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Heterogeneous Treatment Effects of Safe Water on Infectious Disease: Do Meteorological Factors Matter?

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Heterogeneous Treatment Effects of Safe Water on Infectious Disease: Do Meteorological Factors Matter? Cliometrica https://doi.org/10.1007/s11698-017-0169-6 ORIGINAL PAPER Heterogeneous treatment effects of safe water on infectious disease: Do meteorological factors matter? 1 2 Kota Ogasawara • Yukitoshi Matsushita Received: 2 June 2017 / Accepted: 13 November 2017 Ó Springer-Verlag GmbH Germany, part of Springer Nature 2017 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 unob- servable 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. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11698- 017-0169-6) contains supplementary material, which is available to authorized users. & Kota Ogasawara [email protected] Yukitoshi Matsushita [email protected] 1 Graduate School of Social Sciences, Chiba University, 1-33, Yayoicho, Inage-ku, Chiba 263-8522, Japan 2 Graduate School of Economics, Hitotsubashi University, 2-1, Naka, Kunitachi, Tokyo 186-8601, Japan 123 K. Ogasawara, Y. Matsushita Keywords Climate Á Heterogeneous treatment effects Á Panel-data analysis Á Public health Á Semi/nonparametric estimation JEL Classification C14 Á I18 Á Q54 Á N55 1 Introduction The global burden of waterborne infectious diseases remains considerable. Typhoid fever, for instance, caused 11.9 million illnesses and 129 thousand deaths in low- and middle-income countries in 2010 (Mogasale et al. 2014). Launching efficient social programs to reduce the risk factors for typhoid fever is thus an important agenda. Therefore, experience from countries that previously had developing status can offer valuable lessons for present-day industrializing societies. During the nineteenth and early twentieth centuries, typhoid fever was one of the most common waterborne infectious diseases in the world. In the USA, the impact of clean water on waterborne diseases has thus been widely studied. By using data on 13 major US cities in 1900–1936, Cutler and Miller (2005) found that the installation of clean water technologies led to the near-eradication of typhoid fever. Ferrie and Troesken (2008) also argued that the installation of water-purification technology and subsequent eradication of typhoid fever led to 35–56% of the reduction in the crude death rate in Chicago between 1850 and 1925. Moreover, the recent study by Beach et al. (2016) found that eliminating early-life exposure to typhoid fever increases later-life earnings, suggesting the long-run positive impact of safe water. Similar mitigating effects of clean water via water-purification systems are found in current developing countries (Nandi et al. 2017).1 While the growing body of the literature has revealed the mitigating effects of water-supply systems, the heterogeneous treatment effects of clean water have been understudied. Although a few studies investigate the varying effects of clean water on mortality rates across or within municipalities, most previous works have assumed that the effect of water-supply systems does not depend on the physical environment of societies.2 The important fact related to this point is that a growing body of the literature in epidemiology and bacteriology has found a significant association between meteorological conditions and the infection risk of waterborne infectious diseases 1 See also Jalan and Ravallion (2003), Gamper-Rabindran et al. (2010), and Devoto et al. (2012) for the cases of India, Brazil, and Morocco, respectively. Daley et al. (2015) provide an interesting evidence on the importance of residents’ perceptions of the functionality of current water and water sanitation systems in a remote Arctic Aboriginal community. 2 For example, Jalan and Ravallion (2003) found a variation in the effects of piped water on the prevalence and duration of diarrhea across mothers’ education levels. Ogasawara et al. (2016) also found varying effects of piped water with respect to poverty levels in prewar Tokyo. However, neither study directly modeled the nonlinearity of these effects. A few exceptions include Gamper-Rabindran et al. (2010), employed a panel quantile regression approach, and Ogasawara and Matsushita (2017), employed a semiparametric fixed-effects approach. However, we are the first to use the fixed-effects semivarying coefficient panel-data model to bridge the gaps in the body of the literature. 123 Heterogeneous treatment effects of safe water on infectious disease such as typhoid fever, which are directly improved by safe water. Infectious agents such as protozoa, bacteria, and viruses are devoid of thermostatic mechanisms. Thus, their temperature and fluid levels are determined by local meteorological conditions such as temperature and precipitation (Patz et al. 2003). For instance, a higher temperature drives the transmission of pathogens through the contamination of food and/or drinking water, whereas heavy rainfall is associated with outbreaks of enteric pathogens because of the contamination of water supplies, usually river water (Tseng et al. 2009).3 Moreover, a recently growing literature in economics has also demonstrated causal links between these meteorological conditions and human health (Descheˆnes 2014). A more recent study by Barreca et al. (2016) found striking evidence that the temperature-mortality gradient declined over the course of the twentieth century. In other words, the heavy mortality penalty for living in warm climates has slowly diminished. Considering this fact, it is then important to explore the mechanisms that have helped attenuate the temperature–mortality relationship. To bridge this gap in the body of knowledge, therefore, we first investigate the heterogeneous treatment effects of clean water with respect to meteorological factors by implementing a fixed-effects semivarying coefficient panel-data model. To ensure a reliable estimation, we use the nationwide city-level panel dataset with populations of above 20,000 between 1922 and 1940 in Japan, which covers approximately 90% of the total city population at that time.4 We find that the heterogeneity of the impacts of clean water is obvious with respect to temperature. While the estimate from the parametric specification suggests that a 1% increase in the coverage of tap water decreased the typhoid death rate by 0.117, that from the semiparametric specification suggests declines by 0.018, 0.137, and 0.158 per 10,000 people under the condition of the 5th, 50th, and 95th percentiles of temperature, respectively. This result suggests that the increased availability of clean water accounts for approximately 11, 40, and 61% of the improvements in the typhoid death rate from 1922 to 1940 in cities with an average annual temperature of around 8.9, 14.6, and 15:8 C, respectively. A set of robustness checks confirms our results. This article contributes to the literature in three main ways. The first contribution is to implement a semiparametric fixed-effects model to examine nonlinear effects in public health intervention. Heterogeneous treatment effects when evaluating social programs have attracted wide scholarly attention (Imai and Ratkovic 2013). For instance, recent studies have considered both treatment effect heterogeneity and multiple treatments and examined the most appropriate way in which to make statistical inferences (Athey and Imbens 2016; List et al. 2016; Lehrer et al. 2016). In accordance with these studies, we first extend the approaches of Fan et al. (2005) and Lee and Mukherjee (2014) to estimate a fixed-effects semivarying coefficient panel-data model by using a comprehensive city-level dataset than previous studies 3 See also Guzman Herrador et al. (2015), Wang et al. (2012), Dewan et al. (2013), and Listorti and Doumani (2001) for this epidemiological evidence. 4 The total city population is derived from the population in all cities reported in the vital statistics for each year (Appendix B in ESM). While Noheji and Kato¯(1954) and Ogasawara et al. (2016) investigated the impacts of water supply on mortality in Gifu and Tokyo, respectively, the present study aims to offer a more thorough discussion on this topic by using a comprehensive city-level dataset. 123 K. Ogasawara, Y. Matsushita (Cutler and Miller 2005). A related minor contribution is that we investigate treatment effect heterogeneity in the context of post-reform covariates that are random. Predetermined characteristics such as racial and gender differences are usually used to estimate the heterogeneous effects (Flory et al. 2015a, b). By contrast, the present study
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