Advertisement

Cliometrica

, Volume 13, Issue 1, pp 55–82 | Cite as

Heterogeneous treatment effects of safe water on infectious disease: Do meteorological factors matter?

  • Kota OgasawaraEmail author
  • Yukitoshi Matsushita
Original Paper

Abstract

Mortality from waterborne infectious diseases remains a serious issue globally. Investigating the efficient laying plan of waterworks to mitigate the risk factors for such diseases has been an important research avenue for industrializing countries. While a growing body of the literature has revealed the mitigating effects of water-purification facilities on diseases, the heterogeneous treatment effects of clean water have been understudied. The present study thus focuses on the treatment effect heterogeneity of piped water with respect to the external meteorological environment of cities in industrializing Japan. To estimate the varying effects, we implement fixed-effects semivarying coefficient models to deal with the unobservable confounding factors, using a nationwide city-level panel dataset between 1922 and 1940. We find evidence that the magnitude of safe water on the reduction in the typhoid death rate is larger in cities with a higher temperature, which is consistent with recent epidemiological evidence. These findings underscore the importance of the variations in the external meteorological conditions of the municipalities that install water-purification facilities in developing countries.

Keywords

Climate Heterogeneous treatment effects Panel-data analysis Public health Semi/nonparametric estimation 

JEL Classification

C14 I18 Q54 N55 

Notes

Acknowledgements

This study was supported by JSPS KAKENHI Grant No. 16K17153. There are no conflicts of interest to declare. The authors wish to thank the editor, two anonymous referees, and Badi Baltagi for helpful comments on the paper. We also thank Tatsuki Inoue for excellent research assistance.

Supplementary material

11698_2017_169_MOESM1_ESM.pdf (3.8 mb)
Supplementary material 1 (pdf 3855 KB)

References

  1. Alsan M, Goldin C (2015) Watersheds in infant mortality: the role of effective water and sewerage infrastructure, 1880 to 1915. National Bureau of Economic Research, Working Paper No. 21263, Cambridge, MAGoogle Scholar
  2. Antman FM (2016) For want of a cup: the rise of tea in England and the impact of water quality on economic development. Working Paper, pp 1–32Google Scholar
  3. Athey S, Imbens G (2016) Recursive partitioning for heterogeneous causal effects. PNAS 113(27):7353–7360Google Scholar
  4. Banerjee AB, Duflo E (2007) The economic lives of the poor. J Econ Perspect 21(1):141–167Google Scholar
  5. Barreca A, Clay K, Deschenes O, Greenstone M, Shapiro JS (2016) Adapting to climate change: the remarkable decline in the US temperature-mortality relationship over twentieth century. J Polit Econ 124(1):105–159Google Scholar
  6. Beach B, Ferrie J, Saavedra M, Troesken W (2016) Typhoid fever, water quality, and human capital formation. J Econ Hist 76(1):41–75Google Scholar
  7. Crump JA, Mintz ED (2010) Global trends in typhoid and paratyphoid fever. Clin Infect Dis 50(2):541–546Google Scholar
  8. Cutler D, Miller G (2005) The role of public health improvements in health advances: the twentieth-century United States. Demography 42(1):1–22Google Scholar
  9. Daley K, Castleden H, Jamieson R, Furgal C, Ell L (2015) Water systems, sanitation, and public health risks in remote communities: Inuit resident perspectives from the Canadian Arctic. Soc Sci Med 2015:124–132Google Scholar
  10. Deschênes O (2014) Temperature, human health, and adaptation: a review of the empirical literature. Energy Econ 46:606–619Google Scholar
  11. Deschênes O, Greenstone M (2007) The economic impacts of climate change: evidence from agricultural output and random fluctuations in weather. Am Econ Rev 97(1):354–385Google Scholar
  12. Deschênes O, Kolstad C (2011) Economic impacts of climate change on California agriculture. Clim Change 109(s1):365–386Google Scholar
  13. Devoto F, Duflo E, Dupas P, Parienté W, Pons V (2012) Happiness on tap: piped water adoption in urban Morocco. Am Econ J Econ Policy 4(4):68–99Google Scholar
  14. Dewan AM, Robert C, Hashizume M, Ongee ET (2013) Typhoid fever and its association with environmental factors in the Dhaka metropolitan area of Bangladesh: a spatial and time-series approach. Negl Trop Dis 7(1):1–14Google Scholar
  15. Drixler FF (2016) Hidden in plain sight: stillbirths and infanticides in imperial Japan. J Econ Hist 76(3):651–696Google Scholar
  16. Ebi KL, Lindgren E, Suk JE, Semenza JC (2013) Adaptation to the infectious disease impacts of climate change. Clim Chnage 118(2):355–365Google Scholar
  17. Exner M, Vacata V, Gebel J (2003) Heterotrophic plate counts and drinking-water safety: the significance of HPCs for water quality and the human health. In: Bartram J, Cotruvo J, Exner M, Fricker C, Glasmacher A (eds) Public health aspects of the role of HPC: an introduction. World Health Organization, Geneva, pp 12–19Google Scholar
  18. Fan J, Peng H, Huang T (2005) Semilinear high-dimensional model for normalization of microarray data. J Am Stat Assoc 100(471):781–796Google Scholar
  19. Ferrie JP, Troesken W (2008) Water and Chicago’s mortality transition, 1850–1925. Explor Econ Hist 45(1):97–130Google Scholar
  20. Flory JA, Leibbrandt A, List JA (2015a) Do competitive workplaces deter female workers? A large-scale natural field experiment on gender differences in job-entry decisions. Rev Econ Stud 82(1):122–155Google Scholar
  21. Flory JA, Uri G, Kenneth L, List JA (2015) Gender, age, and competition: the disappearing gap. Mimeo, New York CityGoogle Scholar
  22. Fukushima T, Mizuno K, Horiuchi T (1940) Epidemic of cholera by the simple waterworks. Nihon hoken kyōkai zasshi 16(1):520–529 (in Japanese)Google Scholar
  23. Gamper-Rabindran S, Khan S, Timmins C (2010) The impact of piped water provision on infant mortality in Brazil: a quantile panel data approach. J Dev Econ 92(2):180–200Google Scholar
  24. Guzman Herrador BR, de Blasio BF, MacDonald E, Nicholas G, Sudre B, Vold L, Semenza JC, Nygard K (2015) Analytical studies assessing the association between extreme precipitation or temperature and drinking water-related waterborne infections: A review. Environ Health 14(29):1–12Google Scholar
  25. Hunter J (2014) “Extreme confusion and disorder”? The Japanese economy in the Great Kantō Earthquake of 1923. J Asian Stud 73(3):753–773Google Scholar
  26. Hunter J, Ogasawara K (2016) Price shocks in disaster: the Great Kantō Earthquake in Japan, 1923. LSE Working Paper 253, pp 1–45Google Scholar
  27. Imai K, Ratkovic M (2013) Estimating treatment effect heterogeneity in randomized program evaluation. Ann Appl Stat 7(1):443–470Google Scholar
  28. Jalan J, Ravallion M (2003) Does piped water reduce diarrhea for children in rural India? J Econ 112(1):153–173Google Scholar
  29. Japan Water Works Association (1967) A history of water works in Japan (general review part) [in Japanese]. Japan Water Works Association, TokyoGoogle Scholar
  30. Javaid Siddiqui F, Rabbani F, Hasan R, Qamaruddin Nizami S, Ahmed Bhutta Z (2006) Typhoid fever in children: some epidemiological considerations from Karachi, Pakistan. Int J Infect Dis 10(3):215–222Google Scholar
  31. Johnston W (1995) The modern epidemic: a history of tuberculosis in Japan. Harvard University Asia Center, CambridgeGoogle Scholar
  32. Kesztenbaum L, Rosenthal J-L (2017) Sewers’ diffusion and the decline of mortality: the case of Paris, 1880–1914. J Urban Econ 98:174–186Google Scholar
  33. Lehrer SF, Pohl RV, Song K (2016) Targeting policies: multiple testing and distributional treatment effects. NBER Working Paper Series, No. 22950, pp 1–32Google Scholar
  34. Lee Y, Mukherjee D (2014) Nonparametric estimation of the marginal effect in fixed-effect panel data models: an application on the environmental Kuznets curve. Syracuse University Working Paper, pp 1–24Google Scholar
  35. List JA, Shaikh AM, Xu Y (2016) Multiple hypothesis testing in experimental economics. NBER Working Paper Series, No. 21875, pp 1–23Google Scholar
  36. Listorti JA, Doumani FM (2001) Environmental health: bridging the gaps. World Bank Discussion Paper, No. 422, The World BankGoogle Scholar
  37. Mogasale V, Maskery B, Ochiai RL, Lee JS, Mogasale VV, Ramani E, Kim YE, Park JK, Wierzba TF (2014) Burden of typhoid fever in low-income and middle-income countries: a systematic, literature-based update with risk-factor adjustment. Lancet Glob Health 2(10):e570–580Google Scholar
  38. Nagashima T (2004) Sewage disposal and typhoid fever: the case of Tokyo 1912–1940. Annales de démographie historique 108(2):105–117Google Scholar
  39. Nandi A, Megiddo I, Ashok A, Verma A, Laxminarayan R (2017) Reduced burden of childhood diarrheal diseases through increased access to water and sanitation in India: a modeling analysis. Soc Sci Med 180:181–192Google Scholar
  40. Noheji K, Katō T (1954) The relationship between sewers and water lines and the annual transition of typhoid fever in Gifu city. Nihon koushū eisei zasshi 1(1):2–6 [in Japanese]Google Scholar
  41. Nukada S (1925) Diagnostics for the differential Diagnosis in the internal medicine. Kanehara Shōten, Tokyo [in Japanese]Google Scholar
  42. Ogasawara K, Matsushita Y (2017) Public health and multiple-phase mortality improvement: Evidence from industrializing Japan. Working PaperGoogle Scholar
  43. Ogasawara K, Shirota S, Kobayashi G (2016) Public health improvements and mortality in interwar Tokyo: a Bayesian disease mapping approach. Cliometrica.  https://doi.org/10.1007/s11698-016-0148-3 (forthcoming)Google Scholar
  44. Parry CM, Tinh Nien T, Dougan G, White NJ, Farrar JJ (2002) Typhoid fever. N Eng J Med 347(22):1770–1782Google Scholar
  45. Patz JA, Githeko AK, McCarty JP, Hussein S, Confalonieri U, de Wet N (2003) Climate change and infectious diseases. In: McMicharel AJ, Campbell-Lendrum DH, Corvalan CF, Ebi KL, Githeko AK, Scheraga JD, Woodward A (eds) Climate change and human health-risk and responses. World Health Organization, Geneva, pp 103–132Google Scholar
  46. Ray S (2014) Managing outbreaks of scarlet fever. Nurs Times 110(39):23–34Google Scholar
  47. Rodó X, Pascual M, Doblas-Reyes FJ et al (2013) Climate change and infectious diseases: Can we meet the needs for better prediction? Clim Change 118(3):625–640Google Scholar
  48. Sedgwick WT (1902) Principles of sanitary science and the public health. The Macmillan Company, New YorkGoogle Scholar
  49. Tornevi A, Bergstedt O, Forsberg B (2014) Precipitation effects on microbial pollution in a river: lag structures and seasonal effect modification. PLoS ONE 9(5):e98546Google Scholar
  50. Tseng W-C, Chen C-C, Chang C-C, Chu Y-H (2009) Estimating the economic impacts of climate change on infectious diseases: a case study on dengue fever in Taiwan. Clim Change 92(1):123–140Google Scholar
  51. Wang LX, Li XJ, Fang DC, Wang DC, Cao WC, Kan B (2012) Association between the incidence of typhoid and paratyphoid fever and meteorological variables in Guizhou, China. Chin Med J 125(3):455–460Google Scholar
  52. Whipple GC (1908) Typhoid fever: its causation, transmission and prevention. Wiley, New YorkGoogle Scholar
  53. World Health Organization (2011) Guidelines for drinking-water quality, 4th edn. World Health Organization, GenevaGoogle Scholar

Statistical reports

  1. Federation of Water Authorities (1923) Jyōsuidō tōkei oyobi hōkoku [Statistics and reports of water supply, vol. 1]. TokyoGoogle Scholar
  2. Ministry of Railways (1930) Nihon zenkoku tetsudō senro kirotei [General Railway map of Japan, 1930 edition]. Ministry of RailwaysGoogle Scholar
  3. Population Bureau of the Department of Welfare (1942–1943) Eisei nenpō [Annual report on sanitation, 1939–1940 editions]. TokyoGoogle Scholar
  4. Sanitary Bureau of the Home Department (1877–1938) Eiseikyoku nenpō [Annual report of the sanitary bureau, 1922–1936 editions]. TokyoGoogle Scholar
  5. Sanitary Bureau of the Department of Welfare (1939–1940) Eisei nenpō [Annual report on sanitation, 1937–1938 editions]. TokyoGoogle Scholar
  6. Statistics and Information Department, Minister’s Secretariat, Ministry of Health and Welfare (1999) Jinkō dōtai tōkei hyakunen no dōkō [Trends in vital statistics of Japan for 100 years]. Tokyo: Health, Labour, and Welfare Statistics AssociationGoogle Scholar
  7. Statistics Bureau of the Cabinet. (1924a–1932a). Nihonteikoku jinkōdōtai tōkei [The vital statistics of the empire of Japan, 1922–1931 editions]. TokyoGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Graduate School of Social SciencesChiba UniversityChibaJapan
  2. 2.Graduate School of EconomicsHitotsubashi UniversityKunitachiJapan

Personalised recommendations