Abstract

Although earnings generally increased in rural areas in the 1990s, Hispanic population growth

led to lower wages for at least one segment of the rural population—workers with a high

school degree (skilled workers), particularly men in this skill group. Using data from the

Bureau of Economic Analysis and the Current Population Survey, this report examines the

effects of Hispanic population growth on rural wages. The analysis combines approaches

from earlier immigration-impact studies and more recent work that incorporates the role of

labor demand in the labor market. The analysis finds that labor demand shift factors and

other area-specific factors that often are not included in immigration studies are important.

Results indicate that labor demand increases favored skilled workers (those with a high

school degree) overall but favored unskilled and professional workers in some rural industries. Thus, the increased supply of unskilled labor from Hispanic population growth led to

lower wages for skilled men as a result of production changes in some parts of the rural

economy.

Keywords: Immigration, wages, labor demand, and Hispanic population growth

United States

Department

of Agriculture

www.ers.usda.gov

Electronic Report from the Economic Research Service

Impacts of Hispanic Population

Growth on Rural Wages

Constance Newman September

2003

Agricultural

Economic

Report

Number 826
ii ✥ Impacts of Hispanic Population Growth/AER-826 Economic Research Service/USDA

Contents

Summary......................................................................................................................iii

Introduction ..................................................................................................................1

Background...................................................................................................................3

Conceptual Framework, Data, and Methods of Analysis.........................................5

Conceptual Framework..............................................................................................5

Data Sources ..............................................................................................................5

Wage Analysis Method ..............................................................................................6

Sector Decomposition Method ..................................................................................7

Empirical Results .........................................................................................................8

Nonmetro Employment Changes by Occupation, Industry, and Ethnicity ...............8

Wage Analysis Results...............................................................................................8

Sector Decomposition Results .................................................................................16

Conclusions .................................................................................................................20

References ...................................................................................................................22
Economic Research Service/USDA Impacts of Hispanic Population Growth/AER-826 ✥ iii

Summary

Although earnings generally increased in rural areas in the 1990s, Hispanic population

growth led to lower wages for at least one segment of the rural population—workers

with a high school degree (skilled workers), particularly men in this skill group. This

report examines the implications of Hispanic population growth for rural wages by

skill group. Results suggest that forces of technological (and/or organizational) change

in some rural industries led employers to demand more unskilled and professional

workers relative to skilled workers, and rural Hispanic immigrants filled the new labor

demand for unskilled workers. General output growth in most rural industries, however, favored skilled workers, so their earnings increased overall.

New census data show that the number of Hispanics in nonmetro counties grew 70.4

percent from 1990 to 2000, whereas the number of Whites (non-Hispanic) grew 8.6

percent. Very little is known about the economic effect of the population growth of

Hispanics in rural areas, but the rapid change has led to social tensions in some areas

and questions about the effects on rural wages.

Using data from the Bureau of Economic Analysis (BEA) and the Current Population

Survey (CPS), this technical report examines the effects of Hispanic population growth

on rural wages. The analysis combines approaches from earlier immigration-impact

studies and more recent work that incorporates the role of labor demand in the labor

market. A wage regression analysis is conducted and estimated changes in the structure

of labor markets over the decade are broken down for a better understanding of change

in labor demand by skill level. The analysis is based on the 1993 Office of

Management and Budget definition of metropolitan and nonmetropolitan status. The

terms “rural” and “nonmetropolitan” are used interchangeably in this report.

Traditional analyses of the economic effects of immigrants have focused on labor supply and have assumed a minimal role for labor demand and other area-specific factors.

This study measures the effects of changes in labor demand and labor supply (implied

by the share of Hispanics), together with other area-specific factors, on rural Statelevel wages. The analysis is carried out in two stages using CPS data. First, individual

wages are estimated for different subpopulations (divided by gender, race, and education group) using human capital, occupation, State dummies, and other variables.

Second, the estimated coefficients for the State dummies are used to estimate the

effects of labor demand, the share of Hispanics, and other variables on State wage

changes during 1990-2000.

Results show that labor demand shifts are important determinants of wage changes for

some groups in the population and that the share of Hispanics in an area is linked to

lower wages for some groups. We divided the population into three skill groups based

on education: unskilled, those who have less than a high school degree; skilled, those

with a high school degree; and professional, those with a college degree. Wages of

skilled rural men increased with labor demand and declined with Hispanic population

growth. Wages of unskilled rural men rose with increases in manufacturing and service

sector labor demand but fell with labor demand in other sectors. And the wages of

unskilled men were not affected by Hispanic population growth. Wages of rural

women of all skill levels were affected by labor demand in some industries but not

negatively affected by the share of Hispanics. Wages for professional men and women

often rose with Hispanic population growth.
iv ✥ Impacts of Hispanic Population Growth/AER-826 Economic Research Service/USDA

Using a recently developed method of decomposing changes in the structure of labor

demand, the study finds evidence that helps explain the wage effects. The analysis decomposes changes in the labor market into changes in the total value of output produced

across industries (output mix) and changes in the labor skill-mix used within industries

(which is assumed to be a result of technological change). Results show that output-mix

changes led to large increases in demand for skilled workers and that the effects of the

output-mix changes were larger than the technology change effects. However, the smaller

technology changes led to relative increases in demand for unskilled and professional

workers, which helps explain the negative effect of immigration on skilled wages in some

parts of the economy. While the findings do not reflect on the efficiency of changes in

labor demand structure, they demonstrate the importance of including labor demand factors in an analysis of immigration and wage effects.
Introduction

The tremendous growth of the Hispanic population in

rural America presents new opportunities as well as

challenges for rural communities that seek to revitalize

their economies. New census data show that the number

of Hispanics in nonmetro counties grew 70.4 percent

from 1990 to 2000, whereas the number of Whites

(non-Hispanic) grew 8.6 percent. The population growth

of Hispanics in rural areas is widespread, with rates over

200 percent in many Southern and Midwestern States

(table 1). The data show that nonmetro Hispanics tend to

have larger families and to be significantly younger than

nonmetro Whites—two demographic characteristics that

some consider beneficial for economic growth. The

analysis is based on the 1993 Office of Management

and Budget definition of metropolitan and nonmetropolitan status. The terms, “rural” and “nonmetropolitan” are used interchangeably in this report.

Very little is known about the economic impacts of the

population growth of Hispanics in rural areas. Most of

the information is limited to specific regional studies,

which show that Hispanics have had a huge impact on

the labor force, such as in the meatpacking and poultry

and carpet manufacturing industries. On one hand,

their presence is valued in small communities: Many

local governments have actively recruited Hispanics to

move to their towns. Iowa recently awarded grants of 

$50,000 to three counties to attract immigrants, and as

a Chamber of Commerce official from one of those

communities put it: “Economic development is about

attracting people” (Rural Migration News, 2002). On

the other hand, rapid demographic change in many of

these communities has led to significant social tension

over issues of crime, public service needs, and basic

cultural differences. Other concerns include the potentially negative effect that Hispanic migration may be

having on unskilled worker wages (Kandel and

Parrado, 2002; Massey, Durand, and Malone, 2002).

This report focuses on the impact of Hispanic population

growth on rural wages, although its effect on wage is

only one part of the overall economic impact. For example, population growth can affect income, government

spending, and tax revenues. This analysis looks at the

effect of Hispanic population growth on the wages of

rural workers by workers’ education levels and gender

while controlling for other economic factors. In addition,

the analysis separates the impact of labor demand on

wages from the impacts of labor supply, which Hispanic

population growth represents. Studies on the economics

of immigration often ignore the impact of changes in

labor demand as a factor that can affect wages independently of immigration. This study combines approaches

from the immigration literature and recent work that

incorporates the role of labor demand in the market.

Economic Research Service/USDA Impacts of Hispanic Population Growth/AER-826 ✥ 1

Impacts of Hispanic

Population Growth

on Rural Wages

Constance Newman
2 ✥ Impacts of Hispanic Population Growth/AER-826 Economic Research Service/USDA

Table 1—Change in nonmetro total and Hispanic population by State, 1990 and 2000 

Nonmetro population Nonmetro Hispanic population Share Hispanic

State 1990 2000 Change 1990 2000 Change 1990 2000

---------Number-------- Percent ---------Number-------- Percent ----Percent---

All States1 50,886,468 56,159,326 10 1,864,283 3,175,953 70 4 6

Alabama 1,330,857 1,453,233 9 5,198 26,155 403 0 2

Alaska 319,320 366,649 15 8,469 11,053 31 3 3

Arizona 559,476 719,952 29 96,006 133,073 39 17 18

Arkansas 1,310,724 1,434,529 9 9,559 36,504 282 1 3

California 961,303 1,121,254 17 177,669 275,669 55 18 25

Colorado 608,053 809,860 33 79,135 126,052 59 13 16

Connecticut 276,617 291,284 5 6,052 11,631 92 2 4

Delaware 113,229 156,638 38 1,221 6,915 466 1 4

Florida 914,571 1,144,881 25 40,822 95,689 134 4 8

Georgia 2,126,654 2,519,789 18 26,270 124,296 373 1 5

Hawaii 271,998 335,381 23 24,062 28,970 20 9 9

Idaho 710,898 861,608 21 34,508 63,768 85 5 7

Illinois 1,856,803 1,877,585 1 21,820 38,857 78 1 2

Indiana 1,581,713 1,690,582 7 12,260 36,921 201 1 2

Iowa 1,576,857 1,600,191 1 11,807 35,611 202 1 2

Kansas 1,144,646 1,167,355 2 42,458 86,016 103 4 7

Kentucky 1,905,535 2,068,667 9 8,479 24,465 189 0 1

Louisiana 1,060,433 1,098,766 4 14,915 17,505 17 1 2

Maine 732,933 760,599 4 4,002 4,964 24 1 1

Maryland 342,581 385,446 13 2,995 6,958 132 1 2

Massachusetts 87,743 96,042 9 886 1,792 102 1 2

Michigan 1,597,654 1,768,978 11 18,362 33,510 82 1 2

Minnesota 1,364,205 1,456,119 7 11,283 34,860 209 1 2

Mississippi 1,797,542 1,932,670 8 7,774 24,321 213 0 1

Missouri 1,626,202 1,800,410 11 10,822 27,807 157 1 2

Montana 607,903 692,486 14 7,658 11,344 48 1 2

Nebraska 791,050 811,425 3 16,641 44,564 168 2 5

Nevada 187,926 250,521 33 17,113 32,813 92 9 13

New Hampshire 423,101 465,353 10 2,439 3,854 58 1 1

New Mexico 673,385 783,991 16 232,457 292,788 26 35 37

New York 1,475,170 1,503,399 2 29,358 44,795 53 2 3

North Carolina 2,252,775 2,612,257 16 16,714 98,846 491 1 4

North Dakota 381,412 358,234 -6 2,472 4,277 73 1 1

Ohio 2,021,046 2,139,364 6 22,744 32,947 45 1 2

Oklahoma 1,275,743 1,352,292 6 28,400 54,881 93 2 4

Oregon 857,597 997,186 16 33,852 67,924 101 4 7

Pennsylvania 1,798,645 1,889,525 5 11,004 27,403 149 1 1

Rhode Island 87,194 85,433 -2 1,723 2,409 40 2 3

South Carolina 1,064,088 1,205,050 13 5,830 27,853 378 1 2

South Dakota 475,425 493,867 4 2,809 5,206 85 1 1

Tennessee 1,579,336 1,842,679 17 7,119 32,737 360 0 2

Texas 2,820,852 3,159,940 12 649,539 859,880 32 23 27

Utah 387,033 530,719 37 13,917 31,168 124 4 6

Vermont 385,699 409,938 6 2,361 3,644 54 1 1

Virginia 1,407,096 1,550,447 10 8,136 28,258 247 1 2

Washington 830,311 994,967 20 43,672 99,973 129 5 10

West Virginia 1,045,317 1,042,776 0 4,713 6,619 40 0 1

Wisconsin 1,560,597 1,723,367 10 11,098 28,893 160 1 2

Wyoming 319,220 345,642 8 15,680 19,515 24 5 6

1

New Jersey and the District of Columbia do not appear because they have no nonmetro areas.

Source: Kandel (2002), using data from the U.S. Bureau of the Census.
Background

Much of the impact of Hispanic growth on rural labor

markets can be seen as a type of immigration impact, in

which migrants—whether from within the U.S. or from

abroad—increase labor supply. Studies have focused on

immigrants’ impacts on the wages and employment of

native-born workers (Borjas, 1999, 1997; Friedberg and

Hunt, 1995). Because the addition of immigrants to a

community implies a shift in labor supply, the main

question has been: Have immigrants affected the labor

market outcomes of natives? In a simple model with a

closed economy, if immigrant labor were a substitute

for native labor supply, then the wages and employment

of natives would be expected to decline. But if immigrants were to provide a complementary labor supply

(such as if native labor were skilled and immigrant labor

were nonskilled), then native wages and employment

would be expected to increase. Employers would be

expected to gain from the influx of immigrants, whether

the immigrants provide complementary labor or not.

Empirical studies traditionally compared immigration

impacts across regions (usually metropolitan areas). This

approach was criticized, however, because it assumed

implicitly that immigration could affect wages in a

given area but did not assume that local wages could

also draw immigrants to the area—the opposite direction of causality. This method proved especially problematic because areas with high immigration empirically tended to have higher median wages, suggesting

that immigrants had a positive effect on wages.

Another problem with this approach was that inflows

of immigrants might lead to outflows of natives, neutralizing the increase in labor supply caused by immigration. Attempts to correct for these problems in past

analyses have included the use of instrumental variables and the use of natural experiments when available. Increasingly, researchers have used individuallevel data, which make it possible to control for worker characteristics that may lead to immigration as well

as for area-specific features that may attract workers.

Most of the results from individual-level analyses show

that the impact of immigration on U.S. wages has been

negligible (Borjas, 1999; Card, 1990, 2001). These

studies typically include metropolitan dummy variables

to control for area-specific conditions or estimate wages

using fixed-effects models. Easton (2001) argues that

models using geographic fixed effects have failed to

account for area-specific factors that happen to be correlated with immigration. Rather than measuring

changes in labor market outcomes by skill level within

cities as is done in a fixed effects approach, he examines

changes in wages across areas and includes three important area-specific wage determinants: inflation rates,

prior wage estimates, and labor demand shifts. Different

costs of living could drive wages, and shifts in industry

structure could lead independently to wage increases in

the same areas where immigration is high. Easton is the

only author in the immigration literature that we are

aware of who has incorporated these impacts, though

many authors cite their importance.

Easton finds inflation and prior wages to be important

determinants of wages, but he does not find strong evidence of labor demand effects. The labor demand coefficient was significant for 5 of 10 female subpopulations

(for less-educated and Black women in particular) but

not significant for male subpopulations. On the contrary, Easton points out that Bound and Holzer (2000)

find evidence of labor demand effects in a study of

wage determination. Easton finds that, even after controlling for other area effects, the percentage of immigrants in an area had a positive, significant effect on

the wages of 11 out of 20 subpopulations and that the

effect of immigrants was never negative. He concludes

that, unless he has missed some omitted variable bias,

the results suggest that general complementarities exist

between native and immigrant workers.

In other analyses of wage changes in the U.S., economists have examined the role of labor demand shifts

and have noted that production changes are an alternative way for the labor market to absorb immigration

impacts. Hanson et al. (2002) describe two possible

roles for labor demand shifts in wage changes. First, a

Heckscher-Olin model of trade would predict changes

in the output mix of a country as a possible response

to immigration. This would entail shifts in production

toward sectors that use relatively more of the new factor supply types. This model works only for an open

economy, wherein production changes can be absorbed

through international trade. The impacts on wages

from a change in output mix would depend on the relative size of the country, the size of the immigration

shock, and the initial product mix. With large enough

changes in the supply of unskilled versus skilled workers and with a product mix and size that affects world

prices, the effects of immigration could induce

changes in both relative wages and real wages.

The second role of labor demand shifts in wage

changes that Hanson et al. hypothesize is that of “technological” change. The use of the term “technological”

is generic in the sense that it describes a change in the

Economic Research Service/USDA Impacts of Hispanic Population Growth/AER-826 ✥ 3
mix of labor types demanded to produce a given product. This effect could be the result of structural change

in an industry where the processes of production

change because of either organizational change or the

addition of new physical equipment. This effect, unlike

the output-mix effect that could be induced by immigration itself, is not necessarily related to immigration

(although one can also imagine scenarios where it

could be related). Technological change that is exogenous to labor supply characteristics may drive employers to demand a different combination of skill-level

inputs. A large body of research on U.S. wages in the

1980s finds a shift toward hiring more skilled workers

at increasing real wages and explains it as being a

result of technological change. Indeed, in reviewing

the empirical literature on the effects of immigration in

the U.S., Hanson et al. conclude that this “skill-biased

technological change,” as it is commonly referred to,

has been a chief reason that immigration has not led to

a significant decline in wages.

Recent work on rural employment trends suggests that

changes in the industrial sector and their implications

for skill-level demand shifts are important. McGranahan

and Ghelfi (1998) found that most rural job growth in

the 1980s was for low-skill labor but that this trend

weakened in the early 1990s. They found that rural

trends became more similar to urban trends from 1989 

to 1995 and that rural job growth increased with education levels. Earnings for rural college graduates

(expressed as a percentage of national average earnings) were relatively stable in this period, but the earnings for younger workers in this group rose rapidly.

Earnings for rural high school graduates and those

with some college rose somewhat; again, the increase

was greater for younger workers. Earnings declined

for both older and younger urban workers in this middle education group. The earnings of rural unskilled

workers (those with less than a high school education)

for all ages rose in rural areas but fell in urban areas.

Overall, rural earnings were lower than urban earnings

in the 1990s, but they rose faster than urban earnings

at both high and low education levels.

Unlike traditional immigration studies, this study

includes labor demand shift factors and other area-specific factors in the analysis of wages. Then employment changes are analyzed by skill type for a better

understanding of the different ways labor demand

affects wages. The study also examines changes in

occupation and industry employment by gender and

ethnic group at the individual level to inform the labor

demand patterns. This approach of incorporating labor

demand in the wage analysis and separately exploring

its changes provides a more rounded understanding of

wage dynamics than do traditional studies.

4 ✥ Impacts of Hispanic Population Growth/AER-826 Economic Research Service/USDA
Conceptual Framework, Data,

and Methods of Analysis

Conceptual Framework

The conceptual framework for this study is a combination of the frameworks used in the immigration-impact

and labor demand literature. In the simple model where

labor demand changes are not considered, the most

unambiguous impact of immigration (via labor supply)

is to depress the wages of unskilled native workers.

However, when the simple model is expanded to one

where trade is permitted, labor demand changes indirectly caused by immigration could lead to changes in the

wages of other skill groups. This is the hypothesis of

Hanson et al. where immigration leads to a change in a

country’s output mix toward a mix that reflects the lower

relative costs of the type of labor represented by the

immigrants. In the typical U.S. case, where the bulk of

Hispanic immigrants are unskilled laborers, immigration

would lead U.S. producers to switch some amount of

production toward output that requires relatively more

unskilled laborers. Since this argument implies a rightward shift in a relative labor supply curve (where labor

is defined as the proportion of unskilled to skilled

laborers) and then a rightward shift in relative labor

demand, the effects on relative wages are ambiguous.

Another possibility linked to labor demand is that technological change could induce changes in labor demand

independent of immigration that lead to changes in relative wages. Evidence shows that production has shifted

toward the use of higher skilled workers, but no one has

examined the data for rural areas alone. If some rural

industry has become more specialized in low-skill work

in the last decade, it may be a result of a technological

change that has particularly affected that industry. This

would cause a rightward shift in labor demand and thus

an unambiguous increase in wages for unskilled workers

compared with skilled workers. Such a change toward

the use of unskilled labor to produce the same product

could also be a firm’s response to a different mix of

available labor. In that case, both labor supply and

demand would shift to the right and the relative wage

impact would be ambiguous (as in the output-mix case).

This analysis tests for the impact of labor demand

changes on wage changes and then decomposes labor

demand changes into those due to technological or

“labor-mix” change and those due to output-mix change.

The decomposition is done separately for different skill

groups for a better understanding of how the two types

of production changes (technological and output mix)

have affected relative demand for college-educated,

skilled, and unskilled labor. For example, if output-mix

changes lead to wage increases for unskilled workers,

one could conclude that a preceding labor supply shift

from immigration helped cause the change. If technological changes lead to wage increases for unskilled workers, the conclusion about the role of immigration would

be more ambiguous. If, however, either kind of labor

demand change leads to wage increases for skilled

workers, one can more confidently conclude that

immigration-induced labor supply shifts for unskilled

workers did not contribute to labor demand changes.

Thus, the analysis seeks to understand, first, the independent roles of immigration and labor demand in wage

determination and, second, whether or not immigration

factors can explain some of the labor demand changes.

Data Sources

The data used in this analysis are from the Bureau of

Economic Analysis (BEA) and the Current Population

Survey (CPS) for 1990 and 2000. These periods are

roughly comparable by business cycle, and relatively

little analysis has been done on rural labor market

changes over the period of 1990 to 2000. This analysis

uses CPS data to describe relative changes in wages,

educational attainment, and employment patterns by

ethnic group. It then uses individual-level CPS wage

data for 1990 and 2000 to estimate relative State-level

wages. The estimated 2000 wages are regressed on

estimated labor demand shifts; cost of living changes;

estimated 1990 wages; and percentage growth in the

Hispanic population. CPS and BEA data are used to

decompose changes in labor demand from 1990 to 2000.

The March CPS supplement data is used because they

contain broader data on economic welfare and because

the representation of Hispanics is more accurate (due

to an oversampling of Hispanics in March for that purpose). Since the CPS samples are not very large and a

sizable proportion of Hispanics was needed in the

data, data from 1989 and 1991 and then separately

data from 1999 and 2001 were combined. Neither the

1990 or 2000 years of CPS March data were used

because the respondents are already included in the

preceding and following years. The interview cycle of

the CPS results in half of the households surveyed in

March 1989 being resurveyed in March 1990 and the

other half of those surveyed in March 1990 being

resurveyed in March 1991. The matches are often

imperfect because people move or cannot be found

again, but to make sure that people were not counted

twice, the middle years were excluded. Finally, standard errors for the CPS are based on the assumption of

a random sample. Since the CPS is not conducted as a

Economic Research Service/USDA Impacts of Hispanic Population Growth/AER-826 ✥ 5
random sample, the analysis uses a method developed

by Jolliffe (2002) that corrects the standard errors by

mimicking the sample design.1

Wage Analysis Method

This part of the analysis looks at how nonmetro wages

have changed over the last 10 years. Based on the literature, one would expect less-educated groups to be

more likely to see wages decrease as a result of

Hispanic population growth. However, as noted previously, labor demand shifts and cost of living changes

can also be important. The analysis includes these factors and looks at how labor demand shifts in different

industries may have affected wages.

It is highly likely that local changes in the cost of living have influenced wages over time. To measure cost

of living differences, we use changes in median rent

by county from 1990 and 2000 Census data. This

measure is far from ideal and may be said to be

endogenous to wages, but it is considered the best

available. Easton (2001) used two estimates of

changes in price levels, neither of which is available

for nonmetro areas (the Consumer Price Index and an

index from the American Chamber of Commerce

Research Association).

Positive labor demand shifts in an area are likely to

attract people, thereby confounding the effects of labor

supply and demand shifts on wages. Labor demand

shifts are not directly observable, however, so a proxy

must be found. The proxy of actual changes in

employment is closest, but it is also determined by

labor supply shifts. Following Bound and Holzer

(2000) and Easton (2001), the analysis uses an estimate of demand that is exogenous to labor supply. Let

ηk, represent actual changes in employment in State k.

The estimate of ηk

, which we call is based on

nationwide growth in employment by industry for all

industries j (ηj

) and State-specific industry shares (γ jk)

averaged over the decade and calculated as follows:

The aggregate estimate is thus the sum of expected

growth in each industry, where expected growth is

measured as the product of the industry’s share in the

State economy and national growth in that industry.

The analysis is extended by using this aggregate predicted labor demand in one model and using the

expected growth in each industry j as separate independent variables (γ jkηk ) in a second model.

The analysis follows the two-stage method of wage

estimation originally suggested by Dickens and Katz

(1987) and commonly used to avoid standard error

bias. Moulton (1986) showed that combining individual- and metropolitan-level observations in one regression could lead to an underestimate of the standard

errors of the coefficients. This analysis focuses on the

year 2000 to measure the potential impacts of Hispanic

population growth. The first stage regresses rural individual wages on individual characteristics along with

dummy variables for the States in which the individual

resides. We ran separate regressions for different subpopulations. The model estimated is as follows:

1n(Wi

) = a + Xijbj + Zilcl + Sikdk + ui. (4)

Using CPS data, individual wages (Wi

) are calculated

from the total annual earnings for full-time employees

(working 50-52 weeks) divided by the product of

weeks worked and usual hours worked. We regress the

log of individual wages (Wi

) against several variables

(represented by Xij, Z, and S) where the lower case letters depict the estimated effects of the corresponding

variables. The variable matrix Xij includes the individual characteristics of education, age (and age-squared),

gender, ethnic groups, and whether the person moved

in the last year. The matrix Z contains dummies for the

l major occupation groups, and the matrix S contains

the dummies for the k States for which there are data

(excluding the District of Columbia and New Jersey

because they have no nonmetropolitan areas).

In the second stage, the first-stage estimated coefficients

for the k = 49 State dummies are used as the dependent variable measuring relative differences in rural

wages by State These relative wages are regressed

on the growth rate of the Hispanic population in rural

areas of the State (Hk

), an estimate of the cost of living

differences by State (Ck

), an estimate of relative wage

differences by State in 1990 2 and changes in

labor demand by State The model is as follows:

6 ✥ Impacts of Hispanic Population Growth/RDRR-XX Economic Research Service/USDA

1

Jolliffe shows that this method performs better than assuming a random

sample in the presence of incomplete information about the sampling parameters. He compares results from different methods in specific cases where

formulas are available from CPS to calculate the correct standard errors.

2

The estimate for 1990 relative wage differences by State is estimated

from the same wage model as that used for 2000, shown in equation (3)

with 1990 data.

η$ . k jk

j

= ∑ j γ η (3)

$ $ d d HC v $ . k k k kk k =+ + + + + λ τ θ ηδ 90 (5)

η$ , k

(η$

k )

(η$

k ).

( ). $dk

( ), $90 dk
The second model is the same but includes the separate j components of predicted industry labor demand

(γjkηk

) instead of aggregate demand The results

of the estimation are described in the next section.

Sector Decomposition Method

The wage analysis provides a general estimate of the

impact of labor demand on wages. One can better

understand that impact by decomposing changes in

labor demand according to whether they were due to

increases in output or to technological shifts. As just

discussed, these two kinds of labor demand shifts have

different implications for the role of immigration.

Motivated by the notion that the labor market could be

affected by production decisions, Hanson and Slaughter

(2002) decompose changes in State employment into

changes in output mix and changes in production techniques within sectors. Following their methodology,

assume that there are N industries and M factors of

production. In this case, 10 major industries are used,

coinciding with the number of industries used in the

wage analysis, and 3 factors of production—unskilled,

skilled, and professional. In each State, a general equilibrium labor market is characterized as:

V = EO, (6)

where V is an M x 1 vector of factor supplies; E is an

M x N matrix of unit factor requirements for each factor

type and industry; and O is an N x 1 vector of real

value-added output for each industry. Each element in

E shows the units of factor m needed to produce one unit

of real value-added output in industry n. In equation

(6), factor supply equals factor demand. Decomposing

(6) taking first differences results in:

where ∆ represents changes from 1990 to 2000, is

the mean across 1990 and 2000 of real value added,

and is the mean across 1990 and 2000 of unit labor

requirements. The first element in the decomposition

of the demand side holds the unit labor requirements,

or production technology, constant while allowing output to change. The second element holds output constant while allowing the production technology within

industries to change.

A combination of BEA and CPS data are used to perform

the estimation (see box for explanation) for each of the

49 States. All of the data elements in the decomposition

analysis are strictly estimates: The CPS is a statistical

sample, and the BEA value-added data are available only

at the State-level—and not for the nonmetro areas alone.

Economic Research Service/USDA Impacts of Hispanic Population Growth/AER-826 ✥ 7

Data Sources for Sector

Decomposition Components

Real Value-Added Output

The real value-added output data at the State level for 1990 and 2000 were obtained from the U.S. Department of

Commerce, Bureau of Economic Analysis (BEA). These data are available by industry and State, but not by metro/nonmetro distinction. The data are available at: www.bea.gov/bea/regional/gsp/

State Labor Supplies by Education Category

The estimates for State employment levels for skilled and unskilled groups were obtained by combining Current

Population Survey (CPS) education and industry of employment data with BEA State-level employment data. The CPS

data were used to first calculate the proportions of individuals of each skill type by industry and by State. These proportions were then used to apportion the BEA State-level employment data to skilled and unskilled categories by industry.

Unit Labor Requirements

The unit labor requirements were calculated from a combination of BEA and CPS data. From the CPS data, we first calculate the share of each skill group in total employment by industry and State. The unit labor requirements are then estimated by the ratio of employment by State, industry, and skill group to value added by State and industry.

(η$

k ).

∆ ∆∆ V = + E O EO, (7)

Ο

Ε
Empirical Results

Nonmetro Employment Changes by

Occupation, Industry, and Ethnicity

This section first reviews basic labor market trends

shown in both the BEA and the CPS data. These trends

inform the wage analysis and decomposition results by

showing from an individual-level perspective where

different kinds of workers have moved and how their

wages have changed over the last decade. The results

from these descriptive statistics and the two types of

analysis combine to provide a good understanding of

how Hispanic migrants have affected rural wages.

Using CPS data to look at trends by ethnic group during

1990-2000, we see that Whites earned substantially more

total personal income in both periods than did Blacks or

Hispanics, and their gain over time was larger (table 2).

Blacks earned the least of the three, but they had the

highest percentage increase in income over the decade.

Hispanics had the second highest increase. Wages show

a similar pattern. Wages also increased with the level of

education. Wage growth was highest for college-educated workers and second highest for high school graduates.

Education indicators improved for both Whites and

Blacks over the decade, but show no change or just

slight improvement for Hispanics. The percentage of

rural Hispanics with less than a high school education

was 49 percent in both years, which is much higher

than for Whites at 17 percent and Blacks at 36 percent

by 2000. A high proportion of immigrants and language barriers could explain the low formal education

levels of Hispanics.

Rural occupation trends between 1990 and 2000

improved most for Blacks, showed some pointed

changes for Hispanics, and changed little for Whites

(table 3). The share of Blacks rose from 8 percent to

11 percent in professional and executive positions and

from 37 percent to 43 percent in service occupations,

such as sales and administrative support. The share of

Blacks in blue-collar jobs dropped from 48 percent in

1990 to 41 percent in 2000. The share of Whites rose

from 19 percent to 23 percent in professional jobs and

fell from 33 percent to 31 percent in blue-collar jobs.

The share of Whites in service-sector jobs stayed the

same, at 40 percent, in both years.

The biggest change for Hispanics was a rapid movement

away from farm labor (table 4). The share of Hispanics

doing farm work dropped by 5 percentage points, from

14 to 9 percent. The next largest occupation drop was

less than 1 percent. The share of Hispanics rose from

35 percent to 38 percent in white-collar jobs and from

39 percent to 41 percent in blue-collar jobs. The share

of Hispanics in professional occupations stayed about

the same.

Occupation trends show where different groups may

fall within the income ranges of rural areas, while

industry employment trends illustrate how different

groups were affected by growth in different parts of

the economy. Hispanics left agriculture at a rapid rate

(6 percent), more rapidly than Blacks and Whites combined (see table 3). Blacks showed a large movement

out of nondurable goods manufacturing (7 percent)

and Whites a smaller movement out (2 percent), while

Hispanics showed strong movement into that industry

(4 percent). Overall, the three industries where all

three groups had the largest presence were manufacturing, retail, and professional services.

Wage Analysis Results

Next, the relative wage regression estimates for different

subpopulations are examined. In the first stage, the determinants of individual wages were estimated as depicted

in equation (4). Different regressions were run for each

gender and race and for three categories of educational

attainment: (1) less than a high school education, or

“unskilled,” (2) a high school education and/or some

college, or “skilled,” and (3) a college degree, or “professional.” The schooling categories were also combined

separately with race and gender categories.3 Table 5

shows the results of two regressions—Whites and skilled

people for the year 2000—and table 6 shows a summary

of the R-squared, number of observations, and significant t-statistics of the other regression groups. The

results of these regressions are typical of individual wage

analysis results: Age has an increasing and diminishing

effect, education has a positive effect, female gender and

minority race have strong negative effects, and the State

wage coefficients follow typical regional patterns.

Estimates of State-level wages were obtained for each

subgroup for each period, 1990 and 2000. The 1990

estimates were used as independent variables, and the

2000 estimates were used as the dependent variables in

the year 2000 State-level regressions. The results of the

State-level wage regressions for subgroups are shown in

8 ✥ Impacts of Hispanic Population Growth/AER-826 Economic Research Service/USDA

3

The estimates for Blacks were hampered by the fact that there were not

enough Blacks in several States to yield State-level wage estimates; only

36 States had enough observations. This small number of observations was

even more of a problem once we split the sample by gender and skill level.

So the results for Blacks are not presented.
tables 7 and 8.4 The regressions were weighted by State

nonmetro population in 2000. Note the strong correlation

between the estimate we use for exogenous labor demand

changes and actual employment changes by State

ηk). A simple regression shows this result (with the

standard error of the estimated coefficient in parentheses):

The high R2 suggests that actual labor demand at the

State level closely followed that of the national level

since the estimate of labor demand is based on 

national trends.

The results show many interesting patterns. First, it is

clear that including an estimate for the State’s rural wage

in 1990 is important. For most subgroups, the coefficient

estimate on prior wages is highly significant, suggesting

that rural wage levels and their differences across States

were fairly persistent over time. Second, the percentage

growth of Hispanics shows a negative impact on rural

wages for skilled workers and for skilled males, but it

is insignificant for the other two skill groups. This

negative impact on the rural wages of skilled males

and skilled workers (of both genders) was found in the

two versions of the model, where labor demand is

aggregated and where it is separated by industry.

Economic Research Service/USDA Impacts of Hispanic Population Growth/AER-826 ✥ 9

Table 2—Economic statistics for nonmetro population by ethnicity and education level, 1989-2001

1989-91 1999-2001

Item White Black Hispanic White Black Hispanic

Mean:

Total personal earnings 16,036 10,967 11,728 23,897 17,869 18,415

Change—

Dollars NA NA NA 7,861 6,902 6,687

Percent NA NA NA .49 .63 .57

Hourly wage 9.22 6.80 7.02 13.49 10.52 10.44

Change—

Dollars NA NA NA 4.27 3.72 3.42 

Percent NA NA NA .46 .55 .49

Highest grade of education

(>18 years old) 12.29 10.83 10.47 12.56 11.30 10.36

Percent attending high school

(<=24 years old) .10 .10 .09 .12 .13 .11

Percent attending college

(<=24 years old) .07 .04 .03 .07 .05 .03

Percent with less than 

high school education

(>18 years old) .23 .43 .49 .17 .36 .49

Percent moved since 

last year 14.42 14.99 26.01 11.95 13.78 17.50

1989-91 1999-01

Less than High school/ Less than High school/

high school some college College high school some college College

Mean 7.00 8.73 12.99 9.53 12.50 19.43

Standard error .09 .05 .13 .28 .13 .44

Number of observations 7,494 26,728 7,255 2,419 10,111 2,738

Level of change NA NA NA 2.53 3.77 6.44

Percent change NA NA NA .36 .43 .50

NA = Not applicable.

Sources: March Current Population Survey, 1989, 1991, 1999, and 2001.

4

The results for the White subgroup regressions (such as White with college degree) have been left out of the tables because they are virtually the

same as the general subgroups reported.

(η$

k )

η η k k =− + = 0 54 123 0 845 R2 . . $ .

(0.077)
These results are important in light of the common

expectation that Hispanic population growth would have

the most impact on the unskilled or those with less than a

high school education. But the results are not inconsistent

with the theory that wages could be affected by changes

in labor demand toward more or less skilled labor.

Alternately, the results could reflect a situation where

skilled workers are working in jobs that require lesser

skills. The result brings to mind the replacement of White

and Black males in higher paying jobs by less-educated

Hispanics that occurred in the meat-processing industry.5

10 ✥ Impacts of Hispanic Population Growth/AER-826 Economic Research Service/USDA

Table 3—Nonmetro employment by occupation, industry, and ethnicity, 1990-2000

1990 2000 Change

Job types White Black Hispanic White Black Hispanic White Black Hispanic

-----------------------------Percent----------------------------- ---Percentage point---

By major occupation group:

Executive, administrative, 

and managerial 8.97 2.66 4.28 10.93 4.08 4.71 1.96 1.42 .43

Professional specialty 10.29 5.45 4.81 11.94 6.73 4.25 1.65 1.28 -.56

Total 19.26 8.11 9.09 22.87 10.81 8.96 3.61 2.70 -.13

Technicians and related support 2.31 1.72 .71 2.78 2.23 1.00 .47 .51 .29

Sales 10.98 5.17 7.78 10.85 7.87 9.36 -.13 2.70 1.58

Administrative support, 

including clerical 12.80 6.36 8.74 12.75 9.28 8.53 -.05 2.92 -.21

Private household .92 3.02 1.21 .51 1.16 .93 -.41 -1.86 -.28

Protective service 1.54 1.75 .89 1.73 2.94 1.89 .19 1.19 1.00

Service, except household 

and protective 11.83 18.89 15.62 11.80 19.67 16.57 -.03 .78 .95

Total 40.38 36.91 34.95 40.42 43.15 38.28 .04 6.24 3.33

Farming, forestry, and fishing 7.46 6.33 15.98 5.75 3.62 10.84 -1.71 -2.71 -5.14

Precision production:

craft, and repair 12.96 9.98 14.11 12.96 11.58 14.35 0 1.60 .24

Machine operators, assemblers, 

and inspectors 9.23 19.85 11.73 7.58 14.41 10.86 -1.65 -5.44 -.87

Transportation and 

material moving 5.44 6.78 5.29 5.28 6.28 5.98 -.16 -.50 .69

Handlers, equipment cleaners, 

helpers, and laborers 4.97 11.15 8.18 4.78 9.04 10.21 -.19 -2.11 2.03

Total 32.6 47.76 39.31 3.6 41.31 41.40 -2.00 -6.45 2.09

By major industry group:

Agriculture, forestry, and fisheries 7.30 5.22 16.97 5.70 3.01 10.60 -1.60 -2.21 -6.37

Mining 1.40 .43 2.01 1.07 .40 1.58 -.33 -.03 -.43

Construction 7.09 5.96 6.54 8.02 4.75 7.23 .93 -1.21 .69

Durable goods manufacturing 11.09 12.16 7.28 10.99 13.85 7.01 -.10 1.69 -.27

Nondurable goods manufacturing 8.47 2.56 12.08 6.71 14.00 15.80 -1.76 -6.56 3.72

Transportation, communications, 

and public utilities 5.84 4.56 3.78 5.93 5.27 4.56 .09 .71 .78

Wholesale trade 3.29 1.73 3.25 3.40 2.72 4.95 .11 .99 1.70

Retail trade 17.86 14.54 16.45 17.45 16.98 17.69 -.41 2.44 1.24

Finance, insurance, and 

real estate 4.20 1.49 1.67 4.15 1.58 2.22 -.05 .09 .55

Business and repair services 3.81 2.61 3.16 4.37 4.40 3.28 .56 1.79 .12

Personal services, including 

private households 4.37 6.80 6.22 3.29 4.15 4.14 -1.08 -2.65 -2.08

Entertainment and 

recreation services .95 .61 1.09 1.52 .43 .76 .57 -.18 -.33

Professional and related services 19.73 18.83 14.84 22.35 22.00 15.66 2.62 3.17 .82

Public administration 4.59 4.50 4.67 5.06 6.46 4.52 .47 1.96 -.15

Sources: March Current Population Survey, 1989, 1991, 1999, and 2001.

5

See MacDonald, Ollinger, Nelson, and Handy (2000) for an analysis of

consolidation in U.S. meatpacking and accompanying wage trends.
The results also show that labor demand factors are

important determinants of wages, especially in the first

two skill-level categories. Growth in aggregate demand

for labor reduced unskilled wages, increased skilled

wages, but did nothing for college wages. For males,

females, and Whites as separate groups with combined

education levels, labor demand factors had no significant 

effect.6 But when these groups were broken down by

education level, demand mattered, and aggregate labor 

Economic Research Service/USDA Impacts of Hispanic Population Growth/AER-826 ✥ 11

Table 4—Nonmetro Hispanic employment by occupation, 1990 and 2000

Occupation 1990 2000 Change

Percent

Farm workers and related 14.15 9.02 -5.13

Other professional specialty 1.61 .78 -.83

Personal service 3.16 2.36 -.80

Construction trades 4.66 3.94 -.72

Financial records, processing 1.47 .77 -.70

Machine operators and tenders, except precision 7.09 6.56 -.53

Health service 2.73 2.29 -.44

Sales workers, retail and personal services 5.69 5.28 -.41

Freight, stock and material handlers 3.03 2.69 -.34

Mail and message distributing .58 .29 -.29

Food service 6.69 6.45 -.24

Other executive, administrators, and managers 3.11 2.92 -.19

Private household service 1.05 .87 -.18

Engineers .27 .10 -.17

Computer equipment operators .27 .11 -.16

Health diagnosing .30 .15 -.15

Engineering and science technicians .26 .13 -.13

Supervisors—administrative support .38 .26 -.12

Teachers, except college and university 1.68 1.61 -.07

Teachers, college and university .31 .26 -.05

Technicians, except health engineering .09 .08 -.01

Other transportation and material moving 2.22 2.22 0

Natural scientists .05 .07 .02

Health technologists and technicians .60 .62 .02

Mechanics and repairers 3.55 3.58 .03

Forestry and fishing .35 .40 .05

Farm operators and managers .46 .52 .06

Management related .77 .97 .20

Construction laborer 1.51 1.71 .20

Motor vehicle operators 3.46 3.80 .34

Sales representatives, finance, and business service .44 .78 .34

Secretaries, stenographers, and typists 1.42 1.79 .37

Fabricators, assemblers, inspectors, and samplers 4.56 4.96 .40

Administrators and officials, public administration .27 .69 .42

Sales representatives, commodities, except retail .51 1.06 .55

Health assessment and treating .43 1.03 .60

Supervisors and proprietors, sales 2.00 2.60 .60

Protective service .67 1.57 .90

Other administrative support 4.08 5.31 1.23

Other precision production 5.73 7.22 1.49

Cleaning and building service 4.08 5.66 1.58

Other handlers, equipment cleaners, and laborers 4.17 6.34 2.17

Sources: March Current Population Survey, 1989, 1991, 1999, and 2001.

6

All impacts reported are statistically significant unless otherwise noted.

This study reports significant results up to 10 percent significance since there

are relatively few observations (48) in the State-level regressions and thus

low statistical power. Different levels of significance are noted in table 8.
12 ✥ Impacts of Hispanic Population Growth/AER-826 Economic Research Service/USDA

Table 5—Determinants of individual wages for different populations, 2000

High school graduates Whites

Standard Standard

Independent variables Coefficient error t-Statistic Coefficient error t-Statistic

Age 0.05 0 13.13 0.04 0 12.77

Age-squared 0 0 -10.48 0 0 -9.60

Highest grade level achieved .06 .01 7.24 .05 0 12.39

Female -.28 .02 -17.04 -.27 .02 -17.63

Race/ethnic groups:

Black -.09 .03 -3.24 NA NA NA

Asian, Pacific Islander .16 .10 1.59 NA NA NA

Native American -.09 .06 -1.48 NA NA NA

Hispanic -.03 .03 -.79 NA NA NA

Moved in the last year -.05 .02 -2.37 -.04 .02 -1.92

Occupations:1

Professional 0 .04 .10 0 .03 .02

Technical .06 .04 1.50 0 .04 -.01

Sales -.24 .03 -7.27 -.30 .03 -10.02

Adminstrative support -.16 .03 -5.57 -.24 .03 -8.81

Private household -.74 .16 -4.48 -.89 .12 -7.34

Protective service -.08 .05 -1.70 -.18 .05 -3.85

Service (all other) -.47 .03 -14.83 -.52 .03 -17.76

Farming, Forestry -.39 .05 -7.40 -.49 .04 -10.88

Precision production -.08 .03 -2.49 -.16 .03 -5.69

Machine operators -.09 .03 -2.95 -.17 .03 -5.86

Transportation -.19 .04 -5.04 -.23 .04 -6.47

Handlers, laborers -.35 .04 -8.69 -.39 .04 -10.34

Civilian military -.23 .08 -2.74 -.25 .08 -3.14

States:2

New Hampshire .15 .06 2.59 .09 .05 1.99

Vermont .01 .05 .11 .02 .04 .58

Massachusetts .11 .08 1.39 .12 .06 1.90

Rhode Island .26 .06 4.08 .28 .08 3.68

Connecticut .24 .10 2.36 .22 .09 2.44

New York .10 .05 1.91 .08 .05 1.83

Pennsylvania .06 .05 1.08 .04 .05 .85

Ohio .12 .06 2.17 .12 .05 2.55

Indiana .02 .05 .47 -.01 .04 -.14

Illinois .07 .06 1.25 .06 .05 1.38

Michigan 0 .07 -.06 .02 .06 .43

Wisconsin .05 .05 .88 .02 .05 .35

Minnesota 0 .06 0 .06 .05 1.17

Iowa .05 .05 .99 .04 .04 .85

Missouri .10 .07 1.45 .01 .06 .24

North Dakota -.07 .05 -1.20 -.08 .05 -1.70

South Dakota -.04 .06 -.68 -.03 .05 -.57

Nebraska -.05 .05 -1.03 -.07 .04 -1.75

Kansas .03 .05 .64 .03 .04 .73

Delaware .12 .08 1.65 .05 .06 .84

Maryland .04 .08 .49 .07 .08 .90

Virginia .12 .06 1.82 .11 .06 1.81

West Virginia -.12 .05 -2.34 -.09 .04 -2.19

North Carolina .10 .05 1.98 .05 .05 1.03

See notes at end of table. Continued—
Economic Research Service/USDA Impacts of Hispanic Population Growth/AER-826 ✥ 13

Table 5—Determinants of individual wages for different populations, 2000—Continued

High school graduates Whites

Standard Standard

Independent variables Coefficient error t-Statistic Coefficient error t-Statistic

South Carolina 0.07 0.06 1.15 0.06 0.06 1.01

Georgia .09 .06 1.63 .07 .05 1.44

Florida -.06 .07 -.88 -.09 .07 -1.34

Kentucky .02 .05 .37 .03 .04 .68

Tennessee -.03 .06 -.52 -.01 .05 -.15

Alabama .01 .06 .18 0 .05 .08

Mississippi -.01 .05 -.23 .07 .05 1.51

Arkansas -.03 .05 -.63 -.04 .04 -.91

Louisiana -.11 .06 -1.69 -.07 .06 -1.26

Oklahoma -.05 .06 -.84 -.07 .05 -1.46

Texas -.05 .05 -.99 -.05 .04 -1.26

Montana -.03 .05 -.65 -.03 .04 -.70

Idaho .01 .05 .21 .02 .04 .39

Wyoming .04 .05 .85 .05 .04 1.26

Colorado .09 .07 1.28 .09 .05 1.63

New Mexico -.19 .06 -3.27 -.10 .07 -1.53

Arizona -.01 .08 -.09 .01 .08 .15

Utah .09 .08 1.17 .07 .05 1.29

Nevada .09 .09 .92 .15 .08 1.82

Washington .10 .06 1.74 .07 .05 1.26

Oregon .12 .06 1.99 .13 .05 2.58

California -.03 .12 -.22 .03 .10 .32

Alaska .31 .06 5.27 .24 .05 4.45

Hawaii -.08 .10 -.77 -.02 .11 -.23

Constant -.88 .35 -2.49 -.13 .16 -.82

R-squared .2172 .2665

Number of observations 10,111 12,964

NA = Not applicable.

1

The occupation group that was excluded is “Executive, administration, and managerial.”

2

The State that was excluded from the regression is Maine.

Sources: March Current Population Survey, 1989, 1991, 1999, and 2001.

Table 6—Summary of results from individual wage regressions for different populations

Less than High school/

Item Whites Males Female high school some college College

R-squared 0.2665 0.2587 0.2397 0.2384 0.2172 0.1884

Number of observations 12,964 8,057 7,211 2,419 10,111 2,738

Significant t-statistics 22 of 67 18 of 70 17 of 70 17 of 71 25 of 71 20 of 71

Less than High school/

high school College some college

Female Male Female Male Female Male

R-squared 0.1816 0.2710 0.2070 0.2074 0.1659 0.1912

Number of observations 981 1,438 1,319 1,419 4,911 5,200

Significant t-statistics 10 of 69 18 of 70 20 of 70 20 of 69 16 of 70 14 of 69

Sources: March Current Population Survey, 1989, 1991, 1999, and 2001.
14 ✥ Impacts of Hispanic Population Growth/AER-826 Economic Research Service/USDA

Table 7—Determinants of relative State wages by gender and education level

Less than High school/

Item Whites Males Females high school some college College

Estimated wage, 1990 0.76 0.68 0.65 0.25 0.72 0.46

(5.21)*** (4.91)*** (4.02)*** (1.03) (4.18)*** (2.08)**

Aggregate labor demand .02 0 0 -.01 .003 -.002

(1.19) (.65) (.99) (-1.93)* (1.79)* (-.62)

Percent change median rent .31 .30 .24 -.04 .27 .29

(2.55)** (2.36)** (1.50) (-.15) (1.82)* (1.11)

Percent Hispanic, 2000 -.22 -.26 -.15 .08 -.38 .14

(-2.26)** (-2.41)** (-1.16) (.35) (-3.11)*** (.61)

Constant -.05 -.06 -.01 .17 -.05 -.09

(-1.06) (-1.28) (-.16) (1.60) (-.81) (-.91)

Number of observations 48 48 48 48 48 48

Adjusted R-squared .415 .396 .257 .112 .330 .040

Estimated wage, 1990 .94 .69 .87 .18 1.11 .30

(5.18)*** (3.62)*** (5.03)*** (.64) (5.44)*** (1.06)

Agriculture -.15 -.08 -.06 .05 -.33 .46

(-.89) (-.34) (-.31) (.13) (-1.62) (1.17)

Agricultural services, mining .12 -.01 .18 -.15 .23 -.11

(2.27)** (.13) (3.49)*** (-1.37) (3.68)*** (-.97)

Construction .03 -.15 .04 -.19 .13 -.14

(.43) (-.15) (.49) (-1.04) (1.47) (-.77)

Manufacturing -.03 .77 -.78 2.68 -.88 1.09

(-.06) (1.25) (1.37) (2.29)** (-1.51) (.84)

Transportation, utilities -.06 -.05 -.15 -.51 -.03 -.27

(-.37) (-.26) (-.78) (-1.24) (-.17) (-.68)

Wholesale trade -.32 -.18 -.33 -.46 -.51 .37

(-1.06) (-.47) (-1.02) (-.66) (-1.42) (.52)

Retail trade -.06 -.12 .01 -.43 .07 -.06

(-.71) (-1.07) (.12) (-1.94)* (.65) (-.27)

Finance, real estate .13 .04 .23 -.19 .19 -.11

(1.48) (.41) (2.27)** (-.90) (1.93)* (-.53)

Services .02 .05 -.01 .28 -.06 .13

(.41) (.92) (-.19) (2.53)** (-1.19) (1.15)

Government .41 0 .06 .05 .09 -.14

(.81) (.01) (1.12) (.41) (1.56) (-1.15)

Percent change median rent .33 .29 .26 .10 .27 .19

(2.33)** (1.89)* (1.54) (.34) (1.73)* (.60)

Percent Hispanic, 2000 -.16 -.23 -.08 .09 -.28 .44

(-1.08) (-1.28) (-.48) (.26) (-1.72)* (1.19)

Constant -.07 -.07 -.03 .07 -.06 -.14

(-1.20) (-1.09) (-.54) (.59) (-.87) (-1.19)

Number of observations 48 48 48 48 48 48

Adjusted R-squared .436 .299 .463 .130 .480 .041

Numbers in bold = 10 percent or greater significance. *10 percent significance. **5 percent significance. ***1 percent significance.

Numbers in parentheses = t-statistics.

Sources: March Current Population Survey, 1989, 1991, 1999, 2001, and Bureau of Economic Analysis, 1990 and 2000.
Economic Research Service/USDA Impacts of Hispanic Population Growth/AER-826 ✥ 15

Table 8—Determinants of relative State wages by combined gender and education levels

Less than High school/

high school College some college

Item Female Male Female Male Female Male

Estimated wage, 1990 0.66 0.18 0.03 0.86 0.58 0.62

(2.92)*** (.79) (.14) (3.76)*** (2.94)*** (3.70)***

Aggregate labor demand 0 -.01 -.003 -.001 .002 .004

(-.02) (-2.11)** (-.54) (-.33) (.75) (2.00)*

Percent change median rent -.07 .07 -.54 .91 .28 .16

(-.24) (.22) (-1.36) (2.98)*** (1.44) (.96)

Percent Hispanic, 2000 .37 .16 -.11 .67 -.13 -.63

(1.27) (.53) (-.29) (2.38)** (-.83) (-4.24)***

Constant .04 .23 .16 -.31 .00 -.06

.30 (1.77)* (1.13) (-2.64)** (.02) (.93)

Number of observations 48 48 48 48 48 48

Adjusted R-squared .149 .069 -.010 .290 .120 .340

Estimated wage, 1990 .74 .21 -.18 .73 .98 .78

(2.90)*** (.80) (-.61) (2.77)*** (5.04)*** (3.22)***

Agriculture .27 -.24 -.04 .68 -.17 -.19

(.57) (-.44) (-.08) (1.46) (-.86) (-.59)

Agricultural services, mining -.25 -.06 .26 -.35 .26 .11

(-1.96)* (-.39) (1.73)* (-2.62)** (4.30)*** (1.20)

Construction -.31 -.07 .02 -.12 .16 .07

(-1.41) (-.29) (.07) (-.55) (1.72) (.56)

Manufacturing 2.19 2.95 -.38 1.35 -1.32 -.32

(1.52) (1.84)* (-.22) (.92) (-2.12)** (-.39)

Transportation, utilities -.40 -.38 .04 -.38 -.19 .07

(-.80) (-.69) (.08) (-.80) (-.92) (.27)

Wholesale trade -.81 -.40 -1.59 1.75 -.09 -.51

(-.96) (-.42) (-1.55) (2.13)** (-.25) (-.95)

Retail trade -.17 -.64 .26 -.30 .02 .10

(-.63) (-2.14)** (.84) (-1.18) (.18) (.68)

Finance, real estate 0 -.27 -.21 -.13 .35 .06

(.04) (-.92) (-.66) (-.55) (3.18)*** (.43)

Services .16 .32 .10 .15 -.08 -.05

(1.23) (2.14)** (.63) (1.14) (-1.35) (-.65)

Government .07 .09 -.21 -.12 .10 .04

(.48) (.58) (-1.25) (-.89) (1.62) (.46)

Percent change median rent .32 .18 -1.07 1.14 .31 .10

(.86) (.47) (-2.26)** (3.36)*** (1.71)* (.51)

Percent Hispanic, 2000 -.02 .33 .90 .37 -.07 -.56

(-.04) (.70) (1.62) (.90) (-.40) (-2.36)**

Constant -.11 .15 .12 -.37 .002 -.05

(-.68) (.96) (.73) (-2.69)** (.03) (-.65)

Number of observations 48 48 48 48 48 48

Adjusted R-squared .148 .022 .030 .330 .490 .240

Numbers in bold = 10 percent or greater significance. *10 percent significance. **5 percent significance. ***1 percent significance.

Numbers in parentheses = t-statistics.

Sources: March Current Population Survey, 1989, 1991, 1999, 2001, and Bureau of Economic Analysis, 1990 and 2000.
demand had strongly opposite effects. For skilled

males, increased aggregate labor demand increased on

wages, and for unskilled males, it reduced them.

Sector differences in demand did not affect the wages

of skilled males but did affect the wages of unskilled

and professional males. For unskilled men, increases in

manufacturing and service sector demand led to wage

increases, while increases in retail trade demand led to

wage decreases. For professional men, growth in the

wholesale trade sector increased wages, while growth

in agricultural services and mining decreased wages.

For females, the story is very different. Increased

aggregate labor demand was never as significant a

determinant of female wages as it was for males, but

increased labor demand in certain sectors was strongly

so. Growing labor demand in agricultural and financial

services raised wages for all females regardless of education. These results were similar to the results for

skilled women, except that skilled women’s wages

were lowered by growth in manufacturing labor

demand. Wages for unskilled women fell from

increased labor demand in agricultural services. The

opposite was true for professional women. The results

suggest that women’s wages depended more on specific industry shifts, which is consistent with their historically higher degree of occupational segregation.

To test the robustness of the results, the four States—

Alaska, Rhode Island, Connecticut, and New Mexico—

with the largest estimated wage outliers were excluded

and the regressions were re-run. Alaska, Rhode Island,

and Connecticut had the highest wage coefficients (for

Whites) and low percentages of Hispanics. New

Mexico had the lowest wage coefficients and a high

percentage of Hispanics. The strong negative results in

some of the regressions of the percentage Hispanic

variable could have been driven by the four outlier

States. The results of the test indicate that the Hispanic

effect was not as strong without those States, but it

was still significant in the smaller model regressions

for Whites, skilled workers, and male skilled workers.

See tables 9 and 10 for a summary of the results.

Sector Decomposition Results

The results from the decomposition of labor demand

changes over the decade of the 1990s are shown in

table 11, which shows changes in the share of State

employment of each skill type and their decomposition

into production technology changes and output changes.

In columns 1, 4, and 7 (∆Vu for unskilled, ∆Vs for

skilled, and ∆V

p for professional), we see that the share

of unskilled workers increased in most States, while that

of skilled workers decreased and professional workers

stayed the same. The average shares of unskilled workers in both 1990 and 2000 were very low, an average

12 percent in 1990 and 15 percent in 2000. The average

shares of skilled workers fell from 72 percent in 1990

to 69 percent in 2000, and the average shares of professional workers stayed at 17 percent over the decade

(only the percentage changes are shown in the table).

The decomposition results show that rural trends differ

markedly from the national trends found in the 1980s

by Hanson and Slaughter (2002) who found clear evidence of a production technology shift toward higher

skill levels. This analysis finds a bimodal effect for

nonmetro areas in the 1990s: Production technology

effects caused a shift away from skilled workers much

faster than from unskilled and professional workers.

Technological change for unskilled labor positively 

16 ✥ Impacts of Hispanic Population Growth/AER-826 Economic Research Service/USDA

Table 9—Robustness test of Hispanic effect on State relative wages by gender and education level

Less than High school/

Models Whites Males Females high school some college College

Small model:

Percent Hispanic, 2000 -0.22 -0.26 -0.15 0.08 -0.38 0.14

Full (-2.26)** (-2.41)** (-1.16) (.35) (-3.11)*** (.61)

Percent Hispanic, 2000 -.22 -.21 -.05 .06 -.27 .30

Test (-1.87)* (-1.57) (-.30) (.20) (-1.79)* (1.02)

Bigger model:

Percent Hispanic, 2000 -.16 -.23 -.08 .09 -.28 .44

Full (-1.08) (-1.28) (-.48) (.26) (-1.72)* (1.19)

Percent Hispanic, 2000 -.18 -.13 .14 .04 -.09 1.42

Test (-.84) (-.48) (.56) (.06) (-.40) (2.56)**

Numbers in bold = 10 percent or greater significance. *10 percent significance. **5 percent significance. ***1 percent significance.

Numbers in parentheses = t-statistics.

Sources: March Current Population Survey, 1989, 1991, 1999, and 2001.
affected 15 States and technological change for professional labor positively affected 4 States. Skilled labor

had no positive shifts, and their technology shifts were

much more negative on average than for the other two

groups. These negative numbers could indicate that the

productivity of individual workers was rising and at a

faster rate than among skilled labor. Thus, the few positive and less-negative estimates of the technology

change for unskilled labor indicate that a larger share

of unskilled labor was required to produce the same unit

value added of output. However, the analysis is unable

to confirm whether this resulted from a substitution of

unskilled for skilled labor in production or from faster

productivity growth among skilled workers. On average, the technology effect was the least negative for

unskilled labor at -0.03, while the average for skilled

labor was -0.29 and -0.06 for professional labor.

Changes in employment shares due to output-mix

changes show the opposite of the bimodal technology

effect. The skilled group benefited most from output

changes. The average across States for the change in

shares due to output changes was 0.26 for skilled labor,

0.05 for unskilled labor, and 0.06 for professionals.

The results on output effects imply that rural industries

shifted production toward sectors that used available

labor supply types more intensively and that despite the

Hispanic population growth, the relatively more abundant laborers were skilled workers. Given that the U.S.

is an open economy, we can assume that many of these

production changes led to changes in exports and

imports as well. At the same time, production technology

shifts—that is, the relative use of different skilled

labor within industries—played an important role. The

trend estimated here reflects change toward higher

proportions of unskilled labor in some industries,

although we do not know which ones. This result differs from national findings, which could mean that

there are important differences between metro and

nonmetro areas. Or it could reflect differences by

decade, since all of the national studies have used the

1980 and 1990 Censuses. The results here suggest that

rural industries generally expanded production that

demanded more skilled labor but that some proportion

of industries experienced technological change that led

to higher demand for unskilled labor.

The greater growth in higher skill jobs found by

McGranahan and Ghelfi (1998) is consistent with our

finding that output growth alone led to an increased

demand for skilled workers. And our finding that technology shifts led to an increased relative demand for

unskilled and professional workers is consistent with

McGranahan and Ghelfi’s finding of higher growth in

wages for rural unskilled and college-educated workers

compared with their urban counterparts. McGranahan

and Ghelfi characterize the rural economy as dominated by production activities: agriculture, mining, manufacturing, and production services, which include

legal, financial, and accounting services. The urban

economy by contrast, has a large and similarly sized

production sector, but it also has a significant consumer service sector. Further analysis should test for

technological changes in particular production activities driving this result.

Economic Research Service/USDA Impacts of Hispanic Population Growth/AER-826 ✥ 17

Table 10—Robustness test of Hispanic effect on State relative wages by combined 

gender and education level

Less than High school/

high school College some college

Item Female Male Female Male Female/ Male

Small model:

Percent Hispanic, 2000 0.37 0.16 -0.11 0.67 -0.13 -0.63

Full (1.27) (.53) (-.29) (2.38)** (-.83) (-4.24)***

Percent Hispanic, 2000 .25 .18 -.11 .99 .09 -.53

Test (.73) (.47) (-.22) (2.89)** .52 (-2.71)**

Bigger model:

Percent Hispanic, 2000 -.02 .33 .90 .37 -.07 -.56

Full (-.04) (.70) (1.62) (.90) (-.40) (-2.36)**

Percent Hispanic, 2000 -.66 .35 2.62 1.16 .32 -.53

Test (-1.09) (.45) (2.80)** (1.97)* (1.35) (-1.50)

Numbers in bold = 10 percent or greater significance. *10 percent significance. **5 percent significance. ***1 percent significance.

Numbers in parentheses = t-statistics.

Sources: March Current Population Survey, 1989, 1991, 1999, and 2001.
18 ✥ Impacts of Hispanic Population Growth/AER-826 Economic Research Service/USDA

Table 11—Sector decomposition: Changes in share of nonmetro State employment by skill type, 1990-2000

Unskilled Skilled Professional

(less than high school) (high school/some college) (college)

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Region/State

New England 0 -0.03 0.03 0.02 -0.18 0.20 -0.02 -0.10 0.08

Maine 0.01 -.01 .02 0 -.14 .14 -.01 -.05 .04

New Hampshire .01 -.05 .06 .02 -.32 .33 -.03 -.15 .12

Vermont .02 -.03 .04 .02 -.15 .17 -.04 -.09 .05

Massachusetts .04 .03 .01 0 -.26 .26 -.04 -.12 .09

Rhode Island -.08 -.13 .05 .09 -.05 .15 -.01 -.12 .11

Connecticut .02 .02 .01 0 -.14 .14 -.03 -.09 .07

Mid-Atlantic .02 0 .02 -.03 -.22 .19 0 -.03 .03

New York .01 -.02 .02 -.01 -.16 .15 0 -.02 .03

Pennsylvania .04 .02 .03 -.04 -.27 .23 0 -.04 .04

East North Central .04 .02 .03 -.05 -.30 .25 .01 -.04 .04

Ohio .05 .03 .02 -.13 -.37 .24 .08 .04 .05

Indiana .03 -.02 .05 0 -.25 .26 -.03 -.06 .03

Illinois .04 .02 .02 -.04 -.27 .23 0 -.04 .04

Michigan .08 .05 .03 -.06 -.32 .26 -.02 -.05 .03

Wisconsin .03 0 .03 -.03 -.29 .27 0 -.06 .06

West North Central .01 -.03 .04 -.02 -.27 .25 .01 -.03 .05

Minnesota .02 -.05 .07 -.08 -.36 .28 .06 .01 .05

Iowa -.02 -.07 .05 .01 -.21 .22 .01 -.02 .04

Missouri .02 -.03 .04 -.07 -.34 .27 .06 .01 .04

North Dakota .02 -.01 .03 -.04 -.27 .23 .02 -.03 .04

South Dakota .02 -.01 .03 .01 -.29 .30 -.03 -.10 .07

Nebraska .01 -.03 .04 -.01 -.25 .25 0 -.04 .04

Kansas .01 -.02 .04 .02 -.19 .21 -.03 -.07 .04

South Mid-Atlantic .04 .01 .03 -.07 -.28 .21 .02 -.02 .05

Delaware .06 .01 .05 -.05 -.27 .22 -.01 -.05 .04

Maryland .04 .01 .02 -.12 -.29 .18 .08 .03 .04

Virginia .03 0 .03 -.04 -.29 .25 0 -.06 .06

West Virginia .05 .01 .03 -.06 -.25 .19 .02 -.01 .03

South Atlantic .08 -.01 .09 -.07 -.39 .32 -.01 -.07 .06

North Carolina .10 .04 .06 -.08 -.47 .39 -.02 -.09 .07

South Carolina .06 -.01 .06 -.06 -.34 .28 .01 -.05 .06

Georgia .07 -.10 .18 -.07 -.41 .35 0 -.05 .04

Florida .09 .04 .05 -.06 -.35 .29 -.03 -.08 .05

East South Central .02 -.06 .08 0 -.27 .27 -.01 -.06 .05

Kentucky .01 -.07 .08 -.01 -.28 .27 -.01 -.06 .05

Tennessee 0 -.10 .11 .01 -.30 .31 -.01 -.05 .04

Alabama -.01 -.07 .06 .04 -.21 .25 -.03 -.09 .06

Mississippi .06 -.01 .06 -.06 -.29 .23 0 -.06 .06

West South Central .01 -.06 .07 -.01 -.23 .22 0 -.04 .04

Arkansas .05 -.01 .06 -.04 -.34 .30 -.01 -.07 .06

Louisiana -.02 -.06 .04 .02 -.09 .11 0 -.03 .02

Oklahoma .01 -.04 .05 .01 -.19 .19 -.02 -.05 .04

Texas 0 -.13 .12 -.02 -.31 .29 .03 -.03 .05

See notes at end of table. Continued—

∆E Ou E Ou ∆ ∆E Os E Os ∆ ∆E Oc E Oc ∆V ∆ u ∆Vs ∆Vc
Economic Research Service/USDA Impacts of Hispanic Population Growth/AER-826 ✥ 19

Table 11—Sector decomposition: Changes in share of nonmetro State employment 

by skill type, 1990-2000—Continued

Unskilled Skilled Professional

(less than high school) (high school/some college) (college)

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Region/State

Mountain 0.03 -0.07 0.09 -0.02 -0.43 0.42 -0.01 -0.11 0.10

Montana .01 -.02 .03 -.06 -.27 .22 .05 -.02 .07

Idaho .03 -.06 .08 -.01 -.46 .45 -.01 -.09 .08

Wyoming .02 -.03 .04 -.01 -.26 .26 -.01 -.06 .06

Colorado -.03 -.15 .12 0 -.40 .39 .03 -.09 .12

New Mexico .02 -.14 .16 .03 -.61 .64 -.04 -.15 .11

Arizona .03 -.12 .14 -.06 -.51 .45 .04 -.08 .12

Utah .06 -.04 .10 -.03 -.43 .40 -.03 -.14 .11

Nevada .08 .01 .07 .02 -.52 .54 -.10 -.20 .11

Pacific .05 -.03 .07 -.08 -.37 .29 .04 -.03 .07

Washington 0 -.08 .08 .02 -.24 .26 -.02 -.08 .05

Oregon .02 -.08 .10 -.11 -.49 .38 .09 .01 .08

California .11 .08 .04 -.15 -.39 .23 .04 -.03 .07

Hawaii, Alaska .05 .06 -.01 -.03 -.03 0 -.02 -.02 0

Alaska .09 .12 -.03 -.05 -.05 0 -.04 -.03 -.01

Hawaii .01 .01 0 0 0 0 0 -.01 0

State average .03 -.02 .05 -.03 -.29 .26 0 -.06 .06

Sources: March Current Population Survey, 1989, 1991, 1999, 2001, and Bureau of Economic Analysis data, 1990 and 2000.

∆E Ou E Ou ∆ ∆E Os E Os ∆ ∆E Oc E Oc ∆V ∆ u ∆Vs ∆Vc
20 ✥ Impacts of Hispanic Population Growth/AER-826 Economic Research Service/USDA

Conclusions

The literature sources brought together in this report

combine to provide a new framework for understanding the impacts of immigration on the rural U.S. economy. By including both labor supply and demand factors and other important area-specific determinants in

the wage analysis, this analysis found not only an

apparent labor supply effect from the increase in the

Hispanic population, but also a labor demand effect.

Other studies of immigration impact that do not

include labor demand, prior wages, and estimates of

the cost of living may be subject to omitted variable

bias. The findings in this analysis were further

enhanced by an examination of changes in occupation

and industry participation at the individual level and

by a decomposition of changes in industry skill-level

requirements by State.

Growth in the rural Hispanic population reduced

wages of skilled workers, particularly male skilled

workers. This result implies a twist on the simple

immigration model prediction in which increases in

unskilled labor supply affect only the unskilled labor

market, but it is consistent with other models in which

labor demand shifts can play a role. This study found

that such shifts were important. Wages for skilled

workers rose in response to increases in labor demand

whether at the aggregate level for men or in different

industrial sectors for women.

An important caveat regarding the results is that

because the regressions have relatively few observations, the probability of not finding significance, even

though it exists, is high. On one hand, the effect of

Hispanics or of any of the hypothesized determinants

may be important to other subgroups but may not be

evident in this analysis. On the other hand, finding

consistently significant impacts with so few observations strengthens the value of the significant results.

The significance levels decline when the four States

with outlying wage estimates are excluded, but the

negative relationship between the percentage of

Hispanics and wages of skilled male workers is still

quite evident.

The provisional sector decomposition analysis suggested possible reasons for the different impacts of

Hispanic population growth and labor demand shifts

on rural wages. Most employment growth favored

skilled workers because it was driven by large changes

in output mix, which favored skilled workers. Within

some industries, however, changes in the occupational

mix led to increases in demand for unskilled and, to a

lesser extent, professional labor, relative to that for

skilled labor. The occupational mix changes help

explain the positive association with aggregate labor

demand for skilled workers wages in the wage regressions: the positive association is consistent with

increases in labor demand resulting from general output growth. The occupational mix changes also help

explain why greater labor demand in only certain

industries was associated with wage growth for

unskilled and professional workers. At the same time,

growth in the Hispanic labor force is negatively correlated with skilled worker’s wages, consistent with a

relative shift in demand toward unskilled labor within

some industries.

The analysis found that Hispanics moved away from

agriculture and into manufacturing and services, while

Whites and Blacks moved out of manufacturing.

Hispanics increased their presence in blue- and whitecollar occupations, while both Blacks and Whites

moved out of blue-collar jobs and into white-collar, or

professional, jobs. The economic status of all three

ethnic groups improved over the decade. Nevertheless,

the findings in this report taken all together suggest

that the increased supply of and increased relative

demand for unskilled Hispanic laborers in parts of the

manufacturing and service industries led to lower

demand and lower wages for skilled labor in those

industry subsectors. Other analyses of the rural manufacturing sector in the 1990s identified the converse,

where rural manufacturers generally sought to hire

workers with higher skill levels (McGranahan, 2000).

This finding could suggest either that the differentiation in the skill mix across detailed manufacturing

industries is greater or that increases in unskilled labor

demand may be coming from service industries. The

exact roots of these demand changes need to be further

examined.

The increased demand for unskilled labor found in

some parts of the rural economy may have been indirectly caused by the change in the labor pool available

to firms resulting from Hispanic immigration, but it

could also be a pure labor demand “pull” effect as postulated by Hanson et al. That relationship is ambiguous

and is probably a combination of dynamic responses

of supply and demand to each other. But this study

shows that the impact of immigration on wages was

isolated because overall employment and wage growth

favored skilled workers and outweighed the effect of

technology changes.
It is possible that the technological changes have led to

greater efficiency and thus greater economic benefit

overall, although the benefit may be going more to

employers than to labor. However, we cannot conclude

from this analysis whether or not the shift to more

unskilled labor in some industries is more efficient.

That conclusion would require a more detailed investigation, which is beyond the scope of this analysis, into

the industries that are changing their labor demand and

the reasons for the change.

This study has identified significant changes in rural

labor markets. A deeper understanding of these changes

will require more detailed study of the industry, as data

become available. For example, changes in skill mix 

identified from the CPS sample can be confirmed by

2000 Census data. The Equal Employment Opportunity

Special Tabulation of these data will provide detailed

industry information by occupation educational attainment, which is needed to refine and confirm the estimates presented here. These data will help us better

understand underlying production changes and the

changing occupational skill requirements in contrast to

the indirect skill measure provided by changes in education levels alone. Finally, the variation across States

in the decomposition analysis suggests that there may

be important differences in changes in skill demand

across rural labor markets. Indeed, this would appear

to be the key to understanding the differential effect of

Hispanic population growth on rural wages.

Economic Research Service/USDA Impacts of Hispanic Population Growth/AER-826 ✥ 21
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