Ec-980u: Estimating the labor market impact, descriptive

studies

George J. BorjasHarvard University

Fall 2010

2. Percent of adult population that is foreign-born

0

10

20

30

40

1950 1960 1970 1980 1990 2000

Year

Perc

ent

3. Percent of adult population that is foreign-born

The “other immigrant states” are New York, Florida, Texas, Illinois, and New Jersey.

0

10

20

30

40

1950 1960 1970 1980 1990 2000

Year

Perc

ent California

Other immigrant states

Rest of country

4. Measuring the labor market impact

First academic study appeared only in 1982 (by Jean Baldwin Grossman).

The literature has already gone through three phases: The “spatial correlation” approach The “factor proportions” approach The “national labor market” approach

5. The spatial correlation approach

Most studies of the labor market impact of immigration exploit the geographic clustering of immigrants to measure how immigrants affect native economic opportunities.

The typical study correlates wages and some measure of immigrant penetration across cities. Or correlates changes in wages with measures of changes in immigrant penetration across cities.

The presumption is that if immigration is “bad” natives working in cities that are penetrated by immigrants should be worse off than natives working in cities that immigrants avoid.

Done both in cross-section & panel data (i.e., fixed effects)

6. Simple econometrics of fixed effects

Suppose you have data on wages and the immigrant share (i.e., % of workforce that is foreign-born) for 100 cities. And you have the data for 2 cross-sections, 1990 and 2000.

One can imagine differencing out the data within a city and regressing the change in the wage on the change in the immigrant share, and getting a coefficient b.

One can also imagine stacking the data, so you have 200 observations. Running a regression of the wage level in a particular year on the immigrant share in that year, PLUS 100 dummies, one for each city. You will get the exact numerical estimate of the coefficient b. (This is true even if there are other regressors as long as every regressor is differenced).

Interpretation: Including “fixed effects” differences out the data, and estimates b from within-city variation.

7. Altonji and Card, empirical model

8. Altonji and Card, results

9. The Mariel boatlift (Card, 1991)

Between May and September 1980, 125,000 Cuban immigrants arrived in Miami on a flotilla of privately chartered boats.

Half of the Marielitos settled in Miami, increasing Miami’s labor supply by 7 percent. This increase in supply was equivalent to a 20 percent increase in the number of Cuban workers in Miami.

The Marielitos were much less educated than other Cuban immigrants: 57 percent did not have a high school diploma, as compared to 25 percent for other Cuban immigrants.

10. Immigration in Miami

Unemployment rate of blacks in:

The Mariel flow(Card, 1991)

Before (1979)

After (1981)

Miami 8.3 9.6

Comparison cities

10.3 12.6

The comparison cities are Atlanta, Houston, Los Angeles, and Tampa-St. Petersburgh.

The Mariel flow that didn’t

happen(Angrist & Krueger, 1999)

Before (1993)

After (1995)

10.1 13.7

11.5 8.8

11. The Mariel boatlift that did Not happen (Angrist and Krueger, 1999)

In 1994, economic and political conditions in Cuba were ripe for the onset of a new boatlift of refugees into the Miami area, and thousands of Cubans began the hazardous journey.

Due to political pressures (mainly a gubernatorial election in Florida), the Clinton administration acted to prevent the refugees from reaching the Florida shores. It ordered the Navy to direct all refugees towards the American military base in Guantanamo. Few of the potential migrants were able to migrate to Miami in 1994—though many eventually moved to Florida in subsequent years.

12. Problems with spatial correlations

Immigrants may not be randomly distributed across labor markets. If immigrants cluster in cities with thriving economies, there would be a spurious positive correlation between immigration and local employment conditions.

Local labor markets are not closed. Natives may respond to the immigrant supply shock by moving their labor or capital to other cities, thereby re-equilibrating the national economy.

Measurement error. Small sample used to calculate key independent variable, the immigrant share. Altonji & Card limit data to largest cities, and use “total” immigrant share to minimize problem.

13. Modeling the native migration response

DollarsDollars

PPT

w0

PLA

w0

wLA

Demand

PittsburghLos Angeles

Employment

Employment

S0

S1

S2

Demand

S0

S3

w* w*

14. Possible native response to Mariel

From 1970 to 1980, Miami’s population grew at an annual rate of 2.5 percent, and the rest of Florida grew at an annual rate of 3.9 percent.

After April 1, 1980, Miami’s rate of growth slowed down to 1.4 percent, and that of the rest of Florida to 3.4 percent.

The actual population of Dade County in 1986 was equal to the pre-Boatlift projection of the University of Florida’s Bureau of Economic and Business Research.

15. Implications of a native response

The spatial correlation approach cannot identify the impact of immigration on the local labor market. All markets are affected by immigration, not only those penetrated by immigrants

The unit of observation is the national labor market, not the locality

16. Borjas, Freeman, Katz, AER, 1996

17. The factor proportions approach (Borjas, Freeman and Katz, 1997)

Let the CES production function have two inputs, skilled labor (L2) and unskilled labor (L1): Q = [aL1

β + (1-a) L2β]1/β.

It can be shown that the marginal products of the two types of workers are given by:

MP1 =Q 1-βa L1β-1 and MP2 =Q 1-β(1-a) L2

β-1 In a competitive market the ratio of wages equals the

ratio of marginal products:

w2

w1

=MP2

MP1

=1−a

aL2

β−1

L1β−1

Taking logs:log (w2/w1) = constant + (β-1) log(L2/L1)

18. The factor proportions approach, continued

If we had assumed a more general production function (e.g., CES), the regression equation would be

log (w2/w1) = constant + b log(L2/L1) Let group 1 be high school dropouts; group 2 be

everyone else. Katz and Murphy (1992) estimated this regression

for the period 1963-1987 indicated that b = -.322 with a standard error of .14.

One can then use this regression estimate to predict the value of the wage ratio between skilled and unskilled natives if immigration had not changed factor proportions

19. Impact on high school dropouts

Immigrants increased supply of high school dropouts by:

20.7 percent

Immigrants increased supply of workers with at least a high school diploma by:

4.1 percent

Wage gap between skilled and unskilled natives in 1979:

30.1 percent

Wage gap in 1995: 41.0 percent

Percent of the change in the wage gap attributable to immigration:

44.0 percent

20. Problems with the factor proportion approach

Does not really estimate the impact of immigration. Instead it simulates the impact. So the answer is mechanically determined by the assumptions.

One key unanswered puzzle: Why should it be that many other regional variations persist over time, but that the local impact of immigration on native workers is arbitraged away immediately?

21. The national labor market approach (Borjas, 2003)

Switch focus to wage trends in national labor market.

Immigration is not balanced evenly across all age groups in a particular schooling group. The immigrant influx will tend to affect some native workers more than others. And the nature of the supply “imbalance” changes over time.

22. Data

Use the 1960, 1970, 1980, 1990 and 2000 Public Use Microdata Samples (PUMS) of the Decennial Census (in QJE version, I used the pooled 1999, 2000, and 2001 Annual Demographic Supplement of the Current Population Surveys). The 1960-1970 Census extracts form a 1% random sample of the population; the 1980-2000 extracts form a 5% random sample.

Millions of persons are contained in these data sets. The analysis is restricted to men who participate in

the civilian labor force and are not enrolled in school. A person is defined to be an immigrant if he was born abroad and is either a non-citizen or a naturalized citizen; all other persons are classified as natives.

23. Skills

Schooling and work experience are used to define a skill group.

Four schooling groups: high school dropouts (< 12 years of completed schooling), high school graduates (12 years), some college (13 to 15 years), and college graduates (≥ 16 years).

Experience = the number of years elapsed since the person completed school. Let AT be age of entry into the labor market. Work experience is (Age – AT).

The Census does not report AT . Assume the typical high school dropout enters at age 17; the typical high school graduate at 19; the typical worker with some college at 21; and the typical college graduate at 23.

The analysis is restricted to persons with 1 to 40 years of experience. All persons are grouped into five-year experience bands (Welch’s baby boom paper, 1979).

24. Supply shock for high school dropouts

0

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0.2

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0.5

0 10 20 30 40

Years of experience

1960

1970

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1990

2000

Imm

igra

nt s

hare

25. Supply shock for high school graduates

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0.05

0.1

0.15

0 5 10 15 20 25 30 35 40

Years of experience

1960

1970

1980

1990

2000

Imm

igra

nt s

hare

26. Supply shock for college graduates

0

0.05

0.1

0.15

0.2

0 5 10 15 20 25 30 35 40

Years of experience

Imm

igra

nt s

hare

t

1960

1970

19801990

2000

27. Scatter diagram relating wages and immigration (removing decade effects)

-0.2

-0.1

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-0.1 -0.05 0 0.05 0.1 0.15 0.2

Decadal change in immigrant share

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ca

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in lo

g w

ee

kly

wa

ge

28. Regression model Let ysxt be the mean value of a particular labor market

outcome for native men with education s, experience x, in year t. Stack the data across skill groups and calendar years and estimate:

ysxt = θ psxt + S + X + T + (S × T) + (X × T) + (S × X) + esxt

p is the immigrant share; S are fixed effects indicating educational attainment; X are fixed effects indicating work experience; T are fixed effects indicating calendar year.

The interactions control for the experience profile of y differing across schooling groups, and for the impact of education and experience changing over time. The fixed effects effectively difference the data.

All regressions are weighted by the sample size of the education-experience-year cell.

29. Interpreting the adjustment coefficient

The regression model is:

ysxt = θ psxt + S + X + T + (S × T) + (X × T) + (S × X) + esxt

Key coefficient needs to be rescaled to interpret as wage elasticity (i.e., d log w/d log L)—the percent change in wages associated with a percent change in supply. Multiply the coefficient by around 0.7 in US context.

30. Key descriptive results in Borjas, 2003

31. Estimated adjustment coefficients

Study Immigration Emigration

2. Canada (Aydemir and Borjas, 2007) -0.507 ---

(0.202)

4. Mexico (Mishra, 2006) --- 0.440

(0.110)

5. Great Britain (Peev, 2007) -0.508 ---

(0.198)

6. Puerto Rico (Borjas, 2007) -0.583 0.405

(0.267) (0.184)