\chapter{The Effect of Municipal Water Filtration on Children's School Enrollment and Employment in American Cities, 1880--1920}\label{ch1}

\section{Introduction}
Water is important for life. However, many people in developing countries still lack access to safe drinking water. In 2015, there were still 663 million people who did not have access to safe drinking water \parencite{who2015}.\footnote{Safe water in the report is defined by water source which is adequately protected from outside contamination, particulary faecal matter.} This poor access to clean water exposes human health to great risks of deadly waterborne diseases such as cholera, diarrhea, malaria, typhoid and paratyphoid enteric fevers, and other water-related diseases.\footnote{WHO webpage: http://www.who.int/water\_sanitation\_health/diseases/diseasefact/en/}
For instance, the World Health Organization (WHO) reports that diarrhoeal disease alone causes the death of 1.5 million people every year.\footnote{WHO webpage: http://apps.who.int/gho/data/node.main.CODWORLD?lang=en}

This study focuses on the United States from 1880 to 1920 to investigate the effect of access to clean water on human capital investment, particularly school enrollment and child labor. During the late nineteenth and early twentieth centuries, many American cities started to install water filtration plants to purify their water supply. The city-time variation in the installation of filtration plants permits me to use a difference-in-differences strategy to identify the causal impact of municipal water filtration. Intuitively, this estimate is obtained by taking the post-intervention/pre-intervention difference in child outcome in an adopting city, and using cities that never adopt or adopt it later to control for the changes in child outcome over time that would have occurred for reasons unrelated to the policy.

The effects of water access and quality on health improvement are widely discussed in the literature \parencite{tk2002, jm2003, cm2005, ferrie2008, mangyo2008, galiani2005, galiani2009, gamper2010, kremer2010, kremer2011, zhang2012, goldin2015}. One closely related study is David Cutler and Grant Miller (2005), which uses city-time variation in water purification technologies for 13 U.S. cities during the early twentieth century, and finds that water purification reduced infant mortality and child mortality by 46\% and 50\%, respectively.\footnote{The authors look at four water purification technologies in 13 cities: water filtration, water chlorination, sewage treatment, and sewage chlorination. They conclude that water filtration and chlorination accounted for a large decline in typhoid, total, infant, and child mortality in the early twentieth century, their regression results always show statistically significant effect from water filtration, but small and not statistically significant effect from water chlorination. Furthermore, they also mention that their analysis could not examine the effect of sewage technologies since these modern technologies were not in widespread use in the U.S. until the 1930s and 1940s.}
Given such a large effect of clean water on child health, and in the context of a growing body of evidence indicating complementarities between childhood health and education \parencite{glewwe2001, mk2004, miguel2006, bleakley2007, bleakley2010}, it is important to understand how access to clean water affects human capital investment such as school enrollment and child labor.

To date, there have been only a few studies that look at the possible effects of water on educational outcomes \parencite{duflo2012, bv2013, tk2014, kosec2014, xz2014}. This study adds to this under-studied topic by using a different source of variation in exposure to clean water: municipal water filtration policies in the United States. Furthermore, unlike most of the previous studies which use natural experiments that change only water access, or that change both water access and quality at the same time, I examine an intervention which changes water quality. That is, the American cities I analyze had piped water at the outset, and what the filtration policy brought to city dwellers was higher quality water. It may be that the impacts of water access and water quality are different, and I provide some of the first evidence of water quality on children's school enrollment and employment. In addition, I provide evidence on the effect of municipal water filtration for a much larger set of cities than has previously been done. I assemble filtration plants information for 74 cities during 1880--1920 from various historical sources, enabling me to evaluate these policies on a wider scale than has been done.\footnote{I consulted various historical books, articles, and reports to obtain complete information on filtration plants installation for 74 U.S. cities from 1880 to 1920. The data section describes the formation of city sample.} Did the U.S. municipal water filtration policies play a role in the rise in educational attainment observed for people who were children at the turn of the twentieth century? This question is important both from a historical standpoint---possibly these municipal infrastructural projects had more far-reaching effects than has been previously documented---and may be relevant for developing countries that are considering these investments by providing more information about the benefits and costs.

A priori, the effect of municipal water filtration on school enrollment is unclear. For example, after municipal water filtration, children are healthier and are less likely to get sick, which allows those previously weak and sick children to go to school. At the same time, water filtration may also improve children's ability to focus and concentrate in class, so that they may perform better in school. All of these reduce the costs of going to school and increase the return to education, so parents may be more likely to send their kids to school. On the other hand, healthier children can be more productive and, therefore, be more valuable in the labor market. This increases their opportunity costs of going to school and has a negative effect on school enrollment. These two opposing stories imply that the effect of municipal water filtration on school enrollment is an open question.

There is substantial time variation in water filtration adoption in my city sample.\footnote{For the 74 cities which I am able to obtain complete information on water filtration from 1880 to 1920, different cities adopted the filtration at different time, and eventually 56 cities adopted the intervention by 1920. The details of the time variation of filtration adoption is discussed in Section \ref{timing}.}
I merged this city-level water filtration adoption data with the U.S. Census microdata from 1880 to 1920 to estimate the impacts of water filtration on children's school enrollment and employment. Applying a difference-in-differences strategy which exploits variation across cities and across time, my results show that municipal water filtration increases school enrollment by 2 percentage points (compared to the sample mean of 86\%). These results are not sensitive to controlling for city-specific trends or state-specific year effects, raising confidence that they reflect causal impacts of the policies rather than differential trends between earlier adopters and later/never adopters. On the other hand, water filtration has a negative effect on child labor, but the effect is not statistically significant. I further investigate which subgroups of people are more affected by the policy intervention. I find that filtration has the largest effect on children who are 14 to 15 years old, which are the ages of finishing elementary school and in some states beyond compulsory schooling age during my studied time periods. Furthermore, I also find that when children are at their sensitive ages of finishing elementary school (14 to 15 years old), early childhood exposure to clean water has a larger impact on school enrollment and child labor. Finally, I investigate other heterogeneous effects of water filtration by legal school dropout status, by household socioeconomic status, by gender, and by race. The results suggest that the effects come from children who can legally drop out of school. The effects are also larger for children from lower socioeconomic status households. The results of gender difference indicate that, compared to males, females have stronger responses to water filtration in terms of increases in school enrollment and decreases in child labor. This is consistent with female's comparative advantage in skill acquiring activities. I also find suggestive evidence that black children in the South benefit more from the intervention.

The rest of this study is structured as follows. I review the literature in Section \ref{lit}. Section \ref{bck} discusses the background of water filtration, how it affects water quality in the U.S. cities, and filtration adoption in American cities. In Sections \ref{data}, I present data of children's school enrollment and employment and municipal water filtration. Section \ref{resul} discusses the identification strategy and presents empirical results. Finally, Section \ref{conclu} concludes this study.





\section{Literature Review}\label{lit}
The research on the effect of clean water on education is limited. \textcite{duflo2012} employ a randomized design in Morocco to examine the non-health effect of household water connection. They find that household water connection had no effect on children's school participation, but had a positive effect on household well-being such as increased leisure time.\footnote{In the paper, since all sample households have access to clean water from public tap, the randomized design of in-home water connection will not improves water quality.} \textcite{bv2013} study a clean water program in Mexico in 1991 and find that infant exposure to clean water program increases test scores for girls by 0.1 standard deviation, and has no effect on boys' test score. \textcite{kosec2014} examines the effect of increase in access to piped water by the private sector participation in the piped water sector in 39 African countries during 1986--2010. The author finds that increase in access to piped water reduces diarrhea of children under age five in urban area, and is associated with a 7.8 percentage points increase in school attendance. Furthermore, the positive association between access to piped water and school attendance is only statistically significant for children aged 11--13 and 17, where children are at transition points in their education. To investigate the long-term effect of clean water, \textcite{xz2014} employ a large drinking water treatment program in rural China and shows a positive effect on schooling attainments.\footnote{Education attainments are grades of education completed, binary variables of graduated from middle school and graduated from high school.} Similarly, \textcite{tk2014} use typhoid fatality rate as a proxy of water quality, then link males in 1900 and 1940 in the U.S. They find that eradicating early-life exposure to typhoid fever had positive effect on years of schooling and later life earnings. The present study contributes to this handful of literature on the effect of clean water on educational outcomes, which highlights this important channel in the larger literature of the benefits of clean water programs.

Numerous papers have examined the impact of clean water on health, including mortality \parencite{tk2002, jm2003, cm2005, galiani2005, ferrie2008, gamper2010, goldin2015} and health conditional on survival \parencite{mangyo2008, galiani2009, kremer2010, kremer2011, zhang2012}. These studies look at the contexts within the U.S. and across different countries. Several are in the setting I examine, the U.S. in the later 1800s and early 1900s. \textcite{tk2002} finds that water filtration reduced both black and white typhoid mortality during 1880--1925. \textcite{tk2001} finds, compared to private ownership of municipal water company, public ownership had larger effect in reducing black typhoid mortality. \textcite{ferrie2008} show that water purification programs in Chicago from 1850--1925 decreased death rates from typhoid fever and diarrheal diseases, the program had diffused effect on other water-unrelated diseases. \textcite{cm2005}, using data on 13 cities from 1900 to 1936, find that water purification technologies strikingly reduced typhoid mortality by 25\%, total mortality by 13\%, infant mortality by 46\%, and child mortality by 50\%. In addition, they find a larger mortality reduction on the poor. Given the dramatic health benefits of clean water found by these studies, it is of interest to examine what consequences there were for child school enrollment and labor. This study provides a more comprehensive picture of the effect of clean water on human capital investment from a broader set of cities (74 cities, see data section). This extends the knowledge of policy makers about the various benefits from the clean water program, especially the knowledge that clean water also influences human capital of survivors, which is essential to economic development. Ignoring the causal effects of water-related programs on human capital investment will underestimate the benefits on implementing water improvement programs.

More generally, this study is relevant to a large literature on the effect of early childhood environment. Given the well documented causal effect of childhood health environment on later life outcomes in the literature (see handbook chapter of \textcite{ac2011}), the present study complements this literature by examining the effect of early childhood exposure to clean water.

My study also relates to the literature on determinants of child labor, which mostly focuses on the effect of child labor laws and changes in household incomes on child labor (see handbook chapter of \textcite{edmonds2007}). I investigate the effect of a health intervention---municipal water filtration---on children's employment. This adds another dimension in determinants of child labor.




\section{Background}\label{bck}
\subsection{Water Filtration and Water Quality}\label{quality}
The initial purpose of water filtration was for aesthetic improvement---reduce turbidity, bad taste, and discoloration. However, before the understanding of disease transmission by microbes, public health experts acknowledged that unclean drinking water could be the source of disease epidemics \parencite{mcguire2006}. By the 1870s and 1880s, with the development of bacteriology and the understanding of how waterborne diseases could be transmitted, public health protection became the primary reason for water filtration \parencite{jama1903a, lr2007}. A water filter is constructed by multiple layers of large and smaller stones, coarse sand, and gravel. When turbid raw water passes through the filter, color and particulate matter are removed, and the slimy matting organic material on the surface of the sand will entrap, digest, and break down bacteria and organic matter contained in the water \parencite{slow1991}.

Water filtration has been shown to be effective at improving water quality. One type of evidence is from scientific testing of water for presence of harmful matter. A 1903 paper in the Journal of the American Medical Association shows that filtration dramatically reduces bacterial concentration. Table \ref{bact} shows the bacterial concentration of the water before and after filtration in the city of Lawrence, Massachusetts. After the water filtration, the average number of bacteria per cubic centimeter decreased from 10,800 to 110, a 99\% reduction, in 1894. Statistics show the filter is effective in the following years, with later years' data focusing on E. coli concentration; E. coli is especially harmful to human health and it is clear that filtration reduces the prevalence of E. coli. A second type of evidence is based on studies of the impact of water filtration installation on health outcomes. For example, \textcite{cm2005} find a decline in child mortality using a sample of 13 U.S. cities, and \textcite{lr2007} find that a decline in deaths due to typhoid fever.\footnote{The cities analyzed in \textcite{lr2007} are: Albany, NY; Cincinnati, OH; Columbus, OH; Indianapolis, IN; Lawrence, MA; Louisville, KY; McKeesport, PA; New Orleans, LA; and Washington, DC.}



\subsection{Water Filtration Adoption in American Cities}\label{timing}
Many American cities started to install water filtration plants to purify their water supply in the late nineteenth and early twentieth centuries. Although there is no major city which adopted water filtration by 1880, the distinguished performance of the sand filter in the the city of Lawrence, MA marked ``the opening of the modern era in water purification in America" \parencite{jama1903b}. Historical accounts show substantial time variation in the municipal adoption of water filtration.

My empirical analysis uses 74 major cities which are identified in Census microdata and for which I was able to obtain complete information on municipal water filtration for the 1880--1920 period.\footnote{The details of how the city sample is formed is described in the data section.} Figure \ref{map} illustrates the 74 cities in my main sample. The cities were spread over the U.S.---in 30 different states---though concentrated more on the east coast (this is where most populous cities were located at that time). Consistent with historical accounts, Table \ref{filyr} shows the considerable time variation of filtration plants adoption in my sample period. None of the 74 cities had installed a water filtration plant by 1880, but three-quarters installed by 1920, with 11 cities installing between 1881-1900, 29 between between 1901-1910 and 16 between 1911-1920. I use the year in which the city's filtration plant is first operational as year of adoption for the city.\footnote{Note that cities usually kept expanding their water filtration system after the first operation. This implies that more households would have access to clean water after the municipal adoption of filtration. Therefore, while my estimates below will always give the effect of municipal water filtration, since not all households get access to filtered water after the first year of adoption, my estimates of the effect of water filtration in the empirical analysis could possibly be underestimated. In other words, from the perspective of obtaining causal effects of having filtered water, my estimates give intention to treat effects, and one would have to divide by the take-up rate (e.g., share of households in the city getting filtered water) to get the effect of treatment on the treated.}

There are many reasons for the variation in timing of water filtration adoption by American cities. Bureaucratic, legal, and political factors can speed up or slow down the process of filtration adoption. Some obstacles to adoption included: lobbying state legislatives to raise debt; convincing the public that the benefits of filtration plant outweighs the costs; debates of whether the plant should be operated by city government or by private contract; and lawsuits by opponents against the major city expenditure. Even after the city government approves a filtration plant, it could take years for construction to be completed. For example, for Philadelphia, because of the political complications and length of construction time, it took more than a decade from the approval to actual operation of the plant. This example highlights that cities cannot precisely time when water filtration is operational. These various hurdles to water filtration adoption introduce a certain degree of randomness to whether, and when, a city adopts water filtration plants installation, so it is unlikely that timing of adoption is endogenous when examining children's school enrollment and employment (at least, conditional on city fixed effects).\footnote{See \textcite{mccarthy1987}, \textcite{tk2002}, \textcite{cm2005}, \textcite{cm2006} for discussions.}

It is worth mentioning that water filtration is not the only water purification technology available during the early twentieth century. Other methods or technologies such as water chlorination or modern sewage treatment were also available to purify water. However, \textcite{cm2005}'s mortality reduction results show a large and statistically significant effect from water filtration, but a small and not statistically significant effect from water chlorination,\footnote{The authors claim that this could be due to two possible reasons. The first reason is because major cities generally adopted filtration before chlorination, so there is little mortality variation left to identify the effect of chlorination. The second reason is the less time variation of chlorination adoption cross cities due to its inexpensive costs of implementation; after the first full-scale application of chlorination in Jersey City, NJ in 1908, many other cities started to use chlorine in their water supple in the following decade due to its inexpensive costs \parencite{mcguire2006}.} while the modern sewage treatment was not common until the 1930s and 1940s. Note that in the context of the present study, even if the cities actually adopted water chlorination or other water purification technologies, my municipal water filtration intervention still serves as the effect of ``cleaner water", given the effectiveness of water filter described in Section \ref{quality}.\footnote{In fact, reports and articles claim that there is an increase of interest in using filtration since the application of chlorine alone cannot remove particulate matter in surface waters and chlorine-resistant pathogens \parencite{la2004, mcguire2006}. Moveover, historical accounts show that there was objections and complaints in applying chlorine in drinking water because the unbalanced dosage causes a distinct and disagreeable taste and smell remain in the water \parencite{mcguire2013}.}





\section{Data}\label{data}
To investigate the effect of municipal water filtration on human capital investment, I require data on children's school enrollment and employment and date of city installation of water filtration plants. I obtain the former from U.S. Census microdata, and the latter from various historical sources, as I describe below.

Individual-level data on school enrollment and employment are taken from the U.S. \emph{Integrated Public Use Micro Sample} (IPUMS) \parencite{ipumsUSA}. I use four censuses: 1880, 1900, 1910, and 1920.\footnote{Microdata from the 1890 census is not available because much of the records were destroyed by fire.} To maximize the number of cities in my analysis, I use the 1\% and 10\% samples in 1880, the 1\% and 5\% samples in 1900, and the 1\% sample in both 1910 and 1920.\footnote{In some instances, different samples under one census year provide information for different cities. For example, in 1880 census, information for Austin, TX and Denver, CO is available in the 1\% sample but not in the 10\% sample. For my analyses I use sample weights that are adjusted for the fact that the population share of the sample varies by census year.} In my analysis, school enrollment and employment are binary variables. The school enrollment dummy equals one if the child attended or was enrolled in school in the months before the census day. The employment dummy equals one if the child reported an occupation.\footnote{Different censuses use different occupational classifications, and for constructing the employment variable I used IPUMS' harmonized occupation variable, which translates occupational classifications in different years into one classification system.} The occupation question is only available for individuals age 10 or higher in 1880 and 1900, leading me to restrict my analysis of employment outcomes to individuals at least age 10.

To construct my data set on city water filtration policies, I began with a list of the large cities in that period (specifically, those having a population of over 30,000 in 1920). I then consulted numerous historical sources to find out each city's water filtration policy over the 1880--1920 period.\footnote{My main sources were: \emph{General Statistics of Cities: 1915}\nocite{gen1916} by the United States Bureau of the Census; ``Filtration Plant Census, 1924"\nocite{filcens1925}  published in \emph{Journal of the American Water Works Association}, which is compiled by C.G. Gillespie who was director of the Sanitary Engineering Bureau of the California State Board of Health; a set of three papers titled ``Design and Operation Data on Large Rapid Sand Filtration Plants in the United States and Canada" published in \emph{Journal of the American Water Works Association} \parencite{rapid1932, rapid1942, rapid1956}. Supplementary sources included: various issues from ``Water Survey Series\nocite{wss}"; ``Water Supply Paper\nocite{wsp}"; ``The Purification of Public Water Supplies" \parencite{johnson1913}; ``Present Day Water Filtration Practice" \parencite{johnson1914}; ``Census of Municipal Water Purification Plants in the United States, 1930-1931" \parencite{filcens1933}; ``Manual of Design for Slow Sand Filtration" \parencite{slow1991}.} From these sources, I found filtration plant information for 154 cities. For 73 of these cities, I have both complete information of filtration plant installation for the 1880--1920 period, as well as Census microdata for each of the four censuses. There are 21 cities which have complete city filtration policy data, but lacks Census data from 1880.\footnote{These 21 cities are not identified in the 1880 microdata. According to IPUMS, city's identity is given for households in any city with over 10,000 inhabitants in 1880, and over 25,000 inhabitants in 1900, 1910, and 1920.} I also have partial filtration policy information for 59 cities, in which they either did not have filter until 1914 (lacking information between 1915 to 1920) or had water filtered by private corporations but installation dates are not available. In my main analysis, I exclude these 80 (21 + 59) cities which are not identified in the 1880 Census or which have incomplete information on filtration policy.\footnote{As I discuss below, adding in these 80 cities with incomplete data (either missing a census year or missing some years of water filtration information) lead to substantially similar results (see Section \ref{robust}).} Finally, following the same logic of \textcite{cm2005} who use information for smaller cities with good and readily available historical information, one city with population slightly below the 30,000 in 1920---Steubenville, Ohio---is also used in my analysis.\footnote{The city of Steubenville, Ohio has population of 28,500 according to 1920 census. My results are similar by when this city is excluded from the analysis.} The 74 cities entering my main analysis are mapped in Figure \ref{map}.

To the Census microdata I merged in city-level data on municipal water filtration (when, if at all, the city installed a water filtration plant). My main analysis focuses on native born white and black children who are 10 to 15 years old. The age 10 cutoff is motivated by my interest in analyzing both enrollment and employment, and the employment variable is available for individuals age 10 and higher in the earlier censuses. On the other hand, age 15 would typically be the end of elementary school, in a period when completing elementary school was far from universal and not many attended secondary school. Early in the twentieth century, the typical school system in the U.S. had eight years of elementary school and four years of secondary school \parencite{report1912}. Table \ref{schlsys} describes the typical school structure in 1911. Children typically entered first grade at age 7 or 8, which means that with on-time grade progression students would be finishing elementary school at eighth grade at age 14 or 15. It can be noted that for some analysis, I use a broader age range, 7 to 19 years old, to provide evidence to support the age criteria of 10 to 15 years olds being responsive to municipal water filtration policy while younger and older children are not.

Table \ref{sumstat} shows the summary statistics for the sample used in my main analysis. The average age is 12 years old, 86\% of the sample reported that they had been enrolled or attended school at least one day preceding the Censuses and 10\% of the sample reported an occupation. 7\% of the sample is black children and approximately 7\% of the sample had an illiterate household head. Around 49\% of the sample is male.





\section{Empirical Strategy and Results}\label{resul}
The staggered installation of water filtration plants by the U.S. cities over the 1880--1920 periods permits me to use a difference-in-differences framework to investigate the effect of municipal water filtration.


\subsection{Model Allowing for Age-Specific Effects}\label{agespec}
I begin by estimating the effect of municipal water filtration for each age (7 to 19). To allow the model to be fully flexible with age, I estimate the following equation:
\begin{align}\label{did1}
    y_{ict} = \alpha &+ \Sigma^{19}_{a=7} \beta_{a} (Filter_{ct} \cdot d_{a})
    + \gamma_{ca} + \lambda_{ta} + \Sigma^{19}_{a=7} (\textbf{x}_{ict} \cdot d_{a}) \delta_{a} + (\rho_{c} \cdot t) + \varepsilon_{ict}
\end{align}
where $y_{ict}$ is either the school enrollment dummy or employment dummy for individual $i$ who lives in city $c$ in year $t$. $Filter_{ct}$ is an indicator variable which equals one if city $c$ adopted water filtration in year $t$ or zero otherwise. $d_{a}$ is a dummy that equals one if the individual is age $a$. $\gamma_{ca}$ represents vector of city-age fixed effects, which is city dummies time age dummies. $\lambda_{ta}$ represents vector of year-age fixed effect, which is year dummies time age dummies. $\textbf{x}_{ict}$ is a vector of demographic characteristics such as household head illiteracy, gender, and race dummies. $\rho_{c} \cdot t$ represents city-specific linear time trends which account for the possibility that cities are different in systematic and time-varying way between each other. $\varepsilon_{ict}$ is the error term. In this unrestricted model, each coefficient $\beta_{a}$ represents the effect of water filtration for children of a given age. The identifying assumption of equation (\ref{did1}) is that in the absence of water filtration, cities with and without water filtration have had the same changes over time in children's outcomes (this is the parallel trend assumption, however note it is made after conditioning on city-specific time trends, so it is less restrictive than the standard parallel trend assumption for basic difference-in-differences specifications).

Figure \ref{fig:did1} plots the estimated coefficient $\widehat{\beta}_{a}$ with 95\% confidence intervals from equation (\ref{did1}). The dependent variable is children's school enrollment in Panel A and employment in Panel B. In Panel A, the estimates show that water filtration has the largest positive effect on school enrollment for ages around 14 years old, in particular age 14, and the effect is close to zero for younger and older ages. The estimates in Panel B show a similar pattern in which water filtration has the largest negative effect on children's employment for ages around 14 years old, again largest effect at age 14, then the coefficients fluctuate around zero for other ages. Note that no estimated effect is shown for ages 7 to 9 in Panel B because the occupation question is asked only for children age 10 and above. In sum, Figure \ref{fig:did1} shows that municipal water filtration affects school enrollment and child labor, and the effect is different at different ages.

The possible explanations for the observed pattern in Figure \ref{fig:did1} are that, first, given the school structure mentioned in Section \ref{data}, 14 and 15 are typical ages in which children are at the last year of elementary school during my studied period (see Table \ref{schlsys}). Second, most of the states allow children to legally drop out of school at around 14 years old during my sample period (see Table \ref{sumstat:candropout}). These imply ages around 14 are sensitive ages for educational transition. Similar to \textcite{kosec2014} (see Section \ref{lit}), I observe the largest effect of water filtration when children are at their transition points of education.

In the following analysis, I pool children who are ages 10 to 15 to gain more power and investigate the average effect of water filtration on this sensitive age group of children.


\subsection{Baseline Model}\label{main}
I use a difference-in-differences approach to examine the average effect of water filtration on children who are 10 to 15 years old. Specifically, I estimate the following equation:
\begin{align}\label{did2}
    y_{ict} = \alpha + \beta Filter_{ct} + &\gamma_{c} + \lambda_{t} + \textbf{x}_{ict} \tau + (\rho_{c} \cdot t) + \varepsilon_{ict}
\end{align}
$\gamma_{c}$ represents vector of city dummies which control for city characteristics that do not vary over time, $\lambda_{t}$ represents vector of year dummies which account for shocks or changes that are common to all cities. $\textbf{x}_{ict}$ is a vector of demographic controls including household head illiteracy, gender, race, and age dummies. All other variables are defined in equation (\ref{did1}). In this equation, $\beta$ is the difference-in-differences estimator of the effect of municipal water filtration. In order to interpret the difference-in-differences estimate from equation (\ref{did2}) as the causal impact of municipal water filtration, the parallel trend assumption must hold: in the absence of municipal water filtration, the change over time in enrollment (or employment) is the same between earlier-adopting cities and cities that adopt later or never adopt.

Table \ref{filter} shows the estimated results of equation (\ref{did2}). Each column reports the results of a separate regression with model specification denoted in the last two rows, and the dependent variable and its mean indicated on top of the columns. All regressions control for age fixed effects, city fixed effects, year fixed effects, and are weighted by sample weight. Robust standard errors are clustered at the city level (74 clusters), and they are consistent under heteroscedasticity as well as within-city serial correlation.

Columns (1) to (2) show the estimated effect of municipal water filtration on children's school enrollment with and without city-specific time effects. Without controlling for the city-linear trends in column (1), the coefficient of filter shows a positive effect of water filtration on school enrollment that is statistically significant at the 10\% level. It turns out that the inclusion of city-specific time trends does not materially change the estimated policy effect, as can be seen by comparing column (2) to column (1). This lends confidence that the difference-in-differences estimate is really capturing the causal impact of municipal water filtration, as differential trends in enrollment between adopting and later/never-adopting cities do not appear to be a confounding factor (if differential trends were a problem, then the estimates in the two columns would differ). The estimate in column (2) indicates that municipal water filtration increases school enrollment by 1.9 percentage points, and this is statistically significant at the 5\% level.

Columns (4) to (5) show the estimated effect of municipal water filtration on child labor. Without controlling for any city-specific time effects, the coefficient in column (4) shows that water filtration has a negative and statistically significant effect on child labor. However, the estimate becomes very small and is not statistically significant after controlling for the city-specific linear trends in column (5). The difference in results is suggestive of differential trends in child labor between adopting and later/never-adopting cities, and in subsequent analysis the preferred specification includes city-specific time trends. Based on column (5), I find there is no statistically significant effect of municipal water on child labor, although it can be noted that the point estimate is negative and meaningfully sized (as the mean employment rate is 10\% in the sample).

The coefficients on the control variables are statistically significant and with expected sign. Children from lower socioeconomic status household, which is captured by household head illiteracy, are less likely to enroll and more likely to work. In addition, both blacks and males are less likely to go to school and more likely to work.





\subsection{Assessing the Parallel Trend Assumption}\label{parall}
A possible concern to the difference-in-differences strategy is that there could be differential trends across treated and never treated cities. To handle this issue, I employ model specifications that allow for differential trends across cities, which have been shown and discussed in Table \ref{filter} columns (2) and (5). However, there could still be concerns about time effects that are different between adopting and later/never-adopting cities but not of the smooth variety that could be accounted for using city-specific trends.  I explore this below.

One concern might be that local economic and political conditions might affect the adoption of various amenities, not only water filtration.  Perhaps the difference-in-differences estimates are also picking up the effects of other variables, such as better economic conditions, tougher compulsory schooling laws or tougher child labor laws.  To assess if this is indeed a problem, I take advantage of the fact that my policy variation is at the level of city-time, which means that it is still possible to identify the difference-in-differences estimate even when I control for state-specific time effects. State-year fixed effects control for all time-varying variables that have common effects among cities within the same state, and include state economic conditions, programs and policies (note compulsory schooling laws and child labor laws tend to be set at the state level).

Table \ref{filter}, column (3) shows the results of estimating equation (\ref{did2}) after adding state-year fixed effects, which take care of state-specific time shocks and policy changes, notably state's compulsory schooling laws and child labor laws. Even though this specification throws away all of the cross-state variation in the policy variable, and is still allowing for differential trends across cities, the estimated effect of water filtration on school enrollment is essentially unchanged. The remarkable stability of the difference-in-differences estimate of the effect of municipal water filtration on school enrollment across the specifications encapsulating different assumptions about city-specific time effects, which provides evidence that the estimate is not driven by any city-specific time patterns.\footnote{I also try other model specifications such as state-specific linear trends or state-year fixed effects along, the estimated effect of water filtration on school enrollment are unchanged (The results are not reported and are available upon request).} For child labor, it can be seen that adding state-year fixed effects yields (column (6)) yields similar results as column (5), suggesting that while there appear to be smooth trend differences in child labor between filter cities and other cities, state time-varying variables do not appear to be a source of incremental bias.





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Another way to assess the validity of the parallel trend assumption is to graph the mean outcome by year and filter status, and consider whether in the pre-policy period the filter and no-filter cities do indeed have parallel trends. To preview the results, the graphical analysis indicates support for the parallel trend assumption in the case of school enrollment, but not in the case of child labor, mirroring the earlier results in which estimates of the effect on schooling are insensitive to controls for city time effects while estimates of the effect on child labor are sensitive.

In my context, different cities adopt filtration in different years, so there is no straightforward way to depict all the data used in my regression analysis on the same graph. An additional issue is that for cities adopting filtration in 1900 or earlier, I only have one pre-policy data period (the 1880; recall the 1890 census are not available because records were destroyed in a fire), which does not permit an assessment of pre-policy trends. To make progress, I compare cities which never adopted the intervention to cities which adopted water filtration in a between (1901--1910) or a later (1911--1920) period separately in each graph. Because of the lack of 1890 census, even the comparison between cities with intervention during 1901--1910 versus never-treated cities, and comparison between cities with intervention during 1911--1920 versus never-treated cities are not ideal since some of the pre-treatment periods have 20-year intervals and others have 10-year intervals.

Figures \ref{fig:para:schatt} and \ref{fig:para:worker} show the mean comparison of school enrollment and child labor for treated versus never treated cities, respectively. The solid line represents the mean enrollment in each census year for cities which adopted water filtration in the mentioned time period. The dashed line represents the mean enrollment for cities which never installed a filtration plant. The dotted vertical line located in specified year indicates the census before the intervention, with left side of the line representing pre-treatment periods and right side representing post-treatment periods. Panel A compares cities which never had the intervention to cities which adopted water filtration during 1901--1910. Panel B compares cities which never had the intervention to cities which adopted water filtration in 1911--1920. Broadly speaking, the school enrollment in Figure \ref{fig:para:schatt} shows similar pre-trends between never treated and treated cities (left side of the dotted vertical line). In the post-treatment periods (right side of the dotted vertical line), treated cities started to catch up after the intervention and eventually surpassed the never-treated cities.

Doing the same comparisons in Figure \ref{fig:para:worker}, children's employment in treated cities started to catch up after the intervention (right side of the dotted vertical line) in both Panels A and B. Panel B shows a parallel pre-trends pattern, but the trends of employment in pre-treatment periods do not look parallel in Panel A. This pre-trend pattern of children's employment is consistent with the earlier finding that the estimated effect of filtration on child labor is sensitive to the inclusion of city-specific trends (columns (4)--(5) in Table \ref{filter}).\footnote{Borrowing the logic from Figures \ref{fig:para:schatt} and \ref{fig:para:worker}, I implement placebo tests which compare two pre-policy years. Specifically, I compare cities which adopted water filtration in census year 1910 (or year 1920) to cities which never adopted the intervention, then I assign a fake treatment dummy to the census year right before the actual adoption year to estimate the placebo treatment effect of water filtration. Although these placebo tests are not ideal (for the same reasons mentioned for the graphical analysis), the results generally support the parallel trend assumption in the case of enrollment, and do not support it in the case of employment (The results are not reported and are available upon request).}

Taken all together, the similar results on school enrollment under different assumptions about time effects in Table \ref{filter}, plus the pre-trends comparison in Figure \ref{fig:para:schatt}, give us confidence about the validity of the parallel trend assumption. However, there do appear to be differential trends for the child labor outcome. Therefore, in the following analyses, I regard model specification which controls for city-specific linear trends as the preferred specification throughout this study.





\subsection{Early Childhood Exposure}
One natural question is whether the effect of municipal water filtration varies by age at first exposure. The existing literature shows that conditions and environments at different stages of life could affect later life outcomes differently \parencite{martorell1997, martorell1999, my2009}. The causal effect of early childhood exposure to negative shocks on adverse later life outcomes are well documented (see handbook chapter of \textcite{ac2011} for details). In the context of this study, germ and bacteria in contaminated water generate great risks of exposure to infectious diseases to young children because they are more susceptible to waterborne diseases. The poor nutritional status and risks of infectious diseases during infancy or early childhood may cause nutrition deficiency which has adverse effect on various short-term and long-term outcomes, such as brain development, cognitive performance, adult health, and educational attainment, etc. \parencite{my2009, eppig2010, ac2011, bv2013}. These imply that early childhood exposure to municipal water filtration, which reduces germ and bacteria, may have a greater health benefit.

To investigate the effect of early childhood exposure to water filtration on school enrollment or child labor, I estimate the following model:
\begin{align}\label{1stexpo}
    y_{ict} = \alpha + \Sigma^{15}_{j=0} \beta_{j} I(&\text{1st exposed at age}=j) + \gamma_{c} + \lambda_{t}
    + \textbf{x}_{ict} \tau + (\rho_{c} \cdot t) + \varepsilon_{ict}
\end{align}
where $I(\text{1st exposed at age}=j)$ is an indicator variable equals to one if a child is first exposed to water filtration at age $j$ or zero otherwise. Other variables are defined in equation (\ref{did1}). The control group in equation (\ref{1stexpo}) is children who were never exposed to water filtration. $\beta_{j}$ shows the effect of water filtration when a child was first exposed at age $j$.

In order to have a more complete picture of the effect of age at first exposure, I estimate equation (\ref{1stexpo}) separately by each age. Figures \ref{schatt:1stexpo:cyr} and \ref{worker:1stexpo:cyr} show the estimated effect of the age at first exposure to water filtration on school enrollment and child labor, respectively. Each graph represents estimation for each specific age of children, each point graphed indicates estimated $\widehat{\beta}_{a}$ with the 95\% confidence interval from equation (\ref{1stexpo}), with children who are never exposed to water filtration as the control group. The results in Figure \ref{schatt:1stexpo:cyr} suggest that for children who are currently at ages 10 to 13, early childhood exposure to water filtration has no effect on school enrollment and child labor; all estimates for ages 10 to 13 are small and close to zero. However, for children who are currently at ages 14 to 15, although the pattern of estimated effect of age at first exposure is rather noisy and most of the estimated coefficients are not statistically significant, the bottom two graphs show a rough pattern that children who are first exposed to water filtration at ages 0 to 3 have larger positive effect on school enrollment than those who first exposed at age 4 and older, but the pattern in Figure \ref{worker:1stexpo:cyr} is rather unclear.

Guided by Figures \ref{schatt:1stexpo:cyr} and \ref{worker:1stexpo:cyr}, which suggest that children aged 14--15 and first exposed at ages 0-3 experience larger impacts than children first exposed later, I estimate the following model to get the average effect of early and later exposure to filtration, which is allowed to differ for age 14 and 15 years old (as suggested by Figure \ref{schatt:1stexpo:cyr}):
\begin{align}\label{expo0to3age14to15}
    y_{ict} = \alpha &+ \beta_{1} \text{1st exposed at ages 0-3}
    + \beta_{2} (\text{1st exposed at ages 0-3} \cdot \text{Ages 14-15}) \notag\\
    &+ \beta_{3} \text{1st exposed at ages $\geq$ 4}
    + \beta_{4} (\text{1st exposed at ages $\geq$ 4} \cdot \text{Ages 14-15}) \notag\\
    &+ \gamma_{c} + \lambda_{t} + \textbf{x}_{ict} \tau + (\rho_{c} \cdot t) + \varepsilon_{ict}
\end{align}
where variable $\text{1st exposed at ages 0-3 ($\geq$ 4)}$ is an indicator variable equals to one if children are first exposed to water filtration at ages 0 to 3 ($\geq$ 4) or zero otherwise. Variable Ages 14-15 is a dummy equal to one if a child is currently at age 14 or 15. The control group in equation (\ref{expo0to3age14to15}) is children who are never exposed to water filtration. $\beta_{1} + \beta_{2}$ indicates the total effect of early childhood exposure (first exposed at ages 0-3) to water filtration for children who are aged 14 to 15. $\beta_{3} + \beta_{4}$ represents the total effect of first exposure to water filtration at older than age 4 for children who are aged 14 to 15.

Table \ref{a1415exp0to3} shows the regression results from equation {\ref{expo0to3age14to15}}. The total effects to children who are currently at ages 14 to 15 and first exposed to water filtration at different age ranges are shown in the second and third rows from the bottom, with the joint test p-value shown immediately below. The last row shows the differential effect for first exposed at ages 0-3 and first exposed at ages $\geq$ 4 on children aged 14-15 ($(\beta_{1} + \beta_{2}) - (\beta_{3} + \beta_{4})$). The results clearly show that early childhood exposure to water filtration has largest impact on school enrollment and child labor for children who are aged 14 to 15. The estimate in the third row from the bottom suggests that, compared to children who are never exposed to the intervention, children's school enrollment (employment) increases (decreases) by around 15 (7.5) percentage points if first exposed to water filtration at ages 3 or younger (sample mean of school enrollment and employment for children aged 14-15 is 0.69 and 0.27, respectively). The effect dropped by more than 40\% (60\%) to 8.3 (2.8) percentage points if children are first exposed at ages 4 or older. The estimates in the last row indicate that, for children aged 14-15, the effect of age at first exposure to water filtration at ages 0-3 and $\geq$ 4 is statistically different. Finally, similar to the observed patterns of early childhood effect in Figures \ref{schatt:1stexpo:cyr} and \ref{worker:1stexpo:cyr}, early childhood exposure to water filtration has small and not statistically significant effect to children who are aged 10 to 13. The results here are consistent with the fact that 14 and 15 years old are ages in which children are sensitive to the intervention since they are finishing elementary school and can legally drop out of school in these ages (see discussion in Section \ref{agespec}).

The findings are similar when I used an alternate definition of early childhood exposure, ages 0--5, which is commonly used in the early childhood literature \parencite{ac2011}. I re-estimate equation (\ref{expo0to3age14to15}) by replacing variable ``1st exposed at ages 0-3" with ``1st exposed at ages 0-5" and ``1st exposed at ages $\geq$ 4" to ``1st exposed at ages $\geq$ 6". I find similar results in Table \ref{a1415exp0to5}. The results again suggest that, for children aged 14 to 15, early childhood exposure (defined as first exposed at ages 0-5) to water filtration has larger effect on school enrollment and child labor. Furthermore, as indicated in the last row, the effect of early childhood exposure is statistically different from the effect of later life exposure. The point estimates in the last three rows are substantial similar to that of Table \ref{a1415exp0to3}.



\subsection{Compulsory Schooling Laws}
Clean water can have differential effects on children who are bound or not bound by the compulsory schooling laws. There are likely families whose decisions about child enrollment are constrained by compulsory schooling laws, such that some parents may always send their children to school when they are below the legal school leaving age. So averaged across all families there would be less responsiveness to the health intervention which affects the health status of their children. Therefore, access to clean water may have larger effect on children who are not bound by the compulsory schooling laws and can legally drop out of school. I collect state compulsory schooling laws information from various years of \emph{Report of the Commissioner of Education}\nocite{reportEdu} and data appendix from \textcite{gk2011}, then merge the state's laws to the 30 states in my sample.\footnote{\textcite{gk2011}'s data appendix provides different state compulsory school laws and child labor laws from 1910 to 1939. Few variables also available from 1900 to 1909.}

I construct a dummy variable---\emph{Can drop out}---which indicates whether a child can drop out of school or not:
\begin{center}
    \emph{Can drop out} = 1 if \\
    Child's age $\geq$ Min[(\emph{Minimum compulsory school entrance age} + \emph{yrs of schooling for exemption}), (\emph{Maximum age of compulsory schooling})]
\end{center}
where \emph{Minimum compulsory school entrance age} denotes state's compulsory school entrance age, \emph{Maximum age of compulsory schooling} denotes state's maximum age of compulsory education, \emph{yrs of schooling for exemption} denotes the years of education for exemption from maximum age rule.

Note that I have data for \emph{Minimum compulsory school entrance age} and \emph{Maximum age of compulsory schooling} in all of my studied time periods (years 1880, 1900, 1910, 1920), but I only have data for \emph{yrs of schooling for exemption} in years 1910 and 1920. In constructing the \emph{Can drop out} variable, I assumed that all of the states in my sample did not have the laws of \emph{yrs of schooling for exemption} in years 1880 and 1900. In other words, I assigned the value of zero to this variable to all states in years 1880 and 1900.\footnote{This assumption sounds reasonable since the majority of states did not have the law of \emph{yrs of schooling for exemption} in 1910, which somehow implies there would be even fewer states have this law in 1880 and 1900. The estimated results show similar intuitions if I use an alternative definition of \emph{Can drop out} = 1 if Child's age $\geq$ \emph{Maximum age of compulsory schooling}.} This \emph{Can drop out} dummy variable indicates whether a child can drop out of school legally.

During the years 1880 to 1920, most of the states allowed children to drop out of school at 14. Table \ref{sumstat:candropout} shows the distribution of variable \emph{Can drop out age} for the whole sample and in each census year.

Table \ref{afildrop} shows the estimation results of adding can drop out of school as a control variable and its interaction with water filtration. Columns (1) and (3) show estimation of equation (\ref{did1}) by adding \emph{Can drop out} as a control variable. The coefficient of water filtration in column (1) equals to 0.020 which is very similar to the baseline results shown in Table \ref{filter} (the coefficient equals to 0.019 in Table \ref{filter}). After adding the interaction term between water filtration and \emph{Can drop out} variable in columns (2) and (4), the results show that the positive (negative) effect of water filtration on children's school enrollment (employment) is driven by children who can legally drop out of school. In order words, after the access to clean water, children who are legally allowed to drop out are more likely to continue their education and less likely to work. However, water filtration has no effects on children who are still bound by the compulsory schooling laws and cannot legally drop out of school. The finding is consistent with the municipal water filtration policy inducing children who are ``compliers" to compulsory schooling laws (i.e., attend if required by law, not attend if not required by law) to attend more school.



\subsection{Household Socioeconomic Status}
Existing water studies suggest that water improvement programs have a larger health impact on the poor \parencite{cm2005, galiani2005, gamper2010}. A natural question is to understand whether the effect of water filtration is heterogeneous across rich and poor households. In fact, the benefits of clean water to lower socioeconomic status households are unclear. One may think, on average, that the poor are less healthier than the rich, so they may receive greater health benefits from water quality improvement. On the other hand, the poor households could be less likely to have connections to municipal water supplies, then municipal water filtration may not benefit the poor households. Another consideration is that child labor is used more by poor families, so the improved health from filtration may pull a child into employment instead of schools. I use household head illiteracy as a measure of household socioeconomic status.

Table \ref{filterXilliterHH} shows estimated results of the baseline model when the treatment variable is interacted with the household head illiterate dummy. The total effect of water filtration to lower socioeconomic status children with joint test p-value is shown in the last row of the table. The estimated total effect in Table \ref{filterXilliterHH} show that water filtration increases (decreases) children's school enrollment (employment) of children from lower socioeconomic status household by 5.4 (2.5) percentage points at the 1\% (10\%) significant level. This is consistent with improvements in public health infrastructure reducing inequality in school enrollment between higher and lower socioeconomic status students. It is not possible to pinpoint why the response in school enrollment is greater for the poor. For example, it could be due to health improving more for the poor (because the rich have other mechanisms to protect health, such as better medical care, health practices, or nutrition), the poor are at a point in the health distribution where a marginal increase in health delivers greater returns from schooling, or the rich have relatively inelastic demand for schooling since they always attend schools.

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\subsection{Racial Differences}
I also investigate whether the effect of water filtration is different between white and black children in the North and South.\footnote{I classify the North and South by whether the city is located in the confederate states or not. The confederate states in my sample include: Alabama, Georgia, Louisiana, North Carolina, South Carolina, Tennessee, Texas, and Virginia. } The results in Table \ref{filterXblackXsouth} show suggestive evidence that water filtration has a larger effect on school enrollment to black children in the South.\footnote{It can be noted that the estimates in this table might not be precise since white and black children could have different time trends even within a city. However, given the data limitation that there are only a few black children in some cities (e.g., some cities only have 8 to 10 black children in my sample), it is not possible to control for the differential trends between white and black children in the estimation.}



\subsection{Gender Differences}
Recent literature shows that, compared to males, females have stronger responses to health improvement programs \parencite{mk2004, my2009, pitt2012, bv2013}. In this section, I investigate the heterogeneous effects of water filtration across gender and try to explore the mechanisms behind the gender differences.

One relevant hypothesis is the difference in comparative advantage between male and female suggested by \textcite{pitt2012}. They construct a theoretical model which illustrates the idea that the improvement of nutritional status for everyone will increase schooling more for females relative to males and increase the gender division of labor, given that females have comparative advantage in skill intensive occupations such as education. The idea, suggested by medical literature, is that males will gain more physical strength than girls when there is an improvement of their nutritional status \parencite{round1999}. So a health intervention will increase males comparative advantage in working on physically intensive occupations, which raises their relative returns to working (given that child labor for boys primarily requires physical stamina). This type of reasoning suggests that a health intervention increases female comparative advantage in skill intensive occupations (occupations with higher skill returns) such as education.

I estimate the effect of water filtration separately by male and female. The results are in Table \ref{gender}. In Panel A, columns (1) and (2) show estimated effect of water filtration on school enrollment and child labor for male sample, while columns (3) and (4) show that for female sample. Both males and females are more likely to go to school (columns (1) and (3)). However, for child labor (columns (2) and (4)), the effect for females is negative and significant while it is positive and insignificant for males. These results are consistent with a story like the aforementioned one. Water filtration improves health, which in turn lowers the costs of going to school given the health-education complementarities, which has the effect of raising enrollment.  However, it raises the enrollment of females more than males because an improvement in health has an offsetting effect on males of raising child labor: males have a comparative advantage in physically strenuous occupations, an improvement in health raises the relative returns to working for males. Note that the coefficients on school enrollment between male and female in columns (1) and (3) are not statistically different from each other, while the coefficients on children's employment between male and female in columns (2) and (4) are statistically different from each other at the 1\% level.

There are other hypotheses which could also explain the gender differences in Table \ref{gender}. The first possibility is that the differential results reflect catch-up if female children had initially a lower enrollment rate or higher employment rate than males. However, I can rule out this explanation because, as reported in Table \ref{gender}, both males and females have the exact same school enrollment rate, and females have a lower employment rate compared to males.

Another possibility is the survival selection of males. After the municipal water filtration, children who are relatively weak and fragile will most likely be the marginal survivors. Since literature suggests that males are biologically more vulnerable than females \parencite{kraemer2000}, one would expect a greater survival margin among males. This can possibly explain my findings of relatively weaker response from males. More importantly, this also implies there would be an even greater selection among male children from families of lower socioeconomic status. In other words, among males, one might expect that poor male children would have a weaker response to the health intervention compared to their nonpoor counterparts. However, the results in columns (5) and (6) in Panel B of Table \ref{gender} show that, compared to their counterparts from nonpoor families, males from lower socioeconomic status families have stronger responses from the clean water intervention.\footnote{In Panel B, the total effect of water filtration to lower socioeconomic status children shown in the last row of the table. The results here provide similar intuitions as in Panel A. } This suggests that selection effects are not large.





\subsection{Robustness Checks}\label{robust}
In this section, I explore the sensitivity of the results to using alternative samples of cities and an alternative way of measuring exposure to the water filtration policy.

First, one might be concerned that cities which never adopted water filtration during my studied time periods are cities with backward and disadvantaged situations compared to the treated cities, so perhaps they are not a plausible comparison group for constructing the counterfactual change over time for the cities that do adopt filtration. To address this concern, I re-estimate equation (\ref{did2}) by restricting the sample to cities which ever adopted water filtration plants in Table \ref{filter:NoNev}. The analysis here contains 56 cities that have adopted water filtration. The sample size is reduced by around one-fourth, but the estimated effects of water filtration remain the same.

Second, as mentioned in Section \ref{data}, in addition to the 74 cities which I have complete information of both water filtration data and Census data for 1880 to 1920, I also have partial information of two additional groups of cities: 21 cities which I have water filtration data from 1880--1920 but their city's identity is not available in the 1880 census, and 59 cities where identity is available in the 1880 census but I only have partial information of water filtration until 1914 (lacking filtration information from 1915 to 1920). For the first group of cities, I include them in my analysis to form an unbalanced city-year panel (these 21 cities lack observations from 1880). For the second group of cities, I assume that they never adopted water filtration (i.e., assign the years 1915-1920 with zero for the filter variable) and add them to my analysis. The results are in Table \ref{filter:MoreCities}. Each column represents a separate regression with dependent variables and its mean reported at the top of the table. Columns (1) and (5) repeat estimation of the baseline models in Table \ref{filter}. Columns (2) and (6) show unbalanced city-year panel estimations including 21 cities. Columns (3) and (7) show estimations by assuming the 59 cities with partial filtration information never adopted the intervention. Finally, Columns (4) and (8) show estimations including all cities. The results show that regardless of the number of cities being used in the sample, the estimated effect of water filtration on children's school enrollment and employment is largely unchanged. This provides evidence that my finding of water filtration is not sensitive to the city selection criteria.

Third, so far I have examined the effect of water filtration by using a treatment dummy specified in equation \ref{did2}. An alternative way to estimate the effect of clean water is to calculate the number of years children are exposed to water filtration. This alternative treatment measure captures the accumulation effect of health improvement, which means that the longer the years of exposure to clean water, the larger the positive effect on health. Table \ref{yrexp} shows regression results by changing the treatment dummy variable to years of exposure to water filtration in equation (\ref{did1}) (with 3.8 mean years of exposure in the sample). The results show that, regardless of model specification, children who have longer exposure to water filtration have a larger positive effect on school enrollment and negative effect to child labor. The estimate from the main specification (model specification with city-linear trends) shows that one more year of exposure to clean water increases school enrollment by 0.8 percentage points and decreases child labor by 0.4 percentage points, these effects are statistically significant at 5\% level. This finding is consistent with the results in the baseline model discussed in Section \ref{main}.





\section{Discussion and Conclusion}\label{conclu}
This study quantifies the effect of access to clean water on school enrollment and child labor. Many American cities adopted water filtration during the late nineteenth and early twentieth centuries. From various historical sources, I am able to obtain complete filtration plant information for 74 cities from 1880--1920. By using a difference-in-differences method which exploits variation across time and across cities, I find that municipal water filtration increases school enrollment by 2 percentage points (compared to a sample mean of 86\%) and has no detectable effect on child labor in the baseline analysis. These effects are more pronounced for children aged 14--15, who are in their last year of elementary school and can legally drop out of school. I explore the hetergoeneity in policy effects along several additional dimensions. I find a larger positive effects on enrollment for children who are exposed at younger ages, children who can legally drop out of school, children from lower socioeconomic status families, and female children. I also provide suggestive evidence that black children who lived in the South benefits more from water filtration.

Note that since I define filtration adoption as the year of filtration plants which are operational, which does not necessarily mean that the entire city would be exposed to clean water right away. Given that more households would enjoy the benefits from filtration after the first operation of water filters, my estimation results of water filtration could be a lower bound of the true effect.

How does my estimated effect of clean water intervention compare to other studies on various types of health improvement programs? \textcite{mk2004} find that a deworming program in Kenya increases school participation by 7.5 percentage points, and the effect is more pronounced for girls. \textcite{bleakley2007} looks at a hookworm eradication program in American South happened in the early twentieth century and find that the program increases school enrollment by 3--5 percentage points. \textcite{kosec2014} finds that private sector participation in the piped water sector in African countries is associated with a 7.8 percentage points increase in school attendance, and the effect is age specific. My estimated treatment effect in the baseline model suggests a gain of around 2 percentage points in school enrollment, which is smaller than the other health programs in the literature. Note that most of these studies examine programs in different countries which have different social and demographic contexts compared to the U.S. An article which is germane to my study is \textcite{bleakley2007}. The author looks at the American South in a similar time period and finds a little larger point estimate than my main treatment effect (3--5 vs. 2 percentage points), which could be due to the fact that the hookworm eradication program is implemented in rural areas but my municipal water filtration intervention focuses only in urban areas.

My findings suggest that some basic infrastructure investments such as municipal water filtration which provides safe water not only improves health, but also affect human capital investment (i.e., school enrollment and child labor). This provides policy makers with a better understanding that water improvement programs have benefits beyond lowering mortality and morbidity. The findings in this study are particularly important to developing countries where poor health environment may impede the development of human capital in schools.






%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% FIGURES %%%%%
\clearpage
\section{Figures}
\begin{figure}[!htbp]
\centering
\caption{Cities}\label{map}
\includegraphics[width=.95\textwidth]{map_completed.jpg}
\end{figure}
\begin{minipage}{14.5cm}\vspace*{-0.5cm}
\singlespace
\footnotesize
Notes: Displayed are the 74 cities (located in 30 states) in my main sample. As described in Section \ref{data}, these are the cities for which both complete data on filter installation and IPUMS microdata are available for census years 1880--1920.
\end{minipage}



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% AGE SPECIFIC EFFECTS %%%%%
\clearpage
\begin{figure}[!htbp]
\centering
\caption{The Effect of Municipal Filter Installation by Age}\label{fig:did1}
\medskip
{\footnotesize Panel A: School Enrollment} \\
\includegraphics[width=0.6\textwidth]{est_reg_afilageX_schatt_AllInter_cyr.png}

{\footnotesize Panel B: Employment (Reported an Occupation)} \\
\includegraphics[width=0.6\textwidth]{est_reg_afilageX_worker_AllInter_cyr.png}
\end{figure}
\begin{minipage}{14.5cm}\vspace*{-0.5cm}
\singlespace
\footnotesize
Notes: Each point shows estimated $\widehat{\beta}_{a}$ with 95\% confidence interval from equation (\ref{did1}). Robust standard errors clustered at city level were used. The controls in this equation are: age fixed effects, year fixed effects, city fixed effects, household head illiteracy, black, male, interaction between age dummies and all aforementioned control variables, and city-specific linear time trend. In panel B, no estimated effect is shown for ages 7 to 9 because the occupation question is asked only for children age 10 and above.
Sample consists of children between ages 7 to 19 in the IPUMS census 1880, 1900, 1910, 1920.
Regressions are weighted by sample weight.
\end{minipage}



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% PARALLEL TREND %%%%%
\clearpage
\begin{figure}[!htbp]
\centering
\caption{Assessing Parallel Trend: School Enrollment}\label{fig:para:schatt}
\medskip
{\footnotesize Panel A: Cities that Adopted Water Filtration During 1901--1910 vs. Cities that Never Adopted} \\
\includegraphics[width=0.6\textwidth]{schatt_year_parall_both_compare2_a10-15.png}

{\footnotesize Panel B: Cities that Adopted Water Filtration During 1911--1920 vs. Cities that Never Adopted} \\
\includegraphics[width=0.6\textwidth]{schatt_year_parall_both_compare3_a10-15.png}
\end{figure}
\begin{minipage}{14.5cm}\vspace*{-0.5cm}
\singlespace
\footnotesize
Notes: Solid line represents the mean enrollment in each year for cities which adopted Filtration Plant in mentioned time period. Dashed line represents the mean enrollment for cities which never installed a filtration plant during the entire time periods 1880-1920.
The dotted vertical line located in specified year indicates the first census before the intervention. So to the left are the pre-intervention periods and to the right are the post-intervention periods.
Sample consists of children between ages 10 to 15 in the IPUMS census 1880, 1900, 1910, 1920.
The census of 1890 is not available because the records were destroyed by fire.
Statistics are weighted by sample weight.

\end{minipage}



\clearpage
\begin{figure}[!htbp]
\centering
\caption{Assessing Parallel Trend: Employment}\label{fig:para:worker}
\medskip
{\footnotesize Panel A: Cities that Adopted Water Filtration During 1901--1910 vs. Cities that Never Adopted} \\
\includegraphics[width=0.6\textwidth]{worker_year_parall_both_compare2_a10-15.png}

{\footnotesize Panel B: Cities that Adopted Water Filtration During 1911-1920 vs. Cities that Never Adopted} \\
\includegraphics[width=0.6\textwidth]{worker_year_parall_both_compare3_a10-15.png}
\end{figure}
\begin{minipage}{14.5cm}\vspace*{-0.5cm}
\singlespace
\footnotesize
Notes: Solid line represents the mean reported occupation (child labor) in each year for cities which adopted Filtration Plant in mentioned time period. Dashed line represents the mean reported occupation (child labor) for cities which never installed a filtration plant during the entire time periods 1880-1920.
The dotted vertical line located in specified year indicates the first census before the intervention. So to the left are the pre-intervention periods and to the right are the post-intervention periods.
Sample consists of children between ages 10 to 15 in the IPUMS census 1880, 1900, 1910, 1920.
The census of 1890 is not available because the records were destroyed by fire.
Statistics are weighted by sample weight.
\end{minipage}





\clearpage
\begin{figure}[!htbp]
\centering
\caption{The Effect of Municipal Filter Installation on School Enrollment by Age and Age at First Exposure}\label{schatt:1stexpo:cyr}
\tiny

\includegraphics[width=0.44\textwidth]{est_reg_F_schatt_a10_cyr.png}
\includegraphics[width=0.44\textwidth]{est_reg_F_schatt_a11_cyr.png}

\includegraphics[width=0.44\textwidth]{est_reg_F_schatt_a12_cyr.png}
\includegraphics[width=0.44\textwidth]{est_reg_F_schatt_a13_cyr.png}

\includegraphics[width=0.44\textwidth]{est_reg_F_schatt_a14_cyr.png}
\includegraphics[width=0.44\textwidth]{est_reg_F_schatt_a15_cyr.png}
\end{figure}
\begin{minipage}{14.5cm}\vspace*{-0.5cm}
\singlespace
\footnotesize
Notes: Each point graphed shows the coefficient for filter for the given age at exposure (i.e., estimated $\widehat{\beta}_{a}$ with 95\% confidence interval from equation (\ref{1stexpo})).
Robust standard errors clustered at city level were used. The controls in this equation are: age fixed effects, year fixed effects, city fixed effects, household head illiteracy, black, male, and city-specific linear time trend. Control group: children never exposed to water filtration.
Regressions are weighted by sample weight.
\end{minipage}



\clearpage
\begin{figure}[!htbp]
\centering
\caption{The Effect of Municipal Filter Installation on Employment by Age and Age at First Exposure}\label{worker:1stexpo:cyr}
\tiny

\includegraphics[width=0.44\textwidth]{est_reg_F_worker_a10_cyr.png}
\includegraphics[width=0.44\textwidth]{est_reg_F_worker_a11_cyr.png}

\includegraphics[width=0.44\textwidth]{est_reg_F_worker_a12_cyr.png}
\includegraphics[width=0.44\textwidth]{est_reg_F_worker_a13_cyr.png}

\includegraphics[width=0.44\textwidth]{est_reg_F_worker_a14_cyr.png}
\includegraphics[width=0.44\textwidth]{est_reg_F_worker_a15_cyr.png}
\end{figure}
\begin{minipage}{14.5cm}\vspace*{-0.5cm}
\singlespace
\footnotesize
Notes: Each point graphed shows the coefficient for filter for the given age at exposure (i.e., estimated $\widehat{\beta}_{a}$ with 95\% confidence interval from equation (\ref{1stexpo})).
Robust standard errors clustered at city level were used. The controls in this equation are: age fixed effects, year fixed effects, city fixed effects, household head illiteracy, black, male, and city-specific linear time trend. Control group: children never exposed to water filtration.
Regressions are weighted by sample weight.
\end{minipage}





%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% TABLES %%%%%
\clearpage
\section{Tables}
\begin{table}[!htbp]
\caption{Average Number of Bacteria Per Cubic Centimeter in Lawrence, MA}\label{bact}
\centering
\footnotesize
\newcolumntype{Y}{>{\raggedleft\arraybackslash}X}
\begin{tabularx} {6cm} {@{} l Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y@{}}
\toprule
Year & Water Before Filtration & Water After Filtration \\\hline
     & \multicolumn{2}{c}{All Bacteria} \\[-3pt]\cmidrule(l{.75em}){2-3}
1894 & 10,800 & 110 \\
1895 & 11,100 & 80 \\
1896 &  7,600 & 85 \\
1897 & 10,900 & 84 \\
     & \multicolumn{2}{c}{B. coli communis} \\[-3pt]\cmidrule(l{.75em}){2-3}
1898 &  4,400 & 66 \\
1899 &  5,800 & 42 \\
1900 &  8,970 & 49 \\
1901 &  3,017 & 26 \\
\bottomrule
\addlinespace[.75ex]
\end{tabularx}
\tabnote{Source: \textcite{jama1903b}. \\
Notes: Lawrence, Massachusetts installed its first water filter in 1893. In 1898, the test of water quality changed to measure only B. coli communis concentration.}
\normalsize
\end{table}



\begin{table}[!htbp]
\caption{Municipal Filtration Plants Installation, 1880-1920}\label{filyr}
\footnotesize
\centering
\newcolumntype{Y}{>{\raggedleft\arraybackslash}X}
\begin{tabularx} {9cm} {@{} l Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y@{}}
\toprule
\input{\ChOnePath/sumstat_treated_yr.txt}
\bottomrule
\addlinespace[.75ex]
\end{tabularx}
\tabnote{ }
\normalsize
\end{table}



\begin{table}[!htbp]
\caption{Typical School Structure in 1911}\label{schlsys}
\footnotesize
\centering
\newcolumntype{Y}{>{\raggedleft\arraybackslash}X}
\begin{tabularx} {\linewidth} {r|YY|YYYY|YYYY|YYYY|YYYY}
\toprule
      &       &    & \multicolumn{8}{c|}{Elementary} & \multicolumn{4}{c|}{Secondary} & \multicolumn{4}{c}{Higher} \\
      &       &    & \multicolumn{4}{c|}{First four grades} & \multicolumn{4}{c|}{Second four grades}   & \multicolumn{4}{c|}{} & \multicolumn{4}{c}{} \\
\hline
Grade \& year &    &    & 1  & 2  & 3  & 4  & 5  & 6  & 7  & 8  & 1  & 2  & 3  & 4  & 1  & 2  & 3  & 4 \\
Age           & 5  & 6  & 7  & 8  & 9  & 10 & 11 & 12 & 13 & 14 & 15 & 16 & 17 & 18 & 19 & 20 & 21 & 22 \\
\bottomrule
\addlinespace[.75ex]
\end{tabularx}
\tabnote{Source: \emph{Report of the Commissioner of Education, 1912}. U.S. Bureau of Education}
\end{table}



\clearpage
\begin{table}[!htbp]
\caption{Summary Statistics For Main Analysis Sample, Children Aged 10-15}\label{sumstat}
\footnotesize
\centering
\newcolumntype{Y}{>{\raggedleft\arraybackslash}X}
\begin{tabularx} {14cm} {@{} l Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y@{}}
\toprule
\input{\ChOnePath/sumstat_SumVar.txt}
\bottomrule
\addlinespace[.75ex]
\end{tabularx}
\tabnote{Notes: Sample consists of children between ages 10 to 15 in the IPUMS census 1880, 1900, 1910, 1920 in the 74 cities with complete filtration information. Statistics are weighted by sample weight.}
\normalsize
\end{table}





%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% BACIC RESULTS %%%%%
\clearpage
\begin{table}[!htbp]
\caption{The Effect of Water Filtration on School Enrollment and Employment}\label{filter}
\footnotesize
\centering
\newcolumntype{Y}{>{\raggedleft\arraybackslash}X}
\begin{tabularx} {15cm} {@{} l Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y@{}}
\toprule
                           & \multicolumn{6}{c}{Dependent variable} \\\cmidrule(l{.75em}){2-7}
                           & \multicolumn{3}{c}{School enrollment} & \multicolumn{3}{c}{Employment} \\
                           & \multicolumn{3}{c}{0.86} & \multicolumn{3}{c}{0.10}  \\
                           & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)}
                           & \multicolumn{1}{c}{(3)} & \multicolumn{1}{c}{(4)}
                           & \multicolumn{1}{c}{(5)} & \multicolumn{1}{c}{(6)} \\
                           \cmidrule(l{.75em}){2-4}\cmidrule(l{.75em}){5-7}
\input{\ChOnePath/reg_filter_a1_m1_3spec.txt}
Model specification: \\
City-linear trends         & No  & Yes & Yes & No  & Yes & Yes \\
State-year FE              & No  & No  & Yes & No  & No  & Yes \\
\bottomrule
\addlinespace[.75ex]
\end{tabularx}
\tabnote{Notes: ***$p<0.01$, **$p<0.05$, *$p<0.1$.
Sample consists of children between ages 10 to 15 in the IPUMS census 1880, 1900, 1910, 1920 in the 74 cities with complete filtration information.
Regressions are weighted by sample weight.
Robust standard errors clustered at the city (74 cities) level are in parentheses.
Dependent variable school enrollment (employment) is an indicator variable equals to one if a child is enrolled (reported an occupation) and zero otherwise.
Filter is an indicator variable equals to one if city installed the water filtration plant. Additional control variables not reported in the table are: age fixed effects, year fixed effect, city fixed effects. }
\normalsize
\end{table}





%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% EXPOSED AT AGES 0-3 X AGES 14-15
\clearpage
\begin{table}[!htbp]
\caption{The Effect of Early Childhood Exposure to Water Filtration on School Enrollment and Employment}\label{a1415exp0to3}
\footnotesize
\centering
\newcolumntype{Y}{>{\raggedleft\arraybackslash}X}
\begin{tabularx} {11cm} {@{} l Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y@{}}
\toprule
                           & \multicolumn{2}{c}{Dependent variable} \\\cmidrule(l{.75em}){2-3}
                           & \multicolumn{1}{c}{School} & \multicolumn{1}{c}{} \\[-3pt]
                           & \multicolumn{1}{c}{enrollment} & \multicolumn{1}{c}{Employment} \\
                           & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)} \\
                           \cmidrule(l{.75em}){2-2}\cmidrule(l{.75em}){3-3}
\input{\ChOnePath/reg_a1415exp0to3_a1_cyr.txt} \\
City-linear trends         & Yes & Yes \\
   \\
Effect on Ages 14-15 + Exposed 0-3      & 0.147*** & -0.075*** \\
~~~Joint test [p-value]$^a$             & [0.000]  & [0.002]   \\
Effect on Ages 14-15 + Exposed $\geq 4$ & 0.083*** & -0.028*   \\
~~~Joint test [p-value]$^b$             & [0.000]  & [0.093]   \\
Difference in effect for \\[-3pt]
~~~Exposed 0-3 and $\geq 4$ on Ages 14-15 & 0.064**  & -0.047*  \\
~~~Joint test [p-value]$^c$             & [0.015]  & [0.060]  \\
\bottomrule
\addlinespace[.75ex]
\end{tabularx}
\tabnote{Notes: ***$p<0.01$, **$p<0.05$, *$p<0.1$.
Sample consists of children between ages 10 to 15 in the IPUMS census 1880, 1900, 1910, 1920 in the 74 cities with complete filtration information.
Regressions are weighted by sample weight.
Robust standard errors clustered at the city (74 cities) level are in parentheses.
Dependent variable school enrollment (employment) is an indicator variable equals to one if a child is enrolled (reported an occupation) and zero otherwise.
The sample mean of school enrollment and employment for children aged 14-15 is 0.69 and 0.27, respectively.
1st exposed at ages 0-3 ($\geq$ 4) is an indicator variable equals to one if children are first exposed to water filtration at ages 0 to 3 ($\geq$ 4).
Ages 14-15 is a dummy equal to one if a child is currently at age 14 or 15.
Reference group is children who never exposed to water filtration.
Additional control variables not reported in the table are: age fixed effects, year fixed effect, city fixed effects, head illiterate, black, male.

$^a$Joint test: $\beta_{1} + \beta_{2} = 0$.

$^b$Joint test: $\beta_{3} + \beta_{4} = 0$.

$^c$Joint test: $(\beta_{1} + \beta_{2}) - (\beta_{3} + \beta_{4}) = 0$. }
\normalsize
\end{table}





%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% CAN DROP OUT %%%%%
\clearpage
\begin{table}[!htbp]
\caption{The Effect of Water Filtration by Legal School Dropout Status}\label{afildrop}
\footnotesize
\centering
\newcolumntype{Y}{>{\raggedleft\arraybackslash}X}
\begin{tabularx} {13cm} {@{} l Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y@{}}
\toprule
                           & \multicolumn{4}{c}{Dependent variable} \\\cmidrule(l{.75em}){2-5}
                           & \multicolumn{2}{c}{School enrollment} & \multicolumn{2}{c}{Employment} \\
                           & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)}
                           & \multicolumn{1}{c}{(3)} & \multicolumn{1}{c}{(4)} \\
                           \cmidrule(l{.75em}){2-3}\cmidrule(l{.75em}){4-5}
\input{\ChOnePath/reg_afildrop_a1.txt} \\
City-linear trends & Yes & Yes & Yes & Yes \\
\bottomrule
\addlinespace[.75ex]
\end{tabularx}
\tabnote{Notes: ***$p<0.01$, **$p<0.05$, *$p<0.1$.
Sample consists of children between ages 10 to 15 in the IPUMS census 1880, 1900, 1910, 1920 in the 74 cities with complete filtration information.
Regressions are weighted by sample weight.
Robust standard errors clustered at the city (74 cities) level are in parentheses.
Dependent variable school enrollment (employment) is an indicator variable equals to one if a child is enrolled (reported an occupation) and zero otherwise.
Filter is an indicator variable equals to one if city installed the water filtration plant.
\emph{Can drop out} is an indicator variable equals to one if a child can legally drop out of school.
Additional control variables not reported in the table are: age fixed effects, year fixed effect, city fixed effects, head illiterate, black, male. }
\normalsize
\end{table}





%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% SOCIOECONOMIC STATUS %%%%%
\clearpage
\begin{table}[!htbp]
\caption{The Effect of Water Filtration by Household Socioeconomic Status}\label{filterXilliterHH}
\footnotesize
\centering
\newcolumntype{Y}{>{\raggedleft\arraybackslash}X}
\begin{tabularx} {8.5cm} {@{} l Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y@{}}
\toprule
                           & \multicolumn{2}{c}{Dependent variable} \\\cmidrule(l{.75em}){2-3}
                           & \multicolumn{1}{c}{School} & \multicolumn{1}{c}{} \\[-3pt]
                           & \multicolumn{1}{c}{enrollment} & \multicolumn{1}{c}{Employment} \\
                           & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)} \\
                           \cmidrule(l{.75em}){2-2}\cmidrule(l{.75em}){3-3}
\input{\ChOnePath/reg_filter_a1_m2.txt} \\
City-linear trends         & Yes & Yes \\
   \\
Filter + lower SES           & 0.054*** & -0.025* \\
~~~Joint test [p-value]$^a$    & [0.000]  & [0.085] \\
\bottomrule
\addlinespace[.75ex]
\end{tabularx}
\tabnote{Notes: ***$p<0.01$, **$p<0.05$, *$p<0.1$.
Sample consists of children between ages 10 to 15 in the IPUMS census 1880, 1900, 1910, 1920 in the 74 cities with complete filtration information.
Regressions are weighted by sample weight.
Robust standard errors clustered at the city (74 cities) level are in parentheses.
Dependent variable school enrollment (employment) is an indicator variable equals to one if a child is enrolled (reported an occupation) and zero otherwise.
Filter is an indicator variable equals to one if city installed the water filtration plant.
Additional control variables not reported in the table are: age fixed effects, year fixed effect, city fixed effects, black, male.

$^a$Joint test: Filter + Interaction = 0. }
\normalsize
\end{table}





%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% GENDER %%%%%
\clearpage
\begin{table}[!htbp]
\caption{The Effect of Water Filtration by Gender}\label{gender}
\footnotesize
\centering
\newcolumntype{Y}{>{\raggedleft\arraybackslash}X}
\begin{tabularx} {13cm} {@{} l Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y@{}}
\toprule
                           & \multicolumn{4}{c}{Panel A: By gender} \\
                           \cmidrule(l{.75em}){2-5}
                           & \multicolumn{2}{c}{Male} & \multicolumn{2}{c}{Female} \\
                           \cmidrule(l{.75em}){2-3}\cmidrule(l{.75em}){4-5}
                           & \multicolumn{1}{c}{School} & \multicolumn{1}{c}{}
                           & \multicolumn{1}{c}{School} & \multicolumn{1}{c}{} \\[-2pt]
                           & \multicolumn{1}{c}{Enrollment} & \multicolumn{1}{c}{Employment}
                           & \multicolumn{1}{c}{Enrollment} & \multicolumn{1}{c}{Employment} \\
                           & \multicolumn{1}{c}{0.86} & \multicolumn{1}{c}{0.12}
                           & \multicolumn{1}{c}{0.86} & \multicolumn{1}{c}{0.08} \\
                           & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)}
                           & \multicolumn{1}{c}{(3)} & \multicolumn{1}{c}{(4)} \\
                           \cmidrule(l{.75em}){2-2}\cmidrule(l{.75em}){3-3}
                           \cmidrule(l{.75em}){4-4}\cmidrule(l{.75em}){5-5}
\input{\ChOnePath/reg_male_a1_m1_cyr.txt} \\
City-linear trends         & Yes & Yes & Yes & Yes \\
\bottomrule \\

                           & \multicolumn{4}{c}{Panel B: By Gender and household socioeconomic status} \\
                           \cmidrule(l{.75em}){2-5}
                           & \multicolumn{2}{c}{Male} & \multicolumn{2}{c}{Female} \\
                           \cmidrule(l{.75em}){2-3}\cmidrule(l{.75em}){4-5}
                           & \multicolumn{1}{c}{School} & \multicolumn{1}{c}{}
                           & \multicolumn{1}{c}{School} & \multicolumn{1}{c}{} \\[-2pt]
                           & \multicolumn{1}{c}{Enrollment} & \multicolumn{1}{c}{Employment}
                           & \multicolumn{1}{c}{Enrollment} & \multicolumn{1}{c}{Employment} \\
                           & \multicolumn{1}{c}{0.86} & \multicolumn{1}{c}{0.12}
                           & \multicolumn{1}{c}{0.86} & \multicolumn{1}{c}{0.08} \\
                           & \multicolumn{1}{c}{(5)} & \multicolumn{1}{c}{(6)}
                           & \multicolumn{1}{c}{(7)} & \multicolumn{1}{c}{(8)} \\
                           \cmidrule(l{.75em}){2-2}\cmidrule(l{.75em}){3-3}
                           \cmidrule(l{.75em}){4-4}\cmidrule(l{.75em}){5-5}
\input{\ChOnePath/reg_male_a1_m2_cyr.txt}
     \\
City-linear trends         & Yes & Yes & Yes & Yes \\
     \\
Filter + lower SES          & 0.063*** & -0.010  & 0.044*** & -0.035*** \\
~~~Joint test [p-value]$^a$ & [0.000]  & [0.615] & [0.007]  & [0.005]   \\

\bottomrule
\addlinespace[.75ex]
\end{tabularx}
\tabnote{Notes: ***$p<0.01$, **$p<0.05$, *$p<0.1$.
Sample consists of children between ages 10 to 15 in the IPUMS census 1880, 1900, 1910, 1920 in the 74 cities with complete filtration information.
Regressions are weighted by sample weight.
Robust standard errors clustered at the city (74 cities) level are in parentheses.
Dependent variable school enrollment (employment) is an indicator variable equals to one if a child is enrolled (reported an occupation) and zero otherwise.
Filter is an indicator variable equals to one if city installed the water filtration plant.
Additional control variables not reported in the table are: age fixed effects, year fixed effect, city fixed effects, black, male.

$^a$Joint test: Filter + Interaction = 0. }
\normalsize
\end{table}




%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% ROBUSTNESS CHECK %%%%%
%%%%% OMIT NEVER ADPOTED CITIES %%%%%
\clearpage
%\vspace*{-3cm}
\begin{table}[!htbp]
\caption{The Effect of Water Filtration on School Enrollment and Employment (Restricting Sample to Cities that Ever Adopted Water Filtration during 1880--1920)}\label{filter:NoNev}
\footnotesize
\centering
\newcolumntype{Y}{>{\raggedleft\arraybackslash}X}
\begin{tabularx} {7.5cm} {@{} l Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y@{}}
\toprule
                           & \multicolumn{2}{c}{Dependent variable} \\\cmidrule(l{.75em}){2-3}
                           & \multicolumn{1}{c}{School} & \multicolumn{1}{c}{} \\[-3pt]
                           & \multicolumn{1}{c}{enrollment} & \multicolumn{1}{c}{Employment} \\
                           & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)} \\
                           \cmidrule(l{.75em}){2-2}\cmidrule(l{.75em}){3-3}
\input{\ChOnePath/reg_omit_afilpt_a1_cyr.txt} \\
City-linear trends & Yes & Yes \\
\bottomrule
\addlinespace[.75ex]
\end{tabularx}
\tabnote{Notes: ***$p<0.01$, **$p<0.05$, *$p<0.1$.
Regressions in this table only include sample of cities which ever adopted the water filtration plant.
Sample consists of children between ages 10 to 15 in the IPUMS census 1880, 1900, 1910, 1920.
Regressions are weighted by sample weight.
Robust standard errors clustered at the city (56 cities) level are in parentheses.
Dependent variable school enrollment (employment) is an indicator variable equals to one if a child is enrolled (reported an occupation) and zero otherwise.
Filter is an indicator variable equals to one if city installed the water filtration plant.
Additional control variables not reported in the table are: age fixed effects, year fixed effect, city fixed effects, head illiterate, black, male. }
\normalsize
\end{table}





%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% ADD OTHER CITIES %%%%%
\clearpage
\begin{table}[!h]
\caption{The Effect of Water Filtration on School Enrollment and Employment, Using Cities in Main Sample and Cities with Incomplete Information}\label{filter:MoreCities}
\footnotesize
\centering
\scalebox{.89}{
\newcolumntype{Y}{>{\raggedleft\arraybackslash}X}
\begin{tabularx} {15cm} {@{} l Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y@{}}
\toprule
                           & \multicolumn{8}{c}{Dependent variable} \\\cmidrule(l{.75em}){2-9}
                           & \multicolumn{4}{c}{School enrollment} & \multicolumn{4}{c}{Employment} \\
                           & \multicolumn{1}{c}{0.86} & \multicolumn{1}{c}{0.86}
                           & \multicolumn{1}{c}{0.86} & \multicolumn{1}{c}{0.86}
                           & \multicolumn{1}{c}{0.10} & \multicolumn{1}{c}{0.10}
                           & \multicolumn{1}{c}{0.10} & \multicolumn{1}{c}{0.10} \\
                           & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)}
                           & \multicolumn{1}{c}{(3)} & \multicolumn{1}{c}{(4)}
                           & \multicolumn{1}{c}{(5)} & \multicolumn{1}{c}{(6)}
                           & \multicolumn{1}{c}{(7)} & \multicolumn{1}{c}{(8)} \\
                           \cmidrule(l{.75em}){2-5}\cmidrule(l{.75em}){6-9}
\input{\ChOnePath/reg_filter_a1_m1_MoreCities.txt}
    \\
No. of city/cluster        & 74$^a$  & 95$^b$  & 133$^c$ & 154$^d$ & 74$^a$  & 95$^b$  & 133$^c$ & 154$^d$  \\
City-linear trends         & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes  \\
\bottomrule
\addlinespace[.75ex]
\end{tabularx}
}
\tabnote{Notes: ***$p<0.01$, **$p<0.05$, *$p<0.1$.
Sample consists of children between ages 10 to 15 in the IPUMS census 1880, 1900, 1910, 1920.
Regressions are weighted by sample weight.
Robust standard errors clustered at the city level are in parentheses.
Dependent variable school enrollment (employment) is an indicator variable equals to one if a child is enrolled (reported an occupation) and zero otherwise.
Filter is an indicator variable equals to one if city installed the water filtration plant.
Additional control variables not reported in the table are: age fixed effects, year fixed effect, city fixed effects, head illiterate, black, male.

$^a$74 cities in main sample.

$^b$95 = 74 cities in main sample + 21 cities which city's identity is not available in IPUMS 1880 census (Unbalanced city-year panel).

$^c$133 = 74 cities in main sample + 59 cities which either do not adopt water filtration until 1914 or have filtration but water filtered by private corporations.

$^d$154 = 74 + 21 + 59 (Unbalanced city-year panel).
}

\normalsize
\end{table}



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% SENSITIVITY TEST: YEARS OF EXPOSURE %%%%%
\clearpage
\begin{landscape}
\begin{table}[h!]
\caption{Results Using Years of Exposure to Water Filtration As Treatment Measure}\label{yrexp}
\footnotesize
\centering
\newcolumntype{Y}{>{\raggedleft\arraybackslash}X}
\begin{tabularx} {15cm} {@{} l Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y@{}}
\toprule
                           & \multicolumn{6}{c}{Dependent variable} \\\cmidrule(l{.75em}){2-7}
                           & \multicolumn{3}{c}{School enrollment} & \multicolumn{3}{c}{Employment} \\
                           & \multicolumn{3}{c}{0.86} & \multicolumn{3}{c}{0.10} \\
                           & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)}
                           & \multicolumn{1}{c}{(3)} & \multicolumn{1}{c}{(4)}
                           & \multicolumn{1}{c}{(5)} & \multicolumn{1}{c}{(6)}  \\
                           \cmidrule(l{.75em}){2-4}\cmidrule(l{.75em}){5-7}
\input{\ChOnePath/reg_yrexpafil_a1_m1_3spec.txt}
Specification:          \\
City-linear trends         & No  & Yes & Yes & No  & Yes & Yes  \\
State-year FE              & No  & No  & Yes & No  & No  & Yes  \\
\bottomrule
\addlinespace[.75ex]
\end{tabularx}
\tabnote{Notes: ***$p<0.01$, **$p<0.05$, *$p<0.1$.
Each column represents separate regression.
Sample consists of children between ages 10 to 15 in the IPUMS census 1880, 1900, 1910, 1920 in the 74 cities with complete filtration information.
Regressions are weighted by sample weight.
Robust standard errors clustered at the city (74 cities) level are in parentheses.
Dependent variable school enrollment (employment) is an indicator variable equals to one if a child is enrolled (reported an occupation) and zero otherwise.
Yrs of exposure measures the number of years exposed to the water filtration, with range = [0, 15] and mean = 3.79.
Additional control variables not reported in the table are: age fixed effects, year fixed effect, city fixed effects, head illiterate, black, male. }
\normalsize
\end{table}
\end{landscape}






%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% APPENDIX %%%%%
\clearpage
%\appendix
%\setcounter{table}{0}
%\setcounter{figure}{0}
%\renewcommand{\thetable}{\thesection.\arabic{table}}
%\renewcommand{\thefigure}{\thesection.\arabic{figure}}
\section{Appendix} \label{apdx}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% EXPOSED AT AGES 0-5 X AGES 14-15
\begin{table}[!htbp]
\caption{The Effect of Early Childhood Exposure to Water Filtration on School Enrollment and Employment (Define Early Childhood Exposure As Ages 0-5)}\label{a1415exp0to5}
\scriptsize
\centering
\newcolumntype{Y}{>{\raggedleft\arraybackslash}X}
\begin{tabularx} {8.5cm} {@{} l Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y@{}}
\toprule
                           & \multicolumn{2}{c}{Dependent variable} \\\cmidrule(l{.75em}){2-3}
                           & \multicolumn{1}{c}{School} & \multicolumn{1}{c}{} \\[-3pt]
                           & \multicolumn{1}{c}{enrollment} & \multicolumn{1}{c}{Employment} \\
                           & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)} \\
                           \cmidrule(l{.75em}){2-2}\cmidrule(l{.75em}){3-3}
\input{\ChOnePath/reg_a1415exp0to5_a1_cyr.txt} \\
City-linear trends         & Yes & Yes \\
   \\
Effect on Ages 14-15 + Exposed 0-5      & 0.144*** & -0.090*** \\
~~~Joint test [p-value]$^a$             & [0.000]  & [0.000]   \\
Effect on Ages 14-15 + Exposed $\geq 6$ & 0.085*** & -0.031*   \\
~~~Joint test [p-value]$^b$             & [0.000]  & [0.076]   \\
Difference in effect for \\[-3pt]
~~~Exposed 0-5 and $\geq 6$ on Ages 14-15 & 0.059**  & -0.058** \\
~~~Joint test [p-value]$^c$               & [0.021]  & [0.023]  \\
\bottomrule
\addlinespace[.75ex]
\end{tabularx}
\tabnote{Notes: ***$p<0.01$, **$p<0.05$, *$p<0.1$.
Sample consists of children between ages 10 to 15 in the IPUMS census 1880, 1900, 1910, 1920 in the 74 cities with complete filtration information.
Regressions are weighted by sample weight.
Robust standard errors clustered at the city (74 cities) level are in parentheses.
Dependent variable school enrollment (employment) is an indicator variable equals to one if a child is enrolled (reported an occupation) and zero otherwise.
The sample mean of school enrollment and employment for children aged 14-15 is 0.69 and 0.27, respectively.
1st exposed at ages 0-5 ($\geq$ 6) is an indicator variable equals to one if children are first exposed to water filtration at ages 0 to 5 ($\geq$ 6).
Ages 14-15 is a dummy equal to one if a child is currently at age 14 or 15.
Reference group is children who never exposed to water filtration.
Additional control variables not reported in the table are: age fixed effects, year fixed effect, city fixed effects, head illiterate, black, male.

$^a$Joint test: $\beta_{1} + \beta_{2} = 0$.

$^b$Joint test: $\beta_{3} + \beta_{4} = 0$.

$^c$Joint test: $(\beta_{1} + \beta_{2}) - (\beta_{3} + \beta_{4}) = 0$. }
\normalsize
\end{table}





%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% STATE COMPULSORY SCHOOLING LAWS %%%%%
\clearpage
\begin{table}[h!]
\caption{State Legal School Leaving Age by Census Year}\label{sumstat:candropout}
\footnotesize
\centering
\newcolumntype{Y}{>{\raggedleft\arraybackslash}X}
\begin{tabularx} {15.5cm} {@{} l Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y@{}}
\toprule
                           & \multicolumn{10}{c}{Can drop out age = } \\
                           & \multicolumn{10}{c}{Min[(Minimum compulsory school entrance age + yrs of schooling for exemption),} \\
                           & \multicolumn{10}{c}{(Maximum age of compulsory schooling)]} \\
                           \cmidrule(l{.75em}){2-11}
                           &        &       & \multicolumn{8}{c}{By census year} \\
                           \cmidrule(l{.75em}){4-11}
                       Age & \multicolumn{2}{c}{All sample} & \multicolumn{2}{c}{1880} & \multicolumn{2}{c}{1900} & \multicolumn{2}{c}{1910} & \multicolumn{2}{c}{1920} \\
                           \cmidrule(l{.75em}){2-3}\cmidrule(l{.75em}){4-5}\cmidrule(l{.75em}){6-7}
                           \cmidrule(l{.75em}){8-9}\cmidrule(l{.75em}){10-11}
\input{\ChOnePath/sumstat_compschoolage_state.txt}
\bottomrule
\addlinespace[.75ex]
\end{tabularx}
\tabnote{Notes: Unit of observation = state. Information of state compulsory schooling laws comes from various issues from \emph{Report of the Commissioner of Education} and data appendix of \textcite{gk2011}. }
\normalsize
\end{table}




%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% RACIAL DIFFERENCE %%%%%
\clearpage
\begin{table}[!htbp]
\caption{The Effects of Water Filtration by Race in the North and South}\label{filterXblackXsouth}
\footnotesize
\centering
\newcolumntype{Y}{>{\raggedleft\arraybackslash}X}
\begin{tabularx} {9.5cm} {@{} l Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y@{}}
\toprule
                           & \multicolumn{2}{c}{Dependent variable} \\\cmidrule(l{.75em}){2-3}
                           & \multicolumn{1}{c}{School} & \multicolumn{1}{c}{} \\[-3pt]
                           & \multicolumn{1}{c}{enrollment} & \multicolumn{1}{c}{Employment} \\
                           & \multicolumn{1}{c}{(1)} & \multicolumn{1}{c}{(2)} \\
                           \cmidrule(l{.75em}){2-2}\cmidrule(l{.75em}){3-3}
\input{\ChOnePath/reg_afilbksouth_a1_cyr.txt} \\
City-linear trends         & Yes & Yes \\
   \\
Effect on blacks in the South & 0.084*  & -0.015  \\
~~~Joint test [p-value]$^a$   & [0.090] & [0.546] \\
Effect on blacks in the North & 0.021   & -0.003  \\
~~~Joint test [p-value]$^b$   & [0.124] & [0.810] \\
Difference in effect for \\[-3pt]
~~~blacks and whites in South & 0.074*** & -0.030  \\
~~~Joint test [p-value]$^c$   & [0.000]  & [0.112]  \\
\bottomrule
\addlinespace[.75ex]
\end{tabularx}
\tabnote{Notes: ***$p<0.01$, **$p<0.05$, *$p<0.1$.
Sample consists of children between ages 10 to 15 in the IPUMS census 1880, 1900, 1910, 1920 in the 74 cities with complete filtration information.
Regressions are weighted by sample weight.
Robust standard errors clustered at the city (74 cities) level are in parentheses.
Dependent variable school enrollment (employment) is an indicator variable equals to one if a child is enrolled (reported an occupation) and zero otherwise.
Filter is an indicator variable equals to one if city installed the water filtration plant.
Additional control variables not reported in the table are: age fixed effects, year fixed effect, city fixed effects, head illiterate, male.
Southern cities in the samples are cities located in confederate states: Alabama, Georgia, Louisiana, North Carolina, South Carolina, Tennessee, Texas, and Virginia.

$^a$Joint test: $\beta_{1} + \beta_{2} + \beta_{3} + \beta_{4} = 0$.

$^b$Joint test: $\beta_{1} + \beta_{2} = 0$.

$^c$Joint test: $[(\beta_{1} + \beta_{2} + \beta_{3} + \beta_{4}) - (\beta_{1} + \beta_{3})] = \beta_{2} + \beta_{4} = 0$. }
\normalsize
\end{table}



\clearpage
\begin{table}[!htbp]
\caption{City Installation of Water Filtration Plants}\label{tab:city-yr}
\footnotesize
\centering
\newcolumntype{Y}{>{\raggedright\arraybackslash}X}
\begin{tabularx} {\textwidth} {@{} l| Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y@{}}
\toprule
\textbf{Year of}        &                                  \\[-3pt]
\textbf{installation}   & \multicolumn{1}{c}{\textbf{City, State}}  \\\hline
\input{\ChOnePath/city-year_panel.txt}
\bottomrule
\addlinespace[.75ex]
\end{tabularx}
\tabnote{Notes: There are the 74 cities (located in 30 states) in my main sample. As described in Section \ref{data}, these are the cities for which both complete data on filter installation and IPUMS microdata are available for census years 1880--1920.

Sources: \emph{General Statistics of Cities: 1915}\nocite{gen1916} by the United States Bureau of the Census; ``Filtration Plant Census, 1924"\nocite{filcens1925}  published in \emph{Journal of the American Water Works Association}, which is compiled by C.G. Gillespie who was director of the Sanitary Engineering Bureau of the California State Board of Health; a set of three papers titled ``Design and Operation Data on Large Rapid Sand Filtration Plants in the United States and Canada" published in \emph{Journal of the American Water Works Association} \parencite{rapid1932, rapid1942, rapid1956}. Supplementary sources included: various issues from ``Water Survey Series\nocite{wss}"; ``Water Supply Paper\nocite{wsp}"; ``The Purification of Public Water Supplies" \parencite{johnson1913}; ``Present Day Water Filtration Practice" \parencite{johnson1914}; ``Census of Municipal Water Purification Plants in the United States, 1930-1931" \parencite{filcens1933}; ``Manual of Design for Slow Sand Filtration" \parencite{slow1991}.
}
\end{table}

