Long-term and Intergenerational Effects of Education: Evidence from - PowerPoint PPT Presentation

Introduction Empirical Strategy 1G Results 2G Results CBA Robustness Conclusion Long-term and Intergenerational Effects of Education: Evidence from School Construction in Indonesia Richard Akresh § , Daniel Halim ¥ , Marieke Kleemans § § University of Illinois, Urbana-Champaign ¥ World Bank August 2019

Introduction Empirical Strategy 1G Results 2G Results CBA Robustness Conclusion Motivation • Governments in developing countries spend 1 trillion dollars annually on education, households spend hundreds of billions more (Glewwe and Muralidharan, 2016) • Macroeconomic growth models stressing the importance of schooling for economic development • Challenge in measuring causal effects of schooling • Direction of causality and effects differ across identification strategies (e.g. Bills and Klenow, 2000, Foster and Rosenzweig, 1996) • Major strides forward with randomized experiments but sometimes difficult to generalize and often focus on short-run effects only (McEwan, 2015) • Questions remain whether effects persist or fade over time (Evans and Ngatia, 2018, Blattman et al, 2018) • Do effects spillover to the next generation? Little evidence on intergenerational transmission of schooling in developing country setting

Introduction Empirical Strategy 1G Results 2G Results CBA Robustness Conclusion This paper • Research Question: What are the long-term and intergenerational effects of additional schooling as a child? • School construction program in 1970s Indonesia provides natural experiment (Duflo, 2001) • Calculate wage return to education for working age male in 1995 • Exploit variation across districts in the number of schools built and across birth cohorts in their exposure to the new schools • Use 2016 nationally representative household survey

Introduction Empirical Strategy 1G Results 2G Results CBA Robustness Conclusion This paper • Study effects of education on a range of outcomes, many of which not previously studied • Persistence of effects 43 years after the program using 2016 nationally representative cross-sectional data • Study intergenerational effects of children whose parents were exposed to the program • Gender and marriage market dynamics • For school construction that has large up-front costs and benefits dispersed over time, can perform a detailed cost-benefit analysis

Introduction Empirical Strategy 1G Results 2G Results CBA Robustness Conclusion Preview of Results • First generation • Large effects on education • Men more likely to be formal workers, work outside agriculture, and migrate • Women more likely to migrate and have fewer children • Households: improved living standards and pay more taxes • Second generation • Parents transmit effects to next generation: particularly large increases in secondary and tertiary education • Mother’s effect larger than father’s • Effect on daughters seems larger than sons • Policy implications: School construction pays for itself in higher government taxes

Introduction Empirical Strategy 1G Results 2G Results CBA Robustness Conclusion Institutional Context • Indonesia is the 4th most populous country in the world, 261 million people • 7th lagest economy in the world in terms of total GDP at PPP • GPD per capita at PPP is $12,432, and 5.0% GDP growth rate in 2016 • In 1972, enrollment rates were low in Indonesia at 71% among primary school-aged children, large disparities across regions • Average adult in 65 out of 290 districts did not complete primary education (Census 1971)

Introduction Empirical Strategy 1G Results 2G Results CBA Robustness Conclusion Institutional Context • Between 1973 and 1979, Indonesia built 61,800 INPRES primary schools with the goal to reduce regional disparities • On average, program added over 200 schools per district or two schools for every 1,000 children of primary school age • Duflo (2001) finds gains in educational attainment and short-term wage effects in 1995 for men exposed to program. • Program also featured in Ashraf et al, 2018, Karachiwalla and Palloni, 2019, Bharati et al, 2019, Mazumder et al, 2019, and Rohner and Saia, 2019. Number of school constructed per 1,000 children between 1973 and 1979

Introduction Empirical Strategy 1G Results 2G Results CBA Robustness Conclusion Data • National Socioeconomic Survey, Susenas 2016 • Nationally representative household survey conducted by Indonesian Central Bureau of Statistics • Covers all 34 provinces and all 511 districts in Indonesia • 109,847 households and 143,790 individuals interviewed for cohorts we focus on • District of birth data • Rich dataset with detailed questions on education, labor force participation (work, sector, hours worked), migration, expenditures, health, housing, assets, nutrition, taxes, demographics, and education/work of children • Average age ≈ 50 for first generation; ≈ 15 for second generation Summary Statistics 1st Generation Summary Statistics 2nd Generation Susenas 2016

Introduction Empirical Strategy 1G Results 2G Results CBA Robustness Conclusion Summary Statistics 1st Generation

Introduction Empirical Strategy 1G Results 2G Results CBA Robustness Conclusion Identification Strategy • Following Duflo (2001), use difference-in-differences in which year and district of birth jointly determine exposure to school construction • Children young enough in 1974 could benefit from the program, but older cohorts would not • More INPRES schools constructed in regions with lower schooling enrollment at baseline • Specifically, compare children aged 2-6 and 12-17 in 1974 across high and low intensity program regions • Identifying assuming: the change in outcomes across birth cohorts in the regions that built many schools would have been the same as the change across birth cohorts in the regions that did not build many schools

Introduction Empirical Strategy 1G Results 2G Results CBA Robustness Conclusion Difference-in-differences ′ y ijt = α + β School j · Young it + ( X j B t ) γ t + µ j + δ t + ε ijt • y ijt is the outcome of individual i born in district j in year t • School j is the number of INPRES schools constructed per 1,000 children between 1973 and 1979 • Young it is a dummy if individual i is aged 2-6 in 1974 • X j is a vector of district controls ′ • B t is a vector of birth year dummies • µ j are district fixed effects • δ t are birth year fixed effects • Standard errors clustered at the district level

Introduction Empirical Strategy 1G Results 2G Results CBA Robustness Conclusion Difference-in-differences Second Generation ′ y ijtca = α + β School j · Young it + ( X j B t ) γ t + µ j + δ t + θ a + ε ijtca • y ijtca is the outcome of child c who is age a , born to parent i who was born in district j in year t • School j is the number of INPRES schools constructed per 1,000 children in the father’s or mother’s birth district between 1973 and 1979 • Young it is a dummy if the father or mother belongs to the young cohort (ages 2-6 in 1974) • θ a are child c ’s age fixed effects • Standard errors clustered at the father’s or mother’s birth district

Introduction Empirical Strategy 1G Results 2G Results CBA Robustness Conclusion Parallel trends on men’s years of education Parallel Trends All Variables

Introduction Empirical Strategy 1G Results 2G Results CBA Robustness Conclusion Strategies to Address Large Number of Outcomes • Creation of indexes for families of outcomes • Index of standardised outcomes relative to old cohorts in low-intensity areas amongst a family of outcomes (Kling, Liebman and Katz, 2007) • Following Banerjee et al. (2015) and Ajayi and Ross (2017) • Multiple hypothesis testing • False discovery rate (FDR) to allow inference when many tests are being conducted (Benjamini and Hochberg, 1995) • FDR allows researcher to tolerate a certain number of tests to be incorrectly discovered • FDR adjusted q-value of 0.05 implies that 5% of significant tests will result in false positives, compared with unadjusted p-value of 0.05 implying that 5% of all tests will result in false positives

Introduction Empirical Strategy 1G Results 2G Results CBA Robustness Conclusion Effects on Indexes of Long-run Outcomes

Introduction Empirical Strategy 1G Results 2G Results CBA Robustness Conclusion Effect of School Construction on Education • Duflo (2001) difference-in-differences in Indonesia • Handa (2002) difference-in-differences in Mozambique • Alderman, Kim, Orazem (2003) RCT in Pakistan • Burde and Linden (2013) in Afghanistan • Kazianga, Levy, Linden, Sloan (2013) RD in Burkina Faso • Khanna (2018) in India

Introduction Empirical Strategy 1G Results 2G Results CBA Robustness Conclusion Long-run Effects on Education Outcomes: Education Mean/SD Effect on: Men Women Men Women Years of schooling 8.022 7.105 0.268*** 0.234*** (4.230) (4.215) (0.047) (0.042) [0.000] [0.000] Completed Primary 0.813 0.727 0.026*** 0.041*** (0.390) (0.446) (0.006) (0.006) [0.000] [0.000] Completed Lower Secondary 0.385 0.312 0.023*** 0.008 (0.487) (0.463) (0.006) (0.007) [0.000] [0.422] Completed Upper Secondary 0.338 0.261 0.026*** 0.005 (0.473) (0.439) (0.006) (0.006) [0.000] [0.422] Completed Tertiary 0.095 0.077 -0.001 -0.003 (0.293) (0.267) (0.003) (0.003) [0.741] [0.422]