metadata: authors: 0: author: Francois,John Nana Darko
1: author: Gyimah-Brempong,Kwabena
2: author: Kakeu, Johnson
3: author: Kouame, Christelle Signo
count: World
docna: 0: docna: The People’s Voice and Access to Sanitation
docty: Policy Research Working Paper
abstracts: cdata!: This paper estimates the effect of voice
            and accountability, which captures transparent electoral
            processes, free media, and freedom of expression, on access
            to sanitation services in developing countries. The core
            argument is that voice and accountability increases the
            visibility of sanitation as a public good and raises
            awareness of its benefits; hence, increasing its supply and
            demand. The analysis utilizes data from 73 developing
            countries and an instrumental variable approach to identify
            the causal effect of voice and accountability on access to,
            and use of, sanitation. The paper also employs a novel
            instrument-free estimator as both an alternative estimator
            for the analysis and an empirical strategy to formally
            assess the validity of the instrument in a just-identified
            model&#x2014;a previously untestable just-identifying
            exclusion restriction. The paper finds robust evidence that
            voice and accountability increase access to sanitation and
            help close the persistent rural-urban inequality in access
            to sanitation. The results suggest that key tenets of
            democracy such a freedom of speech, free media, and power of
            electoral incentives are not a luxury of the
            rich&#x2014;they are relevant to the world&#x2019;s
            poor as they can shape the demand and distribution of
            sanitation services.
display_title: The People’s Voice and Access to Sanitation
listing_relative_url: /research/2023/05/34054345/people’s-voice-access-sanitation
topicv3: Sanitation m1326453 2000,Spatial Inequality m1327181 1166,Governance in Public Sector m1327255 831,Sustainable Development Goals m1504460 3134,Rural Development m1504505 2667
publishtoextweb_dt: 2023-05-02T00:00:00Z
dois: 10.1596/1813-9450-10430
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 2166801
1: author_key: NA
2: author_key: NA
3: author_key: 1095985
model_extractions: Policy Research Working Paper 10430 The People’s Voice and Access to Sanitation _John Nana Francois Kwabena Gyimah-Brempong Johnson Kakeu Cristelle Kouame_ Macroeconomics, Trade and Investment Global Practice May 2023
Policy Research Working Paper 10430

# **Abstract**

This paper estimates the effect of voice and accountability, which captures transparent electoral processes, free media, and freedom of expression, on access to sanitation services in developing countries. The core argument is that voice and accountability increases the visibility of sanitation as a public good and raises awareness of its benefits; hence, increasing its supply and demand. The analysis utilizes data from 73 developing countries and an instrumental variable approach to identify the causal effect of voice and accountability on access to, and use of, sanitation. The paper also employs a novel _instrument-free_ _estimator_ as both an alternative estimator for the analysis and an empirical strategy to formally assess the validity of the instrument in a just-identified model—a previously untestable just-identifying exclusion restriction. The paper finds robust evidence that voice and accountability increase access to sanitation and help close the persistent rural- _urban inequality_ in access to sanitation. The results suggest that key tenets of democracy such a freedom of speech, free media, and power of electoral incentives are not a luxury of the rich—they are relevant to the world’s poor as they can shape the demand and distribution of sanitation services.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jfrancois1@worldbank.org_and ckouame 1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **The People’s Voice and Access to Sanitation**<sup>*</sup>

John Nana Francois<sup>†</sup> Kwabena Gyimah-Brempong<sup>‡</sup> Johnson Kakeu<sup>§</sup> World Bank University of South Florida and IZA University of Prince Edward Island Cristelle Kouame<sup>¶</sup> World Bank _JEL Classification_ : C26, D72, H41, H42, O18, P16 _Keywords_ : Sanitation; Voice and accountability; Awareness and visibility; Rural and urban; Instrumental variable; Instrument-free estimator

*We are grateful to Michael Bleaney, Robert Johann Utz, Chiara Bronchi, Yaw Nyarko, Zalda Feliciano, Maty Konte, Leonce Ndikumana, Mina Baliamoune-Lutz, Jesse Mora, Grace Phillips, Eric Hoffmann, Anne C. Barthel, Kodama Toyoko, Berta Macheve, and Guy Tchuente for useful comments, feedback, and discussions on earlier versions of the paper that have significantly improved the paper. We would also like thank conference participants and discussants at the 2021 American Economic Association (AEA), the 2022 97th Western Economic Association International meeting in Portland, Oregon, and University of Prince Edward Island for useful discussions and feedback. We are solely responsible for any remaining errors.

**Disclaimer** : The findings, interpretations and conclusions expressed in this paper are entirely those of the authors and should not be attributed to the World Bank, its Executive Directors, or the countries they represent.

†Fiscal Policy and Sustainable Growth Unit, World Bank, 701 18th Street NW, Washington, DC20433, US.

‡Department of Economics, University of South Florida, Tampa, FL 33620, Tel: (703) 798-2438 email: kgyimah@usf.edu and IZA-Institute of Labor Economics, Bonn.

- §Email: jkakeu@upei.ca. Department of Economics, 550 University Avenue Charlottetown, Prince Edward Island Canada C1A 4P3 ¶Email: ckouame1@worldbank.org. World Bank, 701 18th Street NW, Washington, DC20433, US (J10-042)
# 1. INTRODUCTION

“I stuck my neck out today when I said we have to break taboos because people didn’t even mention the word toilet in the UN [United Nations] five years ago ... We need to break taboos ... We don’t speak about [defecation], there’s a shame factor which is horrible. It is the most natural of human needs, which causes enormous problems which relate both to health and dignity.”— Jan Eliasson UN Deputy Secretary-General on his speech at the World Water Week in Stockholm on September 2, 2013 to the Guardian.

This paper studies the causal effect of voice and accountability ( _Voice_ , henceforth), which broadly captures transparent electoral processes and free media and accountability, on the access to sanitation in developing countries.<sup>1</sup> The paper also investigates the effect of _Voice_ on rural/urban gaps in access to sanitation. We do so by: (1) estimating the effect of _Voice_ on the percentage of people using basic sanitation for 73 developing countries; (2) examining whether _Voice_ increases access to sanitation in rural and urban areas; and (3) investigating whether _Voice_ closes the persistent rural-urban gap in access to sanitation in developing countries. Our core argument is that _Voice_ increases sanitation’s visibility as a public good, raises awareness of its benefits to households who demand its production, and pushes governments to respond to pressure to increase the supply of sanitation.<sup>2</sup> We instrument for _Voice_ using the average years of schooling in 1900. We find that _Voice_ has a positive, statistically and economically significant effect on access to sanitation services. This positive effect generally holds true in urban and rural areas. The results are also robust to different model specifications and estimation methods. Furthermore, the results show that _Voice_ can be used as an effective vehicle to close the persistent rural-urban inequality in access to sanitation.

Access to sanitation, measured as the percentage of people using at least basic sanitation services—improved sanitation facilities that are not shared with other households—is fundamental to improved human health. This measure encompasses both people using basic sanitation services as well as those using safely managed sanitation services.<sup>3</sup> Recent updates of Sustainable Development Goal 6 (SDG 6) show that the developing world has seen significant progress in the number of people with access to sanitation.<sup>4</sup> However, globally, 3.6 billion people still live without access to basic sanitation, and approximately 8 percent of the world’s population still practice open defecation (United Nations, 2021). Furthermore, it is well-documented that the sanitation crisis is most acute in rural areas, home to 91 percent of the people who defecate in the open and 72 percent of those without basic sanitation. Additionally, the lack of access to sanitation poses a serious threat to vulnerable populations as it exposes them to serious environmental health risks (Alz ́ua, Djebbari, and Pickering, 2020). In fact, poor sanitation is a leading risk factor for infectious diseases, including cholera, diarrhoea, dysentery, hepatitis A, typhoid, and polio. There is also strong evidence that lack of access to sanitation exacerbates malnutrition, and in particular, childhood stunting (WHO , 2019).<sup>5</sup> Access to sanitation also improves education outcomes. For example, Adukia (2017) finds that latrine construction increases female school enrollment in India, particularly those who may be vulnerable to illnesses caused by uncontained waste. Similarly, Spears and Lamba (2016) find that early exposure to a sanitation campaign through India’s Total Sanitation

> 1Throughout the paper, we often use voice and accountability to also capture free media and transparent elections.

> 2This argument is in line with Kaufmann et al. (2019), who find evidence that suggests that “voice”-related variables such as citizen voice and transparency has a larger effect on the public sector service delivery performance and corruption. More practically, recent sanitation policy iterations have targeted demand for sanitation through media campaigns, capacity building, and community activities (Pakhtigian et al., 2022). The focus has shifted to education and communication, coupled with a continued emphasis on direct infrastructure provision and subsidization. 3We follow the World Health Organization’s (WHO) definition of sanitation as access to facilities for the safe disposal of human waste as well as having the ability to maintain hygienic conditions. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs.

> 4See details on SDG 6 here: https://www.un.org/sustainabledevelopment/water-and-sanitation/ at United Nations.

> 5https://www.who.int/news-room/fact-sheets/detail/sanitation

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Campaign improves childhood cognitive skills. The direct role of sanitation on educational outcomes suggests that lack of access to sanitation is a threat to human capital formation, which is an important driver of long-term growth and development. Despite its benefits, governments in developing countries have not provided sufficient investment, administrative support, and continuous oversight for the substantial provision of basic sanitation services.

Table 1 provides a first look at the progress made in access to electricity and water compared to sanitation services in low-income countries. Over the 2010 to 2019 period, access to electricity and water services increased by 14.07 and 7.97 percentage points, respectively. Meanwhile, access to sanitation services increased by only 0.68 percentage points in low-income countries. This marginal increase in access to sanitation may be partly driven by the fact that there is less public awareness of, and perhaps, lower priority given to, the sanitation problem— i.e., sanitation is a “low visibility good” in developing countries— compared to electricity or water services.

**Table 1:** The change in access to basic public goods in low-income countries, 2010-2019

|Publicgood/service|2010|2019|Change(% Points)|
|---|---|---|---|
|Electricity (% of population)|26.85|40.92|14.07|
|Water services (% of population)|49.85|57.80|7.95|
|Sanitation services (% of population)|44.56|45.24|0.68|

Source: Authors’ calculation of World Bank’s World Development Indicators. Access to electricity is the percentage of population with access to electricity. Water services (% of population) is the percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water.

Access to sanitation is both a demand and supply problem. Specifically, households demand to use sanitation services, and governments have a key leadership and facilitating role (e.g., partnering with the private sector) in providing sanitation services. Households may be able and willing to pay for improved sanitation services, but the government’s supply of public infrastructure is required to facilitate the final provision of these services. Without government provision of sanitation infrastructure, household demand for, and use of, sanitation may not be possible. In several rural areas in low-income countries, households may not be able to afford sanitation services— i.e., demand is nonexistent simply because income is too low. In such a case, the government can provide sanitation services (e.g., public toilets) as public goods to serve the poor at little to no cost.<sup>6</sup> On the other hand, if the supply of sanitation service is available, people will have the incentive to access and use these necessities. For several people in the developing world, however, the immediate and long-term benefits of accessing and using quality sanitation services and the facilitating role of government in providing these services are not well-communicated. Hence, the demand, and consequently, the supply for basic sanitation services might be low partly because of low awareness from lack of information. Moreover, even if there is awareness of the sanitation problem, the suppression of discussions and media campaigns around it can keep the problem hidden and hence, make it less visible as a priority policy issue. These may be attributed to weak institutions related to voice and accountability.

Using average years of schooling in 1900 as an instrumental variable to identify the exogenous component of voice and accountability, we estimate the latter’s impact on access to sanitation in 73 developing countries over the period 2002-2020. Our empirical analysis uncover the following results: First, we find that higher levels of _Voice_

> 6In situations where building sanitation infrastructure can generate positive externalities on the demand side, the empirical study by Gautam (2023) shows that a price subsidy is more effective than a targeted cash transfer in increasing the average sanitation adoption rate.

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increases access to and use of sanitation. Specifically, after accounting for key covariates, our point estimate indicates that a 1 unit increase in _Voice_ causes an 18 percentage points increase in access to and use of basic sanitation service. Second, we find that a unit increase in _Voice_ leads to 12 and 32 percentage point increase in sanitation access in urban and rural areas, respectively. Third, the results reveal that increases in _Voice_ decreases the rural/urban gap in access to sanitation. These results are robust across different estimators, specifications, and samples.

The causal impact of _Voice_ on access to sanitation in developing countries suggests that _Voice_ makes the sanitation problem visible. Once the problem is made visible, solutions may be seriously considered; hence, help increase the provision and access of sanitation services. Moreover, our findings imply that policies targeting the strengthening of institutions along the dimension of voice and accountability in developing countries will not only improve the latter but also induce an increase of access to sanitation. More precisely, policies that target the improvement of _Voice_ can generate positive second order effects with important development implications. Furthermore, the results strongly suggest that _Voice_ can be used as an effective vehicle to close the rural-urban gap in access to sanitation.

Our study makes four key contributions to the literature: First, we establish _causality_ between _Voice_ and access to, and use of, sanitation— previous studies have not established a casual impact of _Voice_ on access to sanitation. Relatedly, our work focuses exclusively on the effect of voice and accountability on access to, and use of, sanitation in developing countries as opposed to simply using it as another control variable in previous studies. Second, we use a simple conceptual framework to shed light on the mechanisms—i.e., the demand and supply channels— through which voice and accountability might affect access to sanitation in developing countries. Third, our analysis identifies a rural-urban gap in access to sanitation and investigates the degree to which voice and accountability can close this gap. The result establishes that _Voice_ can serve as an important vehicle in solving this critical and structural spatial inequality in access to sanitation. This can in turn improve well-known rural-urban gap inequalities in education and health. Fourth, the study applies a novel instrument-free estimator in kinky least squares (KLS) to formally test the validity of our instrumental variable— a previously untestable just-identifying exclusion restriction. Importantly, as a robustness exercise, we employ the KLS as an alternative estimator to establish a causal effect of voice and accountability on access to and use of sanitation.

The rest of the paper is organized as follows: Section 2 briefly summarizes the related literature on the subject, Section 3 introduces a simple conceptual framework to shed light on how voice and accountability can impact access to sanitation. Section 4 describes the data and discusses the methodology. Section 5 presents and discusses the estimation results. Section 6 concludes.

# 2. RELATED LITERATURE

Despite its importance, the development economics literature has paid scant attention to sanitation (Ravilla et al., 2021). However, this is rapidly changing as the literature has begun to pay serious attention to the subject in light of the prominence given to it in the SDGs. Recent studies of sanitation in the development economics literature have focused on three main areas: the impact of sanitation on development outcomes; the supply of, access to, and use of sanitation services; and the role of external aid in the provision of sanitation. Our paper focuses on the effects of voice and accountability on access to and use of sanitation; hence, we do not focus on the literature that investigates the effect of sanitation on development outcomes. We therefore limit our literature review to the supply and demand for sanitation, and the role of foreign aid in the supply, access and use of sanitation.

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On the demand side, several studies have stressed the importance of income (Immurana et al., 2022); education and information (Alexander et al., 2019), Adil et al. (2021); Legge et al. (2021); Pakhtigian et al. (2022), resource constraints, as well as socio-cultural environment (Munamati et al., 2016) as important determinants of the access to, and use of, sanitation infrastructure and services. The argument is that information and education shift household preferences towards sanitation use while increasing income and relaxing resource constraints, generally, make sanitation services affordable to households. These studies have been conducted mostly at the aggregate level and do not establish causality. Other researchers stress the importance of accountability and advocacy as important factors in access and use of sanitation services. Some studies also highlight the critical role of information and peer effects in overcoming social and cultural barriers in the use of sanitation (Adil et al., 2021; Alexander et al., 2019; Legge et al., 2021; Pakhtigian et al., 2022, among others) Researchers interested in the supply of infrastructure services have focused on the characteristics of sanitation infrastructure and services, political institutions and the environment in which services are provided as well as political accountability and advocacy generated through voice and accountability. Ashraf et al. (2016) and Batley and Mcloughlin (2015) argue that sanitation infrastructure and services are characterized by indivisibility as well as externalities; hence, have to be provided by the public sector else the quantity provided will be sub-optimal. In low income countries, private provision may not even be feasible because of affordability. Other researchers have focused on the type of political institutions that provide sanitation services more effectively. Deacon (2009) argue that democracies provide better public services, including sanitation, to citizens relative to autocracies, probably because of increased accountability. Similarly, Keefer and Khemani (2005); Khemani (2015) argue that political incentives emanating from visibility and political pressure determines which public goods will be provided in a society.<sup>7</sup> Looking at sanitation issues through an institutional lens, data from 85 countries are analyzed by Anbarci et al. (2009) to show that corruption negatively affects both efforts to improve drinking water and sanitation. Breen and Gillanders (2022) using data on 45,000 households’ across Africa provide strong evidence that local corruption in the utilities sector impedes everyone’s access to clean water and sanitation. Similarly, some researchers argue that the supply of sanitation is influenced by the institutional environment (Francois et al., 2021; Hepworth et al., 2022; Hout et al., 2022).

An aspect of the provision of public services generally, and sanitation in particular, is _visibility_ and accountability. The more visible a project is and the more policy makers are held accountable for its provision, the greater the quantity of such services that will be provided; the reverse is also true—policy makers tend to provide less of services that are not visible and for which they are not held accountable. _Voice_ shines a spotlight on sanitation thus making it _visible_ as well as helping to keep policy makers _accountable_ for its provision. Adil et al. (2021) finds that information and increased accountability improves access to safe drinking water and sanitation in Punjab, Pakistan. Similarly, Hout et al. (2022) finds that increased accountability increases both access and sustainability of sanitation in Kenya.

A large amount of research focuses on testing the effects of foreign aid in providing access to sanitation (Abell ́an and Alonso, 2022; Gopalan and Rajan, 2016; Ndikumana and Pickbourn, 2017; Pickbourn et al., 2022, among others) and generally finds a significant and positive effect of aid on access to sanitation. These studies are generally conducted at the aggregate level using a variety of estimation methods. These researchers argue that international aid affects the demand for sanitation by changing the marginal utility of sanitation through health education and other information 7An interesting case is provided by Narzetti and Marques (2021), who seem to suggest that in vulnerable areas in Brazil, sanitation issues have a very low degree of visibility in the eyes of regulators than they do elsewhere. One way to see it is that governmental entities resign from their roles there, as evidenced by the lack of inclusion and application of regulation and public policies in vulnerable areas.

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channels. On the supply side, foreign aid works to reduce the marginal cost to household as well as decrease the cost of provision to governments tat otherwise may not be able to afford to provide the infrastructure.

With the exception of Abell ́an and Alonso (2022), previous research focuses exclusively either on the demand side or the supply side without integrating supply and demand for access to and use of sanitation. We do so in this paper. Second, although some papers have mentioned voice and accountability, they do not focus on it as the central issue in their analyses. Finally, none of these studies establishes the direction of causation between voice and accountability on the one hand and access and use of sanitation on the other. Our paper addresses these issues by investigating the _causal effects_ of _Voice_ on sanitation access and does so within a supply and demand framework. In addition, our study extends the analysis to study rural and urban areas alongside the role of voice and accountability in closing the rural/urban gap in access to sanitation. Besides using instrumental variables estimation to overcome possible endogeneity problems, we also use a novel instrument free estimator in kinky least squares developed by Kiviet (2013, 2022) as a robustness check on our results. Our primary approach in this study is the use of crosssection data and thus does not explicitly account for time dimension. The use of cross sectional data allows us to use our estimation methodology and draw causality in the process.

# 3. CONCEPTUAL FRAMEWORK: DEMAND AND SUPPLY CHANNEL OF VOICE & ACCOUNTABILITY

Access to and use of sanitation services is a two-part problem— a demand side and a supply side issue. In this section, we provide a brief description of the channels through which _Voice_ can directly broaden access to sanitation by stimulating the demand and supply.

On the demand side, households are the main beneficiaries of sanitation services and hence, have the key responsibility of gaining access to it. In low information environments, lack of awareness of the benefits of sanitation as well as government’s inability or unwillingness to provide sanitation services can lower the demand for these services by households. The lack of information issue is likely to be more pervasive in rural areas compared to urban areas. Notice that when the freedom of the media is suppressed by government, certain socioeconomic issues may not be publicly discussed and certain educative information may not reach the populace. In contrast, a free media can effectively serve as a medium in communicating key information to citizens on the benefits of sanitation. Consequently, by providing the appropriate health information and education about the benefits of sanitation through TV and radio advertisements for example, _Voice_ can stimulate the demand for sanitation, all things equal.

Formally, we can consider that households maximize a utility function that is positive and concave in sanitation services and a bundle of all other consumption goods and services ( _X_ ) subject to budget constraints. Additionally, we assume that households gain utility from an exogenous level of _Voice v_ in the country that is complementary to sanitation services. Specifically, voice increases the marginal utility of sanitation and hence, the demand for sanitation. Algebraically, the household utility is given as _U_ = _u_ ( _s, X, v_ ) _, ∂U/∂ s >_ 0 and _∂U/∂ s∂ v >_ 0. The last term captures the complementarity (in the Edgeworth pareto sense) between voice and sanitation services in utility, so that an improvement in voice and accountability raise the marginal utility of sanitation services and hence, its demand. We can summarize the demand function for sanitation service as _s_<sup>_d_</sup> = _g_ ( _v, Z_ ), where _Z_ captures other factors that may affect the demand for sanitation and _∂ s_<sup>_d_</sup> _/∂ v >_ 0.<sup>8</sup>

> 8Other factors such as lack of basic income and credit constraints can reduce demand for the adoption of safely managed sanitation services. Indeed, credit constraints often limits poor households in the adoption of privately optimal technology (e.g., flushable toilets or energy efficient technologies) that can improve household lifestyle. See for instance, Berkouwer and Dean (2022) for the case of adoption of energy efficient

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On the supply, there is a key role for governments in broadening access to sanitation. A primary responsibility of governments is to provide investment in sanitation infrastructure in order to increase sanitation delivery services to households.<sup>9</sup><sup>_,_10</sup> Given scarce public resources, there may be incentives for government facing elections not to provide sanitation services, as they channel these resources to the more visible public goods such as roads, electricity, and water services that can win them elections relative to low visibility services such as sanitation. However, effective media can spark social action by highlighting the pervasive nature of lack of sanitation and the role of governments in facilitating sanitation services, thus increasing its visibility. Another argument, often discounted in the literature, is the inherent characteristic of sanitation. Sanitation is more of a merit good rather than a pure public good; hence, governments can remain agnostic on its role in the provision of sanitation services and shift responsibility to the private sector unless forced to do so through civic/political action.<sup>11</sup> An effective media and environment that allows for peaceful protests can help hold governments accountable by spelling out the government’s role in the provision of sanitation services.

As discussed above, politicians are likely to supply more high visibility public goods/services for which they can take credit and be held publicly accountable by the electorate in order to boost their electoral chances. We assume that the politician’s objective is to be (re) elected, which is an increasing function of the quantity and quality of public services (including) sanitation, provided i.e., _Pr_ ( _E_ ) = _f_ ( _s,_ Ω) _, ∂ Pr_ ( _E_ ) _/∂ s >_ 0, where _Pr_ ( _E_ ) is the probability of the politician getting (re)elected, Ω is all other variables that affect the politician’s re-election chances, all other variables as defined above. Sanitation services may not always fall in the priority list of “high-visibility” expenditure programs, such as electricity, famine relief, road construction, and water, geared to the groups of voters most beneficial to the incumbent government (Mani and Mukand, 2007) in low income, low information countries. Free media acting as information multipliers can, not only make sanitation services visible, but can also hold politicians accountable for lack thereof. We assume that because politicians want to increase their electoral chances, the push for the increase in sanitation services by the electorate through voice and accountability will induce government to supply more sanitation services, i.e. _s_<sup>_s_</sup> = _ψ_ ( _v_ ) _, ∂ s_<sup>_s_</sup> _/∂ v >_ 0,<sup>1213</sup> The role of the media and communicators as information multipliers is therefore paramount to public advocacy and awareness-raising, which can increase the visibility of sanitation and hence, access to sanitation.<sup>14</sup> technologies in Kenya.

9Ashraf et al. (2016) illustrate the complementarity between infrastructure and institutions and shows how institutional weaknesses determine whether fines, subsidies, both or neither are optimal. With general strong institutions, the latter (i.e., fines and/or subsidies) is one of the ways government can play a leading role in sanitation delivery services.

> 10A practical example of government’s role in leadership and administrative support and oversight in the provision of sanitation delivery service is the Greater Accra Metropolitan Area Sanitation and Water Project (GAMA) project in Ghana. GAMA is being implemented by the Ghana Water Company and the Ministry of Sanitation and Water Resources with funding support from the World Bank.

11Merit goods could be, and indeed are, provided through the market, but not necessarily in sufficient quantities to maximize social welfare.

12 Conceptually, _∂ s/∂ v_ = 0 since the politician will choose not to respond to the news or might conclude that the available sanitation service is enough. We rule out these possibilities because we assume that the politician wants to maximize electoral probability and that the level of sanitation service is very low in our setting.

> 13We also provide a simple stylized model to further explain how voice and accountability may impact access to and use of sanitation. We embed the concept of visibility of a public good into a tractable political economy framework to model the delivery of sanitation services in a country with the key tenets of voice and accountability—i.e., free media, transparent elections, and freedom of speech as inputs. See Appendix A for details. See, https://www.un.org/waterforlifedecade/waterandsustainabledevelopment2015/stakeholders ~~m~~ edia.shtml for additional details on the role of the media in raising awareness to the sanitation problem

14Another argument discussed in the corruption literature is that governments tend to invest less in small projects where they cannot gain a lot of rent. In general corrupt governments would prefer to invest more in big infrastructure such as roads where they can get a big share of the money. However, education or perhaps sanitation projects are not attractive enough for government officials to gain maximum rent (see for example Mauro, 1998). Here, voice and accountability can serve as first line of imposing checks and balance to reduce corruption in how funds are used by the government and make sanitation services an important public good that needs attention from politicians by unearthing the 6
Combining the effects that _Voice_ has on the demand for, and supply of sanitation, we see that equilibrium access to and use of sanitation services in developing countries positively depends on voice and accountability and other factors— _s_<sup>_∗_</sup> = _s_ ( _v, Z_ ) _, ∂ s_<sup>_∗_</sup> _/∂ v >_ 0, where _s_<sup>_∗_</sup> is the observed amount of sanitation services provided in a country. In this simple formulation, _Voice_ can increase sanitation access through the demand side by households, the supply side by government, and/or other factors.

# 4. DATA AND ESTIMATION METHODS

This section describes the various layers of the country-level analyses of the influence of _Voice_ on access to sanitation, discusses key variables employed, as well as the empirical strategies implemented to identify the causal effect of _Voice_ on access to sanitation. Our analysis focuses on measures of access to sanitation, exploiting variations in cross-country data. We estimate the effect of _voice_ on: (i) overall access to sanitation, (ii) access to sanitation in rural and urban areas, and (iii) closing the rural/urban gap in access to sanitation. The first sub-section describes the data while the second sub-section briefly describes our estimation methods and empirical strategy.

4.1 DATA AND DESCRIPTIVE STATISTICS In this section, we present descriptions and data sources for our main variables. We utilize data from 73 developing countries to study the effect of voice and accountability on access to sanitation. We measure our dependent variable, the access to sanitation ( _S_ ) in four different ways: (1) the proportion of the entire population using basic sanitation ( _St_ ), (2) proportion of rural population using basic sanitation ( _Sr_ ), (3) proportion of urban population using basic sanitation services ( _Su_ ), and (4) the rural-urban gap in access to sanitation ( _Sg_ ). The data for _S_ and its various components is from _World Development Indicators (2021)_ . Our main explanatory variable is the institutional variable, voice and accountability ( _Voice_ ). This variable captures perceptions of the extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. We employ the standard measure of freedom of expression, of association and free media as our measure of _Voice_ . The standard measure of this variable ranges from -2.5 to 2.5 with higher values being associated with better outcomes and hence, higher levels of quality _Voice_ . We use this index because it provides the most up-to-date measure of broad institutions relevant to the time period we consider. The data on _Voice_ comes from the _Worldwide Governance Indicators_ constructed by Kaufmann, Kraay, and Mastruzzi (2011).

We include potentially important control variables in our empirical estimation. In particular, we include GDP per capita and population density. GDP per capita is included as a measure of income level as well as a proxy for overall economic development in each country. Per capita GDP is measure is real terms (constant 2015 US$). We expect a positive relationship between access to sanitation and the level of per capita income across countries. We include population density in the analysis to control for possible scale or scope economies effects. The relationship between population density and access to sanitation can be positive or negative. A positive relationship may arise because higher population density could mean lower administrative costs as well as scale economies and thus translate lower cost of access to improved sanitation facilities for a larger segment of the population (Gopalan and Rajan, 2016). On the other hand, sanitation is a rival good at least in low and lower-middle income countries, hence, higher population density could be associated with lower access to sanitation as a share of the population. Data on per capita GDP and population density were obtained from the World Development Indicators (World Bank, 2021). Controlling for these factors may help with minimizing specification errors.

severity of the sanitation problem.

7
**Table 2:** Summary Statistics

|Variable|obs|Mean|SD|Min.|Max.|
|---|---|---|---|---|---|
|People using sanitation services, total (share of population)|73|0.63|0.28|0.11|0.99|
|People using sanitation services, urban (share of urban population)|72|0.71|0.25|0.21|0.99|
|People using sanitation services, rural (share of rural population)|72|0.54|0.29|0.05|0.97|
|Rural-Urban Gap|72|0.17|0.12|-0.03|0.49|
|Voice and accountability index (lowest = -2.5 highest =2.5)|73|-0.41|0.69|-1.79|1.04|
|Mean year of schooling, 1900|73|0.27|0.39|0.01|1.52|
|Log GDP per capita|72|7.84|0.97|5.85|9.44|
|Log population density|73|4.20|1.10|1.26|7.04|

**Notes:** SD is the standard deviation of the variables. The mean difference (i.e., rural - urban gap) of 0.17 is statistically different from zero with _p_ -value of 0.0002. Source: Authors’ illustration from the World Development Indicators World Bank (2021)

Table 2 presents the summary of the data. The data are for 73 developing countries over the 2002 to 2020 period. We also report the country-by-country data for the four measures of the dependent variable used in our cross-sectional analysis in Table 3. We observe from Table 2 that, on average, 63% of the population in our sample has basic sanitation services. The standard deviation, at 28% is, however, relatively large, indicating high variability in the proportion of the population using basic sanitation across countries. In particular, several Sub-Saharan African (SSA) countries in the sample, have less than 30% of their population using basic sanitation services. These countries include Sudan (29.1%), Cˆote d’Ivoire (27.8%), Zambia (27.7%), Malawi (24.1%), Mozambique (23.7%), the Democratic Republic of Congo (19.3%), Uganda (18.5%), Liberia (16.2%), Ghana (15.7%), Togo (14.2%), Sierra Leone (13.6%), Benin (13.2%), and Niger (10.7%) (see, Table 3). In contrast, there are countries in our sample with more than 90% basic sanitation services access and usage. Additionally, more than a third of countries in our sample have less than 50% of their population accessing and using sanitation services over the period. Finally, Table 2 reports the summary statistics for the standard control variables in our estimation—i.e., logarithm of GDP per capita and population density. The mean value of log GDP per capita is 7.84 with a standard deviation of 0.97 while that of lop population density is 4.20 with a standard error of 1.12. These suggest large variances in per capita income and population density across countries in our sample.

We now switch our attention to rural and urban access to sanitation. As shown in Table 2 the means of the percentage of people with access to sanitation in rural and urban areas are 54% and 71%, respectively, suggesting the existence of a rural-urban gap in access to sanitation. A simple t-test confirms that the rural-urban gap is statistically _−_ significant at conventional levels (mean difference = 0.17, _p_ value = 0.0002). The country-by-country summary in Table 3 provides a granular look and reinforces rural-urban inequality. With the exception of Sri Lanka, Thailand, and Jamaica, where the rural-urban gap is negative, the gap is positive in all other countries. Additionally, while the ruralurban gap is highest in Yemen Rep. (49.2%) and Cambodia (42.2%); the gap is generally high in Latin American and African countries— e.g., Argentina, Brazil, Cameroon, Cˆote D’Ivoire, Mozambique, Peru, Panama, Niger, Liberia, Guatemala, Zambia, and Tunisia, where it is above 20 percent. (See, Table 3).<sup>15</sup> It is worth highlighting that while the rural-urban gap is small in countries such as Congo, Dem. Rep., Lesotho, and Kenya, these countries have low access to sanitation in both rural and urban areas. Hence, the low gap in access to sanitation is not indicative of a lack of sanitation problem in in these countries; it is in fact the result of a chronically low access to sanitation in both rural and urban areas. In contrast, countries like Malaysia, Jordan, Serbia, and Costa

> 15Romania has one of the highest rural-urban gap in access to sanitation outside Latin America and Africa

8
**Table 3:** Percentage of people using basic sanitation services

|||Access to sanitati|on (%)||||Access to sanitat|ion (%)||
|---|---|---|---|---|---|---|---|---|---|
|Country|% Pop.|% Rural Pop.|% Urban Pop.|Gap|Country|% Pop.|% Rural Pop.|% Urban Pop.|Gap|
|Malaysia|98.7|97.3|99.4|2.10|Philippines|70.5|65.9|76.0|10.1|
|Jordan|97.8|96.7|97.9|1.30|South Africa|69.9|63.0|74.5|11.4|
|Serbia|96.7|94.9|98.1|3.20|Myanmar|69.2|64.9|79.9|14.9|
|Costa Rica|96.1|92.9|97.5|4.70|Nicaragua|67.4|54.6|77.1|22.5|
|Thailand|95.8|96.3|94.9|-1.4|Guatemala|65.4|51.7|80.0|28.3|
|Albania|95.5|92.9|98.1|5.10|Indonesia|63.9|52.1|76.1|24.0|
|Egypt, Arab Rep.|94.6|92.4|97.5|5.10|Eswatini|57.2|57.0|58.1|1.00|
|Mauritius|93.8|93.0|95.0|1.90|Pakistan|51.4|39.6|73.4|33.8|
|T ̈urkiye|93.7|83.9|98.0|14.1|Bolivia|50.3|29.0|61.0|31.9|
|Argentina<br>|91.9<br>|74.3|94.6|20.3|Yemen, Rep.<br>|50.1|34.3|83.6<br>|49.3|
|Libya|91.5|—|—|—|Senegal|48.1|34.7|65.4|30.7|
|Fiji|90.5|86.6|94.3|7.80|Gambia, The|46.5|41.0|49.7|8.70|
|Syrian Arab Republic|90.5|88.1|92.6|4.50|Nepal|46.2|44.5|55.1|10.6|
|Cuba|89.4|82.1|91.7|9.60|India|45.4|36.8|64.8|28.0|
|Tunisia|87.9|73.0|95.5|22.5|Cambodia|41.2|32.5|74.7|42.2|
|Russian Federation|87.1|66.3|94.5|28.2|Cameroon|41.0|23.3|57.2|33.9|
|Sri Lanka|86.8|87.1|85.6|-1.4|Bangladesh|40.5|37.5|48.0|10.5|
|Algeria|86.4|77.2|90.8|13.7|Zimbabwe|40.5|33.8|53.9|20.1|
|Bulgaria|85.9|83.7|86.8|3.10|Afghanistan|36.6|32.7|48.7|16.0|
|Belize|85.9|82.5|90.0|7.50|Lesotho|32.2|31.1|35.8|4.70|
|Mexico|85.8|72.0|89.9|17.9|Kenya|31.9|30.7|35.5|4.80|
|Iran, Islamic Rep.|85.6|73.2|90.9|17.7|Mali|31.2|22.8|45.9|23.1|
|Iraq|85.5|77.9|88.9|11.0|Sudan|29.3|18.3|51.2|32.9|
|Colombia|85.3|72.4|88.9|16.5|Haiti|28.3|19.2|37.9|18.7|
|Jamaica|84.9|86.7|83.5|-3.2|Cˆote d’Ivoire|27.8|13.6|43.4|29.8|
|Guyana|83.5|81.5|88.8|7.30|Zambia|27.7|17.4|43.6|26.3|
|El Salvador|83.2|72.8|88.5|15.6|Malawi|24.1|22.4|33.2|10.8|
|Paraguay|83.1|72.0|90.9|18.8|Mozambique|23.7|12.3|46.9|34.6|
|Dominican Republic|82.9|73.9|86.0|12.1|Congo, Dem. Rep.|19.3|16.9|22.5|5.60|
|<br>Brazil|82.6|51.0|88.5|37.5|<br>Uganda|18.5|16.0|28.4|12.4|
|Ecuador|81.6|72.9|86.9|13.9|Liberia|16.2|5.30|27.9|22.6|
|Romania|80.0|63.4|94.4|30.9|Ghana|15.7|10.1|21.0|10.9|
|Morocco|78.8|62.2|90.6|28.4|Togo|14.2|5.50|28.1|22.6|
|China|77.0|67.5|87.3|19.8|Sierra Leone|13.6|7.20|23.6|16.3|
|Honduras|74.8|67.3|81.9|14.6|Benin|13.2|5.30|23.4|18.1|
|Panama|74.2|55.7|83.9|28.2|Niger|10.7|5.40|37.9|32.5|
|Peru|72.1|46.4|80.1|33.7||||||

Notes: Access to sanitation is People using at least basic sanitation services (% of population). Gap is the difference between People using at least basic sanitation services, urban (% of urban population) and People using at least basic sanitation services, rural (% of rural population). _Source_ : Authors’ illustration from the World Development Indicators World Bank (2021).

Rica have low rural-urban gaps because access to sanitation in both areas is high.

4.2 INSTRUMENTAL VARIABLE A difficulty in estimating the effect of _Voice_ on access to and use of sanitation is the possible endogeneity of _Voice_ . While _Voice_ may have a positive effect on access to sanitation, sanitation could also improve human capital and economic and institutional development of which _Voice_ is a part. It is also possible that both _Voice_ and access to sanitation are being driven by an unobserved common factor. In addition to these factors, institutional variables, such as _Voice_ are measured with substantial errors (Rodrik et al., 2004). All these factors could introduce endogeneity when _Voice_ is used in a regression analysis, making it difficult to infer causal effects.

Given the possible endogeneity of our independent variable, OLS estimates are likely to be biased. To address this endogeneity issue, we use primary school enrollment rate in 1900 as an instrument to achieving identification. The core argument is that historical levels of education plays an important role in teaching civic education and highlighting the role of governments while raising awareness of basic individual rights in civil society (Glaeser et al., 2007). This means that countries with historically higher levels of education will likely have societies with active civic 9
participation. Moreover, with citizens being aware of their rights, they are more likely to push governments, through voting, mobilization and demonstrations, to act to address socioeconomic issues. Such civic participation induced by education can be passed down to later generations. Consequently, historical levels of education can be a durable good that impacts today’s _Voice_ in those countries. Beyond this intuitive explanation, a long-standing theory in political science argues that education is a key determinant of the emergence and sustainability of democracy, because it promotes political participation at the individual level and fosters a collective sense of civic duty. Specifically, findings from studies such as Lutz, Cuaresma, and Abbasi-Shavazi (2010) confirms a strong positive relationship between past levels of education and democracy even after controlling for many other country characteristics. While current levels of _Voice_ may be correlated with primary school school enrollment in 1900, it is unlikely that primary school enrolment in 1900 will be correlated with current levels of access and use of basic sanitation. This makes primary school enrolment in 1900 a potentially good instrument for _Voice_ . In summary, we can exploit a measure of education from historical times as an instrument for today’s level of _Voice_ across countries while assuming that it only impacts current levels of access to sanitation through its correlation with _Voice_ .

In addition to using instrumental variable estimation, we apply a novel instrument-free identification strategy in kinky least squares (KLS), a non-parametric estimator, developed by Kiviet (2020, 2022). The primary motivation for this additional empirical step is as follows: Our model is a just-identified model hence, we cannot test whether our proposed instrument does not violate the exclusion restriction. The KLS estimator, which is an instrumentfree estimator, allows us to formally test the exclusion restriction to validate our instrumental variable estimations. Furthermore, the KLS serves as an alternative estimator to examine the effect of _Voice_ on access to sanitation, which we can compare with the estimates from the instrumental variable estimation. We also apply the KLS estimator to panel data to exploit the relevance of time dimension in the causal effect in question. Consequently, the KLS is used for model validation and robustness exercises. We discuss the details of the KLS estimator in Section 5.4.

4.3 EMPIRICAL STRATEGY The main regression equation we estimate is: where the subscript _i_ refers to countries, _Si_ is access to sanitation in country _i_ , _Voice_ is voice and accountability, **X** is a vector of the two control variables in our models, _ε_ is a stochastic error term, and _α_ and _β_ are coefficients to be estimated. We begin with a parsimonious specification of a bivariate regression of access to sanitation on voice and accountability. We then proceed to our baseline estimation that includes the standard covarates that may affect access to sanitation. We follow the convention in the sanitation literature in specifying the covariates. The analysis implements empirical strategies to mitigate issues of endogeneity. To address these endogeneity issues, we employ an instrumental variable estimator for the causal analysis. Our instrument for _Voice_ in our cross-country analysis is primary school enrollment in 1900.

We present ordinary least squares (OLS) estimates to show the correlation between _Voice_ access to sanitation. We present the bivariate estimation as well as our estimations where we include the standard control variables. The estimates from the OLS estimates will help contextualize the severity of the endogeneity issue, which the 2SLS estimator will address. After presenting the OLS estimates, we proceed to present and discuss the 2SLS estimates. In addition to the OLS and the IV estimates, we also use an instrument-free non-parametric estimator, Kinky Least 10
Square (KLS) estimator to estimate the model as an added robustness check. In all the regressions, we control for geographical differences by including regional dummies. Throughout the paper, the standard errors that we report are robust against arbitrary heteroskedasticity.

# 5. RESULTS

5.1 ORDINARY LEAST SQUARE REGRESSIONS Table 4 presents results from the OLS regressions showing the correlation between basic sanitation use and _Voice_ in total, rural and urban populations. Before proceeding to discuss the results, a few features of the table needs highlighting: Odd numbered columns report the results from the bivariate specification whereas even number columns report results that include the control variables in the regression model.

We now discuss the results for column 1 and 2. While the estimated coefficients are positive, they are small and statistically insignificant (Column 1). Furthermore, once we control for the level of GDP per capita and log population density, the coefficient of _Voice_ becomes even smaller, close to zero and remains statistically insignificant. The relationship between per capita income and basic sanitation use is positive and statistically significant at conventional levels in all the regressions. On the other hand, we find no significant relationship between access to sanitation and population density. These results generally hold for access to sanitation in urban and rural areas (Columns 3 – 6).

**Table 4:** Ordinary least squares (OLS) cross-country regressions (Dependent variable: Sanitation)

|||Total<br>||Urban<br>||Rural<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Voice and accountability|0.074|0.006|0.053|0.001|0.083|0.012|
||(0.047)|(0.024)|(0.041)|(0.022)|(0.053)|(0.033)|
|log real GDP per capita||0.197***<br>(0.025)||0.152***<br>(0.023)||0.191***<br>(0.031)|
|log population density||0.012<br>(0.015)||-0.007<br>(0.015)||0.031<br>(0.022)|
|Regional dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|_R_-squared|0.677|0.858|0.716|0.857|0.578|0.751|
|Observations|73|72|72|71|72|71|

Notes:<sup>_∗∗∗_</sup> indicates significance at the 1% and 5% level. Robust standard errors in parentheses. South Asia is the region excluded in the regional dummies.

The finding of no significant relationship between access to sanitation and _Voice_ is not surprising for the following reasons: The OLS regression is likely to be plagued with endogeneity problems. Consequently, the OLS estimates can not be relied on for causal inference. We therefore employ instrumental variable estimation to address the possible endogeneity problem and draw casual inference on the relationship between _Voice_ and sanitation access and use.

5.2 INSTRUMENTAL VARIABLES ESTIMATION Table 5 presents the results from the two-stage least squares (2SLS) model. Odd (even) numbered columns present results for estimations where we exclude (include) the standard control variables. Panel A of the table reports the 2SLS estimates of the effect of _Voice_ on access to sanitation services.

11
**Table 5:** The effect of voice and accountability on access to sanitation

||Full|Population|Urban|Population|Rural P|opulation|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_Panel A: Second-stage regression (Depend_|_ent variable is ac_|_cess to sanitation)_|||||
|Voice and accountability|0.323***|0.177**|0.241***|0.127*|0.423***|0.322**|
||(0.079)|(0.076)|(0.073)|(0.067)|(0.090)|(0.126)|
|**AR confdence intervals**|[0.16, 0.50]|[0.06, 0.44]|[0.09, 0.39]|[0.01, 0.34]|[0.27, 0.65]|[0.15, 0.8]|
|log real GDP per capita||0.150***<br>(0.040)||0.117***<br>(0.031)||0.103<br>(0.065)|
|log population density||-0.006<br>(0.016)||-0.017<br>(0.014)||0.004<br>(0.024)|
|_Panel B: First-stage regression (Dependen_|_t variable is voic_|_e)_|||||
|Mean years of schooling, 1900|0.826***|0.689***|0.823***|0.680***|0.823***|0.680***|
||(0.172)|(0.204)|(0.172)|(0.204)|(0.172)|(0.204)|
|Regional dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|_R_-squared|0.412|0.750|0.520|0.780|0.105|0.410|
|Observations|73|72|72|71|72|71|
|F-stat of excl. instruments|23.01|11.36|22.84|11.17|22.84|11.17|
|Effective F-stat of excl. instruments|14.44|8.99|14.23|8.677|14.23|8.677|
|Kleibergen & Paap (2006)test(_p_-value)|0.009|0.007|0.009|0.007|0.009|0.007|

Notes:<sup>_∗∗∗_</sup> and<sup>_∗∗_</sup> indicate significance at the 1% and 5% level. Robust standard errors in parentheses. AR confidence intervals correspond to the 95% Anderson-Rubin confidence intervals robust against weak instruments and heteroscedasticity. The _p-values_ of the Kleibergen & Paap (2006) test correspond to a test in which the null hypothesis is that the equation is underidentified, and under the null, the statistic is distributed as chi-squared.

We begin by discussing the results in column 1 of Table 5, where we only include _Voice_ and regional dummies in the model. The dummies of the regions included are Europe and Central Asia, Middle East and North Africa, East Asia and Pacific, Sub-Sahara Africa, Latin America and the Caribbean. The omitted group is South Asia. From column 1, we see that _Voice_ has a large and positive effect on access to sanitation with a coefficient of 0.323, which is fairly precisely estimated, with standard error of 0.079. This results implies that a one unit increase in _Voice_ increases access to sanitation by 32.3 percentage points. Furthermore, Panel B of the Table shows that our instrumental variable explains a large proportion of the variation in _Voice_ and is statistically significant showing that there is a strong firststage association with _Voice_ . This suggests that our instrument passes the instrumental relevance test. The associated F-statistic and Effective F-statistic of excluded instrument are approximately 23 and 14, respectively. This is larger than the rule-of-thumb value of 10 (see Andrews et al., 2018). Furthermore, the AR confidence interval shows that the interval excludes a zero effect.

Column 2 in Table 5 reports the results from the specification that includes the control variables. The controls are the log income per capita and the log population density. Introducing these controls almost halves the coefficient of _voice_ to 0.177 implying that a unit increase in _Voice_ increases access to sanitation by 17.7 percentage points. The coefficient is also statistically significant at the 5% level and it is fairly precisely estimated, with standard errors of 0.076. Additionally, the first-stage regression in Panel B for column 2 shows that the coefficient of the instrumental variable is large and statistically significant. However, the associated F-statistic and the Effective F-statistic of excluded instrument are 11.36 and 8.99, respectively. These values are close to the Staiger and Stock (1997) rule-ofthumb value of 10 although the F-statistic is marginally greater than 10. Given the closeness of the Effective F statistic to the threshold value, we rely on the AR confidence interval for inference. The AR confidence interval clearly shows 12
that the effect of _Voice_ on basic sanitation service access and use exclude a zero effect.

Columns 3–6 in Table 5, present the results for urban and rural areas. The coefficient estimates on _Voice_ is fairly large and statistically significant at least at the 10% level in all columns. A unit increase in voice and accountability causes an increase in access to sanitation by approximately 13 and 32 percentage points in urban and rural areas, respectively. The estimates suggest that the relationship between _Voice_ and sanitation access is stronger in rural areas than in urban areas. Similar to the results for the full population, Panel B of the Table shows that the coefficient of the instrumental variable is large and statistically significant. The effective F-statistic for the even numbered columns (i.e., Columns 4 and 6) are smaller than the rule-of-thumb of 10. Hence, we again rely on the AR confidence interval for inference. The interval does not include a zero. We can therefore conclude that _Voice_ has an unambiguous positive impact on broadening access to sanitation in the developing countries we consider. This implies that improvements in the populace’s ability to actively participate in selecting governments, freedom of speech through protests, and strong and free press can help broaden access to sanitation in developing countries via the mechanisms previously discussed.

5.3 THE RURAL-URBAN GAP As discussed earlier, there exists a significant rural-urban gap in access to sanitation in developing countries (Table 6). The results in Table 5 provides a preamble for the question of whether _Voice_ can serve as an effective vehicle to help close this gap in access to sanitation. Specifically, Table 5 showed that while _Voice_ has positive effect on access to sanitation in both rural and urban areas, the effect is generally larger in rural areas than in urban areas. In this section, we investigate whether _Voice_ can help close this gap by regressing the gap, measured as the difference between the share of rural population with sanitation access and the share of urban population with sanitation access, on _Voice_ .

**Table 6:** The effect of voice on the rural-urban gap

||(1)|(2)|
|---|---|---|
|_Panel A: OLS estimates_|||
|Voice and accountability|-0.029|-0.011|
||(0.024)|(0.023)|
|_Panel B: Two stage least squares estimates_|||
|Voice and accountability|-0.1833<sup>_∗∗∗_</sup><br>(0.057)|-0.195<sup>_∗∗_</sup><br>(0.082)|
|AR confdence intervals|[-0.344,-0.094]|[-0.503,-0.082]|
|Regional dummies|Yes|Yes|
|Other controls|No|Yes|
|Number of countries|72|72|
|First-stage Effective_F-_statistic|14.28|11.17|
|First-stage_F-_statistic|22.84|8.68|
|Kleibergen & Paap (2006)test(pvalue)|0.009|0.007|

_Notes:_ *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1. Robust standard errors in parentheses. AR confidence intervals correspond to the 95% Anderson-Rubin confidence intervals robust against weak instruments and heteroscedasticity. The _p_ -values of the Kleibergen & Paap (2006) test correspond to a test in which the null hypothesis is that the equation is underidentified, and under the null, the statistic is distributed as chisquared. _The dependent variable is rural-urban gap in access to sanitation_ Table 6 presents the results. We include the estimations from the OLS estimator for completeness. Two findings stand out. First, the estimates reveal that that there is a strong, negative, and statistically significant effect of _Voice_ on the rural-urban gap. Increased _Voice_ is likely to increase sanitation access and use in rural areas faster than it will 13
be for urban areas, which generally may be nearing the upper limit of access (100%) hence, the gap is likely to close with increased _Voice_ .<sup>16</sup> This suggests that improvement in institutions along the lines of which the populace have the ability to participate in selecting government and express freedom through associations and free media can narrow the rural-urban gap.

5.4 ROBUSTNESS, VALIDITY, AND SENSITIVITY In this section we present robustness checks along the lines of sensitivity analysis and validity of our instrumental variable. We also present estimation results from the KLS estimator for both cross-section data and panel data. The latter allows as to control for time fixed effects.

5.4.1 SENSITIVITY ANALYSIS In this section, we conduct sensitivity analysis to ensure that the positive effect of _Voice_ on access to sanitation is not driven by a set of outlier countries or a specific region. To this end, we re-estimate the model with data which exclude countries from one regional grouping at a time from the full sample.

**Table 7:** Sensitivity analysis on the effect of voice on access to sanitation

|||Region E|xcluded in the Es|timation|||
|---|---|---|---|---|---|---|
||South Asia|EAP|MENA|EAP|SSA|LAC|
|_Panel A: Second-stage regression (De_|_pendent variable i_|_s access to sanitatio_|_n)_||||
|Voice and accountability|0.172**|0.214**|0.184**|0.158**|0.184*|0.137|
||(0.075)|(0.094)|(0.080)|(0.065)|(0.104)|(0.097)|
|**AR confdence intervals**|[0.06, 0.43]|[0.08, 0.58]|[0.06, 0.46]|[ 0.05, 0.37]|[0.03, 0.59]|[-0.02, 0.52]|
|Regional dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Other controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|66|66|63|64|49|52|
|F-stat of excl. instruments|11.42|8.53|10.90|12.34|6.00|7.81|
|Effective F-stat of excl. instruments|8.70|7.91|7.92|10.43|5.39|5.16|
|Kleibergen & Paaptest(pvalue)|0.007|0.012|0.008|0.005|0.026|0.075|

Notes:<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at the 1%, 5%, and 10% level. Robust standard errors in parentheses. AR confidence intervals correspond to the 95% Anderson-Rubin confidence intervals robust against weak instruments and heteroscedasticity. The _p_ -values of the Kleibergen & Paap (2006) test correspond to a test in which the null hypothesis is that the equation is underidentified, and under the null, the statistic is distributed as chi-squared. ECA is Europe & Central Asia, MENA is Middle East & North Africa, East Asia & Pacific is EAP, sub-Saharan Africa is SSA, and Latin American and Caribbean is LAC.

Table 7 reports the estimates from the sensitivity exercise. All regressions include regional dummies and the standard control variables as in log GDP per capita and the log population density. The results show that there is sufficient evidence of a positive and statistically significant effect of _Voice_ on access to sanitation when a region is excluded from the sample. Specifically, excluding countries in South Asia, Europe and Central Asia (ECA), Middle East and North Africa (MENA), East Asia and Pacific (EAP) or Sub-Sahara Africa (SSA) from the sample does not impact the positive effect or the statistical significance of the effect. However, when Latin America and Caribbean (LAC) countries are excluded from the sample, the coefficient remains positive but statistically insignificant. More importantly, the AR confidence include zero, suggesting that we can not rule out a zero effect for some regions in the sample. However, we note that the fact that five out of the six sensitivity exercise provide strong evidence in support of the conclusion that _Voice_ increases the percentage of people using basic sanitation services is reassuring.

16After all, sanitation use in urban areas is getting close to the upper limit in most regions, hence can only grow slowly while low levels of access in rural areas imply that it can grow faster than in urban areas.

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5.4.2 AN INSTRUMENT-FREE ESTIMATOR So far we have used instrumental variable estimation to establish a causal effect of _Voice_ and accountability on access to sanitation. In this section, we apply a novel instrument-free non-parametric estimator, the kinky least squares (KLS) estimator, recently developed by Kiviet (2013, 2020, 2022). Before discussing the results, we provide a brief background on the estimator. The estimator is designed as a complement, and in scenarios where obtaining valid and relevant instrument is difficult, a viable alternative to the standard IV estimator.

The primary benefit of the KLS is that it is an instrument-free estimator under more relaxed assumptions. In the context of valid inference under conventional asymptotic theory, instrumental variables must be relevant and exogenous. The former condition requires that the instruments are sufficiently strongly correlated with the endogenous regressor(s) while the second condition requires that the instrument is directly related to the dependent variable (exclusion restriction) (Kripfganz and Kiviet, 2021). The KLS utilizes an identification strategy that does not rely on such exclusion restrictions but instead imposes assumptions on the degree of regressor endogeneity, which is left unrestricted in an IV estimation Kripfganz and Kiviet (2021). More precisely, the KLS makes use of a non-orthgonality condition for the endogenous regressor in the model, which is a function of the correlation coefficient between the endogenous regressor and the error term and the standard deviation of the endogenous variable and the error term in the regression (see, Kiviet, 2022; Kripfganz and Kiviet, 2021, for additional details). Consequently, for identification of the regression coefficient of the endogenous variable, the KLS restricts the plausible correlation of the regressor with the error term within plausible bounds. In this case, no excluded instruments are needed, and instead, the bias of the OLS estimator is analytically corrected for all values on a grid of endogeneity correlations (Kripfganz and Kiviet, 2021).

The KLS estimator is given by: where _ρ_ is the unknown correlation coefficient, _β_ 1 _,KLS_ is the KLS estimator, and _β_ 1 _,OLS_ is the standard OLS estimator, which is inconsistent when _ρ_ is non-zero. The bias correction term, which is analytically derived and described in Kiviet (2020, 2022); Kripfganz and Kiviet (2021) is a function of _ρ_ , the estimated variance of the OLS residual, the variance of the explanatory variable, covariance between the explanatory variable and the covariates.<sup>17</sup> The KLS estimator _βKLS_ coincides with the standard OLS estimator _βOLS_ if _ρ_ is equal zero. To estimate _βKLS_ , the researcher has to postulate values for _ρ_ , which includes the direction of bias of the OLS estimator. For a reasonably narrow range of postulated endogeneity correlations, the KLS confidence intervals are — as a general rule— narrower than those from 2SLS estimations, in particular if the instruments are relatively weak (Kripfganz and Kiviet, 2021). The KLS inference in this case can be more informative while avoiding the challenges associated with finding strong and valid external instruments. As described in Kripfganz and Kiviet (2021), the KLS approach enables testing of any potential exclusion restrictions, which is not plausible under a just-identified model like our model in section 5.

In our IV-estimation, it is evident that once we include the control variables in the specification, the standard F- statistic for the excluded instrument gets close to the rule-of-thumb value of 10. Furthermore, the effective F-statistics is consistently less than 10, although close, in several of the regressions. Although the robust AR confidence intervals

> 17See Kripfganz et al. (2021) and Kiviet (2020, 2022) for detailed discussion on the analytical derivation of the bias correction term. For exposition purposes we present the full mathematical representation of the KLS estimator in the appendix.

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suggests that there may not be any issues related to weak instruments, the large differences between the OLS and IV estimates raises concerns other concerns of the relevance of the instruments.(Kiviet, 2020, 2022; Kripfganz and Kiviet, 2021).<sup>18</sup> As a first step, we employ the KLS estimator as an additional estimator to help reinforce our results. Because the KLS estimator does not rely on instruments, we are able to use it along with the 2SLS estimation to test for any potential exclusion restriction violation in our just-identified model. In particular, it allows the user to assess the validity of previously untestable just-identifying exclusion restrictions. The KLS estimator therefore serves as both a complement to the IV-estimation as well as a validation tool.

5.4.3 KLS RESULTS Because the correlation coefficient, _ρ_ , is unknown, the KLS requires the postulation of admissible correlation of the regressors with the error term within plausible bounds. To do so, we combine information from the OLS and IV estimates in previous sections for guidance. The coefficients of _Voice_ from the OLS and IV estimations in Tables 4 and 5 respectively suggest that the OLS estimator is downward biased in our case. To this end, we specify a negative sign for the correlation as well as a reasonable interval of -0.6 to -0.2 for the unknown correlation coefficient, _ρ_ . Given that the KLS requires a postulated interval, as a more illuminating approach, the KLS plots the estimated coefficient with their corresponding 95% confidence intervals over the chosen range of endogeneity correlations (i.e., -0.6 to -0.2). This shows immediately for which values of the correlation coefficient we can reject (or not reject) the null hypothesis that a coefficient of interest equals a certain value. Furthermore, we are able to check whether the coefficient is statistically significantly different from zero. We also report formal exclusion test for our instrumental variable from postestimation tests from the KLS estimator.<sup>19</sup> Figure 1 presents the results. The top row of the figure, Panels A and B, reports the estimates of the effect of _voice_ on access to sanitation from the KLS estimator and the IV-regression. The light gray shaded area is the 95% confidence for the 2SLS estimator and the solid black line is the point estimate from the 2SLS estimator of _Voice_ , _β_ 1 _,IV_ . The dark gray shaded area in Panels A and B is the 95% confidence interval for the KLS estimate and the dashed lines are the KLS point estimates ( _β_ 1 _,KLS_ ) of _Voice_ corresponding to the postulated correlation coefficients given between -0.6 and -0.2. Panel A is the specification with no control variables and Panel B is the model with control variables. Both specifications include the regional dummies. The bottom figures, Panel C and D report the respective exclusion tests for the instrument used in the IV regressions in Panel A and B.

It is clear from panels A and B of figure 1 that for plausible range of the postulated values of the correlation coefficient, _Voice_ has an unambiguous and statistically significant effect on access to sanitation. Two notable points need further highlighting: First, the effect of _Voice_ from the KLS is decreasing in the postulated values of the correlation coefficient. Recall that the OLS estimates of the coefficient of voice and accountability are biased downwards. Hence, because the KLS estimator coincides with the OLS estimator at _ρ_ = 0, it is intuitive to see that as the postulated endogeneity gets closer to zero, the KLS estimates of the effect of _Voice_ will get closer to the OLS estimate.<sup>20</sup> Second, at approximately -0.56 and -0.52 for the postulated correlation coefficient in panel A and B, respectively, the estimates from the KLS coincides with the estimate from the 2SLS (i.e., _β_ 1 _,KLS_ = _β_ 1 _,IV_ ). Additionally, for most of the postulated values, the KLS confidence lies within the confidence interval associated with the IV-estimation. This

> 18We thank Aart Kraay and S. Kripfganz for raising this issue. We intend on further investigating this issue and expanding the KLS postulated correlation interval to include positive correlation. The analysis is ongoing.

> 19We use the Stata command `kinkreg` developed by Kripfganz and Kiviet (2021) to implement the KLS estimations and postestimations. 20 Indeed, Kripfganz and Kiviet (2021) show that the KLS estimator _β_ 1 _,KLS_ is by construction, point-symmetric around _ρ_ = 0 and that _β_ 1 _,KLS_ is a monotonically decreasing function in _ρ_ . See also Kiviet (2020) and Kiviet (2022) for additional details.

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<!-- Start of picture text --> 2<br>=<br>34} i<br>3, 3”<br>5, 3"<br>iot iea<br>i. J<br>-8 -5 -4 -3 -2 -8 -5 —4 -3 -2<br>Postulated endogeneity of voice & accountability Postulated endogeneity of voice & accountability<br>\——— V95%Clestimate ENN_----- KLSestimaeKLS>=95% Cl —— 95%WestimateCl -----NINN KLSestimateKLS>=95% Cl<br>PanelvariateA:specification Effeof voi c e andt accountabilityon sanitationusage (Bi- Paneltrols B: Effect of voice on sanitation usage (specification with con-<br>22<br>”<br>.<br>i ai<br>z2<br>.”<br>J o<br>-8 -5 -4 -3 -2 -8 -5 -4 -3 -2<br>Postulated endogeneity of voice & accountability Postulated endogeneity of voice & accountability<br><!-- End of picture text --> Panel C: p-values for the KLS exclusion restriction test of primary school enrollment Panel D: p-values for the KLS exclusion restriction test of primary school enrollment
We have some prior information from the IV-KLS analysis from above that informs us about the sign of the endogeneity. The KLS estimates in Figure 1 suggests that the plausible range of the correlation coefficient lies approximately between -0.6 to -0.1; this also lies within the confidence interval of the IV estimates. For our panel estimation we specify a negative sign for the endogeneity coefficient, _ρ_ , and restrict is to lie between zero and -0.2.<sup>22</sup> We now estimate the panel data equivalent version of Equation 1 given as where the subscript _i_ refers to countries and _t_ refers to time in year, _αi_ and _δ_ are country fixed-effects and time effects respectively, and all other variables as defined above.

**Figure 2:** KLS Panel-data estimates of the effect of voice & accountability on access to sanitation

<!-- Start of picture text --> Panel A : Total access to sanitation<br>Panel B : Urban access to sanitation<br>−.2 −.15 −.1 −.05 0 −.2 −.15 −.1 −.05 0<br>Postulated endogeneity of Voice & Accountability Postulated endogeneity of Voice & Accountability<br>KLS >=95% CI KLS estimate KLS >=95% CI KLS estimate<br>Panel B : Rural access to sanitation Panel D : Rural-Urban gap in access to sanitation<br>−.2 −.15 −.1 −.05 0 −.2 −.15 −.1 −.05 0<br>Postulated endogeneity of Voice & Accountability Postulated endogeneity of Voice & Accountability<br>KLS >=95% CI KLS estimate KLS >=95% CI KLS estimate<br>.25<br>.3<br>.2<br>.15 .2<br>.1<br>.1<br>.05<br>Estimated Voice & Accountability Coefficient 0 Estimated Voice & Accountability Coefficient 0<br>.3<br>.3<br>.2<br>.2<br>.1<br>.1<br>0<br>Estimated Voice & Accountability Coefficient 0 Estimated Voice & Accountability Coefficient −.1<br><!-- End of picture text -->

**Notes:** The light gray shaded area in Panel A is the 95% confidence for the KLS estimate and the solid blue line is the coefficient point estimates of voice and accountability corresponding to the postulated correlation coefficient, _ρ_ set between -0.2 and 0. We control for country-specific effects and time-specific effect. The estimation comprise 73 countries over the period 2002-2020 with a total number of observation of 1,378.

Figure 2 depicts the results for the KLS panel estimates. For the admissible range of correlation coefficients ( _ρ <_ 0), we find strong evidence that _Voice_ has a positive and statistically significant impact on access to sanitation (Panel A, B, and C). Additionally, Panel D in the figure shows that _Voice_ helps to close the rural-urban gap in access to sanitation. These panel-data results complements the findings from the cross-sectional analysis.

assumptions. As pointed out by the authors, in several applications, it might be easier to specify a credible range for the correlation of an endogenous regressor with the error term than to convincingly present strong and valid instruments. However, on one hand, if the postulated endogeneity range is too narrow, it may not include the true correlation value, potentially leading to serious bias. In contrast, if it is too wide, the resulting confidence intervals could be less informative than those from a 2SLS estimation with strong and valid instruments.

> 22We conduct preliminary estimation with _ρ_ specified between (-0.6, 0). We found that the corresponding confidence intervals for _ρ_ values less than -0.25 are extremely wide. We therefore rule out these values for the purpose of inference.

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# 6. CONCLUSION AND POLICY DISCUSSION

Globally, 2.4 billion people live without access to basic sanitation. Developing countries also face serious governance challenges due to weak political institutions and processes. In this paper, we estimate the causal effect of voice and accountability, characterized broadly as transparent electoral processes, free media, and freedom of expression, on access to sanitation. Our core argument is that voice and accountability increase sanitation’s visibility as a public good and raise awareness of its benefits, thereby increasing the supply and demand for sanitation services and access to sanitation. In a cross-section of 73 developing countries, we find a positive and economically significant effect of voice and accountability on access to sanitation. Importantly, voice and accountability can be used as an effective vehicle to close the persistent rural-urban inequality in access to sanitation. Our findings reinforce the argument that the institutionalization of monitoring and evaluation systems for sanitation delivery that sufficiently relies on citizens’ voices and perceptions is not a luxury of the rich. Instead, it is relevant to the world’s poor as its tenets, like the power of electoral incentives and free media, can shape the demand and distribution of sanitation services.

Our results are consistent with the results of previous research that finds that increased resources and education increases access to and use of sanitation services. However, our results are also different from the results of previous research. This is one of the few studies that establishes a causal relationship between voice and accountability and access to and use of sanitation services. While most research on sanitation provision focuses on the effects of external aid to increase the demand for and supply of sanitation, this study focuses on free media, freedom of expression, and accountability as the drivers of increased provision and use of sanitation. In this regard, increased access to and use of sanitation can be seen as the results of domestic institution building, hence more durable than the oft reliance on external aid.

This paper analyzed access to and use of sanitation as the results of a combination of supply and demand forces. In the empirical analyses, we did not distinguish between demand side effects and supply side effects; distinctions that may be important in policy formulation and implementation. In the robustness exercise, we extend our analysis to panel data to capture the relevance of the time dimension in the relationship in question. Our baseline empirical analyses (i.e., the instrumental variable estimation) was based on cross-country data; hence, we were not able to analyze how the relationship between voice and accountability and sanitation use across countries changed over time. Such changes could be potentially important and significantly affect our results. Our results should therefore be interpreted with these caveats.

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