The Place Premium

1. The Place Premium Lant Pritchett (paper with Michael Clemens, CGD and Claudio Montenegro, World Bank) LEP Lunch/Development Seminar Sept 29, 2008

2. Outline of the presentation • Empirical estimates of wages differences for observationally equivalent workers on opposite sides of the US Border • Addressing the issue of migrant self-selection • Simulation with residuals • Data from Latin America • One true experiment • Comparison with macro growth accounting • Experiences with spatially distinct but open borders • Comparisons of (adjusted) wage gaps with other “similar” numbers (wage discrimination, etc.)

3. Drilling down through wage surfaces USS (born, educated) workers In USA ln(w) Bolivian (born, educated) workers In USA Bolivian (born, educated) workers in Bolivia X (e.g. education, age)

4. New collection of data sets 2,015,411 formal-sector wage-earners in 43 countries 42 different countries wage surveys—of wage earners Wages (converted to monthly, PPP) Country of birth Amount and country of schooling Age/experience Gender Rural/urban

5. Comparison of our wage survey results with labor value added per worker The “formal sector” is a big Issue for the poor African Countries In the sample We just drop Honduras

6. Combine with PUMS US Census • Wages of individuals, with country of birth and age at arrival plus • Schooling • Age • Sex • Urban/rural residence

7. Results of the wage surface drilling: foreign born, foreign educated (late arrivers), high school or less educated, 35 year old, males, in urban areas in USA vs home

8. Estimates of R0 (predicted wages of observationally workers across the US border) for 42 countries with 95% confidence intervals 38/42 can reject bigger than 1.5 32/42 cannot reject bigger than 4

9. All kinds of comparability issues: but the biggest is PPP • Gross versus net • Inclusion of benefits (in-kind, entitlements) or not • Valuation of workplace amenities (e.g. safety regulation) • But we suspect the biggest is imputation of the location of consumption (in US versus home) • Remittances about 20 percent • For Mexicans • Remittances/savings about 60 • For Philippines overseas workers • Think “optimal” savings of temporary • worker

10. How much of the observed wage differentials of observationally equivalent workers represent border restrictions vs. selection or home preference? • Six different methods/data for examining wage selection, all of which suggest our predicted mean wage ratios of observationally equivalent workers over-state wage ratios of equal intrinsic productivity workers by between 1 and 1.4.

11. The question of selection on unobservable • Our estimates of compare what those who moved to US make versus what those who are observationally equivalent make in home. • But those who did move might have made more than the o.e. counter-parts so R0 overstates the gain • We are not talking about the upper end but the low skill end—people making 10$/hour • Not obvious that there is positive self-selection on unobservable productivity in the home market—theory is that people would maximize the gain from moving if either: • productivity is a market match phenomena (e.g. having an uncle with a good business), or • Individually differential obstacles (e.g. family unification visas) then one might expect zero or negative selection.

12. R0 compares means India Could compare to other percentile of the home distribution of unobservables, e.g. 70th

13. 1st approach: Wage ratios under various assumptions about where in the home distribution of unobservables migrants came

14. Wage ratios of equally productive workers at various assumptions of source of migrants in distribution of unobservables

15. 2nd Approach: Data from the Latin American Migration Project (LAMP) • Tracks migrants from seven Latin American countries and does surveys in their origin localities of non-migrants • Wage histories of migrants including last wage before migrating • Compare wages of migrants before moving and non-migrants, with distribution of residuals

16. Distribution of the unobserved component on wages (residuals) in home for migrants and non-migrants: Mexico Mean migrant at 53rd Percentile of non-migrants

17. Mexico Actual distribution Of residuals for Mexico So we can compute 50th Of movers to 53rd of home

18. Distribution of the unobserved component on wages (residuals) in home for migrants and non-migrants: Haiti Mean migrant at 61st Percentile of non-migrants

19. Haiti

21. Results from 7 countries • The medians of the migrant and non-migrants are exactly the same for 5 of the 7—the selection is mostly an upper tail thing • Using the means to adjust out Ro estimates lowers them by a ratio of between 1 (no adjustment for Guatemala) to 1.46 (Haiti) • In no country is the typical migrant from as high as the 70th percentile of non-migrants (which, from table above, implies an adjustment of 1.34 using the actual residuals data).

22. 3rd Approach: Comparison with experimental estimates of wage effects • Movers from Tonga to New Zealand chosen from applicants based on a lottery • OLS wage ratio: 6.14 (chosen versus all stayers) • Experimental wage ratio: 4.91 (foreign wages of randomly selected chosen versus home wages of applicants). • Bias from not correcting for selection: 6.12/4.91=1.25

23. 4th Approach: Comparison to macro growth decomposition (Hall and Jones)

24. 5th approach: Use comparisons of average wages of observationally equivalent in home and foreign (allowing for country specific schooling) • Doesn’t involve movers at all—so should understate the marginal mover if there is positive selection. • In fact, these are larger than bilateral estimates • But one has to correct for the quality of schooling as S in Bolivia is not S in USA • Under various plausible adjustments of S “evaporation” suggest selection at most increases R0 by factor of 1.2

25. 6th approach: wage ratios in spatially distinct but legally integrated labor markets: Puerto Rico Guam=1.36

26. When borders were open wage ratios above 2 caused massive mobility, leading to wage convergence

27. Shall I compare thee to a summer’s day…thou are much bigger • Wage discrimination—comparing wage discrimination against disfavored social groups within borders to consequence of local of birth/citizenship/market access based wage differentials • Border differentials in prices of goods or capital • Impacts of poverty programs

28. Our average cross-border wage differential (5.1) is larger by a factor of 3 than racial discrimination in the US in 1939 Using our wage data we can estimate the largest discrimination against females in the world, Pakistan, 3.1 Using historical data one can estimate the gap between marginal product (rental price) and subsistence wage for 19th century North American slaves: around 3.8

29. Estimates of the remaining price gaps across countries Source: Bradford and Lawrence, 2004

30. ` Combination of small price gaps and large wage gaps implies the estimated gains from even minor relaxations in labor mobility are big relative to the largest gains in remaining trade liberalization Source: Winters et al 2004

33. Comparing estimated gains from anti-poverty interventions in poor countries to wage differences

34. Conclusion • Massive gaps in wages between observationally equivalent workers in 42 poor countries—average 5.1, median 4.1--$15,000 per year (PPP) • The bulk of the evidence suggests that the self-selection might cause these to overstate gains from movement of unskilled workers by a modest amount (scale back by between 1 and 1.4) • These make the wage differentials across borders: • Bigger than any wage discrimination • Bigger than any price distortion due to borders • Bigger than any poverty impact by factor multiples (if not orders of magnitude)