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 Front Cover
 Center information
 Abstract
 Introduction
 Research methodology
 Results and discussion
 Conclusions
 Reference
 Tables






Group Title: Policy Brief Series - International Agricultural Trade and Policy Center. University of Florida ; no. 07-01
Title: Implications of proposed immigration reform for the U.S. farm labor market
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Title: Implications of proposed immigration reform for the U.S. farm labor market
Series Title: Policy Brief Series - International Agricultural Trade and Policy Center. University of Florida ; no. 07-01
Physical Description: Book
Language: English
Creator: Walters, Lurleen M.
Emerson, Robert D
Iwai, Nobuyuki
Publisher: International Agricultural Trade and Policy Center, Institute of Food and Agricultural Sciences, University of Florida
Institute of Food and Agricultural Sciences, University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007
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Table of Contents
    Front Cover
        Page i
    Center information
        Page ii
    Abstract
        Page iii
    Introduction
        Page 1
        Page 2
    Research methodology
        Page 3
        Page 4
        Page 5
    Results and discussion
        Page 6
        Page 7
        Page 8
        Page 9
        Page 10
        Page 11
        Page 12
        Page 13
    Conclusions
        Page 14
        Page 15
        Page 16
    Reference
        Page 17
    Tables
        Page 18
        Page 19
        Page 20
        Page 21
        Page 22
        Page 23
        Page 24
        Page 25
        Page 26
Full Text

PBTC 07-01


I ional Agricultural Trade and Policy Center




Implications of Proposed Immigration Reform for the
U.S. Farm Labor Market
By
Lurleen M. Walters, Robert D. Emerson, and Nobuyuki Iwai

PBTC 07-01 February 2007


POLICY BRIEF SERIES


'V


UNIVERSITY OF
FLORIDA


Institute of Food and Agricultural Sciences


i~fr









INTERNATIONAL AGRICULTURAL TRADE AND POLICY CENTER


THE INTERNATIONAL AGRICULTURAL TRADE AND POLICY CENTER
(IATPC)

The International Agricultural Trade and Policy Center (IATPC) was established in 1990
in the Institute of Food and Agriculture Sciences (IFAS) at the University of Florida
(UF). The mission of the Center is to conduct a multi-disciplinary research, education and
outreach program with a major focus on issues that influence competitiveness of specialty
crop agriculture in support of consumers, industry, resource owners and policy makers.
The Center facilitates collaborative research, education and outreach programs across
colleges of the university, with other universities and with state, national and
international organizations. The Center's objectives are to:

* Serve as the University-wide focal point for research on international trade,
domestic and foreign legal and policy issues influencing specialty crop agriculture.
* Support initiatives that enable a better understanding of state, U.S. and international
policy issues impacting the competitiveness of specialty crops locally, nationally,
and internationally.
* Serve as a nation-wide resource for research on public policy issues concerning
specialty crops.
* Disseminate research results to, and interact with, policymakers; research, business,
industry, and resource groups; and state, federal, and international agencies to
facilitate the policy debate on specialty crop issues.









IMPLICATIONS OF PROPOSED IMMIGRATION REFORM
FOR THE U.S. FARM LABOR MARKET


Lurleen M. Walters
International Agricultural Trade & Policy Center
Food and Resource Economics Department
P.O. Box 110240, University of Florida
Gainesville, FL 32611
lwalters@ufl.edu


Robert D. Emerson
International Agricultural Trade & Policy Center
Food and Resource Economics Department
PO Box 110240, University of Florida
Gainesville, FL 32611
remerson@ufl.edu


Nobuyuki Iwai
International Agricultural Trade & Policy Center
Food and Resource Economics Department
PO Box 110240, University of Florida
Gainesville, FL 32611
niwai@ufl.edu



Abstract

Specialty crop agriculture may be affected by immigration reform given that most farm workers
are foreign-born and unauthorized for U.S. employment. Controlling for selection on legal status and job
type according to skill level, this research examines the wage effects for workers with different
characteristics in the U.S. and South.

Keywords: immigration reform, legal status, skill, specialty crop agriculture, wages
JEL Code: J430


Selected Paper prepared for presentation at the Southern Agricultural Economics Association
Annual Meeting, Mobile, Alabama, February 4-7, 2007.

Copyright 2007 by Lurleen M. Walters, Robert D. Emerson and Nobuyuki Iwai. All rights
reserved. Readers may make verbatim copies of this documentfor non-commercial purposes by
any means, provided that this copyright notice appears on all such copies.









IMPLICATIONS OF PROPOSED IMMIGRATION REFORM
FOR THE U.S. FARM LABOR MARKET1

Introduction

Immigration continues to be an issue engendering heated political debate. In

signing P.L. 109-367, the Secure Fence Act of 2006, the President remarked that "It is an

important step toward immigration reform. ... We have more to do." (White House,

2006) Two earlier proposals in the 109th Congress one introduced in the U.S. House of

Representatives and the other in the U.S. Senate provoked much debate across

America, and serve to frame U.S. immigration issues. The 109th Congress failed to reach

a compromise between these two sets of views on immigration.

Legislative proposal H.R. 4437 (the Border Protection, Antiterrorism, andlllegal

Immigration Control Act of 2005) was passed by the House of Representatives in

December 2005. It is arguably one of the more restrictive proposals introduced for

consideration in Congress in that it contains no provisions for legalization of

unauthorized workers or for a guest worker program. It is pro-enforcement, advocating

criminal penalties for unauthorized immigrants and hefty fines for the U.S. employers

who hire them. Further, it makes no modifications to existing laws on legal immigration,

and is quite strict on I-9 document reform and worksite/interior/border enforcement. In

contrast, S. 2611 (the Comprehensive Immigration Reform Act of 2006) passed by the

U.S. Senate in May 2006 proposes earned legalization for unauthorized immigrants and



1The authors are grateful to Susan Gabbard, Alberto Sandoval and their associates at Aguirre International
for assistance with the NAWS data, and to Daniel Carroll at the U.S. Department of Labor for granting
access and authorization to use the NAWS data and for providing valuable comments on the paper. This
research has been supported through a partnership agreement with the Risk Management Agency, U.S.
Department of Agriculture; by the Center for International Business Education and Research at the
University of Florida; and by the Florida Agricultural Experiment Station. The authors alone are
responsible for any views expressed in the paper.









modifications to existing laws on legal immigration. It is not as severe as H.R. 4437 but it

favors I-9 document reform and stricter enforcement (for example, tougher penalties for

U.S. employers who hire unauthorized immigrants). S.2611 also contains specific

provisions for agriculture under AgJOBS (Agricultural Job Opportunity, Benefits and

Security Act of2005 (S. 359/H.R. 884; S.2611 Subtitle B). AgJOBS is intended to

streamline the H-2A program to improve wages, working conditions and minimum

benefits (housing and transportation) for farm workers and to establish a pilot program

for earned legalization of unauthorized workers who meet certain requirements.

Given that the majority of the crop farm workforce is foreign-born (78%) and

more than 50% is unauthorized for U.S. employment (NAWS, 2004), the U.S. farm labor

market and the specialty crop sector may be directly affected by immigration reform,

specifically labor availability and wages. Based on the assumption that farm workers

self-select into specific legal status groups, previous work on this issue examined the

impact of legal status on the farm wage and the types of jobs (skilled or unskilled) for

which workers are hired. First, Taylor (1992) explained wages separately for primary

(skilled) and secondary (unskilled) jobs in agriculture, arguing that there was self-

selectivity into the two types of work. Legal status of the worker entered the earnings

equations as an exogenous influence, argued to affect earnings differently for the two job

types. Ise and Perloff (1995) explained farm wages based on a model with self-

selectivity into four legal status categories and specified separate earnings equations for

each status. Job type (as distinguished by skill level) was not considered in their analysis.

Iwai et al. (2006) also examined farm workers' self-selectivity into different legal

statuses. Similar to Ise and Perloff (1995), they simulated how the wages of unauthorized









workers would change with adjustment to legal status, but did not consider self-selection

into skilled or unskilled employment.

This study goes a step further to examine farm workers' joint self-selection into

U.S. employment as authorized or unauthorized and for skilled or unskilled jobs.

Focusing on job type as a potential source of selection bias in conjunction with legal

status is motivated in part by an interesting conclusion by Taylor (1992) that there may

be wage differentials for workers of different legal status in the same jobs, such that the

expected earnings for unauthorized workers in skilled positions could be less than those

for their authorized cohorts. The major distinction from previous work is that these two

employment status indicators are modeled jointly to reflect the potential joint choices by

workers. We use data for 1989-2004 from the National Agricultural Workers Survey

(NAWS) and restrict our sample to the foreign-born population since U.S. citizens do not

select into legal status for US employment.

Research Methodology

The double selection model proposed by Tunali (1986) is used to model the two

potential sources of selectivity. A bivariate probit model is used to explain the joint

decisions/selections on employment status in the first stage. We constructed selectivity

parameters from the estimated coefficients and included them as explanatory variables in

wage regressions in the second stage. We assume that the ith individual's (unobserved)

decisions on legal status and job type are specified as shown:


yli = x/i + uli Legal status decision (1)
Y2i = X2i= 2 + U2i Job (skill) type decision (2)

The log wage regression equation is specified as:









inW,3 = x ,,83 + su (3)

The explanatory variables and unknown coefficients are represented by x and f,

respectively, and the joint normal error terms (uh, u2,u, ) have zero mean and covariance

1 p p13
matrix= p 1 p3 Since y,* and y, are unobserved, we use the observed
LP13 P23 wI

outcomes of the dichotomous variables D1 (indicating whether the farm worker is

authorized or unauthorized for U.S. employment) and D2 (indicating whether he is

employed for a skilled or unskilled job) only:


D, 1 if y, >0 D 1 f y > (4)
S if y3 < 0 0 f 3y2 < 0

Four subgroups G, (j = 1,...,4) are generated, the elements of which are combinations of

D1 and D2, i.e. G = (0,0), G2 = (0,),G3 = (1,0)andG4 = (1,1). Subgroup Gi denotes

foreign-born farm workers who are unauthorized & unskilled, and G2, Gs and G4 are

foreign-born farm workers who are unauthorized & skilled, authorized & unskilled, and

authorized & skilled, respectively2. If the four subgroups are distinct and completely

classified, the probability S, that an individual is assigned to thejth subgroup is:


S, =Pr(D, =0,D2 =0)=Pr(y, <0,y, <0)
=Pr(uh <-C,,u2, <-C,) (5)
= 0p)

S =Pr(D, =0,D2 =1)=Pr(y, <0,y, >0)
=Pr(,, <-C1,u >-C2) (6)
= (-C,,C2;-p)



2 Per the NAWS dataset, pre-harvest, harvest and post-harvest jobs are classified as unskilled positions,
whereas semi-skilled and supervisory jobs are classified as skilled.
4








S =Pr(D =1,D2 =0)=Pr(y,* >0,y:* <0)
=Pr(i,> >-C,u2 <-C2) (7)
=02(C,- C;-p)C
S4 =Pr(D =,D2 =1) =Pr(y1i >0,y2 >0)
=Pr(ili >-CU2 >-C2) (8)
= (C2, C2,p)
In equations 5 through 8, C, = x', ,8, and C2 = x'2 2, 2 is the standard bivariate

normal distribution function and p is the correlation coefficient between ul and u2. For

each subgroup with complete observations, E(W3, x3,, ) = x 3,'f + a3E(u31 x3, 0) where

0 denotes the joint outcome of the double selection process. Selectivity bias arises

ifE = (u3, x31,) 0 (Tunali, 1986; Vella, 1998). The inverse Mills ratios (selectivity

variables) corresponding to each subgroup are calculated as shown below. Symbols

(.) and (P.) represent the standard univariate normal density and distribution functions,

respectively3

(i) For i e G, (i.e. D1=D2=0): E(u3, ul, < -C1,u2, < -C2)= p3 +P23212

~(cc)(-c2} __ 2 -c1
F q (Cs) C 12 (S,)
L (si (s) (10.1)
,* C, -PC2 C2_ pC
where C1 =- 2-2, C2 =- C==


(ii) For i e G (i.e. D =0; D2=1): E(u3z u,, < -C,u1, > -C2)= p-2 + 2322


A21 = (Ci)S C2 22 L (2)2 1 (10.2)
(S22) (S-2)





3 See Tunali (1986) for the complete set of derivations.
5









(iii) For i e G3 (i.e. D1=1; D2=0): E(u3, u1 > -C1,22 < -C2)= p1323 + P23232


A31 1 3(C )0C2 32 J _[4C2)(SC] (10.3)


(iv) For i e G4 (i.e. D=1; ,D2= ): E(u31 u, > -C],u2 > -C)= pi 1 + 23p42


A41(_ (C) C2* !(C2)PC* 1 (10.4)


The selectivity variables are used as covariates in the log wage equations for each

subgroup of the foreign-born farm workers, for example, for subgroup G1 we have

In W = x' f + c-3p13 A1 + 3P23 12 + 3v (11
= x' P + Pi/,1 + P8212 + (3v


where PI = 03P13 2 = 73P23, and v = u3i P13/21 P2312


Results & Discussion

Table 1 defines the explanatory variables that were used in the bivariate probit

and wage equation models. Tables 2(a) and 2(b) report the summary statistics for each

foreign-born worker subgroup (authorized & skilled, authorized & unskilled,

unauthorized & skilled, unauthorized & unskilled) for the U.S. and South4, respectively.

Unauthorized & unskilled workers comprise 44%, authorized & unskilled workers

comprise 32%, authorized & skilled workers comprise 13%, and unauthorized & skilled

workers comprise 11% of our U.S. sample (n=29364).5 In our sample of the South



4 The South is defined as Florida, Delta-Southeast (AR, LA, MS, AL, GA, SC) and Appalachia (NC, VA,
KY, TN, WV).
5 Throughout the paper, both summary statistics and parameter estimates are based on unweighted sample
data. In the absence of a compelling argument to weight the data for parameter estimation (Deaton 1997),
the parameter estimates reported later in the paper use unweighted data. Correspondingly, unweighted
summary statistics are presented to characterize the sample data used for model estimation. Consequently,
other reports (Carroll et al. 2005; Walters et al. 2006) focusing solely on representative summaries of the
data which properly incorporate the weights in generating the summary statistics may differ from summary
6









(n=6238), the unauthorized & unskilled subgroup comprises the majority (64%) followed

by the authorized & unskilled (24%) subgroup. The unauthorized & skilled and

authorized & skilled subgroups each account for less than 10% of the entire sample, at

8% and 4%, respectively.

The summary statistics for the subgroups reveal that foreign-born workers who

select into farm employment at the national level (US) and in the South are more likely to

be male and Mexican. They speak little or no English and have some foreign farm work

experience. In both samples, workers in the unauthorized & unskilled and unauthorized

& skilled subgroups are generally younger than their counterparts in the other subgroups:

for example, workers' mean age on average in either of these subgroups is 28 years (U.S.

sample) and 27 years (South). Workers represented in the authorized & skilled and

authorized & unskilled subgroups tend to be in their mid- to late 30s.

Approximately 15-20% of the workers in the US and the South reported receiving

free housing from their employer. The exception was for unauthorized workers in the

South where 28% of the unskilled workers and 54% of the skilled workers reported free

housing from their employer. The proportions of workers reporting having attended adult

education classes since arriving in the US were understandably higher in both the US and

the South for authorized workers than for unauthorized workers. Among authorized

workers, 28-34% reported adult education in the US while only 10-14% of unauthorized

workers reported the same. Although the percentages do not differ greatly between the

US and the South, the percentages for adult education were slightly higher for authorized

workers in the South relative to the US, and slightly lower for unauthorized workers in

the South relative to the US.

characteristics reported here. The weighted data are more appropriate as a representative summary of the
data.
7









Most of the workers are employed with growers. Not surprisingly, authorized

workers tended to have been employed longer with their current employer; in the U.S.,

they had been employed for over five years whereas they had been employed for only

two years on average if they were unauthorized. Similarly, workers in the South had

been with their current employer for four years on average, whereas unauthorized

workers had been employed for two years on average. Regardless of skill level, workers

who are authorized report longer work periods on average, and have more experience in

U.S. farm work than their unauthorized cohorts. Although an analysis of duration of

farm work in the US is beyond the scope of this paper, the substantial differences

between authorized and unauthorized workers in years of US farm work are undoubtedly

dependent on variations between the two groups in the number of years since arriving in

the US. By contrast, farm work completed in the last year is quite similar for both

groups, varying only between 33 and 39 weeks, but again with slightly higher averages

for authorized workers.

Bivariate Probit Model Selections

Foreign-born workers' choices on employment as authorized or unauthorized

workers and for skilled or unskilled jobs were estimated with bivariate probit models of

the U.S. and South. Table 3 reports the estimated coefficients and asymptotic standard

errors. The rho (p) coefficients are positive and statistically significant at 10% (level of

significance) in both models. These results suggest that in the farm labor markets of the

U.S. and the South, workers' choices on employment as authorized or unauthorized

workers and in skilled or unskilled jobs are interrelated. Thus, foreign-born farm workers

who are likely to self-select into U.S. employment as unauthorized workers are also

likely to be employed in unskilled jobs.









The coefficients of the two equations of the U.S. model are statistically

significant at the 10% significance level or better and have the expected signs. All else

being the same, the characteristics that increase the probability that a foreign-born farm

worker is unauthorized for U.S. employment are: (being) male, single, Mexican, having

few years of formal education, US farm experience and limited English speaking ability.

The probability that a worker is unauthorized for U.S. employment increases if he is hired

in Florida as opposed to California. However, if he was interviewed before 1993 and has

attended adult education courses since arriving in the U.S., there is a higher probability

that he is authorized for U.S. employment, holding all other factors constant. The

probability of selection into skilled employment is increased if workers can speak English

well, if they are educated, and have U.S. and foreign farm work experience. All else

being the same, there is also an increased probability of selection into skilled employment

if the worker is employed on a seasonal basis and has worked for several years with his

current employer. On the other hand, the probability decreases if the farm worker is

employed with a grower, paid a piece rate, and employed in specialty crop agriculture.

With the exception of the variables reflecting Mexican ethnicity, foreign farm

work experience, years with current employer and age, the coefficients of the bivariate

probit model for the South are statistically significant at the 10% level. The

characteristics that increase the probability of being authorized and skilled are similar to

those discussed previously for the U.S. model, the only notable (and statistically

significant) exceptions being the coefficients for the grower and education variables in

the skilled equation. Holding all other factors constant, the probability that a worker

selects into skilled employment in the South increases if he is employed with a grower -

this is directly opposite to what was determined for the U.S. model. The negatively

9









significant coefficient for education is interesting, more so because it suggests that the

probability of farm workers selecting into skilled employment in the South decreases if

they are educated. This is in contrast to the finding on education in the skill equation of

the U.S. model.

Table 4 reports the marginal effects of each variable on the probability of a

worker's selection into each of the four subgroups identified in the U.S. and South

samples. Holding all other characteristics constant, foreign-born workers who are

interviewed before 1993 are 9% and 3% more likely to be authorized & skilled (US and

South, respectively) than if they were interviewed after 1993. In contrast, workers are

9% and 3% less likely to self-select into the unauthorized & skilled subgroup if they were

interviewed before 1993.6 The marginal effects are similar for the US and South for the

authorized & unskilled (unauthorized & unskilled) subgroups, in that workers are about

30% more (less) likely to self-select into these employment statuses if they were

interviewed before 1993. These effects are expected since roughly 1.3 million farm

workers gained legal status under the 1986 Immigration Reform and Control Act (IRCA);

this particular variable is therefore capturing this effect. Comparison with the after 2001

dummy variable shows the opposite marginal effects, in that foreign-born farm workers

are more likely to select into the unauthorized subgroups (regardless of the job/skill level)

than into the authorized subgroups.

Many of the farm workers in specialty crop agriculture are unskilled. This effect

comes through in the specialty crop dummy variable in our models of the US (South) in

that foreign-born workers are 5% (4%) more likely to be authorized & unskilled and 6%

(14%) more likely to be unauthorized & unskilled if they are employed in specialty crop

6 Note that for any variable included in one equation but not the other, the marginal effects are the mirror
image for the opposite group.
10









agriculture. We observe the opposite marginal effects if the workers are skilled

(regardless of legal status). The marginal effects induced by the location variables

(California and Florida) show that workers are more likely to be unauthorized if

employed in Florida but more likely to be authorized if employed in California.


Wage Equation Models with Selectivity Bias Corrections


Tables 5 (a) and 5 (b) report the estimated coefficients and asymptotic standard

errors for the four wage equation models for the US and South, respectively. Most

parameter estimates are statistically significant at 10% or better and have the expected

signs. The selectivity variable for legal status, 1A, accounts for potential selection bias

from foreign-born workers' choices on employment as authorized or unauthorized.

Selectivity variable A2 measures the potential bias arising from workers' selection into

skilled or unskilled employment. The estimated coefficients for both selectivity variables

are statistically significant across all worker subgroups suggesting the presence of

selectivity bias in the system. Thus, if these wage equations were estimated without the

appropriate corrections, the coefficients would be biased.

In the four worker subgroups shown for the US in Table 5(a), workers who have

attended adult education courses, are employed in California, employed with a grower

and paid by piece rate are more likely to have higher earnings. Education and English-

speaking ability also have significantly positive effects on farm worker earnings across

all worker subgroups. US farm experience has a significant positive nonlinear effect in


7 Although this result may seem counterintuitive, it is a result of excluding native born citizen workers from
the analysis. Most authorized workers on the East Coast are native born so that when native born citizen
workers are excluded, virtually all workers are unauthorized, e.g. there tend to be few green card farm
workers on the East Coast. By contrast, on the West Coast most authorized workers were foreign born
(green card, naturalized citizen, or other form of authorization). Consequently, the exclusion of native born
citizens on the East Coast results in a very large change in the proportions of authorized and unauthorized
workers from what they would be over all types of workers, including native born citizens.
11









all subgroups, and so does age except in the unauthorized & unskilled subgroup. Free

housing has a significant and negative effect on farm worker earnings, consistent with the

theory of compensating differentials. Regardless of the subgroup that they select into,

seasonal workers are likely to have lower earnings.

Overall, our findings are broadly consistent with those of Ise and Perloff (1995)

and Iwai et al. (2006). Taylor (1992) found that education and farm work experience

were significant in explaining skilled employment, but were not so in explaining

differences in earnings among skilled workers. The effect of employment in Florida is

somewhat unclear judging from the results of our model: it is negative and significant in

the unauthorized & unskilled subgroup (implying that earnings are lower for those

workers) but positive and significant in the authorized & skilled subgroup (implying that

this employment status is more advantageous for workers). Ise and Perloff (1995) and

Iwai et al. (2006) found that workers employed in Florida had lower earnings on average

-- Ise and Perloff noted that they were paid significantly less than workers employed in

California, whereas Iwai et al. were able to confirm this only for unauthorized workers.

In the earnings equations for the South, females, workers for whom free housing

is provided, and those who are hired for seasonal work tend to have lower earnings if

they belong to any subgroup other than authorized & skilled. In contrast to the results

based on the total US data, these variables have no statistically significant effect on

workers' earnings in the latter subgroup. Age has a significant nonlinear effect in the

authorized (skilled or unskilled) subgroups only. Conversely, workers who have attended

adult education courses since arriving in the U.S., who are employed by a grower, paid

by piece rate, and educated are likely to earn more, as was the case for farm workers

throughout the US. In addition, English speaking ability has a statistically significant

12









effect on Southern workers' earnings only if the workers are authorized for US

employment, in contrast to all farm workers regardless of authorization status for the total

US data. Farm work experience has a statistically significant effect with the typical

earnings profile for all groups in the South except authorized & skilled workers for which

log earnings increase with experience at a constant rate rather than a decreasing rate; all

groups were statistically significant and appropriately shaped for the total US data.

The piece rate dummy variable is dominant in both the US and Southern earnings

equations. All else being the same, payment by piece rate increases earnings by more

than 20% in unskilled subgroups regardless of authorization, but to a lesser extent in the

skilled subgroups (<13%). The magnitude of influence is less in the Southern earnings

equations but is comparable across all subgroups, increasing earnings by 18% or more

regardless of skill level. The grower dummy variable also yields some interesting

differences between the US and Southern subgroups. Based on the total US data, our

results suggest employment with a grower would increase workers' earnings by 7% or

more if they are authorized for US employment. In the South however, the effect on

earnings is larger for skilled than for unskilled workers for example, authorized &

(un)skilled and unauthorized & (un)skilled workers' earnings increase by 9% (4%) and

6% (5%), respectively.

Our results show that employer-provided free housing has a statistically

significant effect on earnings for most of the US and Southern worker subgroups. The

US data show rather modest negative effects varying between 2% and 4% of earnings,

with unauthorized workers having the larger differential. The situation is reversed in the

South: authorized & unskilled workers' earnings are approximately 7% less in the









presence of free housing, but unauthorized workers' earnings are about 4% less in the

presence of free housing regardless of skill level.

Finally, we report the average predicted earnings for each subgroup in the US and

South in Table 6. The predicted earnings are highest for workers who self-select into the

authorized & unskilled subgroups ($8.13 and $7.77, respectively, for the US and the

South). In the US market, workers self-selecting into the authorized & skilled subgroup

have the second highest average earnings ($7.82), followed by those individuals who

choose to be employed as unauthorized & unskilled workers ($7.20). In the South,

individuals who opt for employment as authorized & skilled workers also have higher

average earnings ($7.24) than those who self-select into the unauthorized & unskilled

subgroup ($6.97). Interestingly, the lowest average earnings are for workers who self-

select into skilled employment as unauthorized workers: $6.92 (US); $6.36 (South). This

result suggests that self-selection into skilled employment without legal status is

disadvantageous to the average foreign-born farm worker; unauthorized workers doing

skilled work are significantly penalized for their legal status. These findings are

consistent with those reported by Taylor (1992) who concluded that unauthorized status

could hamper workers' chances of skilled employment or at least result in lower wages

that would in turn discourage them from moving into those jobs.

Conclusions

Two employment status indicators, legal status and the job type (distinguished by

skill), were modeled jointly to reflect the potential joint choices by foreign-born farm

workers. This is the major distinction between our study and previous examinations of

the workers' self-selectivity into legal status in the farm labor market. Further, we









restricted our sample to the foreign-born population since U.S. citizens do not select into

legal status for employment in the US.

We found a statistically significant relationship between foreign-born workers'

selection into skilled or unskilled employment as authorized or unauthorized; this implies

that workers who are likely to select into skilled (unskilled) employment are more likely

to be authorized (unauthorized). In our models of the US and South, we determined

similar sets of characteristics associated with workers who select into the different

statuses (authorized & skilled, authorized & unskilled, unauthorized & skilled,

unauthorized & unskilled). In general, unauthorized and unskilled status is associated

with workers who have limited English-speaking ability, few years of formal education

and US farm experience. We determined that education was less of a factor for skilled

employment in the South in contrast to the US overall. Further, Southern farm workers

have a higher probability of selecting into skilled employment if they are employed with

growers; this is directly opposite to our results based on the total US data.

Our earnings results indicate some interesting differences between the US and the

South. First, English-speaking ability appears to matter for earnings in the South only if

the workers are authorized; in contrast, the total US data indicate that it positively

influences earnings regardless of authorization status. Second, employment with

growers in the South tends to increase workers' earnings more so if they are employed in

skilled positions; conversely, the US data suggest larger earnings increases if the workers

are authorized for US employment. Third, in the South, a housing provision decreases

the earnings of authorized & unskilled workers more so than unauthorized workers

(regardless of skill level); in the US, unauthorized workers would experience slightly

larger earnings decreases than their authorized cohorts.

15









The group of workers in the South of most interest in the context of immigration

reform is the unskilled group. A few key findings of most interest in terms of worker

earnings are the following. Unauthorized workers earn 22% more when paid by the piece

rate, while authorized workers earn 30% more with the piece rate. Being a seasonal

worker reduces earnings by 2% for unauthorized workers, but 6% for authorized workers.

And finally, English ability has no significant effect for unauthorized workers, but

improves earnings for authorized workers by about 4%.

Finally, our results on earnings differences by legal status and job (skill) type

concur with those reported by Taylor (1992). Regardless of whether they are employed

in the US or South, unauthorized workers holding skilled jobs are penalized for doing so.









References


Carroll, D., R. Samardick, S. Bernard, S. Gabbard, T. Hernandez. Findings from the
National Agricultural Workers Survey (NAWS) 2001 2002. A Demographic and
Employment Profile of United States Farm Workers. U.S. Dept of Labor, Office
of the Asst. Secretary for Policy, Office of Programmatic Policy, Research Report
No. 9. March 2005.

Deaton, A. The Analysis of Household Surveys: A Microeconometric Approach to
Development Policy. Baltimore, MD: Johns Hopkins University Press, 1997.

Ise, S., and J. M. Perloff. "Legal Status And Earnings Of Agricultural-Workers."
American Journal ofAgricultural Economics 77, no. 2(1995): 375-386.

Iwai, N., R. D. Emerson, and L. M. Walters. "Legal Status and U.S. Farm Wages."
Selected Paper prepared for presentation at the Southern Agricultural Economics
Association Annual Meeting, Orlando, Florida, February 5-8 (2006).

Taylor, J. E. "Earnings and Mobility of Legal and Illegal Immigrant Workers in
Agriculture." American Journal ofAgricultural Economics 74, no. 4(1992): 889-
896.

Tunali, I. "A General Structure for Models of Double-Selection and an Application to
a Joint Migration/Earnings Process with Re-Migration." In Ronald G. Ehrenberg,
ed.. Research in Labor Economics, Vol. 8 (Part B). Conn.: JAI Press, pp. 235-84,
1986.

Vella, F. "Estimating Models with Sample Selection Bias: A Survey." Journal Of
Human Resources 33, no. 1(1998): 127-169.

Walters, L. M., O. Napasintuwong, N. Iwai, and R. D. Emerson. "The U.S. Farm Labor
Market Post-IRCA: An Assessment of Employment Patterns, Farm Worker
Earnings and Legal Status." Selected Paper for presentation at the Southern
Agricultural Economics Association Annual Meeting, Orlando, FL. February
2006.

White House, Office of the Press Secretary, October 26, 2006. "President Bush Signs
Secure Fence Act." Available at:
http://www.whitehouse.gov/news/releases/2006/10/20061026.html










Table 1: Explanatory Variables for Bivariate Probit & Wage Models-


Definition


LnWage


Authorized



Skill


Piece Rate


Seasonal Worker


Female


Mexican


Education


Adult Educationb


Before 1993

After 2001

California (CA)



Florida (FL)



Housing


Natural logarithm of the real farm wage in 2004 dollars. Conversions
from the nominal wage were made using the consumer price index for all
urban consumers

=1 if farm worker is authorized for U.S. employment (citizen, permanent
resident, or has other work authorization)
= 0 if otherwise (i.e. unauthorized)

=1 if task is semi-skilled or supervisory job
=0 if otherwise (pre-harvest, harvest, post harvest jobs)

= 1 if worker is paid by piece rate
= 0 if otherwise (by the hour, hour/piece combination, or salary)

=1 if worker is employed on a seasonal basis
= 0 if otherwise (year-round)

=1 if female
=0 if male

= 1 if worker is of Mexican nationality
=0 if otherwise

Highest grade level of education completed by the farm worker, ranging
from 0 to 16

=1 if worker had attended any adult education classes or school in the
U.S.
=0 if otherwise
Dummy variable reflecting the interview years after the 1986 IRCA but
before 1993
Dummy variable reflecting the interview years following September 2001

Dummy variable reflecting employment in California at the time of the
interview

Dummy variable reflecting employment in Florida at the time of
interview

=1 if worker (and family, if applicable) receives free housing from current
employer
=0 if otherwise


Variable










Table 1: Explanatory Variables for Bivariate Probit & Wage Models, (continued)


Definition


English speaking
ability


Married


1 if 'none at all'
2 if 'a little'
3 if 'somewhat'
4 if 'well'


= 1 if 'married/living together'
=0 if otherwise


Years with current
employer

Farmwork weeks



Foreign Farm
Work Experience


Grower


Specialty Crop



Age

Age2

Experience

Experience2

h1


Number of years of employment worker has completed with current
employer. One year is measured as one or more days per year (NAWS)

Farmwork weeks completed in the last year


=1 if worker had been employed in agriculture, either full-time or part-
time, while living in native (foreign) country
=0 if worker had been employed in non-agricultural sector or had never
worked while living in native (foreign) country

= 1 if employed by a grower
= 0 if employed by a farm labor contractor

= 1 if worker was employed in specialty crop production at the time of the
interview
=0 if otherwise


Respondent age in years


Age squared


Years of U.S. farm work

Experience squared

Selectivity correction term from the legal status (authorized) decision
equation
Selectivity correction term from the job type (skill) decision equation


a Data were sourced from the National Agricultural Workers Survey. Definitions enclosed in quotation
marks are as they appear in the NAWS Codebook.

bThis would include English/ESL, citizenship, literacy, job training and Adult Basic Education classes,
GED/high school equivalency classes, college or university classes, and Even Start and Migrant Education
classes (NAWS Codebook, 2004).


Variable











Table 2(a): Summary Statistics for Explanatory Variables: US Sample
Authorized Unauthorized Unauthorized
& Skilled Subgroup Authorized & Unskilled & Skilled Subgroup & Unskilled Subgroup
(n=3903) Subgroup (n=9434) (n=3125) (n=12902)
Standard Standard Standard Standard
Variable Mean Deviation Mean Deviation Mean Deviation Mean Deviation

Housing 0.15322 0.36024 0.16218 0.36863 0.19264 0.39444 0.21710 0.41229
Adult Education 0.29029 0.45395 0.28344 0.45069 0.14080 0.34787 0.13223 0.33875
Before 1993 0.22700 0.41895 0.29924 0.45795 0.07488 0.26324 0.09247 0.28969
After 2001 0.24648 0.43101 0.26087 0.43913 0.36224 0.48072 0.39304 0.48844
California 0.66308 0.47272 0.45177 0.49769 0.48736 0.49992 0.31662 0.46517
Florida 0.03485 0.18341 0.11925 0.32410 0.03488 0.18351 0.16145 0.36796
Female 0.12042 0.32549 0.22186 0.41552 0.09504 0.29332 0.15439 0.36134
Married 0.80656 0.39505 0.74698 0.43477 0.50912 0.50000 0.49310 0.49997
English 2.00000 0.88614 1.89177 0.89538 1.50912 0.68899 1.48295 0.69390
Mexican 0.90161 0.29787 0.87799 0.32731 0.93248 0.25096 0.89374 0.30819
Education 5.59672 3.29263 5.64755 3.45410 6.13696 3.06551 6.05550 3.17261
Experience 16.26057 8.85396 14.11363 8.97386 5.96288 5.62182 5.01984 5.30795
Experience2 342.77860 357.31130 279.71610 365.99610 67.15072 160.43600 53.37095 156.38060
Age 39.00692 11.08921 37.77411 11.60935 28.53664 9.51811 28.30422 9.64938
Age2 1644.47900 930.86990 1561.64600 960.38850 904.90530 683.31520 894.23200 686.38450
Seasonal worker 0.50602 0.50003 0.52258 0.49952 0.52192 0.49960 0.48279 0.49972
Foreign Farmwork
Experience 0.71253 0.45264 0.60695 0.48845 0.72960 0.44424 0.68803 0.46331
Years with Current
Employer 6.56444 5.90306 5.39029 5.28969 2.79104 2.58669 2.44389 2.33974
Farmwork weeks 38.64935 11.82657 36.37304 13.27431 34.50295 15.05507 33.20103 16.35835
Piece rate 0.10838 0.31090 0.22006 0.41431 0.13504 0.34182 0.21446 0.41047
Grower 0.81655 0.38708 0.79309 0.40511 0.74208 0.43756 0.76190 0.42594
Specialty crop 0.84653 0.36049 0.92315 0.26637 0.81600 0.38755 0.88947 0.31356











Table 2(b): Summary Statistics for Explanatory Variables: South Sample
Authorized Unauthorized Unauthorized
& Skilled Subgroup Authorized & Unskilled & Skilled Subgroup & Unskilled Subgroup
(n=227) Subgroup (n=1524) (n=492) (n=3995)
Standard Standard Standard Standard
Variable Mean Deviation Mean Deviation Mean Deviation Mean Deviation

Housing 0.21586 0.41233 0.21785 0.41292 0.54268 0.49868 0.28511 0.45152
Adult Education 0.34361 0.47596 0.29134 0.45453 0.09553 0.29424 0.10738 0.30964
Before 1993 0.40529 0.49203 0.45801 0.49840 0.11382 0.31792 0.14919 0.35632
After 2001 0.21586 0.41233 0.21457 0.41066 0.13821 0.34547 0.32466 0.46830
Female 0.13216 0.33941 0.27034 0.44428 0.10976 0.31290 0.16446 0.37073
Married 0.68722 0.46465 0.66470 0.47225 0.43699 0.49652 0.44856 0.49741
English 2.15859 1.02251 1.89042 0.91054 1.50000 0.66785 1.44631 0.65494
Mexican 0.81938 0.38555 0.77034 0.42075 0.82317 0.38191 0.80075 0.39949
Education 5.62996 3.21289 5.21588 3.48583 5.68089 3.07573 5.61151 3.31153
Experience 12.05286 7.50497 11.37336 8.13843 4.12602 4.79990 4.27384 4.54947
Experience2 201.34800 231.35190 195.54400 345.47110 40.01626 130.45890 38.95820 144.65990
Age 35.78414 10.83350 35.77887 11.60180 27.13211 8.83872 27.50713 9.16937
Age2 1397.35200 839.99970 1414.64100 921.37150 814.11590 597.43770 840.69860 629.38020
Seasonal worker 0.41410 0.49365 0.44488 0.49712 0.67276 0.46968 0.50738 0.50001
Foreign Farmwork
Experience 0.63436 0.48267 0.58136 0.49350 0.70935 0.45452 0.66984 0.47033
Years with Current
Employer 4.12335 4.21047 4.11221 3.96354 1.93902 1.66072 2.09011 1.86953
Farmwork weeks 40.71743 13.43007 39.97666 13.17330 30.70006 16.70126 34.31050 17.26394
Piece rate 0.10132 0.30242 0.34449 0.47536 0.10976 0.31290 0.31840 0.46591
Grower 0.87225 0.33455 0.76640 0.42326 0.81301 0.39030 0.70113 0.45782
Specialty crop 0.68722 0.46465 0.88320 0.32128 0.29675 0.45729 0.77397 0.41831










Table 3: Bivariate Probit Model Estimates for Foreign-Born Workers' Selections"
Parameter Parameter Parameter Parameter
Authorized Estimate Estimate Skilled Estimate Estimate
(US) (South) (US) (South)


Constant


Adult Education


Before 1993


After 2001


California


Florida


Female


Married


English


Mexican


Education


Experience


Experience2


Age


Age2


-3.4908**
(0.10029)

0.18184**
(0.02405)

1.03700**
(0.02668)

-0.47995**
(0.02197)

0.31474**
(0.02054)

-0.15734**
(0.03253)

0.43924**
(0.02493)

0.20063**
(0.02148)

0.31770**
(0.01321)

-0.13471**
(0.03128)

0.03023**
(0.00329)

0.17934**
(0.00325)

-0.00273**
(0.00008)

0.04627**
(0.00521)

-0.00037**
(0.00007)


-3.6980**
(0.21608)

0.2823 1**
(0.05483)

1.14035**
(0.05172)

-0.29124**
(0.05543)


N/A


N/A

0.38664**
(0.05173)

0.12968**
(0.04714)

0.34427**
(0.03000)

0.01453
(0.05384)

0.02790**
(0.00724)

0.16634**
(0.00683)

-0.00233**
(0.00017)

0.03764**
(0.01189)

-0.00024
(0.00015)


Constant


Seasonal Worker

Foreign Farmwork
Experience

Years with Current
Employer


Farmwork Weeks


Piece rate


Grower


Age


Age2


English


Female


Experience


Education


Specialty Crop


US sample size = 29364; Log-likelihood = -26995.78; Rho (p) = 0.034*

South sample size = 6238; Log-likelihood = -4152.83; Rho (p) = 0.0759*
a Standard errors are given in parentheses. Asterisks (**, *) indicate statistical significance at 5% and 10%
levels of significance, respectively.


-1.12476**
(0.08221)

0.11192**
(0.01716)

0.11340**
(0.01974)

0.01360**
(0.00213)

0.00235**
(0.00061

-0.38749**
(0.02320)

-0.08955**
(0.02032)

0.0203 1**
(0.00401)

-0.00031**
(0.00005)

0.05519**
(0.01108)

-0.25616**
(0.02554)

0.01786**
(0.00142)

0.00627*
(0.00295)

-0.33139**
(0.02458)


-1.2679**
(0.22207)

0.14062**
(0.05136)

0.04591
(0.05213)

-0.01400
(0.00953)

0.00277*
(0.00153)

-0.60320**
(0.06472)

0.18350**
(0.05859)

0.01948
(0.01199)

-0.00031*
(0.00017)

0.13665
(0.03149)

-0.13900*
(0.06765)

0.00932*
(0.00454)

-0.01396*
(0.00769)

-0.87597**
(0.05210)












Table 4: Marginal Effects of Bivariate Probit Estimates of Selection into Legal Status & Job (Skill) Type (US & South)

I Marginal Effect


Variable


Authorized Authorized Unauthorized Unauthorized
& Skilled & Unskilled & Skilled & Unskilled


Adult Education
Before 1993
After 2001
California
Florida
Female
Married
English
Mexican
Education
Experience
Experience2
Age
Age2
Seasonal worker
Foreign
Farmwork
Experience
Years with
Current
Employer
Farmwork Weeks
Piece rate
Grower
Specialty Crop


US
0.01650
0.08918
-0.04226
0.02839
-0.01404
0.00012
0.01801
0.03590
-0.01223
0.00355
0.01853
-0.00025
0.00683
-0.00007
0.01464


0.01463


0.00178
0.00031
-0.04617
-0.01192
-0.04697


South
0.00774
0.03369
-0.00710
n/a
n/a
0.00473
0.00333
0.01344
0.00037
0.00025
0.00459
-0.00006
0.00162
-0.00002
0.00473


0.00153


-0.00047
0.00009
-0.01754
0.00585
-0.03842


US
0.05522
0.30312
-0.14031
0.09483
-0.04665
0.17351
0.06000
0.08843
-0.04092
0.00828
0.05166
-0.00082
0.01127
-0.00007
-0.01464


South
0.07454
0.33733
-0.06679
n/a
n/a
0.11010
0.03155
0.07926
0.00353
0.00727
0.04020
-0.00057
0.00851
-0.00005
-0.00473


-0.01463 -0.00153


-0.00178
-0.00031
0.04617
0.01192
0.04697


0.00047
-0.00009
0.01754
-0.00585
0.03842


US
-0.01650
-0.08918
0.04226
-0.02839
0.01404
-0.07256
-0.01801
-0.01919
0.01223
-0.00165
-0.01312
0.00025
-0.00069
-0.00002
0.01923


South
-0.00774
-0.03369
0.00710
n/a
n/a
-0.02510
-0.00333
0.00783
-0.00037
-0.00242
-0.00313
0.00006
0.00141
-0.00003
0.01717


0.01920 0.00555


0.00234
0.00040
-0.06031
-0.01568
-0.06216


-0.00171
0.00034
-0.06294
0.02111
-0.14481


US
-0.05522
-0.30312
0.14031
-0.09483
0.04665
-0.10107
-0.06000
-0.10514
0.04092
-0.01018
-0.05707
0.00082
-0.01742
0.00016
-0.01923


South
-0.07454
-0.33733
0.06679
n/a
n/a
-0.08973
-0.03155
-0.10053
-0.00353
-0.00509
-0.04166
0.00057
-0.01155
0.00010
-0.01717


-0.01920 -0.00555


-0.00234
-0.00040
0.06031
0.01568
0.06216


0.00171
-0.00034
0.06294
-0.02111
0.14481










Table 5(a): Wage Models for Each Worker Subgroup (US)a
Authorized Authorized Unauthorized Unauthorized
& Skilled & Unskilled & Skilled & Unskilled


Constant


Housing


Adult Ed.


California


Florida


Grower


Piece Rate


Seasonal Worker


Female


Education


English


Age


Age2


Experience


Experience2


;i


1.7053**
(0.0779)

-0.0340**
(0.0097)

0.0541**
(0.0077)

0.0548**
(0.0082)

0.0364*
(0.0187)

0.0746**
(0.0088)

0.1217**
(0.0151)

-0.0551**
(0.0070)

0.0102
(0.0131)

0.0041**
(0.0012)

0.0345**
(0.0048)

0.0077**
(0.0023)

-0.0001**
(0.00003)

0.0120**
(0.0019)

-0.0002**
(0.00004)

0.0487**
(0.0156)

-0.1277**
(0.0325)


1.5298**
(0.0369)

-0.0190*
(0.0077)

0.0373**
(0.0064)

0.0676**
(0.0065)

-0.0109
(0.0091)

0.0938**
(0.0069)

0.3466**
(0.0102)

-0.0746**
(0.0057)

0.0108
(0.0089)

0.0042**
(0.0009)

0.0272**
(0.0038)

0.0035*
(0.0016)

-0.00005**
(0.00002)

0.0076**
(0.0013)

-0.0002**
(0.00003)

0.0415**
(0.0101)

-0.3043**
(0.0407)


1.5922**
(0.0640)

-0.0408**
(0.0092)

0.0181*
(0.0099)

0.0415**
(0.0078)

-0.0103
(0.0179)

0.0382**
(0.0083)

0.0891**
(0.0143)

-0.0348**
(0.0072)

-0.0491**
(0.0140)

0.0036**
(0.0011)

0.0259**
(0.0057)

0.0065**
(0.0020)

-0.0001**
(0.00003)

0.0166**
(0.0018)

-0.0002**
(0.00005)

0.0630**
(0.0137)

0.0582*
(0.0327)


1.7172**
(0.0210)

-0.0385**
(0.0054)

0.0260**
(0.0065)

0.0455**
(0.0052)

-0.0472**
(0.0061)

0.0563**
(0.0051)

0.2297**
(0.0077)

-0.0396**
(0.0045)

-0.0246**
(0.0073)

0.0052**
(0.0007)

0.0148**
(0.0036)

0.0012
(0.0011)

-0.00002
(0.00002)

0.0145**
(0.0011)

-0.0003**
(0.00003)

0.0431**
(0.0090)

-0.1213**
(0.0367)


-










Table 5(b): Wage Models for Each Worker Subgroup (South)


Authorized Authorized Unauthorized Unauthorized
& Skilled & Unskilled & Skilled & Unskilled


Constant


Housing


Adult Ed.


Grower


Piece Rate

Seasonal
Worker


Female


Education


English


Age


Age2

Experience


Experience2


xi


0.8074**
(0.2396)

-0.0587
(0.0377)

0.0260
(0.0302)

0.0928*
(0.0422)

0.1793**
(0.0519)

-0.0023
(0.0298)

-0.0935*
(0.0430)

0.0094*
(0.0048)

0.0609**
(0.0172)

0.0220*
(0.0092)

-0.0003*
(0.0001)

0.0146*
(0.0075)

0.00001
(0.0002)

0.1299**
(0.0484)

0.1671**
(0.0523)


1.5601**
(0.0853)

-0.0675**
(0.0175)

0.0332*
(0.0168)

0.0403*
(0.0178)

0.2993**
(0.0203)

-0.0656**
(0.0152)

-0.0525**
(0.0173)

0.0058*
(0.0023)

0.0358**
(0.0093)

0.0099**
(0.0037)

-0.0002**
(0.00005)

0.0079**
(0.0027)

-0.00004
(0.0001)

0.0482*
(0.0221)

-0.0584
(0.0635)


1.5853**
(0.1146)

-0.0412*
(0.0202)

0.0308
(0.0310)

0.0553*
(0.0232)

0.2136**
(0.0337)

-0.0439*
(0.0244)

-0.0481*
(0.0286)

0.0075*
(0.0029)

-0.0111
(0.0152)

0.0050
(0.0053)

-0.0001
(0.0001)

0.0263**
(0.0048)

-0.0005**
(0.0001)

0.0779*
(0.0438)

0.0591
(0.0387)


1.7483**
(0.0384)

-0.0266*
(0.0103)

0.0412**
(0.0145)

0.0507**
(0.0097)

0.2174**
(0.0114)

-0.0181*
(0.0095)

-0.0703**
(0.0121)

0.0068**
(0.0013)

-0.0054
(0.0075)

-0.0002
(0.0023)

-0.00001
(0.00003)

0.0096**
(0.0020)

-0.0002**
(0.00005)

-0.0421*
(0.0203)

-0.1083**
(0.0372)


a Asterisks (**, *) indicate statistical significance at 5% and 10% levels of significance, respectively.
Standard errors have not been corrected for the two-step estimation.









Table 6: Average Predicted Conditional Wage for Each Legal Status & Job Type
Subgroup in the US and South"

U.S. Wage South Wage
Legal Status & Job Type Subgroups ($) ($)

Authorized & skilled (Gil) 7.82 7.24
Authorized & unskilled (GlO) 8.13 7.77
Unauthorized & skilled G(01) 6.92 6.36
Unauthorized & unskilled G(OO) 7.20 6.97

a Average wages are conditioned on the selectivity variables for legal status and skill
type.




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