Fiscal decentralization and local expenditure policy in China

China Economic Review 28 (2014) 107–122

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China Economic Review

Fiscal decentralization and local expenditure policy in China☆ Junxue JIA 1, Qingwang GUO 2, Jing ZHANG ⁎ China Financial Policy Research Center, School of Finance, Renmin University of China, 59 Zhongguancun Street, Beijing 100872, China

a r t i c l e

i n f o

Article history: Received 24 March 2012 Received in revised form 10 January 2014 Accepted 10 January 2014 Available online 21 January 2014 Keywords: Fiscal decentralization Local expenditure policy Common-pool problem Vertical fiscal imbalance

a b s t r a c t Since the tax-sharing reform in 1994, the Chinese fiscal system has exhibited a marked vertical fiscal imbalance—a mismatch between expenditure and revenue assignments—at the local levels, which may cause the common-pool problem in local governments' behavior. Using a large fiscal dataset at the county level from 1997 to 2006, this paper studies the effects of fiscal decentralization on local expenditure policy and analyzes how the vertical fiscal imbalance shapes these effects. The estimation results show that expenditure decentralization increases government spending and leads to a fund allocation with a larger weight on capital construction and smaller weights on education and administration. In contrast, revenue decentralization has little influence on local government expenditures. We show that the differences in the effects of expenditure and revenue decentralization can be attributed to the distortionary effects caused by the vertical fiscal imbalance and, thus, offer support to the importance of the common-pool problem at the county level of China's fiscal system. © 2014 Elsevier Inc. All rights reserved.

1. Introduction Conventional wisdom holds that fiscal decentralization enhances government efficiency by restraining the size of public sectors or by providing public goods that better cater to local residents' preferences (Brennan & Buchanan, 1980; Hayek, 1948; Oates, 1972; Qian & Roland, 1998; Tiebout, 1956). However, whether the benefits of fiscal decentralization do materialize depends on complementary institutions in a country (Rodden, Eskeland, & Litvack, 2003). In fact, one strand of the literature argues that fiscal decentralization could boost government size if governments' expenditure and revenue responsibilities are not adequately balanced, which has been described as the common-pool problem (Ehdaie, 1994; Grossman, 1989; Rodden, 2003; Stein, 1999). In addition to the overall effects, how different components of government expenditures (for example, spending on capital construction, education or social security) react to fiscal decentralization is another important issue that is under-explored. Understanding the effects of fiscal decentralization on specific components of fiscal expenditures sheds light on the preference of the local governments when they are granted more autonomy. Indeed, it has been conjectured that local governments tend to invest in the expenditure categories that are useful for attracting mobile factors and to neglect those benefiting immobile factors (Keen & Marchard, 1997). Whether this conjecture holds is of interest to both researchers and policy advisors. The existing empirical evidence on the relations between fiscal decentralization and government expenditure is mainly based on cross-country or state-level data (for example, Ehdaie, 1994; Fiva, 2006; Jin & Zou, 2002; Oates, 1972, 1985; Rodden, 2003; Stein, 1999).3 Nevertheless, it is reasonable to expect that a country's politics, history, culture, and other institutions can simultaneously affect the levels of fiscal decentralization and government expenditure. For instance, a new governor who supports the free market and competition would alter the fiscal structure by allowing a more decentralized governance mode. Meanwhile, because the new ☆ The research is supported by the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (2013030214). ⁎ Corresponding author. Tel.: +86 1082500614; fax: +86 1082509287. E-mail addresses: [email protected] (J. Jia), [email protected] (Q. Guo), [email protected] (J. Zhang). 1 Tel.: +86 1082500646; fax: +86 1082509287. 2 Tel./fax: +86 1082509287. 3 These studies include cross-sectional, time series and panel data analysis. 1043-951X/$ – see front matter © 2014 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.chieco.2014.01.002

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governor may prefer to be less involved in the economy, the government expenditure may shrink as well. If we are ignorant of such political transitions, we could reach an incorrect conclusion that decentralization itself restrains government size. Without perfect proxies for this institutional background, the estimation of the relationship between government decentralization and government behavior may not be accurate. Our paper studies the influences of fiscal decentralization on government size and its composition by using the most comprehensive fiscal data of 1920 Chinese counties from 1997 to 2006. The contribution of our studies lies in the following several aspects. First, in contrast to the joint fiscal and political decentralization found in most democratic countries, China has a decentralized fiscal regime coupled with a highly centralized political system (World Bank, 2002; Xu, 2011; Zhang, 2006). Particularly, since the 1994 tax-sharing reform, China's fiscal system has demonstrated a strong mismatch between expenditure and revenue responsibilities, that is, vertical fiscal imbalance. This specific governance structure with a significant vertical fiscal imbalance provides us with an interesting context to examine the relevance of the current theories of fiscal decentralization. Second, understanding the performance of fiscal decentralization among sub-provincial governments is essential to evaluate the efficiency of current governance structure and to guide future government reforms in China. So far, the fiscal relations between the central and provincial governments in China have been well documented and studied (Bai, Tao, & Tong, 2008; Huang & Chen, 2012; Jin, Qian, & Weingast, 2005; Lin & Liu, 2000; Qiao, Martinez-Vazquez, & Xu, 2008; Zhang & Zou, 1998). Yet, corresponding knowledge of the sub-provincial governments remains sparse. Only a few studies have been conducted to understand their decentralization practice (Shih & Zhang, 2007; Tsui, 2005; World Bank, 2002; Zhang, 2006). Our study sheds some new light in this area. Third, we use the county-level panel data from 1997 to 2006 for the empirical test of theoretical hypothesis in fiscal decentralization. One advantage of employing this county-level data is that some aforementioned unobserved aggregate shocks can be captured by year or provincial-times-year fixed effects in this context. Thus, our analysis better avoids omitted variable bias caused by macro- or provincial-level unobservables. Meanwhile, employment of this panel data allows us to control for the time-invariant county heterogeneity in the analysis. By employing a dynamic panel data model to account for county heterogeneity and persistence in government expenditures, we find that expenditure decentralization raises the amount of county governments' total expenditures and the ratio of capital construction spending but reduces the proportions of education and administrative expenditures. In contrast, revenue decentralization has little influence on the size or the composition of county governments' expenditures. These two findings imply that the divergence of expenditure and revenue assignments is associated with a larger government. By splitting the sample based on counties' average degrees of vertical fiscal imbalance, we further find that the influences of expenditure decentralization are stronger in the counties with higher levels of vertical fiscal imbalance. The above findings suggest that the common-pool problem is prevalent in China's fiscal system. In China, the 1994 tax-sharing reform initiated a series of fiscal reforms that recentralize the tax revenues while devolving expenditure responsibilities to the lower levels of governments. In this fiscal regime, county governments do not have enough revenue capacities to finance the public goods and services that they are assigned and have to resort to transfers from the upper-level governments or to other common-pool resources to finance their expenditures. This fiscal arrangement provides the county governments with incentive to overspend because their residents only pay for part of their government expenditures. Indeed, only expenditure decentralization without matched revenue decentralization in China undermines government efficiency. Furthermore, since in China capital is mobile while labor is much less so due to the current household registration (hukou) system, the county governments prefer to spend much more in infrastructure construction to attract capital inflow rather than in education to attract labor. The rest of this paper is organized as follows. Section 2 summarizes the theoretical and empirical literature that discusses the relationships between fiscal decentralization and government expenditures. Section 3 describes the fiscal system in China and several major reforms of fiscal disciplines during our study period (1997–2006). The descriptions of the data, estimation strategy, and variables are in Section 4. Section 5 presents the regression results, and the final section concludes. 2. Literature review 2.1. Theoretical framework In the literature, two classic theories—asymmetric information and fiscal competition—explain the mechanisms through which fiscal decentralization might influence government behavior. Oates (1972) first brings up the role of asymmetric information in determining the fiscal structure. He emphasizes that local governments are better informed of diverse local conditions and of preferences than the national government. In that case, devolving expenditure responsibilities to local governments can make such governments meet the needs of local residents better, and the allocation of government resources becomes more efficient. This prominent strand of literature does not put forward a conclusive conjecture on the correlation between fiscal decentralization and government size. Nevertheless, the theories of fiscal competition render the most unambiguous predictions of the links between fiscal decentralization and smaller governments. Assuming a benevolent government that seeks to maximize civil welfare, several studies have argued that fiscal competition could lead to a “race to the bottom” in public goods provision and inefficiently low levels of government expenditures (Wildasin, 1991; Wilson, 1986, 1999; Zodrow & Mieszkowski, 1986). In contrast, rested on a more realistic assumption that governments, as the Leviathan, always maximize revenues instead of social welfare, Brennan and Buchanan (1980) predict that interjurisdictional competition in attracting mobile factors (such as capital and labor) limits

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governments' excessive taxing power. Through intensifying interjurisdictional competition and enforcing accountabilities of local governments to their own jurisdictions, fiscal decentralization serves as a disciplinary device to mitigate agency problems. Hence, the Leviathan model implies that fiscal decentralization induces smaller governments. Despite its theoretical appeal, the Leviathan model may not suit the reality well. In fact, central and local governments usually collude to avoid such interjurisdictional competition by allowing central governments to collect a majority of the revenues and then to transfer money to local governments to finance their expenditures (Ehdaie, 1994; Grossman, 1989). One problem that has been discussed about this fiscal arrangement is that the expenditure decisions of local governments are not independent of fund source. Right now, local public expenditures come partially from the taxes collected from residents outside the jurisdictions, which incentivizes the local governments to spend more than they would when using their own tax revenues. This outcome has been described as the common-pool problem in the literature (Rodden, 2003; Stein, 1999). The larger the gap is between expenditure and revenue responsibilities, the weaker the disciplinary role of fiscal decentralization is and, thus, the larger the local governments may become. As for the fiscal decentralization and government expenditure composition, Tiebout (1956) argues that interjurisdictional competition leads to efficient public goods provision when both labor and capital are free to move. Under the scenario with only mobile capital, Keen and Marchard (1997) show that fiscal competition may cause systematic inefficiencies in the composition of government expenditures; that is, local governments are willing to spend more on public inputs to attract capital and to underprovide public services that benefit local residents. 2.2. Empirics Starting with Oates (1972), numerous studies have tested the relationship between fiscal decentralization and government expenditures in a variety of contexts, with special focus on the Leviathan hypothesis that fiscal decentralization restrains government expenditures. The results of these studies become inconclusive (Feld, Kirchgässner, & Schaltegger, 2010; Marlow, 1988; Oates, 1985). To reconcile the findings of the empirical literature, one section of the literature has created the concept of vertical fiscal imbalance to measure the gap between expenditure and own-source revenue and has emphasized its role in shaping the relationship between fiscal decentralization and government size. Ehdaie (1994), Stein (1999), Jin and Zou (2002), Rodden (2003), and Fiva (2006) all find that vertical fiscal imbalance is associated with the expansion of governments. Studies on the influences of fiscal decentralization on the composition of government expenditures are relatively limited. Faguet (2004) finds that decentralization increases government responsiveness to local needs—leading to larger public investments in education and other social services in Bolivia. Fiva (2006) shows that social security transfers decrease with revenue decentralization and that government consumption increases with expenditure decentralization. Kappeler and Välilä (2008) present that tax revenue decentralization boosts public infrastructure investments and reduces economically less productive investments, such as recreational facilities. In the case of China, the existing studies have been conducted mostly at the provincial level. Zhu and Krug (2005) use time-series data at the national level from 1953 to 2002 and cross-sectional data at the provincial level for 2000 to test the Leviathan hypothesis. They find that expenditure decentralization imposes constraints on government size at both the national and provincial levels. In contrast, Chen (2004) shows a positive correlation between expenditure decentralization and provincial government size using panel data from 1986 to 1998. Similarly, using provincial-level panel data from 1998 to 2006, Wu and Lin (2010) find that both expenditure and revenue decentralization increase government size, while vertical fiscal imbalance does not have any statistically significant influences. Nonetheless, as Jin and Zou (2005) point out, there is no reason to believe that the findings at the provincial level also hold true at the lower levels of governments. With special interests in Chinese county governments, this paper explores the relationships between fiscal decentralization and both the size and the composition of their government expenditures. By using the most comprehensive fiscal data of 1920 Chinese counties from 1997 to 2006, our study provides a new piece of evidence on the practice of fiscal decentralization at the county-level in China. 3. Institutional background (1997–2006) China has a decentralized fiscal system coupled with a highly centralized political system (World Bank, 2002; Xu, 2011; Zhang, 2006). Its fiscal system contains five hierarchical levels: Central, provincial, prefectural, county, and township. As of 2006, the system comprises one central government; 31 provincial-level units (4 municipalities, 22 provinces, and 5 autonomous regions)4; 333 prefectures; 2860 county-level units5; and 41,040 townships (National Bureau of Statistics, 2007). With the introduction and development of a market economy since 1978, the fiscal relations between central and provincial governments have been revamped by several major reforms. Those reforms mainly focused on the revenue-sharing rules and only made a few changes in expenditure responsibilities. The current tax-sharing scheme stipulated in 1994 recentralized the majority of the tax revenues. The share of total budgetary revenues assigned to the central government increased from 22.0% in 1993 to 55.7% in 1994 and has remained at around 52.0% since then (National Bureau of Statistics, 2011). Nevertheless, the fiscal regime at

4 Hainan was established as a province in 1988, and Chongqing was designated as a municipality in 1997. In this paper, we do not include Taiwan Province and the two special administrative regions of Hong Kong and Macau in China. 5 For simplicity, county-level units are called counties in the rest of the paper.

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40 10

20

30

percent

50

60

70

110

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

year

expenditure decentralization vertical fiscal imbalance

revenue decentralization

Fig. 1. Expenditure decentralization, revenue decentralization, and vertical fiscal imbalance at the county level, 1997–2006. Notes: Expenditure decentralization and revenue decentralization are the ratios of the per capita expenditures/revenues of a county government in the total of per capita expenditures/revenues of (1) its own and (2) the governments that the county is subordinated to, including the prefectural, provincial, and central governments. Vertical fiscal imbalance is measured by the ratio of the county governments' fiscal gap (including grants net of remittances and deficits) to their expenditures. Data sources: Statistical Material for Prefectures, Cities, and Counties Nationwide (Ministry of Finance, 1998–2007).

the sub-provincial level is quite autonomous and complex. In China, the upper-level governments usually have the decisive power on the fiscal schemes of their directly subordinate governments with little discretion of the central government. That is, the provinces specify the revenue sharing with their respective prefectures, which, in turn, set the rules with their counties. Consequently, the fiscal schemes of prefectural and county governments are the results of case-by-case ad hoc negotiation and present a substantial variety throughout the country. For example, the sub-provincial (prefectural, county, and township) governments in Jiangsu Province was allowed to retain 50% of the local revenues collected from value-added taxes (VATs) in 2000, as compared to 80% in Hunan Province (World Bank, 2002). Because governments' revenues have been centralized, local governments have fewer financial resources than before and have the tendency to devolve expenditure responsibilities to the lower levels (World Bank, 2002). As the second-lowest tier in the fiscal hierarchy, county governments have faced greatly expanded expenditure obligations, including support to agricultural production, finance of compulsory education, provision of public health services, and so forth. Furthermore, since China's social security net started to rebuild in the 1990s, they are even assigned to manage some of the social security programs that are usually run by central or provincial/state governments in international practice (for example, the New Cooperative Medical Scheme for rural residents and support for low-income families). During our study period (1997–2006), county governments on average accounted for 28.98% of total government expenditures and 49.38% of government education expenditures.6 Fig. 1 plots the index of expenditure and revenue decentralization of counties in our sample over those years. The expenditure decentralization—the share of a county government's per capita spending in the total of per capita spending of its own and its superordinate governments—has increased continuously since 2000. This number was around 41.97% in 2006. On the contrary, the proportion of the county governments' per capita revenues, defined as revenue decentralization, has decreased from 23.61% in 1997 to 15.2% in 2006. This downward trend of revenue decentralization is attributed to several tax reforms during this period. One of the most prominent reforms concerned the sharing of income taxes in 2002. Before 2002 the local governments were allowed to retain all individual and enterprise income tax revenues except for those collected from centrally owned enterprises. The reform on income taxes in 2002 reassigned the revenue split: The central government owned 50% of income taxes in 2002 and 60% thereafter. Another significant adjustment in the revenue collection of local governments was the Rural Tax for Fee reform in 2000, which revoked a variety of user fees and surcharges levied by county and township governments and cut the agricultural tax rate at the same time. Along with expenditure decentralization and revenue centralization in China's fiscal system, the fiscal gap in county governments' budgets continues to increase. Transfers from upper-level governments and other common-pool resources mainly fill this fiscal gap. Our data show that in 1997 county governments' expenditures amounted to 108.7 million Chinese Yuan on average, of which 44.91% was financed by intergovernmental grants (net of remittances to upper-level governments). And this ratio rose to 69.81% in 2006. Albeit the large scale of transfers, some counties still run significant budget deficits and have to borrow directly or indirectly from banks or other sources to supplement the budget (Li, 2010; World Bank, 2002). In our data, 609 counties registered budget deficits in 2006 with the average level at 12.36 million Chinese Yuan. Given that Chinese county governments face a severe mismatch between their expenditure responsibilities and revenue capacities, uncovering whether and to what extent their expenditures may be distorted would have profound policy implications to future government reform. 6 These percentages are calculated using data from the Statistical Material for Prefectures, Cities, and Counties Nationwide (Ministry of Finance, 1998–2007) and China Statistical Yearbook (National Bureau of Statistics, 2011).

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4. Data and variables 4.1. Data The data used in this paper are collected primarily from Statistical Material for Prefectures, Cities, and Counties Nationwide (Ministry of Finance, 1998–2007), the most comprehensive disaggregated data on public finance in China. The data cover all county-level administrative units, including counties, county-level cities, and urban districts and contain detailed information on the local governments' revenues, expenditures, and transfers in various categories as well as some basic socioeconomic variables in these units (for example, gross domestic product (GDP) and population size). We limit our sample period between 1997 and 2006 because some of the key variables, such as GDP and the main categories of government expenditure, are consistently measured and recorded in those yearbooks during that period. All the county-level urban districts are excluded because they are believed to be systematically different from their rural counterparts in the literature (Ahmad, 1998; Shih & Zhang, 2007; Shih, Zhang, & Liu, 2010; Zhang, 2006).7 The county population data after 2001 are collected from China Statistical Yearbook for Regional Economy (National Bureau of Statistics, 2003–2007) because they are no longer recorded in the previous yearbooks. The area data are from China County Statistical Yearbook (National Bureau of Statistics, 2001–2007).8 To select the sample for this study, we exclude counties in accordance with the following rules: (1) counties that changed their judiciary boundaries from 1997 to 2006, (2) all of the counties in Tibet because of vast missing values, and (3) county observations whose values of the regression variables themselves are above the 99.5th percentile or below the 0.5th percentile.9,10 Finally, our sample comprises 1920 counties, and it is unbalanced panel data because some counties were newly formed during the study period. 4.2. Estimation strategy Considering the slow adjustments in both the size and the composition of county government expenditures, we use a dynamic panel model to investigate the influences of fiscal decentralization: Gipt ¼

X m

α m Gipt−m þ Fdecipt β þ X ipt δ þ upt þ υi þ εipt ;

ð1Þ

where Gipt refers to the variables that depict local expenditure policy: The government expenditure size or the share of each expenditure category of county i in province p in year t. Gipt − m is the m-th lag of dependent variable accounting for the persistence of government expenditures and their structure. Fdecipt includes the two types of decentralization index—expenditure decentralization and revenue decentralization. β stands for the corresponding coefficients for those two variables. Xipt represents a set of control variables that shape local government expenditures. And δ is the vector of the coefficients of those control variables. To capture the time patterns of the outcome variables that might be different across the provinces, we control for a full set of province-times-year fixed effects, denoted by upt. υi refers to the county-level fixed effects that capture any unobserved time-invariant county heterogeneity. And εipt is the idiosyncratic error term. To decide the maximum lag length to be included in the regression specification, following Han, Phillips, and Sul (2012), we truncate the sample to the observations for t ≥ 3 and choose the optimal lag length based on the Schwarz (Bayesian) Information Criterion (BIC). The results suggest the inclusion of both the first- and the second-period lags of the dependent variables,11 and the basic specification becomes: Gipt ¼ α 1 Gipt−1 þ α 2 Gipt−2 þ Fdecipt β þ X ipt δ þ upt þ υi þ εipt :

ð2Þ

7 Ahmad (1998) suggests that district governments are less fiscally independent. Yang (2011) shows district governments have very limited autonomy in their fiscal decision. They usually follow the planning of the prefectural governments and collaborate in public goods provision for whole urban districts. For example, some districts are classified as the high and new technology development zone, while some districts are mainly for recreation or tourism. 8 Because China County Statistical Yearbooks have only been published from 2001, the data of counties' area before 2000 are proxied with the data in 2000. 9 To eliminate the possibility of misreports, we delete the observations whose values of regression variables are below 0.5th percentile or above 99.5th percentile. The variables are the following: Government expenditure size, the shares of capital construction, education, social security subsidy, administrative expenditures in total expenditures, expenditure decentralization, revenue decentralization, vertical fiscal imbalance, log of real GDP per capita, log of population density, fiscally supported population (percentage of total population), and own-source revenue size. 10 Our regression specification controls for the province-times-year fixed effects. Therefore, Chongming County, the only county in Shanghai, does not contribute to our estimation. In addition, “any dummy that is 0 for almost all individuals, or 1 for almost all, might cause bias” (Roodman, 2009a, p. 115). Thus, we exclude the observations whose values of the province-times-year dummy variables are very close to 0. The Stata code is available on request. 11 To keep more observations, our choice on the optimal lags concerns whether to include the second-period lag. Following Han, Phillips, and Sul (2012), we truncate the data to the observations for t ≥ 3 and calculate BIC for the specifications with and without the second-period lags of dependent variables for each outcome. For the regressions with only lag 1, the BICs for government expenditure and the ratios of capital construction, education, social security, and administration are 84,499.07, 54,931.84, 73,827.18, 56,597.49, and 79,397.93, respectively. And for the specifications with both lag 1 and lag 2, BICs are 83,975.52, 54,915.6, 73,819.89, 56,567.98, and 79,405.63, respectively. A lower BIC indicates a better fit of specification. Considering the consistency of our specification, we choose to include both the first- and the second-period lags in our model.

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The regular Ordinary Least Squares (OLS) estimates of the coefficients are biased because of the correlation between the lags of dependent variables and county fixed effects. We take the first differences of Eq. (2) to cancel the county fixed effects: ΔGipt ¼ α 1 ΔGipt−1 þ α 2 ΔGipt−2 þ ΔFdecipt β þ ΔX ipt δ þ Δupt þ Δεipt :

ð3Þ

Here Δ is the first-difference operator—for example, ΔGipt = Gipt − Gipt − 1. Given that cov(ΔGipt − 1, Δεipt) ≠ 0 because of the correlation between Gipt − 1 and εipt − 1, Holtz-Eakin, Newey, and Rosen (1988) and Arellano and Bond (1991) introduce all further available lags Gipt − j for j = 2,…, t − 1 as the instruments for ΔGipt − 1. This method is named the difference GMM estimation. However, if the dependent variable is highly persistent over time, then the past levels of the dependent variable become weak instruments for differenced lags, and the difference GMM estimation suffers from finite sample bias. To deal with this problem, Arellano and Bover (1995) and Blundell and Bond (1998) propose the system GMM estimation that combines regressions both in differences and in levels into one system, where the differenced lags of the dependent variable are instrumented by the further lags as the difference GMM. For the equation in levels, the instruments are the differenced lags of the dependent variable. In this paper, we use the two-step system GMM method with the small sample correction for robust standard errors developed by Windmeijer (2005). All of the standard errors are clustered at the prefectural level. The consistency of the two-step system GMM estimation relies on the validity of the instruments, which is usually addressed by three specification tests. Two of them are the over-identification tests to check the orthogonality conditions between instruments and error terms in difference and level equations—the Hansen and difference-in-Hansen (incremental Hansen) tests.12,13 Their null hypothesis is that the instruments are exogenous, and the model gains better support with the failure to reject the null. The third test is the second-order serial correlation test, AR(2), which examines whether the first-differenced error term is second-order serially correlated. This second-order serial correlation indicates that the non-differenced error term is serially correlated, which implies that the chosen instruments are inadequate. We report the p-values of those tests for each regression result. In addition, Roodman (2009b) shows that the conventional approach to choose instruments could cause an instrument proliferation problem and weaken the power of the Hansen over-identification tests in a finite sample. Based on this concern, we substitute one-period lags of the endogenous variables for all their further lags as the instruments to ensure the robustness of the estimation results. 4.3. Variables Next, we explain our key variables and present their descriptive characteristics in Table 1. 4.3.1. Local expenditure policy The size and composition of county governments' expenditures are used to describe local expenditure policy. Government expenditure size is measured by a ratio of the county governments' expenditures over their local GDPs. In Table 1, the average of the government expenditure size is 13.32%, varying from 2.29% to 104.6%.14 Following Hsiao, Shen, and Fujiki (2005), we draw a 5% random sample of the counties15 and plot their government expenditure size over time in Fig. 2. The figure shows that counties' government size varies substantially across those counties and yet presents persistence over time. Moreover, Fig. 3 lays out that, on average, county government size increased rapidly during this period, from 9.76% in 1997 to 16.7% in 2006. As for the composition of county governments' expenditures, we study four major categories that are consistently recorded in our data: Capital construction, education, social security subsidies, and administration. Note that the categories of education expenditures and social security subsidies are available only from 1998. According to the functional classification of fiscal expenditures used in China before 2007, capital construction expenditures mainly comprise infrastructure investments, such as road construction and water conservancy facilities. These expenditures generate the most direct influences in stimulating local economies. Table 1 shows that they amount to only 4.68% of total county government expenditures on average. This number indicates that county governments have relatively limited capacities to make such investments in the economy, as compared to prefectural and provincial governments with around 12.9% and 18.09%, respectively, of expenditures on capital construction during this period.16 Nevertheless, counties' average proportion of capital construction spending kept rising and almost tripled during our study period—from 2.08% in 1997 to 5.55% in 2006 (see Fig. 3). Chinese county governments take the main responsibility of providing local education: They finance the operation costs of basic education (elementary and middle schools), vocational education, adult education, and teachers' training. Around a quarter (24.75%) of county government expenditures are used on education on average. The third category of government expenditures, social security subsidies, includes subsidies to social security funds, employment support, and support to laid-off workers of state-owned enterprises. Different from international practice, China has assigned some social security programs to county governments, such as the New Cooperative Medical Scheme, and subsidies for low-income families. In reality, county governments usually need to subsidize those programs using money in their budget. The 12

The difference-in-Hansen tests are implemented to check the validity of system GMM instruments for the level equations (Roodman, 2009b). To gain valid instruments and to pass the Hansen or difference-in-Hansen tests, we use the forward orthogonal deviation transformation of the instruments for some of the outcome variables (see notes under the regression tables), as suggested by Roodman (2009a). The magnitudes and significances of the estimated coefficients are barely affected by the use of this transformation. 14 In our sample, some national poverty counties' government expenditures are larger than their GDPs. For example, government expenditure of Shilou County in Shanxi Province is 156.93 million Yuan in 2004 and its GDP is 150 million Yuan. 15 Here we only include the counties whose government expenditures are not missing in all 10 waves. 16 Authors' calculation based on the data from Statistical Material for Prefectures, Cities, and Counties Nationwide (Ministry of Finance, 1998–2007). 13

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Table 1 Descriptive statistics of key variables. Variable

Observations

Mean

Std. dev.

Min.

Max.

P25

P50

P75

Government expenditure size (% of GDP) Capital construction expenditure (% of government expenditure) Education expenditure (% of government expenditure) Social security subsidy (% of government expenditure) Administrative expenditure (% of government expenditure) Expenditure decentralization (%) Revenue decentralization (%) Vertical fiscal imbalance (% of government expenditure) Dummy variable of the PGC fiscal system Number of counties in one prefecture Log of real GDP per capita Log of population density Fiscally supported population (% of total population) Own-source revenue size (% of GDP)

18,309 11,845 16,456 16,277 18,227 18,382 18,382 18,268 18,382 18,382 18,382 18,382 18,382 18,382

13.32 4.68 24.75 2.49 20.30 36.41 18.50 56.87 0.08 9.05 3.92 5.11 3.19 4.20

11.35 5.27 6.22 2.31 5.26 9.83 10.14 22.07 0.28 4.95 0.71 1.30 1.27 2.36

2.29 0.01 8.01 0.04 7.46 16.83 2.79 −4.76 0.00 1.00 1.53 −0.52 1.53 0.98

104.60 34.44 42.75 16.09 39.05 73.09 62.74 96.94 1.00 43.00 6.12 7.13 11.31 38.01

6.14 1.01 20.47 0.87 16.80 29.37 10.96 40.38 0.00 6.00 3.44 4.54 2.40 2.75

9.61 2.91 24.66 1.77 20.03 34.99 16.60 59.32 0.00 8.00 3.90 5.31 2.87 3.76

16.31 6.41 29.09 3.38 23.48 41.86 23.79 74.60 0.00 11.00 4.38 6.09 3.59 5.04

Notes: 1. Government expenditure size is the ratio of a county government's expenditure to its local GDP. The variables of capital construction expenditure, education expenditure, social security subsidy, and administrative expenditure refer to the shares of the corresponding items in total government expenditures. The two variables—expenditure decentralization and revenue decentralization—are the ratios of the per capita expenditures/revenues of a county government in the total of per capita expenditures/revenues of (1) its own and (2) the governments that the county is subordinated to, including the prefectural, provincial, and central governments. Vertical fiscal imbalance is measured by the ratio of the county governments' fiscal gap (including grants net of remittances and deficits) to their expenditures. Dummy variable of the PGC fiscal system equals one if a county adopts the Province Directly Governing County (PGC) fiscal system in a certain year and zero otherwise. Real GDP per capita is deflated by the provincial-level consumer prices in 1997 and its unit is 10,000 Chinese Yuan. Population density is constructed by dividing the population by the area, and its unit is 1/km2. Fiscally supported population size is the ratio of fiscally supported population of a county to its local population. Own-source revenue size is measured by the ratio of the own-source revenue of a county to its local GDP. 2. P25, P50, and P75 refer to the 25th, 50th, and 75th percentiles of the variables, respectively. Data sources: Statistical Material for Prefectures, Cities, and Counties Nationwide (Ministry of Finance, 1998–2007); China Statistical Yearbook for Regional Economy (National Bureau of Statistics, 2003–2007); China County Statistical Yearbook (National Bureau of Statistics, 2001–2007).

mean of the share of such subsidies was only 2.49% from 1998 to 2006, but it has increased steadily over time (see Fig. 3). On average, more than 20% of county government expenditures are on government administration, public safety, judiciaries, and inspectorates. Note that administrative expenditures here are an incomplete count of all costs associated with government administration; some items, such as the spending of local tax bureaus, are not included (World Bank, 2002).

60 40 0

20

percent

80

100

4.3.2. Fiscal decentralization Following relevant studies on fiscal decentralization (Fiva, 2006; Jin & Zou, 2002; Stein, 1999; Wu & Lin, 2010), two measures are usually created to describe the decentralization level of a government: Expenditure and revenue decentralization. The construction of these two variables at the county level in China is slightly complicated because of some unique features in China's hierarchical fiscal management system. Generally, county governments are subordinated directly to their prefectural governments in both fiscal and

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

year Fig. 2. The variation of the county government expenditure size, 1997–2006. Notes: Government expenditure size is measured by the ratio of the county governments' expenditures to their local GDPs. Data sources: Statistical Material for Prefectures, Cities, and Counties Nationwide (Ministry of Finance, 1998–2007).

percent

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8 10 12 14 16

114

1997

1998

1999

2000

2001

2002

year

2003

2004

2005

2006

2003

2004

2005

2006

percent

0 5 10 15 20 25

ratio of government expenditure to GDP

1997

1998

1999

2000

2001

2002

year

share of capital construction expenditure

share of education expenditure

share of social security subsidy

share of administrative expenditure

Fig. 3. Size and composition of county government expenditures, 1997–2006. Notes: The shares of capital construction expenditure, education expenditure, social security subsidy, and administrative expenditure refer to the shares of the corresponding items in county governments' expenditures. Data sources: Statistical Material for Prefectures, Cities, and Counties Nationwide (Ministry of Finance, 1998–2007).

administrative systems. Yet, some counties exist as exceptions: They circumvent prefectures and interact directly with their corresponding provinces in fiscal disciplines, which we call the Province Directly Governing County (PGC) fiscal system.17 For those counties, we measure their levels of decentralization by comparing their own expenditures and revenues only with the central and their superordinate provincial governments. In light of Zhang and Zou (1998) and the differences in the vertical fiscal management system across counties, we construct the index of expenditure decentralization as follows: Expenditure decentralization ¼

Cexp ; Cexp þ Pexp  ð1−PGC Þ þ Sexp þ Eexp

ð4Þ

where Cexp, Pexp, Sexp, and Eexp refer to expenditures per capita of the county, prefectural, provincial, and central governments, respectively. The PGC is a dummy variable, which equals one if a county adopts the PGC fiscal system in a certain year and zero otherwise. Revenue decentralization is measured in a similar way: Revenue decentralization ¼

Crev ; Crev þ Prev  ð1−PGC Þ þ Srev þ Erev

ð5Þ

where Crev, Prev, Srev, and Erev are the own-source revenues per capita of the county, prefectural, provincial, and central governments, respectively. These two measures of fiscal decentralization are not perfect because they do not fully present the extent of local governments' autonomy in the fiscal regime. However, they do inform their decentralization levels in some way and, hence, are widely used in the literature. To capture the extent of the discrepancy between expenditure responsibilities and available funding resources confronted by the county governments, we follow the literature and create another variable—vertical fiscal imbalance. In China's current fiscal transfer system, counties may submit remittances and receive grants at the same time (Shih & Zhang, 2007; Tsui, 2005; World Bank, 2002). Sometimes the differences between their expenditures and their own-source revenues cannot be completely met by the grants net of remittances. And thus, those counties register budget deficits even though such deficits are not legally allowed (World Bank, 2002). Considering these features of China's fiscal system, we define the variable of vertical fiscal imbalance as the ratio of a county government's fiscal gap to its expenditures, where the fiscal gap includes (1) grants net of remittances and (2) deficits. In some 17 This specific fiscal arrangement first showed up in Zhejiang Province in the early 1990s. Because of its success in improving government efficiency, the PGC fiscal system has expanded to other provinces since the early 2000s (Li, 2010). By 2006, all counties in Ningxia Province and some selected counties in Anhui, Henan, Hubei, Jilin, and Jiangxi Provinces had switched to this fiscal system (the information is collected from the official documents in regard to the PGC fiscal system reform in every province). Moreover, all counties in the four municipalities of Beijing, Chongqing, Shanghai, and Tianjin; all counties in Hainan Province; and some counties in other provinces (for example, Jiyuan City in Henan Province) have been directly subordinated to the provinces in the administrative system and, thus, perform under the PGC system as well. In our sample the total number of counties with the PGC fiscal system increased from 107 (5.93% of total sample counties) in 1997 to 271 (15.24% of total sample counties) in 2006.

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Table 2 Regression results of fiscal decentralization on local expenditure policy. DVt

Estimation method: two-step system GMM Government expenditure size (% of GDP)

DVt − 1 DVt − 2 Expenditure decentralizationt (%) Revenue decentralizationt (%) Log (real GDP per capitat) Log (population densityt) Fiscally supported population sizet (% of total population) Own-source revenue sizet (% of GDP) PGCt Number of counties in one prefecturet Constant AR(1) test AR(2) test Hansen test Difference-in-Hansen test Number of instruments Observations Counties

Capital construction expenditure (% of government expenditure)

Education expenditure (% of government expenditure)

Social security subsidy (% of government expenditure)

Administrative expenditure (% of government expenditure)

(1)

(2)

(3)

(4)

(5)

0.329⁎⁎⁎ (0.059) 0.128⁎⁎⁎

0.382⁎⁎⁎ (0.026) 0.052⁎⁎

0.631⁎⁎⁎ (0.015) 0.124⁎⁎⁎

0.581⁎⁎⁎ (0.156) 0.192⁎

0.617⁎⁎⁎ (0.021) 0.185⁎⁎⁎

(0.025) 0.481⁎⁎⁎ (0.051) 0.159 (0.129) −12.111⁎⁎⁎ (2.065) −0.419 (0.381) −0.074 (0.521) −0.476 (0.503) −7.800⁎⁎⁎

(0.021) 0.273⁎⁎⁎ (0.058) −0.080 (0.096) −0.006 (1.526) −0.174 (0.262) −1.186⁎⁎ (0.568) 0.096 (0.201) −2.625 (2.525) −0.082⁎⁎ (0.038) 0.384 (8.394) 0.000 0.630 0.999 1.000 373 8100 1396

(0.013) −0.163⁎⁎⁎ (0.024) 0.077⁎ (0.046) −1.108 (0.726) −0.220⁎

(0.110) −0.007 (0.052) 0.024 (0.141) −0.343 (2.252) 0.009 (0.423) −0.003 (0.633) −0.062 (0.279) 0.367 (3.475) −0.013 (0.050) 2.241 (11.435) 0.000 0.735 0.226 0.103 375 12,446 1904

(0.015) −0.119⁎⁎⁎ (0.027) 0.110⁎⁎ (0.048) −1.109 (0.803) −0.476⁎⁎⁎

(2.902) −0.043 (0.058) 47.703⁎⁎⁎ (9.857) 0.001 0.730 0.605 1.000 415 14,338 1913

(0.129) 0.160 (0.225) −0.238⁎⁎ (0.094) 0.972 (1.009) 0.032⁎⁎ (0.016) 17.209⁎⁎⁎ (3.736) 0.000 0.047 0.185 0.160 369 12,674 1904

(0.141) 0.459 (0.299) −0.188⁎ (0.098) 1.274 (1.586) 0.031 (0.023) 8.678⁎ (4.636) 0.000 0.427 0.703 1.000 417 14,468 1910

Notes: 1. Government expenditure size is the ratio of a county government's expenditure to its local GDP. The variables of capital construction expenditure, education expenditure, social security subsidy, and administrative expenditure refer to the shares of the corresponding items in total government expenditures. DV denotes dependent variable. The two variables—expenditure decentralization and revenue decentralization—are the ratios of the per capita expenditures/ revenues of a county government in the total of per capita expenditures/revenues of (1) its own and (2) the governments that the county is subordinated to, including the prefectural, provincial, and central governments. Real GDP per capita is deflated by the provincial-level consumer prices in 1997 and its unit is 10,000 Chinese Yuan. Population density is constructed by dividing the population by the area, and its unit is 1/km2. Fiscally supported population size is the ratio of fiscally supported population of a county to its local population. Own-source revenue size is measured by the ratio of the own-source revenue of a county to its local GDP. The PGC is a dummy variable, which equals one if a county adopts the Province Directly Governing County (PGC) fiscal system in a certain year and zero otherwise. 2. The province-times-year fixed effects are also controlled for each regression. Standard errors clustered at the prefectural level are reported in parentheses. p-Values of AR(1), AR(2), Hansen, and difference-in-Hansen tests are reported in their rows. We use the forward orthogonal deviation transformation of the instruments for the outcome of capital construction expenditure. Data sources: Statistical Material for Prefectures, Cities, and Counties Nationwide (Ministry of Finance, 1998–2007); China Statistical Yearbook for Regional Economy (National Bureau of Statistics, 2003–2007); China County Statistical Yearbook (National Bureau of Statistics, 2001–2007). ⁎ Denote the significance at 10%. ⁎⁎ Denote the significance at 5%. ⁎⁎⁎ Denote the significance at 1%.

counties, governments' remittances to the upper-level government are greater than the grants they receive, a factor that generates negative values of vertical fiscal imbalance.18 The expenditures of county governments in our sample are, on average, 17.91 percentage points more decentralized than the revenues from 1997 to 2006 (see Table 1) and are 26.77 percentage points in 2006 (see Fig. 1). As compared to only 9.67 percentage points for U.S. local governments in 2006 (calculated from Baicker, Clemens, & Singhal, 2011), Chinese county governments have presented relatively larger unbalanced fiscal responsibilities. The mean of vertical fiscal imbalance is 56.87%, ranging from −4.76% to 96.94%, and has displayed a significant growth trend since 1998 (see Fig. 1).

18 For example, in our sample, Akesu City in Xinjiang Province in 1998 made two types of remittances totaling 33.72 million Chinese Yuan while receiving grants of 25.77 million Chinese Yuan, and its deficits were 5.65 million Chinese Yuan.

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Table 3 Regression results of fiscal decentralization on local expenditure policy: Reducing the number of instruments. DVt

Estimation method: two-step system GMM Government expenditure size (% of GDP)

DVt − 1 DVt − 2 Expenditure decentralizationt (%) Revenue decentralizationt (%) Log (real GDP per capitat) Log (population densityt) Fiscally supported population sizet (% of total population) Own-source revenue sizet (% of GDP) PGCt Number of counties in one prefecturet Constant AR(1) test AR(2) test Hansen test Difference-in-Hansen test Number of instruments Observations Counties

Capital construction expenditure (% of government expenditure)

Education expenditure (% of government expenditure)

Social security subsidy (% of government expenditure)

Administrative expenditure (% of government expenditure)

(1)

(2)

(3)

(4)

(5)

0.507⁎⁎⁎ (0.106) 0.066⁎⁎

0.409⁎⁎⁎ (0.148) 0.045 (0.054) 0.208⁎⁎⁎ (0.067) −0.088 (0.122) 0.454 (1.899) −0.022 (0.201) −0.561 (0.624) 0.085 (0.274) −0.157 (0.918) −0.066⁎⁎⁎ (0.025) −3.304 (8.207) 0.001 0.276 0.652 0.692 251 8100 1396

0.593⁎⁎⁎ (0.014) 0.112⁎⁎⁎

0.559⁎⁎⁎ (0.062) 0.193⁎⁎⁎

0.699⁎⁎⁎ (0.069) 0.083⁎

(0.012) −0.117⁎⁎⁎ (0.031) 0.063 (0.050) −1.234 (0.837) −0.069 (0.152) −0.264 (0.328) −0.264⁎⁎⁎ (0.097) −0.013 (0.996) 0.016 (0.018) 15.545⁎⁎⁎ (4.756) 0.000 0.329 0.524 0.298 260 12,674 1904

(0.028) −0.007 (0.015) 0.028 (0.033) −0.538 (0.540) 0.036 (0.063) 0.011 (0.169) −0.034 (0.085) −0.434 (0.994) −0.014⁎ (0.008) 3.337 (2.733) 0.000 0.189 0.160 0.196 260 12,446 1904

(0.048) −0.079⁎⁎⁎ (0.024) 0.100 (0.063) −1.575 (0.994) −0.182 (0.135) 0.385 (0.282) −0.070 (0.147) −0.451 (1.867) 0.062⁎⁎⁎ (0.019) 4.950 (5.129) 0.000 0.000 0.750 0.266 295 14,468 1910

(0.031) 0.387⁎⁎⁎ (0.080) 0.132 (0.153) −9.846⁎⁎⁎ (3.331) −0.027 (0.381) −0.711 (0.649) −0.238 (0.548) −6.091⁎⁎ (2.900) −0.031 (0.073) 32.444⁎⁎ (14.402) 0.000 0.384 0.058 0.112 293 14,338 1913

Notes: 1. Government expenditure size is the ratio of a county government's expenditure to its local GDP. The variables of capital construction expenditure, education expenditure, social security subsidy, and administrative expenditure refer to the shares of the corresponding items in total government expenditures. DV denotes dependent variable. The two variables—expenditure decentralization and revenue decentralization—are the ratios of the per capita expenditures/ revenues of a county government in the total of per capita expenditures/revenues of (1) its own and (2) the governments that the county is subordinated to, including the prefectural, provincial, and central governments. Real GDP per capita is deflated by the provincial-level consumer prices in 1997 and its unit is 10,000 Chinese Yuan. Population density is constructed by dividing the population by the area, and its unit is 1/km2. Fiscally supported population size is the ratio of fiscally supported population of a county to its local population. Own-source revenue size is measured by the ratio of the own-source revenue of a county to its local GDP. The PGC is a dummy variable, which equals one if a county adopts the Province Directly Governing County (PGC) fiscal system in a certain year and zero otherwise. 2. The province-times-year fixed effects are also controlled for each regression. Standard errors clustered at the prefectural level are reported in parentheses. p-Values of AR(1), AR(2), Hansen, and difference-in-Hansen tests are reported in their rows. In the regressions we substitute one-period lags of the endogenous variables for all their further lags as the instruments. We use the forward orthogonal deviation transformation of the instruments for the outcomes of capital construction expenditure, education expenditure and administrative expenditure. Data sources: Statistical Material for Prefectures, Cities, and Counties Nationwide (Ministry of Finance, 1998–2007); China Statistical Yearbook for Regional Economy (National Bureau of Statistics, 2003–2007); China County Statistical Yearbook (National Bureau of Statistics, 2001–2007). ⁎ Denote the significance at 10%. ⁎⁎ Denote the significance at 5%. ⁎⁎⁎ Denote the significance at 1%.

4.3.3. Other control variables In addition to the lags of dependent variables and the decentralization indicators, we control for other factors that could possibly affect local expenditure policy in the regressions. One is the aforementioned PGC fiscal system to capture the mechanisms through which this specific fiscal arrangement could affect local expenditure policy other than through fiscal decentralization. The mean of the dummy variable of PGC is 0.08 during the study period. Moreover, we control for the number of counties in one prefecture19 to account for the effects of interjurisdictional competition, including both expenditure and tax competition. In our sample, the number of counties in one prefecture is 9.05 on average. Several other possible determinants of government expenditures are also included in the regressions. Real GDP per capita deflated by the provincial-level consumer prices in 1997 is considered to capture Wagner's Law (government size increases with 19

For the counties that are directly subordinated to their provinces in the administrative system, we use the total number of those counties in their provinces.

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income growth). Population density accounts for possible scale effects in the provision of public services. We use their log values in the regressions. Another control variable is the size of the fiscally supported population (caizheng gongyang renkou), which consists of civil servants and employees in public service sectors. This variable is measured by the share of fiscally supported population expressed as a percentage of local population. Following Baicker, Clemens, and Singhal (2011), we control for own-source revenue size, measured by the proportion of a county's own-source revenue in its local GDP. In this paper, we use the two-step system GMM to estimate the influences of fiscal decentralization on county governments' expenditure policy in China. Three control variables—real per capita GDP, own-source revenue size, and the fiscally supported population—are treated as endogenous. 5. Results 5.1. Effects of fiscal decentralization on local expenditure policy Table 2 presents the regression results for the effects of fiscal decentralization on local expenditure policy: Column (1) for the government expenditure size and columns (2)–(5) for the shares of different expenditure categories. To ensure the validity of GMM Table 4 Regression results of vertical fiscal imbalance on local expenditure policy. DVt

Panel A: Baseline results DVt − 1 DVt − 2 Vertical fiscal imbalancet (% of government expenditure) AR(1) test AR(2) test Hansen test Difference-in-Hansen test Number of instruments Observations Counties

Estimation method: two-step system GMM Government expenditure size (% of GDP)

Capital construction expenditure (% of government expenditure)

Education expenditure (% of government expenditure)

Social security subsidy (% of government expenditure)

Administrative expenditure (% of government expenditure)

(1)

(2)

(3)

(4)

(5)

0.353⁎⁎⁎ (0.061) 0.142⁎⁎⁎

0.408⁎⁎⁎ (0.024) 0.074⁎⁎⁎

0.609⁎⁎⁎ (0.014) 0.108⁎⁎⁎

0.585⁎⁎⁎ (0.022) 0.195⁎⁎⁎

0.630⁎⁎⁎ (0.018) 0.186⁎⁎⁎

(0.023) −0.160⁎⁎⁎

(0.020) 0.073⁎⁎ (0.035) 0.143 0.380 0.999 1.000 375 8116 1391

(0.012) −0.063⁎⁎⁎ (0.016) 0.000 0.090 0.551 0.473 367 12,746 1905

(0.022) −0.0004 (0.008) 0.000 0.146 0.186 0.177 380 12,517 1903

(0.015) −0.036⁎ (0.022) 0.000 0.304 0.728 0.999 415 14,536 1910

0.492⁎⁎⁎ (0.131) 0.047 (0.057) 0.068⁎⁎ (0.030) 0.061 0.592 0.742 0.412 253 8116 1391

0.611⁎⁎⁎ (0.123) 0.119⁎ (0.063) −0.114⁎⁎⁎ (0.027) 0.000 0.407 0.559 0.136 259 12,746 1905

0.524⁎⁎⁎ (0.058) 0.191⁎⁎⁎ (0.026) 0.006 (0.010) 0.000 0.185 0.254 0.181 259 12,517 1903

0.697⁎⁎⁎ (0.073) 0.070 (0.050) −0.063⁎⁎⁎ (0.021) 0.000 0.134 0.065 0.686 287 14,536 1910

(0.043) 0.000 0.197 0.746 1.000 414 14,432 1915

Panel B: Reducing the number of instruments 0.852⁎⁎⁎ DVt − 1 (0.065) −0.002 DVt − 2 (0.024) 0.009 Vertical fiscal imbalancet (% of government expenditure) (0.040) AR(1) test 0.000 AR(2) test 0.571 Hansen test 0.056 Difference-in-Hansen test 0.510 Number of instruments 292 Observations 14,432 Counties 1915 Notes:

1. Government expenditure size is the ratio of a county government's expenditure to its local GDP. The variables of capital construction expenditure, education expenditure, social security subsidy, and administrative expenditure refer to the shares of the corresponding items in total government expenditures. DV denotes dependent variable. Vertical fiscal imbalance is measured by the ratio of the county governments' fiscal gap (including grants net of remittances and deficits) to their expenditures. Other control variables are the same as the ones in Table 2. 2. The province-times-year fixed effects are also controlled for each regression. Standard errors clustered at the prefectural level are reported in parentheses. p-Values of AR(1), AR(2), Hansen, and difference-in-Hansen tests are reported in their rows. In panel B, we substitute one-period lags of the endogenous variables for all their further lags as the instruments in the regressions. We use the forward orthogonal deviation transformation of the instruments for the outcomes of capital construction expenditure and education expenditure in panel A, and for the outcomes of government expenditure size, capital construction expenditure, and administrative expenditure in panel B. Data sources: Statistical Material for Prefectures, Cities, and Counties Nationwide (Ministry of Finance, 1998–2007); China Statistical Yearbook for Regional Economy (National Bureau of Statistics, 2003–2007); China County Statistical Yearbook (National Bureau of Statistics, 2001–2007). ⁎ Denote the significance at 10%. ⁎⁎ Denote the significance at 5%. ⁎⁎⁎ Denote the significance at 1%.

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estimation strategy, the results of the AR(2), Hansen, and difference-in-Hansen tests are listed in Table 2. The p-values of those tests are all greater than 0.1 except for the AR(2) test for the outcome of education expenditure. This result suggests that there are no second-order serial correlations of the differenced error terms and that the instruments are orthogonal to the error terms in most of the regressions. And we obtain similar results for those tests when decreasing the number of instruments as shown in Table 3. The results of those tests confirm the validity of the two-step GMM estimation strategy. Moreover, the relatively large and statistically significant coefficients on the lags of the dependent variables imply strong persistence of local expenditure policy, which coincides with the fact that the counties' budget formation has been built up incrementally on the resource allocations of past years. The dynamic panel data model thus suits the context. Table 5 Regression results of fiscal decentralization on local expenditure policy for different levels of vertical fiscal imbalance. DVt

Estimation method: two-step system GMM Government expenditure size (% of GDP)

Capital construction expenditure (% of government expenditure)

Education expenditure (% of government expenditure)

Social security subsidy (% of government expenditure)

Administrative expenditure (% of government expenditure)

(1)

(2)

(3)

(4)

(5)

−0.142⁎⁎⁎ (0.018) 0.088 (0.086) 0.000 0.013 0.414 0.407 173 3768 571

−0.006 (0.008) −0.043 (0.031) 0.000 0.157 0.361 0.362 173 3645 569

−0.064⁎⁎⁎ (0.015) 0.198⁎⁎

−0.117⁎⁎⁎ (0.026) −0.010 (0.167) 0.000 0.571 0.416 0.276 64 3768 571

−0.020⁎⁎ (0.008) −0.060 (0.047) 0.000 0.065 0.131 0.124 64 3645 569

−0.055⁎⁎⁎ (0.022) 0.175 (0.122) 0.000 0.835 0.057 0.325 71 4267 571

−0.122⁎⁎⁎ (0.020) 0.043 (0.047) 0.000 0.283 0.618 0.951 173 3774 569

0.009 (0.008) 0.013 (0.016) 0.000 0.551 0.242 0.774 173 3747 574

−0.075⁎⁎⁎ (0.014) 0.083⁎⁎⁎ (0.021) 0.000 0.808 0.254 0.744 193 4349 574

−0.100⁎⁎⁎ (0.028) −0.003 (0.054) 0.000 0.326 0.217 0.134 64 3774 569

−0.004 (0.012) 0.008 (0.023) 0.000 0.950 0.081 0.102 64 3747 574

−0.078⁎⁎⁎ (0.021) 0.099⁎⁎ (0.050) 0.000 0.763 0.207 0.823 71 4349 574

Panel A: Counties with high vertical fiscal imbalance (above the 70th percentile) Baseline results Expenditure decentralizationt (%) 0.738⁎⁎⁎ 0.267⁎⁎⁎ (0.085) (0.051) −0.788⁎⁎⁎ 0.159 Revenue decentralizationt (%) (0.227) (0.120) AR(1) test 0.000 0.000 AR(2) test 0.333 0.867 Hansen test 0.705 0.999 Difference-in-Hansen test 1.000 1.000 Number of instruments 193 193 Observations 4255 2407 Counties 574 429 Reducing the number of instruments Expenditure decentralizationt (%) Revenue decentralizationt (%) AR(1) test AR(2) test Hansen test Difference-in-Hansen test Number of instruments Observations Counties

0.417⁎⁎⁎ (0.055) −0.076 (0.389) 0.000 0.356 0.179 0.579 64 4255 574

0.205⁎⁎⁎ (0.077) 0.084 (0.228) 0.000 0.015 0.181 0.216 71 2407 429

Panel B: Counties with low vertical fiscal imbalance (below the 30th percentile) Baseline results Expenditure decentralizationt (%) 0.224⁎⁎⁎ 0.122⁎⁎⁎ (0.022) (0.027) −0.194⁎⁎⁎ −0.160⁎⁎⁎ Revenue decentralizationt (%) (0.033) (0.040) AR(1) test 0.066 0.000 AR(2) test 0.126 0.979 Hansen test 0.130 0.820 Difference-in-Hansen test 0.786 1.000 Number of instruments 193 193 Observations 4272 2621 Counties 575 425 Reducing the number of instruments Expenditure decentralizationt (%) Revenue decentralizationt (%) AR(1) test AR(2) test Hansen test Difference-in-Hansen test Number of instruments Observations Counties

0.192⁎⁎⁎ (0.019) −0.287⁎⁎⁎ (0.075) 0.073 0.355 0.002 0.317 64 4272 575

0.110⁎⁎⁎ (0.033) −0.096 (0.063) 0.000 0.786 0.238 0.153 71 2621 425

(0.079) 0.000 0.121 0.750 0.995 193 4267 571

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As for the influences of decentralization, as shown in column (1) of Table 2, expenditure decentralization has a positive effect on government expenditures, and it is statistically significant at the 1% level. In contrast, revenue decentralization does not present a statistically significant effect on this outcome. These findings stay when the number of the instruments is reduced in Table 3. The regression results of the asymmetric influences of expenditure and revenue decentralization are in line with some studies in other countries (Ehdaie, 1994; Fiva, 2006; Jin & Zou, 2002; Rodden, 2003) and also echo some provincial-level studies in China (Chen, 2004; Wu & Lin, 2010). As we mention before, this phenomenon is described as the common-pool problem in the literature (Fiva, 2006; Jin & Zou, 2002; Rodden, 2003). Our data shows more than half (56.87%) of county governments' expenditures are financed through intergovernmental grants and other common resources (see the mean of vertical fiscal imbalance in Table 1). This fiscal arrangement loosens the connection of the benefits and the costs of government expenditure because the amount a county government spends in its own jurisdiction partially comes from the tax revenues collected from the residents outside the jurisdiction. Under this situation, the county governments are apt to spend more than they would if their revenues were fully collected from their own jurisdictions. Hence, the disciplinary role of fiscal decentralization becomes weaker, and expenditure decentralization itself induces an overspending bias. With regard to the specific components of government expenditures, in both Tables 2 and 3, expenditure decentralization shows positive and statistically significant effects on the share of capital construction expenditures, with the coefficients around 0.27 at the 1% significance level. Revenue decentralization does not present any statistically significant effects on this outcome. In contrast, decentralization's influences on education expenditures portray a different picture: Expenditure decentralization shows negative and statistically significant effects on its share, whereas revenue decentralization presents positive effects but the effect does not stay statistically significant with fewer instruments. A similar pattern is also found for administrative expenditures. Neither of the decentralization indicators shows a statistically significant influence on the share of social security subsidies. The estimation results on the composition of government expenditures suggest that fiscal decentralization does not encourage the county governments to be more responsive to the needs of local residents on education and social security services. On the contrary, granting more fiscal autonomy to the local governments incentivizes them to invest more on infrastructure construction. The findings are consistent with the empirical evidence by Kappeler and Välilä (2008) and the prediction of fiscal competition theory. Keen and Marchard (1997) illustrate the idea of fiscal competition that jurisdictions would like to invest more on public inputs to attract capital and are reluctant to provide public goods and services benefiting local residents. The key assumption of their model lies in mobile capital and immobile labor, which fits the Chinese institution well where the household registration (hukou) system restricts free labor mobility. The results on administrative spending seem counterintuitive because the governments normally would like to consume more when they have more autonomy in spending (Fiva, 2006). We do not have a clear explanation for the results, but we suspect that it might be because the administrative spending used here does not cover all of the costs associated with government administration, such as the management costs of local tax bureaus (World Bank, 2002). The institutional variable of the PGC fiscal system shows a negative and statistically significant effect on the government size, but does not have any robust effects on the composition of government expenditures. The number of counties in one prefecture is negatively correlated with the government size, but this correlation is not statistically significant. The coefficients of the log of real GDP per capita in column (1) of Tables 2 and 3 are negative and statistically significant, thereby suggesting that Wagner's Law does not hold true at the county level in China. This result corresponds to the evidence at the provincial level in China found by Wu and Lin (2010). They argue that the fact that poorer localities have larger governments might be because of more prevalent rent-seeking behavior in those areas. Neither local population density nor fiscal supported population generates a robust influence on the county government's expenditure policy, as seen in Tables 2 and 3. Own-source revenue size has a negative and statistically significant influence on the share of education expenditure. 5.2. Common-pool problem In the previous section, we argue that asymmetric influences of expenditure and revenue decentralization are rooted in the mismatch between benefits and costs of government expenditures (defined as vertical fiscal imbalance) at the county level of Notes to Table 5: 1. Government expenditure size is the ratio of a county government's expenditure to its local GDP. The variables of capital construction expenditure, education expenditure, social security subsidy, and administrative expenditure refer to the shares of the corresponding items in total government expenditures. Vertical fiscal imbalance is measured by the ratio of the county governments' fiscal gap (including grants net of remittances and deficits) to their expenditures. DV denotes dependent variable. The two variables—expenditure decentralization and revenue decentralization—are the ratios of the per capita expenditures/ revenues of a county government in the total of per capita expenditures/revenues of (1) its own and (2) the governments that the county is subordinated to, including the prefectural, provincial, and central governments. Other control variables are the same as the ones in Table 2. 2. The year fixed effects are also controlled for each regression. Standard errors clustered at the prefectural level are reported in parentheses. p-Values of AR(1), AR(2), Hansen, and difference-in-Hansen tests are reported in their rows. We substitute one-period lags of the endogenous variables for all their further lags as the instruments in the regressions when the number of instruments is reduced. For counties with higher levels of vertical fiscal imbalance, we use the forward orthogonal deviation transformation of the instruments for the outcome of social security subsidy under “Baseline results” and for the outcomes of capital construction expenditure and administrative expenditure under “Reducing the number of instruments.” For counties with lower levels of vertical fiscal imbalance, we use the forward orthogonal deviation transformation of the instruments for the outcome of education expenditure under “Baseline results” and for the outcome of government expenditure size under “Reducing the number of instruments.” Data sources: Statistical Material for Prefectures, Cities, and Counties Nationwide (Ministry of Finance, 1998–2007); China Statistical Yearbook for Regional Economy (National Bureau of Statistics, 2003–2007); China County Statistical Yearbook (National Bureau of Statistics, 2001–2007). ⁎ Denote the significance at 10%. ⁎⁎ Denote the significance at 5%. ⁎⁎⁎ Denote the significance at 1%.

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China's fiscal system. In this section, we study how the discrepancy in expenditure and revenue assignments shapes the local government expenditure policy directly. First, we substitute the variable of vertical fiscal imbalance for the decentralization index in all of the regressions, as suggested by Stein (1999), Jin and Zou (2002), and Wu and Lin (2010). The regression results are presented in Table 4. The vertical fiscal imbalance shows a negative effect on county governments' expenditure size with the 1% level of significance, but this effect does not exist in the regression with smaller number of instruments. Yet, it presents statistically significant influences on the expenditure composition. The vertical fiscal imbalance raises the share of capital construction spending and reduces the share of education and administrative spending in the total county governments' expenditures. Next, we select two subsamples based on the average level of counties' vertical fiscal imbalance across years: One comprises the counties whose average level of vertical fiscal imbalance is above the 70th percentile of the distribution of all the sample counties, and the other only includes the counties below the 30th percentile.20 Employing the baseline specification, we run the regressions for those two subsamples separately, and the results are shown in Table 5.21 The signs and significances of the coefficients of expenditure decentralization for both subsamples are quite similar and also in accordance with the estimation results for the whole sample. Expenditure decentralization shows positive influences on county government expenditure size and the share of capital construction and has negative influences on the shares of education and administrative spending. Moreover, a comparison of the magnitudes of the coefficients for the two subsamples further proves the existence of the common-pool problem in China's fiscal system. We find that the estimates for expenditure size, capital construction, and education are larger in absolute values for counties with higher levels of vertical fiscal imbalance than for those with lower levels. For example, the coefficient for government expenditure size is 0.738 for the counties exceeding the 70th percentile of vertical fiscal imbalance, and it is 0.224 for the counties below the 30th percentile. Consistent with the empirical study by Stein (1999), the results imply that in the counties with a severer mismatch between benefits and costs of government expenditures their expenditure policy would be distorted to a larger extent when they are granted more fiscal autonomy over the spending. Meanwhile, revenue decentralization does not generate statistically significant and robust effects on the size and the composition of counties' expenditures in counties with higher levels of vertical fiscal imbalance, but a negative effect on government size and a positive effect on administrative spending for counties with lower levels of vertical fiscal imbalance (both effects are statistically significant at least at the 5% level.) These regression results for the subsamples with different levels of vertical fiscal imbalance reveal that the disciplinary effects of decentralization on government expenditures are relatively weaker in the counties with a larger unbalanced budget between expenditure and revenue assignments. These results reiterate the findings in the existing literature that fiscal decentralization tends to be associated with faster growth in the size of government when common-pool resources mainly fund local governments (Ehdaie, 1994; Jin & Zou, 2002; Rodden, 2003; Stein, 1999). To summarize, our regression results offer the first empirical evidence that the commonpool problem is influential in shaping the effects of fiscal decentralization on county governments' expenditure policy in China. 6. Conclusion Since the tax-sharing reform in 1994, China has experienced expenditure decentralization and revenue centralization simultaneously in its fiscal regime, thereby leading to a marked vertical fiscal imbalance at the local levels. This imbalance may distort local governments' expenditure policy and result in larger government size. The underlying reason is this fiscal structure attenuates the link between the benefits of public goods and services and their costs to local taxpayers. Consequently, the local governments have incentive to spend more because they only bear part of the costs of their spending. This phenomenon is described as the common-pool problem in the literature. To examine the prevalence of such a problem in China's fiscal system, this paper uses a large county-level fiscal dataset from 1997 to 2006 to study the influences of China's fiscal arrangements on the size and composition of county government expenditure during this period. The regression results show that expenditure decentralization creates more government spending, with an increase in its weight on capital construction and declines in its weights on education and public administration. Nevertheless, revenue decentralization does not have robust correlations with the size or the composition of government expenditures. The differences in the effects of these two types of decentralization on expenditure size suggests an expansion of government size caused by the mismatch between governments' expenditure and revenue responsibilities at the county level in China's fiscal system. The findings about the influences of fiscal decentralization on the composition of local government expenditure echo the predictions of fiscal competition theories. Given that their autonomy on tax revenue is limited, the jurisdictions choose to invest more in public inputs to attract mobile capital and underprovide public services for local residents. This inclination becomes stronger with the severity of the imbalances in their fiscal budget. Most theories on federalism claim that fiscal decentralization can serve as a disciplinary and incentive device to enhance government efficiency. However, this hypothesis depends critically on complementary institutional arrangements that are generally lacking in developing countries (Rodden, Eskeland, & Litvack, 2003). Our findings suggest that unbalanced expenditure– revenue fiscal structure at the local level stimulates opportunistic behaviors of overspending among local governments in China. To mitigate the opportunistic urge, a feasible reform for the current fiscal regime would be to grant the local governments more power of revenue collection or to give them a lighter expenditure burden. This proposed fiscal scheme generates better-matched fiscal responsibilities of local governments and bolsters their accountabilities on spending. In that case, local governments are 20 Each county's relative position for vertical fiscal imbalance is quite stable in the sample period. The 70th and 30th percentiles of the distribution of the average of vertical fiscal imbalance for the sample counties are 67.54 and 46.97%, respectively. 21 Given the much smaller sample size, we choose to control for year fixed effects instead of province-times-year fixed effects to save power.

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