Ec-980u: Estimating the labor market impact, descriptive studies George J. Borjas Harvard University Fall 2010

1. Percent of adult population that is foreign-born

2. Percent of adult population that is foreign-born The “other immigrant states” are New York, Florida, Texas, Illinois, and New Jersey. California Other immigrant states Rest of country

3. 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

4. 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)

5. 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 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.

6. Altonji and Card, empirical model

7. Altonji and Card, results

8. 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.

9. Immigration in Miami Unemployment rate of blacks in: The Mariel flow (Card, 1991) Before (1979) After (1981) Miami Comparison cities 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. 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.

11. 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.

12. Modeling the native migration response Dollars P PT w0w0 P LA w0w0 w LA Demand Pittsburgh Los Angeles Employment S0S0 S1S1 S2S2 Demand S0S0 S3S3 w*

13. 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.

14. 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

15. Borjas, Freeman, Katz, AER, 1996

16. The factor proportions approach (Borjas, Freeman and Katz, 1997) Let the CES production function have two inputs, skilled labor (L 2 ) and unskilled labor (L 1 ): Q = [aL 1 β + (1-a) L 2 β ] 1/β. It can be shown that the marginal products of the two types of workers are given by: MP 1 =Q 1-β a L 1 β-1 andMP 2 =Q 1-β (1-a) L 2 β-1 In a competitive market the ratio of wages equals the ratio of marginal products: Taking logs: log (w 2 /w 1 ) = constant + (β-1) log(L 2 /L 1 )

17. The factor proportions approach, continued If we had assumed a more general production function (e.g., CES), the regression equation would be log (w 2 /w 1 ) = constant + b log(L 2 /L 1 ) Let group 1 be high school dropouts; group 2 be everyone else. Katz and Murphy (1992) estimated this regression for the period indicated that b = 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

18. 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

19. 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?

20. 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.

21. 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 Census extracts form a 1% random sample of the population; the 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.

22. 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 A T be age of entry into the labor market. Work experience is (Age – A T ). The Census does not report A T. 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).

23. Supply shock for high school dropouts

24. Supply shock for high school graduates

25. Supply shock for college graduates

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

27. Regression model Let y sxt 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: y sxt = θ p sxt + S + X + T + (S × T) + (X × T) + (S × X) + e sxt 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.

28. Interpreting the adjustment coefficient The regression model is: y sxt = θ p sxt + S + X + T + (S × T) + (X × T) + (S × X) + e sxt 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.

29. Key descriptive results in Borjas, 2003

30. Estimated adjustment coefficients StudyImmigrationEmigration 2. Canada (Aydemir and Borjas, 2007) (0.202) 4. Mexico (Mishra, 2006) (0.110) 5. Great Britain (Peev, 2007) (0.198) 6. Puerto Rico (Borjas, 2007) (0.267)(0.184)