\chapter{The Impact of Privatization of Water Services on Educational Attainment: The Case of Argentina}\label{ch2}

\section{Introduction}
Access to safe drinking water is still a very important issue today. According to a report from the World Health Organization \parencite{who2015}, as of 2015, there were still 663 million people who did not have access to safe drinking water.\footnote{Safe water is defined by water source which is adequately protected from outside contamination, particulary faecal matter.} While the way of increasing access to safe water is debatable, one possible approach is to allow the private sector to provide municipal water infrastructure.

This study investigates the effect of early childhood exposure to a municipal water services privatization program on educational attainment such as primary, compulsory, or secondary school completion in Argentina. During the 1990s, Argentina experienced one of the world's largest privatization campaigns which comprise municipal privatization of water services in 1991 to 1999. Different municipalities privatized their water services at different times. By 1999, municipalities which cover about 60 percent of country's population transferred their water services from public to private ownership. An influential study by \textcite{galiani2005} suggests that this water services privatization program improves both access to water and water quality,\footnote{The author adopted a difference-in-differences strategy to show that the number of households connected to water networks experienced a large increase (4.2 percentage points) after privatization. The authors also provide a number of case studies to demonstrate that privatized firms significantly improved water service performance, are more efficient, and invest more on physical infrastructure.}
which reduces child mortality and the impact is most pronounced in poorest areas. Furthermore, \textcite{galiani2009} find that the privatization program also improves child health; the program induces a large reduction of diarrheal cases among children. The reduction in child mortality and illness documented in these two studies clearly suggest that the privatization program improves health environment in municipalities.

Establishing causal inference between clean water and later life outcomes is difficult because of the possibility of other unobserved factors which affect both human capital accumulation and access to clean water. The municipal water services privatization program provides a great opportunity to investigate the long term effect of access to clean water. To examine the causal relation between access to clean water and school completion, I exploit two sources of variation from this intervention. In particular, the municipality-cohort variation in water services privatization permits me to use a difference-in-differences strategy to identify the causal impact of access to clean water. Intuitively, this effect is estimated by taking the early/late childhood exposure difference in educational attainment in a privatized municipality, and using cohorts never exposed to the privatization to control for the changes in educational outcomes over time that would have occurred for reasons unrelated to the privatization program.

The impacts of private sector participation in public sector are widely discussed in both empirical and theoretical studies \parencite{shapiro1990, mnv1994, barberis1996, schmidt1996, laporta1999, galiani2005, galiani2009, kosec2014}. The most relevant studies are \textcite{galiani2005} and \textcite{galiani2009}, which look at the same privatization program in Argentina and find that this program reduced child mortality and improve children health, which imply an improvement in health environment. Given these health benefits from the water services privatization program, and the established complementarities between childhood health and education \parencite{glewwe2001, mk2004, miguel2006, bleakley2007, bleakley2010}, it is important to understand how private sector participation in public infrastructure affects human capital accumulation among survivors. This paper contributes to the literature by providing the first evidence of the effect of private sector participation in water infrastructure on educational outcomes such as primary, compulsory, and secondary school completion.

Existing literature shows that access to clean water reduces mortality and provides a healthier environment to survivors \parencite{tk2002, jm2003, cm2005, ferrie2008, mangyo2008, galiani2005, galiani2009, gamper2010, kremer2010, kremer2011, zhang2012, goldin2015}. Surprisingly, 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 examining the impact of a municipal water services privatization program. More specifically, given the knowledge of child mortality reduction and health improvement after the private sector participation in water infrastructure \parencite{galiani2005, galiani2009, kosec2014}, the results on human capital accumulation in this study extend our knowledge about various benefits of private sector participation in water sector. This is particularly important to the developing world where under-provision of safe water may impede human capital development. Furthermore, given the well documented causal effect of childhood health environment on later life outcomes in the literature (see handbook chapter of \textcite{ac2011} for discussion), it is reasonable to believe that early childhood exposure to contaminated water will have long-term consequences---because germ and bacteria in contaminated water create great risks of exposure to infectious diseases in young children who are more susceptible to waterborne diseases. The present study complements this literature by examining the effect of early childhood exposure to clean water.

A priori, the effect of water services privatization on educational attainment is an open question. For example, the access to clean water allows those previously weak and sick children to have the stamina go to school. At the same time, the privatization program may also improve children's ability to focus and concentrate in class, which will allow them to perform better in school. All of these reduce the costs of education and increase the return to schooling, so individuals may be more like to invest in human capital. On the other hand, healthier children can be more productive and, therefore, be more valuable in the labor market. This increases their relative return in working, in order words, increases their opportunity costs of going to school, which have a negative effect on schooling. These two opposing stories imply that the effect of municipal water services privatization program on school attainment is unclear. Furthermore, the effect of privatization can also be affected by the mortality selection effect. This means that after the access to clean water, children who are relatively weak and fragile will most likely be the marginal survivors, so they will lower the estimated average effect of clean water on educational attainment.

There is considerable time variation in municipal adoption of water services privatization. I merged this municipality-year privatization data with the individual level microdata of Argentina 2010 Census to estimate the impacts of early childhood exposure to water services privatization on educational attainment. Applying a difference-in-differences strategy which exploits variation across municipalities and across cohorts, the results suggest that privatization has a zero effect on primary and compulsory school completion, but a small negative effect on secondary school completion. I also find that the effect of water services privatization is heterogeneous for individuals who lived in nonpoor and poor municipalities. The results show that, for primary and compulsory school completion, early childhood exposure to privatization has a zero effect in nonpoor municipalities and a negative effect in poor municipalities. Moreover, the negative effect goes to zero when the first age of exposure becomes older. This is consistent with the early childhood literature that claims conditions and environments in early life has a larger effect on later life outcomes. To further understand the negative effect in poor municipalities, I implement a supplemental analysis to investigate at which grade of primary education individuals are dropping out of school. The results suggest that privatization induces individuals to drop out of school at 5th, 6th, and 7th grade among poor municipalities, which map into the final years of primary school.

The rest of this study is organized as follows. I review the literature in section \ref{lit}. Section \ref{background} discusses institutional background of privatization program in Argentina. In section \ref{data}, I present data of educational attainment and municipal water services privatization. Section \ref{para} assesses the parallel trend assumption. Section \ref{results} discusses the identification strategy and presents empirical results. Finally, Section \ref{conclu} concludes this study.



\section{Literature Review}\label{lit}
This paper provides some of the first empirical evidence of how private sector participation in municipal water services affects educational outcomes. Related to this study, in the context of Argentina, \textcite{galiani2005} investigate the causal effect of the privatization of water services on child mortality. Their results show that privatized municipalities experienced a significant increase in water network connections and improvement in water quality. These led to an 8 percent decrease in child mortality and the effect is largest (26 percent) in the poorest areas. \textcite{galiani2009} use the same privatization program and show that the expansion of water networks improved child health and induced savings in household water expenditure in urban shantytowns.

There is limited research on the effect of clean water on education. \textcite{duflo2012} use a randomized design in Morocco to examine the non-health effect of household water connection. Their results show that household water connection had no effect on children's school participation, but had a positive effect on household well-being such as increased in leisure time.\footnote{In the paper, since all sample households have access to clean water from public tab, the randomized design of in-home water connection will not improves water quality.} \textcite{bv2013} examine the effect of a clean water program in Mexico in 1991 and find that infant exposure to clean water program has positive effect on girls' test scores, their test scores increased by 0.1 standard deviation, and there is no effect on boys. \textcite{kosec2014} investigates the effect of private sector participation in the piped water sector, which increases access to piped water, in 39 African countries during 1986 to 2010. The author shows that privatization in piped water sector reduces diarrhea of children under age five in urban areas, 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 who are at transition points in their education, which is aged 11--13 and 17. Regarding the long-term effect of clean water, \textcite{xz2014} look at a large drinking water treatment program in rural China and find 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} link males in 1900 and 1940 in the United States and use typhoid fatality rate as a proxy of water quality. Their results show that the effect of eradicating early-life exposure to typhoid fever is positive on years of schooling and later life earnings.

My paper contributes to this limited literature on the effect of clean water on educational outcomes, which features this important channel in the larger literature of the benefits of clean water programs on mortality reduction \parencite{tk2002, jm2003, cm2005, galiani2005, ferrie2008, gamper2010, goldin2015} and improvement in child health \parencite{mangyo2008, galiani2009, kremer2010, kremer2011, zhang2012}.

Given the reduction in mortality and morbidity found by the existing literature, the present study provides policy makers a better understanding about the various benefits from the clean water program, especially the knowledge that clean water also influences educational outcomes of survivors, which is crucial to economic development. Furthermore, the benefits of water improvement programs will be understated if we neglect the causal effects of water-related programs on human capital.

This study is also relevant to a large literature on the effect of early childhood environment. Since the causal effect of childhood health environment on later life outcomes is well documented in the literature (see handbook chapter of \textcite{ac2011}), it is important to understand the effect of early childhood exposure to clean water through privatization of water infrastructure.



\section{Background}\label{background}
\subsection{Privatization Campaigns in Argentina}
Argentina launched one of the world largest privatization campaigns in the 1990s. The privatization of municipal water services is part of this enormous privatization campaigns which transferred almost all of the country's state owned enterprizes (SOEs) to private ownership.\footnote{These SOEs include infrastructure services such as water and sanitation, electricity, oil and gas, telecommunications, transportation, and mail services.} The goal of these massive privatization campaigns is to deal with the long prevailed economic depression in the past decades. Argentina experienced a hyperinflation and huge government deficits in the late 1980s. In order to save the economy, the newly elected government in 1989 decided to implement a reform program which consisted of trade and capital market liberalization, monetary reform to stabilize currency exchange rate, tax system restructuring, transforming public-run pension system to privately managed fund, and privatization of public enterprizes \parencite{blake1998, galiani2005, galiani2009}.

There are several objectives in this massive privatization. First, it is to alleviate the huge budget deficit in the late 1980s in which a nontrivial amount are created by sizable SOE losses. Another intention is to induce private sector investment in infrastructural development. After two decades of stagnant economic growth in 1970s and 1980s, there was lack of investment in public utilities. To resolve this inadequate infrastructure investment, privatizing public utilities provides a positive force to improve infrastructural facilities \parencite{cer1999, ep2005}.


\subsection{Water Services Privatization in Municipalities}
Before the privatization campaigns in the 1990s, municipal water services in Argentina were operated by the public sector or a number of non-profit cooperatives, most companies usually provided services on both water and sewage, but a few of them only provided water \parencite{anu2000, galiani2005}. The water services privatization program started in 1991 and continued until 1999, in which municipalities that cover around 60 percent of the country's population switched its ownership from public to private control. There is considerable time variation in municipal privatization of water services during the 1990s in Argentina. Figure \ref{fig:perc} plots the percentage of privatized municipalities over the water services privatization periods (1991 to 1999). The details of how the municipal privatization sample is formed is described in the data section. Within the 275 municipalities in my data, the progress of privatization is slow before 1995. There were only 8 percent of municipalities that privatized their water services in 1994. The progress sped up since 1995 and by 1999, more than 35 percent of municipalities switched their water services from private to public ownership in my sample.

There are several reasons for the variation in timing of water services privatization adoption in Argentina. Political factor is one of them. Although the privatization started in early 1990s, the privatization of water services was slow at the beginning of the decade and sped up after the newly elected government was appointed in 1995 (as shown in Figure \ref{fig:perc}). In addition, time-invariant factors such as municipal socioeconomic conditions can also affect the privatization decision. For example, larger and poorer municipalities with less developed public infrastructure are more likely to adopt the privatization program sooner \parencite{galiani2005}.

One might be concerned that municipalities could be more likely to privatize their water services if they respond to bad economic shocks or other time-varying factors. A key threat to the difference-in-differences strategy is that the decision of privatization is driven by unobserved time-varying shocks which affect both privatization decisions and individual educational outcomes. To test the effect of time-varying shocks on privatization decisions, \textcite{galiani2005} implemented a discreet-time hazard model analysis and showed that
%, although privatization decision is correlated with time-invariant variables such as level of GDP per capita and unemployment rate,
privatization decision is not correlated with observed time-varying shocks such as GDP growth, change in unemployment rate, and change in income inequality. So the authors claim that privatization decision is unlikely to be correlated with unobserved time-varying shocks, given that privatization is not associated with observed time-varying factors. The discreet-time hazard model analysis in \textcite{galiani2005} provides evidence which validates the subsequence difference-in-differences analysis in the present study.

The water services privatization program has been shown to improve both water access and water quality \parencite{aas2002, anu2000}, which induce an improvement in health environment. For example, \textcite{galiani2005} find a decline in child mortality, and \textcite{galiani2009} find that children are less likely to suffer from diarrhea.


\section{Data}\label{data}
To investigate the effect of privatization on human capital accumulation, I require data on educational attainment and date of municipal water services privatization. I obtain the former from the 2010 Census microdata, and the latter from \textcite{galiani2005} (GGS).

Individual-level data on educational attainment are taken from the \emph{Integrated Public Use Micro Sample} (IPUMS)\nocite{ipumsINT}. This study uses a single cross-sectional 2010 Census of Argentina which covers 10 percent of total population. Human capital accumulation is measured by primary school completion, compulsory school completion, and secondary school completion. It is worth mentioning that school system in Argentina experienced two reforms in last two decades. Argentinian had seven years of primary school and five years of secondary school (7-5) before 1993, in which seven years of primary school is compulsory education. Followed by an education reform in 1993 which changed the system to nine years of compulsory education and three years of higher secondary school (9-3). The nine years of compulsory education includes both primary school and lower secondary school. In 2006, the government implemented another reform which allowed education jurisdictions to choose discretionarily between a structure of 7-5 (seven years of primary and 5 years of secondary) or a 6-6 (six years of primary and secondary).\footnote{As mentioned in the following, because the youngest individual in my sample is 17 years old in 2010, the 2006 reform is not going to affect my results since my sample is old enough and cannot be affected by this latest reform.}

The IPUMS 2010 Census provides information of individual year of schooling, which is generated by individual school attainment questions from the questionnaire. According to the instruction of the IPUMS Census, it treated more than 7 years of primary education as 7 years and coded secondary education begins at 8 years. Therefore, this years of schooling variable is not capturing the actual years of completed schooling. In my empirical analysis, I regard individuals who have completed primary school if they have 7 years of schooling. I can also construct compulsory school completion dummy since I am able to identify whether individuals belong to 7-5 or 9-3 system in the data. Finally, secondary school completion is dummy equals one if individuals have 12 years of schooling.

The Census also contains information about working status, dwelling characteristics, and other demographic variables. For geographic information, the data cannot identify respondents' place of birth. Furthermore, for household living location, it suppresses municipalities with less than 20,000 residents in one geographic category in each province due to requirement of confidentiality. There are totally 299 municipalities in the IPUMS data, 276 of them represent individual municipalities with more than or equal to 20,000 residents and 23 of them are suppressed municipalities in each province which contain municipalities with less than 20,000 residents.\footnote{There are 23 provinces in Argentina. Each province contains suppressed municipality which represents municipalities with less than 20,000 residents.}

The data of municipal year of water services privatization are from GGS,\footnote{I thank Sebastian Galiani for sharing me the data and Stata code used in their paper.} which contains 476 municipalities from 1990 to 1999.\footnote{Note that the empirical analysis in \textcite{galiani2005} does not contains all municipalities in Argentina due to no water service or missing information.} Privatization is measured by a dummy variable equals one if a private water company supplies services to the largest fraction of the population in the municipality.

I merged these individual level microdata from the Census to the timing of municipal privatization of water services. Notice that the IPUMS 2010 Census of Argentina provides fewer municipality's identity than the data in GGS. To figure out the year of privatization for each municipality, I merged the municipalities in the 2010 Census with the privatization information from GGS's data.\footnote{As illustrated in Figure \ref{fig:dept}, after merging the two datasets, section A shows that 256 municipalities in the 2010 Census are matched with year of privatization information. Among the unmatched municipalities in the 2010 Census, section B shows that 20 of them are either ``no service or missing information" of water services, or they are identifiable in the 2010 Census but not exist in GGS's data. Section C shows that 23 of them are suppressed municipality in the 2010 Census. In section C, for the suppressed municipality under each province in the 2010 Census, I go back to GGS's data to figure out their year of privatization. I realized that most of these suppressed municipalities with under 20,000 residents are either never privatized or all privatized at the same year within each province. Eventually, as indicated in section D, I can keep individuals who lived in 19 suppressed municipalities since I can identify their municipal year of privatization. Overall, my empirical analysis contains 275 municipalities (sections A + D) which represent 84\% of municipalities (400 out of 476 municipalities) in GGS's data. Among these 275 municipalities with identifiable year of privatization in the IPUMS, 256 of them represent 256 municipalities in GGS's data, 23 of them are suppressed municipality in each province representing 144 municipalities in GGS's data.}
There are eventually 275 municipalities in my analysis.\footnote{Table \ref{tab:muni-yr} presents the year of privatization of these 275 municipalities.}
My empirical analysis focus on native born individuals who are between ages 17 to 35. The age 17 cutoff was chosen because students start their first grade (first year of schooling) at the age of 6 and will complete their secondary school at age 17 if they do not repeat in any grade.

Table \ref{sumstat} shows the summary statistics for my sample. Panel A shows the summary statistics of the overall sample. Panels B and C show statistics of subsamples for nonpoor and poor municipalities, respectively. In Panel A, on average, 92 percent of the sample completed primary school, 91 percent completed compulsory school, and around 53 percent of the sample are secondary school graduate. Around 58\% of individuals were ever exposed to the water services privatization in their life. 50 percent of the samples are female. The average age is about 26 years old. About 18\% of individuals were living in poor municipalities. Compared to panel A, educational attainment is higher in nonpoor municipalities in Panel B, and lower in poor municipalities in Panel C. Larger proportion of individuals were ever exposed to the privatization program in the nonpoor municipalities (63\% vs. 35\%). Finally, gender and age distribution are similar across all Panels.



\section{Assessing the Parallel Trend Assumption}\label{para}
Since my empirical analysis exploits variation across municipalities and across cohorts. A possible concern to the difference-in-differences method is the differential trends on educational attainment between privatized and not-privatized municipalities in the pre-intervention period. To assess the parallel trend assumption, Figure \ref{fig:para} graphs the mean outcome by birth cohort and privatization status, and consider whether in the pre-intervention period the privatized and not-privatized municipalities do indeed have parallel trend. Panels A.1, A.2, and A.3 show the mean primary school completion, compulsory school completion, and secondary school completion, respectively. The solid line represents the mean school completion in each birth cohort for municipalities which adopted water services privatization. The dashed line represents the mean school completion for municipalities which never adopted privatization. The dotted vertical line located in 1981 indicates 10 years before the first year of the water services privatization program in 1991. In other words, in municipality privatized in 1991, a child who was born in 1981 was first exposed to the privatization at age 10, which regards as late childhood exposure to the intervention. So to the left of the dotted vertical line are the pre-intervention periods and to the right are the post-intervention periods. In general, treatment and control groups in Figure \ref{fig:para} indicate support for the parallel trend assumption in all cases of primary, compulsory, and secondary school completion; Figure \ref{fig:para} shows similar pre-trends on left side of the dotted vertical line.

Both the observed parallel pre-trends in Figure \ref{fig:para} between treatment and control groups for older cohorts, and the evidence provided by \textcite{galiani2005} that the adoption of privatization is not correlated with time-varying factors provide us confidence about the validity of the parallel trend assumption. It validates my difference-in-differences strategy in empirical analysis.



\section{Empirical Strategy and Results}\label{results}
The considerable time variation of water services privatization during the 1990s permits me to use a difference-in-differences method which exploits variation across municipalities and across cohorts to identify the effect of privatization program. To investigate the effect of early childhood exposure to privatization on educational attainment, I estimate the following model:
\begin{equation}\label{did}
y_{ijc} = \alpha + \Sigma^{16}_{a=0} \beta_{a} I(\text{1st exposed at age}=a) + \gamma_{c} + \delta_{j} + \textbf{x}_{ijc} \tau + \varepsilon_{ijc}
\end{equation}
where $y_{ijc}$ is a dummy variable of primary school completion, compulsory school completion, or secondary school completion for individual $i$ who lives in municipality $j$ and born in year $c$. $I(\text{1st exposed at age}=a)$ is an indicator variable which equals one if an individual was first exposed to the privatization program at age $a$ or zero otherwise. $\gamma_c$ is a vector of year of birth dummies which control for cohort characteristics that do not vary over time. $\delta_{j}$ represents vector of municipality dummies which control for municipality characteristics that do not vary over time. $\textbf{x}_{ijc}$ is a vector of demographic and region-specific variables including, gender dummy, provincial real GDP per capita and public expenditure per capita when individual was at age 6 (typical age of first year of schooling). The control groups in this regression are individuals who first exposed to the privatization at ages 17 or older, or individual who never exposed to the privatization. In this equation, $\beta_{a}$ represents the difference-in-differences estimator of the effect of privatization when an individual was first exposed at age $a$. The key identifying assumption is that in the absence of the privatization program, the difference of educational attainment across cohorts would have remained the same between individuals who live in municipalities with or without the privatization program.

Figures \ref{fig:ps}, \ref{fig:pscomp}, and \ref{fig:hscomp} show the estimated effect of the age of first exposure to privatization on primary school completion, compulsory school completion, and secondary school completion, respectively. Each point graphed indicates estimated $\widehat{\beta}_{a}$ with 95\% confidence interval from equation (\ref{did}). The control groups are individuals who never exposed to the privatization or who first exposed to the privatization at ages 17 or older. Robust standard errors in regressions are clustered at the municipality level, and they are consistent under heteroscedasticity as well as within-municipality serial correlation.

In Panel A.1 of Figures \ref{fig:ps}, \ref{fig:pscomp}, and \ref{fig:hscomp}, the results show that early childhood exposure to privatization has a zero effect on primary and compulsory school completion and a negative effect on secondary school completion, although the coefficients are only marginally significant.
The zero effect in primary and compulsory completion and negative effect in secondary completion could be due to the reason that there may be more job opportunities for children who are older than the primary school ages.
This is consistent with the story that the health intervention increases the relative return of working, which induces children to drop out from secondary school earlier (although the negative coefficients of early childhood exposure are only marginally significant), but has no effect on primary and compulsory school completion. It is worth mentioning that, especially in Figure \ref{fig:hscomp}, the effect of privatization declines to zero as the first age of exposure become older. This means that, compared to the control group, only individuals who has early childhood exposure experienced the largest effect from the intervention. There is a zero effect on educational attainment for those who experienced the intervention at late childhood. This is consistent with the early childhood literature that environments in early childhood have a larger impact on later life outcomes since young children are more sensitive to health and sanitary environments.


\subsection{Municipal Socioeconomic Status}
\textcite{galiani2005} suggest that the privatization program had no impact on child mortality in nonpoor municipalities, and child mortality had the largest decrease in poorest municipalities. One interesting question is to understand whether the effect of privatization is heterogeneous across nonpoor and poor municipalities. Although empirical evidence clearly suggests that the privatization program improves municipal health environment \parencite{galiani2005, galiani2009}, the effect of privatization across nonpoor and poor municipalities is unclear. For example, households that lived in poor municipalities may be less likely to have household connections to water network as well as fewer health technologies (e.g., access to clinic and doctor, private water treatment) to mitigate negative health shocks before the privatization, so these households may enjoy greater health benefits from the privatization program. On the other hand, families which lived in poor municipalities may be more likely to send a healthy child to work to maintain households level of subsistence. In addition, the mortality selection effect can be more pronounced in poor municipalities, which suggests that the effect in poor municipalities can also be zero or even negative. The reason is that relatively weak and fragile children will most likely be the marginal survivors after the privatization. Depending on the cohort size of these marginal survivors, these children will attenuate my estimated effect on educational attainment or even drive the effect of clean water to negative in poor municipalities.
\begin{comment}
Second, healthier childhood could be more likely to go to school (increases educational attainment) or go to work (decreases educational attainment). The latter case are more likely to happen among households which lived in poor areas because they might be more likely to send a healthy child to work to maintain household's level of subsistence.
\end{comment}
I divide my sample into nonpoor and poor municipalities following the definition in \textcite{galiani2005}.\footnote{A municipality's socioeconomic status is classified by fraction of households that have unmet basic needs (UBN) according to 1991 census. A household has UBN if at least one of the following happened: there are more than three people per room (overcrowded housing), there are no fecal evacuation system (no toilet), household members are living in poor housing (poor housing), and there are four or more members per working member and low household head education (below subsistence). In my analysis, nonpoor municipalities are municipalities where less than 25 percent of households have UBN, poor municipalities are municipalities where at least 25 percent of households have UBN.}

Each point estimate in Figures \ref{fig:ps}, \ref{fig:pscomp}, and \ref{fig:hscomp} shows the difference-in-differences coefficient for individuals who lived in nonpoor municipalities (Panel A.2) and poor municipalities (Panel A.3). The primary school completion results in Figure \ref{fig:ps} Panels A.2 and A.3 show that early childhood exposure to privatization has a zero effect in nonpoor municipalities and a negative effect in poor municipalities, respectively. This finding is consistent with a greater impact of privatization on poor municipalities.
%The possible explanations for the negative effect in poor municipalities can be due to the child labor story or the mortality selection effect mentioned at the beginning of this section.
The compulsory school completion results in Figure \ref{fig:pscomp} Panels A.2 and A.3 show similar patterns, except that early childhood exposure to privatization shows a slightly larger positive effect in nonpoor municipalities. In Figure \ref{fig:hscomp}, the secondary school completion results in Panel A.2 indicate a zero effect in nonpoor municipalities. Panel A.3 show that the coefficients of early childhood exposure are positive, although they are not statistically significant.

Overall, the estimated negative effect on primary and compulsory school completion and positive effect (although not statistically significant) on secondary school completion in poor municipalities are interesting. The possible explanation is that since many people in poor municipalities do not go to secondary school, the health intervention induces people to drop out of primary school earlier and go to work. However, for individuals who are likely to go on to secondary school, early childhood exposure to privatization has a small positive effect on secondary school completion, but the effect is not statistically significant at the 5\% level.


\subsection{Gender Difference}
Existing literature suggests that female and male have different responses to health improvement programs \parencite{mk2004, my2009, pitt2012, bv2013}. The reason is that female has competitive advantage in skill acquiring activities (such as education) while male has competitive advantage in labor intensive activities. A natural question is to understand the heterogeneous effect of privatization across gender.

I estimate the effect of privatization separately by female and male. Figure \ref{fig:ps:female} shows the effect of privatization on primary school completion. Panel A shows the estimates for female, in which Panels A.1, A.2, and A.3 show the estimated effect for all, nonpoor, and poor municipalities, respectively. Following the same logic, Panel B shows the estimates for male in different municipalities. Figure \ref{fig:ps:female} Panels A.2 and A.3 show that, for female, early childhood exposure to privatization has a zero effect on nonpoor municipalities and a negative effect on poor municipalities, respectively. We observed a similar pattern for male results in Panels B.2 and B.3, but most of the coefficients are not statistically significant at the 5\% level. The effect on compulsory school completion in Figure \ref{fig:pscomp:female} shows similar results, except that early childhood exposure to privatization show a slightly larger positive effect in nonpoor municipalities for male in Panel B.2. The findings here in Argentina is somewhat different from the existing literature which suggests that female has comparative advantage in education, and therefore, has stronger positive responses on education to health improvement programs. Finally, results in Figure \ref{fig:hscomp:female} show the effect of privatization on secondary school completion. Although most of the coefficients in all panels are not statistically significant, Panel A.3 shows that individuals who are first exposed to privatization at ages 0 to 1 has positive effect on secondary school completion for female. In general, the results for female or male in Figure \ref{fig:hscomp:female} is very similar to the overall results presented in Figure \ref{fig:hscomp}.


\subsection{Effect of Years of Childhood Exposure}\label{childhood}
Guided by Figures \ref{fig:ps} to \ref{fig:hscomp:female}, which suggest that the privatization has a larger effect on children who are first exposed at early childhood, I construct an alternative treatment measure to examine the effect of early childhood exposure to privatization. I estimate the following model to investigate the effect of years of childhood exposure to privatization:
\begin{align}\label{c7expo}
y_{ijc} = \alpha + \beta \text{Yrs of childhood exposure}_{ijc} + \gamma_{c} + \delta_{j} + \textbf{x}_{ijc} \tau + \varepsilon_{ijc}
\end{align}
where I define variable Yrs of childhood exposure as Max$((7 - \text{age of first exposure}), 0)$. The cutoff seven is chosen because Figures \ref{fig:ps} to \ref{fig:hscomp:female} reveal that the effect of early childhood exposure to privatization declines to zero by that age. All other variables are defined in equation (\ref{did}). In this equation, $\beta$ indicates the marginal effect of years of childhood exposure on educational attainment.

Table \ref{tab:ps} shows the estimated results of equation (\ref{c7expo}) for primary school completion, with Panels A, B, and C present the results for female and male, female, or male sample, respectively. Each column in each panel represents a separate regression. Columns (1), (4), and (7) show estimation for all municipalities, Columns (2), (5), and (8) show estimation for nonpoor municipalities, and Columns (3), (6), and (9) show estimation for poor municipalities. Although the effect for all municipalities in Columns (1) and (4) are statistically significant, the point estimates are very small and close to zero. Consistent to the results in Figure \ref{fig:ps}, in poor municipalities, one more year of childhood exposure to privatization has a negative effect on primary school completion. For example, the coefficient in Column (3) shows that an additional year of childhood exposure to privatization decreases primary school completion by 0.8 percentage point. In order to understand the average effect of childhood exposure among treated people (people with any childhood exposure), I can multiple the average years of childhood exposure among treated people by the estimated coefficient. The average years of childhood exposure among people ever exposed to privatization is 3.4, 3.4, and 3.1 years for all, nonpoor, and poor municipalities, respectively. By using the coefficient in Table \ref{tab:ps} Column (3) in poor municipalities, the average effect of an additional years of childhood exposure among exposed people decreases probability to complete primary school by 2.5 percentage points.\footnote{$-0.025 = -0.008 \times 3.1$ years.}
The estimation for compulsory school completion in Table \ref{tab:pscomp} shows similar results as primary school completion, except for the positive but close to zero coefficient in nonpoor municipalities (Table \ref{tab:pscomp} Columns (2), (5), and (8)). Finally, coefficients in Table \ref{tab:hscomp} Columns (1), (4), and (7) show that an additional year of childhood exposure to privatization decreases probability of secondary school completion by around 0.9 to 1 percentage point. For the average effect of childhood exposure among treated people, the average effect of an additional years of childhood exposure among exposed people decreases probability to complete secondary school by 3.1 to 3.4 percentage points.\footnote{$-0.031 = -0.009 \times 3.4$ years. $-0.034 = -0.010 \times 3.4$ years.}
There is no statistically significantly effect on secondary school completion in nonpoor and poor municipalities. In general, the results in Tables \ref{tab:ps} to \ref{tab:hscomp} mirror the results in Figures \ref{fig:ps} to \ref{fig:hscomp:female}.


\subsection{Years of Primary School Completed}
The results in poor municipalities in Figures \ref{fig:ps} and \ref{fig:pscomp} Panel A.3 show that early childhood exposure to privatization has a negative effect on primary and compulsory school completion. It is important to understand at what grade of primary education individuals are dropping out of school.
One can imagine that, since there could be more job opportunities for older children in the labor market, individuals could be more likely to drop out of school at later grades of primary education. I estimate the following equation:
\begin{align}\label{schl}
\text{Completed $k$ yrs of primary school}_{ijc}& = \alpha + \beta \text{Yrs of childhood exposure}_{ijc} \notag\\
&+ \gamma_{c} + \delta_{j} + \textbf{x}_{ijc} \tau + \varepsilon_{ijc}, \notag\\
&k = 1, 2, 3,..., 7
\end{align}
where Completed $k$ yrs of primary school is a dummy variable equals one if individual completed $k$ years of primary school, $k = 1, 2, 3,..., 7$ or zero otherwise. Variable Yrs of childhood exposure is defined as Max$((7 - \text{age of first exposure}), 0)$. All other variables are defined in equation (\ref{did}). $\beta$ represents the marginal effect of years of childhood exposure on probability of completed $k$ years of primary school, $k = 1, 2, 3,..., 7$.

The estimated results are shown in Figure \ref{fig:schl}. Each point represents coefficient estimated by separate regression. The horizontal axis indicates dependent variable completed $k$ years of primary school, $k=1,2,3,...,7$. The vertical axis indicates estimated $\widehat{\beta}$ of years of childhood exposure with 95\% confidence interval from equation (\ref{schl}). Panels A.1, A.2, and A.3 show results for all, nonpoor, and poor municipalities. Results in Panel A.2 for nonpoor municipalities show that, although a few coefficients are statistically significant, the point estimates are close to zero and very small (less than 0.1 percentage point), so there is a zero effect in nonpoor municipalities. For poor municipalities in Panel A.3, the results show that the statistically significant effect appears in completed 5 years, 6 years, and 7 years of primary school. This means that an additional year of childhood exposure induces people to drop out of school in 5th, 6th, and 7th grade.\footnote{Note that the total effect at 7th grade composes effect of people dropping out after 6th grade, dropping out after 5th grade, and dropping out after 4th grade.}
Finally, the results for all municipalities in Panel A.1 also show that people are more like to drop out of school in 5th, 6th, and 7th grade. This small negative effect is basically driven by the results in poor municipalities.

It is useful to think about the magnitude of the estimated coefficients and understand the average effect of early childhood exposure among treated people. I multiply the average years of childhood exposure among people with any exposure by the estimated coefficient.\footnote{As mentioned in section \ref{childhood}, the average years of childhood exposure among treated people is 3.4, 3.4, and 3.1 years for all, nonpoor, and poor municipalities, respectively.}
In Figure \ref{fig:schl} Panel A.3, the average effect of an additional year of early childhood exposure to privatization among exposed people decreases the probability to complete 5, 6, and 7 years of primary school by 1.1, 1.6, and 2.6 percentage points, respectively.\footnote{$-0.011 = -0.0033 \times 3.4$ years, $-0.016 = -0.0048 \times 3.4$ years, $-0.026 = -0.0083 \times 3.1$ years.}
\begin{comment}
These average effects among exposed people are small.\footnote{The sample mean of completed 5th, 6th, and 7th years of schooling conditional on being exposed are 0.97, 0.96, and 0.94, respectively.}
\end{comment}
Taken all together, the privatization program induces individuals in poor municipalities to drop out of primary school at 5th, 6th, and 7th grade, which correspond to the final years of primary school. This finding is consistent with older children could be more likely to drop out of school because there are more job opportunities available to them.



\section{Discussion and Conclusion}\label{conclu}
This study examines the effect of municipal water services privatization on educational attainment in Argentina. Municipalities cover about 60\% of Argentina's population that privatized their water services during 1991 to 1999. Existing literature demonstrates that privatization decision is not correlated with time-varying factors such as economic shocks, and it provides clear evidence that the privatization program improves municipal health environment \parencite{galiani2005, galiani2009}. In this study, by using a difference-in-differences method which exploits variation across municipalities and across cohorts, the results suggest that early childhood exposure to privatization has a zero effect on primary and compulsory school completion, but a negative effect on secondary school completion. Moreover, I find that the effect of early childhood exposure to privatization is heterogeneous across nonpoor and poor municipalities. For primary and compulsory school completion, my results suggest that early childhood exposure to privatization has no effect in nonpoor municipalities and a negative effect in poor municipalities. To get a better understanding of the negative effect in poor municipalities, I use a supplemental analysis to investigate the effect of privatization on years of primary school completed. The results indicate that the program induces individuals to drop out of primary school at 5th, 6th, and 7th grade, which map into the final years of primary school.

There are several possible explanations for the interesting negative effect on primary and compulsory school completion in poor municipalities. For example, since a lot of people in poor municipalities do not attend secondary school, after the water services privatization program, households may require a healthier child to go to work to maintain the family's subsistence. Therefore, the improvement in health status may encourage people to leave school before completing primary school, which may increase child labor. Another potential explanation is that the type of jobs and employment opportunities are different in nonpoor and poor areas. For instance, individuals who lived in poor areas are completing primary school at older ages and, therefore, have better job opportunities in the labor market, so they are more likely to drop out of primary school. Child labor is a pretty serious issue in Argentina. I found a brief report (Understanding Children's Work)\footnote{\url{http://www.ucw-project.org/Pages/Tables.aspx?id=1255}} saying that, in 2004, there are around 11\% of children aged 5-14 were working.\footnote{This number is comparable to developing countries like Philippines.} However, the report only provides me one aggregate statistic for the northern part of the country. I do not have the child labor data to test the hypothesis. Moveover, since \textcite{galiani2005} suggest that poor municipalities are where child mortality reduced the most (also the areas with the larger mortality selection effect), the survival of these relatively weak and fragile children will attenuate the effect of privatization toward zero or even negative.

My findings suggest that privatization of water services in Argentina not only affects health, but also affects other outcomes such as education. This provides policy makers with a better understanding that water improvement programs affect other outcomes beyond mortality and morbidity. The findings in this study are particularly important in understanding the effect of clean water programs since I find that water services privatization may reduce primary school completion in poor areas.




%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% figure %%%%%
%%%%%%%%%%%%%%%%%%
\clearpage
\section{Figures}
\begin{figure}[!htbp]
\centering
\caption{Percentage of Municipalities with Privatized water systems}\label{fig:perc}
\includegraphics[width=.8\linewidth]{perc_privatized.png}
\footnotesize
\end{figure}
\begin{minipage}{14.5cm}\vspace*{-2cm}
\singlespace
\footnotesize
Notes: There are 275 municipalities in my sample. The municipal water services privatization program happened between 1991 to 1999.
\end{minipage}



%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% PARALLEL TREND %%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%
\clearpage
\begin{landscape}
\begin{figure}[!htbp]
\centering
\caption{Assessing Parallel Trend}\label{fig:para}
\begin{multicols}{3}
\centering
{\footnotesize Panel A.1: Primary School Completion \\ (Privatized vs. Never Privatized)} \\
\includegraphics[width=0.43\textwidth]{ps_ParaTrend.png}

{\footnotesize Panel A.2: Compulsory School Completion \\ (Privatized vs. Never Privatized)} \\
\includegraphics[width=0.43\textwidth]{pscomp_ParaTrend.png}

{\footnotesize Panel A.3: Secondary School Completion \\ (Privatized vs. Never Privatized)} \\
\includegraphics[width=0.43\textwidth]{hscomp_ParaTrend.png}
\end{multicols}
\end{figure}
\begin{minipage}{\linewidth}\vspace*{-0.5cm}
\singlespace
\footnotesize
Notes: Sample consists of individuals between ages 17 to 35 in the IPUMS 2010 Census.
Panels A.1, A.2, and A.3 show the mean primary school completion, compulsory school completion, and secondary school completion, respectively.
Solid line represents the mean school completion in each birth cohort for municipalities which adopted water services privatization. Dashed line represents the mean school completion for municipalities which never adopted privatization.
The dotted vertical line located in 1981 indicates 10 years before the first year of the water services privatization program in 1991. In other words, in municipality privatized in 1991, a child who was born in 1981 was first exposed to the privatization at age 10, which regards as late childhood exposure to the intervention. So to the left of the dotted vertical line are the pre-intervention periods and to the right are the post-intervention periods.
\end{minipage}
\end{landscape}




\clearpage
\begin{landscape}
\begin{figure}[!htbp]
\caption{The Effect of Privatization on Primary School Completion}\label{fig:ps}
\begin{multicols}{3}
\centering
{\footnotesize Panel A.1: All Municipalities} \\
\includegraphics[width=0.43\textwidth]{ps_coeff_nt.png}

{\footnotesize Panel A.2: Nonpoor Municipalities} \\
\includegraphics[width=0.43\textwidth]{ps_coeff_p0_nt.png}

{\footnotesize Panel A.3: Poor Municipalities} \\
\includegraphics[width=0.43\textwidth]{ps_coeff_p1_nt.png}
\end{multicols}
\end{figure}
\begin{minipage}{\linewidth}\vspace*{-.5cm}
\singlespace
\footnotesize
Notes: Sample consists of individuals between ages 17 to 35 in the IPUMS 2010 Census.
Dependent variable primary school completion is an indicator variable equals one if an individual completed primary school or zero otherwise.
Each point shows estimated $\widehat{\beta}_{a}$ with 95\% confidence interval from equation (\ref{did}). In Panel A.1, A.2, and A.3 show estimated $\widehat{\beta}_{a}$ for individuals who live in all, nonpoor, and poor municipalities.
The control groups are: never treated or first treated at age 17+.
Robust standard errors were clustered at municipality level.
Control variables are: year of birth fixed effects, municipality fixed effects, gender dummy, provincial real GDP per capita and public expenditure per capita when individual was at age 6.
\end{minipage}
\end{landscape}


\clearpage
\begin{landscape}
\begin{figure}[!htbp]
\caption{The Effect of Privatization on Compulsory School Completion}\label{fig:pscomp}
\begin{multicols}{3}
\centering
{\footnotesize Panel A.1: All Municipalities} \\
\includegraphics[width=0.43\textwidth]{pscomp_coeff_nt.png}

{\footnotesize Panel A.2: Nonpoor Municipalities} \\
\includegraphics[width=0.43\textwidth]{pscomp_coeff_p0_nt.png}

{\footnotesize Panel A.3: Poor Municipalities} \\
\includegraphics[width=0.43\textwidth]{pscomp_coeff_p1_nt.png}
\end{multicols}
\end{figure}
\begin{minipage}{\linewidth}\vspace*{-.5cm}
\singlespace
\footnotesize
Notes: Sample consists of individuals between ages 17 to 35 in the IPUMS 2010 Census.
Dependent variable compulsory school completion is an indicator variable equals one if an individual completed compulsory school or zero otherwise.
Each point shows estimated $\widehat{\beta}_{a}$ with 95\% confidence interval from equation (\ref{did}). In Panel A.1, A.2, and A.3 show estimated $\widehat{\beta}_{a}$ for individuals who live in all, nonpoor, and poor municipalities.
The control groups are: never treated or first treated at age 17+.
Robust standard errors were clustered at municipality level.
Control variables are: year of birth fixed effects, municipality fixed effects, gender dummy, provincial real GDP per capita and public expenditure per capita when individual was at age 6.
\end{minipage}
\end{landscape}


\clearpage
\begin{landscape}
\begin{figure}[!htbp]
\caption{The Effect of Privatization on Secondary School Completion}\label{fig:hscomp}
\begin{multicols}{3}
\centering
{\footnotesize Panel A.1: All Municipalities} \\
\includegraphics[width=0.43\textwidth]{hscomp_coeff_nt.png}

{\footnotesize Panel A.2: Nonpoor Municipalities} \\
\includegraphics[width=0.43\textwidth]{hscomp_coeff_p0_nt.png}

{\footnotesize Panel A.3: Poor Municipalities} \\
\includegraphics[width=0.43\textwidth]{hscomp_coeff_p1_nt.png}
\end{multicols}
\end{figure}
\begin{minipage}{\linewidth}\vspace*{-.5cm}
\singlespace
\footnotesize
Notes: Sample consists of individuals between ages 17 to 35 in the IPUMS 2010 Census.
Dependent variable secondary school completion is an indicator variable equals one if an individual completed secondary school or zero otherwise.
Each point shows estimated $\widehat{\beta}_{a}$ with 95\% confidence interval from equation (\ref{did}). In Panel A.1, A.2, and A.3 show estimated $\widehat{\beta}_{a}$ for individuals who live in all, nonpoor, and poor municipalities.
The control groups are: never treated or first treated at age 17+.
Robust standard errors were clustered at municipality level.
Control variables are: year of birth fixed effects, municipality fixed effects, gender dummy, provincial real GDP per capita and public expenditure per capita when individual was at age 6.
\end{minipage}
\end{landscape}





%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\clearpage
\begin{landscape}
\begin{figure}[!htbp]
\caption{The Effect of Privatization on Primary School Completion by Gender and Poverty Level}\label{fig:ps:female}
\centering
\medskip
{\footnotesize Panel A: Female} \\\vspace*{-0.5cm}
\begin{multicols}{3}
\centering
{\footnotesize Panel A.1: All Municipalities} \\
\includegraphics[width=0.39\textwidth]{ps_coeff_f1_nt.png}

{\footnotesize Panel A.2: Nonpoor Municipalities} \\
\includegraphics[width=0.39\textwidth]{ps_coeff_f1_p0_nt.png}

{\footnotesize Panel A.3: Poor Municipalities} \\
\includegraphics[width=0.39\textwidth]{ps_coeff_f1_p1_nt.png}
\end{multicols}
\vspace*{-0.5cm}
{\footnotesize Panel B: Male} \\\vspace*{-0.5cm}
\begin{multicols}{3}
\centering
{\footnotesize Panel B.1: All Municipalities} \\
\includegraphics[width=0.39\textwidth]{ps_coeff_f0_nt.png}

{\footnotesize Panel B.2: Nonpoor Municipalities} \\
\includegraphics[width=0.39\textwidth]{ps_coeff_f0_p0_nt.png}

{\footnotesize Panel B.3: Poor Municipalities} \\
\includegraphics[width=0.39\textwidth]{ps_coeff_f0_p1_nt.png}
\end{multicols}
\end{figure}
\begin{minipage}{\linewidth}\vspace*{-1.5cm}
\singlespace
\scriptsize
Notes: Sample consists of individuals between ages 17 to 35 in the IPUMS 2010 Census.
Dependent variable primary school completion is an indicator variable equals one if an individual completed primary school or zero otherwise.
Each point shows estimated $\widehat{\beta}_{a}$ with 95\% confidence interval from equation (\ref{did}). Panel A shows estimated results for female sample, in which Panel A.1 shows estimated $\widehat{\beta}_{a}$ for all municipalities, Panel A.2 shows estimates for individuals who live in nonpoor municipalities, and Panel A.3 shows estimates for individuals who live in nonpoor municipalities. Following the same logic, Panel B shows estimated results for male sample.
The control groups are: never treated or first treated at age 17+.
Robust standard errors were clustered at municipality level.
Control variables are: year of birth fixed effects, municipality fixed effects, gender dummy, provincial real GDP per capita and public expenditure per capita when individual was at age 6.
\end{minipage}
\end{landscape}


\clearpage
\begin{landscape}
\begin{figure}[!htbp]
\caption{The Effect of Privatization on Compulsory School Completion by Gender and Poverty Level}\label{fig:pscomp:female}
\centering
\medskip
{\footnotesize Panel A: Female} \\\vspace*{-0.5cm}
\begin{multicols}{3}
\centering
{\footnotesize Panel A.1: All Municipalities} \\
\includegraphics[width=0.39\textwidth]{pscomp_coeff_f1_nt.png}

{\footnotesize Panel A.2: Nonpoor Municipalities} \\
\includegraphics[width=0.39\textwidth]{pscomp_coeff_f1_p0_nt.png}

{\footnotesize Panel A.3: Poor Municipalities} \\
\includegraphics[width=0.39\textwidth]{pscomp_coeff_f1_p1_nt.png}
\end{multicols}
\vspace*{-0.5cm}
{\footnotesize Panel B: Male} \\\vspace*{-0.5cm}
\begin{multicols}{3}
\centering
{\footnotesize Panel B.1: All Municipalities} \\
\includegraphics[width=0.39\textwidth]{pscomp_coeff_f0_nt.png}

{\footnotesize Panel B.2: Nonpoor Municipalities} \\
\includegraphics[width=0.39\textwidth]{pscomp_coeff_f0_p0_nt.png}

{\footnotesize Panel B.3: Poor Municipalities} \\
\includegraphics[width=0.39\textwidth]{pscomp_coeff_f0_p1_nt.png}
\end{multicols}
\end{figure}
\begin{minipage}{\linewidth}\vspace*{-1.5cm}
\singlespace
\scriptsize
Notes: Sample consists of individuals between ages 17 to 35 in the IPUMS 2010 Census.
Dependent variable compulsory school completion is an indicator variable equals one if an individual completed compulsory school or zero otherwise.
Each point shows estimated $\widehat{\beta}_{a}$ with 95\% confidence interval from equation (\ref{did}). Panel A shows estimated results for female sample, in which Panel A.1 shows estimated $\widehat{\beta}_{a}$ for all municipalities, Panel A.2 shows estimates for individuals who live in nonpoor municipalities, and Panel A.3 shows estimates for individuals who live in nonpoor municipalities. Following the same logic, Panel B shows estimated results for male sample.
The control groups are: never treated or first treated at age 17+.
Robust standard errors were clustered at municipality level.
Control variables are: year of birth fixed effects, municipality fixed effects, gender dummy, provincial real GDP per capita and public expenditure per capita when individual was at age 6.
\end{minipage}
\end{landscape}


\clearpage
\begin{landscape}
\begin{figure}[!htbp]
\caption{The Effect of Privatization on Secondary School Completion by Gender and Poverty Level}\label{fig:hscomp:female}
\centering
\medskip
{\footnotesize Panel A: Female} \\\vspace*{-0.5cm}
\begin{multicols}{3}
\centering
{\footnotesize Panel A.1: All Municipalities} \\
\includegraphics[width=0.39\textwidth]{hscomp_coeff_f1_nt.png}

{\footnotesize Panel A.2: Nonpoor Municipalities} \\
\includegraphics[width=0.39\textwidth]{hscomp_coeff_f1_p0_nt.png}

{\footnotesize Panel A.3: Poor Municipalities} \\
\includegraphics[width=0.39\textwidth]{hscomp_coeff_f1_p1_nt.png}
\end{multicols}
\vspace*{-0.5cm}
{\footnotesize Panel B: Male} \\\vspace*{-0.5cm}
\begin{multicols}{3}
\centering
{\footnotesize Panel B.1: All Municipalities} \\
\includegraphics[width=0.39\textwidth]{hscomp_coeff_f0_nt.png}

{\footnotesize Panel B.2: Nonpoor Municipalities} \\
\includegraphics[width=0.39\textwidth]{hscomp_coeff_f0_p0_nt.png}

{\footnotesize Panel B.3: Poor Municipalities} \\
\includegraphics[width=0.39\textwidth]{hscomp_coeff_f0_p1_nt.png}
\end{multicols}
\end{figure}
\begin{minipage}{\linewidth}\vspace*{-1.5cm}
\singlespace
\scriptsize
Notes: Sample consists of individuals between ages 17 to 35 in the IPUMS 2010 Census.
Dependent variable secondary school completion is an indicator variable equals one if an individual completed secondary school or zero otherwise.
Each point shows estimated $\widehat{\beta}_{a}$ with 95\% confidence interval from equation (\ref{did}). Panel A shows estimated results for female sample, in which Panel A.1 shows estimated $\widehat{\beta}_{a}$ for all municipalities, Panel A.2 shows estimates for individuals who live in nonpoor municipalities, and Panel A.3 shows estimates for individuals who live in nonpoor municipalities. Following the same logic, Panel B shows estimated results for male sample.
The control groups are: never treated or first treated at age 17+.
Robust standard errors were clustered at municipality level.
Control variables are: year of birth fixed effects, municipality fixed effects, gender dummy, provincial real GDP per capita and public expenditure per capita when individual was at age 6.
\end{minipage}
\end{landscape}



\clearpage
\begin{landscape}
\begin{figure}[!htbp]
\caption{The Effect of Years of Childhood Exposure on Years of Primary School Completed}\label{fig:schl}
\begin{multicols}{3}
\centering
{\footnotesize Panel A.1: All Municipalities} \\
\includegraphics[width=0.43\textwidth]{schl_c7expo_nt.png}

{\footnotesize Panel A.2: Nonpoor Municipalities} \\
\includegraphics[width=0.43\textwidth]{schl_c7expo_p0_nt.png}

{\footnotesize Panel A.3: Poor Municipalities} \\
\includegraphics[width=0.43\textwidth]{schl_c7expo_p1_nt.png}
\end{multicols}
\end{figure}
\begin{minipage}{\linewidth}\vspace*{-1cm}
\singlespace
\footnotesize
Notes: Sample consists of individuals between ages 17 to 35 in the IPUMS 2010 Census.
Each point represents coefficient estimated by separate regression.
Horizontal axis indicates dependent variable completed $k$ yrs of primary school equals one if individual completed $k$ years of primary school, $k = 1, 2, 3,..., 7$ or zero otherwise.
Vertical axis indicated estimated $\widehat{\beta}$ of years of childhood exposure with 95\% confidence interval from equation (\ref{schl}).
Panels A.1, A.2, and A.3 show estimated $\widehat{\beta}$ for individuals who live in all, nonpoor, and poor municipalities.
Robust standard errors were clustered at municipality level.
Control variables are: year of birth fixed effects, municipality fixed effects, gender dummy, provincial real GDP per capita and public expenditure per capita when individual was at age 6.
\end{minipage}
\end{landscape}





%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% TABLES %%%%%
%%%%%%%%%%%%%%%%%%
\clearpage
\section{Tables}
\begin{table}[!htbp]
\caption{Summary Statistics}\label{sumstat}
\footnotesize
\centering
\newcolumntype{Y}{>{\raggedleft\arraybackslash}X}
\begin{tabularx} {12cm} {@{} l Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y@{}}
\toprule
            & \multicolumn{5}{c}{Panel A: All municipalities} \\\cmidrule(l{.75em}){2-6}
\input{\ChTwoPath/sumstat_SumVar.txt}

\toprule
            & \multicolumn{5}{c}{Panel B: Nonpoor municipalities} \\\cmidrule(l{.75em}){2-6}
\input{\ChTwoPath/sumstat_SumVar_p0.txt}

\toprule
            & \multicolumn{5}{c}{Panel C: Poor municipalities} \\\cmidrule(l{.75em}){2-6}
\input{\ChTwoPath/sumstat_SumVar_p1.txt}
\bottomrule
\addlinespace[.75ex]
\end{tabularx}
\tabnote{Notes: Sample consists of individuals between ages 17 to 35 in the IPUMS 2010 Census in 275 municipalities. Yrs of childhood exposure = Max$((7 - \text{age of first exposure}), 0)$.}
\normalsize
\end{table}



\clearpage
\begin{table}[!htbp]
\caption{The Effect of Privatization on Primary School Completion}\label{tab:ps}
\footnotesize
\centering
\newcolumntype{Y}{>{\raggedleft\arraybackslash}X}
\begin{tabularx} {11.5cm} {@{} l c c c c c c c c c c c c c c c @{}}
\toprule
                    & \multicolumn{3}{c}{Dependent Variable} \\[-3pt]
                    & \multicolumn{3}{c}{Primary school completion} \\\cmidrule(l{.75em}){2-4}
                    & \multicolumn{3}{c}{Panel A: Female and Male} \\\cmidrule(l{.75em}){2-4}
                    & \multicolumn{1}{c}{All} & \multicolumn{1}{c}{Nonpoor} & \multicolumn{1}{c}{Poor} \\
                    & \multicolumn{1}{c}{municipalities} & \multicolumn{1}{c}{municipalities} & \multicolumn{1}{c}{municipalities} \\
                    & \multicolumn{1}{c}{0.92}  & \multicolumn{1}{c}{0.94} & \multicolumn{1}{c}{0.85} \\
                    \cmidrule(l{.75em}){2-2}\cmidrule(l{.75em}){3-3}\cmidrule(l{.75em}){4-4}
                    &   (1)   &   (2)   &   (3)   \\
                    \cmidrule(l{.75em}){2-4}
\input{\ChTwoPath/ps_c7expo_nt.txt}
\bottomrule
     \\
                    & \multicolumn{3}{c}{Panel B: Female} \\\cmidrule(l{.75em}){2-4}
                    & \multicolumn{1}{c}{All} & \multicolumn{1}{c}{Nonpoor} & \multicolumn{1}{c}{Poor} \\
                    & \multicolumn{1}{c}{municipalities} & \multicolumn{1}{c}{municipalities} & \multicolumn{1}{c}{municipalities} \\
                    & \multicolumn{1}{c}{0.94}  & \multicolumn{1}{c}{0.95} & \multicolumn{1}{c}{0.86} \\
                    \cmidrule(l{.75em}){2-2}\cmidrule(l{.75em}){3-3}\cmidrule(l{.75em}){4-4}
                    &   (4)   &   (5)   &   (6)   \\
                    \cmidrule(l{.75em}){2-4}
\input{\ChTwoPath/ps_c7expo_nt_f1.txt}
\bottomrule
     \\
                    & \multicolumn{3}{c}{Panel C: Male} \\\cmidrule(l{.75em}){2-4}
                    & \multicolumn{1}{c}{All} & \multicolumn{1}{c}{Nonpoor} & \multicolumn{1}{c}{Poor} \\
                    & \multicolumn{1}{c}{municipalities} & \multicolumn{1}{c}{municipalities} & \multicolumn{1}{c}{municipalities} \\
                    & \multicolumn{1}{c}{0.91}  & \multicolumn{1}{c}{0.93} & \multicolumn{1}{c}{0.83} \\
                    \cmidrule(l{.75em}){2-2}\cmidrule(l{.75em}){3-3}\cmidrule(l{.75em}){4-4}
                    &   (7)   &   (8)   &   (9)   \\
                    \cmidrule(l{.75em}){2-4}
\input{\ChTwoPath/ps_c7expo_nt_f0.txt}
\bottomrule
\addlinespace[.75ex]
\end{tabularx}
\tabnote{Notes: ***$p<0.01$, **$p<0.05$, *$p<0.1$.
Sample consists of individuals between ages 17 to 35 in the IPUMS 2010 Census in the 275 municipalities.
Robust standard errors were clustered at the municipalities level.
Dependent variable primary school completion is an indicator variable equals one if an individual completed primary school or zero otherwise.
Yrs of childhood exposure = Max$((7 - \text{age of first exposure}), 0)$.
Panels A, B, and C show the results for sample female and male, female, and male, respectively. Each column in each panel represents a separate regression. Columns (1), (4), and (7) show estimation for all municipalities, Columns (2), (5), and (8) show estimation for nonpoor municipalities, and Columns (3), (6), and (9) show estimation for poor municipalities.
Control variables are: year of birth fixed effects, municipality fixed effects, gender dummy, provincial real GDP per capita and public expenditure per capita when individual was at age 6.
}
\end{table}


\clearpage
\begin{table}[!htbp]
\caption{The Effect of Privatization on Compulsory School Completion}\label{tab:pscomp}
\footnotesize
\centering
\newcolumntype{Y}{>{\raggedleft\arraybackslash}X}
\begin{tabularx} {11.5cm} {@{} l c c c c c c c c c c c c c c c @{}}
\toprule
                    & \multicolumn{3}{c}{Dependent Variable} \\[-3pt]
                    & \multicolumn{3}{c}{Compulsory school completion} \\\cmidrule(l{.75em}){2-4}
                    & \multicolumn{3}{c}{Panel A: Female and Male} \\\cmidrule(l{.75em}){2-4}
                    & \multicolumn{1}{c}{All} & \multicolumn{1}{c}{Nonpoor} & \multicolumn{1}{c}{Poor} \\
                    & \multicolumn{1}{c}{municipalities} & \multicolumn{1}{c}{municipalities} & \multicolumn{1}{c}{municipalities} \\
                    & \multicolumn{1}{c}{0.91}  & \multicolumn{1}{c}{0.93} & \multicolumn{1}{c}{0.83} \\
                    \cmidrule(l{.75em}){2-2}\cmidrule(l{.75em}){3-3}\cmidrule(l{.75em}){4-4}
                    &   (1)   &   (2)   &   (3)   \\
                    \cmidrule(l{.75em}){2-4}
\input{\ChTwoPath/pscomp_c7expo_nt.txt}
\bottomrule
     \\
                    & \multicolumn{3}{c}{Panel B: Female} \\\cmidrule(l{.75em}){2-4}
                    & \multicolumn{1}{c}{All} & \multicolumn{1}{c}{Nonpoor} & \multicolumn{1}{c}{Poor} \\
                    & \multicolumn{1}{c}{municipalities} & \multicolumn{1}{c}{municipalities} & \multicolumn{1}{c}{municipalities} \\
                    & \multicolumn{1}{c}{0.92}  & \multicolumn{1}{c}{0.94} & \multicolumn{1}{c}{0.85} \\
                    \cmidrule(l{.75em}){2-2}\cmidrule(l{.75em}){3-3}\cmidrule(l{.75em}){4-4}
                    &   (4)   &   (5)   &   (6)   \\
                    \cmidrule(l{.75em}){2-4}
\input{\ChTwoPath/pscomp_c7expo_nt_f1.txt}
\bottomrule
     \\
                    & \multicolumn{3}{c}{Panel C: Male} \\\cmidrule(l{.75em}){2-4}
                    & \multicolumn{1}{c}{All} & \multicolumn{1}{c}{Nonpoor} & \multicolumn{1}{c}{Poor} \\
                    & \multicolumn{1}{c}{municipalities} & \multicolumn{1}{c}{municipalities} & \multicolumn{1}{c}{municipalities} \\
                    & \multicolumn{1}{c}{0.90}  & \multicolumn{1}{c}{0.92} & \multicolumn{1}{c}{0.81} \\
                    \cmidrule(l{.75em}){2-2}\cmidrule(l{.75em}){3-3}\cmidrule(l{.75em}){4-4}
                    &   (7)   &   (8)   &   (9)   \\
                    \cmidrule(l{.75em}){2-4}
\input{\ChTwoPath/pscomp_c7expo_nt_f0.txt}
\bottomrule
\addlinespace[.75ex]
\end{tabularx}
\tabnote{Notes: ***$p<0.01$, **$p<0.05$, *$p<0.1$.
Sample consists of individuals between ages 17 to 35 in the IPUMS 2010 Census in the 275 municipalities.
Robust standard errors were clustered at the municipalities level.
Dependent variable Compulsory school completion is an indicator variable equals one if an individual completed compulsory school or zero otherwise.
Yrs of childhood exposure = Max$((7 - \text{age of first exposure}), 0)$.
Panels A, B, and C show the results for sample female and male, female, and male, respectively. Each column in each panel represents a separate regression. Columns (1), (4), and (7) show estimation for all municipalities, Columns (2), (5), and (8) show estimation for nonpoor municipalities, and Columns (3), (6), and (9) show estimation for poor municipalities.
Control variables are: year of birth fixed effects, municipality fixed effects, gender dummy, provincial real GDP per capita and public expenditure per capita when individual was at age 6.
}
\end{table}


\clearpage
\begin{table}[!htbp]
\caption{The Effect of Privatization on Secondary School Completion}\label{tab:hscomp}
\footnotesize
\centering
\newcolumntype{Y}{>{\raggedleft\arraybackslash}X}
\begin{tabularx} {11.5cm} {@{} l c c c c c c c c c c c c c c c @{}}
\toprule
                    & \multicolumn{3}{c}{Dependent Variable} \\[-2pt]
                    & \multicolumn{3}{c}{Secondary school completion} \\\cmidrule(l{.75em}){2-4}
                    & \multicolumn{3}{c}{Panel A: Female and Male} \\\cmidrule(l{.75em}){2-4}
                    & \multicolumn{1}{c}{All} & \multicolumn{1}{c}{Nonpoor} & \multicolumn{1}{c}{Poor} \\
                    & \multicolumn{1}{c}{municipalities} & \multicolumn{1}{c}{municipalities} & \multicolumn{1}{c}{municipalities} \\
                    & \multicolumn{1}{c}{0.53}  & \multicolumn{1}{c}{0.56} & \multicolumn{1}{c}{0.39} \\
                    \cmidrule(l{.75em}){2-2}\cmidrule(l{.75em}){3-3}\cmidrule(l{.75em}){4-4}
                    &   (1)   &   (2)   &   (3)   \\
                    \cmidrule(l{.75em}){2-4}
\input{\ChTwoPath/hscomp_c7expo_nt.txt}
\bottomrule
     \\
                    & \multicolumn{3}{c}{Panel B: Female} \\\cmidrule(l{.75em}){2-4}
                    & \multicolumn{1}{c}{All} & \multicolumn{1}{c}{Nonpoor} & \multicolumn{1}{c}{Poor} \\
                    & \multicolumn{1}{c}{municipalities} & \multicolumn{1}{c}{municipalities} & \multicolumn{1}{c}{municipalities} \\
                    & \multicolumn{1}{c}{0.57}  & \multicolumn{1}{c}{0.60} & \multicolumn{1}{c}{0.43} \\
                    \cmidrule(l{.75em}){2-2}\cmidrule(l{.75em}){3-3}\cmidrule(l{.75em}){4-4}
                    &   (4)   &   (5)   &   (6)   \\
                    \cmidrule(l{.75em}){2-4}
\input{\ChTwoPath/hscomp_c7expo_nt_f1.txt}
\bottomrule
     \\
                    & \multicolumn{3}{c}{Panel C: Male} \\\cmidrule(l{.75em}){2-4}
                    & \multicolumn{1}{c}{All} & \multicolumn{1}{c}{Nonpoor} & \multicolumn{1}{c}{Poor} \\
                    & \multicolumn{1}{c}{municipalities} & \multicolumn{1}{c}{municipalities} & \multicolumn{1}{c}{municipalities} \\
                    & \multicolumn{1}{c}{0.48}  & \multicolumn{1}{c}{0.51} & \multicolumn{1}{c}{0.34} \\
                    \cmidrule(l{.75em}){2-2}\cmidrule(l{.75em}){3-3}\cmidrule(l{.75em}){4-4}
                    &   (7)   &   (8)   &   (9)   \\
                    \cmidrule(l{.75em}){2-4}
\input{\ChTwoPath/hscomp_c7expo_nt_f0.txt}
\bottomrule
\addlinespace[.75ex]
\end{tabularx}
\tabnote{Notes: ***$p<0.01$, **$p<0.05$, *$p<0.1$.
Sample consists of individuals between ages 17 to 35 in the IPUMS 2010 Census in the 275 municipalities.
Robust standard errors were clustered at the municipalities level.
Dependent variable secondary school completion is an indicator variable equals one if an individual completed secondary school or zero otherwise.
Yrs of childhood exposure = Max$((7 - \text{age of first exposure}), 0)$.
Panels A, B, and C show the results for sample female and male, female, and male, respectively. Each column in each panel represents a separate regression. Columns (1), (4), and (7) show estimation for all municipalities, Columns (2), (5), and (8) show estimation for nonpoor municipalities, and Columns (3), (6), and (9) show estimation for poor municipalities.
Control variables are: year of birth fixed effects, municipality fixed effects, gender dummy, provincial real GDP per capita and public expenditure per capita when individual was at age 6.
}
\end{table}






%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% APPENDIX %%%%%
\clearpage
\section{Appendix} \label{app}
\begin{figure}[!htbp]
\centering
\caption{Municipalities in IPUMS with Identified Year of Privatization}\label{fig:dept}
\includegraphics[width=.8\textwidth]{dept_match2.png}
\end{figure}
\begin{minipage}{14.5cm}\vspace*{-.2cm}
\singlespace
\footnotesize
Notes: There are 23 provinces and 299 municipalities in the IPUMS 2010 Census, in which 256 municipalities are matched with \textcite{galiani2005} (GGS)'s data (section A). 20 of them are either no service or missing information of water services, or they are identifiable in the 2010 Census but not exist in \textcite{galiani2005} (section B). 23 of them are suppressed municipalities in each province (section C). For section C, I go back to \textcite{galiani2005} to figure out their year of privatization. I realized that most of these suppressed municipalities with under 20,000 residents are either never privatized or all privatized at the same year under each province. Eventually, I am able to identify year of privatization of 19 municipalities (section D). Overall, my empirical analysis contains 275 municipalities (sections A + D) which represent 84\% of municipalities (400 municipalities) in \textcite{galiani2005}.
\end{minipage}


\clearpage
\begin{table}[!htbp]
\caption{Water Services Privatization in Municipalities}\label{tab:muni-yr}
\scriptsize
\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{Privatization}  &                                  \\[-3pt]
\textbf{year}           & \multicolumn{1}{c}{\textbf{Municipality}} \\\hline
\input{\ChTwoPath/municipality-year_panel.txt}
\bottomrule
\addlinespace[.75ex]
\end{tabularx}
\tabnote{Notes: There are 275 municipalities. As mentioned in Section \ref{data}, these are the municipalities after I merged the municipalities in the 2010 Census with the privatization information from \textcite{galiani2005}'s data.

Sources: \textcite{galiani2005}.
}
\end{table}
