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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.18 No.3 pp.495-500
DOI : https://doi.org/10.7232/iems.2019.18.3.495

# Investigating the Public Spending and Economical Growth on the Poverty Reduction in Indonesia

Faculty of Economics and Business, Syiah Kuala University, Banda Aceh, Indonesia
School of Social and Economic Development, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
June 3, 2019 June 17, 2019

## ABSTRACT

Poverty and income inequality are being one of the most challenging issues in many developing countries, including Indonesia. Considering the higher level of the poverty rate in Indonesia, the study about poverty reduction is crucial and currently under discussion in previous studies. Thus, this study is written to examine what is education and health budgets could be reducing the poverty rate in Indonesia. The study utilizes annual data gathered from the Central Bureau of Statistics over the 2007–2017 period and analyzed using panel data by assisting the Eviews-10. The empirical result finds that the budget allocated for education and health significantly reduce the poverty rate in Indonesia. Therefore, it is suggested that to further reduce the poverty level, the government should allocate more budget of education and health for the poor and there is a need to monitor the public service performance with the aim the allocation of expenditure can be more efficient and effective. Besides, it is necessary to increase the number of pro-poor programs such as free public health and education insurance to fulfill the basic needs of the poor.

## 1. INTRODUCTION

Poverty and inequality continue to be one of the most challenging problems in many developing countries, especially Indonesia. Although the number of poverty gradually decrease over the year, the pace of poverty reduction appears to have slowed and even constant. Figure 1 shows the poverty trend in Indonesia. The percentage of people living in poverty steadily decreases every year from 2006 to 2016. In 2016, the poverty rate was approximately 6.55%, lower 0.7% than the previous year of 7.2%. Nevertheless, the change in poverty level remains constant from 2014 to 2016 with a value of 0.7%. The agenda of overcoming poverty for a country relates to the many factors associated with what is caused by poverty itself. According to Ramli et al., (2018), there are three main factors that cause a person to become poor: 1) low level of health, 2) low income-growth, and 3) low level of education. The low level of health will result in a low level of productivity which then further leads to low incomes, and hence, leads to poverty. Therefore, improvement in living standards of society is believed to be one of the most significant factors in reducing poverty. Onrubia-Fernández and Fuentes (2017) stated that to improve one living standard, the importance of public spending is undisputable. In addition, a fall in poverty was inversely proportional to high government spending.

Previous studies estimated the relationship between government spending and poverty reduction gave diversity results. For example (Sourya et al., 2014) argued that although it was assumed to have contributed toward a reduction in poverty, government spending was statistically insignificant in eliminating the poverty level. The same resulted was achieved by other studies such as (Ablo and Reinikka, 1998;Anderson et al., 2018;Elkhdari and Sarr, 2018;Kraay, 2004). However, the research evidence in support of this view is not always impending. Numerous research investigating the impact of the public budget on poverty reduction showed a different result. (Fan et al., 2004) found that public spending was negatively significant in influencing the reduction in the poverty level. Furthermore, Kwon and Kim (2014) found that health spending had a negative and statistically significant effect. Van de Walle (1995) also stated that public spending was an important instrument for eliminating poverty. Moreover, a better appropriate program used by the government would reduce poverty more effectively. These resulted were supported by previous studies (Fan et al., 2000;Hartoyo et al., 2017;Jung and Thorbecke, 2003;Mosley et al., 2004;Ndhleve et al., 2017;Sriyana, 2015)

In addition, Rajkumar and Swaroop (2008) studied the relationship between government spending, and its outcomes and the results differed depending on the quality of governance of a country. The analysis showed that government expenditure was important in determining health and education outcomes in countries with better governance system, nonetheless, in poorly governed countries, public spending had no effect and not effective. Moreover, the Indonesian policy stated that local governments are given the authority to control the use of budget allocation to increase the economic welfare of the people. To regulate the local government, the government created the law regarding the allocation of education and health budget. Law of Republic of Indonesia Number 36 Year 2009 Concerning Health stating that “(1) The amount of Government health budget shall be allocated at the minimum 5% (five per cent) of the state revenue and expenditure budget excluding salary; (2) The amount of regional health budget for provinces, regencies/township shall be allocated at the minimum 10% (ten per cent) of the regional revenue and expenditure budget excluding salary.” In addition, Regulation of the Finance Minister Number 84/pmk.07/2009 On Allocation of Expenditures on Education, function Article 2 (1) stated that” Allocation of expenditures on education function shall be at least 20% (twenty percent).”

Figures 2 shows the total expenditure by the Indonesian government in education from 2012 to 2017. The education budget is maintained at 20% of total state expenditure, focusing on improving access and quality of education services. Further, Figures 3. Shows the health budget by the Indonesian state between 2012 and 2017. The percentage of health budget from total expenditure grows over the year and remains constant in 2016. Health budget is maintained at a 5% level with a focus on improving access and quality of health services. The novelty in this study is because of the different characteristics of each province resulted in the difference in budget allocation, thus, the study of the importance of budget allocation in each province in determining the poverty reduction is significant. Although the government has definite the minimum allocation of health and education budget to be implemented by the local government, the total allocation implemented by each region still differs depending on the characteristic of each region.

Therefore, this paper aims to demonstrate empirically how the importance of government spending on education and health in eliminating the poverty level. It is remarkable to address this issue as Indonesia as one of the countries with practical fiscal decentralization. The rest of the paper is systematized as follows. Section 2 describes the methodology and data in this study. In total, we identify 34 cities to study the effect of government spending on income poverty. Section 3 then shows the results of the panel regression model, testing whether there is evidence that the government spending and poverty have negative and significant effects across the 34 studies. Overall, we discover that government spending plays a significant role in poverty reduction. Section 4 summarizes the main results and brief the policy implication.

## 2. METHODOLOGY

The dataset covers 34 Province in Indonesia from 2007-2017. The explanatory variable of interest is poverty rate, which is the number of poor people in percentage and the independent variables are government expenditures in education and health which are the total public spending on education and health. The data are taken from the Central Bureau Statistic. Panel data is used in this study to examine the effect of public spending on poverty reduction. Panel data involves the same cross-sectional observations over several time periods (Ahmad and Ahmad, 2018). There are considerable benefits of using the panel model compared to using only a time series or only cross-sectional data (Al-Khateeb and Yousif, 2019) describes that, first, panel data allow more accurate estimations and require less assumption. Second, panel data increase the sample size substantially. Third, the panel data are more appropriate in studying the dynamic of the model. However, panel data also have some inference problems because the data combines both the cross-section and time series. Problems that arise in panel data involve problems regarding cross-sectional data (e.g., heteroscedasticity) and time series data (e.g., autocorrelation). Nwakuya and Ijomah (2017) also explains that panel data are more Panel data are more informative as the data are more variability, less collinearity, and have a bigger sample size. Additionally, the estimation of panel data become more efficient in studying the dynamic movement as it minimizes bias because of aggregation.

### 2.1 The Panel Regression Model

The general panel data model can be written as Torres-Reyna (2010), Nwakuya and Ijomah (2017) :

$Y i t = α i + β 1 X 1 i t + β 2 X 2 i t + … + β n X n i t + u i t ~ I I D ( 0 , s 2 u )$
(1)

Panel data in this study can be written as:

$P o v i t = α i + E d u 1 X 1 i t + H e a l t h 2 X 2 i t + u i t ~ I I D ( 0 , s 2 u )$
(2)

where Pov is the poverty rate which is the independent variable. Edu and Health are the explanatory variables in this model which are the total government spending in education and health, respectively, i stands for I Provinces in Indonesia, i = 1, ..., 34 t stands for tth time period, i = 1, .., T (2007-2017).

### 2.2 Fixed and Random Effect Models

In Panel data, it is necessary to address the inference problems due to combining both cross-section and time series which are heteroscedasticity and autocorrelation. There are several estimations used to address this problem, and the two most prominent are the ﬁxed effects model (FEM) or least-squares dummy variable (LSDV) model and the random effects model (REM) or error components model (ECM) (Croissant and Millo, 2008;Asad et al., 2018). Nwakuya and Ijomah (2017) added that in the fixed effect model, the intercept can differ among individuals because each unit may have some special features which make it different on its own. Moreover, the fixed effect model is suitable to use where the intercept may be correlated with one or more regressors. On contrary, the intercept is expected to be random variables in random effect model which are not correlated with the dependent variables. In addition, Hausman (1978) in (Arellano, 1993) developed the Hausman test to select the most appropriate model between fixed and random effects. The model developed based on the comparison between the within-groups (WG) and the GLS estimators. The null hypothesis is that the estimators of fixed effect and random effect do not differ substantially. The asymptotic χ distribution showed that if the null hypothesis is rejected, the conclusion is that the fixed effect model is better appropriate in this model.

## 3. RESULTS

The Hausman Test is used to compare the random effects model with the fixed effects models. Pingxiang (2018) explained that the null hypothesis is (H0) the fixed effect and random effect estimators do not differ and the alternative hypothesis (H1) states that the estimators of fixed and random effects differ. The result in Table 1 shows that since the probability is higher than 0.05 (0.1556 > 0.05) then we do not reject the null hypothesis and assume that the random effect model is more appropriate for this study.

From the random effect, regression result in Table 2 show the probabilities of the p-value of education and health expenditures is less than 0.05, conclude that there is statistical evidence suggesting that both of education and health expenditure are negatively significant in affecting the poverty rate. More importantly, increases in government spending in education by 1% will reduce poverty by 0.000162%. This result is in line with (Anderson et al., 2018; Jung and Thorbecke, 2003) concluded that when the government invests more of their spending in improving the human capital through education will result in the decrease in poverty level.

Likewise, a one percent increase in health budget leads to a 0.000562 % decline in the poverty level. The same result attained by (Ablo and Reinikka, 1998; Fan et al., 2000, 2004) who agreed that investment in health aiming better health condition will improve the income growth of people and then will result in decreasing in poverty rate. Furthermore, the constant of C of 14.94703 explains that when the public spending on health education is zero, the poverty level stays at 14.95%. In general, all the variables in this model are significant in explaining the reduction in the number of poor people in Indonesia (p-value of F-statistic in Table 3. are less than 0.5 (0.000 < 0.05). R-squared coefficient determination test is done to measure how much the independent variables in the research model can explain the dependent variable (Rentai, Zhuo, Shucai, & Qingsong, 2019). The R2 value of 0.941543 shows that poverty can be explained by the government expenditure in education and health by 24.86%, while another 75.14% can be explained by other variables separate from the model.

The result of the estimation by using the random effect model can be written as follow:

$P o v = 14.92970 + C i − 0.000162 E d u i t − 0.000562 H e a l t h i t$

where Pov is the poverty level, Ci is the constant number of random effects of each province, Edu is the total budget in education and Health is the total health budget.

Furthermore, the regression equation for individuals for each Province has a different result since the difference in public spending allocation and the poverty rate. For instance, Aceh Province as one of the poorest cities in Indonesia has a different result with DKI Jakarta as the capital city of Indonesia. Aceh with constant of 6.956213, this means that when there is no allocation of education and health budget implemented in Aceh, the poverty rate will stay at 20.9%. However, if the government spend each 1,000 billion on education and health, then the poverty in Aceh can be reduced by 0.16% and 0.47% becoming 20.74% and 20.43% respectively. Then how about Papua? Papua with a constant of 19.36 shows the highest poverty level reaching 34.29% with zero allocation of education and health. Additional 1,000 billion spent in education and health will decline the poverty rate by 0.16% and 0.47%. On the other hand, the constant of Jakarta of (-9.62) tells that without health and public spending, the poverty rate in Jakarta remains at 5.31%. This shows that there is a huge disparity between Aceh, Papua, and Jakarta. Each 1,000 billion spent by the government in education and health, will reduce the poverty level by 0.16% and 0.47% at 5.15 and 4.84, respectively. This result shows the difference in poverty level and public budget which cannot be inevitable, especially for Indonesia case. As a result, the difference in poverty level in each province needs the different treatment of both the policies and programs and the budget size of each region.

## 5. CONCLUSIONS

In the last decade, there has been an interesting debate regarding the effects of public spending on poverty reduction. The panel data are used in this study to analyze the effect of government expenditure on education and health to lower the poverty rate. The statistical evidence suggesting that both education and health expenditure are negatively significant in affecting the poverty rate. More importantly, increases in government spending in education by 1% will reduce poverty and health by 0.000160% and 0.000466% respectively. Therefore, it is suggested that to further reducing the poverty level, the government should allocate more budget of education and health especially , for the poor, and because of the difference in factors determining the poverty level and characteristic of each province, the differences in the public budget are necessary. Some provinces with lower poverty level, for instance, should allocate more health and education budget, higher than total average spending stated by the Law. Besides, it is necessary to increase the number of pro-poor programs such as free public health and education insurance to fulfill the basic needs of the poor. Moreover, there is a need to monitor public service performance so that the allocation of expenditure can be more efficient and effective.

## ACKNOWLEDGMENT

This research is sponsored by LPPM University of Syiah Kuala of Ministry of Research, Technology and Higher Education of the Republic of Indonesia. I would like to thank the Ministry of Research, Technology and Higher Education of the Republic of Indonesia for the support in this research. I am grateful to Tasdik as the head of Central Bureau Statistics in Subussalam City, Aceh Province, Indonesia for providing me the distribu-tional data of education and health spending, and poverty level in Indonesia.

## Figure

Poverty trend in Indonesia.

Percentage spending in education of total state budget by indonesia government fat 5% level from 2012 to 2017 (in billion IDR)

The health budget of total state expenditure by indonesia government – 2012 to 2017 (in billion IDR).

## Table

The result of the hausman test

Random effect regression results

The result of r-squared and f-statistic

## REFERENCES

1. Ablo, E. and Reinikka, R. (1998), Do budgets really matter? Evidence from public spending on education and health in Uganda, World Bank Policy Research Working Paper No. 1926.
2. Ahmad, I. and Ahmad, S. (2018), Multiple skills and medium enterprises’ performance in punjab pakistan: A pilot study, The Journal of Social Sciences Research, Special, 4, 44-49.
3. Al-Khateeb, B. and Yousif, M. (2019), Solving multiple traveling salesman problem by meerkat swarm optimization algorithm, Journal of Southwest Jiaotong University, 54(3).
4. Anderson, E. , d’Orey, M. A. J. , Duvendack, M. , and Esposito, L. (2018), Does government spending affect income poverty? A meta-regression analysis, World Development, 103, 60-71.
5. Arellano, M. (1993), On the testing of correlated effects with panel data, Journal of Econometrics, 59(1-2), 87-97.
6. Asad, M. , Shabbir, M. , Salman, R. , Haider, S. , and Ahmad, I. (2018), Do entrepreneurial orientation and size of enterprise influence the performance of micro and small enterprises? A study on mediating role of innovation, Management Science Letters, 8(10), 1015-1026.
7. Croissant, Y. and Millo, G. (2008), Panel data econometrics in R: The plm package, Journal of Statistical Software, 27(2), 1-43.
8. Elkhdari, M. and Sarr, B. (2018), Série études et documents decentralization, spending efficiency and propoor outcomes in Morocco, Études et Documents, n°5, CERDI.
9. Fan, S. , Hazell, P. , and Thorat, S. (2000), Government spending, growth and poverty in rural India, Amer. J. Agr. Econ, 82(4), 1038-1051.
10. Fan, S. , Huong, P. L. , and Long, T. Q. (2004), Government spending and poverty reduction in Vietnam, Project Report Prepared for the World Bank-Funded Project “Pro-Poor Spending in Vietnam,” International Food Policy Research Institute, Washington, DC.
11. Hartoyo, S. , Syaukat, Y. , and Oktaviani, R. (2017), The effect of government spending on education in aceh province Indonesia, Journal of Economic Development, Environment and People, 6(4).
12. Hausman, J. A. (1978), Specification tests in econometrics, Econometrica: Journal of the econometric society, 46(6), 1251-1271.
13. Jung, H. S. and Thorbecke, E. (2003), The impact of public education expenditure on human capital, growth, and poverty in Tanzania and Zambia: A general equilibrium approach, Journal of Policy Modeling, 25(8), 701-725.
14. Kraay, A. (2004), When is growth pro-poor ? Crosscountry evidence, IMF Working Paper, WP/04/47, 198-227.
15. Kwon, H. J. and Kim, E. (2014), Poverty reduction and good governance: Examining the rationale of the millennium development goals, Development and Change, 45(2), 353-375.
16. Mosley, P. , Hudson, J. , and Verschoor, A. (2004), Aid, poverty reduction and the ‘new conditionality’, Economic Journal, 114(496), F217-F243.
17. Ndhleve, S. , Obi, A. , and Nakin, M. D. V. (2017), Public spending on agriculture and poverty in Eastern cape province, South Africa, African Studies Quarterly, 17(2), 23-46.
18. Nwakuya, M. T. and Ijomah, M. A. (2017), Fixed effect versus random effects modeling in a panel data analysis: A consideration of economic and political indicators in six African Countries, International Journal of Statistics and Applications, 7(6), 275-279.
19. Onrubia-Fernández, J. and Fuentes, A. J. S. (2017), How costly are public sector inefficiencies? A theoretical framework for rationalising fiscal consolidations, Economics, 11, 1-19.
20. Pingxiang, W. (2018), Study on impact on negative sequence of single three-phase combined co-phase power supply system, Journal of Southwest Jiaotong University, 53(6), 1110-1116.
21. Rajkumar, A. S. and Swaroop, V. (2008), Public spending and outcomes: Does governance matter?, Journal of Development Economics, 86(1), 96-111.
22. Ramli, A. , Shabbir, M. S. , Shukri, M. , Bakar, B. , Mohd, P. , Mohd, N. , and Ahmad, I. (2018), Mediating role of e- learning resources in developing entrepreneurial inclinations amongst undergraduate students at universiti utara Malaysia, International Journal of Engineering & Technology, 7, 51-56.
23. Rentai, L. , Zhuo, Z. , Shucai, L. , and Qingsong, Z. (2019), Pre-excavation grouting to prevent water inflow of jointed rock in underground construction, Journal of Southwest Jiaotong University, 54(1), 48-54.
24. Sourya, K. , Sainasinh, S. , and Onphanhdala, P. (2014), Public spending, aid effectiveness and poverty reduction in Lao PDR, Journal of International Cooperation Studies, 21(2/3), 163-186.
25. Sriyana, J. (2015), Fiscal capacity and poverty alleviation: A panel data analysis for Yogyakarta special province, Indonesia, Jurnal Ekonomi Pembangunan, 16(1), 1-10.
26. Torres-Reyna, O. (2010), Panel data analysis fixed and random effects using Stata, Data and Statistical Services, Princeton University, 1-40.
27. Van de Walle, D. (1995), Public spending and the poor: What we know, what we need to know, Policy Research Working Paper No. 1476, World Bank. Washington DC. June.