Government Credit, a Double-Edged Sword: Evidence from the China Development Bank

Transcription

1 Government Credit, a Double-Edged Sword: Evidence from the China Development Bank Hong Ru Journal of Finance, Forthcoming February 27, 2017 ABSTRACT Using proprietary data from the China Development Bank (CDB), this paper examines the effects of government credit on firm activities. Tracing the effects of government credit across different levels of the supply chain, I find that CDB industrial loans to state-owned enterprises (SOEs) crowd out private firms in the same industry but crowd in private firms in downstream industries. On average, a $1 increase in CDB SOE loans leads to a $0.20 decrease in private firms assets. Moreover, CDB infrastructure loans crowd in private firms. I use exogenous timing of municipal politicians turnover as an instrument for CDB credit flows. JEL classification: E51, G28, G31 G38. Nanyang Technological University. Address: 50 Nanyang Avenue, Singapore, ; ruhong@ntu.edu.sg. I am indebted to my advisors Nittai Bergman, Andrey Malenko, Robert Townsend, and especially Antoinette Schoar for their invaluable guidance and encouragement. I thank Jean-Noel Barrot, Stephen Dimmock, Wei Jiang, Mark Kritzman, Chen Lin, Deborah Lucas, Eric Maskin, Tran Ngoc-Khanh, Stewart Myers, Michael Roberts (the Editor), Stephen Ross, Zheng Song, Richard Thakor, Wei Wu, an associate editor, and two anonymous referees. This paper benefited hugely from seminar participants at Cornell, HBS, MIT, NTU, NUS, Olin, Rotman, SMU, Texas A&M, UIUC, and Wharton for insightful comments. I thank the discussants and participants at CFRC, SFS Cavalcade, SEFM, and TCGC conferences. I am also grateful to Gao Jian. I thank Yue Wu and Endong Yang for their excellent research assistance. I thank all anonymous local government officials in China for long and engaging discussions. I thank Haoyu Gao for the help on the CBRC data. I thank the financial support from the Nanyang Technological University and the in-kind support to the data access from the China Development Bank. To my knowledge, there is no financial or other conflict of interest relevant to the subject of this article. All errors are my own.

2 Government-directed lending programs are pervasive in many countries around the world and are often justified as a way to support economic development. 1 A central question in the debate over government credit is whether it crowds out or crowds in private sector activities. The theoretical literature has suggested that government credit can have many countervailing effects. On the one hand, government credit that supports high social return projects such as infrastructure can have positive spillover effects (e.g., Stiglitz (1993)). On the other hand, government credit might crowd out more productive private sector investments (e.g., King and Levine (1993a, 1993b)). Due mainly to data limitations, previous empirical studies have only been able to explore the net effects of these opposing forces and revealed mixed evidence. Using detailed data from China Development Bank (CDB) on different types of government credit, this paper aims to separate these countervailing channels of government credit by tracing its effects across different levels of the supply chain. The CDB is the largest policy bank in China and lends mainly to state-owned enterprises (SOEs) in strategic industries (e.g., energy and mining) and to local governments for infrastructure development. The CDB loan data record outstanding loan amounts and issuance amounts at the province-industry level between 1998 and I document two main findings. First, CDB loans to SOEs crowd out private firms in the same industry (i.e., decreases in asset investment, employment, and sales), but they notably crowd in private firms in downstream industries. More efficient private firms in downstream industries can benefit significantly more from CDB credit to upstream SOEs. Second, CDB loans to local government infrastructure projects have positive (crowding-in) effects on private firms activities. This detailed analysis in China disentangles the different forces of government credit, and sheds light on the inconclusive results of previous studies regarding the net effects of aggregate government credit. To establish the causal effects of CDB credit on firm activities, I exploit the exogenous variation in CDB credit flows using pre-determined political turnover cycles of municipalities in China, which occur every five years, on average. This allows me to alleviate the concern 1 Lucas (2014) states that the total amount of credit supported by OECD (Organisation for Economic Co-operation and Development) governments was recently estimated at several tens of trillions of Euros. Elliott (2011) states that, in 2010, the US Government s outstanding commitments for loans and guarantees totaled approximately $2.3 trillion, which was roughly one-third the size of the loans of all US banks combined. 2 I mainly categorize CDB loans into two groups: industrial loans to firms and loans to infrastructure projects. Among CDB industrial loans to manufacturing firms, approximately 95% go to SOEs. I consider CDB industrial loans as SOE loans in this paper. See detailed calculation and discussion in Section II.A. 2

3 that the CDB endogenously targets areas with specific economic needs for credit. For example, the CDB may maximize spillover effects by strategically lending to industries where their downstream private firms have high growth potential. I use the predicted timing of municipal government turnover as an instrument for CDB loans. In China, city secretaries are appointed and typically serve a five-year term. 3 Moreover, cities in China have their own five-year turnover cycles. This allows me to exploit variation from different five-year cycles across cities. Instead of the real turnover cycles, I take the real first year of the secretary in the previous term and add five years to establish the predicted first year of the current city secretary. 4 These pre-determined predicted municipal turnover cycles depend solely on past information and are not affected by current economic factors (e.g., local GDP, employment, income). I regress cities borrowing amounts from the CDB on the predicted turnover cycles and find a zig-zag borrowing pattern in most cities; city secretaries borrow significantly more from the CDB during their predicted first year and monotonically decrease borrowing as their terms progress. Borrowing rises again in the predicted first year of the next city secretary. On average, city secretaries reduce total CDB outstanding loan amounts by 15.4% each year during their tenure in office. This zig-zag borrowing cycle is driven primarily by the career concerns of city secretaries. In China, the promotion of local politicians depends largely on their GDP performance (e.g., Li and Zhou (2005)). To increase local GDP quickly over the short term, city secretaries tend to borrow from the CDB and invest as much and as early as possible during their five-year terms. I then investigate the heterogeneous effects of CDB loans on private sector across different levels of the supply chain. First, I explore the private sector consequences of CDB industrial SOE loans in the same industry. I use city-level turnover timing as an exogenous shock to CDB credit at the province-industry level. In particular, I identify each city s largest SOE industry (i.e., focal industry), which does not change much over time. 5 Then, I interact the dummies of the pre-determined focal industry in each city with its predicted turnover cycles. Using these interactions as instruments for CDB province-industry loan amounts, I perform a two-stage least squares (2SLS) regression. If the city secretary is in an early 3 In China, the political leader in a municipal government is called the Secretary of the Municipal Committee of the Communist Party of China (equivalent to a mayor in the US). 4 I follow the strategy in Shue and Townsend (2014) to use predicted cycles as instruments. 5 The distribution of SOE industries across cities is mainly pre-determined for historical reasons and remains stable over time. See detailed discussion in Section III.C. 3

4 year of the term, I consider it a shock to the province-level CDB loans in this city s focal industry. In the first-stage regression, I find that province-level CDB loan amount in an industry is 41.3% higher when the corresponding city secretary is in the first two years of a term. In the second-stage regressions, consistent with the OLS regression results, I find that increasing CDB SOE loan amount to the focal industry by 100% leads to decreases in assets, employment, and sales of private firms in that same focal industry and the same province by 2%, 1.7%, and 4.2%, respectively. By contrast, increasing CDB SOE loans leads to increases in SOEs activities. Second, I study CDB SOE loan effects with regard to upstream and downstream industries. By using an input-output matrix in China, for each focal industry, I identify its downstream industries that source the majority of their inputs from it. On average, each focal industry has 2.3 downstream industries. I find that increasing CDB loan amount to the focal industry by 100% leads to increases in the assets, sales, and sales per worker of downstream private firms in the same province by 3.4%, 2.6%, and 2.6%, respectively. Evidence also suggests that more efficient private firms capture significantly more benefits from these CDB upstream industrial loans. In sum, although CDB industrial SOE loans crowd out private firms within the same industry (i.e., focal industry), they crowd in private firms in downstream industries. These opposing effects could explain the mixed empirical findings in previous studies that use aggregate data on government credit. For the exclusion condition of the instruments, I find that other channels through which the city secretary may influence a city s business are not correlated with the turnover cycles (e.g., borrowing from other banks, selling more land, requesting fiscal transfers, and enforcing tax treaties better). In particular, the joint F-tests of predicted turnover cycle dummies are not significant in regressions of these other channels. I also find that, for cities with potentially better access to CDB credit, the predicted turnover cycles have significantly larger effects on firms. These findings, along with CDB SOE loans opposing effects on private firms and SOEs in focal industries, alleviate the concern that political turnover cycles are associated with other unobservable factors that might affect firm activities (e.g., changes of a city s investment opportunities over its turnover cycles). Besides CDB SOE loans, political turnover cycles also affect firms through CDB infrastructure loans. However, it is hardly the case that the effects of CDB SOE loans across different levels of supply chains are confounded with the effects of CDB infrastructure loans. 4

5 First, infrastructure projects would have an impact on most firms in the area, whereas the crowding-out and crowding-in effects of CDB SOE loans depend on the industrial levels of the supply chain. Second, the distributions of CDB infrastructure vs. SOE loans differ across cities. For example, in 2002, for half of the cities, more than 90% of their CDB loans were for SOEs. The other cities borrowed mainly for infrastructure. I define dummy SOECity as equal to 1 if the city s SOE assets ratio is above the median in Using the interactions between pre-determined dummy SOECity and the predicted municipal turnover cycles as instruments, I can break down various borrowing patterns of CDB industrial SOE loans and infrastructure loans in the first stage. In the second stage, again, I find that increasing CDB industrial SOE loans leads to decreases in private firms assets, sales, and sales per worker. By contrast, increasing CDB city infrastructure loans leads to increases in private firms assets, employment, debt, and total sales. These opposing effects further support the exclusion condition. Finally, based on the regression coefficients, I perform a back-of-the-envelope calculation to estimate the overall effects of CDB loans on individual firms. I multiply the growth of different types of CDB loans (i.e., infrastructure loans, industrial SOE loans, upstream loans) by the estimated coefficients. On average, a $1 increase in CDB SOE loans leads to a $0.20 decrease in private firm assets. This indicates that the crowding-out effects are larger than the crowding-in effects. Moreover, a $1 increase in CDB infrastructure loan amounts leads to a $0.47 increase in private firms assets. This paper adds to the literature that examines whether government credit and spending crowds the private sector in or out. Findings on this question have been inconclusive in both theoretical and empirical studies. On the one hand, the social view (e.g., Stiglitz (1993)) argues that governments should allocate funds to high social return projects with positive externalities when returns from such loans are difficult for private banks to capture. 6 On the other hand, many other studies argue that government credit will crowd out private sector investment, particularly when the subsidized credit is given to firms with distorted incentives, such as SOEs (e.g., King and Levine (1993a, 1993b), Demirguc-Kunt and Maksimovic (1998), Rajan and Zingales (1998), La Porta et al. (2002)). Previous empirical studies are also inconclusive. They use aggregate data to estimate only the overall net effects of government 6 The social view of government intervention is suggested by Atkinson and Stiglitz (1980). They argue that SOEs can be justified under market failures (e.g., Stiglitz and Weiss (1981), Greenwald and Stiglitz (1986)). 5

6 credit and show either crowding-in or crowding-out effects. 7 The major contribution of this paper is to disentangle these different forces of government credit. The findings in this paper suggest that it is important to look into the heterogeneous effects of government credit across different levels of supply chain and different types of credit. This could help researchers and policy makers fully understand the economic consequences of government credit. 8 This paper also relates to another strand of the literature on political business cycle. Starting with Nordhaus (1975) and followed by many other studies, this literature argues that politicians capture firms or use economic policy to increase their chances in election. 9 I show suggestive evidence that politicians career concerns play a role in credit allocation in China, as they do in some other countries. This leads to the zig-zag borrowing pattern over political turnover cycles that helps me tease out the exogenous variation in CDB credit flows to establish the causal effects of government credit, which is another challenge in the literature. 10 The remainder of the paper is organized as follows. Section I outlines the history of the CDB and local government debt in China. Section II describes the data. Section III gives empirical analysis and presents the results. Section IV concludes. 7 See, for example, Gale (1991), Schwarz (1992), Ramey and Shapiro (1998), Burnside et al. (2004), Craig et al. (2007), Shaffer and Collender (2009), Cohen et al. (2011), Banerjee et al. (2012). 8 The CDB focuses on strategic industries at the top of the supply chain. Another agenda of the CDB is to support infrastructure development. The positive spillover effects of CDB infrastructure credit and credit to upstream industries are consistent with the social view on government credit. 9 See, for example, McRae (1977), Kornai (1979), Alesina and Sachs (1988), and Shleifer and Vishny (1994). 10 Many empirical studies use the political turnover cycles as exogenous variation to identify causal effects (e.g., Sapienza (2004), Dinc (2005), Khwaja and Mian (2005), Bertrand et al. (2007), Cole (2009), Dinc and Gupta (2011), Carvalho (2013)). 6

7 I Background: the China Development Bank and Local Government Financing in China A. History of the China Development Bank The China Development Bank was established in 1994 out of six SPC Investment Corporations. 11 Among policy banks, the CDB is the largest with US$1.83 trillion total assets in The CDB s two main shareholders are the Ministry of Finance and the China Investment Corporation. 12 function. The CDB can thus be viewed as an extension of the government s fiscal The CDB is fully state-owned, which is similar to state-owned commercial banks, such as the ICBC, CCB, BOC, and ABC, but the CDB s lending strategy distinguishes it from those commercial banks. 13 The CDB s business typically covers infrastructure sectors and uncontested markets in which commercial banks have little interest. There are three possible reasons for this. First, the CDB s mandate locates the bank in policy-related areas. In particular, the main role of the CDB is to grant subsidized credit to infrastructure projects in undeveloped and underdeveloped areas in China (such as the provinces in western China) and to SOEs in strategic industries, whereas commercial banks weigh their businesses more heavily in the wealthier areas of China (such as the eastern coastal provinces). Second, as a policy bank, profit maximization is not on CDB s agenda. Although commercial banks in China are also state owned, profit is one of their primary performance measurements. Third, the CDB finances its loans by issuing long-term bonds with sovereign ratings, whereas state-owned commercial banks rely primarily on short-term deposits. Therefore, the CDB engages in long-term lending that not only caters to infrastructure-sector requirements but also matches the durations of its assets and liabilities The State Planning Commission (SPC) is under the Chinese State Council and has broad administrative and planning control over the Chinese economy. These six Investment Corporations were policy institutions established in the late 1980s as long-term investment instruments on behalf of the government. 12 The China Investment Corporation is a sovereign wealth fund responsible for managing part of China s foreign exchange reserves. 13 ICBC stands for Industrial and Commercial Bank of China; CCB for China Construction Bank; BOC for Bank of China; and ABC for Agricultural Bank of China. 14 The CDB s long-term loan rates have remained lower than those of state-owned commercial banks - and much lower than private or shareholding commercial banks. The subsidized loans from the CDB can thus be viewed as government spending (Lucas (2012b)). 7

8 B. The Relationship between the CDB and Local Governments The CDB generally has closer relationships with local governments than commercial banks do. Since 1989, budgetary law has prohibited local governments in China from incurring debt. Moreover, under the tax-sharing system, local governments share only approximately 30% of tax revenue. Concurrently, local governments are responsible for infrastructure development but do not have the money to do so. To solve this dilemma, the CDB began to work with local governments in 1998 to help them create 100% state-owned companies as their borrowing platforms. Local governments are thus able to use these companies to borrow from banks off the balance sheet. On the other hand, in November 2008, commercial banks began to lend to local governments aggressively, as part of a four trillion RMB stimulus plan. After this stimulus program, in 2010, many commercial banks pulled back, but the CDB continued to increase its lending to local governments. Between 2006 and 2013, on average, the CDB contributed approximately 50% to 60% of the outstanding loans of local governments in China. In contrast, each commercial bank in China, on average, accounts for less than 3% of total local government debt. Even the big five commercial banks contribute approximately 6% each to total local government debt (Gao et al. (2016)). Compared with commercial banks, the CDB is a long-term, stable financial resource for local governments. This paper focuses on the period from 1998 to 2009, which overlaps with the stimulus plan by only one year. 15 During the sample period, the CDB played an important role in local government borrowing, and local politicians strongly affected CDB credit allocation. Commercial banks also lend massive amounts to SOEs and typically have close connections with SOE executives who do not follow five-year turnover cycles, which is one reason why CDB loans and not commercial bank loans are sensitive to local government turnover cycles. 15 I repeat the main analysis in the paper by restricting the sample period from 1998 to 2008, the results remain the same. Please see Table BIII in internet appendix for details. 8

9 II Data Description A. CDB Loan Data The proprietary loan data in this paper, obtained directly from the CDB, contains both cityand province-level loan data. At the province level, the CDB data record monthly aggregate outstanding loan amounts and loan issuances in 95 industries for each of the 31 provinces in mainland China from 1998 to The industries include infrastructure sectors (such as road, air, rail transportation, water supply, and public facilities) and industry sectors (such as agriculture, tobacco, software, oil refining, and textiles). At the city level, the data record annual aggregate CDB outstanding loan amounts and loan issuances to both infrastructure projects and industrial firms from 1998 to 2010, across 310 cities in China. 16 Moreover, cityand province-level economic variables (such as GDP, income per capita, total employment, and fiscal income) are from the China Statistical Yearbook. Table I presents summary statistics and Table AI in the appendix contains a detailed definition and construction of each variable. For CDB city level loan data, the average total outstanding loan amount is 3.2 billion RMB per city per year. Among these loans, the average outstanding loan amount for infrastructure is 1.1 billion RMB and the average outstanding loan amount for industrial firms is 2.1 billion RMB. Panel B of Table I shows that, for CDB province-industry level loan data, the average outstanding loan amount is 0.8 billion RMB per province, industry, and year. 17 [Place Table I about here] The top panel of Figure 1 shows the ratio of city-level infrastructure loans and industrial loans to total loan amount, respectively. For infrastructure loans, the ratio was almost 0 from 1998 to 2000, and began to increase after Moreover, the gap between the top and bottom quartiles of the infrastructure loan ratio widened from 1999 to 2003 and closed a bit after The bottom panel of Figure 1 shows similar patterns of the industrial SOE loan ratio, which indicates that different cities have different combinations 16 The cities do not include Beijing, Shanghai, Tianjin, and Chongqing, which are classified as provinces. 17 Figure A1 in the appendix shows the time trend of CDB province- and city-level outstanding loan amounts. 9

10 of infrastructure and industrial SOE loans. The CDB industrial loan amounts are significantly higher in cities with more SOEs. These cities with large state-owned sectors borrow relatively less for infrastructure. The distribution of SOEs across cities is pre-determined by historical factors. For example, Baotou, a city in China, has large rare earth mining SOEs because the city has rich rare earth resources. I defined the dummy SOECity for whether the weight of SOE assets in a city was over the median level across all 310 cities in I use this pre-determined dummy to interact with predicted municipal turnover cycles to capture the different borrowing patterns between infrastructure and industrial SOE loans among cities. In other words, from this dummy, I can explore the exogenous variation of how a city chooses to allocate CDB credit into infrastructure as opposed to industrial SOEs. [Place Figure 1 about here] The CDB collaborates primarily with local governments to grant credit. CDB Infrastructure loans were lent directly to local government. For CDB industrial loans to firms, local governments also play a key role, as most of the industrial loans go to local SOEs. From CBRC loan-level data between 2007 and 2012, I am able to explore the allocation of CDB industrial loans between SOEs and private firms. 18 Based on CBRC loan-level data and Chinese Industry Census (CIC) data, among manufacturing firms, approximately 95% of CDB industrial loans go to SOEs. Only 5% of CDB industrial loans go to private firms. Private firms with CDB loans are typically big and have connections with government. In particular, some of these private firms with CDB loans have been privatized from SOEs. For example, Huawei, the largest telecommunications equipment manufacturer in China, was state-owned in 1998, and has been borrowing from the CDB. When I re-define private firms by excluding these privatized ones, only 2% of CDB industrial loans go to the private sector. In sum, the CDB rarely lends to small entrepreneurs in China so that, in this paper, I consider CDB industrial loans as SOE loans. Moreover, a caveat in the analysis using aggregate CDB loan data is that only a few SOEs or small SOEs borrow from the CDB, which might lead to biased results because 18 The China Banking Regulatory Commission (CBRC) loan-level data record all the loan issuances from the 19 largest Chinese banks including the CDB between 2007 and This data set has been used in some other studies. For example, Ai, Bailey, Gao, Yang, and Zhao (2016) and Gao, Ru, and Tang (2016). 10

11 aggregate CDB credit to SOEs cannot represent the majority of SOEs in a certain industry and province. To mitigate this concern, I use the CBRC loan-level data to calculate the coverage of CDB credit at industry-province level. In particular, I first merge the CBRC loan data with CIC manufacturing firm data to get each firm s outstanding loan amount from the CDB. Then, I aggregate the assets of SOEs with outstanding loans from the CDB and divide it by the total assets of all SOEs at province-industry level. This ratio is approximately 86%, which means the majority of the big SOEs have been borrowing from the CDB. The aggregate outstanding CDB loan data in this paper have a good representation of SOEs in China. This mitigates the concern that the crowding-out and crowding-in effects of CDB SOE loans come from only a few small SOEs. B. Politician Profile Data I manually collected local politician profiles from the Zechen Database and the Baidu Encyclopedia. The data record the names of all city mayors and secretaries at the city-month level across all 334 cities between 1949 and 2013 in China. Moreover, the data also record politician demographics (such as gender, age, and birthplace). In total, there are 1,227 city secretaries. I cross check these data with other sources to ensure their quality. When I merged the CDB city-level data with politicians profiles, there were 310 cities in total (the remaining 24 cities did not receive CDB loans). In China, the political leader of a municipal government is called the Secretary of the Municipal Committee of the Communist Party of China (which is equivalent to a U.S. mayor). On average, city secretaries terms are five years. The national political turnover cycle is also five years, and occurs around the National Congress of the Communist Party of China (CPC). Many cities in China were built during the 1990s due to the urbanization process. For a new city, the secretary begins the five-year tenure in the city s incorporation year, which does not coincide with the national cycle. From 1998 to 2010, 67% of the cities are off the national turnover cycle, which allows me to explore variation among different five-year cycles across different cities in China. During the municipal political turnover, as there is no election in China, promotion decisions involving local politicians are frequently made by higher-level Communist Party officials (such as province governors). In total, approximately 38% of city secretaries were 11

12 promoted after their terms ended, 14% of city secretaries were transferred to other cities for another term as city secretary, and the remainder were no longer city secretaries after their terms ended for various reasons (such as retiring, being under arrest, or serving as another type of government officials). I defined the P romotion dummy for whether a city secretary was appointed to a higher political hierarchy in the government. In China, political turnover itself is endogenous. Politicians are assigned by the CPC instead of being elected by voters. For example, those politicians with good connections can be assigned to better cities and can borrow more from the CDB. Notably, the exogenous variation in CDB credit flows I exploit come from pre-determined five-year political turnover cycles rather than from turnover itself. C. Chinese Industry Census Data The firm-level data are the Chinese Industry Census (CIC) data from the Chinese National Bureau of Statistics (NBS). The CIC data is widely used in many academic studies (e.g., Hsieh and Klenow (2009) and Song et al. (2011)). The CIC data cover all manufacturing firms in China with annual sales of over five million RMB (about US$700,000) from 1998 to The CIC features detailed annual accounting data and firm characteristics, such as the number of workers, industry categories, locations, registration types, political hierarchies, government subsidies, and wages. In total, there are 711,892 firms. The CIC appears to be the most detailed database on Chinese manufacturing firms, and the content and quality of the database are both sufficient. In the CIC data, I classify firms as SOEs based on their contemporaneous registration types recorded per year. In particular, I classify two registration types as SOEs: owned by government departments and collective-owned enterprises. In China, the first type is for firms where the majority of the shares are owned by government departments (e.g., Stateowned Assets Supervision and Administration Commission, Ministry of Finance, Ministry of Transport). Collective-owned enterprises (COEs) are firms where assets are owned collectively by all residents in a community (e.g., cities, counties, villages). These COEs are mainly controlled and owned by the local governments. Officially, these two types of firms are considered to be public. 19 Moreover, in the CIC data, approximately 9% of private firms were privatized from SOEs. Consistent with other studies that use the CIC data, I consider these 19 The National Bureau of Statistics in China officially categorizes firms into eight types where governmentowned and collective-owned enterprises are considered to be public firms. 12

13 firms as private firms. In the robustness test, the results of this paper remain by reclassifying these 9% private firms as SOEs. 20 Table I, Panel C, shows the summary statistics of the CIC data. III Empirical Analysis and Results A. Career Concerns of Local Politicians Promotion is one of the most important career aspirations of politicians in China and in other countries (Maskin, Qian, and Xu (2000)). It is well documented that city secretaries and mayors promotions in China depend largely on local GDP growth. When China began its economic reforms in 1978 under Deng Xiaoping, local governments began to play an increasingly important role in developing the local economy. Concurrently, local government officials are also held accountable for local economic growth. In particular, local GDP growth has been the most important determinant of promotion. However, on December 9, 2013, the Organization Department of the Central Committee of the CPC announced a modified performance assessment of local politicians. From that point forward, local political promotion would depend not only on GDP but also on various other performance measurements (such as environmental protection). After this change, many provinces decreased - or even abandoned - the weight attributed to GDP as an assessment measure for local politicians performance. During the sample period of this paper (i.e., between 1998 and 2009), local GDP growth was the main determinant of promotion. Moreover, many empirical studies also find supportive evidence of GDP s importance for promotion; for example, Li and Zhou (2005) find that the likelihood of promotion of Chinese provincial leaders increased with economic performance (i.e., local GDP growth over politicians tenure) between 1979 and Under this promotion system, local politicians in China are incentivized to invest and boost local GDP during their five-year terms. As promotion decisions for city secretaries are made in the last or second-to-last year of their term, the first three or four years of local GDP growth are typically most heavily weighted because GDP data generally take a few months to be released. City secretaries have strong incentives to increase local GDP as much 20 I repeat the analysis in Table V by excluding privatized firms from the private sector. The results remain the same. Please see Table BV in internet appendix for more details. 13

14 as possible and as soon as possible. This GDP growth is fueled mainly by borrowing from the CDB. To verify the hypothesis, I regress the promotion chances on the increase of CDB loans, using a Probit model: promotion i,j = α + β 1 Loan Increase t,i,j + β 2 relation i,j +β 3 age i,j + β 4 gender i + ε i, (III.1) where promotion i,j is a dummy variable for whether city secretary i in city j got promoted during turnover. Loan Increase t,i,j is the logarithm of outstanding CDB loan increase from secretary i s first year in city j in year t. I set t = 1, 2, 3, 4, 5 to examine the effects from the CDB loan increased in various stages of a city secretary s term. relation i,j is a dummy variable for whether city secretary i in city j was from the same hometown as the provincial governor. age i,j is the age of secretary i in city j during the turnover year. gender i is a dummy variable for whether a city secretary i was female. Standard errors are clustered at the city level. This estimation purely explores the correlation between CDB credit and local politician promotions rather than a causal estimation in any sense. Table AII shows that CDB loan increases are positively associated with promotion probabilities, and that this effect was primarily from loan increases during the first two years of secretaries terms. In columns 1 and 2 of Table AII, the coefficients for loan increases are and 0.087, both of which are significant. When I include later years (columns 3 to 5), the coefficients are lower and less significant, which suggests that CDB loans, in particular, taken in the early years of local politicians terms may help their careers. During the most recent 15 years in China, borrowing from the CDB has been the primary method utilized by city secretaries to boost local GDP. Because the loans take time to affect the economy, city secretaries typically intend to borrow from the CDB as early as possible. This is consistent with Li and Zhou (2005) that local GDP growth is an important factor in politicians promotions in China. B. CDB Credit Flows and the Timing of Political Turnover In this subsection, I investigate the effects of municipal politician turnover timing on borrowing from the CDB. Instead of real turnover cycles, I employ predicted turnover cycles using past turnover in a city to predict future city secretary terms, which mitigates the concern that the 14

15 actual timing of city secretary turnover may be potentially affected by other factors. For example, new provincial governors tend to replace city secretaries with their own people. Thus, a provincial governor with greater political power might be assigned to a province with better investment opportunities and might also have greater access to CDB credit. The turnover timing of city secretaries in this province can be potentially correlated with local investment opportunities. To predict the first years of city secretaries terms, I use the following simple prediction algorithm. For each city secretary s term, let y be the first year of the previous city secretary s term, which is also the turnover year of the previous cycle. I predict that year y + 5 will be the first year of the current city secretary. If there was no previous cycle, I simply use the actual first year of the city secretary as the predicted year. For example, to predict the first year of city secretary A, I begin with the term of secretary A s predecessor, secretary B. If secretary B began her term in 2000, I mark 2005 as the predicted first year of secretary A. Overall, I correctly predict approximately 60% of the cycles actual first years. Approximately 28% of the predictions occur within a year before or after the actual first year. Approximately 12% of the predicted first years have differences of greater than one year with the actual first years. These incorrect predictions result from real cycles with lengths that are less than five years. Figure 2 plots the predicted city secretary term length. Approximately 46% of the predicted terms are five years. [Place Figure 2 about here] The predicted turnover cycles depend solely on past information and tease out exogenous variation from the real turnover cycles that might be correlated with concurrent economic conditions. I use the Cox proportional hazard model to test whether predicted political turnover timing is affected by factors such as current local economic conditions and politicians demographics. Table II shows that the one-year lagged local economic conditions (such as GDP, household income, fiscal income, employment, and the CDB s outstanding loans) are uncorrelated with predicted turnover timing. The N ationalcycle dummy had a positive effect on predicted turnover because 33% of city turnovers still occur in national turnover years. The secretary s age had positive effects on predicted turnover timing because city secretaries are more likely to retire as they get older. In columns (2) and (3), I break the 15

16 sample into politicians who are promoted and those who are not (e.g., lateral transfers and demotions), and the results remain the same in these two sub-samples. [Place Table II about here] Then, I explore borrowing patterns over various periods of a city secretary s term. The CDB s primary lending method involves coordinating with local governments to support both infrastructure projects and SOEs. The city secretary is the top-ranking politician in the city, and typically plays a large role during the lending process. The regression is as follows: LogLoan j,t = α + β 1 Y ear 1 i,j,t + β 2 Y ear 2 i,j,t + β 3 Y ear 3 i,j,t +β 4 Y ear 4 i,j,t + β 5 Y ear 5 i,j,t + β 6 Y ear 6 i,j,t +X Control j,t 1 + F ixedeffects + ε j,t, (III.2) where LogLoan j,t is the CDB loan variable in city j in year t. I used the logarithm of the CDB s outstanding loan amounts for industrial SOEs, infrastructure, and total loans as dependent variables. Y ear 1 i,j,t,..., Y ear 6 i,j,t, are dummy variables for those years that a secretary i stayed in city j in year t. Y ear 1 i,j,t equals 1 if it was the first year secretary i stayed in city j, and zero otherwise. Y ear 2 i,j,t to Y ear 6 i,j,t are similarly constructed. Control j,t 1 is a matrix of variables representing economic conditions such as GDP, urban income per capita, fiscal income, and the working population. X is the vector of coefficients of these control variables. I control for year fixed effects, which mitigates the concern that national turnover cycles actually drive the borrowing patterns. I also include city fixed effects and secretaries personal fixed effects because cities are often characterized by unique situations and secretaries have their own investment styles. Standard errors are clustered at the city level. Table III, Panel A, shows the regression results. Columns (1) to (3) represent real turnover cycles. In Column (1), the dependent variable is the logarithm of CDB city total outstanding loan amount. Y ear 1 is the missing category. Y ear 2 s coefficient was on the total outstanding CDB loan with a significance level of 1%; on average, city secretaries borrowed 38.6% less during their second year than during their first year. Y ear 3 s coefficient was 0.749, Y ear 4 s coefficient 1.071, Y ear 5 s 1.429, and Y ear 6 s Borrowing from 16

17 the CDB decreased monotonically the longer a city secretary stayed in a city. In other words, city secretaries borrowed more as soon as they took office and slowed their borrowing monotonically over their terms. In Panel B, I use the variable T urnover as the independent variable which equals the number of years that secretary stayed in the city. In line with Panel A, city secretaries borrowing from the CDB decreases over their tenures. On average, if a city secretary stayed one more year, borrowing from the CDB decreased by 36.4%. I then break loans into infrastructure and industrial SOE loans in Columns (2) and (3), respectively, and both measures have patterns similar to that in Column (1), and the patterns are stronger for industrial SOE loans. Columns (4) to (6) in Table III present predicted turnover cycles. The patterns are similar to real turnover cycles but are a bit weaker. For example, on average, if a city secretary stayed one more year, borrowing from the CDB decreased by 15.4%, which is smaller than the coefficients on real turnover cycles because there are certain incorrect predictions in turnover cycles. These prediction errors eliminate potential endogenous turnover timings while lowering the instruments power. I use these predicted turnover cycles in all the empirical exercises below. [Place Table III about here] Figure 3 plots the average logarithm of the total CDB loan amounts made to cities after eliminating year, city, and politician fixed effects. There are three national turnover cycles during my sample period: 1998 to 2002, 2003 to 2007, and 2008 to I cluster the cities using these three cycles, respectively, and calculate the average logarithm of CDB city total loan amounts for each year in a predicted five-year cycle. Figure 3 shows the zig-zag pattern during these three cycles. On average, city secretaries borrowed significantly more during their predicted first year in office, and this amount monotonically decreased over time. When a new city secretary came in, borrowing spiked again, which verifies the results in Table III. I also examine borrowing patterns in each city, and most follow this zig-zag pattern. This finding alleviates the concern that certain cities with extreme values drive the results in Table III. 21 As discussed in Section III.A, these patterns are consistent with the promotion incentives of city secretaries. 21 In Table AIII in appendix, I select the off-national-cycle cities which exhibited similar borrowing patterns. 17

18 [Place Figure 3 about here] C. CDB SOE Loans Effects on Firms in the Same Industry To explore the effects of CDB loans on private firms, I begin by tracing the different effects of CDB industrial loans across different levels of the supply chain. I use province-industry level CDB loan data covering 31 provinces and 95 industries in China. The data offer the advantage of analyzing the effects of government credit on various industries. Previous studies are mainly based on aggregate government credit or spending to explore only the net effects. As discussed in Section II.A, more than 95% of CDB industrial loans are extended to SOEs. Only 5% of CDB industrial loans flow to private firms, which typically have government backgrounds. Moreover, at the province-industry level, as weighted by total assets, approximately 86% of SOEs have loans outstanding from the CDB. Using this aggregate CDB province-industry loan data, I can capture the effects of CDB credit to major SOEs on nearby private firms. In Table IV, I merge the CDB province-industry outstanding loans per year with firm-level CIC data by firms locations and industry codes. The OLS regressions explore the correlations between CDB loan amounts and a firm s asset investment, employment, borrowing, sales, and firm s efficiencies such as ROA and sales per worker. I control for lagged local economic variables as well as firm fixed effects and year province fixed effects, which eliminates the province time trends. Panel A of Table IV shows the regression results for SOEs. CDB industrial SOE loans demonstrated significantly positive correlations with SOEs total assets, employment, debt, and sales. Panel B shows that CDB industrial SOE loans were negatively correlated with private firms total assets, employment, total sales, ROA, and sales per worker. These results suggest that CDB industrial SOE loans help SOEs grow, which in turn may crowd out the private sector within the same industry. Moreover, infrastructure loans appear to crowd in private firms activities. [Place Table IV about here] However, the correlations in Table IV are hardly evidence of causal effects. The CDB 18

19 credit flows are not random. On the contrary, the CDB may choose to grant credit to SOEs in the areas or industries populated by private firms with relatively poor investment opportunities. Alternatively, the CDB may maximize spillover effects by selecting infrastructure projects in those regions with good investment opportunities. The CDB is mandated to grant credit to SOEs in bottle-necked industries and to infrastructure projects in areas with upside potential. Without the exogenous variation of CDB credit flows, the direction of the expected private firm response to CDB credit is unclear. To explore the causal effects of CDB loans, I employ 2SLS by exploiting the exogenous variation of CDB credit flows from the predicted political turnover timing. In particular, I first identify each city s largest SOE industry as a focal industry. In China, cities in the same province typically focus on different industries, which is particularly true for SOEs that adjust more slowly than private firms. I aggregate total SOE assets at the city-industry level each year and select the largest industry in each city. I call them focal industries. On average, only two cities in the same province focus on the same industry, and cities typically stick to the same industry over time. Among 310 cities, 42% did not change focal industries from 1998 to 2009, and 40% changed once, 14% twice, and only 3% more than twice. Based on these facts, we can view the focal industries as pre-determined. Then, I interact the pre-determined focal industry dummies for each city with predicted city-level political turnover cycles. I use these interaction terms to instrument CDB industry loan amounts and perform 2SLS regressions. More specifically, if city secretaries were in the earlier parts of their terms, based on the results in Table III, the city typically took out more CDB industrial loans for SOEs in the focal industry. I consider this a shock to a province-level CDB loan in the focal industry (i.e., the largest SOE industry) of this city. For example, city A in province B focuses on focal industry C. If city A s current secretary is in the earlier part of her term, I consider it a shock to CDB loans of industry C in province B in that year. As cities in the same province typically focus on different industries, and the focal industry in a city does not change often over time, if a city borrows more for its SOEs, it should be reflected in the province-level CDB loan amount for that industry. Formally, the 19

20 regression can be represented as follows: LogLoan P I k,p,t = α + β 1 F irst k,p,t + β 2 Second k,p,t + β 3 T hird k,p,t +β 4 F ourth k,p,t + β 5 F ifth k,p,t + β 6 Sixth k,p,t + X Control p,t 1 + Y ear P rovincef E + IndustryF E,(III.3) where LogLoan P I k,p,t is the logarithm of the CDB outstanding loan amount in industry k, province p in year t. F irst k,p,t is a dummy variable that equals 1 if there is a city that focuses on industry k (focal industry) in province p with a secretary who is in her first year. Second k,p,t is a dummy variable that equals 1 if a city focuses on industry k in province p with a secretary who is in her second year. T hird k,p,t to Sixth k,p,t are defined similarly. Control p,t 1 includes lagged GDP, urban income per capita, fiscal income, and working population in province p. I control for province time trends by adding year province fixed effects. The regression results are shown in Table A. In the first column of Table AIV, CDB industrial loans are larger if it is a focal industry of a city with a secretary in the early part of her term; the coefficients of F irst k,p,t to F ifth k,p,t are significantly positive and monotonically decreasing. I also combine the first two or three years and create the F irst Second k,p,t and F irst T hird k,p,t dummy variables. In columns 2 and 3 of Table AIV, these two variables also have positive coefficients. On average, a province borrowed 38.6% more from the CDB for focal industries in cities with secretaries in the first three years of their terms. City secretaries borrowed more for a city s focal industry during the early parts of their terms, which is consistent with the previous results in Table III. Next, I use F irst k,p,t to Sixth k,p,t to instrument LogLoan P I k,p,t and perform 2SLS regressions of activities of private firms in the focal industry on the instrumented logarithm of CDB SOE loan amount in the same focal industry. I include all control variables, such as economic variables Control p,t 1, and fixed effects during first-stage regressions. The second-stage regression is: Y l,k,p,t = α+β LogLoan P I k,p,t +X Control p,t 1 +Y ear P rovincef E +F irmf E +ε l,t, (III.4) where Y l,k,p,t are the dependent variables of firm l in industry k province p in year t, such as the logarithm of total assets, number of workers, total debt, and sales. I control for economic 20

21 condition variables and firm fixed effects. I also control for the year province fixed effects to eliminate the province time trends. Panel A of Table V shows the 2SLS regression results for private firms in the same focal industries. In columns 1 to 6, CDB industrial SOE loans exerted negative effects on private firm total assets, employment, debt, total sales, ROA and sales per worker. On average, when CDB industrial loans doubled, private firms in the same industry decreased assets by 2%, decreased employment by 1.7%, decreased total sales by 4.2%, and decreased sales per worker by 2.6%. Panel B of Table V shows the 2SLS regression results for SOEs. Consistent with the OLS results, industrial loans helped SOEs increase total assets, employment, debt, sales per worker, and total sales. When industrial loans doubled, SOEs in the same industry, on average, increased assets by 17.7%, increased employment by 13.9%, increased debt by 21.8%, increased total sales by 13.7%, and increased sales per worker by 4.4%. In sum, CDB industrial loans make SOEs grow larger and sell more. Simultaneously, private firms shrink in size and sell less. Because CDB industrial firm loans typically are made to SOEs, it is not surprising that SOEs become stronger, crowding out the private sector. 22 [Place Table V about here] Moreover, in internet appendix, Table BI, I also disentangle the effects of CDB SOE loans on intensive and extensive margin. I define new firms as firms in their first year of business. Table BI, Panel A, shows that for incumbent private firms, CDB industrial SOE loans exerted negative effects on their total assets, employment, debt, total sales, ROA, and sales per worker. Panel B shows that increases in CDB SOE loans lead to decreases in total number of private firms as well as the number of private firms entering the sector. This suggests that CDB SOE loans crowding-out effects on the private sector in the same industry come from both intensive and extensive margin. D. CDB SOE Loans Spillover Effects on Related Industries It is well known that China s economy has grown dramatically over the past two decades and that the private sector was the primary driver of this growth. Although government credit 22 I repeat the regressions in Table V by controlling for both province year fixed effects and industry year fixed effects. The results are very similar as in Table V which are reported in Table BIV in internet appendix. 21

22 crowds out the private sector in the same industry, it might complement the private sector in related industries. The CDB s strategy is to aid basic industries such as energy and mining to help related industries. Consistent with Stiglitz (1993), loans to basic industries might have positive spillover effects on other sectors of the economy, which can t be captured by commercial banks. In this subsection, I use an input-output matrix to identify inter-industry relationships and study the spillover effects of government credit. I use the national input-output matrix for 2007 from the National Bureau of Statistics of China to define upstream and downstream industries. 23 The input-output matrix has 42 industries, whereas the CDB classifies its loans into 95 industries, which is more detailed. I match these two industrial classifications by combining CDB industries. For each focal industry defined in Section III.C, I use the input-output matrix to identify all its downstream industries that use the majority of inputs from the focal industry. On average, each focal industry has 2.3 downstream industries. At the firm level, I match each firm in these downstream industries with its upstream focal industrial CDB SOE loan in the same province. After this merger, there were 25 industries from the manufacturing sector in the sample. Then, I run the regressions of firms activities in downstream industries on the instrumented CDB loan amount of the focal industry, which is called UpstreamLoan in Table VI. Again, I use city-level turnover dummy variables to instrument U pstreamloan and perform 2SLS regressions, and the first-stage regression is: LogUpstreamLoan l,k,p,t = α + β 1 F irst k,p,t + β 2 Second k,p,t + β 3 T hird k,p,t +β 4 F ourth k,p,t + β 5 F ifth k,p,t + β 6 Sixth k,p,t +X Control p,t 1 + Y ear P rovincef E +F irmf E + ε l,t, (III.5) where LogUpstreamLoan l,k,p,t is the logarithm of CDB outstanding loan amount in the upstream industry of firm l in industry k (i.e., downstream industry), province p in year t. k indexes the upstream industry of k. F irst k,p,t is a dummy variable equal to 1 if there was a city with focal industry k in province p and had a secretary who was in her first year. 23 I also use other years input-output matrices to double-check the definition of upstream and downstream industries and to assess the same inter-industry relationships that do not change substantially over time. 22

23 Second k,p,t to Sixth k,p,t are defined similarly. The second-stage regression is: Y l,k,p,t = α + β LogUpstreamLoan k,p,t + X Control p,t 1 +Y ear P rovincef E + F irmf E + ε l,t, (III.6) where Y l,k,p,t are the dependent variables of firm l in year t, which is in industry k, province p. LogUpstreamLoan k,p,t is the estimated CDB loan to upstream industry k. k indexes the upstream industry of k. Panel A of Table VI presents the results for private firms. Generally, CDB loans to firms upstream industry helped the downstream private sector. In columns 1, 4, 5, and 6 of Panel A, Table VI, the upstream CDB industrial firm loan had significantly positive effects on downstream private firms total assets, total sales, ROA, and sales per worker. On average, when the upstream industrial loan doubled, downstream private firms increased assets by 3.4%, increased total sales by 2.6%, and increased sales per worker by 2.6%. Moreover, I also explore what types of private firms in downstream industries can benefit more from upstream CDB credit. In Table VI, Panel B, I interact the LogUpstreamLoan with dummy HighROA equal to 1 if the firm s lagged ROA was above the median of all firms in the previous year. Panel B shows that firms with higher ROA capture more benefits from upstream CDB credit. This is consistent with the spillover story that more efficient firms are expected to capture more positive spillover effects from government credit. Moreover, in Panel C, I interact Log(U pstream Loan) with the dummy Connected for whether private firms political hierarchy is above the city level or not. In China, all firms (including private firms) have a political hierarchy that defines the level of government the firm must report to. In other words, it determines which level of government the firm is affiliated to. For example, a city-level firm is one that is under a city government and reports to that government. The CIC data classifies the firms into city, province, and national levels in terms of political hierarchy. The dummy Connected is for province and national level firms. From the regression results in Panel C, private firms with better political connections can benefit significantly from upstream loans. The correlation between dummy HighROA and Connected is 0.03, which means there are few overlaps between efficient private firms and connected private firms. The results in Panel B and C are not confounded with one another. In sum, CDB upstream loans have positive effects on downstream private firms, and private firms with connections or with 23

24 high ROA can benefit significantly more. [Place Table VI about here] E. Politicians Other Channels for Affecting the Local Economy One concern regarding the instrument is whether the exclusion condition holds that local political turnover cycles affect the economy only through borrowing from the CDB. In China, local politicians are deeply involved in economic development, and promotion incentives play a role (e.g., Chen et al. (2005), Li and Zhou (2005)). The bar for the exclusion condition is not that city secretaries don t do anything else than borrow from the CDB. Rather, the exclusion condition requires that the other activities of city secretaries do not follow the zig-zag pattern over the turnover cycles as CDB credit flows do. There are several ways that turnover cycle might affect the local economy. When new city secretaries arrive at their new cities, they typically have their own plans or preferences with regard to developing local economies, and they have several tools whereby to do this. For example, a secretary can build business districts to attract investment, speed up approvals of city projects, or provide better government services; however, the biggest constraint is limited fiscal income. Local governments in China share only 20-30% of tax revenue but remain responsible for constructing infrastructure. City secretaries have many good projects piled on their desks, but they lack financial resources. Besides the CDB loans, there are three other common ways to raise money: borrowing from other banks, selling land, and requesting transfers from the central government. Moreover, in China, there are many pro-economic policies determined by the central government, such as export tax rebates and corporate tax breaks for foreign companies and export companies, among others. City secretaries may enforce these policies disparately. For example, they can simply give tax breaks to more firms. To rule out these channels, I repeat the equation III.2 and perform the regressions of outstanding loan amounts from other banks, developed land amounts, export amounts, fiscal income, central government transfers, average effective corporate taxes, and average effective value-added tax for each city on the dummy variable Y ear 1 to Y ear 6. The data on other 24

25 banks outstanding loan amounts are from the CBRC loan dataset between 2007 and I control for the exact same economic variables and fixed effects as in Table III. Table VII shows there were no effects of predicted turnover timing on these other channels. Moreover, the F-tests on these turnover dummies are not significant. This means that, jointly, the excluded instruments together have no predictive power on these channels. The results in Table VII suggest that these other channels do not synchronize cyclically with the predicted turnover cycles of city secretaries as CDB loans do. The exclusion condition is not violated as long as the activities in these other channels show no zig-zag patterns. For example, borrowing from other banks does not violate the exclusion condition as long as the borrowing amounts do not synchronize with predicted turnover cycles. As discussed in Section I.B, unlike the CDB, commercial banks usually have direct connections with SOEs. Executives of SOEs typically do not follow five-year turnover cycles. The CDB, on the other hand, has a stronger relationship with local governments that involve deeply in CDB credit allocations. This is one of the main reasons for the non-cyclicality of the commercial bank loans shown in Table VII. [Place Table VII about here] To further support the exclusion condition, I explore various effects of turnover timing in cities with different CDB loan levels in Table VIII. The hypothesis is that turnover timing should have stronger effects in cities with more CDB credit. Instead of the contemporaneous CDB loan levels which are endogenous, I use the pre-determined CDBCity dummy to interact with turnover cycles. The CDB enjoys various relationships in different cities. Some cities have long-term collaborations with the CDB and some do not. The CDB entered different cities at different times; it began to lend to local governments in 1998 and selected those with better relationships. On average, the cities with longer and better connections with the CDB have been able to borrow more. The CDBCity dummy equals 1 if the city borrowed from the CDB in 1998, the beginning of the sample period. Approximately 30% of the cities are CDBCity and the other 70% began borrowing from the CDB in later years. In Table VIII, I interact P redictedt urnover with the dummy variable CDBCity and regress the SOE variables on the interactions. The interaction term P redictedt urnover CDBCity has significantly negative coefficients for total assets, employment, debt, and sales per worker. 25

26 This suggests that turnover timing has greater effects when the city has better and longer connections with the CDB, which means better access to CDB credit. [Place Table VIII about here] F. Infrastructure Loans vs. SOE Loans In addition to SOE loans, the CDB also lends to infrastructure projects. Table III shows that CDB infrastructure loans also have zig-zag patterns over the predicted political turnover cycles. The results on CDB SOE industrial loans are hardly driven by infrastructure loans. First, infrastructure projects are expected to have positive spillover effects on firms around in most industries. The crowding-out and crowding-in effects of CDB SOE loans depend on the industrial levels of the supply chain. Second, as in Figure 1, the distributions of CDB infrastructure loans and SOE loans are different across different cities. The CDB imposes borrowing constraints on each city. Those cities with more SOEs typically borrow more CDB SOE industrial loans and fewer infrastructure loans. For example, Figure 1 shows that, in 2002, for 50% of cities, more than 90% of their CDB loans were for SOEs. By contrast, 25% of the cities concurrently have mainly CDB infrastructure loans comprising more than 90% of the total CDB loan amounts. This distribution is quite stable over time. I defined the dummy SOECity based on whether the ratio of total SOE assets to total private firm assets in a city was above the median level or not across all 310 cities in I defined the dummy INF City = (1 SOECity) for other non-soe cities that borrow mainly for infrastructure projects. Using the interaction between the pre-determined dummy SOECity and predicted turnover timing, I disentangle the heterogeneous effects of CDB infrastructure loans and SOE industrial loans. There are two sources of endogeneity: the allocation of CDB credit across different cities and the allocation of CDB credit to infrastructure projects and SOEs within a city. Table III shows that although both infrastructure loans and industrial SOE loans have zig-zag patterns over turnover cycles, they have different slopes. CDB SOE loans are more sensitive to turnover cycles than infrastructure loans. One primary reason for this is that infrastructure projects are often long-term and infrastructure loans have longer 26

27 maturities than SOE loans. I use the SOECity dummy as a pre-determined predictor to break down CDB infrastructure loans vs. industrial SOE loans. In particular, I interact dummy SOECity with dummies Y ear 1 to Y ear 5 and also interact dummy INF City with dummies Y ear 1 to Y ear 5. I use these interactions to instrument both CDB infrastructure loans and industrial loans at the city-year level. Figure A2 shows the first stage regression results. Both CDB infrastructure loans and CDB SOE industrial loans follow decreasing patterns over both SOEY ear dummies and INF Y ear dummies. In the top panel, as expected, CDB SOE loans are more sensitive to SOEYear dummies (SOE City) than to INFYear dummies (Non SOE City). By contrast, in the bottom panel, CDB infrastructure loans are more sensitive to INFYear dummies than to SOEYear dummies. This verifies the different zig-zag patterns between CDB infrastructure loans and SOE loans and also verifies that CDB SOE loans and infrastructure loans have different sensitivities to turnover cycles in different cities. Using the interactions between the pre-determined SOECity predictor and predicted turnover cycles as instruments, I can identify the individual causal effects of CDB infrastructure loans and SOE loans. Table IX shows the second stage regression results. CDB infrastructure loans supplement private firms, but industrial SOE loans crowd out private firms. In particular, in columns 1 to 6 in Table IX, for private firms, CDB infrastructure loans increase assets, number of workers, debt, sales per worker, and total sales. Consistent with previous results, CDB industrial SOE loans decreased private firm assets, sales per worker, and total sales. [Place Table IX about here] In sum, for the exclusion condition, the concern is that the political turnover cycle affects firm activities via channels other than CDB loans. The opposing effects of CDB SOE loans and infrastructure loans in Table IX mitigate the concern that the crowding-in and crowding-out effects of CDB SOE loans on private firms across different levels of the supply chain (i.e., the results in Tables 5 and 6) might be driven by CDB infrastructure loans. Moreover, if the political turnover cycle mainly affects firm activities on itself instead of via CDB loans, I would not find these opposing effects between different loan types. Similarly, the opposing effects of CDB industrial SOE loans on private firms vs. SOEs in focal industries (i.e., Table V) also suggest that the political turnover cycle mainly affects firms via CDB loan 27

28 allocations. These asymmetric effects I find can further mitigate, but not entirely eliminate, concerns on the exclusion condition. G. Reduced Form Analysis of Turnover Cycles Effects on Firms Besides the 2SLS regressions, in Table BII in internet appendix, I also report the results of reduced form regressions of firm activities on dummies of predicted politician turnover cycles. In the regressions, Y ear 1 is the missing category. Table BII, Panel A, shows the results for SOEs. In column 1, assets of SOEs, on average, were 1.5% smaller in the second year of a city secretary s term versus the first year. The coefficients for year three to year six were also negative, and decreased monotonically. SOEs employment, debt, and sales also have zig-zag patterns similar to pattern of borrowing from the CDB. It becomes clear that the amount of CDB loans moves in parallel with SOEs activities and performance over the predicted turnover cycles of city secretaries. This is consistent with the crowding-in effects of CDB credit on SOEs in Table V. Moreover, for private firms, I perform the same reduced form regressions and restrict the sample to private firms in SOECity. Since CDB infrastructure loans and industrial SOE loans have opposing effects on private firms, looking only at SOECity allows me to focus on SOE loans effects. Table BII, Panel B, shows that, in contrast to SOEs, private firms assets, employment, and sales increases monotonically over a city secretary s term. These patterns of private firms are opposite to the zig-zag borrowing patterns from the CDB, which suggests that CDB loan amounts move in the opposite direction to private firms activities and performance. This is consistent with the crowding-out effects of CDB credit on private firms in Table V. Together with the results in Table VII, Table BII suggests that political turnover cycles have significant impacts on firm activities, which are mainly the outcome of CDB credit, rather than other channels shown in Table VII. Moreover, the asymmetric effects of political turnover cycles between SOEs and private firms in Table BII and Table V come mainly from the CDB loan allocations, assuming the political turnover cycle itself (other than via CDB loans) has symmetric impacts on all firms. This evidence further supports the exclusion condition. 28

29 H. Overall Effects of Government Credit from the CDB From the analyses above, I find that CDB industrial SOE loans crowd out private firms in the same industry but crowd in private firms in downstream industries. Moreover, it is clear that CDB infrastructure loans help nearby private firms. Thus, what are the overall effects of government credit from the CDB? First, I calculate the net effects of CDB SOE loans. One component of the CDB s mandate is to help basic industries. Although CDB industrial loans crowd out the private sector in the same industry, they help private firms in downstream industries grow. I use the estimated coefficients of CDB SOE loans in Tables 5 and 6 to perform a back-of-the-envelope calculation. In particular, for CDB SOE loans overall effects on private firm assets, the coefficient of CDB SOE loans on private firms total assets was 0.029, as shown in Table V, which means a one-unit increase in the logarithm of CDB SOE loans decreases the logarithm of each private firm s assets by unit in the same industry and in the same province. For each private firm in each year, I calculate the difference in the logarithm of the CDB SOE loans in this industry and province between the last year and the current year. Then, I multiply this difference by to obtain the estimated percentage changes of this private firm s assets caused by CDB SOE loans. Moreover, I use the coefficient of CDB SOE loans on private firms total assets in downstream industries in Table VI and repeat this back-of-the-envelope calculation. On average, between 1998 and 2009 a $1 increase in CDB outstanding SOE loan amount led to a $0.2 decrease in private firms total assets. The crowding-out effects of CDB SOE loans on private firms in the same industry were larger than the crowding-in effects on downstream private firms, which makes the net effects of CDB SOE loans negative on the private sector. When I examine CDB SOE loans net effects over time, I find they were positive during earlier years and became negative during later years. For example, between 1998 and 2004, a $1 increase in the CDB outstanding SOE loan amount led to a $0.43 increase in private firms total assets. From 2005 to 2009, a $1 increase in the CDB outstanding SOE loan amount led to a $0.35 decrease in private firms total assets. On the other hand, CDB infrastructure loans had positive spillover effects on private firms. Between 1998 and 2009, a $1 increase in the CDB outstanding infrastructure loan amount led to a $0.47 increase in private firms total assets, on average. Sales per worker is an important measurement of efficiency in China, particularly in the 29

30 manufacturing sector. An abundant labor supply means cheap labor costs in China, one of the most important reasons for China s dramatic growth in exports and in the economy as a whole. Most manufacturing firms in China are thus labor intensive. Higher sales per worker mean a firm can do more with fewer workers. From the results in Tables 5, 6, and 9, CDB industrial SOE loans decreased sales per worker for private firms in the same industry but increased sales per worker for downstream private firms. CDB infrastructure loans increased sales per worker for private firms. Based on these coefficients, I find that, on average, CDB SOE loans decrease sales per worker by 0.90% for private firms in the same industry but increase sales per worker by 0.95% for private firms in downstream industries. Overall, these two forces cancel one another out. By contrast, CDB infrastructure loans increase sales per worker by approximately 18% for private firms. I further explore the reasons behind the disparate CDB credit effects during various periods. Figure 4 plots total CDB loan issuances between 1998 and 2010 for the top six industries. In 1998, electric power supply and coal mining were the top two industries, followed by petroleum and natural gas extraction, oil processing and refining, chemical products, etc. Except for transportation equipment manufacturing, all these are at the top of the supply chain. Manufacturing firms are typically in their direct downstream industries. CDB s dominant weight on upstream industries can have substantial positive spillover effects on downstream industries, which is one reason industrial credit can have positive net effects on the private sector during earlier years. In 2010, and after 12 years, the top industries that receive CDB loans changed. Electric power supply remains the top industry of CDB loan issuances. However, three of the top six industries are in the manufacturing sectors. Electronic equipment manufacturing ranks third which was not one of the top six industries back to Special equipment manufacturing (e.g., equipment for mining, agriculture, medical, and clothing) ranks fourth, and transportation equipment manufacturing ranks sixth. This might lead to bigger crowding-out effects on the manufacturing industries, and smaller spillover effects on downstream industries. This might explain why CDB industrial loans had positive effects on the private sector during earlier years and negative effects during later years. During the past 20 years, China has experienced dramatic GDP growth, and there have been many shortages of energy supply and raw materials from mining. CDB loans to upstream industries have helped solve these demand constraints, possibly explaining why CDB industrial loans could help the private sector grow faster and become more efficient 30

31 during earlier years. However, in later years, the CDB has focused less on basic industries, shifting to other industries, such as electronic equipment. [Place Figure 4 about here] IV Conclusion This paper traces the heterogeneous effects of government credit across different levels of the supply chain. It also explores the different effects of various types of government credit (infrastructure vs. industrial SOE loans). Using unique and detailed industrial loan data from the China Development Bank, I find that government credit to industries, which typically goes to SOEs, helps SOEs expand but crowds out private firms in the same industry (i.e., decreases in assets, debt, employment, sales, ROA, and sales per worker). However, these industrial loans help private firms in downstream industries grow. Conversely, government credit to infrastructure helps private firms expand. These opposing effects I find shed light on prior mixed empirical findings on the net effects of aggregate government credit. To fully understand the effects of government credit, the different effects across different levels of supply chains and across different types of credit must be explored. The benefits and costs of government credit are central questions in many countries. This paper provides detailed analysis of the benefits of government credit in the context of China, the second-largest economy in the world. Besides China, development banks are important in many other countries, such as Germany and Korea. In addition, numerous multilateral development banks, such as the World Bank, play important economic roles across the globe. Each country has its own industrial structure of supply chain. Based on the empirical evidence in this paper, policy makers should consider the different forces of government credit at different levels of the supply chain and decide where to invest. This paper s findings are therefore important for policy makers worldwide. Although the direct costs of government credit (such as credit default) are essential for evaluating government credit programs, they are beyond the scope of this paper. In future research, it would be important to value the costs of government loans to evaluate their overall costs and benefits. In China, after decades of rapid economic growth, local government indebtedness has recently raised concerns and created risks involving economic stability and growth. These 31

32 looming risks in China also have significant impacts on the global economy. What are the relationships between government credit and China s banking system and shadow banking system? How does government credit in China and the risks it poses affect other countries worldwide? Answering these questions will further elucidate China s government credit and the larger picture of its role in the global economy. 32

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37 Figure 1: Distribution of CDB Infrastructure Loans vs. Industrial SOE Loans Ratio of Infrastructure Loan to Total Loan Amount Ratio Year Top 25 Percentile Bottom 25 Percentile Median Ratio Ratio of Industry Loan to Total Loan Amount Ratio Year Top 25 Percentile Bottom 25 Percentile Median Ratio Figure 1 shows the plots of the ratios of the CDB infrastructure loan amount to the total city-level loan amount and the ratios of the CDB industrial SOE loans to the total city-level loan amount. The top panel shows the distribution of infrastructure loan ratios across 310 cities. Infrastructure includes transportation (e.g., road, railway, airport, bridge, tunnel), water supply, energy supply (e.g., gas, electric), telecommunications, and public service (e.g., sewage discharge). The solid lines represent the median ratios among 310 cities and the dashed lines are the top and bottom quartiles of the ratios among 310 cities each year. The bottom panel shows the CDB industrial SOE loan ratios. City-level loans do not include province-level projects even if a part of such projects may be located in the city, such as a highway. 37

38 Figure 2: Predicted Political Turnover of City Secretaries in China Figure 2 plots the histogram of predicted city secretaries term length in China. It is at city-politician level. The data covers 334 cities and 1,227 city secretaries from 1997 to The term length is calculated from the predicted turnover cycles of city secretaries. Approximately 46% of the city secretaries end their terms in the fifth year. 38

39 Figure 3: Local Government Borrowing Pattern Figure 3 shows the pattern of the logarithm of the CDB total city loan amounts over the turnover cycle. The right vertical axis is the logarithm of CDB total city loan amounts after taking out the year, city, and politician fixed effects in regressions. The horizontal axis is the predicted turnover cycle of city secretary. There are three national five-year turnover cycles between 1998 and 2012: 1998 to 2002, 2003 to 2007, and 2008 to The left vertical axis, Years in Office, is the number of years that the city secretary has served the city (predicted), which is from year 1 to year 5. For example, the first cycle (1 to 5 on the horizontal axis) is from 1998 to I cluster the cities by Years in Office (1 to 5) from 1998 to 2002 and plot the average CDB city loan amounts for each bin of Years in Office. I do the same thing for the second cycle from 6 to 10 in the horizontal line (i.e., year 2003 to 2007). The third cycle is from 11 to 15 in the horizontal line. Since the CDB city-level loan data are between 1998 and 2010, the third cycle has only three years (i.e., year 2008 to 2010). 39