Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-26T19:52:00.588Z Has data issue: false hasContentIssue false

Early-life environment and human capital: evidence from the Philippines

Published online by Cambridge University Press:  29 June 2020

Evan D. Peet*
Affiliation:
RAND Corporation, Pittsburgh, PA, USA
*
*Corresponding author. E-mail: [email protected]

Abstract

This study examines how human capital develops in response to early-life weather and pollution exposures in the Philippines. Both pollution and weather are examined in relation to short- and long-term human capital outcomes. We combine a three-decade longitudinal survey measuring human capital development, a database of historical weather, and multiple databases characterizing carbon monoxide and ozone in the Philippines during the 1980s. We find evidence that extreme precipitation and temperature affect short-term anthropometric outcomes, but long-term outcomes appear unaffected. For long-term cognitive outcomes, we find that early-life pollution exposures negatively affect test scores and schooling. These long-term responses to early-life pollution exposures extend to the labor market with reduced hours worked and earnings. The implication is that a 25 per cent reduction in early-life ozone exposure would increase per person discounted lifetime earnings by $1,367, which would scale to $2.05 billion at the national level (or 2 per cent of 2005 GDP).

Type
Research Article
Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allen, J, Klocke, C, Morris-Schaffer, K, Conrad, K, Sobolewski, M and Cory-Slechta, D (2017) Cognitive effects of air pollution exposures and potential mechanistic underpinnings. Current Environmental Health Reports 4, 180191.CrossRefGoogle ScholarPubMed
Almond, D, Edlund, L and Palme, M (2009) Chernobyl's subclinical legacy: prenatal exposure to radioactive fallout and school outcomes in Sweden. The Quarterly Journal of Economics 124, 17291772.CrossRefGoogle Scholar
Altshuler, K, Berg, M, Frazier, LM, Laurenson, J, Longstreth, J, Mendez, W and Molgaard, CA (2003) Critical Periods in Development. OCHP Paper Series on Children's Health and the Environment, Paper 2003-02. U.S. Environmental Protection Agency.Google Scholar
Baird, S, Hicks, JH, Kremer, M and Miguel, E (2016) Worms at work: long-run impacts of child health gains. Working Paper. The Quarterly Journal of Economics 131, 16371680.CrossRefGoogle Scholar
Benjamini, Y and Hochberg, Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society 57, 289300.Google Scholar
Bennett, CM and McMichael, AJ (2010) Non-heat related impacts of climate change on working populations. Global Health Action 3, 110.CrossRefGoogle ScholarPubMed
Bharadwaj, P, Gibson, M, Graff Zivin, J and Neilson, C (2017) Gray matters: fetal pollution exposure and human capital formation. Journal of the Association of Environmental and Resource Economists 4, 505542.CrossRefGoogle Scholar
Black, SE, Butikofer, A, Devereux, PJ and Salvanes, KG (2019) This is only a test? Long-run and intergenerational impacts of prenatal exposure to radioactive fallout. Review of Economics and Statistics 101, 531546.CrossRefGoogle Scholar
Block, ML and Calderon-Garciduenas, L (2009) Air pollution: mechanisms of neuroinflammation and CNS disease. Trends in Neurosciences 32, 506516.CrossRefGoogle ScholarPubMed
Currie, J, Graff Zivin, JS, Mullins, J and Neidell, M (2014) What do we know about short- and long-term effects of early-life exposure to pollution? Annual Review of Resource Economics 6, 217247.CrossRefGoogle Scholar
Daly, A and Zannetti, P (2007) Air Pollution Modeling – An Overview. Ambient air pollution, pp. 1528.Google Scholar
Dasgupta, S, Laplante, B, Wang, H and Wheeler, D (2002) Confronting the environmental Kuznets curve. Journal of Economic Perspectives 16, 147168.CrossRefGoogle Scholar
Dell, M, Jones, B and Olken, BA (2012) Temperature shocks and economic growth: evidence from the last half century. American Economic Journal: Macroeconomics 4, 6695.Google Scholar
Dell, M, Jones, B and Olken, BA (2014) What do we learn from the weather? The new climate-economy literature. Journal of Economic Literature 52, 740798.CrossRefGoogle Scholar
Deschênes, O, Greenstone, M and Guryan, J (2009) Climate change and birth weight. American Economic Review: Papers and Proceedings 99, 211217.CrossRefGoogle ScholarPubMed
EMB (2010) Environmental Management Bureau: Regional State of the Brown Environment Report. Department of Environment and Natural Resources.Google Scholar
Fernandez, RD and Santos, G (2014) Gravity, distance, and traffic flows in Mexico. Research in Transportation Economics 46, 3035.CrossRefGoogle Scholar
Grace, K, Davenport, F, Hanson, H, Funk, C and Shukla, S (2015) Linking climate change and health outcomes: examining the relationship between temperature, precipitation and birth weight in Africa. Global Environmental Change 35, 125137.CrossRefGoogle Scholar
Graff Zivin, J and Neidell, M (2013) Environment, health and human capital. Journal of Economic Literature 51, 689730.CrossRefGoogle Scholar
Graff Zivin, J, Hsiang, SM and Neidell, M (2018) Temperature and human capital in the short and long run. Journal of the Association of Environmental and Resource Economists 5, 77105.CrossRefGoogle Scholar
Hassan, YA and Barker, DJ (1999) The impact of unseasonable or extreme weather on traffic activity within Lothian region, Scotland. Transport Research Institute 7, 209213.Google Scholar
Heckman, J (1997) Instrumental variables: a study of implicit behavioral assumptions used in making program evaluations. Journal of Human Resources 32, 441462.CrossRefGoogle Scholar
Hocking, C, Silberstein, RB, Lau, WM, Stough, C and Roberts, W (2001) Evaluation of cognitive performance in the heat by functional brain imaging and psychometric testing. Comparative Biochemistry and Physiology 128, 719734.CrossRefGoogle ScholarPubMed
Hsiang, SM (2010) Temperatures and cyclones strongly associated with economic production in the Caribbean and Central America. Proceedings of the National Academy of Science 107, 1536715372.CrossRefGoogle ScholarPubMed
Hsiang, SM and Jina, AS (2014) The causal effect of environmental catastrophe on long- run economic growth: evidence from 6,700 cyclones. National Bureau of Economic Research, Cambridge, MA, NBER Working Paper 20352.CrossRefGoogle Scholar
Isen, A, Rossin-Slater, M and Walker, R (2017) Every breath you take – every dollar you'll make: the long-term consequences of the Clean Air Act of 1970. Journal of Political Economy 125, 848902.CrossRefGoogle Scholar
Kim, Y, Manley, J and Radioas, V (2017) Medium- and long-term consequences of pollution on labor supply: evidence from Indonesia. IZA Journal of Labor Economics 6, article 5.CrossRefGoogle Scholar
Kiyatkin, EA (2007) Brain temperature fluctuations during physiological and pathological conditions. European Journal of Applied Physiology 101, 317.CrossRefGoogle ScholarPubMed
Kolesár, M, Chetty, R, Friedman, J, Glaeser, E and Imbens, GW (2015) Identification and inference with many invalid instruments. Journal of Business and Economic Statistics 33, 474484.CrossRefGoogle Scholar
Lavy, V, Ebenstein, A and Roth, S (2014) The impact of air pollution on cognitive performance and human capital formation. National Bureau of Economic Research, Cambridge, MA, NBER Working Paper 20648.Google Scholar
Levine, DI and Yang, D (2014) The impact of rainfall on rice output in Indonesia. National Bureau of Economic Research, Cambridge, MA, NBER Working Paper 20302.CrossRefGoogle Scholar
Maccini, S and Yang, D (2009) Under the weather: health, schooling, and economic consequences of early-life rainfall. American Economic Review 99, 10061026.CrossRefGoogle ScholarPubMed
Meter, V (2000) Emergency Medicine: Carbon Monoxide Poisoning. New York: McGraw-Hill.Google Scholar
Nelson, RR and Phelps, SS (1966) Investment in humans, technological diffusion, and economic growth. American Economic Review 56, 6975.Google Scholar
Salam, MT, Millstein, J, Li, YF, Lurmann, FW, Margolis, HG and Gilland, FD (2005) Birth outcomes and prenatal exposure to ozone, carbon monoxide, and particulate matter: results from the Children's Health Study. Environmental Health Perspectives 113, 16381644.CrossRefGoogle ScholarPubMed
Sanders, N (2012) What doesn't kill you makes you weaker: prenatal pollution exposure and educational outcomes. Journal of Human Resources 47, 826850.CrossRefGoogle Scholar
Schultz, MG, Heil, A, Hoelzemann, JJ, Spessa, A, Thonicke, K, Goldammer, JG, Held, AC, Pereira, JMC and van Het Bolscher, M (2007) RETRO: Final Report. Available at http://retro.enes.org/pubreports.shtml.Google Scholar
Simes, RJ (1986) An improved Bonferroni procedure for multiple tests of significance. Biometrika 73, 751754.CrossRefGoogle Scholar
Sohn, K (2015) The influence of birth season on height: evidence from Indonesia. American Journal of Physical Anthropology 157, 659665.CrossRefGoogle ScholarPubMed
Stieb, DM, Chen, L, Eshoul, M and Judek, S (2012) Ambient air pollution, birth weight and preterm birth: a systematic review and meta-analysis. Environmental Research 117, 100111.CrossRefGoogle ScholarPubMed
Strand, LB, Barnett, AG and Tong, S (2011) The influence of season and ambient temperature on birth outcomes: a review of the epidemiological literature. Environmental Research 111, 451462.CrossRefGoogle ScholarPubMed
Strauss, J and Thomas, D (1998) Health, nutrition, and economic development. Journal of Economic Literature 36, 766817.Google Scholar
The World Bank (2014) Philippines: World Development Indicators. Available at http://data.worldbank.org/country/philippines.Google Scholar
Waterland, RA and Michels, KB (2007) Epigenetic epidemiology of the developmental origins hypothesis. Annual Review of Nutrition 27, 363388.CrossRefGoogle ScholarPubMed
WHO (2014) The Z-Score or Standard Deviation Classification System. Available at http://www.who.int/nutgrowthdb/about/introduction/en/index4.html.Google Scholar
Supplementary material: PDF

Peet supplementary material

Peet supplementary material

Download Peet supplementary material(PDF)
PDF 7.5 MB