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1. Language: English

Purpose of Study: Regional disparities and inequality continue to be a feature of Indian economy even after seven decades of independence. Many of its social indicators need much improvement. Some states are particularly more backward with large proportions of their population being officially poor while some others are comparatively in better position. Such inter-regional disparities have compounded policy challenges of the governments in the poorer states. Against this background, the present study aims to study the dimension of inter-regional disparity for select less advanced states in India.

Methodology: A double log model was used in this study to analyze government expenditure's impact on development projects or schemes. Health-wise, less advanced states, viz. Bihar and Odisha are chosen for this purpose. The study uses the actual data on government expenditure in the social sector, mainly on health. The data on a per capita basis is used for each state to analyze the impact of the per capita government's expenditure on select social indicators. The analysis is done separately for both states.

2. INTRODUCTION

In a developing country like India where significant part of population are poorer and living under miserable conditions and have to struggle daily for their livelihood, so it is not possible for them to access health care, education and other social services at their own. So, it becomes the duty of the government to provide effective social services at a very reasonable cost. According to (Gupta, 2002), "Health care services have high level of externalities rather than curative services, a minimum package of these services provided by the government would reduce mortality rates". Since, governments in developing countries always have scarcity of funds, so it is necessary to ensure that the funds are used effectively and the desired results are attained at social front. So, it is also important to check the effectiveness of government expenditure on the improvement of social indicators. Further, government's spending is also important to uplift the living standards of the poorer people in the society. As Gera, in her studies also found that government investments in education, health and in the provision of infrastructure can have direct effect on moving household out of poverty (Gera, 2007). Further, Ranjan and Sharma (2008) examined the effect of government development expenditure on economic growth and they discovered a significant positive of government expenditure on economic growth. A study found, educational attainment at basic levels (secondary level) and low infant mortality rates have been shown to have a positive effect on economic growth also (Barro and Lee, 1993). Studies on both developed and developing countries have indicated that sufficient amount of government spending on education and health improves human development and lessens poverty burden as well (Barro and Lee, 1997;Swaroop, 1996). However, it is also necessary to mention that the solely the increase in public spending is not sufficient but the quality of expenditure with good public policies also required. As stated, a government could increase the public spending by a large amount but this does not ensure that it would have desired result on economic and social development as the quality of this spending also matters (Bussato and Brunori, 2011).

Despite the importance of government spending and its role on improvement of social sector, there are not sufficient number of studies have been done in India to evaluate the impact of government spending on social indicators. Thus, present study is an attempt to evaluate the impact of government spending on some selected social indicators and further it will also make a significant contribution to the present literature. As the number of social indicators are very large, so it is not feasible to assess every indicator given the time and data constraint. Hence, the study has selected four indicators i.e., Infant Mortality Rate (IMR), death rate, birth rate and total fertility rate as indicators of health. The study has chosen Bihar and Odisha states.

The following social indicators have been selected for the present study. Anderson al.et (2000), revealed that the USA spent more on health care as compare to other countries. USA spent 14% of GDP on health care in 1998 while OECD median was8% of GDP and results also suggested that Americans enjoys better health care system than other OECD countries. Shenggen Fan et al. (2002) 2012) revealed that government expenditure on health has a significant positive effect on health status while, expenditure on education has no significant impact on either primary or secondary school enrolment. Maitra, B., and C.K. Mukhopadhyay (2012) shown that impact of education and health spending on growth is not an instantaneous but with gestation lags. Initially, expenditure on education and health improves human capital which manifests itself in the form of economic growth. Further, it is found that the gestation lag of education spending was longer than that of health-care spending. Sava? Çevik, M. & Okan Ta?ar (2013) found that government health spending has significant impact on under-5 child mortality rate and on infant mortality rate. Study also concludes that composition of government health expenditure also matters not only the size of expenditure. Tae Kuen Kim and Shannon R. Lane (2013) shown a negative relationship between public the health expenditure and the infant mortality rate (IMR), while positive association between public health expenditure and life expectancy is found. Thus, the study concludes that expanding public health expenditure improves overall health condition. Bhakta, R. (2014) shown that public expenditure on Supplementary Nutritional Program has positive impact on health status of children which also has indirect positive impact on education. Study also concludes that public expenditure on elementary education has direct impact on the enrolment rate. Virupakshapp a D Mulagund (2015) suggested that public health expenditure in India have increasing trend during this period. Further, study concludes that public health expenditure has positive impact on health indicators i.e, it resulted in fall in maternal mortality rate (MMR), infant mortality rate (IMR), fall in total fertility rate (TFR) and improves life expectancy. Wong Sing Yun and Remali Yusoff (2015) indicated there is a unidirectional causal relationship from GDP to education expenditure and from GDP to health care expenditure. Thus, study concludes that GDP affect both the education and health care expenditure. However, reverse causal relationship is not found between them. K. P. K. S.

3. Infant Mortality Rate (IMR):

II. LIT E R AT U R E R E V IE W Gerard F.

4. III. OBJECTIVE(S) OF STUDY

1. To evaluate the impact of government expenditure on selected social indicators in less advanced Indian states. 2. To suggest policy implications for better utilization of public expenditure on social sectors.

5. III. METHODOLOGY

For the purpose of determining the impact of government's expenditure on social indicators, the study has applied log-log or double-log model. In case of Log-log models, the coefficients are used to determine the relative impact of independent variable(s) on relative impact of dependent variable. Here, the independent variable is government expenditure and the social indicator(s) chosen are the dependent variables. The coefficients in a log-log model represent the elasticity of dependent variable with respect to independent variable. Therefore, log-log model presents the empirical interpretation in elasticity term i.e., percentage change in dependent variable due to one percent change in explanatory variable.

Log-log model is represented as:

In Yi= In ?1 + ?2 In X i + ui

(1) Where In= Natural log (i.e., log to the base e, and where e = 2.718)

Equation ( 1) is thus:

In Yi = ? + ?2 In Xi + ui

The coefficients are estimated by OLS regression. Six equations will be fitted/estimated for each selected state.

The if the value of explanatory variable is increased by 1 percent, then the value of dependent variable decreases by 0.10 per cent. From the analysis table we can see the R-squared value is 0.9556 which tells 95.56 percent of variation in dependent variable birth rate is explained by independent variable. The p-value is 0.0000 being less than the significant level of 5% percent which shows that the explanatory variable is statistically significant and, therefore, the null hypothesis that the coefficient of explanatory variable is zero will be rejected. It means we can say that the per capita public health expenditure on health has impact on birth rate. Table 3 provides the results regarding the impact of government's expenditure on health on infant mortality rate (IMR). Here, infant mortality rate is a dependent variable. The squared-R is 0.97 which tells that around 97 percent of the variation in dependent variable is explained by the independent variable. As we can see that the p-value is 0.0000 being less than the significant level of 5% percent which shows that the explanatory variable is statistically significant and, therefore, the null hypothesis that the coefficient of explanatory variable is zero will be rejected. Apart from this, the negative symbol with explanatory variable shows that there is negative relationship between the dependent variable and explanatory one. The explanatory coefficient value is -0.336 which indicates that 1 percent increase in per capita may lead to 0.336 percent fall in IMR. 4.8d provides the results of analysis between per capita health expenditure and total fertility rate (TFR). Here, the total fertility rate is dependent variable while the per capita expenditure on health is independent variable. From the table we can see that the coefficient has a negative sign with value of -0.14 which tells there is an inverse relationship between health expenditure and the TFR i.e., an increase in per capita health expenditure results in 0.14 percent fall in TFR. The R-squared value is 0.911 which tells 91.1 percent of variation in dependent variable TFR is explained by independent variable per capita expenditure on health. The p-value is 0.0000 which is appearing against the explanatory variable is statistically significant because the p-value being less than the significance level of 5 percent (0.05), hence the null hypothesis of that, the explanatory variable is statistically insignificant and being rejected. Table 6 gives the results relating to the impact of government's expenditure on health on death rate in Bihar. Here the death rate is dependent variable. From the table we can see that the per capita health expenditure coefficient has a negative sign which tells there is an inverse relationship between health expenditure and the death rate i.e., an increase in government expenditure on health causes fall in death rate. The coefficient has -0.081 value which means 1 percent increase in per capita health expenditure causes 0.081 percent fall in death rate. The R-squared value is 0.6031 which tells 60.31 percent of variation in dependent variable death rate is explained by independent variable. Further, we can see that the p-value is 0.0007 which is appearing against the explanatory variable is statistically significant because the p-value is being less than the significance level of 5 percent (0.05), hence the null hypothesis of that the explanatory variable is statistically insignificant and being rejected here also. The results of this study are consistent across all variables considered for the study. Our principal conclusion can be summarized as per capita government expenditure on health helps to reduce infant mortality rate, birth rate, death rate and total fertility rate in Bihar and Odisha states. These results indicate that the government should increase its budgetary allocations on health and family welfare as well. These results are also important in considering the fact that there should be the commitment of more funds health. Although only commitment of funds to social sector is not sufficient, better utilization of funds right direction in effective manner is most important. Thus, it is also essential for the government to look after the efficiency and transparency of its budgetary allocations to ensure that these funds are fully utilized (Yun and Yusoff, 2015). Thus, analysis of this study can pave way in determining the optimal mix of It indicates that increase in government spending results in fall in IMR, Birth Rate, Death Rate and TFR. Therefore, the government should further increase its expenditure in health and family welfare. However, merely increasing the allocation of funds to the social sector is not sufficient, effective utilization of funds also necessary. Thus, it is also essential for the government to look after the efficiency and transparency of its budgetary allocations to ensure that these funds are fully utilized. Therefore, policy-makers should address other important factors also apart from allocating public expenditure like the effectiveness of the government schemes in health and family welfare, and proper implementation of such schemes.

6. VI. DATA ANALYSIS AND RESULTS INTERPRETATION

7. VIII. CONCLUSION

From various studies, it can be intuitively explained by the fact that because of extreme poverty and deprivation in India the welfare of the society can be increased by greater involvement of government. At the policy level, the present study recommends that public expenditure should increase further to have a balanced and improved human development of the concerned states. So, an increase in social sector expenditure should also be considered as one of the priorities to promote efficiency in growth and development. Hence, sufficient amount of government funds is recommended to provide support to policies and programs necessary to achieve welfare, growth and development of these states in particular, and the country in general. Therefore, the study is an attempt to analyze the relationship between the public spending on health sector and the selected health indicators in Bihar and Odisha. The study has used the state -level data for the selected states to estimate the direct and indirect effects of government's expenditure on social indicators. The findings clearly indicate that government expenditure does have impact on selected social indicators. The results of the study shows that per capita expenditure on health is inversely related with all the four selected health indicators i.e., increase in per capita expenditure leads to fall in Birth Rate, Death Rate, Infant Mortality Rate (IMR) and Total Fertility Rate (TFR) in both states, however, the amount of decrease will depend on their respective coefficient values.

Figure 1.
Total Fertility Rate (TFR): It is defined as
average number of children that would be born
to a woman if she experiences the current
fertility pattern throughout her reproductive
span (15-49 years). In 2021, TFR was 2.3 in India
i.e., 2.3 births per woman.
Death Rate: The average annual number of
deaths during a year per 1,000 Population at
midyear; also known as crude death rate. Death
rate in 2021 was 7.3 deaths/ 1000 Population in
India.
Birth Rate: The average annual number of births
during a year per 1,000 persons in the
population. In 2021, birth rate was 19 births/
1000 population at midyear; also known as crude
birth rate.
Figure 2.
covered this drawback by considering NER
which is the net of Gross Enrolment Ratio (GER)
and dropout rates. Study further concludes that there is a
unidirectional causality from economic growth to
government expenditure and government
expenditure to economic growth. Sineviciene, L.
(2015), Results show that there is an inverse
relationship between economic development and
government's expenditure on public order and
safety, and economic affairs. While, positive
relationship is found between economic
development and government's expenditure on
social protection and health. Study further
concludes that government should pay more
attention to the needs which ensure sustainable
development in the long-run. Mittal, P. (2016),
shown that there is a direct relationship between
the social sector spending and human
development index (HDI) of the Indian states. So,
study recommends that the public expenditure
should increase further to achieve balanced and
improved human development in India. Solihin,
A., et al. (2017), shown that government spending
in education sector is relatively inefficient.
Further, it states that government's expenditure
for education has no significant impact on
education index. This implies government
expenditure for education sector is not effective in
improving education index. Jiranyakul, K. (2007)
results of Granger causality test reveal the
unidirectional causality from government
expenditure to economic growth. Similarly, the
results of least square method with lagged
variables also show that there is a positive impact
of government expenditure on economic growth.
In doing the above, the present study seeks to fill
up some research gaps found in the literature.
The study has used government's expenditure on
per capita basis while most of the studies have
taken the overall government's expenditure in
their analysis (Yun and Yusoff, (2015), Mello and
Pisu, (2009), Kim and Lane, (2013) and others).
Further, mostly studies have considered gross
enrolment rates as output Lopes, (2002),
Baldacci, Guin-Siu and De Mello (2003),
Craigwell, Lowe and Bynoe, (2012); however,
enrolments do not reflect actual output as it does
not exclude the drop outs. Present study has
© 2023 Great ] Britain Journals Press | Volume 23 Issue | Compilation 1.0 15 29
Figure 3.
Figure 4. Table 1 :
1
Variable Coefficient Std. Error t-Statistic Prob.
Constant 2.486159 0.034363 72.35068 0.0000
ln_ Per Capita Health
-0.100182 0.005986 -16.73666 0.0000
Expenditure
R-squared 0.955649
Adjusted R-squared 0.952237
S.E. of regression 0.014214
Sum squared residual 0.002627
Log likelihood 43.59160
F-statistic 280.1159
Prob(F-statistic) 0.000000
Figure 5. Table 1
1
Note:

provides the results of analysis showing impact of per capita health expenditure on birth rate for state of Odisha for the period 2001 to 2022. Here, the birth rate is dependent variable while the per capita expenditure on health is independent variable. From the table we can see that the explanatory variable's coefficient has a negative sign which tells there is an inverse relationship between health expenditure and the birth rate i.e., an increase in government expenditure on health causes fall in birth rate. Further, coefficient has -0.10 values which mean London Journal of Research in Management and Business

Figure 6. Table 2 :
2
Variable Coefficient Std. Error t-Statistic Prob.
Constant 1.526188 0.048452 31.49869 0.0000
ln_ Per Capita Health
-0.117343 0.008440 -13.90293 0.0000
Expenditure
R-squared 0.936982
Adjusted R-squared 0.932135
S.E. of regression 0.020043
Sum squared residual 0.005222
Log likelihood 38.43740
F-statistic 193.2915
Prob(F-statistic) 0.000000
Table 2 provides the results of analysis between percent fall in death rate. The R-squared value is
per capita health expenditure and the death rate. 0.936 which tells 93.6 percent of variation in
Here, the death rate is dependent variable while dependent variable is explained by independent
the per capita expenditure on health is variable. As we can see that the p-value is 0.0000
independent variable. From the table we can see which is appearing against the explanatory
that the coefficient has a negative sign which tells variable is statistically significant because the
there is an inverse relationship between health p-value being less than the significance level of 5
expenditure and the death rate. The explanatory percent (0.05), hence the null hypothesis of that,
coefficient value is -0.11 which means an increase the explanatory variable is statistically
in per capita health expenditure causes 0.11 insignificant and being rejected.
Figure 7. Table 3 :
3
Variable Coefficient Std. Error t-Statistic Prob.
Constant 2.267239 0.077929 29.09365 0.0000
ln_ Per Capita Health
-0.336219 0.013575 -24.76789 0.0000
Expenditure
R-squared 0.979248
Adjusted R-squared 0.977652
S.E. of regression 0.032236
Sum squared residual 0.013509
Log likelihood 31.30917
F-statistic 613.4486
Prob(F-statistic) 0.000000
© 2023 Great ] Britain Journals Press | Volume 23 Issue | Compilation 1.0 15 31
Figure 8. Table 4 :
4
Variable Coefficient Std. Error t-Statistic Prob.
Constant 0.020812 0.072761 0.286029 0.7794
ln_ Per Capita Health
-0.147003 0.012675 -11.59828 0.0000
Expenditure
R-squared 0.911876
Adjusted R-squared 0.905098
S.E. of regression 0.030098
Sum squared residual 0.011777
Log likelihood 32.33842
F-statistic 134.5201
Prob(F-statistic) 0.000000
Table
Figure 9. Table 5 :
5
Variable Coefficient Std. Error t-Statistic Prob.
Constant 3.066362 0.060341 50.81755 0.0000
ln_ Per Capita Health
-0.047210 0.009447 -4.997567 0.0002
Expenditure
R-squared 0.657676
Adjusted R-squared 0.631343
S.E. of regression 0.031945
Sum squared residual 0.013266
Log likelihood 31.44520
F-statistic 24.97568
Prob(F-statistic) 0.000244
Figure 10. Table 6 :
6
Variable Coefficient Std. Error t-Statistic Prob.
Constant 1.466497 0.116643 12.57247 0.0000
ln_ Per Capita Health
-0.081164 0.018261 -4.444628 0.0007
Expenditure
R-squared 0.603111
Adjusted R-squared 0.572581
S.E. of regression 0.061753
Sum squared residual 0.049574
Log likelihood 21.55844
F-statistic 19.75472
Prob(F-statistic) 0.000661
Figure 11. Table 7 :
7
Variable Coefficient Std. Error t-Statistic Prob.
Constant 3.117815 0.217910 14.30779 0.0000
ln_ Per Capita Health
-0.132202 0.034115 -3.875217 0.0019
Expenditure
R-squared 0.536001
Adjusted R-squared 0.500309
S.E. of regression 0.115365
Sum squared residual 0.173018
© 2023 Great ] Britain Journals Press | Volume 23 Issue | Compilation 1.0 15 33
Figure 12. Table 8 :
8
Variable Coefficient Std. Error t-Statistic Prob.
Constant 0.769332 0.142379 5.403411 0.0001
ln_ Per Capita Health
-0.091269 0.022290 -4.094599 0.0013
Expenditure
R-squared 0.563256
Adjusted R-squared 0.529661
S.E. of regression 0.075377
Sum squared residual 0.073863
Log likelihood 18.56790
F-statistic 16.76574
Prob(F-statistic) 0.001266
Table 8 provides the results of analysis between
per capita health expenditure and total fertility
rate (TFR) in Bihar. Here, the total fertility rate is
dependent variable while the per capita
expenditure on health is independent variable.
From the table we can see that the coefficient has
a negative sign with value of -0.091 which tells
there is an inverse relationship between health
expenditure and the TFR i.e., an increase in per
capita health expenditure results in 0.091 percent
fall in TFR. The R-squared value is 0.5632 which
tells 56.32 percent of variation in dependent
variable TFR is explained by independent variable
expenditure on health. The p-value is 0.0013
which is appearing against the explanatory
variable is statistically significant because the
p-value being less than the significance level of 5
percent (0.05), hence the null hypothesis of that
the explanatory variable is statistically
insignificant and being rejected.
Figure 13.
VII. FINDINGS AND SUGGESTIONS
? One percent increase in per capita government
health expenditure decreases IMR by 0.13
percent, Death Rate by 0.08 percent, Birth
Rate by 0.047 percent and TFR by 0.09
percent in Bihar state.
? And, in Odisha, one percent increase in per
capita government health expenditure
decreases IMR by 0.33 percent, Death Rate by
0.11 percent, Birth Rate by 0.10 percent and
TFR by 0.14 percent.
? At 5 percent level of significance, p-values
indicate that government expenditure has
significant impact on the selected social
indicators.
government's expenditure and good governance.
34 | Volume 23 Issue 7 | Compilation 1.0 © 2023 Great ] Britain Journals Press
Note:

Impact of Government Expenditure on Selected Health Indicators: A Study on Bihar and Odisha

1
2

Appendix A

Appendix A.1 Expenditure and Economic Growth A Case

Impact of Government Expenditure on Selected Health Indicators: A Study on Bihar and Odisha

Appendix B

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Notes
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© 2023 Great ] Britain Journals Press

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Impact of Government Expenditure on Selected Health Indicators: A Study on Bihar and Odisha

Date: 1970-01-01