The Impact of Rural Water Supply and Sanitation on Economic Growth in South Asia

On the other hand, it was found that rural access to improved sanitation Granger causes GDP growth (although not homogenously across countries). It showed that there[r]

The Impact of Rural Water Supply and Sanitation on Economic

Growth in South Asia

M G C N Madadeniya University of Peradeniya

Sri Lanka

Abstract

"Clean water and sanitation" is one of the 17 Sustainable Development Goals set by the United Nations, and the achievement of this goal is crucial for rural South Asia which accounts for around one third of the world’s rural population Clean water and sanitation facilities reinforce economic growth by creating a healthy labour force, increasing environmental sustainability and supporting economic activities in all sectors of an economy This study aims

to analyse the impact of rural people’s access to clean water and sanitation facilities on economic growth in South Asia, as the lack of scientific studies focusing on the particular issue for South Asia is a gap in existing literature A panel data econometric analysis was conducted including four South Asian countries, for the period 1991-2015 The findings of the panel regression indicated that the rural population’s access to improved water sources and improved sanitation facilities has no significant impact on the region’s economic growth during the period studied However, Granger causality analysis showed that rural population’s access to improved sanitation facilities Granger causes economic growth Further, it revealed that rural population’s access to improved water sources as well as capital growth Granger cause rural population’s access to improved sanitation facilities Given that capital growth had

a significant positive impact on economic growth in the regression analysis, more capital investments in the rural water supply and sanitation projects are thus encouraged for the South Asian region to reap growth benefits which still remain undiscovered Although labour force growth had a negative, significant impact on economic growth in the regression analysis, causality analysis revealed that growth in labour force Granger causes rural people’s access

to improved sanitation facilities homogenously across countries Hence, technological developments and other investments in human capital can help improve the productivity of labour and thereby facilitate its contribution to economic growth in South Asia

Key words: economic growth, rural, sanitation, South Asia, water supply

1 Introduction

South Asia is home to around one third of world’s rural population In fact, around 66 percent

of the South Asian population live in rural areas of the region Therefore, rural development

is essential for South Asia to achieve higher economic growth In this context, the provision

of clean water and sanitation facilities to the rural communities can be considered as an important strategy to achieve rural development

However, the supply of water and sanitation facilities to rural South Asia is inadequate In

2015, around 86 percent of rural population in South Asia used at least basic drinking water facilities and only 37 percent used at least basic sanitation facilities During the same year, only 54 percent of rural population in the region used safely managed drinking water facilities and only 26 percent used safely managed sanitation facilities

However, as shown by Figure 1, rural people’s access to improved water sources and sanitation facilities has gradually increased throughout the past

According to the World Bank, improved drinking water sources include piped water, tube wells or boreholes, protected dug wells, protected springs and packaged and delivered water Improved sanitation facilities include latrines where excreta are flushed to piped sewer systems or septic tanks, ventilated and improved pit latrines, as well as composting toilets or pit latrines with slabs

Access to these improved water and sanitation facilities can improve health and nutrition level of rural people, support productive household and commercial activities, save time spent by rural women on carrying water from distant sources to household premises, increase quality of life, ensure environmental sustainability and finally create a healthy and productive rural labour force which is conducive to economic growth

In 1990, only 66 percent of the rural South Asian population had access to improved water sources and only 9 percent of them had access to improved sanitation facilities But in 2015,

91 percent of the rural South Asian population had access to improved water sources and 35 percent of them had access to improved sanitation facilities

Hence, the problem statement of the study can be specified as follows

As the majority of the South Asian population lives in the rural areas of the region, their access

to improved water and sanitation facilities can have massive economic benefits During the period from 1990 to 2015, the South Asian economy grew at an average rate of 6.2 percent

As a matter of fact, South Asia is the fastest growing region in the world today In this context, the problem arises whether rural water supply and sanitation in South Asia, which have increased over the past, have an impact on the region’s economic growth The particular issue motivated the study to address the following questions

 Do rural water supply and sanitation induce economic growth in South Asia?

 Can South Asian countries use rural water supply and rural sanitation as strategies

to achieve higher economic growth?

The main objective of this study is to identify the impact of rural water supply and sanitation

on economic growth in South Asia As specific objectives, it attempts to study the relationship between rural water supply, rural sanitation and economic growth in South Asia, and also to draw policy implications of the findings

Clean water and sanitation is the 6th sustainable development goal in the 2030 Agenda for sustainable development The universal access to clean water and sanitation facilities is a key foundation to achieve not one, but many sustainable development goals such as, no poverty, zero hunger, good health and well-being, decent work and economic growth etc The achievement of this goal in South Asia is very important as it is one of the most populated regions in the world today In this context, this study provides a guideline for the policy makers, emphasizing the impact of rural water supply and sanitation on economic growth in South Asia, and showing how the current situation can be improved for higher economic growth in the future

The rest of this paper are organized as follows Section 2 includes a review of literature which highlights some important studies that have been conducted related to the impact of water supply and sanitation on economic growth Section 3 describes the methodology of the study

Section 4 includes the results and discussion Finally, section 5 gives the conclusions and policy implications of the study

2 Literature Review

Many of the previous studies have identified a strong link between rural development and economic growth in South Asia These studies are noteworthy when beginning a discussion

on the impact of rural water supply and sanitation on economic growth in the region

Naseem (2004) in the book on rural development and poverty in South Asia, have mentioned that for the predominantly agricultural economies of South Asia, rural development is the core issue of development Further, it is stated that due to the low priority given to rural areas

in the national development, South Asian rural societies have suffered a steady erosion in their living conditions and productive infrastructure, as evidenced by the high incidence of poverty Khan (2015) has studied the nexus between rural development, growth and poverty reduction in South Asia In the particular study, it is stated that the development experience

of South Asia suggests a strong link between rural development, growth and poverty reduction It is emphasized that from growth and poverty reduction perspective, rural development must be given priority in the development process of the region The study identifies low access to safe drinking water and proper sanitation facilities as an important indicator which reflects rural poverty in South Asia World Bank (2016) reveals that South Asia accounts for one third of the global poor, 80% of which live in rural areas of the region Therefore, rural development can significantly enhance economic growth by increasing the economic contribution of these people

Many studies are available in the literature that focus on the impact of water and sanitation

on economic growth and development in various regions and countries in the world

Okun (1988) has analysed the value of water supply and sanitation programmes for economic development and listed out their benefits which are: preventing diseases; improving primary health care; improving nutritional status; facilitating health centres, clinics and schools; saving time spent on fetching water; facilitating household irrigation and animal watering; promoting commercial activities; supporting all economic sectors; strengthening community organization; and improving quality of life Thus, the particular research supports the view that water and sanitation increases economic growth

However, Barbier (2004) in his study which involves estimations related to a broad section of countries, has come up with a negative relationship between water and economic growth It is argued that growth is negatively affected by the government's appropriation of output to supply water, but is positively influenced by the contribution of increased water use

cross-to capital productivity, leading cross-to an inverted-U relationship between economic growth and the rate of water utilization A main reason for this is that when freshwater supplies are

limited relative to current and future populations, countries may find it difficult to generate additional growth through more water use It is concluded that privatization, pricing reforms and water markets can establish incentives for more efficient use of water in the economy than the water management solely by public sector Thus, Barbier shows the possibility for a negative water-growth relationship in highly populated regions like South Asia

Nevertheless, most of the studies emphasize water and sanitation as essential factors contributing to economic growth Anderson and Hagos (2008) have explored the potential linkages between access to water and sanitation and growth-related indicators in Ethiopia using an econometric analysis Results show that the improvements in the source of drinking water are likely to report an improvement in household food situation No significant relationship has been found between improvements in drinking water sources and households’ overall welfare Changes in sanitation arrangements also have shown no relationship with changes in households’ food or welfare situations

Hutton et al (2008) have examined the economic impacts associated with poor sanitation in Cambodia, Indonesia, the Philippines and Vietnam They have found that poor sanitation causes considerable financial and economic losses in the four countries The major contributors to overall economic losses are the cost of premature death (mainly of children under five years old), time spent accessing unimproved water and sanitation facilities, tourism losses, health care costs, sickness time etc

Musouwir (2010) has conducted a time series analysis and found a statistically significant relationship between national budget on water supply and sanitation and GDP per capita in

22 African countries The study strongly encourages governments of developing countries to spend more of their annual budgets on the water sector

Minh and Huang (2011) have conducted a study on the economic impacts of unimproved sanitation in developing countries Their evidence prove that the economic cost associated with poor sanitation is more than the economic cost associated with poor water supply Previous studies have also attempted to derive numerical estimates of the costs and benefits associated with water and sanitation OECD (2011) has shown that benefits from the provision

of basic water supply and sanitation services are massive and far outstrip costs It says, benefit-to-cost ratios have been reported to be as high as 7 to 1 for basic water and sanitation services in developing countries In the meantime, their findings reveal that there may be some “disbenefits” in providing access to water, sanitation and hygiene, depending on the sequencing of investments, for example, if access to water is provided without simultaneous access to sanitation However, adequate water and sanitation services are identified as key drivers of economic growth

Tezera (2011) has studied the impacts of poor accessibility to potable water and sanitation

on economic, social and environmental condition of the Soddo district in Ethiopia

Productivity losses due to absenteeism and high medication costs due to various health issues are identified as some economic impacts

According to Frontier Economics (2012) providing universal access to water for all serviced populations worldwide will cost at least USD 175 billion An additional USD 550 billion would be required to provide universal access to sanitation services In India, one-off investment requirements are $4,338 million and $36,911 million for water and sanitation respectively Annual potential economic gain is $16,550 million In Nepal, one-off investment requirements are $142 million and $896 million for water and sanitation respectively Annual potential economic gain is $389 million In Pakistan, one-off investment requirements are

poorly-$965 million and $3852 million for water and sanitation respectively Annual potential economic gain is $1454 million Although the initial investment requirements are large, if properly maintained, they can create cumulated benefits with time

UNU and UNOSD (2013) have found that the inadequate water and sanitation infrastructure increases expenditures in other sectors Costs will be incurred when getting water from informal private providers, health costs increase because of waterborne diseases, and the potential for human productivity is also compromised

Almost all of these studies highlight the fact that lack of access to water and sanitation incurs excessive costs to an economy which exceed the costs that should be borne when providing them However, Ibok and Daniel (2014) have pointed out that along with public spending on water supply and sanitation, some other conditions should also be met to achieve favourable economic outcomes Analysing the rural water supply in Akwa Ibom State in Nigeria, they have identified the lack of maintenance, lack of community participation, lack of coordination and co-operation among the stakeholders, political factors, inefficient monitoring, and poor attitude towards public property etc., as reasons for unproductive government expenditures

on water supply It emphasizes that continuous maintenance, coordination and regulation are needed to make the water supply projects conducive to economic growth

Some more recent studies further prove the importance of water supply and sanitation for

an economy Patunru (2015) has illustrated the importance of water and sanitation facilities

by estimating their impact on diarrhoea incidence in Indonesia The study finds that when it comes to health issues, the relative importance of sanitation is higher than that of water Sadoff et al (2015) have showed that South Asia has the largest global concentration of water-related risks, with the largest global concentration of people without adequate sanitation and growing environmental threats India, with its very large population, is the top-ranked country globally for the number of people without adequate water supply and sanitation This raises the question whether the inadequacy of water supply and sanitation in South Asia has any unfavourable impact on economic growth in the region

UN (2016) has explained how water contributes to economic growth through the generation

of employment in every sector of the economy In fact, half of the global workforce is said to

be employed in eight water and natural resource-dependent industries: agriculture, forestry, fisheries, energy, resource intensive manufacturing, recycling, building and transport

UN (n p) has shown that by managing water sustainably, production of food and energy can

be better managed and it will also contribute to decent work and economic growth It is also mentioned that although the extension of basic water and sanitation services to the unserved will cost a significant amount, the costs are even higher if poor water supply and poor sanitation are left unsolved According to the World Bank estimates, 6.4 percent of India’s GDP is lost due to adverse economic impacts and costs of inadequate sanitation OECD (n p) has shown that the greatest economic losses from water insecurity are resulted by inadequate water supply and sanitation, associated loss of life, health costs, lost time, and other opportunity costs

Through the above literature review, it was clear that water supply and sanitation play an important role in the growth of an economy However, relatively less attention has been given

to the impact of water supply and sanitation on economic growth in South Asia Moreover, the supply of water and sanitation facilities to rural South Asia and its impact on economic growth have hardly been subjected to scientific studies

3 Methodology

This study conducted a panel data analysis including data for four South Asian countries which are, Bangladesh, India, Pakistan and Sri Lanka Annual data were collected from the World Development Indicators database of the World Bank and the sample period was from 1991

to 2015 The four countries as well as the sample period were selected according to the availability of data A balanced panel was used in this analysis where each country has the same number of observations

The Neo-classical growth accounting equation was followed in constructing the model Growth accounting explains what part of growth in total output is due to growth in different factors of production Output grows through increases in inputs and also through increases

in productivity The production function expresses the quantitative relationship between inputs and outputs Assuming labour (N) and capital (K) as the only inputs, the following equation shows that output (Y) depends on inputs and the level of technology (A)

where,

θ = labour’s share of income

1-θ = capital’s share of income

Thus, labour and capital, each contributes an amount equal to their individual growth rates multiplied by the relevant input’s share of total income The last term of the equation, ∆A/A,

is the rate of improvement in technology Hence, output growth can be expressed as a linear function of labour growth, capital growth and technological growth The value of ∆A/A is often described as an estimate of total factor productivity (TFP) growth or the Solow residual It can

be calculated from the above equation as a residual, by subtracting capital growth and labour growth (multiplied by the relevant input’s share of total income) from output growth However, according to Barro (1998), an alternative approach would be to regress the growth rate of output on the growth rates of inputs, so that the intercept will measure the value of

∆A/A, and the coefficients of the factor growth rates will measure the capital’s and labour’s shares of income Although with certain limitations, this alternative approach provides a simple way of decomposing growth rate of output into components associated with factor accumulation and technological progress

Following the alternative approach suggested by Barro (1998), an econometric model was constructed, incorporating the target variables into the growth equation The model is specified below

(3)

where,

GDPG = Growth of Gross Domestic Product (at constant 2010 US $)

GCFG = Growth of Gross Capital Formation (at constant 2010 US $)

LFG = Growth of labour force

LNW = Log of the number of rural people with access to improved water sources

LNS = Log of the number of rural people with access to improved sanitation facilities

t = time or year

The methodological tools used in this study were as follows

i Panel Unit Root Tests

Three panel unit root tests were conducted to check the stationarity property of the data series They are; Levin, Lin & Chu t test, Augmented Dickey Fuller-Fisher Chi-Square test and Philips-Perron-Fisher Chi-Square test

ii Panel Regression

In order to find the relationship between the dependent variable and independent variables, Panel regression methods were used According to Zulfikar (2018), estimating the regression

GDPGt = β0 + β1 GCFGt + β2 LFGt + β3 LNWt+ β4 LNSt + εi

model using panel data can be done through three approaches, among others: Pooled Least Squares regression model (Common Effect model), Fixed Effects model and Random Effects model The study estimated all these three models

 Pooled LS Regression model

In Pooled Least Squares regression model, all observations are simply pooled and regression analysis is carried out, ignoring the cross-section and time series nature of the data, in which case the error term captures everything This camouflages the heterogeneity or individuality that exists between the variables

 Fixed Effects model

The Fixed Effects model differs from the Pooled Least Squares regression, but still uses the ordinary least square principle Nwakuya and Ijomah (2017) states that the fixed-effects model controls for all time-invariant differences between the individual entities In other words, it controls for the average differences across groups (countries) in any observable or unobservable predictors Thus, it assumes the same slopes and constant variance across countries In the basic Fixed Effects model, the effect of each predictor variable (i.e., the slope)

is assumed to be identical across all the groups, and the regression merely reports the average within-group effect Thus, the estimated coefficients of the fixed-effects models cannot be biased because of omitted time-invariant characteristics such as culture, religion etc So Fixed Effects models are considered a method to avoid the problem of omitted variable bias

As Fixed Effects model controls for all the across-group action, what remains is the group action In fact, fixed-effects models are designed to study the causes of changes within

within-a group, within-assuming thwithin-at the time invwithin-ariwithin-ant chwithin-arwithin-acteristics within-are unique to the group within-and should not be correlated with other groups’ characteristics Therefore, one country’s error term and the constant (which captures individual characteristics) should not be correlated with the others Hence, for a Fixed Effects model, a reasonable amount of variation of independent variables is needed within each group However, the effect of the other important variables that have little within-group variation cannot be assessed in this method

Alternatively, estimating Fixed Effects model panel data using dummy variables, i.e Least Squares Dummy Variable (LSDV) method, can capture the differences among groups Here, it

is assumed that differences between groups can be accommodated from different intercept, given that the independent variables are non-stochastic

 Random Effects model

However, in some cases where the error terms are correlated, the Fixed Effects model is not suitable Such correlation is caused by unobservable factors that are correlated with the variables included in the regression As a result, omitted variable bias might occur If these

unobservable factors are assumed to be time-invariant, then Fixed Effects regression will eliminate omitted variable bias If the unobservable factors are not time-invariant, i.e if they move up and down over time within categories in a way that is correlated with the variables included in the regression, then the omitted variable bias is still present In such cases Random Effects model should be estimated But this model is also vulnerable to omitted variable bias (omitted-variable bias occurs when a statistical model leaves out one or more relevant variables, resulting in the attribution of the effect of the missing variables to the estimated effects of the included variables)

Random Effects model does not use the principle of ordinary least square, but uses the principle of Maximum Likelihood or Generalized Least Square (GLS) The difference among groups (or time periods) lies in their variance of the error term, not in their intercepts In other words, residuals may be correlated between time and between groups An advantage of Random Effects model is that the estimates can be generalized as the model assumes that it analyses a sample taken from a population Conversely, in Fixed Effects model, the estimates are specific to the data used In addition, Random Effects model also eliminates heteroscedasticity

To decide the best model out of the three above regression models, three types of tests were conducted

iii Hausman test

The choice between Fixed Effects model and Random Effects model depends on the presence

of individual heterogeneity among groups, which can be tested using Hausman Test If the null hypothesis is rejected, then it is concluded that there is individual heterogeneity and therefore, Random Effects model is not appropriate, alternatively, Fixed Effects model should

be used

iv Redundant Fixed Effects test

Redundant Fixed Effects test can be used to check if fixed effects are necessary in the regression Redundant Fixed Effects-Likelihood Ratio tests the joint significance of the fixed effects estimates in least squares specifications It uses an F-statistic to check if the cross sectional, period or both fixed effects are contributing to the explanation of the dependent variable The null hypothesis in this test is that all cross-sectional or period dummies in the Fixed Effects model are zero and the alternative hypothesis is that at least one of them differs from zero In other words, the null hypothesis states that the fixed effects are redundant and thus unnecessary Thus, Redundant Fixed Effects test provides a way of testing for unobserved heterogeneity

panel-v Lagrange Multiplier (LM) test

LM test helps to decide between a random effects regression and a simple OLS regression The null hypothesis in the LM test is that variances across entities is zero, i.e no significant difference or heterogeneity across groups (no panel effect) Four LM tests were conducted in this study which are, Breusch-Pagan, Honda, King-Wu, Standardized Honda and Standardized King-Wu

Causality tests were also conducted to find the causal relationship between variables in the model

vi Granger Causality test

In line with most of the literatures in econometrics, one variable is said to Granger cause the other if it helps to make a more accurate prediction of the other variable than had we only used the past of the latter as predictor Granger causality between two variables cannot be interpreted as a real causal relationship but merely shows that one variable can help to predict the other one better (Awe 2012: 2)

Two types of Granger Causality tests were conducted which are: Stacked Causality Test and Dumitrescu-Hurlin Causality test The stacked causality test treats the panel data set as one large stacked set of data without taking a lagged value of one cross section to the next cross section This approach assumes that all coefficients are same across all groups (common coefficient) Dumitrescu-Hurlin causality test allows for all coefficients to be different or heterogeneous across groups This approach takes into account two different statistics which are:Wbar-statistic (takes average of the test statistics) and Zbar-statistic (shows a standard, asymptotic normal distribution)

4 Results and Discussion

First, panel unit root tests were conducted to test the stationarity property of the time series1 Three unit root tests were conducted, namely; Levin, Lin & Chu t test, Augmented Dickey Fuller-Fisher Chi-Square test and Philips-Perron Fisher Chi-Square test Levin, Lin & Chu t test assumes common unit root process in the null hypothesis whereas all the other unit root tests assume an individual unit root process in the null hypothesis The following table summarizes the results of the unit root tests

1see appendix

Table 1: Panel unit root test results

Levin, Lin & Chu t ADF – Fisher Chi-square PP – Fisher Chi-Square

56.326***

(0.000) Note: *, ** and *** indicate rejection of null hypothesis at 10%, 5% and 1% respectively

Accordingly, at 5% level of significance, all the variables in the model were stationary at level Therefore, it was clear that Panel Least Squares regression methods can be adopted to find the relationship between variables

The results of the regression analyses are given below

Table 2: Pooled LS Regression results

Variable Coefficient Std Error t-Statistic Prob

S.E of regression 1.333 Akaike info criterion 3.462

Log likelihood -168.085 Hannan-Quinn criter 3.514

Note: *, ** and *** indicate rejection of null hypothesis at 10%, 5% and 1% respectively

The growth in gross capital formation and labour force have a significant impact on GDP growth at 5 percent level of significance Holding other factors constant, when growth in gross capital formation increases by one percent, GDP growth increases by 0.156 percent on average Holding other factors constant, when growth in labour force increases by one percent, GDP growth decreases by 0.281 percent on average Rural people’s access to improved water and sanitation do not have a significant impact on GDP growth

The reason for the particular negative impact of labour force can be justified using Figure 2 Growth in GDP as well as growth in capital to labour ratio in the four countries appear to follow the direction of the growth in gross capital formation This leads to the problem whether South Asian labour is productive, so as to contribute to the region’s economic growth Compared to gross capital formation, labour force has experienced a stable growth rate around 2 percent This stable growth rate along with a fluctuating growth in gross capital formation can have a negative impact on capital to labour ratio or the productivity of labour

in times where labour growth surpasses capital growth However, this problem needs to be addressed through further research

Figure 2: Growth in GDP, capital, labour and capital to labour ratio across the four South Asian

countries: Bangladesh, India, Pakistan and Sri Lanka Source: World Development Indicators

The model’s R-squared value which is equal to 0.541 indicates that around 54.1 percent of the total variability of GDP growth is simultaneously explained by the independent variables

of the model The adjusted R-squared is 52.2 percent It shows that the independent variables can explain 52.2 percent of the variability in GDP growth

According to the individual t tests, all other variables except for capital growth and labour growth are insignificant variables However, individually insignificant variables can be jointly significant in explaining economic growth In such a case overall significance of the model can

be tested using F test where the null hypothesis is that the independent variables of the model jointly have no significant impact on the dependent variable

As the probability value of the F statistic (0.000) is lower even than 0.01 level of significance, the null hypothesis is rejected Hence, independent variables of the model jointly have a significant impact on the dependent variable This implies that rural people’s access to