the labor supply of u.s. agricultural workers hill; us...the data for this paper come from the...

The Labor Supply of US Agricultural Workers

Alexandra E Hill

Abstract

Recent increases in immigration enforcement activities and observed changes in the

profile of US agricultural workers are expected to have large effects on the agricultural

industry This paper documents emerging trends in the demographic characteristics

of US crop workers estimates the implications of these trends for future industry

labor supply and investigates the viability of several employer actions for increasing

the hours and weeks of work that farmworkers are willing to provide Using a nationally

representative survey of employed farmworkers I show evidence that workers are aging

are more likely to have children and are more likely to be female I show that there has

not been a change in the legal status composition of the workforce however trends in

the other demographic characteristics vary significantly over legal status I examine the

specific implications of this for the future labor supply of agricultural workers I find

that the current trends in workforce aging suggest that the future labor force will be

willing to work more hours per week and weeks per year in agriculture However the

trends in gender and family composition move in the opposite direction suggesting that

the future labor force will provide fewer labor-hours and labor-weeks to agricultural

employers Finally I examine the potential of specific employer policies to increase

hours and weeks of work I find that increasing wages and offering health care coverage

have no significant effect on labor supply but that offering a bonus increases both hours

and weeks of work

1

1 INTRODUCTION

1 Introduction

The profile of US agricultural workers is changing and little is understood about the

implications for their employers Farmworkers are aging increasingly female and are

more likely to have children (Hernandez et al 2016) In addition tightening immi-

gration enforcement is expected to reduce the number of undocumented workers in the

labor force (Kostandini et al 2014 Zahniser et al 2012) Previous work has linked

heterogeneity across these demographics with differential behavior in the labor market

These studies suggest that the changing profile of farmworkers will decrease the aggre-

gate labor supply of US agricultural workers This could have large negative effects

on US agricultural production and profits

This paper shows how the changing profile of agricultural workers will affect labor

supply on the intensive margin ie the labor supply of those employed in agriculture I

compare the labor supply of US agricultural workers across four dimensions age gen-

der family composition and legal status The labor supply estimates reflect a workerrsquos

short-run choice of the number of hours to work each week and the number of weeks

worked each year I combine these estimates with an analysis of trends in the work-

force composition to predict future labor supply I then explore several avenues through

which employers can increase labor supply In particular I examine how wages health

care coverage and bonuses affect hours and weeks of work for employed agricultural

workers

Recent estimates of labor supply for US agricultural workers are limited (Emerson

amp Roka 2002 Pena 2010 Taylor amp Thilmany 1993) Significant changes in US im-

migration policy and the profile of workers have resulted in major structural changes

in the agricultural labor market that are not captured in existing estimates (Boucher

et al 2012 Martin amp Calvin 2010 Taylor 2010) Importantly literature studying

other industries and the US labor market more broadly find significant differences in

worker labor supply across many of the demographic dimensions in which the agricul-

tural workforce is changing

A large literature focuses on the labor supply of immigrants and the effects of

legal status on various labor market outcomes Generally this literature finds that

legalization is associated with increases of 5 to 15 in the workersrsquo real wage rate and

that these effects are largest for more skilled workers (Kaushal 2006 Kossoudji amp Cobb-

Clark 2002 Lofstrom et al 2013 Rivera-Batiz 1999) Recent work by Borjas (2017)

is the first to examine labor supply elasticities based on immigrant status He finds a

significant difference in the elasticity of labor supply between US workers of different

immigration statuses He finds that undocumented men have a wage elasticity close to

zero while the elasticity for US native men is closer to 04 Here I add to this finding

2

1 INTRODUCTION

and examine the labor supply differential across the legal statuses of US agricultural

workers Many of these workers are foreign born (76) and undocumented (50)1

This combined with the flexible nature of agricultural employment (ie limited use of

formal employment contracts) makes agricultural workers a useful group for examining

differential labor market behavior across legal statuses Further while Borjas and others

impute worker legal statuses to categorize workers as lsquolikely undocumentedrsquo I rely on

workersrsquo self-reported legal statuses To the best of my knowledge Pena (2010) provides

the only evidence on differences in the labor supply of agricultural workers across self-

reported legal statuses Using the same data source as this paper Pena finds that

foreign born workers are more likely to work in piece rate pay positions and that this

is associated with higher levels of poverty

Another body of literature examines differences in labor supply across gender and

family composition (marital and parental status) This literature shows that the labor

supply of women is lower than that of men that married women are more responsive

to changes in wages (ie have higher wage-elasticities) than single women single men

or married men and that children lead to a reduction in labor supply for females but

not for males (Angrist amp Evans 1998 Blau amp Kahn 2007 Eissa amp Hoynes 2004 Mc-

Clelland amp Mok 2012 Meyer amp Rosenbaum 2001) Because of these well-documented

differences in labor market behavior the common practice is to separately examine the

labor supply of married and single males and females Here I include all workers in the

same regression and control for these characteristics This allows me to compare labor

supply between these groups and ultimately enables me to show how future changes

across these demographics will affect industry labor supply

There is a small but growing literature on the labor market effects of workforce aging

Because workers of different ages behave heterogeneously in the workforce the norm

in the literature is to focus on working-age adults (those ages 16 - 65) and control for

age cohort so that labor supply differences are estimated from within groups of similar-

aged workers (Blundell amp Macurdy 1999) More recently due to the rising average

age of workers in the US and many other countries age has become an explanatory

variable of interest rather than a necessary control Evidence in the United States

suggests that productivity and labor force participation are significantly decreasing in

age (Fallick amp Pingle 2010 2006 Maestas et al 2016 Sheiner 2014) In this paper I

examine the relationship between worker age and labor supply among those employed

in US agriculture To the best of my knowledge this is the first empirical evidence on

the intensive margin effects of an aging workforce

This paper builds on existing literature in three important ways This paper is

1Estimates are from my own analysis of the NAWS data for the most recent survey round (2015-2016)

3

1 INTRODUCTION

the first comprehensive study of how the changing demographic profile of agricultural

workers will affect industry labor supply I find that native born citizens work fewer

hours per week and fewer weeks per year than foreign born legal or undocumented

workers Mid-aged workers (25 - 44 year olds) work more hours per week than younger

or older workers (16 - 24 and ge 45 respectively) but older workers work more weeks

each year Parents work significantly more weeks per year than non-parents but work a

similar number of hours Males work significantly more hours and weeks than females

In light of ongoing trends in these demographic characteristics my results imply that

the way in which the agricultural workforce is aging will cause the hours and weeks of

labor provided by employed farmworkers to increase while the changes in gender and

family composition will cause labor supply to decrease

Second this paper provides the first empirical evidence on multidimensional differ-

ences in labor supply across legal statuses and key demographic characteristics Existing

evidence has focused on separately identifying effects of legal status gender age mar-

ital status and parental status on labor supply This is the first study to examine

how these interact and provides important insights into heterogeneity in labor supply

across worker legal statuses Among my sample of US agricultural workers I find

surprisingly little heterogeneity across legal statuses and within demographic groups

This suggests that while on average natives foreign born legals and undocumented

workers have different labor market behavior they behave similarly within age gender

marital status and parental status groups Two notable exceptions to this are single

women with children who are legal immigrants work 65 more hours per week than na-

tives and single men with children who are both legal and undocumented immigrants

work 55 more weeks per year than natives

Finally this paper provides empirical evidence on how specific employer actions can

influence the labor supply among those employed in agriculture I examine whether

employers can increase hours or weeks of work by offering higher wages health benefits

or pay bonuses I show evidence from naive OLS regressions that these employer policies

are significantly and positively correlated with labor supply outcomes However causal

evidence from an IV approach indicates that little of this correlation is causal Among

these policies I find that bonuses are the only ones that causally affect labor-hours and

labor weeks I find that offering a bonus causes the average worker to increase weekly

hours of labor by ten percent and to increase annual weeks working in agriculture by

65 weeks I also find evidence that bonuses paid to workers for staying through the

end of the season increases weeks of work (by 35 weeks per year) but not hours

The paper proceeds as follows In the next section I introduce the data used for this

study and provide summary statistics on worker demographics employment employer

4

2 DATA

behavior and variation in state minimum wages In section 3 I introduce the data and

show recent trends in worker demographics and employment that are relevant to this

study In section 4 I introducte my empirical methodology and show estimates of labor

supply across demographic characteristics In section 5 I discuss employer options for

increasing intensive margin labor supply Section 6 summarizes the findings discusses

policy implications and suggests directions for future research

2 Data

The data for this paper come from the National Agricultural Workers Survey (NAWS)

The NAWS is an employment-based repeated cross-sectional survey administered an-

nually by the US Department of Labor The survey began in 1989 and data are

currently available through the 2016 survey round The survey is randomized and pro-

vides a nationally representative sample of workers employed in US crop production

The survey includes questions on household and worker demographics income legal

status wages hours worked weeks worked and various characteristics of a workerrsquos

current job Because NAWS provides detailed information on worker demographics and

employment it is the best available data for this analysis2

In my analysis I drop the first four years of the survey as several relevant variables

were not collected until the 1993 survey round I remove workers from the sample

who fall under the lsquosupervisorrsquo category and those who are salaried I keep low-skilled

and semi-skilled workers who are paid hourly piece rate or a combination of hourly

and piece rate I focus on these workers to minimize demand-side drivers of short-run

labor supply A major concern with examining labor supply comes from conflating

the supply decisions of a worker with the labor demands from the employer In most

industries workers have little control over the number of hours they work each weekmdash

service industry workers are put on a schedule office workers typically work either full-

or part-time (40 or 20 hours per week) and contract workers work until they complete

their task In these industries labor supply determinants can be interpreted as affecting

the workerrsquos willingness to agree to the contract In agriculture most non-managerial

jobs give workers considerable flexibility in their labor provision because most work

without contracts and without consequence for missing work days

Workers in the NAWS are first asked where they were born and foreign born workers

are later asked for their specific legal status Based on these legal statuses workers are

categorized as citizens green card holders undocumented or other work authorized I

2For this analysis I use the NAWS restricted access data so I can include controls and instruments for

potentially endogenous variables at the state rather than region level

5

2 DATA

exclude workers who are categorized as lsquoother authorizedrsquo The number of respondents

in this category is low (750) making it challenging to examine their behavior separately

from other groups of foreign born workers Rather than grouping them together with

foreign born workers I remove them from the sample I additionally divide citizens into

native and foreign born and group together foreign born citizens and green card holders

as foreign born legals3 The final legal status groups that I use are native citizens

foreign born legals and undocumented

A limitation to this study is that I do not examine the demographics or labor

provision of H2A Visa Holders (this is the work visa for temporary agricultural work)

These workers are not captured in the NAWS (the lsquoother authorizedrsquo category does not

include H2A workers) or any other nationally representative survey but are becoming

increasingly important for US agriculture As such my findings are only relevant for

currently employed workers who are not visiting on the agricultural visa

In my analysis I use information on worker demographic characteristics that in-

cludes age gender marital status parental status nativity and legal status I also use

information on employment that includes the payment type crop task wage employer-

offered benefits and weekly hours worked for the current farm job as well as annual

weeks worked in agriculture I exclude workers with missing values for these variables

from my analysis I summarize these variables in Tables 1 and 2

Table 1 shows the mean and standard deviation of the metrics of labor supply job

characteristics and employer provisions The first column shows information for the

entire sample and the remaining three columns divide the sample based on worker legal

status The two labor supply variables I use are hours worked per week and weeks

worked per year Hours per week are the number of hours spent working in the week

prior to the interview for the current farm employer This does not contain farmwork

for other employers or non-farmwork Weeks per year are the number of weeks spent

working in farmwork during the previous year Respondents are told to count all weeks

where they worked at least one day This includes weeks worked for fall farm employers

but does not include for non-farm jobs I remove workers from the sample who worked

zero weeks in the prior year (these are generally recent arrivals) The average worker

in the sample works 37 weeks per year and 44 hours per week This varies across legal

statuses with natives working the least (42 hours per week and 35 weeks per year) and

foreign born legals working the most (45 hours a week and 39 weeks per year)

3Foreign born legals are comprised of 88 green card holders and 12 foreign born citizens

6

2 DATA

Table 1 Sample Summary Statistics Labor Supply and Job Attributes

All workers Natives Foreign born legal UndocumentedLabor Supply

hours per week 436767 424323 452110 433080(1326) (1460) (1286) (1283)

weeks per year 368574 345758 385333 367902(1514) (1665) (1275) (1562)

Payment Typehourly 08461 09466 08422 08089

(036) (022) (036) (039)piece rate 01333 00442 01318 01689

(034) (021) (034) (037)combined hourlypiece rate 00206 00092 00260 00222

(014) (010) (016) (015)Task

pre-harvest 02200 02226 02017 02291(041) (042) (040) (042)

harvest 02671 01649 02448 03206(044) (037) (043) (047)

post-harvest 01166 01549 01093 01051(032) (036) (031) (031)

semi-skilled 02619 02515 03335 02267(044) (043) (047) (042)

other 01344 02061 01107 01186(034) (040) (031) (032)

Crop Categoryfield crops 01468 02759 01176 01125

(035) (045) (032) (032)fruits amp nuts 03583 01423 04473 03953

(048) (035) (050) (049)horticulture 01926 02813 01443 01833

(039) (045) (035) (039)vegetables 02452 02138 02418 02591

(043) (041) (043) (044)miscellaneous 00571 00867 00491 00499

(023) (028) (022) (022)Employer Provisions

wage ($hour) 77947 81674 80100 75270(273) (298) (282) (254)

bonus 03096 04408 03637 02232(046) (050) (048) (042)

season bonus 00978 01293 01236 00692(030) (034) (033) (025)

health coverage (on the job) 08397 08774 08907 07924(037) (033) (031) (041)

health coverage (off the job) 01274 01977 01652 00740(033) (040) (037) (026)

Standard deviation in parentheses

7

2 DATA

Workers in the sample are paid either hourly piece rate or a combination of the

two 85 of all workers are paid hourly and 13 are paid by the piece Almost all

natives are paid hourly (95) while lower proportions of foreign born legal and undoc-

umented workers are paid hourly (84 and 81 percent respectively) A larger proportion

of undocumented workers are paid piece rate (17) compared with natives and foreign

born legals The NAWS records the specific job of each worker interviewed and then

categorizes workers into six broader categories After removing workers categorized as

supervisors from the sample the five remaining task codes are pre-harvest harvest

post-harvest semi-skilled and lsquootherrsquo Most workers report being employed in harvest

or semi-skilled tasks and fewer report doing post-harvest tasks The biggest difference

in tasks between worker legal statuses is for harvesting A larger proportion of undocu-

mented workers are employed in harvesting tasks (32) while a very small proportion

of natives do these tasks (16) The NAWS also collects information on the crop the

farmworker is currently working with and codes these into the five categories listed in

Table 1 Not surprisingly given its high labor requirements fruit and nut production

employs the largest proportion of workers (36) followed by vegetable production and

horticulture This is not the case for native workers who are primarily employed in

horticulture and field crops (28) Relatively few natives work in fruit and nut pro-

duction (14) compared with foreign born legals (45) and undocumented workers

(40)

The last statistics in Table 1 summarize the variables on provisions from the current

farm employer Wage is the self-reported (before tax) hourly wage rate if the worker

is paid by the hour and the imputed hourly equivalent for workers paid by the piece

or combined hourly and piece rate4 The average worker in the sample receives 780

$hour from their current farm employer This is lower among undocumented workers

(750) and higher among foreign born legals and natives (800 and 815)

Bonus is an indicator variable for whether the worker receives any money besides

wages from their current employer Season bonus is an indicator variable for whether

this bonus is an end of season bonus (ie the money is paid to them if they work for

the employer through the end of the season) About 30 of workers receive some form

of bonus and 10 of workers receive an end of season bonus Both types of bonus are

more common for natives and foreign born legals than for undocumented workers 44

of natives receive any bonus and 13 receive an end of season bonus 36 of foreign

born legals receive any bonus and 12 receive an end of season bonus but only 22 of

undocumented workers receive a bonus and 7 receive an end of season bonus

4The hourly equivalent is estimated using the reported piece rate wage average daily productivity and

average hours worked

8

2 DATA

Finally the variables health coverage (on the job) and health coverage (off the job) are

indicators for whether the employer provides heath care coverage for injuriesillnesses

that occur on and off the job respectively The majority of workers (84) receive

health coverage for on-the-job issues while only 13 receive coverage for off-the-job

issues Undocumented workers are least frequently covered by both forms of health care

79 of undocumented workers report coverage for on-the-job illnesses and 7 report

coverage for off-the-job Comparatively 88 of natives report on-the-job coverage and

20 report off-the-job coverage

Table 2 shows summary statistics of the worker characteristics I include in this

study About 20 of workers are natives 29 are foreign born legals and 51 are

undocumented There is considerable variation in the education of workers in the three

legal statuses The majority of natives have completed high school (56 have 12 or

more years of education) whereas the majority of foreign born legals and undocumented

workers have not completed elementary school (72 and 66 have less than 8 years of

education respectively) On average the workforce is close to evenly divided between

the four age categories but this varies across the legal statuses Foreign born legal

workers are older than both natives and undocumented workers 72 of foreign born

legal workers are 35 and older and only 7 are under 25 Comparatively 70 of

undocumented workers are less than 35 years old and only 11 are 45 and older

The family composition variables show that most workers are either single and

childless (34) or married with children (45) Large proportions of natives and un-

documented workers are single and without children (48 and 40 respectively) while

comparatively few foreign born legal workers fall into this category (15) Foreign born

legals have the highest proportion of workers married with children (61) followed by

undocumented workers (44) and natives (26) Across all legal statuses single work-

ers with children are the least common family structure The majority of all workers

are men (80) Natives have the lowest proportion of men (73) and undocumented

workers have the highest (83)

9

2 DATA

Table 2 Sample Summary Statistics Worker Characteristics

All workers Natives Foreign born legal UndocumentedLegal Status

Native citizen 02031(040)

Foreign born legal 02868(045)

Undocumented 05100(050)

Educationle 8 years 05691 01015 07226 06592

(050) (030) (045) (047)8 - 11 years 02510 03410 01826 02563

(043) (047) (039) (044)ge 12 years 01799 05576 00948 00844

(038) (050) (029) (028)Age

16 - 24 02443 02813 00659 03321(043) (045) (025) (047)

25 - 34 02889 02072 02104 03658(045) (041) (041) (048)

35 - 44 02253 01968 03002 01938(042) (040) (046) (040)

ge 45 02416 03147 04235 01083(043) (046) (049) (031)

Family Compositionsingle no kids 03429 04758 01547 03968

(047) (050) (036) (049)single kids 00669 00871 00569 00639

(025) (028) (023) (024)married no kids 01376 01794 01827 00950

(034) (038) (039) (029)married kids 04526 02577 06058 04442

(050) (044) (049) (050)Male 08024 07288 08027 08322

(040) (044) (040) (037)

Observations 54126 10870 15350 27295


10

3 TRENDS IN WORKER CHARACTERISTICS

3 Trends in Worker Characteristics

Descriptive evidence from several sources of data show that the farm workforce is aging

becoming more settled and is increasingly female5 Here I show trends in these worker

demographics as represented in the NAWS Consistent with existing work I show that

the farm workforce is aging I find that this is driven by decreases in the proportion of

workers under 25 and increases in the the proportion of workers above 45 I show that

the proportion of male workers is declining and that this is driven by decreases in the

proportion of undocumented males I show that an increasing proportion of workers

are married with children I find that this is driven by a decrease in the proportion of

single childless men and an increase in the proportion of married women with children

Finally I show that despite widespread claims that increased immigration enforcement

will reduce the number of undocumented workers in the agricultural labor force the pro-

portions of native born citizens foreign born legal workers and undocumented workers

has remained stable over the sample period

Figure 1 shows trends in the age of crop workers surveyed in the NAWS Panel (a)

shows that the average age of workers has been increasing steadily over the sample

period Over the sample period the average age of crop workers has increased by

roughly 25 increasing from 31 years old in 19931994 to 38 years old in 20152016

This is equivalent to an increase of roughly one percent per year Panel (b) shows that

this increase in the average age of workers is driven by a decrease in the proportion

of younger workers (under 25) and an increase in the proportion of older workers (45

and older) The proportion of workers in the middle age groups has remained relatively

stable Over the sample period the proportion of younger workers has decreased by

nearly 20 percentage points falling from 365 in 19931994 to 176 in 20152016

The proportion of older workers has increased by roughly the same amount rising from

147 in 19931994 to 335 in 20152016 This is equivalent to approximately a one

percent increase in workers 45 and older and a one percent decrease in workers under

25 each year

5For example see the summary of American Community Survey Data made available by the Eco-

nomic Research Service (available at httpswwwersusdagovtopicsfarm-economyfarm-labor)

the summary of NAWS data by Martin (2017) or the summary of the California Farm Bureaursquos 2017

Agricultural Labor Availability Survey (available at httpwwwcfbfuswp-contentuploads201710

CFBF-Ag-Labor-Availability-Report-2017pdf)

11


Figure 1 Trends in Age

(a) Average Ages

(b) Age Groups

Average worker ages are computed using self-reported ages and survey weightsprovided in the NAWS Workers under 16 years old are dropped from the sample(this removes 343 workers) Averages are estimated within each NAWS cycle (ieevery two fiscal years) because of the NAWS sampling technique

12


Figure 2 Trends in Age by Legal Status

(a) Average Age (b) Native born citizens

(c) Foreign born legal (d) Undocumented

Legal status is assigned based on work authorization and whether the worker is foreign born TheNAWS categorizes workers into four work authorization categories citizen green card holderother work authorized (including temporary work visas) and undocumented Here workers areseparated into native born citizens foreign born legal workers (including green card holders andcitizens) and undocumented workers Due to the small number of observations rsquoother workauthorizedrsquo workers are removed from the sample

Figure 2 shows trends in average age and age compositions across legal status groups

Panel (a) shows that the increasing average age of the workforce is driven by foreign born

workers The average age of native citizen workers was increasing from 1995 through

2006 but has remained relatively stable since Until very recently undocumented

workers have had the lowest average age followed by native born citizen workers and

with foreign born legal workers having the highest However in the last survey round

the average age of undocumented workers surpassed that of native born citizens

Panels (b) (c) and (d) show the age composition for native born citizens foreign

born legal workers and undocumented workers respectively Panel (b) shows that the

trends in the average age of native citizens were driven by the share of native born

citizens aged 45 and older The proportion of these workers decreased until 2006 and

has been increasing since The age composition of native born citizens looks remarkably

13


similar in 2016 and 1994 with a small increase in the proportion of older workers and

a small decrease in the proportion of younger workers

Comparatively the age composition of foreign born legal workers and undocumented

workers has changed substantially over the period The majority of foreign born legal

workers are aged 45 and older which might reflect some self-selection into legal status

ie foreign workers who have been in the country longer are more likely to have obtained

citizenship or a green card Over the sample period the proportion of foreign born legal

workers aged 45 and older has risen by nearly 40 percentage points while the proportion

of middle-aged workers (aged 25-44) has fallen by nearly the same amount While the

average age of undocumented workers has also been rising over the sample period the

compositional changes driving these averages is quite different At the start of the

sample workers under 25 make up roughly half of the undocumented population while

at the end of the sample they account for only thirteen percent This decrease in the

proportion of younger workers is accompanied by an increase in workers aged 35 and

older In all Figure 2 suggests that there are important differences in the trends in

worker ages across legal statuses If these trends persist they are likely to influence

aggregate labor market behavior and outcomes

Figure 3 shows trends in the household composition of farmworkers Panel (a) shows

trends in the marital and parental composition of the workforce Workers are categorizes

as married (a parent) based on whether they report having a spouse (child under 18)

regardless of whether the spouse (child) lives with them The most striking trend in

the marital-parental composition of the workforce is the decline in farmworkers who are

single without children This proportion has decreased by almost 15 percentage points

over the sample period falling from 44 percent in 199394 to 30 percent in 201516

This decrease is accompanied by a ten percent increase in the proportion of single

workers with children a three percent increase in the proportion of married workers

with children and a one percent increase in the proportion of married workers without

children

14


Figure 3 Trends in Family Composition

(a) Separated by Marital and Parental Status

(b) Separated by Gender and MaritalParental Status

Marital status and parental status are defined based on whether the farmworkerhas a spouse or child under 18 respectively An alternative approach is to examinetrends based on whether the spousechild live in the same household as the workerbut results (and trends) are similar under this family composition structure Theproportions are estimated for each NAWS cycle using the survey-provided sampleweights 15


Figure 4 Trends in Family Composition by Legal Status

(a) Native born citizens (b) Foreign born legal

(c) Undocumented

Legal status is assigned based on work authorization and whether the worker is foreign bornMarital status and parental status are defined based on whether the farmworker has a spouse orchild under 18 respectively

Existing literature on the labor supply effects of marital and parental status suggest

heterogeneity across gender More specifically the literature suggests that the labor

supply of married women is lower than that of single women or men and that chil-

dren lead to a reduction in labor supply for women but not men (Angrist amp Evans

1998 Blau amp Kahn 2007 Eissa amp Hoynes 2004 McClelland amp Mok 2012 Meyer

amp Rosenbaum 2001) Because of this I further divide family composition based on

worker gender Panel (b) of Figure 3 shows trends across each of these dimensions

ie gender marital and parental status Panel (b) shows that the decrease in single

workers without children is driven by males The proportion of single males without

children decreases by almost 15 percentage points over the period falling from 37 per-

cent in 199394 to 23 percent in 201516 while the proportion of single females without

children remains close to seven percent over the sample period The increase in single

16


workers with children is driven equally by females and males (both increasing by five

percentage points over the period Trends for males and females diverge when exam-

ining married workers with children The proportion of married women with children

increases by six percentage points over the sample while the proportion of males in

this category falls by three percentage points

Figure 4 shows trends across family composition and legal status Similarly to trends

in worker age and gender the trends in family composition varies significantly across

the three legal statuses Panel (a) shows that family composition has been stable across

the sample period for native born citizens This is not the case for foreign born legal

workers or undocumented workers who are depicted in Panel (b) and (c) respectively

Among foreign born legal workers the proportion of married men with children has

decreased by 15 percentage points over the sample period and the proportion of single

men without children has decreased by 9 percentage points while all other groups have

grown proportionally The largest compositional changes have occurred among undoc-

umented workers Panel (c) shows that the proportion of single men without children

has decreased by 25 percentage points over the sample period while the proportions of

married women with children and married men with children have increased by 13 and

4 percent respectively

Figure 5 shows trends in worker genders Unlike the trends for worker ages and

parental status the changes in gender composition of the workforce have emerged re-

cently The percentage of male farmworkers remained near 80 percent until 2006 and

has been steadily declining since In the most recent survey round approximately 67

percent of the workforce is male a 13 percent decrease over the last ten years Panel

(b) shows these trends separately by worker legal statuses Interestingly the stable

proportion of males in the farm workforce until 2006 is simultaneously driven by a

decreasing proportion of undocumented males and an increasing proportion of native

males Following 2006 the proportion of native males has stabilized while the propor-

tion of undocumented males has continued to fall

Finally Figure 6 shows trends in the legal status of crop workers Despite evidence

that increased immigration enforcement decreases immigrant presence (eg Kostandini

Mykerezi amp Escalante 2014) the legal status composition of employed farmworkers

has changed remarkably little over the sample period

17


Figure 5 Trends in Gender

(a) All Workers

(b) By Legal Status

This shows the proportion of farmworkers who are male The proportion is esti-mated for each NAWS cycle using the survey-provided sample weights

18

4 PREDICTORS OF LABOR SUPPLY

Figure 6 Trends in Legal Statuses

Citizens include native born citizens and foreign born (naturalized) citizens Thisdoes not include workers categorized as having rsquoother work authorizationrsquo due tothe limited coverage of agricultural visa holders in the NAWS

4 Predictors of Labor Supply

In this section I estimate heterogeneity in labor supply across the demographic char-

acteristics summarized in the previous section The main estimating equation is an

OLS regression with state year and state-by-year fixed effects This equation can be

written

yi = α+ β1legal statusi + β2age groupi + β3education groupi

+ β4(marriedi times parenti times genderi) +Eprimeiγ + λs + λt + λst + ui

(1)

Where yi is the measure of labor supply either logged weekly hours worked or weeks

worked each year legal statusi is a categorical variable indicating whether the worker

is native foreign born legal or undocumented age groupi is a categorical variable for

worker age divided into four groups (lt25 25-34 35-44 and ge45) education groupi is a

categorical variable for education level (lt9 years 9-12 years and gt12 years) marriedi

is a binary indicator variable for a workerrsquos marital status parenti is an indicator

19


variable for whether the farmworker has children6 genderi is a binary indicator variable

equal to one if the worker is male and zero if they are female and Ei is a vector of

control variables for employment characteristics Employment characteristics include

categorical variables for the payment scheme task and crop of the workerrsquos current

farm job λs λt and λst are state year and state-by-year fixed effects These fixed

effects ensure that the results are not driven by time trends or spacial heterogeneity

and instead are estimated from differences in worker behavior within each state and

year

Table 3 shows coefficient estimates for Equation 1 and Table 4 shows the associated

average adjusted predictions (predictive margins) In both tables the first column

shows results from the regression with logged hours of work as the outcome variable

Table 3 shows that both foreign born legal and undocumented workers on average

provide more hours of labor per week than native workers Foreign born legals work

75 more hours and undocumented work 43 more Workers aged 25 to 44 provide

roughly 3 more hours of labor each week than those under 25 and those 45 and

older Married women with children work significantly fewer hours than single childless

women Men regardless of family composition work significantly more hours than

women On average I find that men work 14 more hours each week than women I find

that married men work significantly more hours per week than their single counterparts

and that married men with children work more than those without children (though

this difference is not significant) This behavior is consistent with the broader labor

supply literature which finds that the effect of an additional child for married couples

is negative for women (ie women decrease labor supply) and insignificant or positive

for men (ie men increase labor supply) (Angrist amp Evans 1998 Lundberg amp Rose

2002)

The first column of Table 4 translates these coefficients into the predicted hours of

work for workers of a given demographic holding all else constant These predictive

margins show that after netting out effects from all other predictive variables the

average foreign born legal worker works 41 hours per week undocumented workers

work 395 and natives work 38 Workers aged 25 to 44 work 40 hours a week while

those under 25 and older than 44 work 39 hours Married women with children work

355 hours a week while single women without children work 37 Men generally work

395 to 415 hours per week much higher than females who work 35 to 37 hours Men

who are married with children work the most at 415 hours each week while women

6I examine the effect of the farmworker being a parent regardless of whether they live with their child

The results are similar if I instead include an indicator variable for the farmworker being a parent and living

with their child Importantly the trends in parental status and parentalliving with the child are also similar

20


who are married with children work close to the fewest at 355 hours each week

The second columns of Tables 3 and 4 show results from the regression with number

of annual weeks working in a farm job as the outcome variable As shown in the second

column of Table 5 foreign born legal workers and undocumented workers work signif-

icantly more weeks per year than natives All workers 25 and older work significantly

more weeks than those under 25 and workers 45 and older work the most Married

women with children work significantly fewer weeks per year than single women with

children Men regardless of family composition work significantly more weeks than

women Men with children regardless of marital status work with most weeks per

year (but the difference between males is not statistically significant) Generally the

sign and relative magnitude of these coefficients mirror those in the first column but

there is one notable difference While the relationship between age and hours of work

is quadratic (ie increasing then decreasing) the relationship between age and weeks

of work is increasing (at a decreasing rate) Workers aged 25 to 44 work the most hours

per week but those 45 and older work the most weeks per year

21


Table 3 Labor Supply Differences Across Demographics

Outcome variablelog(hours) weeks worked

Foreign born legal 00745lowastlowastlowast 2421lowastlowastlowast(00186) (0750)

Undocumented 00429lowastlowast 1419lowast(00168) (0808)

Age 25 - 34 00346lowastlowastlowast 5708lowastlowastlowast(00100) (0318)

Age 35 - 44 00279lowast 7140lowastlowastlowast(00139) (0330)

Age ge 45 00000358 7366lowastlowastlowast(00135) (0585)

Single times child times female -00347 1017(00232) (0615)

Married times no child times female -00525 0418(00378) (1125)

Married times child times female -00375lowast -1120lowast(00191) (0645)

Single times no child times male 00700lowastlowastlowast 3661lowastlowastlowast(00202) (0724)

Single times child times male 00877lowastlowastlowast 4973lowastlowastlowast(00315) (1195)

Married times no child times male 0118lowastlowastlowast 4423lowastlowastlowast(00222) (0637)

Married times child times male 0121lowastlowastlowast 4990lowastlowastlowast(00212) (1047)

Constant 3694lowastlowastlowast 2643lowastlowastlowast(0216) (2829)

Observations 53406 54338R2 0250 0252Notes Robust standard errors in parentheses clustered at state The out-come variable log(hours) is the log of the number of hours worked in theweek prior to the interview for the workerrsquos current farm employer Theoutcome variable weeks worked is the total number of weeks in which theworker worked at least one day in farm work in the last year The base cate-gorical variables are as follows salaried workers for payment type less thanhigh school for education native born citizens for legal status and ages 16 -24 for age All regressions include state year state-by-year task and cropfixed effects Differences in the number of observations come from missingvalues (nonresponse) for the outcome variable lowast p lt 010 lowastlowast p lt 005 lowastlowastlowast

p lt 001

22


Table 4 Labor Supply Differences Across Demographics Predictive Margins

Average adjusted predictions983142hours 983142 weeks

Legal StatusNative 3786 2947

Foreign born legal 4077lowastlowastlowast 3189lowastlowastlowast

Undocumented 3953lowastlowast 3088lowast

AgeAge 16 - 24 3882 2604

Age 25 - 34 4021lowastlowastlowast 3175lowastlowastlowast

Age 35 - 44 3992lowast 3318lowastlowastlowast

Age ge 45 3882 3340lowastlowastlowast

Family Composition amp GenderSingle X no child X female 3678 2750

Single X child X female 3555 2852

Married X no child X female 3492 2792

Married X child X female 3545lowast 2638lowast

Single X no child X male 3945lowastlowastlowast 3117lowastlowastlowast

Single X child X male 4017lowastlowastlowast 3248lowastlowastlowast

Married X no child X male 4143lowastlowastlowast 3193lowastlowastlowast

Married X child X male 4151lowastlowastlowast 3250lowastlowastlowast

Notes Predicted labor supply based on estimates from the fixed effect re-gressions presented in Table 3 Values for predicted hours are computedusing e

983142log(hours) Significance levels come from baseline regression and indi-cate that values are significantly different from the base (first) category ofeach variable lowast p lt 010 lowastlowast p lt 005 lowastlowastlowast p lt 001

The second column of Table 4 translates these coefficients into the predicted number

of annual working weeks These predictive margins show that the average foreign born

legal worker works 32 weeks per year undocumented workers work 31 and natives

work 29 Workers under 25 years old work 26 weeks per year those 25-34 work 32 and

those 35 and older work 33 Single women and married women without children work

roughly 28 weeks per year while married women with children work 26 Single men

without children work the fewest weeks per year (31) followed by married men without

children (32) and then men with children regardless of marital status (325) The

largest differences in annual weeks of labor supplied within these demographic groups

is between the youngest and oldest workers and workers (a seven week difference) and

between men and women who are married with children (a six week difference)

Results from Equation 1 shed light on differences in labor supply within these dis-

23


tinct demographic groups Because my analysis in the previous section suggests that

the trends in these characteristics are heterogeneous across legal status groups I am

also interested in examining labor supply differentials within demographic and legal

status groups The equation that estimates these effects can be written

yi = α+ β1age groupi + β2education groupi + β3(marriedi times parenti times genderi)

+ micro1(legal statusi times age groupi)

+ micro2(legal statusi timesmarriedi times parenti times genderi)

+Eprimeiγ + λs + λt + λst + ui

(2)

Where the new parameters legal statusitimesage groupi and legal statusitimesmarrieditimesparenti times genderi are the legal status and demographic characteristic interaction The

coefficients on these parameters capture differential labor supply across legal status

and within the demographic group For example a significant value of micro1 for foreign

born workers aged 45 and older indicates a statistically significant difference from native

born workers aged 45 and older


predictive margins Again the first columns shows results from the regression with

logged hours of work as the outcome variable and the second columns show results with

annual weeks worked These results show little significant variation across legal statuses

The first column of Table 5 shows that among workers aged 45 and older both foreign

born legal and undocumented work more hours than natives Among single women with

children foreign born legals work significantly more hours than native citizens Finally

among married males with children undocumented immigrants work significantly fewer

hours than native citizens The first column of Table 6 shows that the average foreign

born legal and undocumented workers aged 45 and older work 40 and 39 hours per

week respectively This is roughly two hours more per week than native workers of the

same age Among single women with children legal immigrants work 65 hours more

per week than native citizens And among married males with children undocumented

immigrants work one hour less per week than natives

The second column of Table 5 shows no significant heterogeneity across legal statuses

within age groups but some significant differences within family composition groups

Compared with native citizens in the same groups legal immigrants who are female

married and do not have children work fewer weeks while legal immigrants who are

male single with children and undocumented immigrants who are male and single

regardless of parental status work more weeks

24


Table 5 Labor Supply Differences Across Demographics and Legal Status


Foreign born legal times Age 25 - 34 00389 1901(00400) (1645)


Foreign born legal times Age ge 45 00756lowastlowast -1610(00372) (1692)

Undocumented times Age 25 - 34 00300 -0118(00315) (1266)


Undocumented times Age ge 45 00727lowastlowast -2007(00303) (1246)

Foreign born legal times Single times kids times female 0117lowast 0208(00646) (2031)

Foreign born legal times Married times no kids times female 00779 -3265lowast(00622) (1645)

Foreign born legal times Married times kids times female 00521 -2267(00617) (1576)

Foreign born legal times Single times no kids times male -00217 1220(00363) (1262)

Foreign born legal times Single times kids times male -00532 4463lowastlowast(00353) (2074)

Foreign born legal times Married times no kids times male 00206 1309(00415) (1765)

Foreign born legal times Married times kids times male -00413 1424(00343) (1676)

Undocumented times Single times kids times female 00510 2800(00651) (2143)

Undocumented times Married times no kids times female 00472 -1235(00585) (1570)

Undocumented times Married times kids times female 00424 -0465(00633) (1500)

Undocumented times Single times no kids times male -00201 2276lowast(00224) (1143)

Undocumented times Single times kids times male -00383 5288lowastlowast(00382) (2024)

Undocumented times Married times no kids times male -00271 -1371(00313) (1474)

Undocumented times Married times kids times male -00748lowastlowastlowast -1237(00272) (1400)

State-year FE yes yesTask amp crop FE yes yesObservations 53406 54338R2 0253 0260

Notes Robust standard errors in parentheses clustered at state Outcome variables andbase categorical variables same as prior regression (see Table 3 description) All regressionsinclude state year state-by-year task and crop fixed effects Differences in the number ofobservations come from missing values (nonresponse) for the outcome variable lowast p lt 010 lowastlowast

p lt 005 lowastlowastlowast p lt 001

25


Table 6 Legal Status Interactions Margins

Predicted values983142hours 983142 weeks

Status times AgeNative times Age 16 - 24 3659 2300Native times Age 25 - 34 4023 3114Native times Age 35 - 44 4032 3287Native times Age ge 45 3764 3414Foreign born legal times Age 16 - 24 4013 2681Foreign born legal times Age 25 - 34 4142 3381Foreign born legal times Age 35 - 44 4131 3419Foreign born legal times Age ge 45 4020lowastlowastlowast 3331Undocumented times Age 16 - 24 3954 2694Undocumented times Age 25 - 34 4024 3136Undocumented times Age 35 - 44 3951 3270Undocumented times Age ge 45 3929lowastlowastlowast 3247

Status times Family Composition amp GenderNative times single times no child times female 3539 2693Native times single times child times female 3243 2633Native times married times no child times female 3240 2833Native times married times child times female 3276 2684Native times single times no child times male 3833 2900Native times single times child times male 3965 2857Native times married times no child times male 3999 3152Native times married times child times male 4211 3234Foreign born legal times single times no child times female 3770 2809Foreign born legal times single times child times female 3884lowast 2769Foreign born legal times married times no child times female 3731 2622lowast

Foreign born legal times married times child times female 3677 2572Foreign born legal times single times no child times male 3995 3137Foreign born legal times single times child times male 4005 3419lowastlowastlowast

Foreign born legal times married times no child times male 4348 3398Foreign born legal times married times child times male 4304 3492Undocumented times single times no child times female 3743 2737Undocumented times single times child times female 3609 2957Undocumented times married times no child times female 3593 2753Undocumented times married times child times female 3615 2681Undocumented times single times no child times male 3973 3171lowast

Undocumented times single times child times male 4036 3430lowastlowastlowast

Undocumented times married times no child times male 4116 3058Undocumented times married times child times male 4133lowastlowastlowast 3154

Notes Predicted labor supply based on estimates from the fixed effect regressions pre-sented in Table 5 Values for predicted hours are computed using e

983142log(hours) Signif-icance levels come from baseline regression and indicate that values are significantlydifferent from native citizen workers within the same age or family composition categorylowast p lt 010 lowastlowast p lt 005 lowastlowastlowast p lt 001

26

5 EMPLOYER STRATEGIES

The second column of Table 6 shows that married childless women who are legal

immigrants work 26 weeks per year while natives work 28 Single men with children

who are both legal and undocumented immigrants work 34 weeks per year while natives

work 285 Finally single men without children who are undocumented work 32 weeks

per year while natives work 29

Overall my results show that intensive margin labor supply varies significantly over

age family composition and legal status However I find little significant difference in

the labor supply of workers of different legal statuses within age and family composition

groups This suggests that future changes in the availability of labor-hours and labor-

weeks will be driven by average industry shifts in terms of age gender marital status

parental status and legal status with a few exceptions One of the most prominent

trends in the age composition of the workforce is the increase in the proportion of

foreign born legal workers ages 45 and above My results from estimating Equation 1

show that workers in this age group work more weeks per year and similar hours per

week compared with the youngest workers The results from estimating Equation 2

show no significant differences in the weeks worked between workers within age groups

and between legal status groups but do show significant increases in hours of work

Specifically I find that workers in the fastest growing age-legal status group (foreign

born legal workers ge 45) work significantly more hours per week In all my results

suggest that the way in which the workforce is aging will result in a labor force that is

willing to work more hours per week and weeks per year in agriculture

One of the most prominent trends in the gender-marital-parental composition of the

workforce is the decline in the proportion of undocumented single men without children

The results in Table 5 show that these workers provide among the highest weeks of

labor each year This suggests that the changing gender and family composition of the

workforce will result in a labor force that is willing to work fewer weeks per year in

agriculture Rising prevalence of married legal immigrant women without children will

contribute to this falling labor supply

5 Employer Strategies

The effects of the changing agricultural workforce on future labor supply is of interest to

industry stakeholders researchers and policy makers However a question of particular

interest to industry stakeholders is what can employers do to increase labor-hours and

labor-weeks Here I examine the viability of several alternative employer offerings for

increasing labor supply Naive regressions that do not account for the endogeneity

of these employer policies would suggest that offering health coverage bonuses and

27


higher wages are all viable options However after accounting for the inherent bias

between these employer policies and labor supply (eg employers might pay higher

wages to more motivated workers) I find that bonuses are the only employer policy

that significantly affect labor supply Offering a bonus causes workers to work 10

more hours within a week and 65 more weeks within a year

I use an instrumental variable approach to estimate the causal effect of these em-

ployer policies on labor-hours and labor-weeks The regression equation can be written

in two stages

employer policyi = α+ θ1Pminusi +Dprimeiβ +Eprime

iγ + λs + λt + 983171i

yi = α+ θ1 983142employer policyi +Dprimeiβ +Eprime

iγ + λs + λt + ui(3)

Where the employer policyi is one of an indicator variable for the worker receiving

employer health coverage for off-the-job illnessesinjuries an indicator variable for the

worker receiving a monetary bonus an indicator variable for the worker receiving a

bonus at the end of the season or the logged hourly wage rate Di and Ei are vectors

of controls for the same worker demographic characteristics and employment charac-

teristics in Equation (1) yi is either logged hours of work or number of weeks worked

I include state and year fixed effects

Pminusi is the instrumental variable and represents the average value of the employer

policy for all other workers (ie not including the focal worker i) within the same

state-year group This instrument corrects for the endogeneity of these employer poli-

cies in the labor supply regression Here the concern with an OLS regression comes

from omitted variable bias and potentially reverse causality Some employers likely

offer higher wages bonuses and health coverage to a select group of workers These

workers may put in more hours than their peers (reverse causality) or they may have

some unobserved characteristic (eg motivation) that makes employers like them more

and causes them to work more hours (omitted variable bias) By instrumenting the

employer policy faced by the focal worker with the average of the policy for all other

workers within the state-year the estimated effect of the employer policy is unrelated

to these worker characteristics Instead the effect is measured through increases in

the likelihood of the focal worker receiving the employer policy based on other workers

receiving it Intuitively a worker is more likely to have an employer cover health care

costs if that employer or nearby employers offer their workers health coverage

Table 7 shows the results from this specification as well as the OLS regressions I

use separate first and second stage regressions to analyze each of the employer policies

In addition to the IV approach outlined in Equation (3) Table 7 also shows results

from an alternative instrument (logged state minimum wages) for logged wages The

28


coefficients on the instrumental variables in the first stage regressions are all positive

and significant (ranging from 06 to 08) and F-statistics are sufficiently large

Table 7 Effect of Employer Policies

log(hours) weeks workedOLS IV OLS IV

Health coverage 00680lowastlowastlowast -00518 4698lowastlowastlowast 2408(000967) (00843) (0362) (2749)

Bonus 00950lowastlowastlowast 01026lowast 7360lowastlowastlowast 6465lowastlowastlowast(000765) (00563) (0252) (0704)

Season bonus 00828lowastlowastlowast 00592 3626lowastlowastlowast 3362lowast(00111) (00612) (0333) (0851)

log(wage) 01549lowastlowastlowast -0095 11004lowastlowastlowast 2960(00168) (02474) (05797) (77053)

State minimum wage IV 00561 -1578(00797) (3343)

State-year FE yes no yes noAll controls yes yes yes yesIV no yes no yesNotes Robust standard errors in parentheses clustered at state Reported coefficientsare from separate regressions containing the same set of fixed effects and control vari-ables but changing the predictor variable of interest IV regressions use the proportionof other workers within the same year and state as the focal worker who receives theemployer policy Because instruments vary at the state-year level IV regressions do notinclude state-by-year fixed effects and instead include separate year and state fixed ef-fects For log(wage) I additionally use state minimum wages as the instrument (shownin the bottom row of the table) The state minimum wage IV includes state fixedeffects and a time trend lowastp lt 010 lowastlowast p lt 005 lowastlowastlowast p lt 001

Results from the OLS regressions shown in the first and third columns of Table 7

suggest that all of these policies are positively correlated with labor supply Workers

who receive off-the-job health coverage work 7 more hours per week and 5 more weeks

per year than those who do not Workers who receive a (season end) bonus work 95

(8) more hours and 7 (35) more weeks than those who do not Finally the naive

regressions suggest that a 10 higher hourly wage rate is associated with working

15 more hours per week and one more week per year The IV results however

suggest that the correlations in the OLS regressions are primarily driven by unobserved

characteristics correlated with labor supply and the likelihood of receiving the employer

policy The second and third columns of Table 7 show results from the second stage

of the IV regressions These results show that employer policies have little significant

effect on either labor-hours or labor-weeks The only employer policies causally linked

with higher labor supply are bonuses and end of season bonuses In general receiving

29

6 DISCUSSION AND CONCLUSION

a bonus cause workers to increase weekly hours of work by 10 (note that this is only

significant at the 10 percent level) and to increase annual weeks worked by 65 weeks

End of season bonuses have no significant effect on weekly hours of work but increase

annual weeks of work by 35 Unlike the results from the IV regressions for health

coverage and logged wages these coefficients are similar to the OLS coefficients but

the standard errors are larger

6 Discussion and Conclusion

This paper is the first comprehensive study of the labor supply of US agricultural

workers I estimate differences in short-run labor supply for workers across several

demographic characteristics to show the likely effect of current workforce trends on

future labor supply If the workforce continues to age and in particular if the proportion

of workers aged 45 and older continues to rise I find that this will result in a labor force

that is willing to work more hours per week and weeks per year in agriculture However

if current trends in the gender and family composition of the workforce continue it will

result in a labor force that is willing to work less

This paper is one of few that examines heterogeneity in labor supply across worker

legal statuses I find that among employed US crop workers foreign born legals work

the most hours per week and weeks per year followed by undocumented workers and

then US natives To the best of my knowledge it is the only paper that examines

heterogeneity within worker legal statuses with respect to demographic characteristics

While I find significant variation in labor supply across legal status and demographic

groups separately I find little evidence of heterogeneity within the demographic groups

Notable exceptions are foreign born workers aged 45 and older work 40 hours per week

while natives work 38 single women with children who are legal immigrants work 39

hours per week while natives work 325 single men with children who are both legal

and undocumented immigrants work 34 weeks per year while natives work 285 and

single men without children who are undocumented work 32 weeks per year while

natives work 29

Finally this paper sheds light on the viability of four producer alternatives for in-

creasing labor-hours and labor-weeks To the best of my knowledge this is the first

causal evidence on the effects of these policies on the labor supply of employed agricul-

tural workers I find that bonuses have a positive and significant effect on both hours

and weeks of labor while offering higher wages or off-the-farm health coverage have

no significant effect My results suggest that on average offering workers a monetary

bonus increases labor-hours by 10 and labor-weeks by 65

30

REFERENCES REFERENCES

References

Angrist J D amp Evans W N (1998) Children and Their Parentsrsquo Labor Supply Ev-

idence from Exogenous Variation in Family Size The American Economic Review

88(3) 450ndash477

Blau F D amp Kahn L M (2007) Changes in the Labour Supply Behvaiour of Married

Women 1980-2000 Journal of Labour Economics 25(3) 393ndash438

Blundell R amp Macurdy T (1999) Chapter 27 Labor supply A review of alternative

approaches In O C Ashenfelter amp D Card (Eds) Handbook of Labor Economics

volume 3 chapter 27 (pp 1559ndash1695) Elsevier 1 edition

Borjas G J (2017) The Labor Supply of Undocumented Immigrants Labour Eco-

nomics 46 14ndash15

Boucher S R Smith A Taylor J E Fletcher P L amp Yuacutenez-Naude A (2012)

Immigration and the US farm labour supply Migration Letters 9(1) 87ndash99

Eissa N amp Hoynes H W (2004) Taxes and the labor market participation of married

couples The earned income tax credit Journal of Public Economics 88(9-10)

1931ndash1958

Emerson R D amp Roka F (2002) Income Distribution and Farm Labour Markets In

J L Findeis A M Vandeman J M Larson amp J L Runyan (Eds) The Dynamics

of Hired Farm Labour Constraints and Community Responses chapter 11 (pp 137ndash

150) New York NY CABI Publishing

Fallick B amp Pingle J (2010) The Effect of Population Aging on the Aggregate Labor

Market In K G Abraham M Harper amp J Pingle (Eds) Labor in the New

Economy (pp 31ndash80) National Bureau of Economic Research

Fallick B amp Pingle J F (2006) A Cohort-Based Model of Labor Force Participation

Technical report Federal Reserve Board Finance and Economics Discussion Series

Washington DC

Hernandez T Gabbard S amp Carroll D (2016) Findings from the National Agricul-

tural Employment Profile of Workers Survey United States Farmworkers (NAWS)

2013-2014 Technical Report Research Report No 12 US Department of Labor

Washington DC

Kaushal N (2006) Amnesty Programs and the Labor Market Outcomes of Undocu-

mented Workers The Jounral of Human Resources 41(3) 631ndash647

Kossoudji S A amp Cobb-Clark D A (2002) Coming Out of the Shadows Learning

About Legal Status and Wages from the Legalized Population Journal of Labour

Economics 20(3)

31


Kostandini G Mykerezi E amp Escalante C (2014) The impact of immigration en-

forcement on the US farming sector American Journal of Agricultural Economics

96(1) 172ndash192

Lofstrom M Hill L amp Hayes J (2013) Wage and mobility effects of legalization

Evidence from the new immigrant survey Journal of Regional Science 53(1) 171ndash

197

Lundberg S amp Rose E (2002) The Effects of Sons and Daughters on Menrsquos Labor

Supply and Wages The Review of Economics and Statistics 84(May) 251ndash268

Maestas N Mullen K amp Powell D (2016) The Effect of Population Aging on

Economic Growth the Labor Force and Productivity

Martin P amp Calvin L (2010) Immigration reform What does it mean for agriculture

and rural America Applied Economic Perspectives and Policy 32(2) 232ndash253

McClelland R amp Mok S (2012) A Review of Recent Research on Labor Supply

Elasticities

Meyer B D amp Rosenbaum D T (2001) Welfare the earned income tax credit

and the labor supply of single mothers Quarterly Journal of Economics 116(3)

1063ndash1114

Pena A A (2010) Legalization and Immigrants in US Agriculture The BE Journal

of Economic Analysis amp Policy 10(1)

Rivera-Batiz F L (1999) Undocumented Workers in the Labor Market An Analysis

of the Earnings of Legal and Illegal Mexican Immigrants in the United States

Journal of Population Economics 12(1) 91ndash116

Sheiner L (2014) The Determinants of the Macroeconomic Implications of Aging

American Economic Review Papers amp Proceedings 104(5) 218ndash223

Taylor J E (2010) Agricultural Labor and Migration Policy The Annual Review of

Resource Economics 2 369ndash93

Taylor J E amp Thilmany D (1993) Worker Turnover Farm Labor Contractors

and IRCArsquos Impact on the California Farm Labor Market American Journal of

Agricultural Economics 75(2) 350ndash60

Zahniser S Hertz T Dixon P Rimmer M amp Go W W W W W E R S U

S D A (2012) The Potential Impact of Changes in Immigration Policy on US

Agriculture and the Market for Hired Farm Labor A Simulation Anay Technical

report Economic Research Service Report Number 135 Washington DC

32

1 INTRODUCTION

1 Introduction

The profile of US agricultural workers is changing and little is understood about the

implications for their employers Farmworkers are aging increasingly female and are

more likely to have children (Hernandez et al 2016) In addition tightening immi-

gration enforcement is expected to reduce the number of undocumented workers in the

labor force (Kostandini et al 2014 Zahniser et al 2012) Previous work has linked

heterogeneity across these demographics with differential behavior in the labor market

These studies suggest that the changing profile of farmworkers will decrease the aggre-

gate labor supply of US agricultural workers This could have large negative effects

on US agricultural production and profits

This paper shows how the changing profile of agricultural workers will affect labor

supply on the intensive margin ie the labor supply of those employed in agriculture I

compare the labor supply of US agricultural workers across four dimensions age gen-

der family composition and legal status The labor supply estimates reflect a workerrsquos

short-run choice of the number of hours to work each week and the number of weeks

worked each year I combine these estimates with an analysis of trends in the work-

force composition to predict future labor supply I then explore several avenues through

which employers can increase labor supply In particular I examine how wages health

care coverage and bonuses affect hours and weeks of work for employed agricultural

workers

Recent estimates of labor supply for US agricultural workers are limited (Emerson

amp Roka 2002 Pena 2010 Taylor amp Thilmany 1993) Significant changes in US im-

migration policy and the profile of workers have resulted in major structural changes

in the agricultural labor market that are not captured in existing estimates (Boucher

et al 2012 Martin amp Calvin 2010 Taylor 2010) Importantly literature studying

other industries and the US labor market more broadly find significant differences in

worker labor supply across many of the demographic dimensions in which the agricul-

tural workforce is changing

A large literature focuses on the labor supply of immigrants and the effects of

legal status on various labor market outcomes Generally this literature finds that

legalization is associated with increases of 5 to 15 in the workersrsquo real wage rate and

that these effects are largest for more skilled workers (Kaushal 2006 Kossoudji amp Cobb-

Clark 2002 Lofstrom et al 2013 Rivera-Batiz 1999) Recent work by Borjas (2017)

is the first to examine labor supply elasticities based on immigrant status He finds a

significant difference in the elasticity of labor supply between US workers of different

immigration statuses He finds that undocumented men have a wage elasticity close to

zero while the elasticity for US native men is closer to 04 Here I add to this finding

2

1 INTRODUCTION






































3

1 INTRODUCTION






































4

2 DATA







2 Data





























5

2 DATA



































6

2 DATA



hours per week 436767 424323 452110 433080(1326) (1460) (1286) (1283)

weeks per year 368574 345758 385333 367902(1514) (1665) (1275) (1562)


(036) (022) (036) (039)piece rate 01333 00442 01318 01689


(014) (010) (016) (015)Task

pre-harvest 02200 02226 02017 02291(041) (042) (040) (042)

harvest 02671 01649 02448 03206(044) (037) (043) (047)

post-harvest 01166 01549 01093 01051(032) (036) (031) (031)

semi-skilled 02619 02515 03335 02267(044) (043) (047) (042)

other 01344 02061 01107 01186(034) (040) (031) (032)


(035) (045) (032) (032)fruits amp nuts 03583 01423 04473 03953

(048) (035) (050) (049)horticulture 01926 02813 01443 01833

(039) (045) (035) (039)vegetables 02452 02138 02418 02591

(043) (041) (043) (044)miscellaneous 00571 00867 00491 00499


wage ($hour) 77947 81674 80100 75270(273) (298) (282) (254)

bonus 03096 04408 03637 02232(046) (050) (048) (042)

season bonus 00978 01293 01236 00692(030) (034) (033) (025)




7

2 DATA




















(40)


















8

2 DATA





























9

2 DATA







(050) (030) (045) (047)8 - 11 years 02510 03410 01826 02563

(043) (047) (039) (044)ge 12 years 01799 05576 00948 00844

(038) (050) (029) (028)Age

16 - 24 02443 02813 00659 03321(043) (045) (025) (047)

25 - 34 02889 02072 02104 03658(045) (041) (041) (048)

35 - 44 02253 01968 03002 01938(042) (040) (046) (040)

ge 45 02416 03147 04235 01083(043) (046) (049) (031)


(047) (050) (036) (049)single kids 00669 00871 00569 00639

(025) (028) (023) (024)married no kids 01376 01794 01827 00950

(034) (038) (039) (029)married kids 04526 02577 06058 04442

(050) (044) (049) (050)Male 08024 07288 08027 08322

(040) (044) (040) (037)

Observations 54126 10870 15350 27295


10





























25 each year






11



(a) Average Ages

(b) Age Groups


12


















13





























children

14









(c) Undocumented














16


































17



(a) All Workers

(b) By Legal Status


18








written



(1)







19









year

















2002)













20

















21


















p lt 001

22







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32

1 INTRODUCTION






































3

1 INTRODUCTION






































4

2 DATA







2 Data





























5

2 DATA



































6

2 DATA



hours per week 436767 424323 452110 433080(1326) (1460) (1286) (1283)

weeks per year 368574 345758 385333 367902(1514) (1665) (1275) (1562)


(036) (022) (036) (039)piece rate 01333 00442 01318 01689


(014) (010) (016) (015)Task

pre-harvest 02200 02226 02017 02291(041) (042) (040) (042)

harvest 02671 01649 02448 03206(044) (037) (043) (047)

post-harvest 01166 01549 01093 01051(032) (036) (031) (031)

semi-skilled 02619 02515 03335 02267(044) (043) (047) (042)

other 01344 02061 01107 01186(034) (040) (031) (032)


(035) (045) (032) (032)fruits amp nuts 03583 01423 04473 03953

(048) (035) (050) (049)horticulture 01926 02813 01443 01833

(039) (045) (035) (039)vegetables 02452 02138 02418 02591

(043) (041) (043) (044)miscellaneous 00571 00867 00491 00499


wage ($hour) 77947 81674 80100 75270(273) (298) (282) (254)

bonus 03096 04408 03637 02232(046) (050) (048) (042)

season bonus 00978 01293 01236 00692(030) (034) (033) (025)




7

2 DATA




















(40)


















8

2 DATA





























9

2 DATA







(050) (030) (045) (047)8 - 11 years 02510 03410 01826 02563

(043) (047) (039) (044)ge 12 years 01799 05576 00948 00844

(038) (050) (029) (028)Age

16 - 24 02443 02813 00659 03321(043) (045) (025) (047)

25 - 34 02889 02072 02104 03658(045) (041) (041) (048)

35 - 44 02253 01968 03002 01938(042) (040) (046) (040)

ge 45 02416 03147 04235 01083(043) (046) (049) (031)


(047) (050) (036) (049)single kids 00669 00871 00569 00639

(025) (028) (023) (024)married no kids 01376 01794 01827 00950

(034) (038) (039) (029)married kids 04526 02577 06058 04442

(050) (044) (049) (050)Male 08024 07288 08027 08322

(040) (044) (040) (037)

Observations 54126 10870 15350 27295


10





























25 each year






11



(a) Average Ages

(b) Age Groups


12


















13





























children

14









(c) Undocumented














16


































17



(a) All Workers

(b) By Legal Status


18








written



(1)







19









year

















2002)













20

















21


















p lt 001

22







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32

1 INTRODUCTION






































4

2 DATA







2 Data





























5

2 DATA



































6

2 DATA



hours per week 436767 424323 452110 433080(1326) (1460) (1286) (1283)

weeks per year 368574 345758 385333 367902(1514) (1665) (1275) (1562)


(036) (022) (036) (039)piece rate 01333 00442 01318 01689


(014) (010) (016) (015)Task

pre-harvest 02200 02226 02017 02291(041) (042) (040) (042)

harvest 02671 01649 02448 03206(044) (037) (043) (047)

post-harvest 01166 01549 01093 01051(032) (036) (031) (031)

semi-skilled 02619 02515 03335 02267(044) (043) (047) (042)

other 01344 02061 01107 01186(034) (040) (031) (032)


(035) (045) (032) (032)fruits amp nuts 03583 01423 04473 03953

(048) (035) (050) (049)horticulture 01926 02813 01443 01833

(039) (045) (035) (039)vegetables 02452 02138 02418 02591

(043) (041) (043) (044)miscellaneous 00571 00867 00491 00499


wage ($hour) 77947 81674 80100 75270(273) (298) (282) (254)

bonus 03096 04408 03637 02232(046) (050) (048) (042)

season bonus 00978 01293 01236 00692(030) (034) (033) (025)




7

2 DATA




















(40)


















8

2 DATA





























9

2 DATA







(050) (030) (045) (047)8 - 11 years 02510 03410 01826 02563

(043) (047) (039) (044)ge 12 years 01799 05576 00948 00844

(038) (050) (029) (028)Age

16 - 24 02443 02813 00659 03321(043) (045) (025) (047)

25 - 34 02889 02072 02104 03658(045) (041) (041) (048)

35 - 44 02253 01968 03002 01938(042) (040) (046) (040)

ge 45 02416 03147 04235 01083(043) (046) (049) (031)


(047) (050) (036) (049)single kids 00669 00871 00569 00639

(025) (028) (023) (024)married no kids 01376 01794 01827 00950

(034) (038) (039) (029)married kids 04526 02577 06058 04442

(050) (044) (049) (050)Male 08024 07288 08027 08322

(040) (044) (040) (037)

Observations 54126 10870 15350 27295


10





























25 each year






11



(a) Average Ages

(b) Age Groups


12


















13





























children

14









(c) Undocumented














16


































17



(a) All Workers

(b) By Legal Status


18








written



(1)







19









year

















2002)













20

















21


















p lt 001

22







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32

2 DATA







2 Data





























5

2 DATA



































6

2 DATA



hours per week 436767 424323 452110 433080(1326) (1460) (1286) (1283)

weeks per year 368574 345758 385333 367902(1514) (1665) (1275) (1562)


(036) (022) (036) (039)piece rate 01333 00442 01318 01689


(014) (010) (016) (015)Task

pre-harvest 02200 02226 02017 02291(041) (042) (040) (042)

harvest 02671 01649 02448 03206(044) (037) (043) (047)

post-harvest 01166 01549 01093 01051(032) (036) (031) (031)

semi-skilled 02619 02515 03335 02267(044) (043) (047) (042)

other 01344 02061 01107 01186(034) (040) (031) (032)


(035) (045) (032) (032)fruits amp nuts 03583 01423 04473 03953

(048) (035) (050) (049)horticulture 01926 02813 01443 01833

(039) (045) (035) (039)vegetables 02452 02138 02418 02591

(043) (041) (043) (044)miscellaneous 00571 00867 00491 00499


wage ($hour) 77947 81674 80100 75270(273) (298) (282) (254)

bonus 03096 04408 03637 02232(046) (050) (048) (042)

season bonus 00978 01293 01236 00692(030) (034) (033) (025)




7

2 DATA




















(40)


















8

2 DATA





























9

2 DATA







(050) (030) (045) (047)8 - 11 years 02510 03410 01826 02563

(043) (047) (039) (044)ge 12 years 01799 05576 00948 00844

(038) (050) (029) (028)Age

16 - 24 02443 02813 00659 03321(043) (045) (025) (047)

25 - 34 02889 02072 02104 03658(045) (041) (041) (048)

35 - 44 02253 01968 03002 01938(042) (040) (046) (040)

ge 45 02416 03147 04235 01083(043) (046) (049) (031)


(047) (050) (036) (049)single kids 00669 00871 00569 00639

(025) (028) (023) (024)married no kids 01376 01794 01827 00950

(034) (038) (039) (029)married kids 04526 02577 06058 04442

(050) (044) (049) (050)Male 08024 07288 08027 08322

(040) (044) (040) (037)

Observations 54126 10870 15350 27295


10





























25 each year






11



(a) Average Ages

(b) Age Groups


12


















13





























children

14









(c) Undocumented














16


































17



(a) All Workers

(b) By Legal Status


18








written



(1)







19









year

















2002)













20

















21


















p lt 001

22







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32

2 DATA



































6

2 DATA



hours per week 436767 424323 452110 433080(1326) (1460) (1286) (1283)

weeks per year 368574 345758 385333 367902(1514) (1665) (1275) (1562)


(036) (022) (036) (039)piece rate 01333 00442 01318 01689


(014) (010) (016) (015)Task

pre-harvest 02200 02226 02017 02291(041) (042) (040) (042)

harvest 02671 01649 02448 03206(044) (037) (043) (047)

post-harvest 01166 01549 01093 01051(032) (036) (031) (031)

semi-skilled 02619 02515 03335 02267(044) (043) (047) (042)

other 01344 02061 01107 01186(034) (040) (031) (032)


(035) (045) (032) (032)fruits amp nuts 03583 01423 04473 03953

(048) (035) (050) (049)horticulture 01926 02813 01443 01833

(039) (045) (035) (039)vegetables 02452 02138 02418 02591

(043) (041) (043) (044)miscellaneous 00571 00867 00491 00499


wage ($hour) 77947 81674 80100 75270(273) (298) (282) (254)

bonus 03096 04408 03637 02232(046) (050) (048) (042)

season bonus 00978 01293 01236 00692(030) (034) (033) (025)




7

2 DATA




















(40)


















8

2 DATA





























9

2 DATA







(050) (030) (045) (047)8 - 11 years 02510 03410 01826 02563

(043) (047) (039) (044)ge 12 years 01799 05576 00948 00844

(038) (050) (029) (028)Age

16 - 24 02443 02813 00659 03321(043) (045) (025) (047)

25 - 34 02889 02072 02104 03658(045) (041) (041) (048)

35 - 44 02253 01968 03002 01938(042) (040) (046) (040)

ge 45 02416 03147 04235 01083(043) (046) (049) (031)


(047) (050) (036) (049)single kids 00669 00871 00569 00639

(025) (028) (023) (024)married no kids 01376 01794 01827 00950

(034) (038) (039) (029)married kids 04526 02577 06058 04442

(050) (044) (049) (050)Male 08024 07288 08027 08322

(040) (044) (040) (037)

Observations 54126 10870 15350 27295


10





























25 each year






11



(a) Average Ages

(b) Age Groups


12


















13





























children

14









(c) Undocumented














16


































17



(a) All Workers

(b) By Legal Status


18








written



(1)







19









year

















2002)













20

















21


















p lt 001

22







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32

2 DATA



hours per week 436767 424323 452110 433080(1326) (1460) (1286) (1283)

weeks per year 368574 345758 385333 367902(1514) (1665) (1275) (1562)


(036) (022) (036) (039)piece rate 01333 00442 01318 01689


(014) (010) (016) (015)Task

pre-harvest 02200 02226 02017 02291(041) (042) (040) (042)

harvest 02671 01649 02448 03206(044) (037) (043) (047)

post-harvest 01166 01549 01093 01051(032) (036) (031) (031)

semi-skilled 02619 02515 03335 02267(044) (043) (047) (042)

other 01344 02061 01107 01186(034) (040) (031) (032)


(035) (045) (032) (032)fruits amp nuts 03583 01423 04473 03953

(048) (035) (050) (049)horticulture 01926 02813 01443 01833

(039) (045) (035) (039)vegetables 02452 02138 02418 02591

(043) (041) (043) (044)miscellaneous 00571 00867 00491 00499


wage ($hour) 77947 81674 80100 75270(273) (298) (282) (254)

bonus 03096 04408 03637 02232(046) (050) (048) (042)

season bonus 00978 01293 01236 00692(030) (034) (033) (025)




7

2 DATA




















(40)


















8

2 DATA





























9

2 DATA







(050) (030) (045) (047)8 - 11 years 02510 03410 01826 02563

(043) (047) (039) (044)ge 12 years 01799 05576 00948 00844

(038) (050) (029) (028)Age

16 - 24 02443 02813 00659 03321(043) (045) (025) (047)

25 - 34 02889 02072 02104 03658(045) (041) (041) (048)

35 - 44 02253 01968 03002 01938(042) (040) (046) (040)

ge 45 02416 03147 04235 01083(043) (046) (049) (031)


(047) (050) (036) (049)single kids 00669 00871 00569 00639

(025) (028) (023) (024)married no kids 01376 01794 01827 00950

(034) (038) (039) (029)married kids 04526 02577 06058 04442

(050) (044) (049) (050)Male 08024 07288 08027 08322

(040) (044) (040) (037)

Observations 54126 10870 15350 27295


10





























25 each year






11



(a) Average Ages

(b) Age Groups


12


















13





























children

14









(c) Undocumented














16


































17



(a) All Workers

(b) By Legal Status


18








written



(1)







19









year

















2002)













20

















21


















p lt 001

22







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32

2 DATA




















(40)


















8

2 DATA





























9

2 DATA







(050) (030) (045) (047)8 - 11 years 02510 03410 01826 02563

(043) (047) (039) (044)ge 12 years 01799 05576 00948 00844

(038) (050) (029) (028)Age

16 - 24 02443 02813 00659 03321(043) (045) (025) (047)

25 - 34 02889 02072 02104 03658(045) (041) (041) (048)

35 - 44 02253 01968 03002 01938(042) (040) (046) (040)

ge 45 02416 03147 04235 01083(043) (046) (049) (031)


(047) (050) (036) (049)single kids 00669 00871 00569 00639

(025) (028) (023) (024)married no kids 01376 01794 01827 00950

(034) (038) (039) (029)married kids 04526 02577 06058 04442

(050) (044) (049) (050)Male 08024 07288 08027 08322

(040) (044) (040) (037)

Observations 54126 10870 15350 27295


10





























25 each year






11



(a) Average Ages

(b) Age Groups


12


















13





























children

14









(c) Undocumented














16


































17



(a) All Workers

(b) By Legal Status


18








written



(1)







19









year

















2002)













20

















21


















p lt 001

22







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32

2 DATA





























9

2 DATA







(050) (030) (045) (047)8 - 11 years 02510 03410 01826 02563

(043) (047) (039) (044)ge 12 years 01799 05576 00948 00844

(038) (050) (029) (028)Age

16 - 24 02443 02813 00659 03321(043) (045) (025) (047)

25 - 34 02889 02072 02104 03658(045) (041) (041) (048)

35 - 44 02253 01968 03002 01938(042) (040) (046) (040)

ge 45 02416 03147 04235 01083(043) (046) (049) (031)


(047) (050) (036) (049)single kids 00669 00871 00569 00639

(025) (028) (023) (024)married no kids 01376 01794 01827 00950

(034) (038) (039) (029)married kids 04526 02577 06058 04442

(050) (044) (049) (050)Male 08024 07288 08027 08322

(040) (044) (040) (037)

Observations 54126 10870 15350 27295


10





























25 each year






11



(a) Average Ages

(b) Age Groups


12


















13





























children

14









(c) Undocumented














16


































17



(a) All Workers

(b) By Legal Status


18








written



(1)







19









year

















2002)













20

















21


















p lt 001

22







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32

2 DATA







(050) (030) (045) (047)8 - 11 years 02510 03410 01826 02563

(043) (047) (039) (044)ge 12 years 01799 05576 00948 00844

(038) (050) (029) (028)Age

16 - 24 02443 02813 00659 03321(043) (045) (025) (047)

25 - 34 02889 02072 02104 03658(045) (041) (041) (048)

35 - 44 02253 01968 03002 01938(042) (040) (046) (040)

ge 45 02416 03147 04235 01083(043) (046) (049) (031)


(047) (050) (036) (049)single kids 00669 00871 00569 00639

(025) (028) (023) (024)married no kids 01376 01794 01827 00950

(034) (038) (039) (029)married kids 04526 02577 06058 04442

(050) (044) (049) (050)Male 08024 07288 08027 08322

(040) (044) (040) (037)

Observations 54126 10870 15350 27295


10





























25 each year






11



(a) Average Ages

(b) Age Groups


12


















13





























children

14









(c) Undocumented














16


































17



(a) All Workers

(b) By Legal Status


18








written



(1)







19









year

















2002)













20

















21


















p lt 001

22







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32





























25 each year






11



(a) Average Ages

(b) Age Groups


12


















13





























children

14









(c) Undocumented














16


































17



(a) All Workers

(b) By Legal Status


18








written



(1)







19









year

















2002)













20

















21


















p lt 001

22







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32



(a) Average Ages

(b) Age Groups


12


















13





























children

14









(c) Undocumented














16


































17



(a) All Workers

(b) By Legal Status


18








written



(1)







19









year

















2002)













20

















21


















p lt 001

22







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32


















13





























children

14









(c) Undocumented














16


































17



(a) All Workers

(b) By Legal Status


18








written



(1)







19









year

















2002)













20

















21


















p lt 001

22







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32





























children

14









(c) Undocumented














16


































17



(a) All Workers

(b) By Legal Status


18








written



(1)







19









year

















2002)













20

















21


















p lt 001

22







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32









(c) Undocumented














16


































17



(a) All Workers

(b) By Legal Status


18








written



(1)







19









year

















2002)













20

















21


















p lt 001

22







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32


































17



(a) All Workers

(b) By Legal Status


18








written



(1)







19









year

















2002)













20

















21


















p lt 001

22







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32



(a) All Workers

(b) By Legal Status


18








written



(1)







19









year

















2002)













20

















21


















p lt 001

22







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32








written



(1)







19









year

















2002)













20

















21


















p lt 001

22







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32









year

















2002)













20

















21


















p lt 001

22







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32

















21


















p lt 001

22







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32


















p lt 001

22







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32







AgeAge 16 - 24 3882 2604


























23










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32










(2)


























24



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32



























25












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32












26





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32





































27









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32









in two stages


iγ + λs + λt + 983171i





























28

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32

























29





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32





























natives work 29








30


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32


References



88(3) 450ndash477







nomics 46 14ndash15





1931ndash1958










Washington DC




Washington DC





Economics 20(3)

31




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32




96(1) 172ndash192



197








Elasticities



1063ndash1114

















32

the labor supply of u.s. agricultural workers hill; us...the data for this paper come from the...

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