ECONOMICS FISCAL DECENTRALISATION AND INEQUALITY IN

INDONESIA: 1999-2008

by

M A B Siddique Business School

University of Western Australia

and

Heru Wibowo Ministry of Finance Government of Indonesia

and

Yanrui Wu

Business School University of Western Australia

DISCUSSION PAPER 14.22

FISCAL DECENTRALISATION AND INEQUALITY IN INDONESIA: 1999-2008

M A B Siddique 1*, Heru Wibowo 2 and Yanrui Wu 1

DISCUSSION PAPER 14.22

Abstract: This paper investigates the evolution of expenditure inequality in Indonesia during 1999-2008, a period that coincides greatly with the implementation of fiscal decentralisation policy. In general, the results show increasing expenditure inequality evident in a decreasing share of income in the bottom 10 percent, and an increasing share in the top 10 percent. Further examination employing the two-step system GMM confirms increasing expenditure inequality as a result of fiscal decentralisation. This might be due to the implementation of the hold-harmless clause that was not accompanied by proper income distribution policy within the region. Furthermore, real per capita gross regional product (GRP) and average years of schooling have also contributed toward higher expenditure inequality. In contrast, expenditure inequality may ease in the long term with an improvement in education services. Lastly, expenditure inequality can also be addressed by enhancing public health services.

JEL classification: H72; H77; R11 Keywords: fiscal decentralisation, income inequality, Indonesia

1. UWA Business School 2. Ministry of Finance Government of Indonesia *. Address for correspondence Dr. M. A. B. Siddique Associate Professor, Economics M251, UWA Business School The University of Western Australia 35 Stirling Highway, Crawley, WA. 6009 Phone: +61 (0)8 6488 2941 Fax: +61 (0)8 6488 1016 - E-Mail: abu.siddique@uwa.edu.au - www.business.uwa.edu.au

1. Introduction With the commencement of the New Era (Orde Baru) in 1966, Indonesia embarked on a path of high economic growth that was sustained until 1997 when the country was hit by the Asian financial crisis. From 1966 until 1997, Indonesia’s economic growth averaged about 7.1 percent per annum. During the 1997 economic crisis, Indonesia’s economic growth contracted by over 13.0 percent. The unprecedented scale of the crisis engulfed pre-crisis economic performances, exacerbated social unrest and created political instabilities (Resosudarmo 2005). One observer argued that the 1997 economic crisis intensified the demand from resource-rich regions for larger revenue sharing (Kuncoro and Resosudarmo 2004). The issue of a larger share of revenue for resource-rich regions caught the political attention of the central government. It was believed that unless this was resolved amenably, the national unity of Indonesia would be at risk. In order to appease the demand for larger revenue sharing by the resource-rich regions, former President B.J. Habibie passed two decentralisation laws in 1999 that encompassed both administrative and fiscal decentralisation. McLeod (2005) argued that these laws were somewhat unconstitutional and motivated only by Habibie’s desire to retain the presidency by offering greater revenue sharing to the resource-rich regions. Decentralisation in Indonesia is implemented mainly at the district (kabupaten) and municipality (kota) levels, rather than at the provincial level. Despite the advantage of decentralisation at district and municipality levels being closest to the people, it is argued that the main reason behind this decision was to maintain Indonesian unity as most of the provinces have specific characteristics (for example, ethnicity and religion) that might trigger separatism (Brodjonegoro 2001). The legal frameworks for decentralisation were Law No. 22 of 1999 (amended to Law No. 32 of 2004) on regional governments and Law No. 25 of 1999 (amended to Law No. 33 of 2004) on fiscal balance between central and regional governments. Under these laws, greater authority and accountability in both administrative and fiscal resources have been devolved to regional governments, particularly districts (kabupaten) and municipalities (kota). They become liable to manage and responsible for public works, health, education, cultural and social affairs, labour, cooperatives, environment, land, citizenship, and investment. The central government, on the other hand, maintains authority for international relations, security and defence, monetary and fiscal policy, justice, and religious affairs. Fiscal decentralisation is implemented on the “money follows function” principle, i.e., devolved responsibilities are accompanied by respective intergovernmental transfers in terms of the balance funds (Bahl 1999). The balance funds consist of three major components, namely the general allocation fund (dana alokasi umum/DAU), revenue

1

sharing (dana bagi hasil/DBH), and the specific purpose fund (dana alokasi khusus/DAK). Under the fiscal decentralisation law, resource-rich regions receive a greater share of revenues from natural resources and taxes that have been extracted from their jurisdictions. This has generated scepticism by people who believe that fiscal decentralisation increases inequality rather than reduces it. The reason seems self-evident given the scattered nature of natural resources and the concentrated economic development in particular regions. Those regions with rich natural resources and better economic development are those that gained most from the fiscal decentralisation policy. The impact of fiscal decentralisation on income inequality in Indonesia is worthy of observation. Administrative and fiscal decentralisation are relatively recent phenomena in Indonesia, which may be one of the reasons for the relatively small number of studies examining the impact of fiscal decentralisation on income inequality. Most existing research on Indonesian inequality examines the relationship between economic growth and income inequality (for example, Resosudarmo and Vidyattama 2006; and Hill et al. 2008). Nonetheless, cross-country studies with respect to the impact of fiscal decentralisation on income inequality suggest no conclusive results. Fiscal decentralisation may lead to an increase, decrease, or no change in inequality. Shankar and Shah (2003) argue that regional inequality increases with decentralisation, particularly in developing countries. Conversely, Canaleta et al. (2004), Neyapti (2006), and Lessmann (2009), contend that decentralisation causes a decrease in income inequality. Examination of the relationship between fiscal decentralisation and income inequality becomes more relevant given that inequality in Indonesia appears to have increased since the introduction of fiscal decentralisation. Particularly, during the early days of fiscal decentralisation, due to concentration of natural resources and economic development in a few regions, fiscal decentralisation may not lead to improvement in local welfare. Literature examining the impact of fiscal decentralisation on inequality in Indonesia is increasing. However, as will be shown in the next section, little appears to have been done to examine the impact of fiscal decentralisation on income inequality by employing a formal econometric model for Indonesia. The aim of the study is to fill this gap in the literature. The study uses unbalanced panel data from 33 provinces to investigate the relationship between fiscal decentralisation and inequality in Indonesia during 1999-2008. The study is structured into four main sections: the next section reviews the existing literature on the relationship between fiscal decentralisation and inequality; this is

2

followed by the empirical findings in section 3; while the final section draws some conclusions.

2. Decentralisation and Inequality: A Brief Review Arguments in favour of fiscal decentralisation are motivated mainly by an expectation that fiscal decentralisation brings government closer to the people, which improves efficiency of the decision-making process in matching local preferences. The expected result is better public services provision as well as sound economic performance, including better income distribution. Fiscal decentralisation has gained popularity in both developed and developing countries. In the United States, fiscal decentralisation is highlighted by transfers of various public services to the regional governments, for example, welfare, Medicaid, legal services, housing, and job training (Oates 1999). This is also evident in the United Kingdom and Italy, with demand for their regional parliaments and the breaking up of the nation, respectively. In the European Union, fiscal decentralisation encompasses tax collection (Stegarescu 2005). Fiscal decentralisation is not only implemented in developed countries, but also in developing countries in Asia, Latin America and Africa (Bardhan 2002). For example, in Brazil (represented by the city of Porte Alegre) and Bolivia, fiscal decentralisation has successfully reallocated resources to the poor. In addition, many regional and national governments, as well as international institutions such as the World Bank, have supported the introduction of fiscal decentralisation elsewhere (Becker 2001). The argument in favour of fiscal decentralisation is that it improves public services delivery as local governments have a better understanding of local preferences and needs compared to the central government (Oates 1999). This enhances efficiency and accuracy of the provision of public services due to fewer geographical constraints, both across and within jurisdictions (Tiebout 1956). Furthermore, it is argued that the implementation of fiscal decentralisation needs to be encouraged since it contributes toward higher economic growth (Martinez-Vazquez and McNab 2003). Implementing a fiscal decentralisation policy worldwide is not without criticisms of some of its shortcomings. Some argue that decentralisation may exacerbate inefficiency and disparity amongst regions as resources and utilities are unequally allocated and redistributed (Gordon 1983; Sinn 1997). The fiscally poor regions may face difficulty in delivering public services of appropriate size, and in a timely manner, due to shortage of revenue. The latter may also lead to higher tax rates (Hofman et al. 2006). Moreover, Prud'homme (1995) argues that a centralised rather than a decentralised government is more effective in redistributing income from richer to poorer areas. It is also argued that decentralisation may destabilise the country’s fiscal position (Malik 2008). This happens

3

when regional governments run fiscal deficits that require central government intervention to fill these fiscal gaps. The result is likely to overburden the national budget. In addition, huge domestic debt created by regional governments as they gain access to the market for borrowed funds may endanger fiscal stability at the national level. Many studies on the pros and cons of fiscal decentralisation have been conducted in order to evaluate its relationships with economic indicators, such as poverty and inequality. These relationships have been investigated by employing data from either cross-country or country specific studies (Davoodi and Zou 1998; Woller and Phillips 1998; Lin and Liu 2000; Akai and Sakata 2002; Canaleta et al. 2004; Bonet 2006; and Barrios and Strobl 2009). However, discussion is still far from arriving at a conclusive result as to whether the implementation of fiscal decentralisation contributes toward lower or higher income inequality. Shankar and Shah (2003) challenge the proposition of favourable impact of decentralisation in lessening regional inequality. However, they argue that the negative impact of decentralisation on regional inequality is disproportionately distributed, with developing countries experiencing higher inequality than the developed countries. In addition, the federal countries perform better than unitary ones in restraining the negative impact of decentralisation on inequality. On the pros side, the favourable impact of fiscal decentralisation on reducing inequality in developed countries has been confirmed by Canaleta et al. (2004). Employing cross-sectional data of 17 countries within the Organisation for Economic Co-operation and Development (OECD) from 1975 to 2000, they conclude that regional inequality decreased during the fiscal decentralisation era. Moreover, fiscal decentralisation leads to regional convergence, given that decentralised countries experience higher speeds of convergence. Lessmann (2009) presents further evidence on the advantage of fiscal decentralisation in reducing regional inequality for both developed countries and developing countries. He concludes that the sceptical argument against a higher degree of fiscal decentralisation is not justifiable and that the advancement of fiscal decentralisation reduces regional inequality due to improvement in resources allocation across regions. Neyapti (2006) runs a random effect model, employing revenue of local, state and provincial governments, to estimate the impact of fiscal decentralisation on income inequality. She collected data from 37 countries, both developed and developing. Neyapti concludes that revenue decentralisation contributes towards lowering income inequality, once good governance is present.

4

Relatively lower pre-existing inequality, according to Beramendi (2007), determines whether inequality gets better or worse once decentralisation takes place. This may be one explanation regarding different effects of decentralisation policy on income inequality. Employing panel data of 26 countries (19 developed and 7 developing countries during 1990-2006), Rodriguez-Pose and Ezcurra (2009) show that low and medium income countries with high regional inequality tend to experience greater inequality after the implementation of fiscal decentralisation. On the contrary, fiscal decentralisation policy in high-income countries may have a neutral or positive impact on inequality due to a lower level of pre-existing regional inequality. A recent study by Tselios et al. (2011), employing more than 100,000 individual data in Western Europe, confirms the favourable impact of greater fiscal decentralisation on the decline of interpersonal income inequality. Studies on income inequality in Indonesia are relatively rich. They employ commonly used inequality measures, namely the Gini coefficient and the Theil index. SUSENAS and per capita GRP are the datasets frequently used in those studies. Findings of most of the earlier studies on income inequality in Indonesia suggest that prior to the onset of the crisis in 1997-1998, inequality was relatively low (see, for example, Tadjoeddin et al. 2001; Suryadarma et al. 2005). This trend, however, was not sustained in the post-economic crisis period along with the commencement of fiscal decentralisation in 2001, as shown by the increase in the Gini coefficient from 0.33 in 2002 to 0.35 in 2008 (BPS 2011). This seems to validate concerns raised by scholars that the fiscal decentralisation policy in Indonesia is more likely to increase rather than decrease inequality among people (see, for example, Brodjonegoro and Asanuma 2000; Seymour and Turner 2002; Islam 2003). The phenomenon of increasing inequality in the fiscal decentralisation period is worth investigating in order to ascertain whether there are advantages of ongoing fiscal decentralisation policies adopted in Indonesia. Furthermore, the existing literature, both theoretical and empirical, contains inconclusive results with regard to the impact of fiscal decentralisation on inequality. Some claim that fiscal decentralisation has a detrimental effect on income equality, while others contend that income inequality becomes less marked once fiscal decentralisation takes place. Previous studies undertaken in the early days of fiscal decentralisation in Indonesia predicted that it would worsen inequality amongst regions and people (Ahmad and Hofman 2000; Brodjonegoro and Asanuma 2000; Seymour and Turner 2002). These authors argued that some regions could face difficulty in delivering public services due to limited own-source revenue, lack of natural resource endowments, and lower levels of economic development. However, White and Smoke (2005) argue that issues of inequitable service distribution across regions require careful design of intergovernmental transfers to address such a situation. Brata (2009) states that inequality may always be a problem in Indonesia due to the dispersal of natural

5

endowments and the level of economic development. Hill (2008), on the other hand, argues that fiscal decentralisation in Indonesia has had no major impact on regional inequality given the relatively short period of its implementation. He also observes other challenges in achieving the objective of fiscal decentralisation, such as an ambiguity of demarcation of powers and resources between the central and regional governments and flaws in the political system. 3. Empirical Analysis This section describes the empirical model, discusses the data issues, and contains analysis of the results. 3.1 Econometric model The impact of fiscal decentralisation on inequality is investigated by employing the following model:

tiiktiktititi XFDIGINIGINI ,,,,1,, ενγβα ++++= ∑− (1)

where GINIi,t is the Gini coefficient of province i at time t; GINIi,t-1 is a lag of the Gini coefficient of province i; α is the regression coefficient of the lag of Gini; β is the regression coefficient of fiscal decentralisation variables (FDI); γk are the regression coefficients of control variables Xi,t,k, which comprise region specific features; νi is the fixed effect; and εi,t is the error term. A lagged Gini coefficient is included in the model following the argument that relatively lower pre-existing inequality determines whether post-decentralisation inequality is getting worse or better once decentralisation takes place (Beramendi 2007). In addition, including a lagged Gini coefficient as an explanatory variable is expected to capture the “sluggish” adjustment of the dependent variable (Ziliak et al. 2000). This makes the model dynamic in its nature, in that it might better address the potential endogeneity problem (Lessmann 2009). Some earlier cross country studies confirm the argument regarding the importance of previous period inequality in determining current inequality (for example, Breen and Garcia-Penalosa 2005). For the same reason, the lagged fiscal decentralisation indices are included in the model. Details of variables and sources of data employed in this study are described below. 3.2 Variables and data descriptions 3.2.1 Inequality The inequality measure employed in this study is the Gini coefficient, motivated mainly by its popularity in inequality studies (Nygård and Sandström 1989). It is estimated on

6

real per capita household expenditure derived from the socio-economic national survey (Survei Sosial Ekonomi Nasional, SUSENAS). Referring to Deininger and Squire (1996), SUSENAS satisfies criteria for income inequality studies. As such, it collects data based on household or individual surveys rather than being derived from national accounts data; it covers the entire population and not just subsets; and it collects information on all types of income including in-kind and cash income. In addition, SUSENAS provides a series of data and it is publicly accessible. As such it is one of the most referred-to data sources in Indonesia (Sumarto et al. 2003).

Following Levinsohn et al. (1999)’s argument, the nominal household expenditure was deflated by CPI at 1996 prices and was differentiated between urban and rural areas. Furthermore, Thomas et al. (1999) showed that people who live in rural areas faced higher inflation than urban people after the 1998 economic crisis. They estimated that the difference between urban inflation and rural inflation was about 5 percent. There are some reasons for the choice of expenditure rather than income or assets as inequality measurement in this study. First, income or assets are more difficult to measure and SUSENAS does not collect these data in great detail (Said and Widyanti 2002). Second, expenditure is a better permanent income indicator as it is quite stable in the short-term (Akita and Lukman 1999; Skoufias 2001). Third, expenditure is more preferable than income to minimise the measurement error due to the substantial share the informal sector has in the Indonesian economy (Wu et al. 2008). According to Blunch et al. (2001) the informal sector in Indonesia absorbed 77.9 percent of non-agricultural employment in 1998. 3.2.2 Fiscal Decentralisation As mentioned earlier, the relationship between fiscal decentralisation and inequality is inconclusive. In an attempt to investigate it in the context of Indonesia, this study employs fiscal decentralisation indices developed by Vo (2008). A brief explanation is provided below. Given the wide dispersion of fiscal decentralisation, it is quite difficult to measure the true degree of fiscal decentralisation by one single component (Martinez-Vazquez and McNab 2003; Boex et al. 2006). Taking into account this difficulty, this study follows the method developed by Vo (2008) for measuring fiscal decentralisation, namely the fundamental index of fiscal decentralisation (FFDI) and its enhancement (EFDI).

The FFDI is formulated as follows:

×

=

TEE

EOSRFFDI i

i

ii (2)

7

where OSR is an own-source revenue of regional government i; Ei is expenditure of regional government i; and TE is total national and regional government expenditure, excluding intergovernmental transfers (both transfers from national to regional governments and inter-regional governments transfers). The value of FFDI ranges between 0 and 1; 0 representing perfect fiscal centralisation and 1 representing perfect decentralisation. There are two types of transfers to the regions in nature, namely conditional and unconditional transfers. They are incorporated into the fundamental index of fiscal decentralisation as an adjustment coefficient (k). It is formulated as:

TT

ETTk

UCU

×−

=

where TU, TC, and T are unconditional, conditional, and total fiscal transfers to regional government, respectively; and E is regional government expenditure. The first factor of k represents the share of unconditional transfers (minus conditional ones) received by regional government to its expenditure. This is called the fiscal transfer share. The second factor of k is called the premium factor. The greater the share of unconditional transfers to total transfer, the greater the flexibility of a regional government to use it. Thus, the EFDI for region i is formulated as follows:

×

+=

TEEk

EOSREFDI i

ii

ii (3)

The regional borrowing is added as a component of the fiscal autonomy part of the EFDI in terms of debt autonomy. The regional debt autonomy is formulated as follows (Vo 2008):

laE

D

P

ii

P

ii

=

×=

∑

∑

=

=

1

1autonomyDebt (4)

where ∑=

P

iiD

1

is the total regional government debt, and 0 ≤ a ≤ 1.

Incorporating regional debt autonomy, the EFDI becomes as follows:

8

×

++=∑

∑

∑=

=

=

TE

Elk

E

OSREFDI

P

ii

P

ii

P

ii

1

1

1 (5)

3.2.3 Gross Regional Product This study employs the log of provincial per capita GRP (Gross Regional Product) in 1996 prices to measure the level of regional economic growth. Real per capita GRP is included following Kuznets’ (1955) hypothesis that the relationship between economic growth and inequality follows the inverted U-shape pattern. However, this study is not intended to investigate Kuznets’ hypothesis for two main reasons: first, the observation period is relatively short (Khasru and Jalil 2004); and second, there is no evidence that it is present in the case of Indonesia (Ritonga 2005; Hartono and Irawan 2011). 3.2.4 Ratio of the secondary sector employment to total employment It is argued that the economic sector plays an important role in income inequality (for example, Borjas and Ramey 1994; Partridge et al. 1996). Different sectors require different skills and knowledge from their employees creating labour market segmentation and wage differentiation among sectors. These consequently lead to income inequality among labourers as a particular sector may offer higher wages to attract skilful labourers, for example a secondary sector (manufacturing, energy, and construction areas). This study adopts the BPS (Badan Pusat Statistik) definition in estimating the share of employment in the secondary sector.1 3.2.5 Schooling The average years of schooling and its square term are included to capture the importance of educational attainments in determining the level of a household’s income (for example, Garcia and Soelistianingsih 1998; Akita et al. 1999). Employing an augmented Solow model, Mankiw et al. (1992) find a positive and significant relationship between investment in secondary education and income. In addition, it is argued that more years of schooling is associated with higher educational attainment, thus improving individual competitiveness in accessing higher wage jobs. Assuming that higher income leads to higher expenditure, this will lessen expenditure inequality as more individuals attain higher education. The result will be decreasing inequality in

1 Employment participation is defined as persons 15 year old and over who are working, temporarily absent from work but having jobs, which both are categorized as employed, and those who have not been working but are looking for work (BPS: http://www.datastatistik-indonesia.com/content/view/802/802/). In this study, employment was derived from the SUSENAS rather than SAKERNAS due to data availability and similar definition between them in collecting information on individuals working status during the previous week. Those who work in the manufacturing, energy, and construction sectors are included into the secondary sector.

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the future (Li, Squire and Zou 1998). Jimenez (1986), on the contrary, argues that instead of lessening income inequality, higher education above primary and secondary levels, results in higher income inequality. Some cross-country studies confirm this argument, such as Partridge, Rickman and Levernier (1996) for USA; Sylwester (2000) for cross-country study; and Akita and Miyata (2008) in the case of Indonesia. 3.2.6 Fertility One of the variables which is commonly observed with income inequality studies is fertility (for example, Flórez 1992; Kremer and Chen 2002). It is argued that there is an inverse relationship between fertility and level of education of women. Better-educated women tend to have a smaller family size, which results in higher per capita expenditure compared to those with lower levels of education. This study derives data of fertility from SUSENAS by counting the number of children born alive per women aged above 10. Included are women who are married, divorced, or widowed. 3.2.7 Health Health is a form of human capital that is necessary to sustain good economic performance (Subramanian et al. 2002). Moreover, better health status is essential in addressing inequality (Leigh et al. 2009); first, healthy people have greater employment opportunities that enable them to earn income. Second, healthy people tend to have healthy children that perform better in school, thus improving their competitiveness in the labour market; and third, healthier individuals tend to have better adjustment toward adverse health shocks that may deteriorate their income. This study employs a ratio of healthy people to the total population within a province, estimated from SUSENAS as a proxy for health. Healthy people are described as those who reported no health complaints in the past month from the following conditions; fever, cough, cold, flu, asthma, breathing difficulties, diarrhoea, measles, ear discharges, liver, headache, convulsion, paralysis, senility, accident, toothache, and others. 3.2.8 Urbanisation The relationship between urbanisation and income can be either positive or negative (Jones and Koné 1996). The positive relationship occurs as higher urbanisation increases income inequality as rural migrants cannot be absorbed by the urban formal sector. Most of them, unfortunately, become unemployed or work in urban informal sectors, living in urban slums. This creates a poverty enclave in urban areas. In contrast, some get jobs, and earn income. As a result, income inequality among urban people may increase, which consequently leads to a divergence of consumption expenditure among them. Following SUSENAS, urbanisation is measured in this study by the ratio of urban to total population during the period under investigation.

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3.2.9 Investment Finally, investment is contended as one determinant for economic performance. Consequently, unequal investment distribution among provinces may induce diverse economic growth which affects the income of the people (Akita and Lukman 1995). Nonetheless, the impact of investment on income inequality is also determined by its type. Capital-intensive investment tends to increase inequality, as it requires skilled labour compared to labour-intensive investment. As a consequence, the wage gap between skilled and unskilled labour tends to increase in the capital-intensive industries (Mohtadi 1987; Figini and Görg 2006). On the other hand, Zhang and Fan (2004) found that public investment in rural education and agricultural research and development in the western region of China has had a favourable impact on reducing inequality. 3.3 Empirical Results 3.3.1 Trend in Inequality in Indonesia: 1999-2008 A trend in increasing inequality in the post-fiscal decentralisation era is presented in Figure 1. Increasing inequality during this period was predominantly dictated by increasing inequality between-decile groups rather than within-decile groups. Referring to the Theil T index, share of between-group inequality was, on average, 81.2 percent of total inequality, while the remainder was contributed by within-group inequality.

FIGURE 1. Trend of Expenditure Inequality in Indonesia

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Table 1. Inequality of Real Per Capita Household Expenditure by Decile Groups in Indonesia

Figure 1 and Table 1 show that despite some fluctuations, there was a noticeable trend of increasing inequality in Indonesia during 1999 to 2008. This trend was likely highlighted by growing concerns regarding the unfavourable impact of fiscal decentralisation on inequality. This trend can be observed by transforming the Gini coefficient and Theil indices into the logarithm form and regressing them against time. The slope of coefficients represents the percentage growth rate in each period (Wooldridge 2002). Employing this approach, the Gini coefficient grew 21.9 percent during 1999-2008, while Theil L and Theil T grew 9.9 percent and 0.7 percent, respectively. 3.3.2 Factors contributing to inequality in Indonesia Studies investigating the factors that may contribute to increasing inequality in the fiscal decentralisation period in Indonesia are very rare. This section is an attempt to examine this issue employing the generalised method of moments (GMM). The GMM is the preferred approach in this study since it can deal with “small T, large N”, non-strictly exogenous independent variables, and the presence of heteroskedasticity and autocorrelation (Roodman 2009). It is also preferable when addressing endogeneity in the model. Moreover, since the estimated models may suffer from heteroskedasticity and stationary problems, the present study employs the two-step generalised method of moments (GMM) developed by Arellano and Bond (1991). The two-step GMM is preferable in order to obtain consistent and efficient estimates.2 It employs two and deeper lags of dependent variables, predetermined variables,

2 The GMM is estimated using the xtabond2 command in Stata 11.0. The pedagogic paper on how to perform this method can be followed in Roodman (2009).

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endogenous variables; and time dummies as instruments in the models.3 The two-step GMM is asymptotically more efficient, but “the reported two-step standard errors tend to be severely downward biased” (Roodman 2009). In order to address this issue, the estimated models employ the correction developed in Windmeijer (2005). Robustness tests were performed prior to discussion of the result. The first test investigates serial correlation in the error terms. While carrying out the GMM, it was assumed that there is no serial correlation in the error terms. The presence of serial correlation is identified by observing second-order serial correlation in the first-difference residuals to check first-order serial correlation in levels (Bond 2002). As Table 2 shows, the Arellano and Bond (1991) test for AR(2) in Model 1 is 1.167 (p-value is 0.243) and for Model 2 AR(2) is -0.353 (p-value is 0.724). These results suggest that there are no second-order serial correlations in the models. Second, the Sargan (1958) test of over-identifying restrictions was performed under the null hypothesis of correct model specification and valid over-identifying restrictions (Baum 2006). The Sargan test for Model 1 is 14.503 (p-value is 0.013) and for Model 2 is 7.747 (p-value is 0.052). The results suggest that the null hypothesis of valid instruments is rejected. In other words, it seems that the two-step Difference GMM estimators suffer from weak instruments although their coefficients are relatively reasonable. In order to improve the performance of the Difference GMM, the two-step System GMM was performed as shown by Models 3 and 4 in Table 2. The instruments in the System GMM were a combination of moment conditions for the model in the first differences with moment conditions for the model in the level (Bun and Windmeijer 2010). This consequently increases the number of observations from 218 observations in the Difference GMM to 254 observations in the System GMM. In addition, System GMM has other appealing properties that make it preferable for panel data. Blundell et al. (2000) argue that System GMM greatly reduces finite sample bias and has greater precision when estimating highly autoregressive panel series. Bond (2002) confirms the advantage of System GMM in identifying the parameters when the series in the panel data are random walks and near the unit root processes. The System GMM is, therefore, preferred in our model.

3 The number of instruments employed in the model has been reduced in order to reduce the average bias following Windmeijer (2005). Stata 11.0 gives option to limit the instrument count by collapsing them in the xtabond2 command.

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Table 2. The GMM Result of the Determinants of Expenditure Inequality in Indonesia

Applying the two-step System GMM, the results show that the models do not suffer from a serial correlation problem. The AR(2) for Model 3 is 1.403 (p-value is 0.161) and for Model 4 is 0.121 (p-value is 0.903). Furthermore, the Sargan test indicates that employing System GMM has resolved an issue of invalid instruments. The Sargan test for Model 3 is 7.404 (p-value is 0.687) and for Model 4 is 11.62 (p-value is 0.477). The coefficient of the lagged dependent variable in both models also appears to be lower than the OLS as an upper bound, but higher than the within-group estimates (the fixed effect) as a lower bound (Bond 2002; Weeks and Yao 2003). This suggests that the

Dependent variable Difference GMM System GMMGini coefficientGini coefficient (-1) -0.384 * -0.716 ** 0.075 0.077

[0.223] [0.283] [0.298] [0.270]FFDI -4.428 * -3.703 ***

[2.173] [1.127]F FDI(-1) 7.480 4.240 *

[3.669] [2.438]EFDI 0.885 * 0.310 **

[0.436] [0.124]EFDI (-1) -0.245 -0.327 *

[0.341] [0.193]lnpcgrp96 -0.117 0.069 0.055 *** 0.037 ***

[0.137] [0.175] [0.018] [0.012]sectot 0.471 -0.244 -0.392 -0.099

[1.540] [1.420] [0.481] [0.088]schooling 0.185 -0.202 0.361 ** 0.066 *

[0.217] [0.158] [0.151] [0.036]sqschooling -0.017 0.014 -0.032 ** -0.005 *

[0.017] [0.013] [0.012] [0.003]fertility -0.097 -0.263 ** -0.273 *** -0.021

[0.087] [0.098] [0.074] [0.014]health -0.439 0.050 -0.713 *** -0.512 ***

[0.450] [0.352] [0.191] [0.122]urban growth -0.007 -0.026 0.071 -0.006

[0.034] [0.025] [0.059] [0.011]investment 1.134 * 1.175 * -0.270 0.034

[0.571] [0.625] [0.241] [0.109]No. of observations 218 218 254 254AR(2) 1.167 -0.353 1.403 0.121(p -value) 0.243 0.724 0.161 0.903Sargan test 14.503 7.747 7.404 11.620(p -value) 0.013 0.052 0.687 0.477

Notes : Models 1 and 3 employed FFDI , whilst Models 2 and 4 employed EFDI.

Corrected standard errors are in the brackets.

Time dummies are included in the GMM estimation but not reported.

The models employed two-steps GMM, Windmeijer- corrected standard errors,

and small-sample adjustment.

*** denotes significance at the 1 % level.

** denotes significance at the 5% level.

* denotes significance at the 10% level.

Model 4Model 1 Model 2 Model 3

14

results are more likely unbiased. Therefore, the following discussion refers to the two-step System GMM results of Models 3 and 4. For comparison, discussion focuses on variables which are significant in both models. 4 The main variables of fiscal decentralisation, namely the FFDI (Model 3) and EFDI (Model 4), appear to have different coefficient signs. As Table 2 shows, the FFDI has a negative coefficient (-3.703) which is significant at 1 percent. This suggests that increasing FFDI by 1 unit leads to a decrease in inequality by 3.7 units. The larger share of regional own-source revenue over its expenditure means greater fiscal autonomy for regional governments. There are, however, variations in capacity among regional governments in collecting the revenue from their own sources. For example, DKI Jakarta has the largest share of own-source revenue over its expenditure compared to other provinces. During 1999-2008, more than 50 percent of DKI Jakarta expenditure was supported from its own resources. All provinces in Java and Bali also have a relatively larger share of own-source revenue in their budgets. This might be due to better economic development in these regions. While most of the provinces in Java and Bali enjoy a greater proportion of revenue from their own resources, most provinces, for example Nusa Tenggara Timur, Sulawesi Tenggara, Maluku, and Maluku Utara, do not have that access. These provinces are relatively less developed compared to those in Java and Bali. Lack of own source revenue has motivated regional governments to impose various types of regional taxes and levies (Lewis 2001). Aceh and Papua, on the other hand, have a lower share of own-source revenue to expenditure despite being rich in natural resources. A significant proportion of transfers from the central government to regional governments in terms of balance funds (general allocation fund, revenue sharing, and specific purpose fund) has been widely recognised (Fitrani et al. 2005; Hofman et al. 2006; McCulloch et al. 2008). The share of balance funds received by regional governments to their expenditure was, on average, more than 60 percent every year. Taking into account the unconditional and conditional transfers as components of the fiscal decentralisation index, the finding shows that the EFDI has a positive coefficient (0.31) and a significant relationship at the 5 percent level with the Gini coefficient (Model 4). This suggests that a greater degree of fiscal decentralisation is associated with higher expenditure inequality among the population within a province. Increasing EFDI by 1 unit will increase inequality by 0.31 units. The unfavourable effect of fiscal decentralisation on inequality may be influenced by the period of the study (1999–2008). Prior to 2008, fiscal decentralisation was

4 The results of System GMM with respect to increasing inequality within the region during fiscal decentralisation era remain robust to a variety of fiscal decentralisation measures as presented in Appendix A.

15

implemented with the hold-harmless clause in place. The hold-harmless clause was the National Parliament’s intervention in 2002 following complaints from the regions that they received less of the general allocation fund. This happened due to an implementation of the formula based allocation that taking into account the natural resources revenue as a subtraction factor for the DAU.5 The hold-harmless clause, however, seems to have aggravated horizontal inequality rather than reduced it, namely because the wealthier regions continuously received increasing amounts of transfer in terms of the general allocation fund and revenue sharing. Bahl and Tumennasan (2002) argued that the two transfer systems (general allocation fund and revenue sharing) are mutually reinforcing rather than offsetting. Thus, it works in favour of the rich regions rather than the poor. Increasing horizontal imbalance due to revenue sharing of oil and gas is shown by the increasing coefficient of variation of per capita provincial revenue (including from own sources and shared sources) from 70 percent to 90 percent. Nonetheless, it is apparent from Table 2 that the advantage of fiscal decentralisation has not been equally distributed to the people. Increasing inequality together with a greater degree of fiscal decentralisation suggests that this policy is only benefiting the few as administrative expenditures dominate regional spending. For example, the salary for local government heads, members of local parliaments, local government employees, and building rehabilitation and construction (World Bank 2007). The coefficient of real per capita GRP is positive and significant at 1 percent for Models 3 and 4. The coefficients of real per capita GRP are 0.055 for Model 3 and 0.037 for Model 4. These suggest that expenditure inequality increases as a province becomes richer due to better economic development or natural resources. Increasing real per capita GRP by 1 percent will increase expenditure inequality by 0.0006 percentage points for Model 3 and 0.0004 percentage points for Model 4. Further examination suggests that expenditure inequality is higher in rich provinces, such as DKI Jakarta, Aceh, Riau, Kalimantan Timur, and Papua (Figure 2).

5 The hold-harmless clause intended to maintain the amount of balance funds received by the regions, at least at the level of the previous year.

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FIGURE 2. Gini Coefficient of the Selected Rich Provinces

Source: Estimated by the author using data of SUSENAS.

Comparing the fiscal decentralisation era since 2001 and the New Order era, the latter successfully achieved high economic growth without significant detrimental effects to equality (Alatas and Bourguignon 2005). Strong government equalisation intervention, for example via the central government transfers and the Presidential Instruction (Instruksi Presiden, Inpres), had significantly reduced inequality (Ravallion 1988; Tadjoeddin et al. 2003). During this period, the central government accrued revenue from natural resources and taxes and distributed them to all regions without necessarily giving back the largest share to the producing regions. This policy was believed to be harmful for Indonesian unity and, thus, needed correction in the decentralisation era (Tadjoeddin et al. 2001). The average years of schooling are significant at 5 percent and 10 percent for Models 3 and 4, respectively. The regression coefficient for Model 3 is 0.361 and 0.066 for Model 4. These suggest that, on average, an additional year of school will increase expenditure inequality by 0.361 units for Model 3 and by 0.066 units for Model 4. The positive coefficient suggests that inequality increased as people attained higher education. This tendency has been more evident in recent years as the earning gap between the lower 20 percent and the higher decile has widened (Sakellariou 2009). People with higher educational attainment tend to earn higher income than those with lower levels of education as they have better opportunities to work in the modern sectors and to earn higher wages (Marin and Psacharopoulos 1976). In contrast, the square term of average years of schooling suggests that expenditure inequality may decrease in the long term. It is shown by the negative coefficients of the square term of average years of schooling, which are significant at the 5 percent and 10 percent levels for Models 3 and 4, respectively. Higher education improves the human capital of the nation and plays a critical role in raising household welfare, particularly

0,25

0,30

0,35

0,40

0,45

0,50

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Aceh Riau Jakarta Kaltim Papua

17

for the poor who have limited access to land and other assets (Balisacan et al. 2003). Furthermore, improvement in educational attainment gradually increases returns to education as the supply of educated workers increases and supply of unskilled workers decreases (Todaro and Smith 2009). The coefficient of the ratio of people who have excellent or good health compared to those with fair or poor health is negative and significant at 1 percent for Models 3 and 4. The regression coefficient for Model 3 is -0.713 and -0.512 for Model 4. This suggests that a healthier population is associated with lower inequality. Increasing the ratio of healthier people to those with fair or poor health by 1 unit will decrease expenditure inequality by 0.713 for Model 3 and by 0.512 for Model 4. Healthy people are more productive than those with poor health since they are more likely able to work and, thus, earn more income. This will decrease their expenditure gap with the rich group. 4. Conclusion The implementation of decentralisation in 2001 has substantially altered intergovernmental relationships in Indonesia. Decentralisation initiates the movement of authorities to the regions to deliver and be accountable for a wide array of public services. It is followed by massive fiscal resources in terms of balance funds that have occupied about one third of the national budget. Regions also enjoy greater autonomy to spend them at their discretion to meet local preferences and needs. Employing two-step System GMM, it is found that fiscal decentralisation, as measured by EFDI, increased expenditure inequality among people within each province. The results also highlight the critical contribution of unconditional transfers in regional budgets. An unfavourable effect of fiscal decentralisation on inequality, however, may be partly due to the implementation of the hold-harmless clause. This clause has benefited most of the rich regions. However, increasing inequality within their regions suggests that the benefit of fiscal decentralisation has not been proportionally distributed among its people. The abolishment of the hold-harmless clause in 2008, therefore, is expected to lessen expenditure inequality as poor regions received a greater share of balance funds. This enables them to deliver pro income-equalisation programs that result in decreasing inequality among their people. The real per capita GRP has a positive and significant relationship with expenditure inequality. This suggests that the richer the region, the higher will be inequality. The explanatory variable of the average years of schooling has a positive and significant relationship with inequality. This suggests that higher educational attainment improves the accessibility of higher paid jobs. It consequently enlarges income gaps between higher educated persons and those lacking such education. However, the negative coefficient sign of squared average years of schooling suggests a long-term favourable impact of higher education on lowering inequality as more educated workers engage in

18

high paid jobs. The relationship between health and inequality is negative and significant. This confirms the favourable impact of good health on lowering expenditure inequality as healthy people are more productive than unhealthy people, thus improving their income. Some remarks from this study might lead to further research. First, this study measures fiscal decentralisation by the amount of balance funds and own-source revenue without taking into account budget allocation. Different regional government spending may have different impacts toward income distribution. Findings of the study indicate the significance of health and education services in lessening expenditure inequality. Second, fiscal decentralisation indices employed in the study do not include political measures. More comprehensive fiscal decentralisation measures that consolidate both economic and political features of fiscal decentralisation may provide a more conclusive result with respect to the impact of fiscal decentralisation on expenditure inequality.

19

Appendix A. Regression result of the System GMM for other fiscal decentralisation measures

Dependent variable: Gini coefficient

Gini coefficient(-1) 0.108 0.144 0.124 0.369 0.105 0.130[0.298] [0.353] [0.243] [0.241] [0.145] [0.197]

regexp_te 4.442 **[2.086]

regexp_te(-1) -4.398 *[2.571]

rexp_texp 6.927 **[2.958]

rexp_texp(-1) -6.800 **[3.220]

regrev_tr -2.291[2.945]

regrev_tr(-1) 2.342[3.456]

vertical 0.006[0.029]

vertical(-1) -0.127 ***[0.036]

regexp_grdp 0.096 **[0.043]

regexp_grdp(-1) -0.098 *[0.053]

regrev_grdp 0.115 **[0.056]

regrev_grdp(-1) -0.079[0.078]

lnpcgrp96 0.035 *** 0.037 *** 0.034 *** 0.015 ** 0.022 * 0.024 *[0.011] [0.012] [0.011] [0.007] [0.012] [0.013]

sectot -0.195 -0.210 -0.136 -0.037 0.171 * 0.162[0.165] [0.176] [0.156] [0.073] [0.092] [0.111]

schooling 0.134 ** 0.122 ** 0.169 ** 0.082 *** 0.097 ** 0.086 **[0.053] [0.051] [0.065] [0.028] [0.037] [0.038]

sqschooling -0.011 ** -0.010 ** -0.015 *** -0.006 ** -0.007 ** -0.007 **[0.004] [0.004] [0.005] [0.002] [0.003] [0.003]

fertility -0.045 -0.036 -0.057 -0.010 -0.009 -0.015[0.031] [0.034] [0.039] [0.017] [0.020] [0.020]

health -0.648 *** -0.689 *** -0.651 *** -0.205 *** -0.442 *** -0.418 ***[0.151] [0.179] [0.157] [0.072] [0.145] [0.146]

urban growth -0.016 -0.016 -0.004 0.007 -0.014 -0.012[0.016] [0.017] [0.016] [0.010] [0.010] [0.010]

investment 0.011 0.019 -0.017 0.023 -0.106 -0.088[0.179] [0.184] [0.181] [0.098] [0.073] [0.074]

No. of observations 254 254 254 254 254 254AR(2) -0.488 -0.492 0.550 1.050 0.799 0.840(p -value) 0.626 0.622 0.582 0.294 0.424 0.401Sargan test 11.618 11.116 12.210 9.924 13.104 13.997(p -value) 0.236 0.268 0.271 0.768 0.518 0.450

Coefficient Coefficient Coefficient Coefficient Coefficient CoefficientModel 5 Model 6 Model 7 Model 8 Model 9 Model 10

20

Notes: Fiscal decentralisation indicators follow the World Bank and previous studies in this area. Model 5: ratio of regional to total expenditures (excluding transfers to other levels of government) (=regexp_te). Model 6: proportion of regional governments expenditure to total expenditure (=rexp_texp). Model 7: regional revenue (as a share of total revenue) (=regrev_tr). Model 8: vertical imbalance (ratio of regional intergovernmental transfers to regional expenditure) (=vertical). Model 9: regional expenditure, excluding transfers to other level of government (as a share of GRP) (=regexp_grdp). Model 10: regional revenue (as a share of GRP) (=regrev_grdp). Corrected standard errors are in the brackets. Time dummies are included in the System GMM estimation but not reported. The models employed two-steps System GMM, Windmeijer- corrected standard errors, and small-sample adjustment. Standard errors in brackets. *** denotes significant at the 1% level. ** denotes significant at the 5% level. * denotes significant at the 10% level.

21

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Editor, UWA Economics Discussion Papers: Ernst Juerg Weber Business School – Economics University of Western Australia 35 Sterling Hwy Crawley WA 6009 Australia Email: ecoadmin@biz.uwa.edu.au The Economics Discussion Papers are available at: 1980 – 2002: http://ecompapers.biz.uwa.edu.au/paper/PDF%20of%20Discussion%20Papers/ Since 2001: http://ideas.repec.org/s/uwa/wpaper1.html Since 2004: http://www.business.uwa.edu.au/school/disciplines/economics

ECONOMICS DISCUSSION PAPERS 2012

DP NUMBER AUTHORS TITLE

12.01 Clements, K.W., Gao, G., and Simpson, T.

DISPARITIES IN INCOMES AND PRICES INTERNATIONALLY

12.02 Tyers, R. THE RISE AND ROBUSTNESS OF ECONOMIC FREEDOM IN CHINA

12.03 Golley, J. and Tyers, R. DEMOGRAPHIC DIVIDENDS, DEPENDENCIES AND ECONOMIC GROWTH IN CHINA AND INDIA

12.04 Tyers, R. LOOKING INWARD FOR GROWTH

12.05 Knight, K. and McLure, M. THE ELUSIVE ARTHUR PIGOU

12.06 McLure, M. ONE HUNDRED YEARS FROM TODAY: A. C. PIGOU’S WEALTH AND WELFARE

12.07 Khuu, A. and Weber, E.J. HOW AUSTRALIAN FARMERS DEAL WITH RISK

12.08 Chen, M. and Clements, K.W. PATTERNS IN WORLD METALS PRICES

12.09 Clements, K.W. UWA ECONOMICS HONOURS

12.10 Golley, J. and Tyers, R. CHINA’S GENDER IMBALANCE AND ITS ECONOMIC PERFORMANCE

12.11 Weber, E.J. AUSTRALIAN FISCAL POLICY IN THE AFTERMATH OF THE GLOBAL FINANCIAL CRISIS

12.12 Hartley, P.R. and Medlock III, K.B. CHANGES IN THE OPERATIONAL EFFICIENCY OF NATIONAL OIL COMPANIES

12.13 Li, L. HOW MUCH ARE RESOURCE PROJECTS WORTH? A CAPITAL MARKET PERSPECTIVE

12.14 Chen, A. and Groenewold, N. THE REGIONAL ECONOMIC EFFECTS OF A REDUCTION IN CARBON EMISSIONS AND AN EVALUATION OF OFFSETTING POLICIES IN CHINA

28

ECONOMICS DISCUSSION PAPERS 2012

DP NUMBER AUTHORS TITLE

12.15 Collins, J., Baer, B. and Weber, E.J. SEXUAL SELECTION, CONSPICUOUS CONSUMPTION AND ECONOMIC GROWTH

12.16 Wu, Y. TRENDS AND PROSPECTS IN CHINA’S R&D SECTOR

12.17 Cheong, T.S. and Wu, Y. INTRA-PROVINCIAL INEQUALITY IN CHINA: AN ANALYSIS OF COUNTY-LEVEL DATA

12.18 Cheong, T.S. THE PATTERNS OF REGIONAL INEQUALITY IN CHINA

12.19 Wu, Y. ELECTRICITY MARKET INTEGRATION: GLOBAL TRENDS AND IMPLICATIONS FOR THE EAS REGION

12.20 Knight, K. EXEGESIS OF DIGITAL TEXT FROM THE HISTORY OF ECONOMIC THOUGHT: A COMPARATIVE EXPLORATORY TEST

12.21 Chatterjee, I. COSTLY REPORTING, EX-POST MONITORING, AND COMMERCIAL PIRACY: A GAME THEORETIC ANALYSIS

12.22 Pen, S.E. QUALITY-CONSTANT ILLICIT DRUG PRICES

12.23 Cheong, T.S. and Wu, Y. REGIONAL DISPARITY, TRANSITIONAL DYNAMICS AND CONVERGENCE IN CHINA

12.24 Ezzati, P. FINANCIAL MARKETS INTEGRATION OF IRAN WITHIN THE MIDDLE EAST AND WITH THE REST OF THE WORLD

12.25 Kwan, F., Wu, Y. and Zhuo, S. RE-EXAMINATION OF THE SURPLUS AGRICULTURAL LABOUR IN CHINA

12.26 Wu, Y. R&D BEHAVIOUR IN CHINESE FIRMS

12.27 Tang, S.H.K. and Yung, L.C.W. MAIDS OR MENTORS? THE EFFECTS OF LIVE-IN FOREIGN DOMESTIC WORKERS ON SCHOOL CHILDREN’S EDUCATIONAL ACHIEVEMENT IN HONG KONG

12.28 Groenewold, N. AUSTRALIA AND THE GFC: SAVED BY ASTUTE FISCAL POLICY?

29

ECONOMICS DISCUSSION PAPERS 2013

DP NUMBER AUTHORS TITLE

13.01 Chen, M., Clements, K.W. and Gao, G.

THREE FACTS ABOUT WORLD METAL PRICES

13.02 Collins, J. and Richards, O. EVOLUTION, FERTILITY AND THE AGEING POPULATION

13.03 Clements, K., Genberg, H., Harberger, A., Lothian, J., Mundell, R., Sonnenschein, H. and Tolley, G.

LARRY SJAASTAD, 1934-2012

13.04 Robitaille, M.C. and Chatterjee, I. MOTHERS-IN-LAW AND SON PREFERENCE IN INDIA

13.05 Clements, K.W. and Izan, I.H.Y. REPORT ON THE 25TH PHD CONFERENCE IN ECONOMICS AND BUSINESS

13.06 Walker, A. and Tyers, R. QUANTIFYING AUSTRALIA’S “THREE SPEED” BOOM

13.07 Yu, F. and Wu, Y. PATENT EXAMINATION AND DISGUISED PROTECTION

13.08 Yu, F. and Wu, Y. PATENT CITATIONS AND KNOWLEDGE SPILLOVERS: AN ANALYSIS OF CHINESE PATENTS REGISTER IN THE US

13.09 Chatterjee, I. and Saha, B. BARGAINING DELEGATION IN MONOPOLY

13.10 Cheong, T.S. and Wu, Y. GLOBALIZATION AND REGIONAL INEQUALITY IN CHINA

13.11 Cheong, T.S. and Wu, Y. INEQUALITY AND CRIME RATES IN CHINA

13.12 Robertson, P.E. and Ye, L. ON THE EXISTENCE OF A MIDDLE INCOME TRAP

13.13 Robertson, P.E. THE GLOBAL IMPACT OF CHINA’S GROWTH

13.14 Hanaki, N., Jacquemet, N., Luchini, S., and Zylbersztejn, A.

BOUNDED RATIONALITY AND STRATEGIC UNCERTAINTY IN A SIMPLE DOMINANCE SOLVABLE GAME

13.15 Okatch, Z., Siddique, A. and Rammohan, A.

DETERMINANTS OF INCOME INEQUALITY IN BOTSWANA

13.16 Clements, K.W. and Gao, G. A MULTI-MARKET APPROACH TO MEASURING THE CYCLE

13.17 Chatterjee, I. and Ray, R. THE ROLE OF INSTITUTIONS IN THE INCIDENCE OF CRIME AND CORRUPTION

13.18 Fu, D. and Wu, Y. EXPORT SURVIVAL PATTERN AND DETERMINANTS OF CHINESE MANUFACTURING FIRMS

13.19 Shi, X., Wu, Y. and Zhao, D. KNOWLEDGE INTENSIVE BUSINESS SERVICES AND THEIR IMPACT ON INNOVATION IN CHINA

13.20 Tyers, R., Zhang, Y. and Cheong, T.S.

CHINA’S SAVING AND GLOBAL ECONOMIC PERFORMANCE

13.21 Collins, J., Baer, B. and Weber, E.J. POPULATION, TECHNOLOGICAL PROGRESS AND THE EVOLUTION OF INNOVATIVE POTENTIAL

30

ECONOMICS DISCUSSION PAPERS 2013

DP NUMBER AUTHORS TITLE

13.22 Hartley, P.R. THE FUTURE OF LONG-TERM LNG CONTRACTS

13.23 Tyers, R. A SIMPLE MODEL TO STUDY GLOBAL MACROECONOMIC INTERDEPENDENCE

13.24 McLure, M. REFLECTIONS ON THE QUANTITY THEORY: PIGOU IN 1917 AND PARETO IN 1920-21

13.25 Chen, A. and Groenewold, N. REGIONAL EFFECTS OF AN EMISSIONS-REDUCTION POLICY IN CHINA: THE IMPORTANCE OF THE GOVERNMENT FINANCING METHOD

13.26 Siddique, M.A.B. TRADE RELATIONS BETWEEN AUSTRALIA AND THAILAND: 1990 TO 2011

13.27 Li, B. and Zhang, J. GOVERNMENT DEBT IN AN INTERGENERATIONAL MODEL OF ECONOMIC GROWTH, ENDOGENOUS FERTILITY, AND ELASTIC LABOR WITH AN APPLICATION TO JAPAN

13.28 Robitaille, M. and Chatterjee, I. SEX-SELECTIVE ABORTIONS AND INFANT MORTALITY IN INDIA: THE ROLE OF PARENTS’ STATED SON PREFERENCE

13.29 Ezzati, P. ANALYSIS OF VOLATILITY SPILLOVER EFFECTS: TWO-STAGE PROCEDURE BASED ON A MODIFIED GARCH-M

13.30 Robertson, P. E. DOES A FREE MARKET ECONOMY MAKE AUSTRALIA MORE OR LESS SECURE IN A GLOBALISED WORLD?

13.31 Das, S., Ghate, C. and Robertson, P. E.

REMOTENESS AND UNBALANCED GROWTH: UNDERSTANDING DIVERGENCE ACROSS INDIAN DISTRICTS

13.32 Robertson, P.E. and Sin, A. MEASURING HARD POWER: CHINA’S ECONOMIC GROWTH AND MILITARY CAPACITY

13.33 Wu, Y. TRENDS AND PROSPECTS FOR THE RENEWABLE ENERGY SECTOR IN THE EAS REGION

13.34 Yang, S., Zhao, D., Wu, Y. and Fan, J.

REGIONAL VARIATION IN CARBON EMISSION AND ITS DRIVING FORCES IN CHINA: AN INDEX DECOMPOSITION ANALYSIS

31

ECONOMICS DISCUSSION PAPERS 2014

DP NUMBER AUTHORS TITLE

14.01 Boediono, Vice President of the Republic of Indonesia

THE CHALLENGES OF POLICY MAKING IN A YOUNG DEMOCRACY: THE CASE OF INDONESIA (52ND SHANN MEMORIAL LECTURE, 2013)

14.02 Metaxas, P.E. and Weber, E.J. AN AUSTRALIAN CONTRIBUTION TO INTERNATIONAL TRADE THEORY: THE DEPENDENT ECONOMY MODEL

14.03 Fan, J., Zhao, D., Wu, Y. and Wei, J. CARBON PRICING AND ELECTRICITY MARKET REFORMS IN CHINA

14.04 McLure, M. A.C. PIGOU’S MEMBERSHIP OF THE ‘CHAMBERLAIN-BRADBURY’ COMMITTEE. PART I: THE HISTORICAL CONTEXT

14.05 McLure, M. A.C. PIGOU’S MEMBERSHIP OF THE ‘CHAMBERLAIN-BRADBURY’ COMMITTEE. PART II: ‘TRANSITIONAL’ AND ‘ONGOING’ ISSUES

14.06 King, J.E. and McLure, M. HISTORY OF THE CONCEPT OF VALUE

14.07 Williams, A. A GLOBAL INDEX OF INFORMATION AND POLITICAL TRANSPARENCY

14.08 Knight, K. A.C. PIGOU’S THE THEORY OF UNEMPLOYMENT AND ITS CORRIGENDA: THE LETTERS OF MAURICE ALLEN, ARTHUR L. BOWLEY, RICHARD KAHN AND DENNIS ROBERTSON

14.09

Cheong, T.S. and Wu, Y. THE IMPACTS OF STRUCTURAL RANSFORMATION AND INDUSTRIAL UPGRADING ON REGIONAL INEQUALITY IN CHINA

14.10 Chowdhury, M.H., Dewan, M.N.A., Quaddus, M., Naude, M. and Siddique, A.

GENDER EQUALITY AND SUSTAINABLE DEVELOPMENT WITH A FOCUS ON THE COASTAL FISHING COMMUNITY OF BANGLADESH

14.11 Bon, J. UWA DISCUSSION PAPERS IN ECONOMICS: THE FIRST 750

14.12 Finlay, K. and Magnusson, L.M. BOOTSTRAP METHODS FOR INFERENCE WITH CLUSTER-SAMPLE IV MODELS

14.13 Chen, A. and Groenewold, N. THE EFFECTS OF MACROECONOMIC SHOCKS ON THE DISTRIBUTION OF PROVINCIAL OUTPUT IN CHINA: ESTIMATES FROM A RESTRICTED VAR MODEL

14.14 Hartley, P.R. and Medlock III, K.B. THE VALLEY OF DEATH FOR NEW ENERGY TECHNOLOGIES

14.15 Hartley, P.R., Medlock III, K.B., Temzelides, T. and Zhang, X.

LOCAL EMPLOYMENT IMPACT FROM COMPETING ENERGY SOURCES: SHALE GAS VERSUS WIND GENERATION IN TEXAS

14.16 Tyers, R. and Zhang, Y. SHORT RUN EFFECTS OF THE ECONOMIC REFORM AGENDA

14.17 Clements, K.W., Si, J. and Simpson, T. UNDERSTANDING NEW RESOURCE PROJECTS

32

ECONOMICS DISCUSSION PAPERS 2014

DP NUMBER AUTHORS TITLE

14.18 Tyers, R. SERVICE OLIGOPOLIES AND AUSTRALIA’S ECONOMY-WIDE PERFORMANCE

14.19 Tyers, R. and Zhang, Y. REAL EXCHANGE RATE DETERMINATION AND THE CHINA PUZZLE

14.20 Ingram, S.R. COMMODITY PRICE CHANGES ARE CONCENTRATED AT THE END OF THE CYCLE

14.21 Cheong, T.S. and Wu, Y. CHINA'S INDUSTRIAL OUTPUT: A COUNTY-LEVEL STUDY USING A NEW FRAMEWORK OF DISTRIBUTION DYNAMICS ANALYSIS

14.22 Siddique, M.A.B., Wibowo, H. and Wu, Y.

FISCAL DECENTRALISATION AND INEQUALITY IN INDONESIA: 1999-2008

14.23 Tyers, R. ASYMMETRY IN BOOM-BUST SHOCKS: AUSTRALIAN PERFORMANCE WITH OLIGOPOLY

14.24 Arora, V., Tyers, R. and Zhang, Y. RECONSTRUCTING THE SAVINGS GLUT: THE GLOBAL IMPLICATIONS OF ASIAN EXCESS SAVING

14.25 Tyers, R. INTERNATIONAL EFFECTS OF CHINA’S RISE AND TRANSITION: NEOCLASSICAL AND KEYNESIAN PERSPECTIVES

14.26 Milton, S. and Siddique, M.A.B. TRADE CREATION AND DIVERSION UNDER THE THAILAND-AUSTRALIA FREE TRADE AGREEMENT (TAFTA)

14.27 Clements, K.W. and Li, L. VALUING RESOURCE INVESTMENTS

33