On the Rank-Rank Model of Intergenerational Mobility: Pitfalls for Policy Evaluation

Ahsan, Md Nazmul and Emran, M. Shahe and Shilpi, Forhad (2024): On the Rank-Rank Model of Intergenerational Mobility: Pitfalls for Policy Evaluation.

Abstract

We analyze the challenges in adopting the rank-rank model of intergenerational mobility for policy evaluation. For rank-based analysis of intergenerational mobility, it is standard to calculate cohort-specific ranks from the national distribution, but separately for children's and parents' generations. This ensures that children's inherited socioeconomic status and their life outcomes are measured on common scales irrespective of location and social groups. However, national ranks put the treatment and comparison groups together, and thus, a policy intervention leads to mechanical changes in ranks in the comparison group when the ranks of the treated individuals change because of the policy. We discuss how to deal with this contaminated comparison problem in the context of widely-used research designs: RCTs, Instrumental Variables (IV), and Difference-inDifference (DiD). In a RCT design with a binary treatment assignment, a simple solution is to calculate the ranks separately for the treatment and control groups. In an IV design, the ranks should be calculated separately for different values of the instrument. For a DiD design, an additional concern is how to avoid mechanical changes in the ranks of the pre cohorts following the policy intervention: calculate the ranks separately for pre and post periods. If the policy affects only the children, then, for all research designs, it is desirable to keep the parental ranks at the national level so that children's inherited socioeconomic status is measured on a common scale. As an empirical application, we provide evidence on the effects of Inpres schools on intergenerational educational mobility in Indonesia using the DiD design developed by Duflo (2001). The evidence suggests that the conclusions regarding the impact of Inpres schools depend critically on the way ranks are calculated. If we follow the current practices when calculating the ranks, the DiD estimates suggest that the 61,000 primary schools failed to affect relative mobility even though it improved absolute mobility for the children from low-educated families. In contrast, when the ranks are calculated to tackle the mechanical contamination problem, the evidence, especially from the correct functional form (quadratic), suggests that Inpres schools improved both relative and absolute mobility of the disadvantaged children. The Inpres schools led to higher intercept and quadratic coefficient of the mobility equation while reducing the linear coefficient. The analysis presented here has important implications for economists and sociologists working on intergenerational mobility.

• Item Type: MPRA Paper
• Language: English
• Keywords: Rank-Rank Model, Intergenerational Mobility, Causal Effects, Policy Evaluation, Mechanical Changes in Ranks, Contaminated Comparison, Inpres Schools, Indonesia
• Subjects: D - Microeconomics > D3 - Distribution; I - Health, Education, and Welfare > I2 - Education and Research Institutions > I24 - Education and Inequality; J - Labor and Demographic Economics > J6 - Mobility, Unemployment, Vacancies, and Immigrant Workers > J62 - Job, Occupational, and Intergenerational Mobility; O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O12 - Microeconomic Analyses of Economic Development

References

Agarwal, S., Fan, Y., Qian, W., and Sing, T. F. (2023). Like father like son? social engineering and intergenerational mobility in housing consumption. Working paper, Singapore National University.

Ahsan, M. N., Emran, M. S., and Shilpi, F. (2023). Public Primary School Expansion, Gender Based Crowding Out, and Intergenerational Educational Mobility. SSRN Working Paper Series SSRN ID4106643, SSRN.

Ahsan, N., Emran, M. S., and Shilpi, F. (2024). Complementarities and Intergenerational Educational Mobility: Theory and Evidence from Indonesia. Journal of Economic Behavior and Organization, Forthcoming.

Ahsan, N. M., Emran, M. S., Jiang, H., Murphy, O., and Shilpi, F. J. (2022). When Measures Conflict: Towards a Better Understanding of Intergenerational Educational Mobility. Working paper, The World Bank.

Angrist, J. and Kolesár, M. (2023). One instrument to rule them all: The bias and coverage of just-id IV. Journal of Econometrics.

Asher, S., Novosad, P., and Rafkin, C. (2023). Intergenerational Mobility in India: Estimates from New Methods and Administrative Data. American Economic Journal: Applied Economics, Forthcoming. Forthcoming.

Ashraf, N., Bau, N., Nunn, N., and Voena, A. (2020). Bride price and female education. Journal of Political Economy, 128(2):591–641.

Bazzi, S., Hilmy, M., and Marx, B. (2020). Islam and the state: Religious education in the age of mass schooling. Technical report, National Bureau of Economic Research.

Bjorklund, A. and Salvanes, K. G. (2011). Education and Family Background: Mechanisms and Policies. In Hanushek, E., Machin, S., and Woessmann, L., editors, Handbook of the Economics of Education, volume 3, pages 201–247. Elsevier.

Black, S. E. and Devereux, P. J. (2011). Recent Developments in Intergenerational Mobility. In Handbook of Labor Economics, volume 4, chapter 16, pages 1487–1541. Elsevier.

Chetty, R., Friedman, J. N., Hendren, N., Jones, M. R., and Porter, S. R. (2020). The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility. Working Paper 25147, National Bureau of Economic Research.

Chetty, R. and Hendren, N. (2018a). The Impacts of Neighborhoods on Intergenerational Mobility I: Childhood Exposure Effects. The Quarterly Journal of Economics, 133(3):1107– 1162.

Chetty, R. and Hendren, N. (2018b). The Impacts of Neighborhoods on Intergenerational Mobility II: County-Level Estimates. The Quarterly Journal of Economics, 133(3):1163– 1228.

Chetty, R., Hendren, N., Kline, P., and Saez, E. (2014). Where is the land of Opportunity? The Geography of Intergenerational Mobility in the United States. The Quarterly Journal of Economics, 129(4):1553–1623.

Dahl, M. W. and DeLeire, T. (2008). The Association between Childrens Earnings and Fathers Lifetime Earnings: Estimates Using Administrative Data. Working paper, institute for research on poverty, University of WisconsinMadison.

Dawid, A. P. (2000). Causal inference without counterfactuals. Journal of the American statistical Association, 95(450):407–424.

Deutscher, N. and Mazumder, B. (2023). Measuring intergenerational income mobility: A synthesis of approaches. Journal of Economic Literature, 61(3):988–1036.

Duflo, E. (2001). Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Policy Experiment. American Economic Review, 91(4):795– 813.

Duflo, E. (2004). The medium run effects of educational expansion: evidence from a large school construction program in Indonesia. Journal of Development Economics, 74(1):163– 197.

Edmonds, E. V., Pavcnik, N., and Topalova, P. (2010). Trade Adjustment and Human Capital Investments: Evidence from Indian Tariff Reform. American Economic Journal: Applied Economics, 2(4):42–75.

Emran, M. S., Islam, A., and Shilpi, F. (2020). Distributional Effects of Corruption When Enforcement is Biased: Theory and Evidence from Bribery in Schools in Bangladesh. Economica, 87(348):985–1015.

Emran, M. S., Jiang, H., and Shilpi, F. (2021). Is Gender Destiny? Gender Bias and Intergenerational Educational Mobility in India. GLO Discussion Paper Series 807, Global Labor Organization (GLO).

Kolesár, M., Chetty, R., Friedman, J., Glaeser, E., and Imbens, G. (2015). Identification and Inference With Many Invalid Instruments. Journal of Business & Economic Statistics, 33(4):474–484.

Manian, S., Zhang, Q., and Zhao, B. (2023). A Grand Socioeconomic Reshuffling: The OneChild Policy and Intergenerational Mobility in China. NBER Conference Paper, NBER Chinese Economy Working Group Meeting.

Mazumder, B., Rosales-Rueda, M., and Triyana, M. (2019). Intergenerational Human Capital Spillovers: Indonesia’s School Construction and Its Effects on the Next Generation. AEA Papers and Proceedings, 109:243–249.

Pearl, J. (2000). Causal inference without counterfactuals: Comment. Journal of the American Statistical Association, 95(450):428–431.

Pitt, M. M., Rosenzweig, M. R., and Gibbons, D. M. (1993). The determinants and consequences of the placement of government programs in indonesia. The World Bank Economic Review, 7(3):319–348.

Rajan, R. and Subramanian, A. (2008). Aid and Growth: What Does the Cross-Country Evidence Really Show? The Review of Economics and Statistics, 90(4):643–665.

Solon, G. (1999). Intergenerational mobility in the labor market. In Ashenfelter, O. and Card, D., editors, Handbook of Labor Economics, volume 3 of Handbook of Labor Economics, pages 1761–1800. Elsevier.

Topalova, P. (2010). Factor immobility and regional impacts of trade liberalization: Evidence on poverty from India. American Economic Journal: Applied Economics, 2(4):1–41.

Yu, Y., Fan, Y., and Yi, J. (2020). The One-Child Policy Amplifies Economic Inequality across Generations in China. IZA Discussion Papers 13617, Institute of Labor Economics (IZA).