Daniel Alonso-Soto and Hugo Ñopo

Transcription

1 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

2 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 , 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.

3 Two decades: two different stories

4 Two decades: two different stories

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

6 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)

7 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, 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

8 Descriptives and Sample Sizes Men immigrants educated in country of origin * 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 Education Primary or less Secondary Some tertiary or more Ocupation Managerial Profesional specialty Other white collar Blue collar Total observations Source: (Alonso-Soto & Ñopo, 2015). Compilations based on Public Use Microdata of the U.S. Censuses and the American Community Survey Year sample.

9 Evolution of the premium for an extra year of schooling at the secondary level Global regions Secondary Schooling premium N. Europe S. Europe India LAC EAP

10 Evolution of the premium for an extra year of schooling at the secondary level LAC regions Secondary Schooling premium Mexico Caribean Southern Cone Central America Andean countries

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

12 Evolution of the premium for an extra year of schooling at the tertiary level Global regions Tertiary Schooling premium India N. Europe S. Europe EAP LAC

13 Evolution of the premium for an extra year of schooling at the tertiary level LAC regions Tertiary Schooling premium Mexico Caribean Southern Cone Central America Andean countries

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

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

16 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, 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

17 Descriptives and Sample Sizes Men immigrants educated in the US and US natives * 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 Education Primary or less Secondary Some tertiary or more Ocupation Managerial Profesional specialty Other white collar Blue collar Total observations US Native Source: (Alonso-Soto & Ñopo, 2015). Compilations based on Public Use Microdata of the U.S. Censuses and the American Community Survey Year sample.

18 Diff in Diff Results: Secondary Tertiary Global regions Global regions Secondary Schooling premium Tertiary Schooling premium year of graduation year of graduation LAC EAP LAC EAP India N. Europe India N. Europe S. Europe S. Europe

19 By occupation: Secondary Managerial Professional specialty Secondary Schooling premium Secondary Schooling premium LAC EAP LAC EAP India N. Europe India N. Europe S. Europe S. Europe Secondary Schooling premium Other white collar Secondary Schooling premium Blue collar LAC EAP LAC EAP India N. Europe India N. Europe S. Europe S. Europe

20 By occupation: Tertiary Tertiary Schooling premium Managerial Tertiary Schooling premium Professional specialty LAC EAP LAC EAP India N. Europe India N. Europe S. Europe S. Europe Other white collar Blue collar Tertiary Schooling premium Tertiary Schooling premium LAC EAP LAC EAP India N. Europe India N. Europe S. Europe S. Europe

21 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).

22 After matching results: Secondary Tertiary After Matching After Matching Secondary Schooling premium Tertiary Schooling premium LAC EAP LAC EAP India N. Europe India N. Europe S. Europe S. Europe

23 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)

24 THANK

25 Los premios a la escolaridad han venido cayendo

26 Especialmente entre los más educados

27 Especialmente entre los más educados

28 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

29 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