Daniel Alonso-Soto and Hugo Ñopo

How does Latin America stand on schooling premium? What does it reveal about its education quality in the long run? Evidence from immigrants in the U.S. Daniel Alonso-Soto and Hugo Ñopo

The One Slide Presentation What is this paper about?» We present measures of schooling premium for immigrant workers in the U.S.» Comparing those from Latin America with those from the rest of the world.» Presenting comparative estimates of the evolution of such premia. How do we do it? Methodology» Data: U.S. Census 1980-2010, covering a span of five decades of graduates» Bratsberg and Terrell (2002): schooling premium of migrants workers by region/country of birth (Mincerian eq.)» By census year» By year of graduation (or dropout)» Hanushek and Woessmann (2007): Diff-in-diff exercise.» Ñopo (2008): Matching exercise. Findings? Insights?» The results show that the schooling premium in Latin America has been low and remained low for almost five decades.» This suggests that the education quality of the region has not deteriorated in recent years but rather it has been low for a while.

Two decades: two different stories

Two decades: two different stories

Two decades: two different stories And this is the case for most segments of the labor markets

Motivation: Why is this paper relevant?» Five decades of research on schooling premium» Limitations on comparability: samples and methodologies» By focusing on a single labor market, the differences in premium can be linked to differences in school quality» But non-random selection into migration (and into work)

Methodology 1) ln w iii = α + β t X ii + δs iii + λd iii S iii + μ i w iii = Hourly earnings of immigrant i born in country j and observed in census t. X= Vector of socioeconomic characteristics D iii = Indicator variable set to unity if the immigrant is born in country (region) j and with education s S= Years of schooling s the immigrant obtained in the country of origin» Male migrants, 25-64 y.o., annual income> USD 1,000, at least 50 weeks of work during last year and educated in country of origin» Base category=former soviet republics

Descriptives and Sample Sizes Men immigrants educated in country of origin 1980 1990 2000 2010* Educated in N. S. Europe Europe Other EAP India LAC Educated in Educated in Educated in N. S. N. S. N. Europe Europe Other EAP India LAC Europe Europe Other EAP India LAC Europe S. Europe Other EAP India LAC Age 25-34 30.57 34.11 33.8 10.95 18.49 22.3 19.68 19.04 34.44 16.31 11.2 25.21 12.58 28.97 35.31 21.17 9.4 18.36 8.17 24.85 22.76 12.63 10.42 10.69 35-44 38.06 49.83 30.86 26.65 27.77 25.32 39.64 38.05 31.08 23.72 26.85 35.69 34.27 29.79 33.66 33.52 21.23 35.02 27.26 36.78 33.27 34.15 24.6 28.14 45-64 31.38 16.06 35.34 62.39 53.74 52.38 40.68 42.91 34.48 59.97 61.94 39.1 53.15 41.23 31.03 45.31 69.36 46.62 64.56 38.37 43.97 53.23 64.99 61.17 Education Primary or less 9.25 1.99 48.5 8.68 46.39 23.34 3.24 1.03 28.56 0.82 19.37 7.91 6.23 1.99 44.55 1.32 30.24 9.73 2.87 0.81 19.87 0.62 11.22 6.28 Secondary 21.67 7.57 31.17 39.16 36.77 37.29 22.85 13.16 45.85 29.74 53.32 40.62 21.09 13.43 38.06 21 40.95 37.93 21.62 9.21 56.99 15.33 42.29 37.6 Some tertiary or more 69.08 90.44 20.33 52.16 16.84 39.37 73.91 85.81 25.59 69.43 27.31 51.47 72.68 84.58 17.38 77.68 28.82 52.34 75.51 89.98 23.14 84.05 46.49 56.12 Ocupation Managerial 17.25 17.63 7.31 23.67 10 12.3 19.33 19.62 6.82 28.86 15.26 11.36 17.02 17.89 4.43 30.62 15.75 9.51 15.84 21.83 5.54 35.96 23.03 10.88 Profesional specialty 23.87 52.63 4.48 18.27 5.33 13.11 18.46 37.55 4.63 20.28 6.72 13.26 20.41 28.75 2.97 22.29 7.79 12.91 25.15 28.55 4.1 26.74 16.37 14.55 Other white collar 37 19.08 25.96 23.41 26.03 26.77 41.82 28.56 29.29 25.43 27.64 30.95 41.82 39.34 26.35 27.02 28.76 33.27 41.4 40.77 29.24 24.58 26.84 35.93 Blue collar 21.88 10.66 62.25 34.66 58.64 47.82 20.39 14.27 59.26 25.43 50.37 44.43 20.75 14.02 66.25 20.07 47.7 44.31 17.61 8.85 61.12 12.73 33.76 38.64 Total observations 9239 2416 25340 11147 7786 15666 14956 3876 29709 8741 4409 17640 18627 7082 71164 9248 3744 25985 27765 14868 87035 10786 2736 36353 Source: (Alonso-Soto & Ñopo, 2015). Compilations based on Public Use Microdata of the 1980-2000 U.S. Censuses and the American Community Survey 2008-2012 5-Year sample.

Evolution of the premium for an extra year of schooling at the secondary level Global regions Secondary Schooling premium -.1 -.05 0.05.1 N. Europe S. Europe India LAC EAP 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

Evolution of the premium for an extra year of schooling at the secondary level LAC regions Secondary Schooling premium -.1 -.05 0.05.1 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 Mexico Caribean Southern Cone Central America Andean countries

Evolution of the premium for an extra year of schooling at the secondary level: LAC Countries Secondary Schooling premium -.1 -.05 0.05 Central American countries 1955 1965 1975 1985 1995 Costa Rica El Salvador Secondary Schooling premium -.1 -.05 0.05 Caribbean countries 1955 1965 1975 1985 1995 Guatemala Honduras Cuba Dominican Republic Panama Jamaica Trinidad and Tobago Andean countries Southern cone countries Secondary Schooling premium -.1 -.05 0.05 Secondary Schooling premium -.1 -.05 0.05 1955 1965 1975 1985 1995 1955 1965 1975 1985 1995 Colombia Ecuador Argentina Brazil Peru Chile Uruguay

Evolution of the premium for an extra year of schooling at the tertiary level Global regions Tertiary Schooling premium -.1 -.05 0.05.1 India N. Europe S. Europe EAP LAC 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

Evolution of the premium for an extra year of schooling at the tertiary level LAC regions Tertiary Schooling premium -.1 -.05 0.05.1 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 Mexico Caribean Southern Cone Central America Andean countries

Evolution of the premium for an extra year of schooling at the tertiary level: LAC countries Tertiary Schooling premium -.1 -.05 0.05 Central American countries 1955 1965 1975 1985 1995 Costa Rica El Salvador Tertiary Schooling premium -.1 -.05 0.05 Caribbean countries 1955 1965 1975 1985 1995 Guatemala Honduras Cuba Dominican Republic Panama Jamaica Trinidad and Tobago Andean countries Southern cone countries Tertiary Schooling premium -.1 -.05 0.05 Tertiary Schooling premium -.1 -.05 0.05 1955 1965 1975 1985 1995 1955 1965 1975 1985 1995 Colombia Ecuador Argentina Brazil Peru Chile Uruguay

Migrants in the U.S. are a non-random sample of their source country s population Argentina Brazil Chile Colombia Costa Rica 0 10 20 0 10 20 0 10 20 0 10 20 0 10 20 Cuba Dominican Republic Ecuador El Salvador Guatemala Haiti Honduras Jamaica Mexico Panama Peru Trinidad and Tobago Uruguay EAP India Northern Europe Southern Europe Other 1950 1970 1990 2010 1950 1970 1990 2010 1950 1970 1990 2010 1950 1970 1990 2010 1950 1970 1990 2010 (10 years intervals) Years of education (census) Years of education (Barro&Lee) Graphs by LAC countries and global regions

A refinement: Diff in Diff exercise 2) ln w iii = α + β t X ii + δs iii O + λd iii S iii + θd iii S iii O + μ i w iii = Hourly earnings of immigrant i born in country j and observed in census t. X= Vector of socioeconomic characteristics D iii = Indicator variable set to unity if the immigrant is born in country (region) j and with education s S= Years of schooling s the immigrant obtained in the country of origin O= Indicator that is set to unity if immigrant i was educated entirely in the region of origin» Male migrants, 25-64 y.o., annual income> USD 1,000, at least 50 weeks of work during last year either educated in the US or in country of origin» Base category=former soviet republics

Descriptives and Sample Sizes Men immigrants educated in the US and US natives 1980 1990 2000 2010* Educated in US from Educated in US from Educated in US from Educated in US from N. Europe S. Europe Other US Native EAP India LAC N. Europe S. Europe Other US Native EAP India LAC N. Europe S. Europe Other US Native EAP India LAC N. Europe S. Europe Other EAP India LAC Age 25-34 90.99 66.67 76.33 85.17 74.8 71.33 34.57 76.71 70.59 74.67 66.4 72.03 64.25 32.71 48.68 71.53 48.77 23.7 34.87 52.78 25 33.08 42.19 38.1 16.36 15.67 41.6 21.02 35-44 9.01 33.33 23.67 14.83 25.2 28.67 26.65 21.98 20.59 20.09 28.72 24.06 26.17 32.54 34.39 21.88 35.11 42.73 37.64 27.8 33.07 36.5 44.46 31.9 24.14 32.64 35.94 25.48 45-64 0 0 0 0 0 0 38.79 1.31 8.82 5.23 4.88 3.91 9.58 34.75 16.93 6.6 16.12 33.57 27.49 19.43 41.93 30.42 13.35 30.01 59.49 51.68 22.46 53.5 Education Primary or less 1.8 33.33 17.55 1.23 4.07 6.77 8.5 0.15 0 5.78 0.57 3.19 2.56 3.12 0.34 0.69 6.33 0.38 1.84 1.22 1.49 0.35 0.76 6.78 0.33 1.08 1.19 1 Secondary 27.93 33.33 44.95 37.12 41.46 48.98 46.86 22.75 17.65 36.98 31.96 37.68 31.51 41.38 17.4 4.17 38.7 25.87 34.64 25.9 38.01 14.79 4.25 39.62 22.03 27.62 20.26 32.27 Some tertiary or more 70.27 33.33 37.5 61.65 54.47 44.24 44.65 77.09 82.35 57.24 67.46 59.13 65.92 55.5 82.26 95.14 54.97 73.75 63.52 72.88 60.49 84.87 94.99 53.6 77.64 71.31 78.55 66.73 Ocupation Managerial 15.38 33.33 9.34 17.47 16.67 13.16 17.07 16.99 20.59 15.36 17.03 17.79 15.01 16.58 18.81 29.17 13.74 19.39 18.33 18.22 16.38 22.44 31.8 13.41 21.62 22.29 19.59 18.22 Profesional specialty 20.19 0 6.87 15.71 15.83 12.24 10.98 21.48 29.41 11.7 14.74 11.21 14.77 11.78 23.76 40.97 10.92 17.18 15.56 19.29 12.74 24.56 39.14 11.25 19.8 16.22 22.01 15.22 Other white collar 30.77 33.33 28.3 26.85 32.5 27.25 27.4 35.8 29.41 34.48 33.69 33.18 35.4 29.59 36.06 23.61 34.29 32.07 32.65 36.22 29.93 35.3 25.38 35.67 32.29 34.72 37.75 31.86 Blue collar 33.65 33.33 55.49 39.98 35 47.34 44.56 25.73 20.59 38.45 34.53 37.82 34.82 42.04 21.37 6.25 41.05 31.36 33.47 26.28 40.95 17.69 3.67 39.66 26.29 26.77 20.66 34.69 Total observations 111 3 376 897 123 443 1418953 1301 34 2369 4896 690 898 1618899 3568 288 7078 8620 1735 2378 1774037 6946 659 13287 11349 2227 4625 1936115 US Native Source: (Alonso-Soto & Ñopo, 2015). Compilations based on Public Use Microdata of the 1980-2000 U.S. Censuses and the American Community Survey 2008-2012 5-Year sample.

Diff in Diff Results: Secondary Tertiary Global regions Global regions Secondary Schooling premium -.1 -.05 0.05.1 Tertiary Schooling premium -.05 0.05.1 1965 1970 1975 1980 1985 1990 1995 2000 year of graduation 1965 1970 1975 1980 1985 1990 1995 2000 year of graduation LAC EAP LAC EAP India N. Europe India N. Europe S. Europe S. Europe

By occupation: Secondary Managerial Professional specialty Secondary Schooling premium -.1 -.05 0.05.1 Secondary Schooling premium -.1 -.05 0.05 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 LAC EAP LAC EAP India N. Europe India N. Europe S. Europe S. Europe Secondary Schooling premium -.1 -.05 0.05.1 Other white collar 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 Secondary Schooling premium -.06 -.04 -.02 0.02.04 Blue collar 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 LAC EAP LAC EAP India N. Europe India N. Europe S. Europe S. Europe

By occupation: Tertiary Tertiary Schooling premium -.05 0.05.1 Managerial 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 Tertiary Schooling premium -.02 0.02.04.06 Professional specialty 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 LAC EAP LAC EAP India N. Europe India N. Europe S. Europe S. Europe Other white collar Blue collar Tertiary Schooling premium -.1 -.05 0.05.1 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 Tertiary Schooling premium -.05 0.05.1 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 LAC EAP LAC EAP India N. Europe India N. Europe S. Europe S. Europe

Non-parametric matching» During the half century of our analysis many workers characteristics may have changed.» To try to control for those changes we use the matching-on-characteristics approach developed in Ñopo (2008) to maintain fixed the distribution of observable characteristics of migrant workers into the US 3) ln w iii w mmmmhiii = [α + β t X ii + δs iii + λd iii S iii + μ i ]w mmmmhiii where w matching denotes the weights after matching (that is, after the differences in the distribution of observable characteristics have vanished).

After matching results: Secondary Tertiary After Matching After Matching Secondary Schooling premium -.1 -.05 0.05.1 Tertiary Schooling premium -.05 0.05.1 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 LAC EAP LAC EAP India N. Europe India N. Europe S. Europe S. Europe

Summarizing and concluding» Schooling premium for LAC workers in the U.S. permanently low for the last 50 years, even after controlling for the non-random selection of migrants» On the other hand, the evolution of India, especially at the tertiary level, is remarkable.» Both at the secondary and tertiary levels» In all sorts of occupations (blue collars, white collars and managers)

THANK YOU! @hugonopo

Los premios a la escolaridad han venido cayendo

Especialmente entre los más educados

Especialmente entre los más educados

List of countries by region EAP N. Europe S. Europe Former Soviet republics China Austria Greece Albania Hong Kong Belgium Italy Armenia Indonesia Denmark Portugal Bulgaria Japan Finland Spain Czech Rep. Korea, Rep. France Estonia Macao-China Germany Hungary Malaysia Iceland Latvia Philippines Ireland Lithuania Singapore Liechtenstein Macedonia Taiwan Luxembourg Moldova Thailand Netherlands Poland Norway Romania Sweden Russian Fed. Switzerland Slovak Rep. UK

List of occupations by categories Managerial Professional specialty Other white collar Blue collar Executive, Administrative, and Managerial Occupations Architects Adjusters and Investigators Construction Trades Management Related Occupations Engineers Communications Equipment Operators Extractive Occupations Health Assessment and Treating Occupations Computer Equipment Operators Farm Operators and Managers Health Diagnosing Occupations Duplicating, Mail, and Other Office Machine Operators Machine Operators, Assemblers, and Inspectors Machine Operators and Tenders, Except Precision Lawyers and Judges Engineering and Related Technologists and Technicians Mechanics and Repairers Librarians, Archivists, and Curators Financial Records Processing Occupations Other Agricultural and Related Occupations Mathematical and Computer Scientists Health Technologists and Technicians Precision Production Occupations Natural Scientists Social Scientists and Urban Planners Social, Recreation, and Religious Workers Teachers Therapists Writers, Artists, Entertainers, and Athletes Ocupations by categories Information Clerks Mail and Message Distributing Occupations Material Recording, Scheduling, and Distributing Clerks Miscellaneous Administrative Support Occupations Other service occupation Other technicians Private Household Occupations Protective Service Occupations Records Processing Occupations, Except Financial Sales Related Occupations Sales Representatives, Commodities Except Retail Sales Representatives, Finance and Business Services Sales Workers, Retail and Personal Services Science Technicians Secretaries, Stenographers, and Typists Supervisors, Administrative Support Occupations