U.S. Department of Labor (DOL) 

Employment and Training Administration (ETA) 

The Effect of Continuing Education Participation on 

Agricultural Worker Outcomes

Prepared by: 

Anita Alves Pena, Ph.D.

Economics 

Colorado State University 

Fort Collins, Colorado 

This paper was funded, either wholly or in part, with Federal funds from the U.S. Department of 

Labor, Employment and Training Administration (ETA) under Contract Number 

DOLJ061A20380. The contents of this publication do not necessarily reflect the views or 

policies of the Department, nor does mention of trade names, commercial products, or 

organizations imply endorsement of same by the U.S. Government. This paper was one of 

seven prepared under the 2009 ETA Research Papers Program, which competitively awarded 

doctoral and post-doctoral students funding to conduct original research and prepare scholarly 

papers on topics relating to the workforce investment system. 
Introduction 

The public workforce investment system is an intergovernmental network aimed at 

providing labor force-related business assistance. The system consists of state and local 

workforce investment boards, local One-Stop Career Centers that facilitate employer and 

employee matching and training programs, and activities targeting specific populations such as 

youth, Veterans, Native Americans, and farmworkers. Among these initiatives is the National 

Farmworker Jobs Program (NFJP), a job training and employment assistance program for 

migrant and seasonal farmworkers and their dependents.1

 The Economic Opportunity Act of 

1964 established the NFJP, and the Workforce Investment Act of 1998 currently authorizes it 

(U.S. Department of Labor). The stated goals of this program include assisting migrant 

farmworkers increase their “economic stability” by steadying agricultural employment and by 

helping in the development of skills that can be used in complementary occupations (for 

example, during off-seasons). 

Migrant farmworkers have historically been among the poorest members of the working 

class in the U.S. In fact, the U.S. Department of Labor, in its Farm Labor Fact Book concluded 

that, “The migrant farmworker occupies the lowest level of any major group in the American 

economy” (1959, p. 110). Fifty years after this publication, descriptions of impoverished 

conditions for this largely immigrant population are still relevant. Few studies in agricultural 

labor economics, however, have focused on how educational programs targeting migrant and 

seasonal workers affect outcomes within this population. The aim of this research is to fill this 

 

1

 Complementary to this, the U.S. Department of Education offers vocational rehabilitation, defined to include 

evaluation, counseling, mental and physical rehabilitation, training, work adjustment, job placement, and 

postemployment services to disabled farmworkers through its Migrant and Seasonal Worker Program. The 

Department of Education also administers a Migrant Education Program, which offers support services aimed to 

facilitate primary and secondary education of migrant children. State and local government and privately-funded 

programs also exist in some areas. 

1 
gap by quantifying to what extent farmworker participation in continuing adult education 

programs, such as in the types of programs facilitated by NFJP, results in measurable 

improvements in various economic indicators. Analysis here draws from a large nationallyrepresentative survey of employed U.S. farmworkers. Results suggest that education 

participation is associated with higher wages all else equal, though variation of returns across 

individual programs is large. Program participation is found to be positively related to annual 

weeks worked in agricultural and nonagricultural occupations and negatively related to weeks 

spent outside of the U.S. and to poverty. Understanding links between continuing education 

participation and worker outcomes for both immigrant and U.S.-born workers contributes to the 

very limited academic literature on migrant education programs and is important for establishing 

benefits and costs for strategic planning exercises pertaining to future workforce investments. 

The Effects of Education on Earnings 

The existence of a positive, causal effect of education on earnings is well established in 

general labor economics. Card (1999) summarizes this literature starting with Mincer’s (1974) 

model in which earnings are decomposed into an additive function of schooling and work 

experience (and its quadratic). One relatively recent finding that Card stresses in his overview is 

that the marginal returns to schooling for certain “disadvantaged” subgroups (due, for example, 

to family background or abilities) are higher than average marginal returns to education in the 

overall population. This result suggests that larger effects of education program participation on 

earnings may be found for farmworkers than for other more advantaged groups.2 

Literature in public economics also has modeled education as developing human capital 

and future earnings ability. In addition, public finance has studied the role of education as a 

redistribution mechanism for increasing social equality (e.g., Fernandez and Rogerson (1996)). 

 

2

 In contrast, Chiswick (1988) finds that groups with higher education levels also have higher rates of return. 

2 
Education has been shown to generate a number of favorable externalities (in addition to private 

benefits), including increased economic growth rates and civic involvement, positive peer 

effects, and decreases in crime. This suggests that social returns to education may outweigh 

private ones. 3

 Furthermore, failures in financial markets may prevent current and potential 

students from borrowing fully against future earnings in order to obtain costly education. 

Together these findings support the role of government programs. 

The extent to which farmworker assistance programs effectively achieve stated goals, 

however, is relatively unknown, and official measures used for judging annual NFJP 

performance are limited. Specifically, measures used for annual reporting include percentages of 

farmworkers entering and retaining employment and average earnings among participants.4

 

While these performance measures are useful for summarizing participation and post-program 

employment rates and earnings, all three are unconditional statistics that do not control for 

changes in average worker characteristics and economic conditions, or for self-selection into 

NFJP participation. In contrast to the current measures, this project examines outcome 

differentials between adult education participants and non-participants (treatment and control 

groups) within agricultural labor markets using techniques from the econometrics of program 

evaluation (Imbens and Wooldridge (2009)) and comprehensive and nationally representative 

microeconomic data on farmworkers, their demographic characteristics, and education 

participation histories. Thus, in addition to contributing to an understudied academic research 

 

3

 In their survey of social returns to education, Lange and Topel (2006) argue that many empirical studies of 

education externalities have suffered from statistical imprecision. While evidence is generally consistent with the 

absence of externalities in the negative direction, the authors note that precise estimation of positive externality 

magnitudes is an area warranting continued research. 

4

 Employment entry is calculated as the number of adult participants who are employed in the first quarter following 

exit (training completion or other departure from the program) divided by the number who exit during that quarter. 

Employment retention is the number employed in both the second and third quarters after exit divided by the 

number who exit during the quarter. Finally, average earnings are total earnings in the second and third quarters 

divided by the number who exit during the quarter. 

3 
area within agricultural labor economics, this paper has practical significance by providing 

complementary evidence to what is currently reported for policy purposes. 

Data 

Data for this study come from the U.S. Department of Labor’s National Agricultural 

Workers Survey (NAWS), which is both a nationally and regionally representative survey of 

employed U.S. farmworkers (for 12 agricultural regions with survey weights). Survey 

respondents have been sampled from worksites in three seasons per year since 1989. The data 

are cross-sectional and are pooled for the analysis. This paper uses these data restricted to the 

1993-2006 period as some detailed education participation questions are not asked in the earliest 

waves of the survey. This restriction reduces the total sample size from 46,566 to 37,426 

workers. Of this weighted sample, 73.0 percent reports Mexican origins. Of the overall sample 

(which includes U.S.-born workers), 49.4 percent indicates illegal U.S. work status. Of Mexican 

immigrant workers, 64.1 percent indicate illegal status.5

 

U.S. Education Participation in the NAWS 

While participation in a NFJP-specific program is not identifiable in the data, NAWS 

includes data on whether workers have participated in English/ESL, citizenship, literacy, job 

training, GED/high school equivalency, college/university, adult basic education, Even Start, 

migrant education, or other classes while in the United States. Overall, 23.9 percent of 

farmworkers in the sample report having participated in at least one U.S. education program. 

Table 1 shows participation rates by specific education program. More than 10 percent of 

farmworkers report participation in English or English as a Second Language (ESL) classes or 

school. The next most common education programs are high school equivalency (6.5 percent) 

 

5

 Approximately one percent (416 workers) declined to answer legal status questions. Respondents are given a 

pledge of confidentiality and a nominal financial incentive. Furthermore, the NAWS has a long (and visible) history 

within farming communities. Still, illegal work status statistics should be considered lower bounds. 

4 
and college or university classes (3.5 percent). Other education program participation rates are 

lower. Job training and migrant education categories are closest to specific opportunities offered 

through the NFJP, though overlap is likely to some extent in all categories.6 Low participation 

rates overall across continuing education program categories are related to some extent to 

seasonality of work and participation in northward migrant streams as the agricultural season 

progresses. Only 16.3 percent of farmworkers who report following the crop also report 

participation in any U.S. education programs. This is in contrast to 31.9 percent of non-migrant 

agricultural workers. 

Table 1. Farmworker U.S. Education Participation Rates, By Program (Percentage)

English/ESL 10.62 

Citizenship 1.63 

Literacy 0.10 

Job Training 1.65 

GED, High School Equivalency 6.46 

College or University 3.54 

Adult Basic Education 0.59 

Even Start 0.04 

Migrant Education 0.27 

Other Education Program 2.37 

Any Education Program 23.89 

Observations 37,377 

Source: Author’s calculations, National Agricultural Workers Survey, 1993-2006. Statistics are survey weighted. 

 

6

 U.S. education participation questions at the farmworker level are asked generically. It is possible that timing of 

participation varies over workers and that characteristics such as family structure are determined subsequent to 

participation for some workers. Participation rates based on household member participation in programs within the 

last two years, instead of individual participation at any time are correlated with farmworker participation and are 

presented in Appendix Table A-2. 

5 
Table 2. Mean Demographic Characteristics, by U.S. Education Participation Status

Participants Non-Participants Difference 

Female (%) 28.73 18.23 *** 

Age (years) 31.86 32.29 

Education (years) 9.21 6.48 *** 

Spouse (%) 43.22 32.30 *** 

Children (number) 0.89 0.65 *** 

U.S.-born (%) 40.41 13.58 *** 

Naturalized Citizen (%) 6.88 2.89 *** 

Green Card (%) 25.31 23.75 

Other Authorization (%) 2.33 1.44 *** 

Illegal (%) 25.08 58.35 *** 

Speaks English (%) 61.05 19.89 *** 

Reads English (%) 57.67 17.57 *** 

From Mexico (%) 55.14 80.55 *** 

From Central America (%) 2.24 3.30 *** 

From Puerto Rico (%) 0.95 1.61 *** 

Farm Experience (years) 10.50 8.76 *** 

Tenure (years) 4.76 3.67 *** 

Field Crop (%) 16.63 17.23 ** 

Fruit Crop (%) 27.38 34.59 *** 

Horticulture (%) 21.81 13.19 *** 

Vegetables (%) 24.27 28.96 *** 

Misc. (%) 9.71 5.96 *** 

Pre-harvest (%) 17.33 19.62 *** 

Harvest (%) 23.18 33.89 *** 

Post-harvest (%) 13.12 10.41 *** 

Semi-skill (%) 23.53 20.71 *** 

Supervisor (%) 0.27 0.11 *** 

Other Task (%) 22.57 15.26 *** 

California (%) 23.99 35.47 *** 

East (%) 14.30 17.37 *** 

Southeast (%) 11.97 13.76 *** 

Midwest (%) 24.26 16.55 *** 

Southwest (%) 8.32 7.36 

Northwest (%) 17.16 9.49 *** 

Observations 8,453 27,087 

Source: Author’s calculations, National Agricultural Workers Survey, 1993-2006. Statistics are survey weighted. 

***p< 0.01, **p< 0.05, *p< 0.1 

6 
Summary statistics of demographic and work-related characteristics of participants and 

non-participants are presented in Table 2. On average, participants are more likely to be female, 

to have greater years of education, work experience and tenure with current employer, to be 

married, and to have more children. Survey data distinguish naturalized citizens, green card 

holders, those with other work authorization (e.g., temporary visas), those who are illegally 

working within the U.S., and those who are U.S.-born. Education program participants are more 

likely to be U.S.-born or legal immigrants and to be of higher English language proficiency, 

while non-participants are more likely undocumented and have lower levels of self-reported 

English ability. Further patterns are evident by crop, task, and region of U.S. farm work. 

Specifically, participants are less likely than non-participants to be working fruit and vegetable 

crops, to be harvest workers, and to be surveyed in California.7

 Statistically significant 

differences are evident in most categories in Table 2. 

Economic Outcome Variables in the NAWS 

Worker outcomes of interest include differentials in wages, annual weeks worked and 

spent abroad, and annual incomes between those reporting participation and non-participation in 

the various U.S. educational program categories. Because a large fraction of agricultural 

workers are paid piece rates (i.e. wages based on output) instead of time rates (i.e. wages based 

on time input), hourly-equivalent wages are constructed for piece rate workers based on survey 

questions indicating how much a worker (and his or her crew if applicable) was paid on average 

for each unit of output (e.g., box, bin, etc.) and how many units were produced in an average 

day, along with crew size information. These hourly-equivalent piece rate wages are then 

 

7

 Only six agricultural regions are identifiable in the public use data. The state of California stands alone as one of 

these six. Other regions are groupings of adjacent states with shared agricultural labor market characteristics. 

7 
comparable with hourly rates reported by other workers.8

 Figure 1 depicts farmworker wages in 

treatment and control groups, which is based on whether or not the worker reports participation 

in U.S. education programs. A worker is classified as a participant if he or she reports any of the 

education programs described in Table 1. 

Notably, real wages (adjusted to 2006 dollars) conditional on participation take a Ushaped pattern in this figure, decreasing in the early part of the series and increasing thereafter. 

In terms of wages, there is a distinct breakpoint after which a wage gap between those 

participating and not participating in programs becomes evident. There are several exogenous 

explanations of this pattern in dynamics. This breakpoint occurs in the mid-1990s and 

corresponds to several public policy changes including fundamental welfare reform (the Personal 

Responsibility and Work Opportunity Reconciliation Act), increases to minimum wages, 

legislative initiatives such as the Workforce Investment Act, and immigration-related reforms. 

This timeframe also corresponds to more positive macroeconomic conditions than in other parts 

of the series. 

Survey respondents were asked how many years they have worked with their current 

employer. Figure 2 plots this tenure with current employer. Again, a break point is observable 

after which tenure between the two groups diverges and becomes increasing for the treatment 

group. This point occurs slightly after what is observed in Figure 1. Since farmworker wages 

may be an increasing function of tenure, tenure may be a mechanism explaining the wage 

differences illustrated in Figure 1. Following this reasoning, tenure is modeled in the conditional 

analysis as an explanatory instead of as a dependent variable.9

 

 

8

 The hourly-equivalent wage for piece rate workers used here is the same as that used for reporting purposes by the 

U.S. Department of Labor. It is not known if and to what extent this construction introduces measurement error. 9

 Note that the relationship can be thought to be somewhat bidirectional as higher wages may act to increase tenure. 

8 
Figure 1. Average Farmworker Wages, by U.S. Education Participation 

6

6.5

7

7.5

8

8.5

9

9.5

10

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Real Hourly Wages, Primary Task (2006 USD)

Has participated in U.S. education Has not participated in U.S. education

Source: National Agricultural Workers Survey, pooled cross sections 1993-2006. 

Figure 2. Average Tenure, by U.S. Education Participation 

0

1

2

3

4

5

6

7

8

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Years with Current Employer

Has participated in U.S. education Has not participated in U.S. education

Source: National Agricultural Workers Survey, pooled cross sections 1993-2006. 

9 
Answers to survey questions pertaining to tenure with current employer may represent 

either continuous or annual employment, and therefore increases in tenure may or may not 

correspond to increases in work time. Figures 3 through 5 illustrate weeks worked per year in 

agriculture, weeks worked outside of agriculture, and weeks spent abroad, respectively.10 Like 

the wage and tenure plots, positive differences in agricultural work weeks between program 

participants and non-participants are evident in the latter half of the period. Positive differences 

in nonagricultural work weeks between participants and non-participants and negative 

differences in weeks abroad are evident over the entire survey period. Furthermore, while 

unconditional absolute differences in numbers of weeks worked across participation categories 

are generally small in magnitude, differences in weeks spent outside of the U.S. are larger. 

Figure 3. Average Annual Farm Work Weeks, by U.S. Education Participation 

15

20

25

30

35

40

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Farm Work Weeks per Year

Has participated in U.S. education Has not participated in U.S. education

Source: National Agricultural Workers Survey, pooled cross sections 1993-2006. 

 

10 The three weeks categories may or may not add to 52 as some workers may be unemployed for some weeks. 

10 
Figure 4. Average Annual Non-farm Work Weeks, by U.S. Education Participation 

0

2

4

6

8

10

12

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Nonfarm Work Weeks per Year

Has participated in U.S. education Has not participated in U.S. education

Source: National Agricultural Workers Survey, pooled cross sections 1993-2006. 

Figure 5. Average Annual Weeks Spent Abroad, by U.S. Education Participation 

0

2

4

6

8

10

12

14

16

18

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Abroad Weeks per Year

Has participated in U.S. education Has not participated in U.S. education

Source: National Agricultural Workers Survey, pooled cross sections 1993-2006. 

11 
Figure 6. Family Incomes below Poverty Thresholds, by U.S. Education Participation 

0

10

20

30

40

50

60

70

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Family Income under the Poverty Threshold (% of Farmworkers)

Has participated in U.S. education Has not participated in U.S. education

Source: National Agricultural Workers Survey, pooled cross sections 1993-2006. 

As a final illustration, Figure 6 shows the percentages of farmworkers with annual family 

incomes below U.S. poverty thresholds. Workers are matched to relevant thresholds based on 

their reported family sizes and particular survey year. Overall poverty incidence among families 

whose household head participated in continuing education programs is less than that for 

families whose household head did not participate. This is suggestive of positive returns of 

education on a poverty dimension as well. This pattern persists across survey years. 

Empirical Framework 

The summary statistics presented in figures do not account for differences in observable 

or unobservable characteristics between workers who participate and those who do not. The 

effect of participation on outcomes, however, can be modeled in a multivariate framework. The 

basic econometric framework takes the general form: 

12 
yi = α participatei + Xiβ + εi (1) 

where the dependent variable yi represents a series of outcome variables, including natural log of 

hourly-equivalent wage rates (lnwi), weeks worked in and outside of agriculture (farm_weeksi

and nonfarm_weeksi), weeks spent outside of the U.S. (weeks_abroadi) and the probability of 

falling below the poverty threshold (P(povertyi)). The variable participatei denotes whether a 

worker reports participation in the continuing education classes of interest. The vector Xi

includes nativity, legal status, and general demographic and work-related characteristics such as 

gender, age, education, experience, tenure, family structure, crop, task, geographic region of 

observation, and survey year. Of particular interest is the statistical and economic significance of 

the parameter α. While base line regressions define participation based on use of any U.S. 

education program, extensions relax these groupings and allow for the identification of 

heterogeneity across individual programs categories from Table 1. 

Ordinary least squares estimates of the effects of continuing education program 

participation on hourly and hourly-equivalent farmworker wages are presented in Table 3. The 

effect of participation, broadly-defined to include any U.S. education program is found to be 

approximately two percent. Coefficients in the wage equations follow intuition. Female 

farmworkers earn less all else equal than do male farmworkers. Those with more education, 

experience, and tenure with employer accrue wage premiums. Relative to U.S.-born workers, 

immigrants receive lower wages controlling for demographic and job specific characteristics. 

Regression results presented in columns (3) and (4) of Table 3 allow for heterogeneous 

correlations between specific program categories and wages. Positive and significant wage 

effects of participation are concentrated among English language, citizenship, job training, and 

college and university level study with notable variation across these categories. College or 

13 
university courses are found to be associated with seven to eight percent higher wages, followed 

by citizenship classes (six to seven percent) and job training (three to five percent). 

Results for the effects of continuing education program participation on weeks worked 

within and outside of agriculture and on weeks spent outside of the U.S. are presented in Table 4. 

Due to the seasonal nature of many agricultural tasks, there is a natural limit to increases in 

agricultural work weeks on an annual basis. Education program participation is found to be 

associated with approximately one more week in agricultural employment per year as well as an 

additional 1.6 weeks in non-farm work. These weeks are offset by the approximate three fewer 

weeks spent abroad among participants over nonparticipants. Variation, however, is notable 

across programs, and only English and ESL classes are consistent with this general pattern of 

statistical significance over all week allocation categories. Other program categories display 

results similar to the aggregate patterns though some results are not statistically significant. 

The effect of participation on poverty status is modeled as a final outcome variable of 

interest. For poverty, continuing education participants are found approximately three percent 

less likely to be below their family size U.S. poverty thresholds all else equal. This effect is 

especially notable (order of five percent) for those who participate in English and ESL classes 

within the U.S. relative to non-participants.11

 

11 A caveat is that results should be interpreted in light of the bi-national nature of much of the U.S. farm work 

population. Border commuters and international shuttlers, for example, spend significant annual time both in source 

and receiving countries. U.S. poverty thresholds are based on U.S. cost of living scales and therefore may 

improperly reflect annual outcomes for many workers in this population. Thus, workers who spend significant time 

elsewhere may be more likely to report total annual income below U.S. thresholds yet may be less likely to be living 

in impoverished conditions given differences in exchange rates and living costs. If U.S. poverty thresholds are 

inappropriate for this population, then results may be incomplete even in the presence of selection corrections. 

Adjusting poverty measurement for border crossing populations is the topic of ongoing, related work (Pena (2010)). 

14 
15 

Table 3. Farmworker Education Program Participation and Log(Wages)

(1) (2) (3) (4) 

Participate (Any) 0.0207*** 0.0187*** 

(0.00519) (0.00509) 

English/ESL 0.0152** 0.0103 

(0.00651) (0.00644) 

Citizenship 0.0680*** 0.0668*** 

(0.0167) (0.0165) 

Job Training 0.0468*** 0.0389** 

(0.0162) (0.0157) 

GED/H.S. 0.00868 0.0105 

(0.00859) (0.00845) 

College/University 0.0767*** 0.0725*** 

(0.0132) (0.0128) 

Other U.S. Classes -0.0304*** -0.0235** 

(0.0105) (0.0103) 

Female -0.0596*** -0.0616*** -0.0596*** -0.0615*** 

(0.00532) (0.00528) (0.00532) (0.00529) 

Age 0.000386 0.00901*** 0.000267 0.00867*** 

(0.000255) (0.00113) (0.000256) (0.00114) 

Age Squared -0.000116*** -0.000113*** 

(1.39e-05) (1.40e-05) 

Education 0.00749*** 0.00723*** 0.00683*** 0.00664*** 

(0.000933) (0.000918) (0.000943) (0.000927) 

Farm Experience 0.00142*** 0.00650*** 0.00144*** 0.00646*** 

(0.000373) (0.000841) (0.000373) (0.000842) 

Experience Squared -0.000130*** -0.000128*** 

(1.83e-05) (1.83e-05) 

Tenure 0.00873*** 0.00830*** 0.00872*** 0.00831*** 

(0.000533) (0.000523) (0.000531) (0.000522) 

Naturalized Citizen -0.0270** -0.0360*** -0.0273** -0.0354*** 

(0.0124) (0.0121) (0.0128) (0.0125) 

Green Card -0.0522*** -0.0657*** -0.0386*** -0.0515*** 

(0.0144) (0.0140) (0.0146) (0.0143) 

Other Authorization -0.101*** -0.109*** -0.0856*** -0.0937*** 

(0.0185) (0.0181) (0.0187) (0.0184) 

Illegal -0.0962*** -0.0853*** -0.0829*** -0.0718*** 

(0.0159) (0.0156) (0.0161) (0.0158) 

Speaks English 0.0279** 0.0291** 0.0290** 0.0299** 

(0.0126) (0.0123) (0.0125) (0.0122) 

Reads English -0.00198 -0.00264 -0.000814 -0.00194 

(0.0123) (0.0119) (0.0122) (0.0118) 

From Mexico 0.0871*** 0.0743*** 0.0769*** 0.0650*** 

(0.0129) (0.0126) (0.0131) (0.0128) 

From Central America 0.114*** 0.103*** 0.103*** 0.0932*** 

(0.0161) (0.0159) (0.0163) (0.0161) 

Observations 34,563 34,563 34,563 34,563 

R-squared 0.329 0.341 0.332 0.343 

Notes: Author’s calculations using survey weights, National Agricultural Workers Survey, 1993-2006. Robust 

standard errors in parentheses. Crop, task, region and year fixed effects included. ***p< 0.01, **p< 0.05, *p< 0.1
Table 4. Farmworker Education Program Participation and Annual Week Allocations

(1) (2) (3) (4) (5) (6) 

Farm Weeks Farm Weeks Nonfarm Nonfarm Abroad Abroad 

Participate (Any) 0.927*** 1.625*** -2.977*** 

(0.338) (0.326) (0.251) 

English/ESL 2.181*** 1.188*** -3.424*** 

(0.442) (0.402) (0.370) 

Citizenship 0.331 1.391* -0.459 

(0.826) (0.770) (0.693) 

Job Training 0.967 1.008 -0.721* 

(1.115) (0.978) (0.418) 

GED/H.S. 0.797 0.418 -1.680*** 

(0.565) (0.628) (0.307) 

College/University -0.313 2.098** 0.111 

(0.845) (0.923) (0.366) 

Other U.S. Classes -1.629** 0.673 -2.719*** 

(0.736) (0.887) (0.538) 

Female -3.863*** -3.869*** -0.364 -0.344 -4.555*** -4.566*** 

(0.341) (0.341) (0.311) (0.312) (0.306) (0.306) 

Age 0.288*** 0.271*** 0.447*** 0.437*** 0.154** 0.160** 

(0.0638) (0.0641) (0.0610) (0.0609) (0.0637) (0.0640) 

Age Squared -0.00375*** -0.00355*** -0.00509*** -0.00499*** -0.00174** -0.00182** 

(0.000801) (0.000803) (0.000744) (0.000743) (0.000767) (0.000770) 

Education 0.0841* 0.0815* 0.198*** 0.193*** -0.112** -0.135*** 

(0.0466) (0.0469) (0.0416) (0.0420) (0.0476) (0.0483) 

Farm Experience 1.390*** 1.385*** -0.282*** -0.281*** -1.084*** -1.084*** 

(0.0517) (0.0515) (0.0505) (0.0505) (0.0459) (0.0458) 

Experience Squared -0.0277*** -0.0277*** 0.00495*** 0.00493*** 0.0223*** 0.0224*** 

(0.00121) (0.00121) (0.00102) (0.00102) (0.00107) (0.00107) 

Tenure 0.684*** 0.681*** -0.361*** -0.362*** -0.128*** -0.125*** 

(0.0322) (0.0321) (0.0235) (0.0235) (0.0218) (0.0218) 

Naturalized Citizen -0.0859 -0.541 -2.604*** -2.693*** 4.524*** 5.114*** 

(0.931) (0.950) (0.789) (0.801) (0.800) (0.812) 

Green Card 0.461 0.167 -0.714 -0.678 1.880** 2.583*** 

(1.026) (1.039) (1.058) (1.071) (0.829) (0.832) 

Other Authorization 4.095*** 3.735*** -2.523* -2.459* 0.388 1.178 

(1.422) (1.434) (1.312) (1.325) (1.063) (1.071) 

Illegal 4.104*** 3.793*** -2.829** -2.848** 5.362*** 6.094*** 

(1.081) (1.098) (1.128) (1.142) (0.879) (0.887) 

Speaks English 0.437 0.407 1.503** 1.590** -3.065*** -3.072*** 

(0.710) (0.717) (0.641) (0.642) (0.666) (0.676) 

Reads English -0.507 -0.466 0.777 0.878 -1.784*** -1.863*** 

(0.748) (0.754) (0.674) (0.682) (0.669) (0.668) 

From Mexico 0.797 0.594 0.207 0.171 -0.179 -0.256 

(0.925) (0.945) (0.917) (0.944) (0.799) (0.808) 

From Central America 3.873*** 3.586*** 1.530 1.494 -4.562*** -4.626*** 

(1.154) (1.169) (1.081) (1.103) (1.078) (1.083) 

Observations 35,534 35,534 35,540 35,540 35,540 35,540 

R-squared 0.267 0.269 0.126 0.125 0.291 0.292 

Notes: Author’s calculations using survey weights, National Agricultural Workers Survey, 1993-2006. Robust 

standard errors in parentheses. Crop, task, region and year fixed effects included. ***p< 0.01, **p< 0.05, *p< 0.1

16 
17 

Table 5. Farmworker Education Program Participation and P(Poverty)

(1) (2) (3) (4) 

Participate (Any) -0.0337*** -0.0326*** 

(0.0120) (0.0120) 

English/ESL -0.0533*** -0.0503*** 

(0.0149) (0.0149) 

Citizenship -0.150*** -0.148*** 

(0.0281) (0.0284) 

Job Training 0.0189 0.0289 

(0.0324) (0.0326) 

GED/H.S. 0.0104 0.00799 

(0.0210) (0.0212) 

College/University -0.0539* -0.0501 

(0.0317) (0.0321) 

Other U.S. Classes 0.0363 0.0296 

(0.0302) (0.0302) 

Female 0.0470*** 0.0447*** 0.0475*** 0.0450*** 

(0.0133) (0.0133) (0.0134) (0.0134) 

Age -0.00190*** -0.00796*** -0.00172*** -0.00726*** 

(0.000574) (0.00255) (0.000577) (0.00257) 

Age Squared 7.97e-05** 7.28e-05** 

(3.15e-05) (3.17e-05) 

Education -0.00998*** -0.00987*** -0.00948*** -0.00946*** 

(0.00175) (0.00175) (0.00177) (0.00177) 

Farm Experience -0.00234*** -0.0119*** -0.00227*** -0.0118*** 

(0.000808) (0.00189) (0.000809) (0.00189) 

Experience Squared 0.000252*** 0.000253*** 

(4.29e-05) (4.29e-05) 

Tenure -0.0130*** -0.0121*** -0.0129*** -0.0121*** 

(0.00127) (0.00122) (0.00127) (0.00122) 

Naturalized Citizen 0.142*** 0.152*** 0.162*** 0.171*** 

(0.0318) (0.0317) (0.0322) (0.0321) 

Green Card 0.0802** 0.0969*** 0.0734* 0.0898** 

(0.0373) (0.0373) (0.0378) (0.0379) 

Other Authorization 0.0988* 0.106** 0.0938* 0.101* 

(0.0532) (0.0530) (0.0539) (0.0538) 

Illegal 0.160*** 0.146*** 0.153*** 0.140*** 

(0.0383) (0.0385) (0.0390) (0.0392) 

Speaks English -0.0518** -0.0548** -0.0520** -0.0547** 

(0.0244) (0.0237) (0.0247) (0.0240) 

Reads English -0.0242 -0.0220 -0.0298 -0.0272 

(0.0261) (0.0256) (0.0263) (0.0257) 

From Mexico -0.0939*** -0.0778** -0.0722** -0.0572* 

(0.0330) (0.0330) (0.0335) (0.0335) 

From Central America -0.195*** -0.188*** -0.176*** -0.169*** 

(0.0324) (0.0330) (0.0340) (0.0347) 

Observations 30,011 30,011 30,011 30,011 

Notes: Author’s calculations using survey weights, National Agricultural Workers Survey, 1993-2006. Robust 

standard errors in parentheses. Crop, task, region and year fixed effects included. Probit marginal effects reported. 

***p< 0.01, **p< 0.05, *p< 0.1
Selection Bias 

If there are reasons to suspect selection bias, estimation by OLS alone may not be 

appropriate. Selectivity bias exists if, for example, higher ability people are more likely to 

participate in U.S. education programs than are lower ability workers, or alternately, if those with 

less ability are more likely both to attend classes and to earn lower wages. Other unobserved 

factors that may be correlated with U.S. education participation and that may affect the outcome 

variables of interest include commitment to the U.S. workforce and employment instability. 

Selection on observable characteristics also is possible. Another finding that comes out of recent 

education and earnings literature is that educational returns vary across the population with 

observable factors such as parental education (Card (1999)). Therefore, while there are several 

reasons to suspect selection, the theoretical direction, source, and magnitude of bias is uncertain. 

Education participation can be modeled using both parametric multivariate regression 

analysis (e.g., maximum likelihood treatment effects) and semi-parametric techniques (e.g., 

propensity score matching). The preferred method to deal with selectivity bias depends on 

whether selection occurs on the basis of unobserved or observed factors and whether appropriate 

exclusion restrictions are available for identification. Treatment effects models address bias due 

to correlation between regressors and omitted variables. Propensity score matching methods, on 

the other hand, are based on balancing observable characteristics in the data. 

In literature pertaining to education and earnings in nonagricultural occupations, 

instrumental variable estimates of educational returns, which address endogeneity concerns, have 

been found to be significantly greater than OLS estimates. Consider the propensity to participate 

as an unobserved latent variable: 

ii

*

i   uzeparticipat (2) 

18 
where the treatment decision rule is:



   otherwise eparticipat i

i 0

1 0

. 

Table 6 presents estimates of probit marginal effects of various demographic and labor 

market characteristics on the probability that a worker participates in education program 

categories indicated by survey responses. Estimation results indicate that female gender, 

education, and years of previous farm work experience are positive, significant predictors of 

continuing education program participation. Age, on the other hand, is of significance in the 

negative direction. 

Indicators of legal status also are of statistical and economic significance. Being U.S.-

born is the excluded category in Table 6. In the full regressor version presented in column (5), 

marginal effects indicate that illegal workers are 21.0 percent less likely to participate in U.S. 

education programs all else equal. This is notable, but expected, since illegal workers are 

excluded from participation by some program rules. NFJP assistance, for example, is contingent 

on being a U.S. citizen, a lawfully admitted permanent resident, or a person with other 

employment authorization. English language ability and Mexican or Central American origin 

also are highly and positively correlated with adult education participation. 

 participat if e

*

19 
20 

Table 6. Determinants of Farmworker Education Program Participation (Dependent variable: 

Participation in any U.S. classes or school)

(1) (2) (3) (4) (5) 

Female 0.0639*** 0.0661*** 0.0562*** 0.0532*** 0.0528*** 

(0.00961) (0.00969) (0.00974) (0.00998) (0.00998) 

Age -0.00311*** -0.00796*** -0.00518*** -0.00516*** -0.00471*** 

(0.000416) (0.00175) (0.00175) (0.00177) (0.00177) 

Age Squared 6.56e-05*** 4.13e-05* 3.78e-05* 3.28e-05 

(2.23e-05) (2.25e-05) (2.28e-05) (2.27e-05) 

Education 0.0263*** 0.0265*** 0.0198*** 0.0191*** 0.0198*** 

(0.00117) (0.00117) (0.00123) (0.00123) (0.00124) 

Farm Experience 0.00274*** 0.0101*** 0.00797*** 0.00793*** 0.00780*** 

(0.000556) (0.00126) (0.00128) (0.00128) (0.00128) 

Experience Squared -0.000199*** -0.000167*** -0.000155*** -0.000144*** 

(3.03e-05) (3.08e-05) (3.08e-05) (3.02e-05) 

Tenure 0.00136* 0.000885 0.000200 -5.42e-05 0.000169 

(0.000743) (0.000747) (0.000772) (0.000774) (0.000763) 

Naturalized Citizen 0.0882*** 0.0799*** 0.0466* 0.0463* 0.0478* 

(0.0193) (0.0193) (0.0278) (0.0280) (0.0283) 

Green Card -0.0272** -0.0388*** -0.102*** -0.102*** -0.0991*** 

(0.0106) (0.0109) (0.0253) (0.0252) (0.0245) 

Other Authorization 0.0411 0.0362 -0.0433 -0.0486 -0.0541* 

(0.0288) (0.0288) (0.0339) (0.0328) (0.0312) 

Illegal -0.180*** -0.172*** -0.216*** -0.214*** -0.210*** 

(0.0109) (0.0110) (0.0317) (0.0316) (0.0310) 

Speaks English 0.140*** 0.137*** 0.120*** 

(0.0194) (0.0189) (0.0185) 

Reads English 0.121*** 0.121*** 0.125*** 

(0.0203) (0.0199) (0.0197) 

From Mexico 0.178*** 0.177*** 0.169*** 

(0.0208) (0.0206) (0.0204) 

From Central America 0.359*** 0.346*** 0.347*** 

(0.0504) (0.0510) (0.0498) 

Crop controls? no no no yes yes 

Task controls? no no no yes yes 

Region controls? no no no no yes 

Survey year controls? no no no no yes 

Observations 36,250 36,250 35,563 35,540 35,540 

Notes: Author’s calculations using survey weights, National Agricultural Workers Survey, 1993-2006. Robust 

standard errors in parentheses. Probit marginal effects reported. ***p< 0.01, **p< 0.05, *p< 0.1
Propensity Score Matching

A method to determine effects of participation is to match workers based on a measure of 

their observed characteristics (propensity score). 12 Propensity score matching has become 

increasing popular in recent empirical literature because it relies on fewer distributional 

assumptions than traditional parametric methods (Dehejia and Wahba 2002). 

The first step of the general propensity score technique is to estimate an equation similar 

to (2). In this case, the propensity score is the predicted value of the dependent variable. The 

second step is to examine the effect of U.S. adult education participation on outcomes by 

matching treatment and control variables based on their propensity scores and creating 

counterfactuals. To construct counterfactuals, matching is performed based on both individual 

neighborhood (observations that can be ranked close together) or on smooth weighting (based on 

an assumed population distribution). The assumption imposed is unconfoundedness, or that 

 

12 Several strategies can be used for identifying the effect of participation using simultaneous estimation of 

equations (1) and (2). First, a maximum likelihood treatment effects model can be run without exclusion restrictions 

and the resulting identification is based on the nonlinearities of the probit functional form that is imposed for 

equation (2). Exclusion restrictions, however, help with identification. A first strategy was based on harvest 

activity. Given its time intensive nature, harvest activity was hypothesized to be a significant negative predictor of 

education participation but not a predictor of all outcome variables. Harvest activity, however, is a statistically and 

economically significant predictor in the log (wages) equation during simultaneous estimation. Task-related 

variables such as participation in harvest activity are current, while U.S. education variables are retrospective. This 

suggests that causality may run in the opposite direction indicating that less educated people are less likely to be 

harvesting for other reasons (instead of harvest workers being less likely to participate). A second identification 

strategy could be based on state by state variation in program characteristics because available educational 

opportunities are set at state or local levels. Unfortunately, the public use version of the NAWS has only six 

identifiable agricultural regions. This restricts the use of state-level institutional characteristics as statistical 

instruments. Regional controls, however, are included as regions are based on known migrant streams. Thirdly, a 

family structure variable, specifically the presence of children within the U.S. was considered as an instrument. 

Workers who have children present in the U.S. may be more likely to participate in continuing education programs 

if, for example, they are more likely to anticipate long-term residence and employment in the country. Furthermore, 

correlations between the presence of children who are participating in education and their parents who also 

participate may be present, and family structure can be hypothesized to be unrelated to hourly wage rates. This 

identification method based on presence of U.S. children hypothesizes that parents, for example, are more likely to 

participate in education programs because the family also is doing so. Therefore, family structure characteristics are 

excluded from the wage regression but included in the participation equation. Although results across these 

parametric estimations (available upon request) were somewhat sensitive to specification, the effect of participation 

broadly on log (wages) was generally positive, highly statistically significant, and of greater magnitude than the 

results from OLS and propensity score techniques. 

21 
treatment and control observations with like propensity scores differ only in the error term from 

the propensity score equation. The average treatment on the treated is then: 

E(yi1- yi0|participatei=1) = E(yi1|participatei=1) - E(yi0|participatei=1) (3) 

where E(yi1|participatei=1) and E(yi0|participatei=1) are the actual and counterfactual average 

outcomes for the cases that participants did and did not receive treatment (i.e. did or did not 

participate). 

Table 7: Propensity Score Treatment Effects of Farmworker Education Program Participation on 

Worker Outcomes

Log(wages) Farm Weeks Non-Farm 

Weeks 

Weeks 

Abroad 

P(Poverty) 

Nearest-neighbor 0.052*** 1.890*** 0.799*** -3.380*** -0.072*** 

(0.004) (0.210) (0.133) (0.135) (0.006) 

Kernel 0.049*** 1.528*** 1.003*** -3.930*** -0.057*** 

(0.004) (0.203) (0.153) (0.101) (0.005) 

Notes: Author’s calculations using survey weights, National Agricultural Workers Survey, 1993-2006. Analytical standard errors 

in parentheses for nearest-neighbor match method. Bootstrapped standard errors for kernel match method. 

Propensity score treatment effect estimates for the five outcome variables of interest are 

presented in Table 7. Matching is based on covariates used in parametric estimations. The 

balancing property is satisfied for 19 blocks based on gender, age, U.S.-born and other 

authorization legal status groups, and supervisor task responsibilities with the common support 

option selected.13 Results follow general patterns identified by the OLS technique, though for 

four out of five outcomes, magnitudes of effects are greater. The treatment effect of 

participation on wages, for example, is found to be 5.2 percent by a random draw nearestneighbor math technique and 4.9 percent by kernel matching respectively. The selection 

estimation method therefore leads to higher in magnitude estimates of the effect of continuing 

education on wages than does OLS. This pattern is consistent with literature demonstrating this 

 

13 A disadvantage of balancing being only achievable based on a subset of observable characteristics is that it is 

possible that the remaining error in the propensity score equation is correlated with the error of interest. 

22 
pattern for other industries. Results in Table 7 for annual farm work weeks, non-farm work 

weeks, and weeks spent abroad also are consistent with OLS results with positive, significant 

differences in work week categories and negative significant differences for weeks spent abroad. 

Discussion and Conclusions 

The analysis overall presents evidence as to the effectiveness of U.S. continuing 

education programs for increasing farmworker wages and propensities to secure agricultural and 

nonagricultural work. Hourly wage gains are greatest when education participation is restricted 

to college and university, citizenship, and job training categories. This is notable given the 

presence of programs such as NFJP that aim to assist migrant farmworkers by steadying 

agricultural employment and by helping in the development of general skills that can be used in 

complementary occupations. 

Semi-parametric propensity score matching results, which control for selection into 

participation, suggest that U.S. continuing education program participants earn approximately 

five percent higher wages all else equal and are six to seven percent less likely to fall below 

poverty thresholds than are non-participants. Participants also are found to work one to two 

more weeks per year than non-participants in both agricultural and nonagricultural labor markets 

and to spend these three to four fewer weeks per year outside of the U.S. Results of this paper 

therefore are consistent with farmworker educational opportunities increasing base wages and 

bettering employment options. Thus, this research provides evidence complementary to current 

program performance measures, which also note positive associations between continuing 

education program participation and worker outcomes. 

Several caveats, however, remain. The data used in this paper, although nationally and 

regionally representative, are imperfect. First, data correspond solely to employed U.S. 

23 
farmworkers, meaning that workers who participate in continuing education programs and then 

exit agricultural labor markets are not included in the survey. The extent of substitution from 

agricultural to nonagricultural work therefore is muted in the empirical exercise here. By similar 

reasoning, effects of participation on wages and poverty propensities also are lower bounds. 

Furthermore, the effect of U.S. continuing education participation may vary from the intensive to 

extensive margin. Given the binary nature of survey questions pertaining to education programs, 

it is impossible given current survey data to examine effects of extent and duration of 

participation by individual workers (and likewise effects of milestones such as certificates and 

diplomas which may introduce nonlinearities). While continued data collection and research is 

warranted, this paper contributes a next step to the understudied area of continuing education 

participation among migrant and seasonal workers. 

24 
References 

Card, D. (1999): “The Causal Effect of Education on Earnings,” Chapter 30, Handbook of Labor

Economics. Elsevier Science B.V. 

Chiswick, Barry R. (1988): “Differences in Education and Earnings across Racial and Ethnic 

Groups: Tastes, Discrimination, and Investments in Child Quality,” The Quarterly 

Journal of Economics, 103(3), 571–597. 

Dehejia, R., and S. Wahba (2002): “Propensity Score-Matching Methods for Nonexperimental 

Causal Studies,” The Review of Economics and Statistics, 84, 151–161. 

Fernandez, R., and R. Rogerson (1996): “Income Distribution, Communities and the Quality of 

Public Education,” Quarterly Journal of Economics, 111(1), 135–164. 

Imbens, G., and J. Wooldridge (2009): “Recent Developments in the Econometrics of Program 

Evaluation,” Journal of Economic Literature, 47(1), 5–86. 

Lange, Fabian and Robert Topel (2006): “The Social Value of Education and Human Capital,” 

Chapter 8, Handbook of Education Economics. Elsevier Science B.V. 

Mincer, J. (1974): Schooling, Experience and Earnings. National Bureau of Economic 

Research, New York. 

Pena, A. A. (2010): “Poverty and the Binational Population: A Note on Poverty Measurement,” 

mimeo. 

U.S. Department of Labor, Employment and Training Administration (1959): Farm Labor Fact 

Book. 

----- (2009a): “National Farmworker Jobs Program Overview,” 

http://www.doleta.gov/MSFW/html/facts.cfm. 

25 
----- (2009b): “Summary of Workforce Development Provisions of the Workforce Investment 

Act of 1998 (P.L. 105-220),” 

http://www.doleta.gov/USWORKFORCE/WIA/summarywia.htm. 

26 
Appendix 

The sampling procedure of the National Agricultural Workers Survey (NAWS) is based on four 

levels: region, crop reporting district, county, and employer with probabilities proportional to 

size at each level. Specifically, NAWS uses 12 geographic regions based on USDA Quarterly 

Agricultural Labor Survey of farm employers. The 12 regions are defined in Table A-1 below. 

The public use NAWS sample used here is collapsed to six regions. USDA information also is 

used for cyclical allocation (based on the relative proportions of workers each cycle). There are 

47 crop reporting districts (aggregates of counties with similar agricultural characteristics) from 

which sampling locations are selected. Within crop reporting district, counties are selected 

randomly without replacement with probabilities proportional to the county’s farm labor 

expenses. Employer lists are from the Bureau of Labor Statistics Agricultural Soil and 

Conservation Service and are updated with information from county extension agencies, local 

employment agencies, grower organizations, and farmworker service programs. Employers are 

selected using probabilities proportional to the square root of the seasonal farm workforce. Once 

permission to interview is obtained, the maximum number of interviews per grower is 

determined with probabilities proportional to square root size. The number of interviews per site 

of a particular grower also is determined by a proportional distribution to total number of crop 

workers. Workers are selected and approached randomly when arriving for work, at lunch, or 

when leaving and interviews are scheduled for times away from worksite at locations chosen by 

the workers. Additional information and public access data are available from 

http://www.doleta.gov/agworker/naws.cfm. 

27 
28 

Table A-1. NAWS Agricultural Regions

Region States 

California CA 

Southern Plains TX, OK 

Florida FL 

Mountain III AZ, NM 

Appalachia I, II NC, VA, KY, TN, WV 

Cornbelt Northern Plains IL, IN, OH, IA, MO, KS, NE, ND, SD 

Delta Southeast AR, LA, MS, AL, GA, SC 

Lake MI, MN, WI 

Mountain I, II ID, MT, WY, CO, NV, UT 

Northeast I CT, ME, MA, NH, NY, RI, VT 

Northeast II DE, MD, NJ, PA 

Pacific OR, WA 

Table A-2. Recent Household U.S. Education Participation Rates, By Program (Percentage)

English/ESL 3.92 

Job Training 1.11 

GED, High School Equivalency 4.74 

Migrant Education 1.13 

Head Start 3.13 

Migrant Head Start 1.79 

Other 1.94 

Observations 27,028 

Source: Author’s calculations, National Agricultural Workers Survey, 1993-2006. Statistics are survey weighted 

and based on household member participation in programs within the last two years.