1 4 is to estimate the expected return in Equation 3 1 2 A direct approach to the estimation of the expected return is o rdinary l east s quares procedure s (OLS). In general, parameter estimates obtained via OLS are unbiased and efficient; ho wever, this is not the case when events are clustered, as i s the current case. As shown in Table 3 1 there are simultaneous news releases announcing boycott efforts against several agriculturally related corporations or even the whole food industry. In that case, there is contemporaneous correlation and classical assumptions for OLS do not hold (Salinger 1992). More appropriate approaches to estimate this model are the Feasible Generalized Least Squares (FGLS) or Seemingly Unrelated Regression (SUR) pro cedures (Collins and Dent 1984).
76 Alternatively, the Dummy Variable of Event Studies Model (DVEM), nesting the previous model within it, utilizes data for the pre event, event, and post event windows. ( 3 1 6 ) In this study, the interest is i n two events; therefore Equation 3 1 6 is rewritten to include two corresponding sets of dumm y variables Th is DVEM approach is advantageous when the estimation window is small; it is easy to program, and inferences are based on the parameter estimates which are conveniently estimated via OLS. Statistically significant abnormal return s could be e valuate d to find factors influencing it. This requires a second step, an estimation of a cross sectional model of AR as specified in Equation 3 1 7 : ( 3 1 7 ) Similar t wo step event studies were applied by Godfrey, Merril, and Hansen (2008) and Wang, Strong, Tung, and Lin (2009). The letter Z is a vector of variables representing haracteristics The variables are the Socrates scores ( Socrates ); natural logarithm of sales, ln(sales) ; and ratio of the market to book values mrktbook ; and a dummy variable equal to one if the firm had direct boycott events during the year and zero oth erwise 13 Equation 3 18 is rewritten as follow s : 13 Although the whole food industry was targeted, only some firms in the industry have received letters from the CIW or had picketing events.
77 The Socrates scores are prepared by KLD Research and Analytics as a measure of environmental, social and governance performance of corporations 14 Sales and market to book ratios ar e included to represent corporation size and intangible assets, respectively. 15 The previous for these variables correspond to the most recent information available to stockholders on the event day Al though there are 45 firms, t he number of o bservations was limited to 36 due to data availability in CRSP and Compustat. 16 The subscript j indexes clusters since firms are grouped by SIC. S ubscript i is for firms in the cluster. Probit M odel M ethodology The purpose of the probit model is to eva luate predictors of the boycott and agreement decision by FLG and TFs. A ccording to the game theory analysis in the first stage, the farm labor group decides whether to threat en or launch a boycott. The decision is based on the probability of success. I n the second stage, the TF optimally decides whether or not to agree with the FLG based on the marginal effect of the events TFs agree i f the agreement bring s them greater benefit than cost. The probability of boycott success is modeled as a function of several predictors : ( 3 1 8 ) The predictors (X) in the model are the firm specific characteristics used in the AR regression except for the boycott dummy variable Whenever A=1, the boycott is 14 KLD was acquired by RiskMetric s Group, their web page provides details on Socrates score methodology ( http://www.msci.com/products/esg/global_socrates/methodology.html ) 15 Market to book is the ratio of market value of the firm, which is the common shares outstanding multiplied by the month end price that corresponds to the period end date, to the book value, which is the Common Equity Liquidation Value (CEQL) per share multiplied by common share outstanding. 16 SIC 5148 was not included due to data unavailability.
78 considered successful otherwise, A=0. A boycott is successful if it exerts enough he TF decides to agree with the FLG and pays a wage supplement to workers The number of observations is 36 grouped into five cluster s Standard probit estimates in the presence of clustered data typically result in estimated standard errors that are too small. A standard approach to correct the problem is to utilize robust estimates of the standard errors. However, as noted by Kline a nd Santos (2011) these also perform poorly with small samples. Bootstrap methods are another approach, but again, Klin e and Santos (2011) demonstrate that existing bootstrap methods similarly result in rejecting the null hypothesis too frequently with sm all samples in the presence of clustering. An alternative approach is to estimate the model as a panel data model where each cluster (SIC) is a panel. The only option in the context of a probit model is a random effects model since a fixed effects model cannot be estimated via probit methods. The advantage of this approach is that it allows an estimate of the extent of correlation within clusters to evaluate how serious the clustering problem is. Nevertheless, a maintained assumption of the random effec ts model is that the unobserved effects are uncorrelated with the explanatory variables. Event Study and P robit R esults Table 3 standard errors from the DVEM SUR model. Each individ statistic indicates that the data do not provide enough evidence that boycott or third party agreement announcements affect the financial value of a TF. Table 3 3 shows the test statistics for two set of hypotheses. The null hypotheses are that the boycott and agreement parameters are jointly equal to zero across all firms
79 and that only the agreement parameter is zero or the boycott event parameter is zero The data do not provide enough evidence to reject these hypotheses in favor of the a lternative. T his means that boycott and agreement events do not affect the value of the firm as measured by the stock market return. S tockholders do not re a c t positive ly or negative ly to boycott and agreement events. In the absence of statistically sign ificant abnormal returns, the firm specific model in Equation 3 1 7 is not estimated. T he probit mode l provides an alternative approach to evaluate a characteristics as predictors of the agreement outcome. The results of the probit model in Table 3 4 show that index have a significant effect on the agreement decision, but that t he market to book value ratio is a statistically significant predictor of the agreement outcome The si gn of the coefficient is positive which means that this characteristic positively affect s the probability of the agreement This variable has been shown to have a strong correlation ( Godfrey, Merril, and Hansen 2008). Intangible assets have no physical existence, but could be a I ntangible assets include, among other things brand names trademarks, research and development intellectual prope rty, licenses, copyrights, operating rights, and franchises. For the FLG, this means that it has a higher probability of launching a successful he larger are intangibles th e greater is the probability of facing an intensive boycott. The parameter measuring intra cluster correlation, is not significant ly different from zero. This result indicates a lack of correlation between a firm s propensity to
80 agree with the CIW and its industry classification U nobserved firm characteristics ion do not contribute to the propensity of agreement decisions by target firms. Chapter Summary The game starts with the farm labor group (FLG), player 1, threatening or actually launching a consumer boycott against a target firm (TF), player 2, in exc hange for a wage supplement payment (Figure 3 about the strategy success. Under either threat or actual boycott, the FLG expects a reduction in employment. This is true even if the wage bargaining strat egy is a boycott instead of a strike and the TF is not the direct employer of the FLG (meaning the TF The first essay in this dissertation show s that a competitive TF facing a boycott would reduce the product q uantity, which would reduce employment at the farm market level However, if the boycott strategy were successful, the FLG would expect a wage rate increase for its represented workers. I n the second stage, the TF decides whether or not to agree with the FLG. This decision conveys both certain and uncertain information. The TF is certain about the exact cost of agreeing with the FLG but is uncertain about the total cost of a boycott. product demand and to offset the costs. If the TF agrees, it pays the wage supplement, and the game ends. If the TF does not agree, it takes the risk of a boycott. According to the event study results, n either decision i n the game has a significant e f fect on the financial value of the TF. A boycott announcement may be perceived by stockholders as a threat that may or may not occur at some unknown time in the future.
81 It is also likely that the potential cash flow impact, the cost of removing the threa t, is relatively minor Boycott related events are more subjective than other market value impacting announcements, such as earnings or split announcements. The latter events already have occurred when the announcement is m ade, and can more easily be related to future cash flows. The FLG boycott effort is chosen at the third stage, such that it maximizes its expected utility. Even though boycott efforts are optimally chosen by the FLG, wage supplement agreement gains depend on the difference between the TF maximizing decisions before or after the boycott events in the last stage of the game. nds on its expected ability to thwart boycott threats and if boycotte d, the effects of the boycott on profits. If the agreement costs are high, decisions made by the TF in the last stage may bring significant wealth loss for the FLG. In contrast, if the agreements send positive signals to consumers, stockholders, and inve stors about the ethical behavior of the corporate social responsibility (CSR) of the TF, they may provid e a market opportunity for the TFs, and positive results may subsequently follow for farm workers. The event study results (Table 3.2) show that agree ment and boycott announcements have no significant impact on the value of the TFs regardless of their size, tangible assets, and Socrates index This could be because agreements between the FLG and the TF (agriculturally related corporation) usually are w eak since there is no direct employer employee relationship for the TF. Another possible explanation is that there usually is a long time lag between the boycott announcement (first stage of the game) and the agreement decision announcement. During that time, the TF may be
82 able to put in place some risk management practices to reduce the potential impact from boyc otts. Because the TF knows the FLG demand s it can more accurately nd information to adjust the production process by substituting inputs or introducing new technologies to minimize the negative effect from a settlement. It is probably for this reason that it is not un common to find boycotts that last for several years; examples not involving farm workers are the United Food Workers Union (UFWU) against Winn Dixie (lasted 2.5 years), and the Bakery, Confectionery, and Tobacco Workers International Union boycott against RJ Reynolds (lasted 30 years). Based on the probit model results, an agreement between the FLG and the TF is more likely as the ratio of in tangible to tangible assets increase s Th is is an important result for the FLG strategizing boycott effort s and maximizing its expected utility. The agreement ou tcome prediction increases as a TF s ratio of intangible to tangible assets increase s The r ecent trend of using boycott strategies suggests that FLG s expect to receiv e positive net gains from boycott efforts ending with a wage supplement agreement Howe ver, as results show, it is more likely to reach wage supplement agreements with TFs with large ratio of in tangible to tangible assets. These results are for all targeted firms regardless of their industry classifications. A s material for further resear ch it would be valuable to look at the factors determining the duration of boycott efforts. D ata are currently inadequate to conduct a duration model on the CIW efforts.
83 Table 3 1. CIW Consumer b oycott e fforts against a griculturally r elated c orporati ons Targeted Corporations Ticker Farm Product and State Announcement Date a Supplement Agreement Boycott Agreement Taco Bell (Yum! Brands' Subs.) YUM Tomato FL 2/1/2001 3/8/2005 0.01 Rest of Yum! Brands YUM Tomato FL 4/6/2005 5/18/2007 0.01 Mc Donald's (& Chiportle Mexican Grill as s ubsidiary) MCD Tomato FL 4/6/2005 4/9/2007 0.01 Fast food industry (SIC: 5812) Tomato FL 5/20/2005 n/a n/a Wendy's WEN Tomato FL 4/6/2005 n/a n/a Subway 1 n/a Tomato FL 5/20/2005 12/2/2008 0.01 Food in dust ry (SIC: 5411, 5431, 5141, 5148, 5331, 5399) Tomato FL 3/7/2006 n/a n/a Chipotle Mexican Grill (as public corp) CMG Tomato FL 9/13/2006 9/9/2009 0.01 Burger King BKC Tomato FL 2/6/2007 5/23/2008 0.01 Whole Foods WFMI Tomato FL 6/28/2008 9/10/200 8 0.01 Walmart WMT Tomato FL 3/13/2008 n/a n/a Winn Dixie WINN Tomato FL 3/13/2008 n/a n/a Kroger KR Tomato FL 3/13/2008 n/a n/a Safeway SWY Tomato FL 3/13/2008 n/a n/a Supervalu SVU Tomato FL 3/13/2008 n/a n/a Publix n/a Tomato FL 3/13/2 008 n/a n/a Sysco SYY Tomato FL 3/13/2008 n/a n/a US Food service n/a Tomato FL 3/13/2008 n/a n/a Aramark n/a Tomato FL 3/30/2009 9/25/2009 0.015 Chartwells n/a Tomato FL 3/30/2009 9/25/2009 0.015 Sodexo n/a Tomato FL 3/30/2009 8/24/2010 0.0 1 a Boycott and agreement announcement dates are as first published on major news and business publications found on Factiva and Proquest searches Companies without ticker are not publically owned corporations. Chipotle was a subsidiary of McDonalds Co rporation; its initial public offer (IPO) was in 01/26/2006. Burger King fin ancial data is available after its IPO: 05/18/2006; the 1st news about the CIW boycott to BKC was on 04/06/2005, but after going public the first boycott news 02/05/20 07, the ne ws mentions BKC only. Agreement dates are as of December 2012.
84 Table 3 2 Seemingly u nrelated r egression r esults Ticker Agreement AR Market Index Boycott AR Constant ABS 0.5199 0.0004 0.0007 (0.2751)* (0.0124) (0.0017) BJ 1.0746 0.000 0 0.0009 (0.2718)* (0.0122) (0.0017) BKC 0.0047 0.9563 0.0061 0.0012 (0.0153) (0.2180)* (0.0152) (0.0021) BLI 1.6699 0.0053 0.0019 (0.3437) (0.0149) (0.0020) BWLD 0.8326 0.0072 0.0020 (0.5410) (0.0222) (0.0031) CEC 1.2391 0.0042 0.0009 (0.1830)* (0.0077) (0.0010) CKR 1.2326 0.0238 0.0002 (0.3922)* (0.0164) (0.0022) CMG 0.0004 1.2250 0.0094 0.0012 (0.0188) (0.2974)* (0.0188) (0.0026) CPKI 0.5211 0.0015 0.0008 (0.3255) (0.0138) (0.0019) CQB 0 .9592 0.0266 0.0041 (0.4105)* (0.0181) (0.0025)* DAB 0.8024 0.0146 0.0001 (0.2952)* (0.0142) (0.0019) DG 0.8614 0.0088 0.0004 (0.1832)* (0.0084) (0.0011) DPZ 0.0332 0.0023 0.0051 (0.4555) (0.0189) (0.0026)* DRI 0.8188 0. 0136 0.0001 (0.2059)* (0.0094) (0.0013) EAT 0.8835 0.0066 0.0001 (0.2442)* (0.0107) (0.0015) FDO 0.8819 0.0008 0.0016 (0.2815)* (0.0131) (0.0018) GAP 0.2175 0.0150 0.0115 (0.9420) (0.0397) (0.0054)* IHP 1.5269 0.0032 0.0 005 (0.3145)* (0.0143) (0.0019) JBX 1.1393 0.0210 0.0022 (0.2237)* (0.0097)* (0.0013)* KKD 0.6322 0.0108 0.0066 (0.6880) (0.0316) (0.0044)
85 Table 3 2. Continued Ticker Agreement AR Market Index Boycott AR Constant KR 0.8312 0.00 14 0.0012 (0.1854)* (0.0135) (0.0018) LNY 1.1424 0.0034 0.0006 (0.1883)* (0.0083) (0.0011) LUB 1.1263 0.0008 0.0033 (0.4267)* (0.0192) (0.0026) MCD 0.0067 0.9049 0.0064 0.0004 (0.0065) (0.1286)* (0.0065) (0.0009) NDN 2.4637 0 .0112 0.0020 (0.4373)* (0.0192) (0.0026) OSI 0.6248 0.0061 0.0001 (0.1805)* (0.0078) (0.0011) PFGC 1.6445 0.0131 0.0015 (0.2816)* (0.0131) (0.0018) PTMK 1.3133 0.0133 0.0022 (0.4319*) (0.0211) (0.0028) RDK 1.1398 0.0028 0 .0000 (0.2440)* (0.0108) (0.0015) RI 0.9289 0.0042 0.0012 (0.2446)* (0.0113) (0.0015) RMK 0.5040 0.0009 0.0003 (0.2155)* (0.0093) (0.0013) RVI 1.7784 0.0128 0.0020 (0.5779)* (0.0263) (0.0035) RYAN 1.3137 0.0100 0.0007 (0.2348)* (0.0107) (0.0014) SMF 1.0140 0.0088 0.0048 (0.3742)* (0.0163) (0.0022)* SNS 1.1056 0.0105 0.0022 (0.2320)* (0.0102) (0.0014) SVU 0.5569 0.0136 0.0010 (0.1276)* (0.0096) (0.0013) SWY 0.7511 0.0021 0.0003 (0.17 23)* (0.0136) (0.0018) SYY 0.5785 0.0122 0.0011 (0.1021)* (0.0086) (0.0011) TXRH 0.4612 0.0081 0.0019 (0.3840) (0.0177) (0.0024) WEN 1.1059 0.0043 0.0053
86 Table 3 2. Continued Ticker Agreement AR Market Index Boycott AR Constant (0.2231)* (0.0111) (0.0015)* WFMI 0.0063 1.2202 0.0079 0.0002 (0.0194) (0.2410)* (0.0193) (0.0027) WINN 0.7079 0.0150 0.0005 (0.1802)* (0.0212) (0.0028) WMK 0.8232 0.0017 0.0004 (0.2723)* (0.0126) (0.0017) WMT 0.5267 0.0057 0.0004 (0.0990)* (0.0077) (0.0010) YUM 0.0011 0.9629 0.0024 0.0018 (0.0100) (0.1687)* (0.0101) (0.0019) Note: Standard errors are in parenthes e s, Table 3 3. Linear hypothesis testing of the parameters Chi(2) P>Chi Ho: 0 0.06 0.97 Ha: 0 Ho: 0 0.01 0.92 Ha: 0 Ho: 0 0.06 0.81 Ha: 0 Ho: 0 0.01 0.92 Ha: 0 Table 3 4 Probit estimates: agreement decision Agreement Constant 3.0594* (1.7616) Socrates 0 .0480 (0.1048) Lnsales 0.1079 (0.1929) Market to book value 0.3099* (0.1512) Sigma_u 0.1517 (0.1525) Rho 0.0225 (0.0442) Likelihood ratio test of rho=0: Chibar2(01) 0.13 Prob>= Chibar2 0.359
87 Figure 3 1 Dynamic g ame of c omplete i nformation t ree Game ends ( ) = Y ( B ) = p m (b )( (B) + (1 p m (b ) ) FLG U m = p m (b )( ) + (1 p m (b ) )( )+WL Threat Boycott TF TF Agree Disagree Agree Disagree ( ) = Y ( B ) = p g (b )( (B) + (1 p g (b ) ) Game ends Game ends FLG Game e nds Boycott Boycott U g = p g (b)( ) + (1 P g (b))( )+WL b U g = p g (b)( ) + (1 P g (b))( )+WL b ( ) = Y ( ) = Y TF Agree Disagree Game ends Game ends U g = p g (b )( ) + (1 P g (b ))( )+WL b
88 CHAPTER 4 EMPIRICAL STUDY ON F ARM LABOR MARKETS 1 Overview The farm labor market in the United States is highly dependent on foreign born farm worker s (73%), many of them unauthorized (50%) ; 2 US agricultural labor markets are influenced by federal immigration policies and programs. Previous studies on some of these immigration policies have focused on national and international market effects using general equilibrium mod els and empirical models such as labor demand and supply equations. It is well known that farm worker earnings are low and working conditions are harsh. As discussed in previous chapters, t h i s ha s motivated labor boycotts, such as the one led by a group of Florida farm worker s that end ed with unique wage supplement agreements to be paid by the target food firms. The purpose of this essay is to obtain current labor market elasticities that would guide public policies on immigration and to assist in eval uat ing the economic impact of recent Florida farm worker model is estimated. The model includes an indicator for activity by the Coalition of Immokalee Workers to assess the effects of those effor ts. The model is also designed to provide information useful for evaluating the economic effects of proposed immigration policy. The essay starts by summarizing recent foreign agricultural temporary worker and immigration policies. It continues with a l iterature review guiding the model developed later in the essay. The final section presents the model results and concluding remarks. 1 The work in t his essay was supported in part by funding from the Risk Management Agency of the U.S. Department of Agriculture under the partnership agreement with the University of Florida, Assessing Agricultural Labor Risk for Specialty Crops. 2 The numbers were obtai ned from NAWS data for the years 1989 2006.
89 Recent Immigration Related Policies Although foreign agricultural temporary worker programs date back to 1917, this e ssay only looks at the programs since the 1940s. At that time two guest farm worker programs were established : 1977). In 1952, the Immigration and Nationality Act established the H 2 program to admit foreign contract workers for a short period of time (120 days), especially to cover peak seasonal needs (Heppel and Papademetriou 1999). Between 1952 and 196 4 there were over two hundred thousand annua lly admitted workers under th e s e program s The majority of these workers were Bracero workers from Mexico work ing in Arkansas, Arizona, California, Colorado, Michigan, New Mexico, and Texas (Table 4 1 ). These programs were of great importance for some st ates, particularly during the 1950s (Table 4 2 ). T he number of foreign agricultural guest worker certifications declined from almost 200 thousand in 1964 to around 1 2 thousand in 1976 Between 1977 and 1989, the number of certifications increased to 30,1 89. During this period, the majority of the H 2 certifications were obtained for Florida (50%), New York (12%), and Virginia (9%) (Table 4 4). All H 2 certifications in Florida were for sugarcane; in New York for apples; and in Virginia mainly for appl es and tobacco. There are a number of policies affecting the number of certifications surrounding those years, such as the Sugar Act of 1974 and the Caribbean Basin Economic Recovery Act (CBERA) of 1983. Prior to 1974, sugarcane acreage planted was rest ricted by assigned quotas established by the Sugar Act in effect until that year
90 (Mehra 1984). 3 Any changes in the sugarcane industry or related policies affected H 2 certification numbers. In 1986, under the provisions of the Immigration Reform and Con trol Act (IRCA), the H 2 program (Table 4 3) was divided into two different worker programs: H 2A and H 2B (Heppel and Papademetriou 1999). The H 2A program was specifically established to allow temporary nonimmigrant workers in agriculture while reducing foreign farm labor dependency. Ever since its establishment, it has been criticized as needs to cover labor shortages (Bruno 2006; Effland and Runyan 1998). The restrictio ns of the program include a minimum wage rate for farm worker s so that H 2A in effect at the time the job order is placed, the prevailing hourly or piece rate, the agreed u (U.S. Department of Labor 2010). The idea behind the AEWR was to ensure that domestic workers were not adversely affected by foreign workers. According to Iwai, Emerson, and Wal ters (2006), only 16 percent of farm worker s were considered unauthorized between 1989 and 1992 This percentage was much lower than the preceding and following years since the SAW program, as part of the IRCA, effectively legalized most of the formerly i llegal work force. Ten years after the had almost 3 Later, the 1981 U.S. farm bill established a domestic sugar program with guaranteed loan prices ensured by the Secretary of Agriculture through import quotas and commodity credit corporation purchases (Messina and Seale 1993). According to Messina and Seale (1993), an increase in sugar import quotas established in 1985 resulted in a 20 percent decrease in sugar imports due to increased import quotas; this was offset by a decrease in sugar demand due to the subs titution effect from less expensive High Fructose Corn Syrup (HFCS) (Messina and Seale 1993).
91 reached a 50/50 ratio, and it remained close to this number through 2006 (Figure 4 2). Not only have IRCA enforcement sanctions not b een effective (Thom p son and Martin 1991; Brownell 2005), but Walters (2008) suggests that these sanctions encouraged growers to shift their management responsibilities to farm labor contractors e employment of unauthorized Shortly after the IRCA s 1986 enactment, the number of agricultural guest worker certifications started to decline (Tables 4 4 and 4 5). The 1996 nationwide decline i n H 2A certifications reflected the signif icant decline in H 2A certifications in Florida and New York Florida H 2 certifications went from 10,052 H 2A certifications in 1986 to only 4 in 1997. A 1997 Congress Research Service (CRS) report states that this decline in Florida H 2A job certificat ions was due to mechanization of the Florida sugarcane harvest in the 1990s. One of the main factors leading to mechanization in the Florida sugarcane industry was a change in the method of payment to sugarcane cutters in 1992. This change arose from lit igation that started in Florida in 1986. 4 Sugar Corporation abandoned the task rate system at the heart of this case and began paying workers on a per ton basis using estimates of tonnage cut, with adjustments made after the cane was weighed at the m ill. During the 1992 93 harvesting season, the Department determined that this per ton system had become the prevailing method of payment. As such, it became mandatory on all growers seeking foreign worker certification for the 1993 94 season. At least p artly as a result of that requirement, most of the sugar growers who historically had sought foreign worker certification declined to do so beginning with the 1993 94 season. Instead, they shifted to mechanized harvesting. Only U.S. Sugar stuck with the per ton system for the 1994 95 season, but it too then shifted to mechanized harvesting. Thus, since the end of the 1994 95 season, no sugar grower has sought foreign worker certification. As the Department explains, 4 The United States Department of Labor was challenged in 1982 over apple pickers in West Virginia and in 1986 over sugarcane workers in Florida.
92 The sugarcane task rate system ha s disappeared, and none of the sugarcane growers is using the [foreign worker certification] system (Opinion of the Court filed by Circuit Judge Randolph 84.3d 1432 1996). Coincidently, 1994 was the year when the North American Free Trade Agreement (N AFTA) went into effect. After 1994, the majority of the H 2A certifications were for Mexican farm worker s to harvest specialty crops ; previously, most were from the Caribbean islands (Table 4 6). At about the same time, Florida tomato workers in the Immo kalee area started to organize. In 1998 the group led a 20 day hunger strike resulting in an agreement Workers (CIW) in 2001, leading boycott efforts against food retailer s that purchase Florida tomatoes. Since 2005, the CIW has negotiated supplementary wage rate agreements between farm worker s and various retail companies recent public efforts a t the federal level to control illegal immigration that could impact Florida farm labor markets. One of these is the addition of Section 287(g) to the Immigration and Nationality Act (INA) in 1996. S ection 287(g) allows agreements between the Department of Homeland Security (DHS) secretary and state and local law enforcement agencies that permit selected appropriately trained officers to perform immigration law enforcement functions under the supervision of the Immigration and Customs Enforcement investig ative agency (ICE). Other immigration control initiatives were implemented after the 9/11 terrorist attack. These include the 2005 Secure Border Initiative (SBI) the 2006 Secure Fence Act, and the 2007 IMAGE program. More recently, several federal an d state level legislati ve bills related to immigration have been proposed some of which have been adopted while others
93 expired without action At the federal level, recent legislati ve bills include the Agricultural Job Opportunities, Benefits, and Securi ty Act of 2005 (H.R. 884); Comprehensive Immigration Reform Act of 2007 (S. 1639); Comprehensive Immigration Reform for America s Security and Prosperity Act of 2009 (H.R. 4321); SAVE Act of 2011 (H.R. 2000); the H 2A Improvement Act of 2011 (H.R. 1720 and S. 852) ; the E Verify Modernization Act of 2013 (H.R. 478); the Legal Agricultural Workforce Act of 2013 (H.R. 242); the Electronic Employment Eligibility Verification and Illegal Immigration Control Act (H.R. 502); and the Immigration Reform that Works f or he objectives of these legislative efforts include increasing border security, tightening rule enforcement, improving temporary worker programs, and integrating immigrants into the U.S. culture. Knowing immigration and economic issues are complex and politically contentious, it is not surprising that legislators between the two sets of views [anti immigration/pro enforcement and pro (Walters, E merson, and Iwai 2008 p. 3). While these proposed immigration reforms have not been signed into law a deferred deportation executive order for young immigrants was announced in 2012. Recent state legislative measures have also been approved. However, these measures may not be as comprehensive as the ones proposed at the federal level to deal with the issue. In Florida, several bills have been recently proposed (Table 1 2) including requiring every employer to use E Verify, but none have been passed by the legislature. Currently, Florida does not require E Verify. In other states, some of the legislation recently proposed include Oklahoma S.B. 908; Arizona S.B. 1070; Utah S.B.
94 47 and S.B. 466; Georgia H.B. 87 (252 Act); and Alabama H.B. 56. Three of t hese have l Immigration Reform and Enforcement Act of 2011. Different studies on the effect of immigration on wage rates have been conducted. Some of these studies are in an open economy context while others focus on national or regional farm labor markets. It is worth mentioning that studies on the national farm labor market may not be applicable to all regions. Regional simulations of immigration policies based on national level elasticities may under/overestimate the potential economic impact in a particular re gion, given the characteristics of the region al agricultural markets. Some of the previous studies on immigration and farm labor markets are reviewed in the following section. Policy, Worker Efforts, and Farm Labor Markets: Theoretical Framework There hav e been studies on the effect of immigration on labor markets in an open economy context using computable general equilibrium techniques. One of these is the study by Williamson (1982). He applied a simple general equilibrium model to measure the immigrat ion absorption capacity of the United States, given resource endowments. He shows that during the 1920s decade increase s in unskilled labor negatively affected as skilled immig rants entered the labor force and increased as unskilled immigrants entered the labor force.
95 Also in the context of an open economy, Ethier (1986) developed a two country model to analyze illegal migration of unskilled labor by focusing on the effects of immigration policies on the host country. Some important assumptions specified in im migrant unskilled labor force, and illegal immigration border enforcement pol icies financed exclusively with the taxes paid by legal workers. Under these assumptions, se by Bond and Chen (1987) and Yoshida and Woodland (2006) wh o showed that the optimal level of enforcement depends on the firm s ability to distinguish between illegal and legal workers. This has been one of the supporting arguments for the expanded use of the E Verify program. Hinojosa (2001) examined status quo immigration policies, and 11 alternative s c enarios of U S Mexico migration and economic integration using a computable general equilibrium model. Results for the status quo show how adding Mexi can migrants to the U.S. work force increases GDP, disposable income for consumers, savings for employers, and government revenues. In contrast, NAFTA related migration and trade under the status quo created greater wage and income differentials on both s ides of the border. According to Hinojosa, a restricted immigration policy scenario, such as the H 2A, is inefficient and should be replaced with a comprehensive bi national approach for generating prosperity and equity in both countries. While these stud ies do not focus on agricultural labor markets where a significant number of immigrants work, there are other studies that do focus on immigration and
96 agricultural labor markets. A number of these are included in the Emerson (200 7 ) study 5 in which he cont rasted the potential agricultural economic effects of immigration control established at the federal level to the status quo. The study focuses on the following indicators: wage rates, length of time working in agriculture, technology, crop mix, and capit al flows. In his analysis Emerson concluded that the effects on agricultural wage rates and work duration are minimal as the development and adoption of labor saving technology shifts production to other countries. Other studies estimate farm labor mark et demand and supply elasticities useful for comparative static s analyses. Hammonds, Yadav, and Vathana (1973) present a summary of the different demand elasticity studies of the hired farm labor market from 1912 to 1969. They show that the wage elastici ties of demand estimates obtained from time series and cross section s before the 1950s were less elastic than in later years. They also estimated the demand and supply equations for Oregon to test for the the elastic phase of their demand a 1.64 short run real farm wage demand elasticity and a 3.25 long run elasticity ; on the supply side, they found 4.02 and 5.15 short run and long run supply elasticities. These results were confirmed by Espey and Thilmany (2000) who conducted a meta regression analysis of farm labor demand elasticities. They found that the labor market prior to World War II was less 5 Among the prev ious studies incorporated in to the Emerson (200 7 ) analysis on wage rates and agricultural markets are those by Taylor (1992), Is and Perloff (1995), and Iwai Emerson, and Walters (2006). Previous studies on work duration reviewed in Emerson (200 7 ) are t hose by Hashida and Perloff (1996); Tran and Perloff (2002); Iwai, Napasintuwong, and Emerson (2005); and Iwai, Emerson, and Walters (2006). Regarding the immigration and technology indicator, Emerson (200 7 ) cited Hayami and Ruttan (1970), Schmitz and Sec kler (1970), and Napasintuwong and Emerson (2004). Finally, studies on (2004).
97 elastic than the post 1955 market. Also, they found that farm labor demand in the western United States appears more elastic than the national farm labor. Demand elasticity results also vary if estimated at international levels or by commodity. Wise (1974) found elastic supply of labor when analyzing the eff ect of the Bracero program on the California agricultural labor market by estimating two comparable models (Bracero and post Bracero). He found that without Bracero employment, domestic employment would have increased 262 percent with an associated incre ase in wage rate of 67 percent The impact of guest agricultural worker programs on wage rates may be mitigated by the presence of the AEWR; at least that was a result found by Cornelius (1986) in Florida citrus wage rates. Considering that the AEWR is s et by the U.S. Secretary of Labor, who is appointed by the President and confirmed by Congress, political influence can be viewed as a factor determining wage rates in agriculture. The importance of politics in determining farm wage rates has been measure d by other farm labor economists as well. Emerson, Walker, and Andrew (1976) analyzed this by focusing on the Florida citrus harvesting labor market. Their wage rate was defined as a function of alternative employment opportunities and governmental atti tude toward allowing foreign origin agricultural workers An important indicator of how governmental attitude changed was the termination of the Bracero program. Afterwards, governmental attitude toward labor importation was then modeled by including a d ummy with a value of one for when the Bracero program ended. Among their findings were a highly inelasti c demand for citrus labor and elastic supply of citrus labor ranging from 2.6 to 6.14. They also found positive coefficients for expected nonfarm inco me opportunities and governmental
98 attitude. Mehra (1984) found similar results for Florida sugarcane labor demand and supply corresponding to 1957 to 1982. She modeled government power using a proxy variable, an index reflecting political power by senior ity and related committee memberships. The Morgan and Gard n er (1982) study estimating demand and supply equations on farm labor found that demand and supply elasticities differed by regions. Analyzing the Bracero Program effect on the U.S. farm labor ma rket from 1953 to 1978, they recognized that there was no uniform distribution of state participation i n guest worker programs. They found own price elasticities of demand ranging from 0.80 to 9.5 and of supply from 0.20 to 2.3. The upper bound estimat es correspond to the three region indicate that it increased the farm labor supply by 35 p ercent ; given the estimated supply and demand elasticit ies, wage rates would have been $0.14 per hour higher in the absence of the program and especially after the introd uction of IRCA. This includes the agricultural temporary worker programs and governmental attitude towards immigration. This study has several objectives: 1) to estimate current Florida farm labor demand and supply elasticities; 2) to evaluate the wage r ate effects of the H 2 and H 2A guest farm worker program, worker efforts, and immigration enforcement stipulations after IRCA; and 3) to consider the implications of the empirical results for recently proposed immigration policy in Florida.
99 To achieve the se objectives, this study uses data from 1975 to 2009 and recognizes the importance of all interest groups: policy makers, market stakeholders, and farm labor groups. The studies reviewed here, in one way or another, influence the methodology applied in this study. The focus of this study is on Florida hired and contracted farm worker s, including guest farm worker s under the H 2 and H 2A programs More details on the methodology are discussed in the following section. Model and Data The model has four equations with four jointly determined variables. 6 It assumes that the Florida farm labor market operates under a competitive structure with the wage and quantity of labor determined at the market equilibrium Equation 4 1 shows the market equilibrium, i n which quantity supplied equals quantity demanded. ( 4 1) Equation 4 2 defines Q d t the demand for Florida hired and contracted farm worker s, including H 2 and H 2A workers. The values are farm labor hours directly hired or contrac ted by Florida farmers, including the hours of H 2 and H 2A workers. This variable was calculated using the ERS cash labor expense data for Florida divided by a weighted average of NASS quarterly hired and contracted farm worker wage rates. 7 ( 4 2) 6 The correlation matrix for all variables in the model is in Appendix D. 7 From 1981 to1984, USDA budget cuts affected the farm labor quarterly surveys, so the data for those years are limited. Wage rate weighted average is estimated using available quarters. Ideally, a quality adjusted series should be used. However, the quality adjusted series constructed by Ball Ling, and Nehring (2010) is available only until 2004. More importantly, H 2 and H 2A workers are not included in his labor variable.
100 The farm labor demand is defined in Equation 4 2 as a function of farm wage rate, output price, other complementary or substitute input prices, land and weather. The variables included in this equation and the rest of the equations in the model are explained in this section, but more details and their sources are included in Tables 4 7 to 4 9 All continuous variables in the model are included as the log of the variable. The farm worker wage rate (FWR) is the weighted average of NASS quarterly hired and contracted farm worker wage rate. This variable is treated as endogenous, a function of all other exogenous variables in the simultaneous equation model. The next variable in the farm labor demand equation, PRPP, is the ratio of expec ted price received by farmers (price received index lagged one year) to the price s paid index. In most cases, at the time harvesting decisions are made the current output price of the product is unobserved; harvesting decisions are based on expected pric es. 8 Here the including the ratio of output price to price s paid in place of the two separate variables is to avoid co linearity (Morgan and Gardner 1982). Both series of data are available only at the national level from NASS. P roduct price is an important determinant of labor demand; the relationship between the two should be positive. Acres of crop s planted in Florida annually (LND Q ) were obtained from NASS. The expec ted relationship of this variable with the quantity of labor demand ed is positive. Under ceteris paribus conditions, increases in acres planted should increase the crop supply and require additional units of labor during the harvesting season. In addition included in the demand equation is a variable representing potential labor demand 8 An example of a Florida agricultural product for which this is not true is sweet o ranges being harvested for juice.
101 effects of freezes (FRZ). This variable is the maximum days with temperatures below 28 F during the winter months from 1975 to 2009 as recorded in Florida stations reporte d in the Federal Climate Complex Global Surface Summary of Day Data. Temperatures below this threshold are known to damage Florida crops, and therefore decrease crop supply. Walker Emerson and Andrew (1976) and Mehra (1984) included a weather variable to account for effects in the respective labor markets. Both studies reported weather as having a negative effect on labor demand. The second equation in the model is the supply equation Equation 4 3. ( 4 3) Th is equation includ es the same Florida farm wage rate variable FWR as the demand equation In addition, it includes the number of Florida H 2 certifications (FLH2) and two variables to represent opportunity wage rates for farm worker s. As mentioned earlier a large numbe r of farm worker s are of foreign origin, 73 percent Foreign workers may find jobs in their own countries or in the United States out side of agriculture. O pportunity wages are specified as per capita income (deflated) in Jamaica and Mexico (FOW) and U.S. construction wage rate deflated and adjusted for unemployment (CONW ). The variable CIW is a dummy variable to represent Florida farm worker wage supplement agreements with food retailers purchasing Florida tomatoes It is expected that these unique agr eements will positively impact the quantity of Florida farm labor suppl ied Although labor union status is a standard explanatory factor in the determinant of worker earnings in the general economy (Lee 1978 ), labor organization
102 has not previously been in cluded in farm labor models given the minimal extent of labor organization in the industry. Finally, the number of deportable aliens found by the DHS Investigation Unit and Border Patrol in Florida (DEP) is included (Figure 4 1) This variable is common ly used as a proxy for unauthorized migrant flow. This variable is expected to show the negative effect s on farm labor supply as the volume of illegal immigration declines. Since the percentage of undocumented workers in agriculture is large, it is expec ted that an increase in the level of immigration control reducing undocumented immigrants would negatively impact farm labor availability. The result for this variable will tell us about the effects of immigration enforcement and farm labor supply, and wi ll be helpful in analyzing the potential impact of E V erify on the Florida labor supply and the deferred action policy being proposed. T here are arguments against the use of apprehensions as a proxy for unauthorized migrant presence Briggs ( 1984) One o cannot avoid multiple counts (Briggs 1984). Briggs recognizes however that apprehensions can be used as an indicat or of trend. The most severe problem of this varia ble, pointed out by Briggs (1984), is that it o nly counts those who are caught; most illegal immigrants are never caught. This confirms that apprehensions should not be interpreted as the actual number of illegal immigrants, but as a n approximation Desp ite this argument, the number of deportable aliens remains the best available indicator; this variable has been shown to track the volume of illegal immigration reasonably well (Espenshade 1995).
103 A lternative related variables considered includ e : deported a liens found in agriculture amendments to INA, i mmigration policy dummies fines paid by employers, enforcement man hours, and DHS budget. The first two are not available for all years in the study. The number of laws enacted in each Session of C ongress was readily available up to 2003; however, the objectives of these differ making difficult its use as a proxy f or immigration enforcement or number of undocumented workers available. A n alternative approach is to create immigration control p olicy dummies but this would significantly decrease the degrees of freedom. Another potential variable to represent immigration control enforcement is the number of man hours at the border. Hanson (2006) reports that t he number of man hours at the border increased 28 0 percent between 1990 and 2003 whereas man hours at work sites declined by 62.5 percent between 1999 and 2003. Ano ther study indicate that there is a small but posi tive relationship between man hours engaged to control immigration at the border in a yea r and apprehensions in the same year (Hanson and Spilimbergo 1996) More interestingly, he finds that the relationship between man hours at the border and apprehensions a year later is negative. This result suggests that a high number of deportable alien s found is in part a result of less enforcement a year before. Fines paid by employers declined from 15 in 1990 to zero in 2004 (Hanson 2006). The last alternative variable, D HS annual budget is allocated to different immigration control programs and ser vices. After the 2001 terrorist attack, there were important increases i n the DHS budget for biological terrorism control aviation security and first responders (DHS 2009) In light of these considerations, the number of deportable aliens remains the b est alternative.
104 Two more equations complete the model: the adverse effect wage rate (AEWR) E quation 4 4 and the H 2 certification E quation 4 5. These two equations complete the system by specifying how the AEWR and H 2 certifications are determined, a nd in turn influence the equilibrium quantity of labor and farm wage rate. ( 4 4) The AEWR equation includes a variable to represent the prevailing wage rate for farm worker s, FWR, and another for the federal or state minimum wage ra te lagged one year ( MINW ) As mentioned before, H 2A workers must Adverse Effect Wage Rate (AEWR) in effect at the time the job order is placed, the prevailing hourly or piece rate, the agreed upon collective bargaining rate (C BA), or the The AEWR is an administrative minimum wage set by the Secretary of Labor and is assumed to be subject to political influence. The variable POL is a dummy variable ; it takes the value of one for years in which the Presidency is Democrat and zero otherwise. It is included to represent political influences in determining this wage rate. The variable IRCA represents governmental attitude toward immigration. This is a dummy variab le equal to one from 1989 to 2009 and zero for the earlier years. The latter two variables are also in Equation 4 5 to capture governmental attitude toward enactment, respe ctively. ( 4 5) The H 2 and H 2A programs were written to accommodate the interests of stakeholders, the government, growers, and workers. C ertifications are granted by a
105 governmental agency (now the United States Citizenship and I mmigration Services (USCIS). Employers must demonstrate to the U.S. Department of Labor that (1) are not sufficient able, willing, and qualified United States (U.S.) workers available to perform the temporary and seasonal agricultural employment fo r which an employer desires to import nonimmigrant foreign workers and (2) employment of H 2A workers will not adversely affect the wages and working conditions of similarly employed U.S. the U.S. Department of State H 2A visa at a U.S. embassy or consulate abroad or for H 2A classification to the U.S. Customs and Border Protection for admission to the United States if no visa is needed. Given this procedure, the number of certifications sh ould be influenced by the farm foreign labor; and nonimmigrant s willingness to work in the United States. Based on all these, the H 2 equation ( 4 5) is specified T he AEWR is the minimum that H 2A workers may be paid since the AEWR has always been higher than the FLSA or Florida minimum wage. T he H 2 equation is specified so that application s for H 2A certification are directly affected by the AEWR. The m echanization of sugarcane had a significant impact on the number of H 2A workers in Florida agriculture since H 2 and H 2A workers were the only labor source for Florida sugarcane harvesting until 1995 when full mechanization occurred in the Florida sugarcane industry R epresent ing the adoption of mechanical harvesting technology in the Florida sugarcane industry is the variable ( MECH ) The applications for H 2A visas abroad often out number the actual jobs available, given country of origin economic conditions relative to U.S. economic opportunities.
106 Since there are no data indicating the number of H 2 and H 2A applications abroad by potential workers for the study period, the willingness to come to the United States is represented by the foreign market condition indicator, FO W, the per capita income in the main H 2 or H 2A country of origin. More details and the sources of the data are in T able s 4 7 through 4 9 The model is estimated using annual data covering the years 1975 2009. 9 As defined, there are four equations in the model. In simultaneous equation models the endogenous variables are correlated with the disturbances; therefore, ordinary least square s estimators are inconsistent. Th e simultaneous equation model is estimated in its structural form using 2SLS i nstru mental v ariable techniques and 3SLS to obtain consistent estimates of the structural equation parameters. Structural Model Results The estimate s for the structural equations using instrumental variables (IV) and 3SLS are shown in T able s 4 1 0 to 4 12. T he IV estimates of the demand and supply equation s are in Table 4 10. The FWR coefficient in the demand equation, although positive, is not statistically significant This result is interpreted as a perfectly inelastic demand for farm labor in Florida a nd the restriction is imposed in the second column of Table 4 10. T his result is fairly consistent with the inelastic demand coefficients estimated by Emerson, Walker, and Andrew (1976) for the Florida citrus labor market and by Mehra (1984) for the Flori da sugarcane labor market A perfectly inelastic labor demand is not surprising since at the time of harvest, all other production decisions have been made, and growers are expected to harvest to obtain revenues to help offset 9 The correlation matrix of all variables used in the model is in Appendix D.
107 sunk production costs. The result does differ from the more elastic demands reported by Hammonds, Yadav, and Vathana (1973) and Morgan and Gardner (1982) although their more elastic results are generally for larger labor markets with more diversified commodities than is the case in Florida T he supply equation estimates via IV in Table 4 10 suggest that the supply of Florida agricultural labor is also perfectly inelastic, an implausible result in the presence of a perfectly inelastic demand. An alternative specification recognize s the quantity of labor to be determined by the perfectly inelastic labor demand, thus requiring the quantity of labor to be shifted to the right hand side of the supply equation, and the wage rate shifted to the left hand side of the supply equation. Equ ations 4 2 and 4 3, as modified, are as follows: ( 4 2) ( 4 3) The alternative specification is estimated using IV and 3SLS. The resulting estimates are shown in Tables 4 11 and 4 12, respectively. The IV and 3 SLS estimates are quite similar, suggesting that the 3SLS estimates are preferable on the basis of efficiency considerations. Consequently, the discussion focuses on the 3SLS estimates in Table 4 12. The most pertinent result for the demand equation, a pe rfectly inelastic demand, has already been discussed. The coefficients for LNDQ and that for PRPP were statistically significant at alpha levels smaller than 0.10. As expected, the coefficient for LNDQ was positive. Under ceteris paribus conditions, inc reases in acres planted would require additional units of labor during the harvesting season. Although the coefficient
108 for PRPP is significantly different than zero, it has the wrong sign. This is true for both the IV and 3SLS estimates. Among the suspe cted difficulties is likely the relative importance of citrus as a tree crop in Florida, dampening short term adjustments to price variations. The labor quantity coefficient in the supply equation (Table 4 12) is not statistically significant, suggesting t hat the labor supply is perfectly elastic This is plausible; elastic coefficients previously found range from 2.4 to 4.02 (Emerson, Walker, and Andrew 1976; Mehra 1984; Hammonds, Yadav, and Vathana 1973) The perfectly elastic supply interpretation is c onsistent with the fact that this is a state rather than national level supply to a narrowly defined industry that competes for labor with other industries, and over a time period when there was a seemingly endless supply of foreign workers. Three additio nal coefficient estimates are statistically significant: FLH2, CONW, and DEP. The coefficient for FLH2 was negative ( 0.025), indicat ing that an increase in the number of H 2 certifications for Florida, ceteris paribus rates. CON W was statistically significant and equal to 0. 74; a 10% increase in the The coefficient for DEP is statistically significant and equal to 0.109. The increased number of deportable aliens may simply reflect an increased number of foreign workers in the U.S. economy, thereby potentially exerting downward pressure on wage rates in all low skilled labor markets, including agriculture. Estimates for the AEWR equatio n, in Table 4 11, show statistically significant coefficients for FWR, MINW, IRCA and POL. The coefficients for FWR and MINW are
109 coefficient is negative; the result f or this variable indicates that the AEWR would be 0.135 percent estimated coefficient is 0. 039 which indicates that the AEWR is 0.039 percent lower in years with democrat political cont rol. Statistically significant coefficients in the H 2 equation are those for MECH, FOW, and POL. Each of these represents one of the three stakeholders of H 2 certifications. The coefficient for MECH represents the grower ; i t is negative C ertificati ons granted are negatively correlated with the mechanization of the sugarcane industry This result is not a surprising one, given the high dependency of H 2 workers in Florida sugarcane, which declined as mechanization increased. FOW represents economi coefficient for FOW is negative ; there is a n inverse relationship between foreign per capita GDP and H 2 certifi cations as expected POL represents the interest of public administrators or govern mental attitude toward immigration. The value of the POL estimated coefficient is 0. 9 1 2 which indicates that certifications are 0.912 percent lower in years with democratic political control. The Reduced Form The reduced form estimates of the simultan eous model here are derived from the structural model, which is The reduced form is where and Consistent estimates of the structural model yields reduced form es timates, that are consistent and impose the restrictions specified in the structural model. The estimated covariance matrix for Is derived from the covariance matrix of the structural parameters, as
110 where is the matrix of structural parameters (Theil 1971 ). Theil shows that the derivatives take a very simple form: The results for the reduced form in Table 4 13 show the total effects of a change in an exogenous variable on each of the endogenous variables. Since there is no endogenous variable on the right hand side of the demand equation; the reduced form estimates of the demand equation are the same as the structural estimates. The effects of each exogenous variable on wage rates are shown in the second column of Table 4 13. Four estimates are statistically significant; at alpha levels below 0.10. These correspond to CONW, DEP, POL, and MECH. Of these CONW, POL, and MECH have positive effects. The coefficient for CONW is 0.724; this indicates that a percentage increase in the construction wage rate increases farm wage rates by 0.724 percent. Likewise, a percentage increase in sugarcane mechanization increased wage rates by 0.185 percent. According to the result for POL, wage rates are 0.024 percent lower in years with democratic control. The reduced form estimate for DEP is negative, implying that an increase in deportable aliens negatively affects farm wage rates. More specifically, a one percent increase in the number of deportable aliens would decrease wage rates by 0.106 percent. The interpretation is the same as for the structural coefficient above. The reduced form estimates for the AEWR end ogenous variable in column 3 of Table 4 13 show positive effects from exogenous variables: MINW, CONW, and MECH.
111 A one percent increase in construction wages or minimum wages increases the AEWR by 0.42 and 0.298 percent, respectively. Similarly, as sugar cane mechanization increased, the AEWR also increased with a one percent mechanization increase resulting in a 0.1 percent increase in the AEWR. Negative effects on the AEWR result from DEP and IRCA. For instance, a one percent increase in deportable al iens reduced affected AEWR; IRCA reduced the AEWR by 0.151 percent. In the FLH2 equation, only two coefficients are statistically significant; these correspond to IRCA and M 2 certifications is positive, while MECH effect is negative. A one percent increase in sugarcane mechanization decreased the number of H 2 certifications by 7.3 percent. Implications for Immigration Reform This se ction is to explain the results of this study in the context of recently proposed immigration policy implications for Florida farm workers and producers. V arious elements to consider appear in some combination of past and current immigration proposals T hese are the following : 1) strict border enforcement, 2) employment enforcement, 3) regularizing or deporting existing unauthorized workers, and 4) various guest worker proposals. Results show a negative relationship between the number of deportable aliens in Florida and farm labor wage rates and AEWR. A significant number of deportable aliens in Florida may reflect a significant number of foreign workers in the U.S. economy, perhaps, as a result of ineffective immigration law enforcement efforts. Improvi ng or increasing border enforcement efforts would ultimately reduce the number
112 of deportable aliens. As the empirical results demonstrated above, a smaller number of deportable aliens results in a higher farm wage. Most recent proposals for employment e nforcement are based on an expanded use of the E Verify program. For example, mandating the use of E V erify in a ll labor markets would sharply reduce labor supply in low skill labor markets, resulting in increase d wage rates, overall. Agricultural employ ers would be forced to compete intensely for labor with other sectors such as construction. The empirical results demonstrate that the increased wage rates in construction would result in higher wage rates in agriculture. In addition, in the absence of a ny supplemental labor program, the agricultural labor supply curve would likely bec ome less than perfectly elastic: the industry would no longer be able to attract additional labor without paying higher wage rates. An effective implementation of E Verify o nly in agriculture would not have significantly different effects in agriculture than if it were applied to all labor markets. However, applying it at the state level would place Florida producers at a significant disadvantage relative to other states tha t did not adopt E Ver i fy since wage rates would be driven up in Florida, but not elsewhere An additional consideration of E Verify is what happens to unauthorized workers no longer eligible for employment. This is a contentious issue with some proposal s deporting all unauthorized workers, while others establish some form of regularization of unauthorized workers. The former would result in the farm labor market effects noted in the previous paragraph, while the latter would have much more modest effect s. Even if all unauthorized agricultural workers were regularized in some fashion, there would still remain a longer term problem. Most workers remain in agriculture for
113 only a few years, migrating to some other sector of the economy as they gain experie nce. Guest worker programs are typically suggested to alleviate this problem. The existing H 2A program is one type of guest worker program, and is specifically for agriculture. The empirical results above suggest that increasing the H 2A workers has ha d a modest negative effect in the past on the farm wage rate. In addition, the H 2A program is argued by employers to be overly cumbersome. An alternative has been skilled labor immigration by regulating the number of available visas for workers, and taxing either employers or immigrating workers as a means of allocating foreign workers to those employers in greatest need and to attract the most productive workers. In addition, the supply o f visas would vary with the business cycle. His proposal applies to all low skill labor markets, not just agriculture. This would allow labor markets to perform their allocative function among industries. In the context of the empirical results, this co uld potentially result in an upward sloping labor supply curve for agriculture, requiring higher wage rates for additional labor. In contrast, policies granting visas or other permits to immigrants (deferred action) may ironically increase the number of deportable aliens. At least that was the result of SAW. According to Iwai, Emerson, and Walters (2006), the percent age of farm worker s considered unauthorized increased after the SAW program. Without E Verify granting visas may increase deportable ali ens consequently decreas ing farm wage rates. Summary of Findings The objective of this essay was to estimate a demand and supply model to obtain current and labor market elasticities useful to evaluate the economic impact of recent ly proposed policies an d Florida
114 that the Florida farm labor demand is perfectly inelastic while the farm labor supply is perfectly elastic. R esults in this paper showed that the CIW agreements have not impacted Florid a This may be because wage supplement agreements are passed on entirely from retailers to workers It might also be due to the proportion of the agreement segment relative to the overall Florida farm labor market. However, in this study, it is documented that the late 1980s resulted in a change in the method of payment to sugarcane cutters leading to mechanization in the Florida sugarcane industry. The consequences of this mechanization i nclude a decrease in the demand for seasonal sugarcane labor; reductions in the number of H 2 certifications ; and increases in pay rates Florida sugarcane mechanization was shown to positively impact farm wage rates, including the AEWR. Regarding immig ration enforcement policies, results show a negative relationship between the number of deportable aliens in Florida and farm labor wage rates and AEWR. Any policy changing the number of deportable aliens in Florida, such as E V erify and deferred action, would i mpact Florida farm workers wage rates E verify would decrease deportable aliens, hence farm labor availability positively impacting farm workers wage rates. This effect magnifies if the immigration enforcement system is established at the state level or if it is only for some labor markets The H 2A program or a similar guest worker program would reduce E Verify negative impacts. The data showed evidence that the number of H 2 certifications is negatively affected by factors representing the t hree stakeholders, foreign origin workers, growers,
115 and public administrators. Increases in the country of origin GDP per capita and domestic mechanization, would decrease the number of certifications granted. The model also showed that the number of H 2 certifications is inversely related to democratic control.
116 Table 4 1. Number and percentage of Braceros by main user s tates, 1953 64 Year Total California Number % Texas Number % Percentage 1953 215,321 52,452 24% 62,854 29% 53.55% 1954 32 0,737 77,423 24% 158,704 49% 73.62% 1955 411,966 109,677 27% 200,470 49% 75.28% 1956 459,460 150,877 33% 193,344 42% 74.92% 1957 451,520 149,067 33% 188,824 42% 74.83% 1958 447,198 138,328 31% 206,331 46% 77.0 7% 1959 455,015 136,012 30% 205,959 45% 75.16% 1960 333,866 112,995 34% 122,755 37% 70.61% 1961 310,335 98,733 32% 117,368 38% 69.63% 1962 216,606 116,455 54% 30,152 14% 67.68% 1963 208,295 110,823 53% 26 ,084 13% 65.73% 1964 199,997 112,096 56% 18,171 9% 65.13%
117 Table 4 2 Foreign a gricultural g uest w orkers a dmitted into United States, 1942 76 Year Total Mexican BWI Canadian Number % Number % Number % 1942 4,203 4,203 100% 0% 0% 1943 65,624 52,098 79% 13,526 21% 0% 1944 83,206 62,170 75% 19,622 24% 1,414 2% 1945 72,900 49,454 68% 19,391 27% 4,055 6% 1946 51,347 32,043 62% 13,771 27% 5,533 11 % 1947 30,775 19,631 64% 3,722 12% 7,421 24% 1948 44,916 35,345 79% 3,671 8% 5,900 13% 1949 112,765 107,000 95% 2,765 2% 3,000 3% 1950 76,525 67,500 88% 6,225 8% 2,800 4% 1951 203,640 192,000 94% 9,040 4% 2,600 1% 1952 210,210 197,100 94% 7,910 4% 5,200 2% 1953 215,321 201,380 94% 7,741 4% 6,200 3% 1954 320,737 309,033 96% 4,704 1% 7,000 2% 1955 411,966 398,320 97% 6,616 2% 6,700 2% 1956 459,460 445,197 97% 7,563 2% 6,700 1% 1957 451,520 436,049 97% 8,171 2% 7,300 2% 1958 447,198 432,857 97% 7,441 2% 6,900 2% 1959 455,015 437,643 96% 8,772 2% 8,600 2% 1960 333,866 315,846 95% 9,820 3 % 8,200 2% 1961 310,335 291,420 94% 10,315 3% 8,600 3% 1962 216,606 194,978 90% 12,928 6% 8,700 4% 1963 208,295 186,865 90% 12,930 6% 8,500 4% 1964 199,997 117,736 59% 14,361 7% 7,900 4% 1965 35,871 20,284 57% 10,917 30% 4,670 13% 1966 23,521 8,647 37% 11,194 48% 3,683 16% 1967 23,603 6,125 26% 13,578 58% 3,900 17% 1968 13,323 0% 10,723 80% 2,600 20% 1969 15,830 0% 13,530 85% 2,300 15% 1970 17,474 0% 15,470 89% 2,004 11% 1971 13,684 0% 12,143 89% 1,541 11% 1972 12,526 0% 11,419 91% 1,107 9% 1973 12,837 0% 12,837 100% 2,559 20% 1974 15,151 0% 12,582 83% 1,280 8% 1975 14,093 0% 12,813 91% 952 7% 1976 11,910 0% 10,958 92% n/a n/a Data source: From 1942 to 1972, the 1986 Annual Report, Labor Certifications for Temporar y Foreign Agricultural and Logging Workers (H Labor Certifications; from 1973 to 1976 from Mehra (1984).
118 Table 4 3 H 2 and H 2A c ertifications, 1971 2006 Year Certifications Number 1971 21,893 1972 21,423 1973 20,138 1974 20,634 1975 16,499 1976 15,231 1977 15,281 1978 15,441 1979 18,283 1980 18,371 1981 17,953 1982 19,779 1983 19,506 1984 20,071 1985 20,682 1986 21,177 1989 30,189 1990 18,219 1991 18,440 1992 16,390 1993 14,628 1994 13,185 1995 11,394 199 6 9,635 1998 27,308 1999 32,372 2000 33,292 2001 27,695 2002 15,628 2003 14,094 2004 22,141 2006 46,432 Source: 1986 Annual Report, Labor Certifications fo r Temporary Foreign Agricultural and Logging Workers (H Note: These are not actual numbers of foreign workers admitted for employment. An employer may use all, part or not use the c ertifications granted and admitted foreign workers may work in two or more certified jobs.
119 Table 4 4. Number and percentage of H 2 certifications by main user states 1976 86 Florida New York Virginia Year Total Number % Number % Number % Percenta ge 1976 15,231 8,709 57% 1,151 8% 621 4% 69% 1977 15,281 8,666 57% 1,703 11% 922 6% 74% 1978 15,441 8,706 56% 1,716 11% 1,036 7% 74% 1979 18,283 8,530 47% 2,571 14% 1,809 10% 71% 1980 18,371 8,852 48% 2,308 13% 2,211 12% 73% 1981 17,953 8,801 49% 1,914 11% 2,448 14% 73% 1982 19,779 9,140 46% 2,662 13% 2,352 12% 72% 1983 19,506 9,610 49% 2,548 13% 2,145 11% 73% 1984 20,071 9,639 48% 2,494 12% 2,246 11 % 72% 1985 20,682 10,017 48% 2,494 12% 2,085 10% 71% 1986 21,177 10,052 47% 2,748 13% 1,471 7% 67% Average for the period: 50% 12% 9% Source: 1986 Annual Report, Labor Certifications for Temporary Foreign Agricultural and Log ging Workers (H Table 4 5. H 2A certifications by main user states 1991 2004 Florida New York North Carolina Virginia Year Total Number % Number % Number % Number % 1991 18,440 6,383 35 .00 % 2,442 13 .00 % 1,638 9 .00 % 3,221 17 .00 % 1992 14,566 3,697 25 .00 % 2,202 15 .00 % 1,636 11 .00 % 2,424 17 .00 % 1993 16,257 2,994 18 .00 % 2,228 14 .00 % 2,598 16 .00 % 3,465 21 .00 % 1994 13,185 1,643 12 .00 % 1,927 15 .00 % 2,716 21 .00 % 2,251 17 .00 % 1996 9,635 8 0.08% 49 1 .00 % 3,787 39 .00 % 2,357 24 .00 % 1997 23,352 4 0.02% 2,122 9 .00 % 6,444 28 .00 % 3,706 16 .00 % 1998 27,308 63 0.23% 1,764 6 .00 % 8,884 33 .00 % 3,389 12 .00 % 1999 32,376 311 1 .00 % 1,997 6 .00 % 9,460 29 .00 % 3,004 9 .00 % 2000 33,292 954 3 .00 % 1,70 5 5 .00 % 8,458 25 .00 % 2,055 6 .00 % 2001 39,260 914 2 .00 % 1,472 4 .00 % 5,930 15 .00 % 1,347 3 .00 % 2002 15,628 911 6 .00 % 864 6 .00 % 3,373 22 .00 % 1,155 7 .00 % 2003 14,094 705 5 .00 % 1,126 8 .00 % 2,576 18 .00 % 705 5 .00 % 2004 22,141 249 1 .00 % 1,462 7 .00 % 4,929 22 .00 % 1,605 7 .00 % Source: Department of Homeland Security ; 1997 Data source was the CRS Report for Congress 97 714 EPW
120 Table 4 6. H 2A certifications by leading sender country 1991 2004 Mexico Jamaica Canada Other Year Total Number % Number % Number % Number % 1991 18,440 5,633 31% 11,833 64% 254 1% 259 1% 1992 14,566 4,566 31% 8,178 56% 631 4% 829 6% 1993 16,257 6,771 42% 8,118 50% 443 3% 513 3% 1994 13,185 6,070 46% 5,867 44% 514 4% 711 5% 1995 n/a n/a n/a n/a n/a n/a n/a n/a n/a 1996 9,635 8,833 92% 116 1% 74 1% 546 6% 1997 n/a n/a n/a n/a n/a n/a n/a n/a n/a 1998 27,308 21,594 79% 4,275 16% 759 3% 647 2% 1999 32,376 26,071 81% 3,977 12% 767 2% 1,177 4% 2000 33,292 27,172 82% 3,663 11% 745 2% 1,581 5% 2001 39,260 29,539 75% 5,287 13% 768 2% 2,333 6% 2002 15,628 12,846 82% 1,569 10% 282 2% 865 6% 2003 14,094 9,924 70% 2, 483 18% 357 3% 1,232 9% 2004 22,141 17,218 78% 2,630 12% 484 2% 1,780 8% Source: Department of Homeland Security
121 Table 4 7 Endogenous variables and data source Variable Description Source FLH2 Log of the number of H2 A certifica tions admitted by intended territory of destination Florida 1976 1986 data from 1986 Annual Report on Labor Certifications for Temporary Foreign Agriculture and Logging Workers (H 2's), U.S. Employment Service Division of Foreign Labor Certification. Fro m 1991 to 2008 except for 1995, 1997, and 2005 from Department of Homeland Security (Table 1 Nonimmigrant H2A Admissions by Leading Countries of Citizenship and Leading States of Intended Residence and Table 10 Nonimmigrant H2 A Admissions by State or T erritory of Destination: Fiscal Years 1991 to 2008). 1995 assumed equal to zero because of court litigations. 1997 data source was the CRS Report for Congress 97 714 EPW. 2005 extrapolated. AEWR Log of the real Adverse Effect Wage Rate for Florida (197 5 1994 sugar cane all from 1995 to 2009) divided by CPI. 1975 and 1976 data from Federal Register Vol. 40, No. 159. and Vol. 41, No. 163 respectively. 1981 1986 from 1986 Annual Report on Labor Certifications for Temporary Foreign Agriculture and Loggin g Workers (H 2's), U.S. Employment Service Division of Foreign Labor Certification. 1990 to 2008 from CRS Report for Congress, Farm Labor: The Adverse Effect Wage Rate (AEWR) order code RL32861. 2009 Federal Register Vol 74 No. 102. Q Log of hours of fa rm labor in Florida U sing ERS total cash labor expenses and weighted average hourly wage rate based on NASS quarterly survey FWR Log of the weighted average of Florida hired farm workers hourly wage rate and agricultural service workers wage rate/cpi) NA SS quarterly survey on farm labor
122 Table 4 8 Exogenous variables and data source Variable Description Source FRZ Log of the max no. of days below 28 0 F reported in FL stations: 722015 Key West Nas, 722020 Miami, 722030 West Palm Beach IN, 722056 Daytona Beach Intl., 722057 Orlando Sanford, 722065 Jacksonville Nas, 722106 Ft. Myers Page Fld, 722110 Tampa Intl. Airport, 722140 Tallahassee Municip., 722200 FL Apalachicola Online ordered from Federal Climate Complex Global Surface Summary NCDC Climate Servic e Branch. Electronic access: http://www1.ncdc.noaa.gov/pub/orders/CD O9798665300645.txt DEP Log of number of deportable aliens found by the investigation unit and border patrol in Florida Department of Homeland Security Year Book various issues. Data for 1986 to 1991 was not available; it was extrapolated in Stata. FOW 1975 1995 log of Jamaica per capita GDP and 1996 2009 Mexico GDP times 1 unemployment rate/100 Real historical Gross Domestic Product (GDP) per capita (in billions of 2005 dollars) from E RS Internatoinal Macroeconomic Data Set CONW Log of real United States Construction Hourly Wage Rate; real US construction hourly wage rate=(US construction hourly wage rate/CPI)*(1 unemployment rate) from 1975 2009 Beaurau of Labor Statistics (BLS) Hou rs and Earnings LNDQ Log of annual planted crop acres in Florida farms NASS, Acreage; available at: http://usda01.library. cornell.edu/usda/nass/Acre/ MECH Percentage of mechanization in the Florida sugarcane industry (estimated using sugar cane harve st acres and number of sugar cane cutters (H2) assuming constant labor productivity through the years). Crop havested acres NASS, Acreage; available at: http://usda01.library.cornell.edu/usda/nass/ Acre//2010s/2010/Acre 06 30 2010.txt and number of 1975 H 2 certifications for sugar cane in Florida from 1976 1986 data from 1986 Annual Report on Labor Certifications for Temporary Foreign Agriculture and Logging Workers (H 2's), U.S. Employment Service Division of Foreign Labor Certification. MINW log of Flor ida minimum wage rate lagged one year Wage and Hour Division of the Department of Labor http://www.dol.gov/whd/minwage/chart.htm PRPP log of the ratio of agricultural price received index lagged one year to price paid. Note, this indices are not specifically for Florida. NASS: special request
123 Table 4 9 Dummy variables Variable Description Source IRCA Dummy variables constructed; it equals one for years after 1989 when most of IRCA's stipulatio ns were effective and zero otherwise N/A CIW Constructed dummy equal to 1 for CIW agreement years (2005 and up) and zero otherwise N/A POL Constructed dummy equal to 1 for years in which the U.S. president was democrat and zero otherwise N/A Table 4 1 0 Demand and supply e quation estimates Quantity of l abor (Q) Quantity of l abor a (Q) Quantity of l abor (Q) Farm w age r ate (FWR) Constant 3.181 3.017 2.805 0.753 (3.336) (3.242) (2.322) (1.091) Farm w age r ate (FWR) 0.308 0.781 (0.378) (1.03 2) Price r eceived to p aid i ndex (PRPP) 0.680 0.682 (0.161) (0.157) Acres p lanted (LNDQ) 0.544 0.580 (0.245) (0.234) Days b elow 28 0 F (FRZ) 0.047 0.038 (0.037) (0.035) Quantity of l abor (Q) 0.113 (0.174) Florida H2 c e rtifications (FLH2) 0.035 0.026 (0.031) (0.009) Florida m inimum w age (MINW) 0.135 0.013 (0.250) (0.114) National c onstruction w age r ate (CONW) 0.210 0.789 ( 1.059) (0.281) Foreign c ountry GDP p er c apita (FOW) 0.061 0.005 ( 0.037) (0.020) CIW a greement d ummy (CIW) 0.061 0.0001 (0.064) (0.031) Florida d eportable a liens (DEP) 0.131 0.127 (0.185) (0.060) a The estimates for this demand equation were estimated by ordinary least squares the rest of the columns wer e estimated by instrumental variables The n umber of observations is 34. Statistically significant at alpha < 0.10
124 Table 4 1 1 Single e quation e stimates of farm labor model Demand : a Supply : AEWR : H2 : Quantity of l abor (Q) Farm w age r at es (FWR) Adverse Effect Wage Rate (AEWR) Florida H2 c ertifications (FLH2) Constant 3.017 0.753 0.581 7.930 (3.242) (1.091) (0.559) (13.986) Farm w age r ate (FWR) 0.517 (0.219) Price r eceived to p aid i ndex (PRPP) 0.682 (0.157) Acres p lanted (LNDQ) 0.580 (0.234) Days b elow 28 0 F (FRZ) 0.038 (0.035) Quantity of l abor (Q) 0.113 (0.174) Florida H2 c ertifications (FLH2) 0.026 (0.009) Florida m inimum w age (MINW) 0.013 0.304 (0.114) (0.123) N ational c onstruction w age r ate (CONW) 0.789 (0.281) Foreign c ountry GDP p er c apita (FOW) 0.005 0.690 (0.020) (0.346) CIW a greement d ummy (CIWA) 0.0001 (0.031) Florida d eportable a liens (DEP) 0.127 (0.060) IRCA d ummy (IRCA) 0.144 2.242 (0.0257) (1.448) Political i nfluence d ummy (POL) 0.037 0.756 (0.022) (0.416) Adverse Effect Wage Rate (AEWR) 4.474 (5.938) Sugar c ane m echanization (MECH) 7.690 (1.339) a The demand equation is estim ated by ordinary least squares all other by instrumental variables The n umber of observations is 34. Statistically significant at alpha < 0.10
125 Table 4 1 2. System of Florida farm labor model: 3SLS e stimates Endogenous v ariables in e quations Demand : Supply : AEWR : H2 : Quantity of l abor (Q) Farm wage rate (FWR) Adverse Effect Wage Rate (AEWR) Florida H2 c ertifications (FLH2) Constant 2.280 0.434 0.375 15.210 (3.047) (0.714) (0.500) (10.584) Farm w age r ate (FWR) 0.580 (0.193 ) Price r eceived to p aid i ndex (PRPP) 0.654 (0.147) Acres p lanted (LNDQ) 0.525 (0.220) Days b elow 28 0 F (FRZ) 0.025 (0.032) Quantity of l abor (Q) 0.166 (0.114) Florida H2 c ertifications (FLH2) 0.025 (0.007) Florida m inimum w age (MINW) 0.058 0.338 (0.084) (0.113) National c onstruction w age r ate (CONW) 0.740 (0.203) Foreign c ountry GDP p er c apita (FOW) 0.005 0.720 (0.015) (0.278) CIW a greement d ummy (CIW) 0.014 (0.019) F lorida d eportable a liens (DEP) 0.109 (0.040) IRCA d ummy (IRCA) 0.135 1.319 (0.024) (1.005) Political i nfluence d ummy (POL) 0.039 0.912 (0.021) (0.329) Adverse e ffect w age r ate (AEWR) 1.493 (4.663) Sugar c ane m echaniz ation (MECH) 7.460 (1.074) Statistically significant at alpha < .10 The n umber of observations is 34
126 Table 4 1 3 Reduced f orm e stimates Endogeneous variables in model equations : Quantity of l abor (Q) Farm wage rate (FWR) Adverse Effec t Wage Rate (AEWR) Florida H2 c ertifications (FLH2) Constant 2.280 0.337 0.179 15.477 (3.047) (0.941) (0.774) (9.552) Price r eceived to p aid i ndex (PRPP) 0.654* 0.107 0.062 0.092 (0.147) (0.077) (0.049) (0.276) Acres p lanted (LNDQ) 0.525* 0.08 5 0.050 0.074 (0.220) (0.067) (0.043) (0.222) Days b elow 28 0 F (FRZ) 0.025 0.004 0.002 0.004 (0.032) (0.006) (0.003) (0.012) Florida m inimum w age (MINW) 0.069 0.298* 0.445 (0.081) (0.118) (1.356) National c onstruction w age r ate (CONW) 0.72 4* 0.420* 0.627 (0.186) (0.166) (1.968) Foreign c ountry GDP p er c apita (FOW) 0.023 0.013 0.701 (0.031) (0.019) (1.054) CIW a greement d ummy (CIW) 0.014 0.008 0.012 (0.019) (0.011) (0.039) Florida d eportable a liens (DEP) 0.106* 0.062* 0.09 2 (0.040) (0.031) (0.278) IRCA d ummy (IRCA) 0.028 0.151* 1.094* (0.017) (0.024) (0.632) Political i nfluence d ummy (POL) 0.024* 0.025 0.949 (0.009) (0.019) (0.313) Sugar c ane m echanization (MECH) 0.185* 0.107* 7.300* (0.047) (0.045) ( 0.567) *Statistically significant at alpha level < 0.10 The n umber of observations is 34
127 Figure 4 1. Deportable aliens 1967 200 8 (Notes: Data sources are the U.S. Department of Homeland Security (DHS), Customs and Border Protection (CBP), Office of Border Patrol (OBP), Immigration and Customs Enforcement (ICE), Office of Investigations (OI), and the Office of Detention and Removal Operations (DRO). The 1976 data include 15 months (from July 1, 1975 to September 30, 1976) due to fiscal year end da te change from June 30 to September 30. Deportable aliens located are Border Patrol apprehensions and ICE administrative arrests. ) Figure 4 2. Percent of unauthorized farm workers, 1989 2006 (Note: Data source is the National Agriculture Worker Survey [ NAWS ] ) 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000 1,600,000 1,800,000 2,000,000 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 Deportable Aliens 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Proportion of Unauthorized
128 Figure 4 2007 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% 40.00% 1950 1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 Oranges Green House Sugar Cane Tomatoes and Peppers
12 9 CHAPTER 5 CONCLUSIONS T his dissertation addresses farm workers efforts to improve wage rates and working conditions and public policies impacting farm labor ava ilability. A m ul t i level market model is applied provid ing an interesting way of bringing the structure of the product and factor markets and the production technologies directly to bear on the implications for farm factor, grower, and final product mar kets involved in farm workers boycott efforts and wage supplement agreements in Florida Workers labor relation efforts have ended with unique third party wage supplement agreements improving wage rates for the covered segment of the labor market and d ecreasing wage rates for those not in the agreement. The extent of the agreement is currently small ; as similar agreements are made between other retail level buyers of farm products and labor groups, the higher will be the positive effects on wage rates. The magnitude of the effect also depends upon the stipulations of the agreement. Supplement agreements restricting payments to workers have greater positive effects on farm worker s wage rates than those without restrictions. The grower level impact fr om these stipulations is exactly opposite. The game theory application on the efforts show s that farm labor groups are choosing boycott effort s against target firms threat or actual boycott, which maximize its expected utility. This decision is based on the probability of success, i.e. gett in g a wage sup plement payment. The success of the boycott strategy in obtaining wage supplemental agreements with the TF depends on the ability to sufficiently affect the A successful boycott negatively affects demand, leading the TF to offset those negative demand effects by ac
130 demands in exchange for boycott termination. I t is clear from stage four of the game that the TFs will choose the optimal level of inpu t and output to maximize profits and that the agreement would be made only if the benefit of that decision is greater than the cost of facing the boycott. A ccording to the event study results, a boycott announcement does not affect target firm s value me asured by the stock market abnormal returns. B oycott announcements may be perceived by stockholders as a threat that may or may not occur at some unknown time in the future. The event study also show ed no abnormal return from agreement events. It is als o likely that the potential cash flow impact of the agreement the cost of removing the threat or boycott effort is relatively minor fr om the Th e result of zero abnormal return could be due to several factors: (1) the agreements b etween farm labor groups and target firms are weak because there is no direct employer employee relationship ; (2) there usually is a long time lag between the boycott announcement (first stage of the game) and the agreement decision announcement which allo ws the target firms to reduce the potential impact from boycotts ; (3) b oycott related events are more subjective than other market value impacting announcements that are more easily related to future cash flows ; and (4) farm workers wage supplement agreem ents send positive signals to consumers, stockholders, and investors about the ethical behavior of the target firms or their corporate social responsibility (CSR) providing market opp ortunities with positive impact offsetting the cost of the agreement I n summary, the t decision to
131 sign a supplement wage rate agreement depends on its expected ability to offset a boycott threat s impact and if boycotted, the effects of the boycott on profits. The s and Socrates index, are not predictors of the agreement event, but in tangible assets are. Int angible assets are efforts have higher probability of success as target s in tan gible assets increase. B oycott strategies may in the long run improve farm workers wage rates ; alone they may not be able to help close the existing gap between agricultural and non agricultural wage rates An empirical study on the Florida labor market shows that CIW shows mechanization, construction wage rates, and democratic control positively impact farm worker wage rates. In contrast, tightening immigr ation enforcement lead s to fewer deportable aliens posi tively affecting farm workers wage rates. Establishing E V erify as a requirement for all employers in Florida, including agriculture, may negatively impact the agriculture labor force Therefore, th e establishment of E Verify as mandatory in agriculture may need to be accompanied by p ublic interventions to adjust to the new system. Various approaches to guest worker programs may serve this purpose. As growers adopt mechanized harvesting techniques t he number of H 2 certifications falls T he number of H 2 certifications also decreas e s as the country sourcing H 2 workers face s economic expansion The scope of this study was limited by data availability. F urther research is needed to consider rec ent immigration enforcement initiatives not covered in this study
132 Also, it would be interesting to conduct a duration analysis on the farm worker boycott events ( to further explain strategic decisions of farm worker groups regarding choos ing their targ ets ) and the target firm s responses regarding p articipat ion in the agreements
133 APPENDIX A DERIVATION OF MULTI LEVEL MARKET MODEL Lagrange: (2.0a) First Order Con ditions: (2.1a) (2.2a) (2.3a)
134 Partial derivative of profit with respect to prices of input c and output Y ; (2.4a) ; (2.5a) Conver t Equa tions 2.1a through 2.5a to p ercentage c hanges and divide by percentage change in boycott quantities : (2.1a) (2.6 a) (2.2a)
135 (2.7a) For the AMMM the retailer maximization pro blem is the following : Consequently, E quation 2.2a is re defined as follow: (2.7a)
136 (2.3a) (2.8a) (2.4a) Knowing that, then,
137 (2.9a) (2.5a) (2.10a) Modifying for boycott effects: (2.5a) (2.10a)
138 APPENDIX B DERIVATION OF GROWER LEVEL MARKET MODEL The g rower level m aximization p roblem Lagrange: (2.0b) First Order Conditions: (2.1b) (2.2b) (2.3b)
139 ; (2.4b) ; (2.5b) The next step is to convert Equations 2.1b t hrough 2.5b to p ercentage c hange and divide by EB and EV : (2.1b) (2.6b) (2.2b)
140 (2.7b) (2.3b) (2.8b) (2.4b)
141 (2.9b) For the AMMM re define Equation 2.4b: (2.4b) where, Recall, evaluate d at v=0: (2.9b)
142 (2.9b) (2.5b) (2.10b)
143 APPENDIX C DERIVATION OF SE GMENTED MULTI LEVEL MARKET MODEL T he t otal number of tomatoes include s non agreement and agreement tomatoes: Define Plug these definitions into the maximization problem as follow : Lagrange: (2.0a) Recall, First Order Conditions:
144 (2.1a) (2.2 a) (2.3a) ; (2.4a) ; (2.5a)
145 Equations 2.1a t hrough 2.5a are c onvert ed to percentage changes and divide d by EV (2.1a) (2.6a) Evaluate at v =0 Define: and (2.2a)
146 (2.7a) (2.3a)
147 (2.8a) (2.4a) (2.9a) (2.5a)
148 (2.10a) The grower maximization problem remains the same
149 APPENDIX D CORRELATION MATR IX FOR VARIABLES IN THE FARM LABOR MODEL Table D 1. Correlation matrix for all variables in the labor market model Q FWR FLH2 AEWR IRCA CIW POL FRZ CONW Q 1.000 FWR 0.190 1.000 FLH2 0.221 0.040 1.000 AEWR 0.383 0.367 0.511 1.000 IRCA 0.477 0.270 0.581 0.887 1.000 CIW 0.564 0.095 0.107 0.334 0.387 1.000 POL 0.051 0.412 0.387 0.064 0.118 0.019 1.000 FRZ 0.265 0.455 0.396 0.596 0.606 0.416 0.064 1.000 CONW 0.354 0.601 0.523 0.849 0.835 0 .275 0.035 0.607 1.000 FOW 0.569 0.362 0.573 0.796 0.881 0.492 0.053 0.512 0.864 LNDQ 0.451 0.486 0.476 0.842 0.860 0.420 0.102 0.560 0.861 MINW 0.065 0.280 0.420 0.699 0.641 0.110 0.269 0.528 0.609 PRPP 0.638 0.403 0.466 0.728 0.791 0.498 0.085 0.418 0.779 DEP 0.230 0.281 0.611 0.448 0.497 0.205 0.044 0.409 0.472 Table D 1. Continued FOW LNDQ MINW PRPP DEP FOW 1.000 LNDQ 0.917 1.000 MINW 0.495 0.677 1.000 PRPP 0.957 0.896 0.419 1.000 DEP 0.521 0.435 0.264 0.522 1.000
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158 BIOGRAPHICAL SKETCH Jamille Palacios was bor n in 1971, in Rio Piedras, Puerto Rico. During her early years, she was active in sports and community groups playing leadership roles. In 1989, she enrolled as a full time athlet e student in the Liberal Arts and Science s School at the University of Pue rto Rico, Cayey Campus. While studying full time she also performed a s a time job. In 1995, she graduated with a Bachelor of Arts in e conomics She then continued her studies at the Rio Piedras Campus of the University of Puerto R ico and finished a Master of Arts in e conomics While at the UPR graduate school, she assisted Dr. Ramn J. Cao in two graduate level courses : Cost b enefit a nalysis and Public f inance and f iscal p olicy. In 1997, after finishing all the required credits, she worked as a financial and economic analyst for the Puerto Rico Department of Treasury. The experience of analyzing fiscal policy in a public service environment inspired her to devote her master thesis efforts to evaluating alternative ways to elim thesis greatly contributed to resolving an actual Puerto Rico fiscal policy issue. She received her master degree in 2003. A short time before receiving her master s, she moved to Gainesville, Florida with the goal of pursuing a Ph.D. degree in economics. In fall 2003, the Food and Resource Economics Department at the University of Florida gave her the opportunity to achieve her goal by accepting her into its Ph.D. pr ogram. The program gave her a teaching and research assistantship, providing opportunities to assist excellent professors in their classes and research projects. As a teaching assistant, she worked with Dr. Evan Drummond, Dr. Lisa House, and Dr. Robert D Emerson in their classes, Principles of
159 f ood and r esource e conomics Principles of a gribusiness m anagement and Econometrics, respectively. As a research assistant, she worked on two projects managed by the International Agricultural Trade and Policy Ce nter: (1) an invasive species project that included Puerto Rico as a concerned area and (2) a Florida agricultural labor issues project. Among the professors and researchers she worked with in those projects are Mrs. Carmen I. Alamo, Dr. John J. VanSickle Dr. Hugh Bigsby, Dr. Donna Lee, Dr. Robert D. Emerson, and Dr. Fritz Roka. In 2010, she became a full time faculty member at Ivy Tech Community College in Lafayette, Indiana teaching e conomics in the School of Liberal Arts and the School of Applied Sc ience and Technology. There, s he was promoted and is now one of the Indiana L ouis S tokes A lliance for M inority P articipation (LSAMP) campus directors and the agriculture program chair. She received her Ph.D. degree from the University of Florida in the spring of 201 3