Investigation of Socioeconomic Determinants on Child Death in South Asian Countries: A Panel Cointegration Analysis

Abstract

This article is aimed to examine the relationship between socioeconomic factors and child mortality in South Asia because the relationship between child mortality and socioeconomic factors cannot be overlooked for better progress. Panel data were obtained from (World Development Indicators) and (Human Development Index) for the period 1990–2017. The data were quantitative. Levin, Lin, and Chu and I’m, Pesaran, and Shin test were used to check the stationarity of data. A cointegration test was applied to check the long-run association. Granger causality test was used to determine the direction of the relationship. Fully modified ordinary least squares and dynamic ordinary least squares techniques were used to examine the long-run and short-run impact of socioeconomic determinants on child mortality. The findings from this study showed the significant impact of education, unemployment, and health expenditure, access to improved water and sanitation facilities, and income inequality on child mortality. Overall results showed that there is a negative association between education and child mortality, access to improved water and access to sanitation facilities and child mortality, and health expenditure and child mortality, but there is a positive association between unemployment and income inequality with child mortality. The rate of child mortality is still very alarming in South Asian countries.

Keywords

under-five mortality South Asian countries socioeconomic factors multifaceted approach sustainable development goals

Introduction

The development level of a country must be estimated by more than one-dimensional income statistics. It is necessary for the discussion of the country’s development level to include such indicators that account for the population’s welfare as well as the social and economic state of the country. The child mortality rate, defined as death among children under the age of 5 per 1,000 live births, has generally been considered as an acceptable and measurable indicator of the overall health status of a nation. According to Lemani (2013), the death of a child before reaching the age of 5 is an important factor that determines the quality of life of the population. The child mortality can also be a gauge of the productiveness of public health policies within a country. Globally, child mortality is a major health issue while taking into account social and economic development (Staff, 2015). Some inequities about child mortality and health policy in developing and developed areas exist.

Hence, there is a need to reduce child mortality because a healthy child is an asset of any country. Child mortality reduction is an important agenda at the world level, and this is an important target of sustainable development goals (SDGs) 2030 (Desta, 2011). That is why different health projects such as Heavily Indebted Poor Countries Initiative Funds (HIPC), child mortality reduction programs, and maternal and child health survival programs were launched to reduce the child mortality in developing countries (Bello & Joseph, 2014). The main objective of these projects is to decrease the under-five mortality (see Figure 1).

Figure 1.

Under-Five Child Mortality.

Figure 2 shows the trend of child mortality in South Asian countries. Socioeconomic and demographic indicators are significant for determining the health of children all over the world (Abuqamar et al., 2011; Milat et al., 2011; Shawky & Milaat, 2000). In addition, the causes of malnutrition are also associated with socioeconomic factors. The study used education, unemployment, income inequalities, access to improve water and sanitation facilities, and health expenditures as independent socioeconomic variables. At the same time, child mortality under 5 years of age has been taken as dependent variable.

Figure 2.

Child Mortality in South Asia.

Rationale of the Study

The research focuses on the macroeconomic level effects of socioeconomic determinants on child mortality in South Asian countries. The time framework is selected for this research covers the period from 1990 to 2017. The chosen time is helpful because time provides the requirement to meet econometric and statistical techniques for empirical results, and this will help to examine the impact of socioeconomic indicators on child mortality in South Asian countries.

The set of variables for this study is under research, and the findings of previous research are controversial and heterogeneous. These variables are also taken from future directions of researchers like Patel et al. (2015). A few numbers of studies are found in South Asian countries. The highest rate of child mortality is found in South Asian countries. There is a need to recognize the determinants of child mortality and increase the odds of achieving sustainable development goals in 2030. Moreover, the high rate of child mortality underlines the need to seclude the socioeconomic determinants of child mortality. Recognizing these determinants will help to increase the existing learning and knowledge about increased child mortality rates. This will help in the process of creating and influencing developing policies and meditation techniques to manage issues of child mortality in South Asian countries because these nations are still behind in achieving sustainable development goals in 2030. So, the study especially focuses on child mortality as it is an essential factor of SDG. The study will help for both maternal and child health, their standard of living, and their education. It will help in different development programs in South Asia in the future. These will include increasing the education of both male and female and increasing employment opportunities. Policies will be designed to increase health-care units and to provide safe drinking and sanitation facilities. All these measures will be adopted to reduce under-five mortality.

Problem Statement

South Asia is the second highest region at the world level that is facing child mortality. World Health Organization recently highlighted the issue of child mortality (Doku & Neupane, 2017). Countries facing high mortality rates during the period 1990 to 2009 are India (27.8%), Pakistan (6.9%), and Nepal (7.2%) (Oestergaard et al., 2011). These statistics indicate that most deaths occur in South Asia. Hence, this is an alarming situation in almost all developing countries. The record also shows that every day, 29,000 death occurs among the children less than 5 years. Among these, around 70% of deaths occur due to improper medical facilities or environmental contamination which causes diarrhea or other respiratory infection. There are some other reasons such as lack of proper water and sanitation facilities, lack of oxygen, or improper childcare. It has also been observed that children from low-income families are more vulnerable to this issue compared to the better-off peers (Victora et al., 2003). However, little information is available about factors related to child deaths, particularly in South Asian countries (Kalipeni, 2000). Several studies have approached child mortality on trend analysis (Hong et al., 2009; Rutstein, 1984; Wahab et al., 2018).

Research Objective

The study has been designed to achieve the following objectives.
To examine the association between education and child mortality in South Asian countries.

To examine the association between unemployment and child mortality in South Asian countries.

To examine the association between income inequality and child mortality in South Asian countries.

To examine the association between access to improve water facilities and child mortality in South Asian countries.

To examine the association between access to improve sanitation facilities and child mortality in South Asian countries.

To examine the association between health expenditure and child mortality in South Asian countries.

Literature Review

Related Studies

This section provides the theoretical and conceptual framework of the underlying problem. This section also addresses the literature review of socioeconomic determinants of child mortality in South Asian countries. Furthermore, scholars’ views and empirical researches have also been addressed. The model of child mortality has been adapted from different researchers like Schell et al. (2007). Besides, variables with complete descriptions and definitions and operational frameworks are all present. This section forms the base for this analysis.

Theories

Figure 2 illustrates the pathway which mentions the factors that influence the health of a child and even death. The socioeconomic indicators operate through environmental, nutritional, and maternal and health-seeking behavioral factors leading to a healthy child. Each of the factors is discussed as follows.

Child Mortality

South Asia is the second higher region facing child mortality after Sub Saharan Afrian: 1 out of 19 children die in low-income countries and 1 out of 147 children die each day in high-income nations. In the year 2015, 6 million child deaths occurred from which 30% of deaths were in South Asian countries. From 10 child deaths per 1,000 lives birth worldwide, 3 deaths occur in South Asia. Afghanistan, Pakistan, and India are the top listed nations facing high child mortality. Many indicators are present which cause the state of child mortality (Mosley and Chen, 1984a). The developed analytical framework for the survival of children in many developing nations is the mostly used conceptual framework for the survival of child studies. This study is based on theoretical and conceptual frameworks developed by Mosley and Chen (1984b); Sartorius et al. (2011); Schell et al. (2007); and Mustafa (2007) for child survival.

Education and Child Mortality

H1: There is a significant relationship between education of both male and female and child mortality rates.

According to the Demographic Health Survey of Nepal (2013), the under-five mortality trend was very high among illiterate and low among literate mothers in all Demographic Health Survey report from 1991 to 2011. The mortality gap related to education was in increasing trend in all the studies from 1991 to 2011. Moreover, 80% and above child mortality was seen among illiterate mothers (Sreeramareddy et al., 2013). Similar results were also found in Sri Lanka on the same pattern, and the findings of a comparison of socioeconomic factors of child mortality showed that child mortality among parents having primary education or less was twice higher than that of those children who were born to parents who have high education (Houweling et al., 2005).

Most of the researchers (Ali & Cibas, 2017; Rose, 1999) studied the factors influencing infant mortality and child mortality in the Indian district, Rural Andhra. The main objective of their study was to observe some important and influencing factors that affect infant and child mortality. Some variables that were considered most effective were breastfeeding, immunization, age of mother at birth, and birth interval. These variables were found to be significant indicators of neonatal, post neonatal, and child mortality. Treatment place, parent’s education, and sanitation facility were found to be significant determinants of child mortality in the neonatal period. At postneonatal duration, father’s education was found to be significant.

Unemployment and Child Mortality

H2: There is a significant association between unemployment and the rate of child mortality.

A high level of unemployment is a major concern in different nations, and it is indispensable to assess how people are getting affected by higher levels of unemployment. The phenomenon of unemployment is typically related to loss of income for the individual. Several types of research also suggested that unemployment leads to a decrease in happiness, prosperity, and common welfare (Clark & Oswald, 1994; Theodossiou, 1998; Winkelmann & Winkelmann, 1998). It has also been discussed that unemployment may be a health risk. Several studies in the field of the public health sector have examined that an unemployed person has critical health than employed individuals (Björklund & Eriksson, 1998; Dooley et al., 1996; Jin et al., 1995; Mathers & Schofield, 1998).

Income Inequality and Child Mortality

H3: There is a significant relationship between income inequalities and child mortality rates.

Low- and middle-income countries have encountered rapid financial development, which might be beneficial for adolescent, or child mortality yet may face some inequalities (Bello & Joseph, 2014).

According to Park et al. (2015), disparities in income-restricted economic growth affect the development level of a country. Hence, child mortality also effects as literature shows that economic growth also affects child survival. The data were collected in Korean High Schools for the period 2010 to 2012. Their findings showed that income inequality significantly affects child mortality in the Korean community. The reason behind this is that the increase in income inequalities affects the gross domestic product (GDP) badly, and this low rate of economic growth affects the health of population and, ultimately, child survival. It was stated by Torre and Myrskyla (2014) that the pattern of association between income inequality and child mortality is not well known or confirmed.

Access to Improved Water and Sanitation and Child Mortality

H4: There is a significant impact of water and sanitation on the child mortality rate.

The first article that evaluated the impact of water and sanitation facilities on diarrheal morbidity was written by Ravallion and Jalan (1999). The sample of rural India was selected. National Council of Applied Economics Research New Delhi conducted a household survey in 1993 to 1994. Health gains were measured for children living in the household level with better access to piped water. They found less incidence and time of diarrhea for those children who are living in a household using a clean water facility by applying propensity scores matches at the household level. These benefits of the poor households were passed to that area also, where women were uneducated. The impact of water and sanitation facilities on child mortality has been investigated by a large number of studies worldwide (Berendes et al., 2017; El-Zanfaly, 2015; Maftoon & Kolahi, 2009; Ravallion & Jalan, 1999).

Health Expenditure and Child Mortality

H5: There is a significant relationship between Health Expenditure and child mortality rate.

Most of the researchers (Baldacci et al., 2003) found an insignificant association between health expenditure and both infant and under-five mortality. According to them, if the income of a nation is taken into consideration, the value of the coefficient of public health expenditure will remain insignificant in the equation of health status. In the evidence provided by Filmer and Pritchett (1997), a very limited relationship of health-care spending is found with child mortality. They showed that health-care spending is not a dominant driver to any child mortality. The actual relationship between health spending and child health is still unclear, particularly at the macro level. Some researchers found positive and negative impacts of health-care spending on child health. They also investigated that GDP alone could reduce child mortality by 80%.

Methodology

Conceptual Framework

The review of experimental literature and hypothetical literature for this problem recommends that socioeconomic factors can affect child mortality under five in South Asian countries. These factors are Education, Unemployment, Health expenditure, Income inequality, Access to improved water and sanitation facilities. By utilizing the modelled variables, the conceptual framework is shown in Figure 3.

Figure 3.
Research Model.

Variables and Data Sources

Seven explanatory and dependent variables have been used in this study, which are education, unemployment, income inequality, improved water facilities, improved sanitation facilities, health expenditure, and child mortality. Six variables are explanatory variables, and one dependent variable is child mortality under-five. The research project is based on the Panel data analysis for 27 years of observations, that is, 1990–2017. The data are collected from WDI and HDI annually. Various studies focus on these variables (Amouzou & Hill, 2004; Barros et al., 2005; Machado & Hill, 2005; Maniruzzaman et al., 2018; Mohammad & Tabassum, 2016; Mosley & Chen, 1984b). The general function form of this model is given here (see Table 1).

Table 1.
Variables Description and Source.

Variables Description Units Source

CMU5 Child mortality U5M/1000 live birth WDI

EDU Education School enrollment, primary and secondary WDI

UNEM Unemployment Percentage of the total labor force WDI

HEX Health expenditure Total percentage of gross domestic product HDI

IE Income inequality Annual percentage WDI

IWF Access improve water Percentage of the population with access WDI

ISF Sanitation facilities Percentage of the population with access WDI

Note. WDI = World Development Indicators; HDI = Human Development Index.

Econometric Methodology

This study employed different econometrics steps to investigate the association between child mortality and socioeconomic determinants. Their first step involved in this research is to check the data stationarity through the unit root test. Without checking the stationarity of data, we might lead to spurious results. After confirming the unit root test, if it is clear, the data are stationary at the level and first order, we will check the cointegration. This test is used to find the long-run association among the dependent variable and explanatory variables. For this purpose, Johansen’s Panel cointegration test has been employed in South Asian Countries. This test is very effective and appropriate for Panel study.

The third step is to apply the Granger Causality test to check the direction of the association. The last step is to employ the advance panel data econometric techniques known as fully modified ordinary least square (FMOLS) and dynamic OLS (DOLS) along with descriptive statistics. The study examined the short-run affiliation and long-run association between nominated socioeconomic factors and child mortality less than 5 years of age for South Asian countries (Bangladesh, Afghanistan, Bhutan, India, Pakistan, Nepal, Maldives, and Sri Lanka).

Econometric Equation

Child mortality under five = f (Education, Unemployment, Health expenditure, GDP, Income inequality, Access to improved water, and Access to sanitation facilities).

U5M = βo + β1 (education) + β2 (unemployment) + β3 (health expenditure) + β4 (access to improved water) + β5 (access to improved sanitation) + β6 (income inequality) + $μ$ itU5M = βo + β1 (Lit) + β2 (unmp) + β3 (Hexp) + β4 (iwf) + β5 (isf) + β6 (IE) + $μ$ it

Fully modified ordinary least squares
$t_{{\hat{β}}_{GFMOLS}} = N^{- 1 / 2} \sum_{i = 1}^{N} t_{{\hat{β}}_{FMOLS, i}}, where$

$t_{{\hat{β}}_{FMGLS}} = ({\hat{β}}_{_{FMOLS, i}} - β_{0}) {({\bar{Ω}}_{u}^{- 1} {\sum_{i = 1}^{T} (x_{i t} - {\bar{x}}_{i})}^{2})}^{1 / 2}$

Dynamic ordinary least squares
$y_{i t} = α_{i} + β_{i} x_{i t} + \sum_{k = - K_{i}}^{K_{i}} γ_{i k} Δ x_{i t - k} + u_{i t}$

${\hat{β}}_{^{DOL S_{i} =}}^{} [N^{- 1} {\sum_{i = 1}^{N} (\sum_{t = 1}^{T} z_{i t} z_{i t})}^{- 1} (\sum_{t = 1}^{T} z_{i t} {\tilde{y}}_{i t})]$

These are between-dimensions estimators given by Pedroni (1999). These are based on the Panel Group Mean Panel estimator and can be obtained from the aforementioned regression equation. Here, it is a vector regressor.
$z_{i t} 2 \times (K + 1) \times 1 z_{i t} = (x_{i t} - x_{i}, Δ x_{i t - k, \dots,} Δ x_{i t + k}), {\tilde{y}}_{i t} = y_{i t} - {\bar{y}}_{i}$

Now the DOLS will be constructed here. This is called conventional DOLS.
${\hat{β}}_{GDOLS}^{} = N^{- 1} \sum_{i = 1}^{N} β_{DOLS, i}^{}$

$β_{DOLS, i}^{}$ Conventional DOLS
${\hat{σ}}_{i}^{2} = \lim_{T \to \infty} E [T^{- 1} {(\sum_{t = 1}^{T} u_{i t}^{})}^{2}]$

$t_{{\hat{β}}_{^{DOLS}}^{}} = N^{- 1 / 2} \sum_{i = i}^{N} t_{{\hat{β}}_{^{DOLS}}^{}, i}, where$

$t_{{\hat{β}}_{^{DOLSi}}^{}} = ({\hat{β}}_{^{DOLSi}}^{} - β_{0}) {({\hat{σ}}_{1}^{- 2} \sum_{i = 1}^{T} {(x_{i t} - {\bar{x}}_{i})}^{2})}^{1 / 2}$

Panel Unit Root Tests

The empirical investigation begins with the assessment of stationarity of the factors consolidating in the model. Before applying some other statistical estimation, it is essential to test the unit root of data collected from different websites. The presence of stationary variables is important, within sight of data which is nonstationary, spurious results may be created by estimation, furthermore to check the unit root is likewise helpful for the determination of suitable econometric methodology. There is a variety of techniques available for checking unit root. However, this study used two-unit root tests: Levin, Lin, and Chu (LLC) test and I’m, Pesaran, and Shin (IPS) test (Im & Pesaran, 2003; Levin et al., 2002).

Panel Cointegration

Cointegration is used to check that whether any relationship, in the long run, exists among the variables or not. If the model found that the cointegration equation exists in the variables, then it means there is a clear long-run relationship that exists among the variables. This will show that all variables will move together in the long run.

Granger Causality

This research has used the granger causality to investigate about lead-lag association among child mortality and socioeconomic determinants in the case of South Asian countries. Granger (1969) determined the causality by showing that if a dependent variable, Y_t* can be predicted by using the previous terms of an independent X_t variable, then X_t is called as granger cause Y_t and in alternate way Y_t is granger cause X_t_. Therefore, the leading equations are assessed to uncover any kind of short-run causal relation by using the Granger causality tests at the child mortality and socioeconomic determinants independently.

Fully Modified Ordinary Least Square

For applying Auto Regressive Distributed Lag, certain conditions must be filled. One of them is that the data must be sufficient enough to apply the ARDL. At least 30 observations are required to perform ARDL. But if the data are stationary at the level and at first difference and ARDL is not possible due to insufficient several observations, then the researcher can move toward the FMOLS technique. The technique used in this study is modified OLS. This technique provides the appropriate results even if several observations are less, that is, small sample size. It removes the problem of endogeniety by-taking leads and lags. This technique is more flexible than ARDL. FMOLS fulfills the requirements of cointegration and developed by Phillips and Hansen (1990). FMOLS is applied in two different conditions. First, there should be one cointegration in the model, and second, repressors must not be cointegrated among them (Narayan & Narayan, 2004).

Dynamic Ordinary Least Square

This methodology was first used by Stock and Watson (1993). DOLS is used to determine the long-run relationship and remove the simultaneity bias among all the regressors. This technique resembles the concepts given by Choi et al. (1997), Phillips and Hansen (1990), Phillips and Loretan (1991), and Saikkonen (1991). This technique uses the mixed order variable that is both I(1) variables and I(0) variables. Hence, the problem of endogeniety does not asymptotically affect the robustness of estimates (see Figure 4).

Figure 4.
Flow Diagram. FMOLS = fully modified ordinary least squares; DOLS = dynamic ordinary least squares.

Methodology Flow Chart

Empirical Findings

This section consists of empirical results of Panel data analysis, which has been explained in detail in the previous section.

Unit Root Test Analysis

Table 2 shows that all the variables are stationary at level accept unemployment, which is stationary at first difference. So the order of integration of the series is I(0) and I(1), respectively. We will now proceed to check which test will be suited for the given data. As we know that series are integrated in order I(0) and I(1) and the sample size is small, we can easily apply FMOLS and DOLS, which can handle stationary of both orders and suitable technique for this order (Piabuo & Tieguhong, 2017; see Table 2).

Table 2.
Time Series Panel Unit Root Test.

Variables LLC IPS Decision

t stat and p value t stat and p value Order

D (CMU5) −2.14 (0.015) −2.688 (0.0245) I(1)

IE −5.317 (0.000) −5.3174 (0.000) I(0)

HEX −3.343 (0.000) −2.74382 (0.003) I(0)

IWF −10.375 (0.001) −4.88531 (0.001) I(0)

ISF −8.492 (0.003) −2.57784 (0.005) I(0)

LIT −3.226 (0.000) −3.95220 (0.017) I(0)

D (UNEM) −3.4368 (0.003) 0.60817 (0.02) I(1)

Note. Null Hypothesis: There is a unit root/variable is not stationary. LLC = Levin–Lin–Chu; IPS = Im–Pesaran–Shin.

Panel Cointegration

Results of Table 3 show at least cointegration among given variables, as five out of seven variables are level stationary and two are stationary integrated I(I); the next step is to examine whether cointegration exists or not. This study here uses the Johansen Fisher panel cointegration test, which is of two types of tests: first, trace values, and second, max-Eigen values test. The finding of the cointegration test suggested that due to one cointegration, H0 of no cointegration will be rejected. Therefore, a long-run relationship between socioeconomic determinants and child mortality exists. Hence, we shall reject the H0 of no cointegration and accept the H1 among the given variables (see Tables 3 and 4).

Table 3.
Panel Cointegration.

Hypothesize no CE Eigenvalue Statistic Critical value Prob.^a

None^b 0.634766 141.6725 125.6154 .0036

At most 1 0.528558 92.31880 95.75366 .0842

At most 2 0.441943 55.47283 69.81889 .3994

At most 3 0.270748 26.89146 47.85613 .8583

At most 4 0.173860 11.42042 29.79707 .9502

At most 5 0.038454 2.061871 15.49471 .9941

At most 6 0.002862 0.140458 3.841466 .7078

Note. Null Hypothesis: There is no cointegration.^aMacKinnon-Haug-Michele's (1999) p values. The “numerical distribution functions” obtained in that paper can be used to compute P values as well as critical values.^bRejection of the hypothesis at the 0.05 level. This means that if the P value is less than 0.05, you reject the null hypothesis; if P is greater than or equal to 0.05, you don't reject the null hypothesis.

Table 4.
Unrestricted Cointegration Rank Test (Maximum Eigenvalue).

Hypothesized no of CE Eigenvalue MaxEign statistics 0.05 critical value Prob^a

None^b 0.63477 49.3537 46.2314 .0225

At most 1 0.52856 36.846 40.0776 .1106

At most 2 0.44194 28.5814 33.8769 .188

At most 3 0.27075 15.471 27.5843 .7101

At most 4 0.17386 9.35855 21.1316 .8025

At most 5 0.03845 1.92141 14.2646 .9924

At most 6 0.00286 0.14046 3.84147 .7078

Max-eigenvalue test indicates 1 cointegrating equation(s) at the 0.05 level.

^aMacKinnon–Haug–Michele’s (1999) p values.

^bRejection of the hypothesis at the 0.05 level.

Table 5 shows the Pedroni residual cointegration test, where the first four test statistics are computed by “within” dimensions (panel statistics), and the last three tests are computed by the “between” dimensions (group statistics). Pedroni’s result indicates that the null hypothesis of no cointegration will be rejected.

Table 5.
Pedroni Residual Cointegration Test.

Alternative hypothesis: common AR coefficients (within-dimension)

Tests Statistic Prob. Weighted statistic Prob.

Panel v-Statistic −1.196605 .8843 −1.274804 .8988

Panel rho-Statistic 1.465521 .9286 1.618415 .9472

Panel PP-Statistic −3.412148 .0003 −4.575740 .0000

Panel ADF-Statistic 2.194870 .9859 2.185426 .9856

Alternative hypothesis: individual AR coefficients. (between dimension)

Statistic
Prob.

Group rho-Statistic 2.63707 .9958

Group PP-Statistic −10.293 0

Group ADF-Statistic 2.24758 .9877

Granger Causality Test

Table 6 shows the test of Granger causality. The causality relation is expressed in the fourth column. The result shows that in South Asian countries, there is a unilateral causality which is running from health expenditure to child mortality; child mortality does not granger cause health expenditure. Similarly, other variables also have unidirectional causality. There is no bidirectional causality in the model (see Table 6).

Table 6.
Granger Causality Test.

Null hypothesis F statistics Probability Decision

HEX does not Granger Cause CMU5 8.86768 .04223 Unidirectional causality

CMU5 does not Granger Cause HEX 0.45993 .6323

IE does not Granger Cause CMU5 3.31052 .02721 Unidirectional causality

CMU5 does not Granger Cause IE 1.79944 .3241

IWF does not Granger Cause CMU5 4.22035 .0163 Unidirectional causality

CMU5 does not Granger Cause IWF 0.17246 .8417

EDUCATION does not Granger Cause CMU5 7.70807 .04957 Unidirectional causality

CMU5 does not Granger Cause EDUCATION 0.65889 .5202

SANIT does not Granger Cause CMU5 2.79332 .01693 Unidirectional causality

CMU5 does not Granger Cause SANIT 1.60073 .1769

UNEMP does not Granger Cause CMU5 10.2991 6.E-05 Unidirectional causality

CMU5 does not Granger Cause UNEMP 2.39526 .0939

GDP_IE does not Granger Cause HEX 6.04481 .00462 Unidirectional causality

HEX does not Granger Cause GDP_IE 0.75999 .50016

IWF does not Granger Cause HEX 8.25005 .0004 Unidirectional causality

HEX does not Granger Cause IWF 0.40054 .6709

EDUCTION does not Granger Cause HEX 1.33067 .2719 No causality

HEX does not Granger Cause EDUCATION 0.04240 .9585

SANIT does not Granger Cause HEX 0.44996 .6386 No causality

HEX does not Granger Cause SANIT 0.56336 .5707

UNEMP does not Granger Cause HEX 2.04710 .1331 Unidirectional causality

HEX does not Granger Cause UNEMP 3.34908 .0381

IWF does not Granger Cause GDP_IE 5.66337 .0042 Unidirectional causality

GDP_IE does not Granger Cause IWF 1.33986 .2647

EDCATION does not Granger Cause GDP_IE 0.09368 .9107 No causality

GDP_IE does not Granger Cause EDUCATION 0.86784 .4238

SANIT does not Granger Cause GDP_IE 0.22141 .8016 No causality

IE does not Granger Cause SANIT 0.84455 .4314

UNEMP does not Granger Cause GDP_IE 0.34627 .7078 No causality

IE does not Granger Cause UNEMP 0.78755 .4565

EDUCATION does not Granger Cause IWF 0.21162 .8098 No causality

IWF does not Granger Cause EDUCATION 0.10377 .9016

SANIT does not Granger Cause IWF 0.53514 .5866 Unidirectional causality

IWF does not Granger Cause SANIT 6.19517 .0026

FMOLS and DOLS

The test statistics show that all variables are significant and t value of education, income inequality, access to improved water facility, access to improved sanitation facility and health expenditure, and unemployment strongly affect child mortality and performing a significant role in reducing under-five mortality in case of South Asian countries. The value of R² and adjusted R² shows that 86% of variables are affecting their dependent variables (under-five mortality). The coefficient of health expenditure indicates that by increasing one unit of health expenditure, child mortality reduces by 1.22 units, which are the highly reliable result and support our literature. The study will accept the alternative hypothesis and reject the null hypothesis. The coefficients values in the table show that one unit increase in education leads to reduce child mortality by 3.460 units. In case of improved water and improved sanitation facility, our results show that by increasing one unit in improved water and improved sanitation facilities, child mortality decreased by 1.57 and 0.70, respectively. So again we will accept alternate hypothesis to prove the literature. The relationship between unemployment and child mortality is positive and significant (see Table 7).

Table 7.
FMOLS and DOLS.

Child mortality (Dependent Variable) FMOLS DOLS

Education −1.9926 (4.2476) −3.46009 (−2.2023)

Unemployment 0.6484 (3.5909) 0.2862 (−3.7989)

Income inequality 8.2168 (3.9884) 1.0123 (−3.1579)

Health expenditure −0.5908 (2.4794) −1.2218 (−2.6268)

Access to improve water −2.5044 (−2.64005) −1.5755 (−6.9407)

Access improve sanitation −13.7691 (−2.9624) −0.7046 (−4.7332)

R ² 0.84332 0.86491

Adjusted R² 0.82813 0.85375

Long-run variance 4.3132 6.3129

Note. Figure outside the parentheses shows the coefficient values and inside the parentheses shows t statistics. FMOLS = fully modified ordinary least squares; DOLS = dynamic ordinary least squares.

Diagnostic Tests

It should be noted that if coefficient values are more than 0.8, the problem of multicollinearity exists (Buyuksalvarci, 2010). But in the given results, the coefficient values are not more than 0.8, which provides evidence that the selected model is free from the issues of multicollinearity. Therefore, we can carry out the analysis. The value of health expenditure and child mortality is showing a moderate correlation among these two variables (the value is −0.37). The value of income inequality is 0.04 with a negative sign also indicating the existence of moderate relationship between child mortality and income inequality. The value of the improved water facility is 0.66, which also shows a moderate correlation. Similarly, other variables such as improved sanitation facilities, education, and unemployment are all showing moderate relation with child mortality (see Table 8).

Table 8.
Multicollinearity Test.

t Statistic CMU5 HEX GDP_IE IWF EDUCATION SANIT UNEMP

CMU5 1.000000

–

HEX −0.373836 1.000000

−3.627542 –

IE −0.048593 0.219137 1.000000

−0.437856 2.021367 –

IWF −0.661965 −0.792356 −0.114615 1.000000

−7.948516 −11.68939 −1.038378 –

LIT −0.729021 −0.535851 0.070421 0.534851 1.000000

−13.34218 −5.711930 0.635365 13.64941 –

SANIT −0.613644 −0.441055 −0.050468 0.511840 0.497764 1.000000

−6.994566 −4.422939 −0.454787 5.362197 5.165233 –

UNEMP 0.337182 0.477011 0.184292 −0.590056 −0.462193 0.086342 1.000000

−2.246420 4.884648 1.687533 −6.577589 −4.690833 0.779989 –

Heteroskedasticity Test

The probability value of the Wald test is greater than 0.05, which means we will accept the null hypothesis of no Heteroskedasticity and reject the alternate hypothesis (see Table 9).

Table 9.
Heteroskedasticity.

Test statistic χ² Probability

Wald test 2.478365 .2960

Table 10 shows the findings of serial correlation. The results show that if we use only one lag, in this case, the given results encounter the problem of serial correction in residuals. Still, as we move on increasing optimal lags until Lag 3, the problem of serial correlation is solved, but by utilization of more than three lags, the serial correlation will again present. Hence, to avoid serial correlation in this study, choose Lag 3 as optimal (see Table 10).

Table 10.
Serial Correlation Tests.

Lags LRE stat Rao F stat p

1 74.83045 1.642945 .0114

2 64.95277 1.386250 .0677

3 48.42550 0.986199 .5075

Note. Null Hypothesis: There is no serial correlation. p < 0.05.

Table 11 demonstrates the optimal lag results. The optimal lags selection criteria are quite sensitive tasks as if we add too many lags in the given model; it may result in an error during the forecast. But if we add few lags, it will leave out more accurate and relevant information. For optimal lag selection, experience theory and knowledge are an excellent way. There are different information criterion techniques through which optimal lags can be chosen. Three most commonly and frequently used information criterion are Schwarz’s Bayesian information criterion, Akaike information criterion (AIC), and Hannan and Quinn information criterion. The study selects three optimal lags on the bases of AIC. Furthermore, many other criteria suggest three as optimal lags length, so we continue the study with optimal three lags (see Table 11).

Table 11.
Lag Length Criteria.

Lag Log L LR AIC SC HQ

0 −645.1528 NA 33.44373 33.74232 33.55086

1 −84.19114 891.7852 7.189289 9.577993 8.046336

2 30.21043 140.8019 3.835363 8.314183 5.442325

3 103.3745 63.78403 2.596182 9.165117 4.953059

4 346.6439 124.7536 −7.366354* 1.292698* −4.259561*

Note. LR = likelihood ratio; AIC = Akaike information criterion; SC = Schwarz Criterion; HQ = Hannan–Quinn.*p < 0.05.

Trend of Child Mortality in South Asian Countries

The graphs given here are constructed by taking data on child mortality from World Health Organization. The trend is showing that child mortality rates are decreasing gradually, but still a lot of effort is required to meet the sustainable development goal (see Figure 5).

Figure 5.
Trend Graph of Child Mortality.

Conclusion

Conclusion and Discussion

The primary focus of the study is to determine the socioeconomic determinants of child mortality, their impact, and the extent of the impact on child mortality in South Asian countries (Afghanistan, Pakistan Bangladesh, Bhutan, India, Nepal, Sri Lanka, and the Maldives). The findings of this study revealed that child mortality level is high in South Asian nations with marginal variations, despite several efforts of its reduction. Factors that affect child mortality are the education of both male and female, their unemployment level, access to improved water and access to improved sanitation facilities, income inequality, and health expenditure. All these associated risk factors significantly influence the under-five mortality in all countries and over the South Asian region.

The education of mothers is a cornerstone for the betterment of child health and child survival. The higher level of mother’s education leads to awareness about different risk factors associated with child mortality, which includes early marriage control to avoid adolescent pregnancy. Education of father decreases the child mortality through his higher wages, productivity, increase the financial status of the family, which improve maternally, and child health along with family through efficient consumption pattern. Both maternal and paternal educationist significantly related to child mortality.

The findings of this research about the impact of water and sanitation on child mortality in South Asian countries indicated that children under 5 years of age living at houses with access to unimproved water and unimproved sanitation facilities had increased the danger of infant mortality, child mortality, including both neonatal and postneonatal mortality.

The impact of unimproved water and lack of proper sanitation facilities was substantial at the time of neonatal and postneonatal period. Children learn to crawl and walk at this period, and they experience more exposure to pathogens which is the primary cause of diarrhea from different environmental sources. This also includes contaminated water. At the time of this period, the process of weaning commences, and low-income residents often use this contaminated water to prepare that weaning foods, thereby increasing the chance to transmit pathogens to the children/infants that results in diarrheal diseases, which has been presented in abovementioned results. Similarly, children under 5 years of age exposed to unimproved sanitation and water had increased risk related to mortality. However, the effect was less as compared to the postneonatal period.

Unemployment is also a significant risk factor associated with child mortality because if both males and females are earning a handsome amount, then they both will contribute to increasing the standard of living. But if both are unemployed, they are unable to meet the necessary expenses which are essential for childcare facilities.

The study has examined the effect of public and private health expenditure on child mortality. This health spending reduces child mortality and increases life expectancy at the time of birth. Both public and private health spending are significantly related to health outcomes. The findings of this study show that health expenditures are an essential component of child survival. Proper medical facilities, effective immunization systems, and easy access to the health-care system are crucial ways to reduce child mortality.

Income inequality is found to be an essential determinant of social justice, human well-being, health outcomes, and economic growth. It has been investigated that growing income inequality leads to health problems among the population, especially among children under five. Unequal distribution of income decreases the individual-level income.

The overall results of this study indicate that parent’s education, unemployment level, income inequality, health expenditure, and access to quality water and proper sanitation facilities are all equally important in the South Asian region and help to achieve sustainable development goals 2030.

Policy Recommendations

Although the risk factors associated with child mortality have been discussed in previous literature, yet a lot of improvement is still required. A multifaceted approach that includes health-associated measures is needed for the betterment of child health and child survival. Any particular strategy to improve child health in the South Asian region should base on the abovementioned factors. Child and mother health should be focused on top priorities to achieve sustainable development goals in 2030. Governments should make it a priority in the South Asian region, in policy implementation and formulation, to get more males and females educated. Most of the countries in this region have cultures that prohibit the education of females.

Furthermore, South Asian governments should keep on prioritizing the distribution of improved quality of usable water to residents of each area. Efforts are needed at the delivery of mechanized pipe-borne water and boreholes among other regions. This should be accelerated to decrease the reliance of South Asians on contaminated water bodies for drinking. This could be a long-term way to reduce the spread of water-borne infections, thereby help to improve health outcomes in the particular region.

Policymakers should increase the publicly financed share of health spending. This could be possible by social health insurance, expanding existing medical facilities, and health personnel training, among others. Besides, efforts to finance the health-care units should be made to attain and accelerate the progress in sustainable development goal SDGs.

The unemployment level should decrease to provide the parents, especially fathers, the equal opportunity of occupation, so that they become able to raise the family status and spent on a child’s health in the form of private health expenditure.

Moreover, charities and insurance policies should be designed for the individual for their better future. Public and private health expenditure is essential for decreasing child mortality in South Asian countries. Still, there are some other factors related to that which policymakers should take into account, such as corruption, misallocation of resources, and poor management. All these factors should be considered and removed; otherwise, these will harm child health.

This study recommends that policymakers should build economic strategies to increase employment opportunities. This will decrease employment and bring economic growth. Economic growth is also an important indicator of child health. The government of South Asian countries should collaborate with the ministry of health to develop strategies to improve antibiotic treatment of infection, treatment of malaria, and diarrhea across the state. The government should focus on infrastructure so that health-care units should be in easy access to the community. Easy access to health-care units would enable mothers to visit them during or after pregnancy.

Future Research

Although, in this study, different factors associated with child mortality were considered in South Asian countries, yet various other factors such as foreign direct investment, remittances, and GDP can also be used in research.

The study covers the factors associated with child mortality under five only in South Asian countries. Based on the findings, further research can also be done at the national and provincial level in developing countries or in low- and middle-income countries. This could be more beneficial for policymakers and stakeholders to make decisions, proper strategies, and various interventions.

Further investigation about child mortality can be possible using qualitative data and other variables related to qualitative study or other techniques like Generalized method of moments modeling to make this study more valid, subject to an increase in data availability.

Further studies could also be done by taking socioculture variables.

Study Limitations

Many other variables used in this study do not have enough observation of time series which could improve the panel study. Notwithstanding these listed limitations could be the base for further future research. Furthermore, insufficient observation may lead to biased results.

Besides this, certain other equally important indicators of health and child mortality were excluded from the number of the explanatory variables due to unavailability of the data. The underestimation of these indicators may lead to biased information.

Variables	Description	Units	Source
CMU5	Child mortality	U5M/1000 live birth	WDI
EDU	Education	School enrollment, primary and secondary	WDI
UNEM	Unemployment	Percentage of the total labor force	WDI
HEX	Health expenditure	Total percentage of gross domestic product	HDI
IE	Income inequality	Annual percentage	WDI
IWF	Access improve water	Percentage of the population with access	WDI
ISF	Sanitation facilities	Percentage of the population with access	WDI

Variables	LLC	IPS	Decision
D (CMU5)	−2.14 (0.015)	−2.688 (0.0245)	I(1)
IE	−5.317 (0.000)	−5.3174 (0.000)	I(0)
HEX	−3.343 (0.000)	−2.74382 (0.003)	I(0)
IWF	−10.375 (0.001)	−4.88531 (0.001)	I(0)
ISF	−8.492 (0.003)	−2.57784 (0.005)	I(0)
LIT	−3.226 (0.000)	−3.95220 (0.017)	I(0)
D (UNEM)	−3.4368 (0.003)	0.60817 (0.02)	I(1)

Hypothesize no CE	Eigenvalue	Statistic	Critical value	Prob.^a
None^b	0.634766	141.6725	125.6154	.0036
At most 1	0.528558	92.31880	95.75366	.0842
At most 2	0.441943	55.47283	69.81889	.3994
At most 3	0.270748	26.89146	47.85613	.8583
At most 4	0.173860	11.42042	29.79707	.9502
At most 5	0.038454	2.061871	15.49471	.9941
At most 6	0.002862	0.140458	3.841466	.7078

Hypothesized no of CE	Eigenvalue	MaxEign statistics	0.05 critical value	Prob^a
None^b	0.63477	49.3537	46.2314	.0225
At most 1	0.52856	36.846	40.0776	.1106
At most 2	0.44194	28.5814	33.8769	.188
At most 3	0.27075	15.471	27.5843	.7101
At most 4	0.17386	9.35855	21.1316	.8025
At most 5	0.03845	1.92141	14.2646	.9924
At most 6	0.00286	0.14046	3.84147	.7078

Alternative hypothesis: common AR coefficients (within-dimension)
Panel v-Statistic	−1.196605	.8843	−1.274804	.8988
Panel rho-Statistic	1.465521	.9286	1.618415	.9472
Panel PP-Statistic	−3.412148	.0003	−4.575740	.0000
Panel ADF-Statistic	2.194870	.9859	2.185426	.9856
Alternative hypothesis: individual AR coefficients. (between dimension)
	Statistic	Prob.
Group rho-Statistic	2.63707	.9958
Group PP-Statistic	−10.293	0
Group ADF-Statistic	2.24758	.9877

Null hypothesis	F statistics	Probability	Decision
HEX does not Granger Cause CMU5	8.86768	.04223	Unidirectional causality
CMU5 does not Granger Cause HEX	0.45993	.6323
IE does not Granger Cause CMU5	3.31052	.02721	Unidirectional causality
CMU5 does not Granger Cause IE	1.79944	.3241
IWF does not Granger Cause CMU5	4.22035	.0163	Unidirectional causality
CMU5 does not Granger Cause IWF	0.17246	.8417
EDUCATION does not Granger Cause CMU5	7.70807	.04957	Unidirectional causality
CMU5 does not Granger Cause EDUCATION	0.65889	.5202
SANIT does not Granger Cause CMU5	2.79332	.01693	Unidirectional causality
CMU5 does not Granger Cause SANIT	1.60073	.1769
UNEMP does not Granger Cause CMU5	10.2991	6.E-05	Unidirectional causality
CMU5 does not Granger Cause UNEMP	2.39526	.0939
GDP_IE does not Granger Cause HEX	6.04481	.00462	Unidirectional causality
HEX does not Granger Cause GDP_IE	0.75999	.50016
IWF does not Granger Cause HEX	8.25005	.0004	Unidirectional causality
HEX does not Granger Cause IWF	0.40054	.6709
EDUCTION does not Granger Cause HEX	1.33067	.2719	No causality
HEX does not Granger Cause EDUCATION	0.04240	.9585
SANIT does not Granger Cause HEX	0.44996	.6386	No causality
HEX does not Granger Cause SANIT	0.56336	.5707
UNEMP does not Granger Cause HEX	2.04710	.1331	Unidirectional causality
HEX does not Granger Cause UNEMP	3.34908	.0381
IWF does not Granger Cause GDP_IE	5.66337	.0042	Unidirectional causality
GDP_IE does not Granger Cause IWF	1.33986	.2647
EDCATION does not Granger Cause GDP_IE	0.09368	.9107	No causality
GDP_IE does not Granger Cause EDUCATION	0.86784	.4238
SANIT does not Granger Cause GDP_IE	0.22141	.8016	No causality
IE does not Granger Cause SANIT	0.84455	.4314
UNEMP does not Granger Cause GDP_IE	0.34627	.7078	No causality
IE does not Granger Cause UNEMP	0.78755	.4565
EDUCATION does not Granger Cause IWF	0.21162	.8098	No causality
IWF does not Granger Cause EDUCATION	0.10377	.9016
SANIT does not Granger Cause IWF	0.53514	.5866	Unidirectional causality
IWF does not Granger Cause SANIT	6.19517	.0026

Child mortality (Dependent Variable)	FMOLS	DOLS
Education	−1.9926 (4.2476)	−3.46009 (−2.2023)
Unemployment	0.6484 (3.5909)	0.2862 (−3.7989)
Income inequality	8.2168 (3.9884)	1.0123 (−3.1579)
Health expenditure	−0.5908 (2.4794)	−1.2218 (−2.6268)
Access to improve water	−2.5044 (−2.64005)	−1.5755 (−6.9407)
Access improve sanitation	−13.7691 (−2.9624)	−0.7046 (−4.7332)
R ²	0.84332	0.86491
Adjusted R²	0.82813	0.85375
Long-run variance	4.3132	6.3129

t Statistic	CMU5	HEX	GDP_IE	IWF	EDUCATION	SANIT	UNEMP
CMU5	1.000000
	–
HEX	−0.373836	1.000000
	−3.627542	–
IE	−0.048593	0.219137	1.000000
	−0.437856	2.021367	–
IWF	−0.661965	−0.792356	−0.114615	1.000000
	−7.948516	−11.68939	−1.038378	–
LIT	−0.729021	−0.535851	0.070421	0.534851	1.000000
	−13.34218	−5.711930	0.635365	13.64941	–
SANIT	−0.613644	−0.441055	−0.050468	0.511840	0.497764	1.000000
	−6.994566	−4.422939	−0.454787	5.362197	5.165233	–
UNEMP	0.337182	0.477011	0.184292	−0.590056	−0.462193	0.086342	1.000000
	−2.246420	4.884648	1.687533	−6.577589	−4.690833	0.779989	–

Test statistic	χ²	Probability
Wald test	2.478365	.2960

Lags	LRE *stat	Rao* F stat	p
1	74.83045	1.642945	.0114
2	64.95277	1.386250	.0677
3	48.42550	0.986199	.5075

Lag	Log L	LR	AIC	SC	HQ
0	−645.1528	NA	33.44373	33.74232	33.55086
1	−84.19114	891.7852	7.189289	9.577993	8.046336
2	30.21043	140.8019	3.835363	8.314183	5.442325
3	103.3745	63.78403	2.596182	9.165117	4.953059
4	346.6439	124.7536*	−7.366354*	1.292698*	−4.259561*

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Shazia Kousar is an assistant professor in the Department of Management Science, The Superior College, Lahore. Her research interests are in the areas of financial institutions and international investment. She has published her work in research publications including Journal of Banking and Finance, European Journal of Finance and Applied Economics. She received her PhD in Economics and from The Superior College, Lahore.

Aiza Shabbir is a prospective PhD student. She has don MPhil in Economics from The Superior College, Lahore. Her research interest are Health Economics, Entrepreneurship and Development Economics. She has numerous publications in international journals.

Rukia Shafqat is a prospective PhD student. She has don MPhil in Economics from The Superior College, Lahore. Her research interest are Health Economics, Environmental Economies and Development Economics.

ORCID iD

Aiza Shabbir

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Variables	LLC	IPS	Decision
Variables	t stat and p value	t stat and p value	Order
D (CMU5)	−2.14 (0.015)	−2.688 (0.0245)	I(1)
IE	−5.317 (0.000)	−5.3174 (0.000)	I(0)
HEX	−3.343 (0.000)	−2.74382 (0.003)	I(0)
IWF	−10.375 (0.001)	−4.88531 (0.001)	I(0)
ISF	−8.492 (0.003)	−2.57784 (0.005)	I(0)
LIT	−3.226 (0.000)	−3.95220 (0.017)	I(0)
D (UNEM)	−3.4368 (0.003)	0.60817 (0.02)	I(1)

Alternative hypothesis: common AR coefficients (within-dimension)
Tests	Statistic	Prob.	Weighted statistic	Prob.
Panel v-Statistic	−1.196605	.8843	−1.274804	.8988
Panel rho-Statistic	1.465521	.9286	1.618415	.9472
Panel PP-Statistic	−3.412148	.0003	−4.575740	.0000
Panel ADF-Statistic	2.194870	.9859	2.185426	.9856
Alternative hypothesis: individual AR coefficients. (between dimension)
	Statistic	Prob.
Group rho-Statistic	2.63707	.9958
Group PP-Statistic	−10.293	0
Group ADF-Statistic	2.24758	.9877