Corruption in Indonesia, Challenges for a Sustainable Development Strategy


 This study extends the literature by investigating the relationship between sustainable development and corruption in a panel of thirty-three provinces in Indonesia during 2004-2012. In measuring sustainable development, this study employs composite indices consist of 20 indicators covering economic, social, environmental, and institutional aspects in Indonesia's regional economies. The findings show a significant negative relationship between sustainable development and corruption. This study suggests that anti-corruption initiatives by the government should focus on improving governance and maximizing the social value of natural resource exploitation. Besides, the government should address the issues of productivity and sustainable population growth to ensure economic development sustainability.JEL codes: C33, C43, D73, Q01


Introduction
By balancing economic, environmental, and social interests for the prosperity of present and future generations, sustainable development has drawn scholars' attention globally. The principles of sustainable development are essential to ensure any country's healthy economic growth (Vinnychuk et al., 2013). Further, sustainable development also a part of a non-decreasing element of intertemporal social welfare (Aidt, 2011).
However, sustainable development faces many challenges. One of them is corruption, particularly in developing countries such as Indonesia.
This country is one of the world's largest developing economies but suffering from corruption practices. The Corruption Perception Index (CPI) of Indonesia is ranked 88 out of 168 countries by 2015 (UGM, 2015a).
How far the impacts of corruption on sustainable development remain debatable. This study aims to investigate the corruption -sustainable development connections in Indonesia. This study reveals the effects of corruption on sustainable development and contributes to ensuring successful sustainable development implementation while confronting corruption practices in developing countries. This understanding is helpful to design additional policies on sustainable development in Indonesia. Lessons learned from this study could be adapted not only in Indonesia but also in other countries facing similar situations.
Most recent studies in this topic employed country-level data where the CPI was the proxy of corruption (Aidt, 2011;Guney, 2014;Lameira et al., 2013). This study, however, investigates the effect of corruption on sustainable development at the province level, including all data from 33 provinces across Indonesia. The data of corruption cases released by the Supreme Audit Agency/ SAA (Badan Pemeriksa Keuangan) is employed as the corruption level's proxy. This approach is innovative and empirical, based on evidence from sub-national levels.
This study examines the SAA data in an econometric model constructed from the conventional growth model and is modi ed to meet the research objectives. Twenty indicators covering institutional, economic, environmental, and social features are employed to measure Indonesia's provinces' sustainable development level. Conventional growth and panel regression analysis (i.e., ordinary least square, xed, and random effects) are employed to explain the connections between corruption and sustainable development. This study applies Instrumental Variables Estimation and also General Method of Moments to address endogeneity and multicollinearity issues.
The rest of the paper is as follows. Section 2 presents contemporary issues on sustainable development and corruption in Indonesia, including the positive and negative effects on sustainable development. Section 3 describes the methodology and models employed in this study. Section 4 presents the estimation results. Section 5 discusses the ndings from the model. Section 6 concludes the paper with suggestions for further research.

Contemporary Issues On Sustainable Development And Corruption In Indonesia (a) De nitions of sustainable development and corruption
Many studies agree that sustainable development balances economic, ecological, and socio-cultural aspects. The economic approach refers to maximizing income while maintaining a constant or increasing stock of capital. The ecological approach means preserving resilience and strength of biological and physical systems, and the socio-cultural approach denotes upholding the stability of social and cultural systems.
Further, Khan and Khan (2012) mentioned that the three determinants of sustainable development are consumption, production, and distribution. Scutaru (2013) saw sustainable development as combining economic theory with the study of natural sciences. This combination of economic theory and natural sciences analysis enables creating a comprehensive view of economic, social, and environmental aspects.
Thus, sustainable development is de ned as the interaction between environment and human factors and may combine the economic, natural, and social processes into a single ecological, economic system. Anttila-Hughes (2012) agrees that sustainable development improves welfare, which is limited by natural constraints, while human development plays an essential part in achieving it. Besides, sustainable development leads to human investment in the present with impact in the future. Bayburina and Golovko (2009) considered that human capital and its elements can be viewed as the locomotive of a company's growth and are essential in maintaining sustainable development. Dumitrana et al. (2009) argued that there are two basic concepts of sustainable development: the concept of needs and the environmental capacity and ability to satisfy current and future needs. Goodwin (2003) mentioned that sustainable development must maintain or increase all productive capital stocks, including natural capital, which is often depleted through economic production. Jakimhovski (2011) believed that to achieve sustainable growth, economic improvement has to be achieved, and energy and materials consumption and waste products have to be reduced. Achieving sustainable development needs to be supported through e cient production promotion, reducing unnecessary consumption, and applying energy e ciency and renewable energy policies. Peters (2013) saw sustainable development encompassing overarching issues related to the environment, economy, social issues, globalization, institutional governance, and public governance. The conclusion is that sustainable development calls for multi-stakeholder participation.
Furthermore, corruption is associated with misconducts, including fraud, receiving bribes, and similar actions where persons enrich themselves or other persons, or parties, resulting in a nancial loss to the state. In the context of a state's administration, corruption is an illegal behavior of a person's o cial duties for personal bene ts (individual, family, own group) (Klitgaard, 1988). From a behavioral perspective, corruption is public o cials' behavior for personal interests by breaking the rules (Goodman, 1974). Corruption is also considered government o cials' acts in exploiting public property for private interests (Aidt, 2009).
Measuring the level of corruption is debatable because it is unclear and intangible (Kaufmann et al., 2006). However, there are two approaches to measure corruption level, perception-based and experience-based indicators (Carballo, 2010). Perception-based indicators are composite indexes of corruption perceptions from various stakeholders'. Meanwhile, experience-based indicators are based on citizens' experience when dealing with corruption. Currently, Corruption Perceptions Index (CPI) by Transparency International and Worldwide Governance Indicators (WGI) by World Bank Institute (WBI) are the two most commonly used perception-based indicators provided at the country level.
(b) The impact of corruption on sustainable development Corruption affects development has become a long debate in the literature. Leff (1964) and Huntington (1968) indicate corruption might avoid strict and inelastic bureaucracies at the micro-level and speed up development, known as the concept of 'greasing the growth wheels.' However, based on empirical studies, current scholars argue that corruption negatively affects development (Butler et al., 2009;Carballo, 2010). Aidt (2009) took a neutral stance and found no effect of corruption on the Gross Domestic Product (GDP) growth rate. However, using GDP only to represent development, without considering social and environmental aspects, might result in a wrong conclusion. GDP only measures current economic activity and does not include natural capital, human well-being, and productivity. Therefore, using sustainable development indicators, including economic, environmental, and social aspects, is more reliable to measure corruption's effects on development (Aidt, 2011).
Corruption might slow down the implementation progress of sustainable development. North (1990) indicates that high-level corruption is associated with less effective and less e cient governance, which would increase uncertainty in trade and investment as the economic aspects of sustainable development. Other studies also indicate the adverse effects of corruption on sustainable development's environmental aspects (Damania et al., 2003;Ferreira-Tiryaki, 2008). Carballo (2010) reveals the harmful effects of corruption on the social aspects of sustainable development.

(c) Corruption in Indonesia
Efforts on confronting corruption in Indonesia increased recently, which is indicated by many corruption cases. Many corruption cases by o cials or authorities, exposed by Corruption Eradication Commission/ CEC (Komisi Pemberantasan Korupsi)  Susilo, the next president from 2004 to 2014, did not introduce new legislation or institution, but he implemented the existing anti-corruption laws. Eradicating corruption is among his main political agenda. Signi cant corruption cases involving high-rank o cers and ministers were exposed. Today, efforts on confronting corruption are still ongoing but far from the end since corruption is still standard practice in Indonesia (UGM, 2015b). Java Island is the center of corruption in Indonesia (UGM, 2015a), which might result from higher scal capacity in Java Island compared to the other islands. Figure 2 presents that 1,161 people accused in corruption cases come from four provinces in Java, representing 40% of the national number (3,099 people). Another 60% scattered across the other 29 provinces.
Another indicator of corruption level is the ratio between Cost of Corruption (CC) and Cost for the Accused (CA), as indicated in Table 1. Cost of Corruption is the amount of state loss caused by corruption, while the Cost for the Accused is the amount for which the accused is judged and must pay back. For example, Table 1 shows that the accused need to pay just about 29.4% of the actual state loss caused by their misconduct behavior. Among the ve most signi cant corruption cases, the ratio (CC/CA) is only 4.7%. Even in Jambi province, it is less than 1% (0.68%). This low ratio indicates weak enforcement of law and encourages more corrupt behavior. Source: University of Gadjah Mada (2016) Bribery dominates corruption cases in Indonesia, comprising almost half of all cases (Figure 3). Procurement and budget misuse follow in the following places. Bribery is linked with public service, licensing, and authorization, while procurement is related to tenders, public assets, and amortization. Moreover, budget misuse is associated with budget planning, projections, and preparation.

Methodology And Model Speci cations (a) Variables
This study employs provincial-level data on corruption behavior released by the SAA. Econometric modeling and conventional growth analysis are employed to explain the relationship between corruption and sustainable development. These approaches have been applied in several studies analyzing economic growth in other countries and using country-level data (Chen and Wu, 2012; Wu, 2014). Following the literature, several control variables are used to identify the links between corruption and sustainable development. Variables are employed in ratio because SDI and corruption data are provided in ratio. In this study, corruption is incorporated into the equation as follows: SDI is the independent variable. It is a Sustainable Development Index comprised of two scenarios. SDI 1 is constructed based on equal weights being allotted among the indicators of sustainable development. SDI 2 is an index based on the same weights among the sustainable development aspects. Furthermore, corruption level is approached by two variables, Loss per Capita (LC) and Loss over Expenditures (LE).
Furthermore, GRP, Den, Ed, and Inv represent Gross Regional Product per Capita, Population Density, Education, and Investment of the i th province in period t, respectively. d 1 is the dummy variable of provinces in Java, while d 2 is the dummy variable for Oil and Gas Producer provinces. Table 2 presents all variables and the de nitions included in this study. uses Area (land area in each province) as the instrument variable to explain corruption. The corruption level in Java is higher than in Non-Java, but provinces in Java are smaller than provinces in Non-Java.
This study conducts two types of GMM to address endogeneity and multicollinearity (difference GMM and system GMM). The J-statistic or Sargan value was employed to test the validity of instruments or the problem of over-identi cation.

Results
(a) Preliminary results The disparity is observed in real GRP per Capita, indicating a signi cant difference between real GRP per Capita among the provinces. Table 3 shows that GRP per Capita is 7.8 million on average, where the lowest is only 1.8 million in Gorontalo and the highest is 45.5 million in DKI Jakarta. Log Population Density shows that West Papua is the lowest (1.7268) while DKI Jakarta is the highest (9.6059). The enrolment ratio in primary school as a proxy of Education shows that about 91% of Indonesians had nished their primary school. Table 3 indicates that the lowest enrollment is in Papua (70%), and the highest is in Aceh (97%). Overall, Table 3 presents a low-level investment in Indonesia. On average, there is only 7% of Investment over GRP in Indonesia. The lowest is in West Sulawesi (0.04%), and the highest is in North Maluku (73%).  Table 4 presents the Pearson correlation matrix to observe the pair-wise relationship among variables. GRP per Capita, Population Density, Education, and Investment are all found to correlate with SDI positively. Furthermore, both types of corruption (LC and LE) are negatively correlated with SDI. Table 4 also explains the correlation between corruption and other explanatory variables where all are negatively correlated. The results also suggest that most correlation coe cient indicates potential endogeneity bias which will be addressed by conducting Instrumental Variables estimations.   Tables 6a and 6b show the results based on the two corruption variables, Loss over expenditure and Loss per capita. This study looks at two different speci cations, with and without control variables, to address control variables' appropriateness. In pooled OLS regression models of SDI 1 , when corruption is gured by Loss over Expenditure, the increase of one standard deviation of corruption will decrease the level of sustainable development by 0.017% (model 1) and 0.011% (model 3), ceteris paribus. When corruption is represented by Loss per Capita, the increase of one standard deviation of corruption will decrease the level of sustainable development by 0.007% (model 2) and 0.006% (model 4).
By including control variables in Model 3 and Model 4, GRP per Capita positively affects sustainable development, but Population Density negatively in uences sustainable development. In model 4, dummy variables (Java; and Oil and Gas Producer) positively impact sustainable development.
Pooled OLS regression models of SDI 2 show that the increase of one standard deviation of corruption presented by Loss over Expenditure will decrease in the level of sustainable development by 0.022% (Model 9) and 0.013% (Model 11). When corruption is presented by Loss per Capita, the increase of one standard deviation level of corruption will decrease 0.01% sustainable development level (Model 10 and 12).
In Model 11 and 12, GRP per Capita positively correlates with sustainable development, while Population Density negatively correlates. In model 12, Education represented by the enrolment ratio in primary school positively correlated with SDI 2 .  In Table 7b when SDI2 represented sustainable development, the models also support the negative relationship between corruption and sustainable development. Model 21 and Model 23 also con rm negative connections between Population Density and sustainable development.

(d) General method of moments
The GMM models show that the corruption variables in the models are good predictors to measure the relationships between corruption and sustainable development. GMM estimation results con rm the results of IVE and baseline models indicating that corruption has a negative effect on sustainable development at the province level in Indonesia.

Conclusion
This study aims to analyze the relationship between corruption and sustainable development. The negative trend relationship shows the two different corruption measures used in this study are signi cant. This study suggests that the sustainable development level will follow the corruption level reduction. The xed effect models also con rm robust results about the negative effect of corruption on sustainable development. Finally, the instrumental variables and GMM estimates also point in the same direction and suggest that corruption has a signi cantly negative effect on sustainable development.
In this study, the sustainable development index comprises indicators and groups based on four sustainable development aspects: economic, environmental, social, and institutional. More signi cant comprehensive reforms to eradicate corruption and to enhance governance are needed. Moreover, the anti-corruption program should also consider social and environmental aspects, including corruption in exploiting natural resources that have high social value to the community. Additionally, the government needs to address productivity and birth rate control to boost the sustainable development level. It is expected that in the long run, through a low level of corruption, the level of sustainable development in Indonesia will increase. This improvement can be seen by enhancing each aspect of sustainable development witnessed at the province level.

Declarations
Availability of data and materials All data generated or analysed during this study are available from the corresponding author on reasonable request.

Competing interests
The authors declare they have no competing interests.

Funding
The author's research is funded and supported by the Ministry of Finance of the Republic of Indonesia.