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International Review of Applied Economics
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/cira20
Access to drinking water and sanitation in
developing countries: Does financial development
matter?
Sosson Tadadjeu, Brice Kamguia & Ronald Djeunankan
To cite this article: Sosson Tadadjeu, Brice Kamguia & Ronald Djeunankan (2023): Access to
drinking water and sanitation in developing countries: Does financial development matter?,
International Review of Applied Economics, DOI: 10.1080/02692171.2023.2234837
To link to this article: https://doi.org/10.1080/02692171.2023.2234837
Published online: 11 Jul 2023.
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Access to drinking water and sanitation in developing
countries: Does nancial development matter?
Sosson Tadadjeu
a
, Brice Kamguia
a,b
and Ronald Djeunankan
a
a
The Dschang School of Economics and Management, University of Dschang, Cameroon;
b
CEREG, University
of Yaounde 2, Cameroon
ABSTRACT
The aim of this study is to examine the eect of nancial develop-
ment on access to safe water and sanitation. Using panel data from
a sample of 106 developing countries over the period 2000–2019,
empirical results based on two-step system generalised method of
moments suggest that nancial development improves access to
drinking water and sanitation for the total population and for both
urban and rural populations. In addition, nancial development
reduces the gap between urban and rural populations in terms of
access to these two basic services. Further analysis also suggests
that the nancial market and nancial institutions, as well as their
sub-indices (nancial depth, nancial access, and nancial e-
ciency), also improve access to water and sanitation. These results
underscore the need for continued eorts to design and implement
policies that promote nancial development. In addition, given the
greater impact of nancial institutions, we suggest that reforms to
improve the nancial system should be more oriented towards the
development of nancial institutions.
ARTICLE HISTORY
Received 24 December 2022
Accepted 14 May 2023
KEYWORDS
Financial development;
drinking water; sanitation;
sustainable development
JEL CLASSIFICATION
G2; Q25; Q34; Q01
1. Introduction
The relationship between financial development and economic growth has been the subject
of several theoretical and empirical investigations, with mixed results. Although the first
theoretical reflections on the subject date back to Schumpeter, who already mentioned the
fundamental role of the financial sector in economic development, the theoretical work of
Mac Kinnon (1973) and Shaw (1973) remains prominent in the literature when they
emphasise that financial sector liberalisation would be conducive to development.
However, the results of financial liberalisation policies have been disappointing overall,
given the macroeconomic instability and financial crises they have generated. Endogenous
growth models, drawing on the shortcomings of liberalisation theory, have provided
a theoretical framework for highlighting the impact of financial development on economic
growth (Eggoh 2011). Theoretical advances have subsequently been empirically evaluated,
revealing both favourable and unfavourable effects of the former on the latter (see
Valickova, Havranek, and Horvath 2015 for meta-analyses).
CONTACT Sosson Tadadjeu stadadjeu@yahoo.fr
INTERNATIONAL REVIEW OF APPLIED ECONOMICS
https://doi.org/10.1080/02692171.2023.2234837
© 2023 Informa UK Limited, trading as Taylor & Francis Group
The lack of agreement on the effects of financial development on economic growth has
led to an expansion of work on the effects of financial development on various macro-
economic indicators; including macroeconomic instability (Avom et al. 2021;), inequality
(Chletsos and Sintos 2022), innovation (Hsu, Tian, and Xu 2014), foreign direct invest-
ment (Desbordes and Wei 2017), industrialisation (Shahbaz, Bhattacharya, and Mahalik
2018), shadow economy (Berdiev and Saunoris 2016), export quality (Nguyen and Su
2021), trade openness (Yakubu et al. 2018), firm growth (Haschka et al. 2022), and
sustainable economic development (Hunjra et al. 2022). Despite this extensive and
inconclusive literature on the macroeconomic effects of financial development, very little
attention has been paid to the effects of financial development on social and environ-
mental outcomes, such as health outcomes (Alam et al. 2021; Shahbaz, Shafiullah, and
Mahalik 2019), education (Mamadou and Ongo 2022), environmental quality
(Acheampong 2019; Acheampong, Amponsah, and Boateng 2020), and access to elec-
tricity (Nguyen et al. 2021). This paper is part of this second body of literature and offers
one of the first studies of the effects of financial development on access to drinking water
and sanitation (W&S).
Investments in access to W&S are widely considered essential for improving health
outcomes (Augsburg and Rodriguez-Lesmes 2018; Cameron, Chase, and Suarez 2021). It
is therefore not surprising that one of the Sustainable Development Goals is to provide
universal access to water and sanitation services (SDG 6). There has initially been some
remarkable progress in the coverage of these services through the Millennium
Development Goals. For instance, at the global level, the proportion of the population
using safely managed drinking water services increased from 62% to 86% between 2000
and 2020. Similarly, the proportion of the world’s population using safely managed
sanitation facilities increased from 29% to 54% during the same period (WHO 2021).
Despite this progress, in 2020, nearly 2.2 billion people still lacked access to drinking
water and nearly half of the world’s population also lacked access to safe sanitation
facilities (WHO 2021). As disturbing as these statistics may seem, water and adequate
sanitation are far from being a luxury. Poor water quality and inadequate sanitation
translate directly into a range of diseases such as diarrhoeal disease, cholera, dysentery,
and typhoid fever that affect especially young people. Poor access to W&S services also
results in lower school attendance and an additional burden on women due to the time
spent collecting water.
Given the importance of W&S services, a growing number of studies attempt to
understand the determinants of access to these basic services. In this perspective, several
authors have focused on macroeconomic determinants, further emphasising the role of
foreign aid (Gopalan and Rajan 2016; Ndikumana and Pickbourn 2017; Wolf 2007).
Other economic factors such as household income (Abubakar 2019), financial perfor-
mance of water utilities (Marson and Savin 2015), natural resources (Mazaheri 2017),
remittances (Tsafack and Djeunankan 2021), and globalisation (Fotio and Nguea 2022)
have been highlighted. Despite the growing interest in understanding the macroeco-
nomic determinants of access to W&S, the role of financial development has so far been
ignored.
Theoretically, there are good reasons to believe that financial development
1
indirectly
improves access to basic social services such as W&S, particularly through poverty
alleviation, corruption reduction, and human capital improvement. Indeed, Adams,
2S. TADADJEU ET AL
Boateng, and Amoyaw (2016) point out that inequalities in access to W&S are strongly
correlated with household wealth. In other words, household poverty reduces the like-
lihood of using safe drinking water and improved sanitation facilities (Larson, Minten,
and Razafindralambo 2006). However, a number of studies points out that financial
development reduces poverty directly through McKinnon’s ‘conduit effect’ and Shaw’s
‘intermediation effect’; and indirectly through improved growth.
2
This reduction in
poverty provides people with additional income to invest in the development of water
points and latrines. In addition, a developed financial system also provides opportunities
for individuals to better educate themselves by financing education (Bhuiya et al. 2019;
Mamadou and Ongo 2022). Moving in this direction, Mamadou and Ongo (2022) show
that financial development, financial institutions, and the financial market significantly
improve youth enrolment by increasing access to credit for the poorest households. This
improvement in schooling allows better-educated populations to more adequately
address specific SDGs, such as access to W&S (Larson, Minten, and Razafindralambo
2006). This argument is empirically validated by Adams, Boateng, and Amoyaw (2016),
who show that households with highly-educated heads are more likely to have access to
an improved water source and better sanitation facilities than households with less-
educated heads.
3
Finally, financial development can also improve access to basic services
through better control of corruption. As Anbarci, Escaleras, and Register (2009) and
Breen and Gillanders (2022) point out, public sector corruption, such as bribery in the
construction and operation of water and sanitation systems, significantly reduces access
to safe water and adequate sanitation. In other words, even when a country has the
financial resources to develop and operate adequate water and sanitation systems,
corruption in the public sector prevents these systems from being as effective as possible.
However, financial system development plays an important role in combating corruption
because financial institutions or creditors closely monitor borrowers’ activities and thus
potentially reduce their level of corruption (Altunbaş and Thornton 2012). Sharma and
Paramati (2021) corroborate this finding when they argue that financial development
reduces corruption through greater competition in the market. The authors also argue
that financial sector efficiency through greater participation of private and foreign banks
in a liberal but well-regulated financial market can control corruption by increasing
competition among banks and reducing the cost of credit.
This research contributes to the literature in three ways. First, to the best of the
authors’ knowledge, we propose one of the first studies to analyse the effects of financial
development on access to W&S using the financial development index. Such an analysis
therefore fills the knowledge gap on the link between financial development and access to
these basic services and contributes to the achievement of SDG 6, for which several
developing countries are lagging. Second, this study considers the location of the
populations by examining the effect of financial development on access to W&S for the
urban and rural populations, respectively. This approach helps to understand the extent
to which financial development improves access to basic services for people in different
areas of residence. In addition, because of the large disparities between urban and rural
areas in terms of access to W&S, we also examine the effect of financial development on
the urban-rural gap in terms of access to these services.
4
This second approach identifies
whether financial development increases or decreases inequalities in access to these basic
services between urban and rural populations. Third, while most previous studies, with
INTERNATIONAL REVIEW OF APPLIED ECONOMICS 3
a few exceptions, assess financial development using narrow and inadequate measures of
financial depth (often calling into question the reliability of reported results), we use
a financial development index proposed by Sahay et al. (2017). By incorporating infor-
mation on a wider range of financial development characteristics and a larger number of
financial agents and, in addition, capturing the level of development of financial institu-
tions and financial markets in terms of depth, access, and efficiency, this index better
reflects the overall level of countries’ financial development (Avom et al. 2021). Using
these different measures, it is possible to identify which of the financial institutions and
the financial markets improves access to basic services. Similarly, the use of sub-
indicators such as depth, efficiency, and accessibility of financial institutions and finan-
cial market allows for the identification of the dimension that is most conducive to
progress towards SDG 6 and the formulation of relevant policy implications.
The rest of the paper is organised as follows. Section 2 describes the materials and
methods, while Section 3 presents the empirical results. Section 4 concludes with some
policy implications.
2. Materials and methods
2.1. Data description
This study examines the effect of financial development on access to W&S using data
from an unbalanced panel of 106 developing countries over the period 2000–2019. The
sample size and study period are chosen based on data availability. This study follows the
World Bank classification (2022) and considers low income, lower middle income and
upper middle-income countries among developing countries.
5
2.1.1. Dependent variables
The dependent variables are access to drinking water and sanitation. Ndikumana and
Pickbourn (2017) and Tadadjeu et al. (2020) propose three indicators to measure these
variables. These variables have also been used by the UN-WHO Joint Monitoring
Programme to measure and monitor access to water and sanitation around the world
(WHO & UNICEF 2019). The first indicator is the percentage of the population (total,
urban and rural, alternatively) with access to safe water. Safe drinking water services are
defined as drinking water from an improved source (piped water, boreholes or tubewells,
protected dug wells, protected springs, and packaged or delivered water), provided
collection time is not more than 30 minutes for a round trip (World Bank 2022).
The second indicator is the percentage of the population (total, urban and rural) with
access to improved sanitation. That is, sanitation facilities (septic tanks or pit latrines;
ventilated improved pit latrines, compositing toilets or pit latrines with slabs) that are not
shared with other households (World Bank 2022). The third indicator is the gap between
the percentage of urban population and the percentage of rural population with access to
these services. Specifically, this indicator is calculated as the ratio between the percentage
of urban population and the percentage of rural population with access to these services
(water and sanitation alternatively) (Ndikumana and Pickbourn 2017). The first two
indicators (percentage of the total, urban and rural population with access to water and
sanitation) are collected from the World Bank development indicators while the third
4S. TADADJEU ET AL
indicator (ratio between the percentage of the urban population to the percentage of the
rural population with access to water or sanitation) is constructed by the authors.
2.1.2. Independent variable
We draw on recent works by Nguyen and Su (2021), Nguyen et al. (2021), Mamadou and
Ongo (2022), and Ekoula, Kamguia, and Ndoya (2023) and use the overall financial
development index (OFD), proposed by Sahay et al. (2017) as the main independent
variable obtained from the IMF database. This index is measured on a scale of 0 (low
financial development) to 1 (high financial development).
6
The overall financial development index (OFD) available in the IMF database is
composed of two sub-indices, namely Financial Market (for example, stock markets,
bond markets, wholesale money markets, and bypassing traditional bank lending) and
Financial Institutions (for example, banks, insurance companies, funds, venture capital
firms, and other types of non-bank financial institutions). Each of these indices is also
composed of three other indicators representing financial depth, accessibility, and
financial efficiency. We use these different measures for robustness purposes. Financial
depth measures the size and liquidity of markets, accessibility measures the ability of
individuals and companies to access financial services while financial efficiency captures
the ability of institutions to provide financial services at low cost and with sustainable
revenues, and the level of activity of capital markets.
Financial market depth (FMD) comprises market capitalisation, traded equities,
international government debt, total financial corporate debt, and total non-financial
corporate debt. Financial market accessibility (FMA) is a component of the percentage of
market capitalisation outside the 10 largest firms and the total number of debt issuers.
Finally, financial market efficiency (FME) is calculated from the stock market turnover
ratio.
Financial institution depth (FID) includes private sector credit, pension fund assets,
mutual fund assets, insurance premiums, life and non-life insurance. The financial
institution accessibility (FIA) includes bank branches per 100,000 adults and ATMs per
100,000 adults. Financial institution efficiency (FIE) is calculated using net interest
margin, loan-to-deposit spread, non-interest income to total income, overhead to total
assets, return on assets and return on equity.
2.1.3. Control variables
In order to limit omission variable bias, we draw from related literature (Gopalan
and Rajan 2016; Tadadjeu et al. 2020; Tsafack and Djeunankan 2021) to include
a set of three baseline control variables namely, GDP per capita, natural resources,
and remittances. Theoretically, income is an important predictor of access to basic
services such as W&S because higher income enables the population to invest
more in water point construction and latrine maintenance. Higher incomes also
enable the population to connect to national water networks. Remittances inflows
have been acknowledged in the literature as an important source of economic
growth and also increase the purchasing power of households (Cazachevici,
Havranek, and Horvath 2020). These factors can increase access to W&S, espe-
cially in developing countries where access to these services is still very low. Thus,
we expect a positive effect of remittances on access to W&S (Tsafack and
INTERNATIONAL REVIEW OF APPLIED ECONOMICS 5
Djeunankan 2021). Natural resources represent the total natural resource rents as
a percentage of GDP. Intuitively, one would expect income from commodity
exports to provide opportunities for development financing. However, the volatile
nature of resource rents, poor institutional quality in resources-rich countries, and
conflicts resulting from the extraction of natural resources compromise invest-
ments in access to W&S (Tadadjeu et al. 2020). Thus, we expect a negative effect
of natural resources on access to W&S.
For the purpose of robustness, we use additional covariates including, foreign
direct investment (FDI), trade openness, Polity 2 and ethnic fractionalisation. FDI
has been shown to be a source of economic growth in developing countries (Gui-
Diby 2014) and thus improves access to W&S. According to the recent work of
Fotio and Nguea (2022) who find that overall globalisation improves access to
drinking water in Africa, we expect a positive effect of trade openness on access
to W&S.
7
Deacon (2009) hypothesises that democracies provide more public goods
than their autocratic counterparts. Specifically, he finds that dictatorial governments
provide roads, drinking water, and public sanitation at much lower levels than
democracies. From this perspective, we also expect a positive effect of democracy
on access to W&S. The link between ethnic diversity and the provision of public
goods has received considerable attention in the public economics literature since
the work of Alesina, Baqir, and Easterly (1999). According to Miguel and Gugerty
(2005), we expect a negative effect of ethnic fractionalisation on access to basic
services. Table 1 presents the descriptive statistics and respective sources for each
variable.
2.2. Model specication and methodology
For the empirical analysis, we draw on previous studies to estimate a dynamic model
using two versions of a model (Ndikumana and Pickbourn 2017; Tadadjeu et al. 2020).
The first version estimates the effect of financial development on the percentage of the
population (total, urban and rural, alternatively) with access to W&S and can be
expressed as follows:
Accessit ¼αþβ Accessit1þφ OFDit1þγ Xit1þμiþVtþεit (1)
where Accessit 1 represents the suitable lag for the dependent variable. Accessit is the
percentage of the population (total, rural, and urban, alternatively) of country i in year
t with access to these services. Overall financial development index (OFD) is the main
interest variable, and X is the vector of baseline control variables.
8
μiis the unobservable
country fixed-effect that controls for unobserved, time-invariant and country-specific, Vt
is the time fixed-effect which captures the evolution of unobservable variables assumed to
affect all countries, and εit is the error term.
The second version of this model analyses the effect of financial development on the
urban-rural gap in access to drinking water and sanitation. For this purpose, we specify
the following model:
Gap Ratioit ¼αþβ Gap Ratioit1þφ OFDit þγ Xit þμiþVtþεit (2)
6S. TADADJEU ET AL
where GapRatioit represents the ratio of the percentage of urban population to the
percentage of rural population with access to water or sanitation in country i in year t.
The other terms in Equation (2) have the same meaning as in equation (1).
This study analyses the effect of financial development on access to W&S. However, it
is possible that access to these services also affects financial development. This can be
explained by the fact that access to water improves human capital because it reduces the
time spent collecting water and therefore allows individuals to be healthy and educated.
In addition, economic theory argues that human capital contributes to financial devel-
opment (Lucas 1990). To address this concern, the literature proposes to use external
instruments (i.e. an instrumental variable approach) or internal instruments (i.e. the
Table 1. Descriptive statistics.
Variables Obs Mean
Std.
Devation Min Max Sources
Access to drinking water (% total
population)
2,110 76.374 19.322 18.085 100 World Bank
(2022)
Access to drinking water (% urban
population)
2,113 89.867 9.606 51.267 100 World Bank
(2022)
Access to drinking water (% rural
population)
2,110 66.414 23.542 5.327 100 World Bank
(2022)
Gap access to drinking water 2,110 1.565 0.733 0.915 11.174 Author’s
construction
Access to sanitation (% total
population)
2,108 56.403 29.619 2.755 100 World Bank
(2022)
Access to sanitation (% urban
population)
2,112 66.344 26.124 9.568 100 World Bank
(2022)
Access to sanitation (% rural
population)
2,108 47.789 30.897 0 100 World Bank
(2022)
Sanitation access gap 2,107 2.261 2.313 0.694 39.829 IMF database
OFD 2,120 0.194 0.134 0 0.739 IMF database
FI 2,120 0.279 0.135 0 0.74 IMF database
FM 2,120 0.103 0.162 0 0.735 IMF database
FID 2,120 0.135 0.15 0 0.885 IMF database
FIA 2,120 0.188 0.187 0 1 IMF database
FIE 2,120 0.559 0.13 0 0.86 IMF database
FMD 2,120 0.103 0.157 0 0.863 IMF database
FMA 2,120 0.099 0.18 0 1 IMF database
FME 2,120 0.105 0.24 0 1 IMF database
Income per capita (ln) 2,091 7.626 1.004 5.272 9.93 World Bank
(2022)
Resource rents (% GDP) 2,092 8.664 10.798 0.003 87.459 World Bank
(2022)
Remittances (% GDP) 2,025 5.165 7.016 0 53.826 World Bank
(2022)
Trade openness (% GDP) 1,957 75.993 38.431 0.167 347.997 World Bank
(2022)
Foreign direct investment (% GDP) 2,044 4.311 6.616 −37.155 103.337 World Bank
(2022)
Ethnic fractionalization 2,040 0.525 0.247 0 0.93 Alesina et al.
(2003)
Polity 2 1,958 2.894 5.703 −10 10 Polity IV
Foreign aid (% GNI) 2,060 5.507 7.431 −0.642 92.141 World Bank
(2022)
Period: 2000–2019 for 106 developing countries. FD, FI, FM, FID, FIA, FIE, FMD, FMA and FME denote Financial
Development, Financial Institutions, Financial Market, Financial Institutions Depth, Financial Institutions Accessibility,
Financial Institutions Efficiency, Financial Market Depth, Financial Market Accessibility and Financial Market Efficiency,
respectively. Data on financial development indicators are available in the IMF database available via the following link:
See : https://data.imf.org
INTERNATIONAL REVIEW OF APPLIED ECONOMICS 7
Generalized Method of Moments). The literature on the financial development-
economic growth nexus uses legal origins as external instruments for standard measures
of financial development, such as domestic credit to the private sector. Given the nature
of our variable of interest, which is a composite index (OFD), and the difficulty of finding
a valid exogenous appropriate instrument (Stock, Wright, and Yogo 2002), we resort to
internal instruments through the Generalized Method of Moments (GMM) using the
lags of the independent variables of order t-1 to t-4 as instruments. The second difficulty
in estimating our model is the omission bias. One reason for this bias is that not all the
key determinants of access to W&S may be included in the baseline model. In other
words, unobservable confounders may potentially bias the results. To address this
concern, we adopt an approach commonly used in the literature for estimating our
model with additional observable controls introduced as a robustness check.
To deal with reverse causality, Arellano and Bond (1991) propose to estimate the
difference model using lagged variables as instruments. This is called the difference
GMM (DGMM). However, this estimator has a shortcoming in that it suffers from the
weakness of the instruments (Blundell and Bond 1998). To overcome this problem,
Arellano and Bover (1995) and Blundell and Bond (1998) propose the system GMM
(SGMM). The latter consists in jointly estimating the level equation and the difference
equation by the generalised method of moments. The instruments for the first difference
regression are lagged level of the explanatory variables and the instruments for the level
regression are lagged difference of the explanatory variables. Even though the SGMM is
asymptotically more efficient, this estimator performs standard deviation estimates that
tend to be downward biased. However, this problem can be overcome by using the finite
sample correction of Windmeijer (2005).
Several reasons motivated the choice of the two-step SGMM. First, the SGMM
estimator has been widely used in the literature to address the endogeneity problem
that appears in panel data estimation (Arellano and Bover 1995; Blundell and Bond
1998). Second, we prefer the two-step SGMM estimator over the instrumental variables
two-stage least square estimator (IV-2SLS) because the SGMM controls for the potential
endogeneity of all explanatory variables by using internal instruments (Farhadi, Islam,
and Moslehi 2015). In other words, the SGMM can account for reverse causality by
producing valid instruments under the assumption that current period shocks in the
error term do not affect the past values of the regressors and that the past values of the
regressors do not directly affect the current values of the dependent variable (Hauk and
Wacziarg 2009). In fine, we follow Blundell and Bond (1998) and adopt for the two-step
SGMM because it is asymptotically more efficient than the one-step estimator.
Three conditions must be fulfilled to assure the consistency of the GMM estimator.
First, in order to obtain consistent estimation on the lagged dependent variable, which is
used as a regressor, the idiosyncratic error term should have first-order serial correlation
but not have second-order serial correlation. The second condition is the exogeneity and
validity of instruments (Hansen test insignificant). Third, the ‘rule of Thumb’ is that the
number of instruments should be less than the number of countries.
8S. TADADJEU ET AL
3. Empirical ndings
3.1. Exploratory data analysis
Figure 1 shows the evolution of the overall financial development index (OFD), financial
market (FM) and financial institutions (FI) in developing countries during the period
(2000–2019). We find that despite the increase in these three indicators, financial
institutions remain more developed than financial markets. This suggests that financial
institutions are more developed than financial markets in developing countries. It will
therefore be interesting to compare the effect of these two measures on access to W&S in
order to understand which one better improves access to these two services more.
Figures 2 and 3 show the correlations between financial development and access to
drinking water and sanitation, respectively. First, we observe a positive correlation between
financial development and the percentage of the total population, urban and rural, with
access to these two basic services. This appears to be consistent with our hypothesis that
better financial development is, on average, associated with a larger share of population
having access to basic services. Second, we observe a negative correlation between financial
development and the differential (gap) in access to drinking water (Figure 2) and sanitation
(Figure 3) between urban and rural populations. This negative correlation suggests that
greater financial development appears to be associated with greater access to these basic
services by rural populations compared to their urban counterparts. However, as correla-
tion does not necessarily mean causality, these relationships will be investigated empirically
in section 3.
0.2 .4 .6 .8
2000 2004 2009 2014 2019
excludes outside values
Overall Financial development Financial institutions
Financial market
Figure 1. Evolution of financial development indices in developing countries.
Source: Authors’ construction using data from IMF database
INTERNATIONAL REVIEW OF APPLIED ECONOMICS 9
3.2. Baseline ndings
Table 2 summarises the baseline results of the effect of financial development on access to
W&S. The consistency of the SGMM depends on the validity of the assumption of
no second-order serial correlation of the error terms and the validity of the instruments
(Hansen test). The results of the diagnostic tests show that our models are well specified.
The Hansen test does not reject the validity of the instruments. Furthermore, the test results
validate the absence of second-order serial correlation. Too many instruments can seriously
weaken and bias Hansen’s test of identification restrictions and so the rule of thumb is that
the number of instruments should be less than the number of countries (Roodman 2009).
The results presented in Table 2 generated a maximum of 86 instruments, which is less than
the number of countries. These results are therefore free of instrument proliferation.
Regarding the estimated coefficients of financial development, we find that they are
positive and statistically significant in columns 1 and 5. This suggests that financial
development improves the share of the total population with access to water and
sanitation, respectively. Thus, financial development, by reducing household poverty,
provides households with sufficient income for the construction of W&S facilities.
Similarly, better financial development also allows public authorities and private com-
panies to make investments in social sectors such as water and hygiene in order to
improve the living conditions of the population. Columns 2 and 3 present the results for
access to water for the urban and rural populations, respectively. Once again, we find that
Figure 2. Correlation between different measures of access to drinking water and financial development.
Source: Author’s construction
10 S. TADADJEU ET AL
increased financial development improves access to water for both urban and rural
populations. This finding also holds for access to sanitation (see columns 6 and 7).
However, the coefficient associated with financial development is larger for the rural
population. Thus, financial development by reducing poverty allows rural populations to
invest in the construction of wells, boreholes, and water towers to improve their access to
drinking water. In columns 4 and 8, we find that the coefficient associated with financial
development is negative and statistically significant. This finding suggests that improved
financial development reduces the urban-rural gap in access to basic services. This
finding is consistent with the larger effect associated with financial development for
rural populations. Thus, financial development promotes progress towards equitable
access to water and sanitation.
Regarding the control variables, they all show the expected signs. Specifically, income
improves the percentage of the total, urban and rural population with access to W&S but
reduces the urban-rural gap in terms of access to these services. More importantly, we
observe that the effect is more important for access to water than for sanitation. A plausible
explanation for this result is that, according to the famous expression ‘water is life’, an
increase in income will increase the incentive for people to improve their access to drinking
water, as it is a matter of survival. In addition, the UN-Water Global Analysis and
Assessment of Sanitation and Drinking-Water (GLAAS) 2022 report that approximately
56% of the estimated WASH strategy costs reported by countries are for investments in
drinking water, while only 44% are for investments in sanitation. This further explains why
Figure 3. Correlation between different measures of access to sanitation and financial development.
Source: Authors’ constructions
INTERNATIONAL REVIEW OF APPLIED ECONOMICS 11
the effect of income is higher for access to water than for sanitation.
9
Moreover, according
to Mazaheri (2017), natural resources have on average a negative effect on access to W&S
for the total, urban and rural population but a negative effect on the urban-rural gap in
terms of access to these services. Finally, remittances increase the percentage of the total,
urban and rural population with access to water and sanitation but reduce the gap between
the urban and rural populations with access to W&S. This result can be explained by the
fact that remittances constitute an additional source of income, which can increase the
ability of households to connect to national water networks.
3.3. Robustness checks
We perform two robustness tests to confirm our baseline results. The first one consists in
estimating the baseline model by introducing additional control variables. The second is
to test the robustness of the baseline results by excluding certain regions from the sample.
Table 3 summarises the results of the model estimated with additional control
variables representing determinants of access to basic services (trade openness,
FDI, ethnic fractionalisation, and democracy). We find that the estimated coeffi-
cient associated with financial development shows the expected signs. Indeed,
Table 2. Baseline results.
Water
total
pop.
Water
urban
pop.
Water
rural pop.
Gap access
to water
Sanitation
total pop.
Sanitation
urban pop.
Sanitation
rural pop.
Gap access
to sanitation
Dependent
variables: (1) (2) (3) (4) (5) (6) (7) (8)
Lag
dependent
variables
0.896*** 0.827*** 0.901*** 0.863*** 0.944*** 0.945*** 0.945*** 0.608***
(0.020) (0.060) (0.020) (0.005) (0.013) (0.006) (0.012) (0.064)
OFD 1.803*** 2.016** 2.794*** −0.222*** 1.765** 0.901** 3.052*** −0.388*
(0.658) (0.915) (1.044) (0.047) (0.890) (0.429) (1.094) (0.202)
Income per
capita (ln)
1.176*** 0.904** 1.089*** 0.004 0.971*** 0.870*** 0.828*** −0.273***
(0.331) (0.401) (0.327) (0.005) (0.274) (0.105) (0.294) (0.077)
Natural
resources
−0.023** −0.025** −0.046*** 0.001*** −0.016*** −0.023*** −0.014*** 0.001
(0.009) (0.012) (0.016) (0.000) (0.006) (0.002) (0.005) (0.003)
Remittances 0.051*** 0.038** 0.061*** −0.002*** 0.048*** 0.000 0.061*** −0.046**
(0.016) (0.015) (0.019) (0.001) (0.015) (0.004) (0.015) (0.018)
Constant −0.793 8.537*** −1.100 0.207*** −4.316*** −2.988*** −4.295** 3.144***
(1.228) (2.477) (1.548) (0.039) (1.472) (0.505) (1.802) (0.770)
Observations 1,881 1,884 1,881 1,901 1,881 1,899 1,895 1,894
Number of
countries
105 105 105 106 105 105 105 105
Number of
instruments
86 75 84 48 80 81 63 69
AR(1) 0.008 0.098 0.090 0.053 0.046 0.024 0.000 0.070
AR(2) 0.193 0.104 0.128 0.314 0.464 0.410 0.216 0.110
Hansen OIR 0.543 0.580 0.516 0.197 0.205 0.183 0.268 0.218
*, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. Robust standard errors reported in
parentheses. We use the lag of the explanatory variables as an instrument for the two-step system GMM with
Windmeijer (2005) correction. OFD denotes overall financial development index.
12 S. TADADJEU ET AL
financial development on average improves access to W&S for the total popula-
tion, urban and rural, with a larger effect on the rural population. In addition, the
effect of financial development remains negative and statistically significant on the
urban-rural gap in access to basic services. Thus, by providing additional income
to individuals, financial development promotes the development of drinking water
and sanitation facilities. Moreover, the estimated coefficients of the additional
control variables show the expected signs. While trade openness, FDI, and democ-
racy improve access to water and sanitation by residence, ethnic fractionalisation
has an adverse effect. Table A1 in the Appendix presents the results of
a robustness analysis in which we replace FDI with official development assistance
(foreign aid) as an additional control variable. Once again, we find that the results
remain broadly similar to the baseline results. Our baseline results are therefore
robust to introducing additional control variables.
Second, we test the robustness of the results to different sub-samples and the
results are presented in Table 4. This is deemed necessary to examine whether our
baseline results are due to including countries belonging to certain regions.
Drawing from the literature (Svirydzenka 2016), we first exclude African countries
from our sample because these countries show a lower level of financial
Table 3. Estimations with additional control variables.
Water
total pop.
Water
urban
pop.
Water
rural
pop.
Gap
access to
water
Sanitation
total pop.
Sanitation
urban pop.
Sanitation
rural pop.
Gap access
to sanitation
Dependent
variables: (1) (2) (3) (4) (5) (6) (7) (8)
Lag dependent
variables
0.890*** 0.801*** 0.889*** 0.833*** 0.956*** 0.900*** 0.942*** 0.553***
(0.026) (0.072) (0.027) (0.007) (0.008) (0.033) (0.033) (0.062)
OFD 2.468*** 2.642** 5.531*** −0.117** 1.669** 3.853** 4.431*** −0.510**
(0.929) (1.057) (1.695) (0.053) (0.725) (1.950) (1.681) (0.253)
Baseline control Yes Yes Yes Yes Yes Yes Yes Yes
Trade openness 0.011 0.007* −0.004 0.000 0.005 0.008 0.002 0.001
(0.010) (0.004) (0.006) (0.000) (0.003) (0.010) (0.007) (0.002)
FDI −0.001 0.009 0.016 −0.003* 0.082*** 0.000 0.003 −0.014
(0.012) (0.008) (0.027) (0.001) (0.017) (0.004) (0.010) (0.013)
Ethnic
fractionalization −3.765*** −3.809* −6.904** 0.184** 0.006 −8.955*** −9.081*** 1.058*
(1.357) (2.201) (3.254) (0.077) (0.758) (2.935) (2.723) (0.564)
Polity 2 −0.017 0.037* −0.048 −0.000 0.137*** −0.078 0.045 −0.000
(0.034) (0.022) (0.081) (0.002) (0.027) (0.060) (0.044) (0.004)
Constant 3.214 13.854*** 6.442 0.188 −2.224** 0.843 7.171 2.187***
(2.124) (5.008) (5.271) (0.141) (1.089) (3.207) (4.822) (0.838)
Observations 1,618 1,533 1,618 1,633 1,620 1,616 1,477 1,629
Number of
countries
92 92 92 92 92 92 92 92
Number of
instruments
76 76 70 47 73 75 78 65
AR(1) 0.014 0.063 0.065 0.095 0.087 0.040 0.096 0.043
AR(2) 0.212 0.104 0.116 0.781 0.192 0.128 0.715 0.140
Hansen OIR 0.886 0.882 0.937 0.297 0.962 0.578 0.955 0.407
*, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. Robust standard errors reported in
parentheses. We use the lag of the explanatory variables (except for ethnic fractionalisation) as an instrument for the
two-step system GMM with Windmeijer (2005) correction. OFD and FDI denote overall financial development index and
foreign direct investments, respectively.
INTERNATIONAL REVIEW OF APPLIED ECONOMICS 13
Table 4. Robustness to sample truncation.
Water
total pop.
Water
urban
pop.
Water
rural
pop.
Gap access
to water
Sanitation
total pop.
Sanitation
urban pop.
Sanitation
rural pop.
Gap access
to sanitation
Dependent
variables: (1) (2) (3) (4) (5) (6) (7) (8)
Without Africa
Lag
dependent
variables
0.906*** 0.836*** 0.915*** 0.941*** 0.930*** 0.973*** 0.990*** 0.435***
(0.002) (0.006) (0.004) (0.002) (0.010) (0.012) (0.008) (0.002)
OFD 0.331*** 0.766*** 1.145*** −0.029*** 1.111*** 5.204*** 6.970*** −0.219***
(0.079) (0.072) (0.318) (0.009) (0.336) (0.856) (0.562) (0.055)
Baseline
control
Yes Yes Yes Yes Yes Yes Yes Yes
Constant 3.399*** 10.861*** 1.052 0.027*** −0.366 10.495*** 13.805*** 2.973***
(0.244) (0.457) (0.643) (0.007) (1.332) (0.800) (1.184) (0.070)
Observations 1,055 1,007 1,004 1,061 1,054 1,063 1,059 1,058
Number of
countries
58 58 58 58 58 58 58 58
Number of
instruments
53 54 52 42 46 41 41 55
AR(1) 0.069 0.091 0.000 0.080 0.005 0.020 0.078 0.089
AR(2) 0.232 0.661 0.598 0.931 0.232 0.186 0.139 0.139
Hansen OIR 0.548 0.517 0.233 0.175 0.424 0.365 0.697 0.393
Without Europe and Asia
Lag
dependent
variables
0.873*** 0.966*** 0.824*** 0.820*** 0.930*** 0.850*** 0.830*** 0.853***
(0.007) (0.024) (0.020) (0.003) (0.021) (0.026) (0.015) (0.008)
OFD 1.351*** 1.635*** 7.164*** −0.102*** 4.914** 6.791* 1.832** −0.141**
(0.444) (0.597) (1.338) (0.039) (2.172) (3.983) (0.848) (0.055)
Baseline
control
Yes Yes Yes Yes Yes Yes Yes Yes
Constant −1.867*** 2.547* −2.171 0.460*** −5.734** 4.492 −17.866*** 0.695***
(0.606) (1.363) (1.574) (0.035) (2.323) (3.855) (1.335) (0.151)
Observations 1,184 1,027 1,053 1,198 1,132 1,201 1,074 1,053
Number of
countries
66 66 66 67 66 67 66 66
Number of
instruments
53 19 49 47 27 58 59 36
AR(1) 0.023 0.056 0.060 0.009 0.078 0.380 0.096 0.055
AR(2) 0.166 0.354 0.128 0.825 0.210 0.117 0.519 0.173
Hansen OIR 0.346 0.112 0.853 0.279 0.439 0.928 0.540 0.560
Without America
Lag
dependent
variables
0.923*** 0.957*** 0.928*** 0.854*** 0.966*** 0.987*** 0.914*** 0.597***
(0.013) (0.014) (0.010) (0.004) (0.018) (0.003) (0.013) (0.061)
OFD 2.390*** 1.494** 3.109*** −0.264*** 3.546** 0.590* 6.848*** −0.500**
(0.817) (0.628) (0.999) (0.046) (1.606) (0.348) (1.100) (0.245)
Baseline
control
Yes Yes Yes Yes Yes Yes Yes Yes
Constant 1.192 3.251*** −0.284 0.197*** −2.117 0.232 −8.380*** 3.210***
(0.819) (0.856) (0.839) (0.035) (2.327) (0.210) (2.004) (0.791)
Observations 1,523 1,458 1,525 1,543 1,457 1,47 1,316 1,536
Number of
countries
86 86 86 87 86 86 86 86
Number of
instruments
49 29 59 43 39 66 51 69
AR(1) 0.002 0.023 0.045 0.079 0.054 0.009 0.033 0.064
AR(2) 0.928 0.603 0.309 0.445 0.106 0.282 0.424 0.161
Hansen OIR 0.413 0.197 0.624 0.184 0.775 0.387 0.586 0.661
*, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. Robust standard errors reported in
parentheses. We use the lag of the explanatory variables as an instrument for the two-step system GMM with
Windmeijer (2005) correction. OFD denotes overall financial development index.
14 S. TADADJEU ET AL
development and also have the lowest rate of access to basic infrastructure (W&S).
Second, we exclude European and Asian countries to test whether the results are
influenced by countries with a higher level of financial development. Finally, we
also exclude countries in America. The results show that regardless of the sample;
the coefficient associated with financial development has a positive and statisti-
cally significant sign. This suggests that our baseline results are not due to
including countries with low levels of financial development and access to basic
services (Africa) or introducing countries with high levels of financial develop-
ment and access to W&S (Europe and America).
3.4. Further analysis
The above results support our main hypothesis that development promotes equitable
access to W&S. We conduct additional analyses to examine the effects of financial
institutions, the financial market, and their respective sub-dimensions, including access,
depth, and efficiency, on access to basic services.
Table 5 summarises the results of the effect of financial institutions on access to W&S.
Columns 1–3 present the effect of financial institutions on access to water for the total
urban and rural population. The coefficient associated with our interest variable has
a positive and statistically significant sign suggesting that an improvement in financial
institutions on average, increases the proportion of the population (total, urban and
rural) with access to drinking water. Column 4 presents the results when considering the
urban-rural gap in terms of access to water. The results show a negative and statistically
Table 5. Financial institutions and access to W&S.
Water
total
pop.
Water
urban
pop.
Water
rural
pop.
Gap access
to water
Sanitation
total pop.
Sanitation
urban pop.
Sanitation
rural pop.
Gap access to
sanitation
Dependent
variables: (1) (2) (3) (4) (5) (6) (7) (8)
Lag
dependent
variables
0.899*** 0.797*** 0.890*** 0.830*** 0.944*** 0.933*** 0.947*** 0.844***
(0.022) (0.080) (0.020) (0.023) (0.007) (0.004) (0.008) (0.003)
FI 1.701** 3.009* 3.892** −0.085* 0.896** 1.229*** 1.386*** −0.092**
(0.761) (1.787) (1.862) (0.049) (0.375) (0.133) (0.469) (0.037)
Baseline
control
Yes Yes Yes Yes Yes Yes Yes Yes
Constant −0.707 11.276** −1.517 0.428*** −3.444*** −5.202*** −5.311*** 2.517**
(1.374) (4.421) (2.036) (0.153) (0.732) (0.768) (1.184) (1.059)
Observations 1,881 1,795 1,881 1,901 1,883 1,881 1,879 1,878
Number of
countries
105 105 105 106 105 105 105 105
Number of
instruments
81 82 86 55 74 76 73 52
AR(1) 0.009 0.092 0.078 0.064 0.015 0.070 0.008 0.053
AR(2) 0.126 0.101 0.198 0.700 0.112 0.269 0.614 0.389
Hansen OIR 0.633 0.925 0.847 0.154 0.254 0.210 0.616 0.550
*, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. Robust standard errors reported in
parentheses. We use the lag of the explanatory variables as an instrument for the two-step system GMM with
Windmeijer (2005) correction. FI denotes financial institutions.
INTERNATIONAL REVIEW OF APPLIED ECONOMICS 15
significant sign, suggesting that financial institutions reduce the urban-rural gap in access
to drinking water. Columns 5–7 present the effects of financial institutions on access to
sanitation relative to the total, urban and rural population, while column 8 presents the
results for the urban-rural gap. Like the previous columns, the coefficient associated with
financial institutions is positive for columns 5–7 and negative for column 8, suggesting
that financial institutions improve the proportion of people with access to sanitation and
reduce the urban-rural gap.
Table 6 presents the results of the effect of financial markets on access to W&S. We
find that financial markets also improve access to basic services such as W&S for the
total, urban and rural populations. In addition, the results in columns 4 and 8
highlight that financial markets reduce the urban-rural gap in access to W&S,
respectively. Furthermore, a close look at Tables 4 and 5 shows that financial
institutions have a larger effect than financial markets. This result can be justified
by the fact that financial institutions are more developed in developing countries
compared to financial markets (Avom et al. 2021). In addition, financial institutions
provide funds to businesses at lower costs (Svirydzenka 2016) and therefore increase
business expansion. Another argument for the greater effect of financial institutions is
that, in most cases, economic agents most often resort to financial markets to raise
long-term funds through structured financial products (stocks, bonds, and deriva-
tives), while financial institutions have the advantage of pooling resources and
transferring funds from lenders to borrowers in various amounts and locations
(Nguyen and Su 2021), which can promote the financing of W&S infrastructure
more quickly.
Table 6. Financial markets and access to W&S.
Water
total
pop.
Water
urban
pop.
Water
rural
pop.
Gap access
to water
Sanitation
total pop.
Sanitation
urban pop.
Sanitation
rural pop.
Gap access to
sanitation
Dependent
variables: (1) (2) (3) (4) (5) (6) (7) (8)
Lag
dependent
variables
0.897*** 0.795*** 0.905*** 0.830*** 0.948*** 0.944*** 0.919*** 0.601***
(0.021) (0.080) (0.018) (0.021) (0.015) (0.018) (0.021) (0.069)
FM 1.044** 1.376* 1.462** −0.130* 1.454*** 1.053** 1.768** −0.488**
(0.526) (0.796) (0.680) (0.071) (0.557) (0.524) (0.804) (0.221)
Baseline
control
Yes Yes Yes Yes Yes Yes Yes Yes
Constant −0.964 9.655*** −2.086 0.428*** −4.305** −3.543** −8.061*** 3.617***
(1.173) (3.432) (1.594) (0.116) (1.783) (1.720) (2.829) (1.059)
Observations 1,881 1,900 1,792 1,901 1,879 1,883 1,895 1,894
Number of
countries
105 105 105 106 105 105 105 105
Number of
instruments
81 86 92 55 82 76 84 75
AR(1) 0.018 0.085 0.088 0.070 0.059 0.032 0.020 0.065
AR(2) 0.508 0.107 0.308 0.718 0.420 0.219 0.122 0.118
Hansen OIR 0.575 0.442 0.619 0.183 0.147 0.164 0.112 0.367
*, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. Robust standard errors reported in
parentheses. We use the lag of the explanatory variables as an instrument for the two-step system GMM with
Windmeijer (2005) correction. FM denotes financial markets.
16 S. TADADJEU ET AL
Table 7. Effects of different measures of financial institutions on access to W&S.
Water
total
pop.
Water
urban
pop.
Water
rural
pop.
Gap access
to water
Sanitation
total pop.
Sanitation
urban pop.
Sanitation
rural pop.
Gap access to
sanitation
Dependent
variables: (1) (2) (3) (4) (5) (6) (7) (8)
Panel A
Lag
dependent
variables
0.977*** 0.795*** 0.918*** 0.797*** 0.993*** 0.972*** 0.983*** 0.822***
(0.016) (0.084) (0.006) (0.033) (0.017) (0.016) (0.010) (0.005)
FID 2.131* 0.817* 0.549** −0.082* 5.371** 1.333* 1.259** −0.133*
(1.261) (0.495) (0.236) (0.048) (2.389) (0.792) (0.626) (0.078)
Baseline
control
Yes Yes Yes Yes Yes Yes Yes Yes
Constant 8.311*** 9.781*** −1.442** 0.554*** 15.701*** 11.995*** 15.161 0.539**
(2.213) (3.534) (0.585) (0.210) (4.288) (3.028) (13.683) (0.214)
Observations 1,901 1,724 1,689 1,901 1,899 1,903 1,879 1,898
Number of
countries
106 105 105 106 106 106 105 106
Number of
instruments
84 84 77 61 71 84 56 58
AR(1) 0.021 0.092 0.043 0.099 0.091 0.059 0.036 0.098
AR(2) 0.480 0.112 0.135 0.116 0.113 0.550 0.129 0.223
Hansen OIR 0.205 0.841 0.383 0.161 0.652 0.257 0.844 0.458
Panel B
Lag
dependent
variables
0.892*** 0.795*** 0.893*** 0.840*** 0.923*** 0.935*** 0.944*** 0.868***
(0.025) (0.077) (0.022) (0.005) (0.017) (0.027) (0.009) (0.025)
FIA 1.392** 1.108** 2.783** −0.041** 1.759* 1.581* 1.085** −0.233*
(0.696) (0.516) (1.377) (0.017) (0.922) (0.911) (0.496) (0.123)
Baseline
control
Yes Yes Yes Yes Yes Yes Yes Yes
Constant −0.854 10.110*** −0.780 0.324*** −5.431*** −3.060 −5.885*** 2.744*
(1.374) (2.815) (2.230) (0.041) (2.096) (1.900) (1.556) (1.662)
Observations 1881 1677 1674 1901 1879 1899 1879 1878
Number of
countries
105 105 105 106 105 105 105 105
Number of
instruments
71 65 87 50 71 89 71 68
AR(1) 0.008 0.079 0.057 0.034 0.058 0.049 0.014 0.067
AR(2) 0.116 0.128 0.249 0.238 0.136 0.596 0.340 0.383
Hansen OIR 0.580 0.663 0.210 0.388 0.126 0.227 0.342 0.312
Panel C
Lag
dependent
variables
0.911*** 0.958*** 0.963*** 0.840*** 0.985*** 0.982*** 0.979*** 0.529***
(0.026) (0.016) (0.016) (0.006) (0.022) (0.015) (0.011) (0.057)
FIE 0.785** 1.718*** 2.622*** −0.131*** 3.401** 2.588*** 3.681*** −0.341*
(0.400) (0.613) (0.835) (0.031) (1.325) (0.919) (0.992) (0.192)
Baseline
control
Yes Yes Yes Yes Yes Yes Yes Yes
Constant −0.815 6.587*** 5.437** 0.408*** 7.412*** 6.331** −8.574*** 4.414**
(1.351) (1.594) (2.601) (0.049) (2.091) (2.942) (2.871) (1.923)
Observations 1,881 1,904 1,882 1,901 1,880 1,903 1,899 1,898
Number of
countries
105 106 106 106 106 106 106 106
Water
total
pop.
Water
urban
pop.
Water
rural
pop.
Gap access
to water
Sanitation
total pop.
Sanitation
urban pop.
Sanitation
rural pop.
Gap access to
sanitation
(Continued)
INTERNATIONAL REVIEW OF APPLIED ECONOMICS 17
Tables 7 and 8 present the effects of the sub-dimensions of financial institutions and
markets (depth, access, and efficiency). Overall, there is a positive and statistically
significant effect of these indicators on access to W&S and a negative effect on the urban-
rural gap. However, two observations can be made. First, when looking at the compar-
ison between the indicators of financial institutions versus the financial market, the
results show that, as before, financial institutions have a larger effect on access to water
and sanitation than the financial markets. In addition to the above arguments, this result
can also be justified because financial institutions allow for more expansion of economic
activities in terms of diversification and localisation (Chen 2006), which allows for the
financing of drinking water infrastructure. On the other hand, financial markets allow for
more financing of innovative activities, i.e. the quality of economic activities (Brown,
Martinsson, and Petersen 2013). However, this contribution is likely to be of little benefit
to the construction of drinking water and sanitation infrastructure, as it simply benefits
investors (Nguyen and Su 2021).
Second, when looking at each sub-index of financial development, namely depth,
accessibility, and efficiency, the results show that efficiency has a stronger effect on access
to W&S. A plausible explanation for this result is that efficiency is the most important
element of economic activity, investment, as well as business innovation (Nguyen and Su
2021; Nguyen et al. 2021; Svirydzenka 2016) as it helps determine the cost of funds and
financial services.
4. Conclusion
Inadequate water supply and sanitation infrastructure in developing countries leads to
excessive waste of time, energy, lack of privacy during defaecation and, above all, water
and excreta-related diseases. Indeed, it is estimated that approximately 6,000 people,
mostly children under the age of five, die every day from diarrhoeal diseases caused by
inadequate water and sanitation services (Immurana et al. 2022). In addition, the
problems of access to safe water and sanitation are becoming more imperative in the
current global pandemic of COVID-19. Therefore, there is a need to identify mechanisms
for financing safe infrastructure such as safe water and sanitation. This study combines
two branches of the literature, namely the one on the social effects of financial develop-
ment and the one on the determinants of access to safe water and sanitation. We propose
the first study, to our knowledge, on the effects of financial development on access to safe
Table 7. (Continued).
Dependent
variables: (1) (2) (3) (4) (5) (6) (7) (8)
Number of
instruments
78 82 79 50 78 54 82 57
AR(1) 0.097 0.032 0.030 0.045 0.036 0.032 0.017 0.015
AR(2) 0.228 0.329 0.343 0.476 0.134 0.592 0.215 0.185
Hansen OIR 0.911 0.173 0.750 0.451 0.928 0.428 0.885 0.430
*, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. Robust standard errors reported in
parentheses. We use the lag of the explanatory variables as an instrument for the two-step system GMM with
Windmeijer (2005) correction. FID, FIA and FIE denote Financial Institution Depth, Financial Institution Accessibility
and Financial Institution Efficiency, respectively.
18 S. TADADJEU ET AL
Table 8. Effects of different measures of financial markets on access to drinking water and sanitation.
Water
total
pop.
Water
urban
pop.
Water
rural
pop.
Gap access
to water
Sanitation
total pop.
Sanitation
urban pop.
Sanitation
rural pop.
Gap access to
sanitation
Dependent
variables: (1) (2) (3) (4) (5) (6) (7) (8)
Panel D
Lag
dependent
variables
0.897*** 0.810*** 0.905*** 0.815*** 0.948*** 0.950*** 0.938*** 0.430***
(0.020) (0.077) (0.017) (0.022) (0.012) (0.016) (0.019) (0.005)
FMD 0.532 0.664 1.093* −0.064* 1.062* 0.505 1.737** −0.226***
(0.488) (0.485) (0.639) (0.038) (0.623) (0.467) (0.868) (0.038)
Baseline
control
Yes Yes Yes Yes Yes Yes Yes Yes
Constant −1.183 9.076*** −2.398 0.527*** −4.236*** −3.453** −7.522*** 9.816***
(1.084) (3.296) (1.818) (0.123) (1.477) (1.453) (2.515) (0.442)
Observations 1,881 1,900 1,792 1,809 1,879 1,883 1,895 1,898
Number of
countries
105 105 105 105 105 105 105 106
Number of
instruments
81 80 80 50 82 86 80 73
AR(1) 0.018 0.097 0.097 0.084 0.070 0.029 0.048 0.053
AR(2) 0.489 0.101 0.263 0.183 0.490 0.378 0.239 0.110
Hansen OIR 0.576 0.981 0.481 0.272 0.170 0.102 0.694 0.181
Panel E
Lag
dependent
variables
0.931*** 0.793*** 0.915*** 0.818*** 0.950*** 0.944*** 0.930*** 0.429***
(0.013) (0.090) (0.006) (0.024) (0.012) (0.017) (0.020) (0.005)
FMA 0.748*** 0.833* 0.734*** −0.045* 1.020* 0.987* 1.473** −0.107**
(0.280) (0.496) (0.234) (0.025) (0.559) (0.555) (0.743) (0.046)
Baseline
control
Yes Yes Yes Yes Yes Yes Yes Yes
Constant 0.413 9.548*** −1.317** 0.495*** −3.947*** −3.644** −7.576*** 10.170***
(0.864) (3.458) (0.642) (0.126) (1.409) (1.621) (2.898) (0.402)
Observations 1,881 1,900 1,792 1,809 1,879 1,883 1,895 1,898
Number of
countries
105 105 105 105 105 105 105 106
Number of
instruments
81 80 63 49 82 76 76 73
AR(1) 0.097 0.099 0.086 0.093 0.074 0.047 0.002 0.050
AR(2) 0.598 0.112 0.201 0.408 0.421 0.284 0.109 0.107
Hansen OIR 0.262 0.826 0.440 0.192 0.240 0.177 0.568 0.0980
Panel F
Lag
dependent
variables
0.928*** 0.817*** 0.886*** 0.813*** 0.954*** 0.947*** 0.929*** 0.441***
(0.014) (0.046) (0.021) (0.024) (0.012) (0.018) (0.026) (0.006)
FME 0.520** 0.650* 1.297** −0.067** 0.754** 0.748* 1.807*** −0.248***
(0.226) (0.340) (0.631) (0.027) (0.372) (0.399) (0.584) (0.054)
Baseline
control
Yes Yes Yes Yes Yes Yes Yes Yes
Constant −0.088 8.562*** −2.410 0.523*** −3.973*** −3.291** −9.246*** 9.605***
(0.830) (1.921) (1.517) (0.128) (1.503) (1.580) (3.584) (0.417)
Observations 1,881 1,884 1,881 1,807 1,879 1,883 1,895 1,898
Number of
countries
105 105 105 105 105 105 105 106
Water
total
pop.
Water
urban
pop.
Water
rural
pop.
Gap access
to water
Sanitation
total pop.
Sanitation
urban pop.
Sanitation
rural pop.
Gap access to
sanitation
(Continued)
INTERNATIONAL REVIEW OF APPLIED ECONOMICS 19
water and sanitation. The study covers a sample of 106 developing countries over the
period 2000–2019. The results show that financial development has a positive and
significant effect on access to drinking water and sanitation for the total population as
well as for urban and rural populations. Thus, financial development helps finance
infrastructure for access to safe social services. In addition, the results also suggest that
financial development helps reduce the urban-rural gap in access to water and sanitation.
Given that the poor are primarily those without access to safe water and sanitation, our
results therefore support the vulnerable group theory of financial development. This
result remains robust when we introduce additional control variables and when we
exclude certain country groups from the sample. Furthermore, our results show that
financial institutions have a larger effect than financial markets. Finally, when we look at
the subcomponents of financial development, the results suggest that financial efficiency
has a stronger effect on access to safe social services than financial depth and accessibility.
The results of this study have important policy implications for developing countries.
First, in order to accelerate the achievement of SDG 6, it is necessary to continue efforts
to design and implement policies that promote better financial development. While some
countries are making remarkable progress in this direction, many others, particularly in
Sub-Saharan Africa, are still lagging. Thus, structural reforms aimed at promoting
financial development, will also improve access to safe social services. Second, given
the greater impact of financial institutions, we suggest that reforms to improve the
financial system should be more oriented towards the development of financial institu-
tions. Third, improved policies that promote more efficient financial institutions and
markets are also encouraged to increase access to drinking water and sanitation. Finally,
we recommend the implementation of policies such as microfinance institutions and
rural savings groups, which facilitate granting of credit in favour of rural populations in
order to reduce the urban-rural gap in access to drinking water and sanitation. These
reforms would ensure equitable access to these safe services according to the United
Nations expectations.
Notes
1. Financial development is the set of institutions, instruments, markets, as well as the
legal and regulatory framework that permit transactions to be made by extending
credit.
Table 8. (Continued).
Dependent
variables: (1) (2) (3) (4) (5) (6) (7) (8)
Number of
instruments
86 65 75 50 82 86 86 73
AR(1) 0.042 0.099 0.087 0.067 0.048 0.041 0.054 0.051
AR(2) 0.671 0.374 0.403 0.315 0.440 0.413 0.621 0.121
Hansen OIR 0.215 0.944 0.663 0.109 0.125 0.112 0.271 0.224
*, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. Robust standard errors reported in
parentheses. We use the lag of the explanatory variables as an instrument for the two-step system GMM with
Windmeijer (2005) correction. FMD, FMA and FME denote Financial Market Depth, Financial Market Accessibility,
Financial Market Efficiency.
20 S. TADADJEU ET AL
2. This argument was supported by Kiendrebeogo and Minea (2016), who argue that by
improving the efficiency of savings mobilisation and allocation, monitoring the use of
funds, risk management, and providing increased productivity of financial intermediaries
and access to microcredit for the poorest segments of society, financial sector development
can provide adequate financial services to the poor and thus contribute significantly to
poverty reduction.
3. This is not surprising, as household heads with higher levels of education may be better
informed about the potential health risks associated with unsafe water and poor sanitation
and therefore better able to provide W&S points.
4. Indeed, major disparities between urban and rural areas persist in a context where
nearly half of the population in developing countries lives in rural areas and only
35% of the rural population has access to drinking water compared to 63% of the
urban population. Similarly, only 36% of the rural population has access to safely
managed sanitation, compared to 45% of the urban population in 2019 (World Bank
2022).
5. See https://databank.worldbank.org/source/world-development-indicators
6. The detailed methodology to construct the index is available in the works of Svirydzenka
(2016) and Sahay et al. (2017).
7. To also account for foreign aid, we use official development assistance (% of gross national
income) as an alternative measure of FDI for robustness analyses. Consistent with Gopalan
and Rajan (2016), we expect a positive effect of aid on access to W&S.
8. Note that we lagged all right-hand side variables by one period to alleviate concerns about
reverse causality.
9. https://glaas.who.int/glaas/un-water-global-analysis-and-assessment-of-sanitation-and-
drinking-water-glaas-2022-report
Acknowledgments
We also thank the organizers of the CSAE Conference held on March 19–22, 2023. The feedback we
received during this conference allowed us to further improve the quality of this paper. Finally, we
thank Isaac Ketu and Tii Nchofoung for their help in proofreading the different versions of the
manuscript.
Disclosure statement
Authors declare that they do not have any competing financial, professional, or personal interests
from other parties. We thank the reviewer of this article for all of the comments to improve this
research. No potential conflict of interest was reported by the authors.
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24 S. TADADJEU ET AL
Appendix
Table A1. Robustness check using foreign aid as an additional control variable.
Water
total
pop.
Water
urban
pop.
Water
rural pop.
Gap access
to water
Sanitation
total pop.
Sanitation
urban pop.
Sanitation
rural pop.
Gap access
to sanitation
Dependent
variables: (1) (2) (3) (4) (5) (6) (7) (8)
Lag dependent
variables
0.883*** 0.805*** 0.915*** 0.781*** 0.940*** 0.953*** 0.952*** 0.539***
(0.031) (0.077) (0.016) (0.034) (0.019) (0.038) (0.020) (0.066)
OFD 4.065*** 3.090** 3.575*** −0.179** 1.909** 1.711** 2.727** −0.665**
(1.549) (1.383) (1.150) (0.084) (0.967) (0.747) (1.378) (0.286)
Baseline control Yes Yes Yes Yes Yes Yes Yes Yes
Trade openness −0.001 0.010** 0.001 −0.000 0.007 0.003 0.004 −0.000
(0.003) (0.004) (0.007) (0.000) (0.012) (0.008) (0.010) (0.001)
Foreign aid 0.001 0.016 −0.027 0.007* −0.004 −0.026 0.002 0.044
(0.016) (0.016) (0.021) (0.004) (0.006) (0.024) (0.016) (0.034)
Ethnic
fractionalisation −4.639 −3.674 −5.430*** 0.228** −5.998*** −5.642* −9.721*** 1.548**
(3.002) (2.343) (1.873) (0.092) (2.123) (2.892) (2.328) (0.618)
Polity 2 0.083* 0.011 0.053* 0.002 −0.025 0.021 0.012 −0.008
(0.046) (0.038) (0.030) (0.002) (0.020) (0.028) (0.045) (0.008)
Constant 5.419 14.079** 4.978* 0.054 1.031 5.651** 6.062* 0.394
(3.596) (5.717) (2.874) (0.169) (3.037) (2.488) (3.412) (1.056)
Observations 1,595 1,598 1,593 1,609 1,594 1,609 1,607 1,589
Number of
countries
92 92 92 92 92 92 92 92
Number of
instruments
77 75 72 58 47 73 59 75
AR(1) 0.019 0.073 0.880 0.096 0.049 0.484 0.089 0.914
AR(2) 0.222 0.252 0.467 0.167 0.187 0.463 0.323 0.705
Hansen OIR 0.834 0.882 0.490 0.387 0.819 0.821 0.870 0.204
*, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. Robust standard errors reported in
parentheses. We use the lag of the explanatory variables (except for ethnic fractionalisation) as an instrument for the
two-step system GMM with Windmeijer (2005) correction. OFD denotes overall financial development index.
INTERNATIONAL REVIEW OF APPLIED ECONOMICS 25
