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

Hong Ru∗

January 17, 2015

Abstract

Using unique data from the China Development Bank (CDB), this paper examines the effect of government credit on firm investment, employment, debt, profitability, and survival. I explore the different effects of various types of government credit (credit to infrastructure vs. credit to state-owned enterprises (SOEs)). I also trace the effect of government credit across different levels of the supply chain. I find that CDB industry loans to SOEs crowd out private firms in the same industry but crowd in private firms in downstream industries. Private firms with better political connections benefit significantly more from upstream CDB industry loans. I also find private firms benefit from CDB infrastructure loans. From 1998 to 2009, on average, a $1 million increase in government industry credit from the CDB led to a $0.52 million decrease in the private sector’s total assets. I use the exogenous timing of municipal political leaders’ turnover as an instrument for CDB credit flows to cities. I find that municipal politicians borrow significantly more during the early period of their terms.

∗MIT Sloan. Address: MIT Sloan School of Management, Department of Finance, E62-615, 100 Main Street, Cambridge, MA, 02142; [email protected]. I am indebted to Nittai Bergman, Andrey Malenko, Robert Townsend, and especially Antoinette Schoar for their invaluable guidance and encouragement. I thank Jean-Noel Barrot, Asaf Bernstein, Winston Wei Dou, Yan Ji, Mark Kritzman, Erik Loualiche, Deborah Lucas, Eric Maskin, Tran Ngoc-Khanh, Stewart Myers, Stephen Ross, Felipe Severino, Daan Stuyven, Anjan Thakor, Richard Thakor, Wei Wu, and participates in the MIT Finance Seminar and Olin Business School Conference on Corporate Finance for insightful comments. I am also grateful to Gao Jian. I thank China Development Bank for providing data, and Yue Wu for help with data cleaning. I thank all anonymous, local government officials in China for long and engaging discussions. All errors are my own. The latest version of this paper is available at http://hongru.mit.edu/ 1 Introduction

Government credit programs are pervasive in many countries around the world and play an important role in capital allocation, especially to infrastructure investment and state-owned enterprises (SOEs). For example, the United States established its earliest federal credit program - the Farm Credit System - in 1916 to provide credit to agricultural and rural America. This was followed by a surge in additional government credit programs after the Great Depression. Some credit programs, such as the Rural Electrification Administration and the Small Business Administration, are still in place.1 In 2010, the U.S. Government’s outstanding commitments for loans and guarantees totaled approximately $2.3 trillion. This was roughly one-third the size of the loans of all the U.S. banks combined (Elliott (2011)). Outside the United States, many countries have development banks that typically issue government credit.2 The literature has outlined two opposing views of the effects of government directed credit. On the one hand, government credit can be justified by the existence of credit market failures. Private banks may not allocate funds to high social return projects with positive externalities if the returns are difficult to be captured (Stiglitz (1993)). Prime examples are infrastructure investments such as highways or airport constructions. Private firms could benefit from these projects’ positive externalities. On the other hand, government credit could crowd out private sector investment, especially when the credit is given at below market rates or to firms that have distorted incentives (e.g., Demirguc-Kunt and Maksimovic (1998), King and Levine (1993a, 1993b), Rajan and Zingales(1998)). A concern is that inefficient established firms are subsidized (e.g., SOEs), while more efficient firms are crowded out (e.g., private firms), which harms the economy in

1For detailed histories of the Farm Credit Council, please go to www.fccouncil.com documents. Rural Electrification Administration (REA) was the former agency of the U.S. Department of Agriculture that administered loan programs for electrification and telephone service in rural areas. Reconstruction Finance Corporation (RFC) was a U.S. government corporation started in 1932. It provided financial support to state and local governments, and made loans to banks, railroads, mortgage associations, and other businesses. RFC was the predecessor of the Small Business Administration. Small Business Administration (SBA) was founded in 1953 to provide loans, loan guarantees, contracts, counseling sessions, and other forms of assistance to small businesses. In 2007, the National Infrastructure Reinvestment Bank was proposed, later backed by President Obama. 2For example, Germany formed KfW Bankengruppe in 1948, a German government-owned development bank based in Frankfurt. Korea founded Korea Development Bank in 1954, which is a state-owned policy bank in South Korea. There are also many multilateral development banks such as the African Development Bank, the Asian Development Bank, the European Bank for Reconstruction, and Development and the Inter-American Development Bank Group. In the U.S., the National Infrastructure Reinvestment Bank was proposed in 2007.

1 the long run. Empirical studies have shown mixed results on whether government credit3 crowds out private sector investment and growth or whether it encourages private sector (crowd in) (e.g., Blanchard and Perotti (2002), Cohen et al. (2011), Gale (1991)). Due to limited data, these studies usually explore only the effects of aggregate government credit. In this paper, I am able to add to the prior literature by analyzing the role of different types of government credit (infrastructure loans vs. industry loans to SOEs) in the context of China. In addition, I am able to trace the effect of credit across SOEs and private firms, as well as across different levels of the supply chain. I find that government loans to SOEs crowd out private firms in the same industry, but interestingly they crowd in private firms in downstream industries. Moreover, infrastructure loans appear to have positive effects on private firms. These opposing effects may shed light on the mixed results from previous studies. I study this issue based on a unique industry-level loan data set from the China Development Bank (CDB) for the period 1998 to 2013.4 I combine it with a firm-level panel data set from the Chinese Industry Census, which contains all manufacturing firms with annual sales of more than 5 million RMB ($700K) from 1998 to 2009. The China Development Bank is one of three policy banks with a mandate to provide credit support to infrastructure projects and SOEs in basic industries, primarily by lending to local governments.5 The CDB loan data contain city-level, aggregate, outstanding loan amounts and issuances. Loans are categorized into infrastructure loans and industry loans to SOEs. The data set also contains province-level, aggregate, outstanding loan amounts and issuances, which are categorized into 95 industries.6 I first use a simple OLS regression framework. I find that CDB industry loans are associated with larger investment in assets, greater employment, and more debts of SOEs that receive these CDB

3Government credit can also be viewed as government spending (Lucas (2012b)). CDB loans are subsidized. In China, CDB lending can be viewed as an extension of government fiscal policy. 4I am the visiting scholar in the China Development Bank and have access to their internal data system. This internal data is compiled in CDB headquarter from detailed monthly loan reports of each CDB branch. 5The CDB usually coordinates with local governments and lends to infrastructure projects and SOEs in the province or city. Most of CDB’s loans are lent via local governments, which are implicitly or explicitly responsible for these loans. See more details in Section 3. 6These industries include both infrastructure sectors (e.g., road transportation, water supply, public facilities) and industry sectors (e.g., agriculture, tobacco, software, oil refining, textile). See more details in section 4.

2 loans. On the other hand, the amount of CDB industry loans to SOEs is negatively correlated with investment, employment, total sales ,and sales per worker of private firms in the same industry7. In contrast to the industry SOE loans, the CDB city infrastructure loan amount is positively correlated with private firms’ assets, debt, total sales, and sales per worker. Doubling the CDB industry loans is associated with a 0.6% increase in SOEs’ assets and a 0.3% decrease in private firms’ assets . While the effects are significant, there is a potential endogeneity concern. Government credit might flow into areas or industries with especially high or low growth potential. Moreover, in China, it is the local government that borrows from the CDB for infrastructure projects and SOEs. The local governments, which enjoy closer relationships with the CDB, may be granted more loans. To address these concerns, I use the exogenous timing of political turnovers as a way to identify the causal effects of government directed credit.8 In particular, I use the timing of municipal government turnovers as an instrument for CDB loans. In China, the city secretary (equivalent to a mayor in the U.S.) is replaced every five years on average. This turnover is decided not by an election but by a higher level communist party official. About half of the cities have the same five-year turnover cycle as the National Congress of the Communist Party. But some cities have different turnover cycles if they were newly founded during the 1990s and started on different cycles due to the year they were founded9. The city secretary plays a key role in CDB loan allocations. Infrastructure loans are usually lent to municipal governments directly. Industry firm loans are often lent to SOEs, and municipal governments are deeply involved in and are responsible for arranging these loans. To verify the instrument, I begin by testing the exogeneity of city secretaries’ turnover timings. I find that turnover timing does not correlate with past economic performance. I conduct first-stage regressions by regressing cities’ borrowing from the CDB on the number of years the city secretary

7The majority of the CDB industry credit goes to SOEs. These loans are subsidized. On average, the interest rates of the CDB loans are 100bps below the market rates. 8Previous studies use the exogeneity of political variables (e.g., political cycle, political competitiveness and politicians’ seniority) to overcome endogeneity of the government credit or spending (e.g., Bertrand, Kramarz, Schoar and Thesmar (2007), Carvalho (2013), Cohen et al. (2011), Dinc (2005)), Sapienza (2004)). 9More than 100 new cities were converted from villages during the 1990s. For example, Fuyang, a city in northwestern Anhui province in China, was founded in 1996. Its turnover cycle started from 1996, and the next secretary began in 2000. This is dissimilar from the national cycle.

3 has been in the city. I control for city-fixed and politician-fixed effects to mitigate concerns that the CDB might lend more to the cities or politicians that have better political connections. I find that most cities borrow significantly more during the first year of the secretaries’ terms, and monotonically decrease borrowing during later years. The borrowing of the city goes up again when a new secretary comes in. On average, secretaries reduce total loan amounts by 36.4% each year during their tenure in office. I find similar borrowing patterns in both infrastructure and industry loans. Moreover, I also control for year-fixed effects to take out the macro time trend of the national turnover cycle. When I select the “off-national cycle” cities10, I find that these borrowing patterns still exist. This further shows that the part of the variation from turnover timing is due to the different cycles among different cities, rather than the national turnover cycle only. I also find the secretaries’ chances of promotion are associated with more borrowing from the CDB in their early terms. This is in line with the hypothesis that the political ambitions of the secretaries drive these borrowing patterns. In the second-stage regressions, I regress firm-level dependent variables on the estimated CDB city-level loan amounts. Consistent with OLS results, I find that increasing CDB city- level, aggregate, outstanding loans led to increases in SOEs’ fixed assets, employment, and debt. Conversely, increasing CDB industry SOE loans led to decreases in private firms’ fixed assets, sales, and sales per worker. Moreover, when CDB loans increase, the private sector has fewer firms,11 and higher ROA private firms are crowded out. On the other hand, I find that increasing CDB city infrastructure loans led to increases in private firms’ fixed assets, employment, debt, and total sales. The CDB loans’ opposing effects on private firms and SOEs also alleviate the concern that the timing of city secretary turnovers is driven by changing investment opportunities in a city. I further document that other channels through which the city secretary may influence the business in a city are not correlated with the turnover cycles (e.g., enforcing tax treaties better, raising money by requesting more transfers, and selling more land). Moreover, I find that for cities with no CDB

10I exclude the cities whose turnover are at the same time as the national turnover cycle(year 1998, 2003 and 2008). The remaining ”off national cycle” cities have different turnover cycles than the national cycle. 11Firm-level data used in this paper record all manufacturing firms with annual sales of more than 5 million RMB ($700K). If a firm drops from the sample, it could be a bankruptcy or the firm’s sales was below the threshold. It is also the same for firm enters.

4 loans, turnover timing has no effect on firms. These findings support my hypothesis that turnover timing provides exogenous variation of borrowing timing. The findings also help reduce the concern that alternative channels may play a major role. To explore the heterogeneous effects of government credit on private firms at different levels of the supply chain, I analyze province-level loan data in 41 manufacturing industries. I again use city-level turnover timing as the exogenous shock and match it at the province level. I identify the number of years the city secretary has been in the city and match this data to the city’s biggest SOE industry at province level. If the city secretary is in the early term (within three years of entering the office), I consider it a shock to CDB province-level loans to the industry (i.e., the largest SOE industry of the city). In the first-stage regression, I find that province-level CDB loans in an industry are 33.2% higher if the corresponding city secretary is in the first three years of a term. In the second-stage regressions, I find that increases in CDB loans led to decreases in fixed assets, employment, debts, and sales of private firms within the same industry and same province. SOEs in the same industry and province experience increases in fixed assets, employment, debts, and sales. Therefor, CDB industry loans crowd out private firms in the same industry and crowd in SOEs. Finally, I study CDB industry loan effects on upstream and downstream industries, and I use the input-output matrix to identify inter-industry relationships. For each industry, I pick its largest intermediate input from other industries as an upstream industry. I find that increasing CDB loans to the upstream industry led to increases in downstream private firms’ fixed assets, debts, and sales. Evidence also suggests that private firms with better political connections benefit significantly more from these CDB upstream industry loans. In sum, although CDB industry loans crowd out private firms within the same industry, they crowd in private firms in downstream industries. To calculate the overall effects of the CDB loans on individual firms, I multiply the growth of different types of CDB loans (e.g., infrastructure loans, industry loans, and upstream loans) by the estimated coefficients. The total effect of CDB loans (including both infrastructure and industry credit) on annual asset growth of private firms is 3.4% on average. Annual asset average growth of private firms was 15.4% from 1998 to 2009. Therefore, credit from the CDB contributes 22% of

5 the growth in the private sector. Moreover, the total CDB credit increases private firms’ sales per worker by 10.8% annually. However, CDB industry credit to SOEs has negative overall impacts on private firms. On average, a $1 million increase in the CDB’s outstanding industry loan amount led to a $0.52 million decrease in private firms’ total assets. The remainder of the paper is organized as follows: Section 2 is the literature review. Section 3 provides the history of the CDB and local government debt in China. Section 4 describes the data. Section 5 gives the empirical analysis and presents the results. Section 6 concludes.

2 Literature review

This paper relates to literature that examines whether government credit and spending crowds in or out the private sector. On the one hand, Atkinson and Stiglitz (1980) suggests the “social view” that SOEs can be justified under market failures (e.g., Stiglitz and Weiss (1981) and Greenwald and Stiglitz (1986)). Stiglitz (1993) argues that government directed credit can fund projects with high social returns where private banks might not allocate the funds. On the other hand, government credit could crowd out private sector investment, especially when the credit is given at below market rates or to firms that have distorted incentives. King and Levine (1993a, 1993b) explore the relationship between financial development and growth. They find that credit to SOEs is associated with lower growth of GDP, capital stock, investment, and lower efficiency. Rajan and Zingales(1998) find the fraction of domestic credit going to the private sector is strongly correlated with market capitalization to GDP. One explanation is that government involvement in industry crowds out the private sector. Bertrand, Schoar and Thesmar (2007) suggests that government intervention in banking may create implicit barriers to entry and exit in product markets by subsidizing poorly performing established firms. Many studies shows mixed evidence on whether government credit crowds out or crowds in the private sector. Gale (1991) numerically estimates the effects of federal lending, suggesting credit subsidies are costly and raise private investment slightly. A survey from Schwarz (1992) shows limited evidence regarding the impact of U.S. credit programs on growth. Craig et al. (2007) find a small correlation between SBA loan guarantees and local economic growth.

6 Shaffer and Collender (2009) find that total aggregate federal funding associates with significantly faster employment growth, but also with volatile incomes. In this paper, government credit from the China Development Bank can be treated as fiscal spending. Lucas (2012b) views federal credit programs as fiscal policy since credit subsidies are costly, and affect pricing and allocation in credit markets. For government spending, there has been a long debate between Keynesian and neoclassical12 theories. Vector auto regression on macro data is the standard method to study the effects of government spending shocks (e.g., Blanchard and Perotti (2002), Caldara and Kamps (2008), Fatas and Mihov (2001), Gali et al. (2007), Rotemberg and Woodford (1992), Ramey (2008)). The literature also explores exogenous shocks to government spending. Ramey and Shapiro (1998) use U.S. military buildups as exogenous shocks, finding that product and consumption wages fall after a military buildup. Burnside et al. (2004) use exogenous changes in military purchases as a fiscal shock, suggesting real wages decline and tax rates increase after the shock, and investments rise in the short term. This paper is closely related with Cohen et al. (2011). They use changes in congressional committee chairmanships as an exogenous shock to federal expenditure in states, finding that increasing fiscal spending makes firms reduce investments in new capital and R&D, and increase pay outs to shareholders. They aggregate government spending at the state level. However, we still know little about the effects of government credit or spending. One problem is that there is no detailed analysis on the impact of government credit in individual industries. This paper is based on far more detailed industry categories, which include 95 industries in China (e.g., farming, livestock, food, beverage, tobacco, non-ferrous metals mining and processing, software). I am able to separate the crowding out and crowding in effects of government credit by looking at different levels of the supply chain. This paper also relates to literature that studies political influences on government spending or credit. The political view assumes politicians have political and personal goals that conflict with social welfare maximization (Kornai (1979), Shleifer and Vishny (1994)). Political business-cycle literature started with Nordhaus (1975) and McRae (1977), and was followed by Alesina and Sachs

12Aschauer and Greenwood (1985), Barro (1981, 1989), Baxter and King (1993), Finn (1995), Hall (1980), and Mankiw (1987) use dynamic general equilibrium mode to study the effects of government spending.

7 (1988) to model how the government uses economic policies to influence elections under democratic political systems. Many recent empirical studies support this view. Sapienza (2004) finds that Italian state-owned banks charge low interest rates in a province in which the associated party is stronger. Dinc (2005) uses cross-country data to demonstrate that government-owned banks increase lending during election years in comparison to private banks. Dinc and Gupta (2011) show that in India, the government delays privatization of SOEs in regions with more political competition. Carvalho (2013) uses Brazilian data and finds that politicians influence elections with bank lending. Bertrand, Kramarz, Schoar and Thesmar (2007) show that politically connected CEOs in France create more jobs in politically contested areas, especially during election years. Khwaja and Mian (2005) find that government banks in Pakistan favored politically connected firms by providing greater access to credit. Connected firms received 45% more loans and had 50% higher default rates on these loans. Private banks show no such political bias. However, there is little empirical evidence of the political view in countries without elections. In these countries, politicians are usually much more powerful. In China, government credit affects firms through local government debt instead of a firm’s direct political connections with CDB. Municipal government plays a key role in CDB loan allocations.

3 Background: the China Development Bank and Local

Government Financing in China

3.1 History of the China Development Bank

The China Development Bank was founded in 1994 from six SPC Investment Corporations 13. The CDB, along with Export-Import Bank of China and Agricultural Bank, were assigned as three policy banks during financial system reform in 1994. CDB investment covers basic industries

13State Planning Commission (SPC) is a macroeconomic management agency under the Chinese State Council that has broad administrative and planning control over the Chinese economy. These six Investment Corporations were policy institutions, established in late 1980s, affiliated directly with the State Planning Commission and functioning as long-term investment instruments on behalf of the government.

8 and the infrastructure sector. In 2008, the CDB became a corporation with Ministry of Finance (MOF) and China Investment Corporation14 as its two shareholders. Although the CDB is now a corporation, it can still be viewed as an extension of the government’s fiscal function. CDB’s funding is largely from bond issuances. At its establishment in 1994, the CDB had $6.3 billion capital. The CDB is entitled to receive disbursements from stage budgets, repayments of principal and interest, and fiscal subsidies arranged from the state budget to national projects. In addition, the CDB was entitled to issue financial debentures to state-owned financial institutions. Commercial banks in China are the largest buyers of CDB bond. The total volume of financial debentures issued by the CDB are decided jointly by PBOC15 and SPC based on yearly credit and fixed-asset investment plans. Interest rates of financial debentures are decided by PBOC in consultation with SPC and MOF. The CDB had explicit guarantees from the central government until 200816. The guarantee gives the CDB the advantage to finance the bond market cheaply, and its bond interest rate is slightly higher than the treasuries in China. By the end of 2013, the CDB had approximately $1.3 trillion outstanding loans, and most are financed through bond issuance. As shown in Figure 1, CDB loans started to increase in 2003, and grew over time, especially from 2008 through 2010. At the end of 2013, the CDB has 5.8 trillion RMB ($0.95 trillion) outstanding domestic loans and 1 trillion RMB ($0.16 trillion) overseas loan. The bank’s lending rates were regulated by the Central Bank before 2008. Between 2008 and 2013, the lending rate was required to sit within a range of a referred rate set by the Central Bank. This range was expanded over the years until mid-2013. After July 20, 2013, the lending rate was liberalized. However, the deposit rate is still controlled by the government. The CDB has been regulated like other state-owned commercial banks, but in practice, CDB’s long-term loan rates have been lower than those of state-owned commercial banks, and much lower than private or shareholding commercial banks, because 1) the CDB is less profit driven, and 2) CDB’s

14China Investment Corporation is a sovereign wealth fund responsible for managing part of the People’s Republic of China’s foreign exchange reserves. 15The People’s Bank of China is the central bank of the People’s Republic of China, with the power to control monetary policy and regulate financial institutions in mainland China. 16The guarantee became implicit in 2008 when the CDB changed its corporate governance from SOE to a corporation. However, the interest rate and the issuance volume is unaffected by this change. The CDB is still considered an extension of the government’s fiscal function.

9 administrative costs are lower than commercial banks17. CDB’s loans can be viewed as subsidized loans or government spending. The CDB is fully state-owned, just like other state-owned commercial banks such as ICBC, CCB, BOC and ABC18, but its behavior is different. CDB’s business usually covers infrastructure sectors and uncontested markets in which other state-owned commercial banks have little interest. This phenomenon might due to three reasons. First, CDB’s policy mandate pinpoints the bank in such policy-related areas. Second, the CDB finances its loans by issuing long-term bonds with sovereign ratings, whereas other state-owned commercial banks rely primarily on short-term deposits. Therefore, the CDB conducts long-term lending that not only caters to infrastructure- sector requirements, but also matches its assets and liabilities durations. Third, CDB’s managers and its elites used to work in central government agencies, such as NDRC19 or SPC, MOF, PBOC, etc., whereas those of commercial banks are lifelong bankers. Such career background disparities might led to differences in respective visions, political awareness, and behavioral preferences, which affect a bank’s business. Generally, local governments initiate loan applications by submitting project proposals to the CDB. The CDB decides whether to accept or reject them. Allocation of the CDB loans depends on many determinants. First, based on the central government’s yearly credit plan, the CDB gives each province an annual credit quota. For example, if the total credit amount increases by 20%, each province can also increase loans by 15% from the previous year. Second, the CDB keeps part of the quota, and has flexibility to allocate credit based on other determinants such as supporting national projects, political connections with local governments, how hard local governments lobby, etc. Local governments usually compete for CDB loans, especially in recent years. Although there is a general rule on credit allocations, final loan issuances do not necessarily accord with initial

17Unlike commercial bank, which usually have branches in all cities and villages in China, the CDB has branches only at the province level. One reason is that the CDB does not need local branches to attract depositors. Most of CDB’s money is from bond issuances. The CDB usually focuses only on projects at city or province levels, and it does not invest often at village level or below. 18ICBC stands for Industrial and Commercial Bank of China; CCB for China Construction Bank; BOC for Bank of China; ABC for Agricultural Bank of China. 19The National Development and Reform Commission (NDRC) of the Government of the People’s Republic of China, formerly State Planning Commission and State Development Planning Commission, is a macroeconomic management agency under the Chinese State Council, which has broad administrative and planning control over the Chinese economy.

10 plans.

3.2 Local Government Financing

Since 1989, local governments in China have been prohibited from incurring debt by budgetary law. As a result of tax-sharing system reform between local and central governments in 1994, the central government takes the majority (around 70%) of tax revenue. For example, the central government takes 60% of personal and corporate income taxes and 75% of value-added tax. At the same time, local governments bear the responsibility of infrastructure development but do not have the money to do so. With a thorough understanding of local governments’ desires to boost local economies and develop local infrastructures, the CDB established direct connections with local governments and helped them create borrowing platforms by creating 100% state-owned companies. Local governments are then able to use these companies to borrow from banks and issue bonds legally. The CDB usually commits a certain amount of loans to fund infrastructure development within a certain period (normally 5 years). The CDB has several advantages to support local governments. First, it is mandated to invest in infrastructure sectors and pillar industries. Second, different from other banks, the CDB has special long-term funding resources through CDB bond issuances in domestic bond markets. Before 2008, the CDB was the primary resource for local-government financing, especially concerning long-term borrowing. In November 2008, along with 4 trillion RMB ($586 billion) stimulus plan20, commercial banks started to lend to local governments aggressively. These were usually short-term loans (1 to 3 years). In 2010, the central government decided to pull back the stimulus plan. As a result, many commercial banks either stopped lending or rolled loans over to local governments. However, local governments’ investments are usually long-term (such as infrastructure investments), and they needed loans to continue projects begun under the stimulus program. Therefore, after 2010, many local governments started issuing bonds and borrowing from

20The 4 trillion (US$ 586 billion) stimulus plan was announced by the State Council of the People’s Republic of China on November 9, 2008 to minimize the impact of the global financial crisis. The central government also ordered financial institutions (mainly commercial banks) to lend certain amounts within a limited period. Commercial banks started to increase lending dramatically, including lending to local governments.

11 shadow banking systems in China. The CDB is still a long-term, stable finance resource for local governments. This paper focuses on the period from 1998 to 2009, which overlaps with the stimulus plan by only one year. During the sample period, the CDB played the most important role in local government borrowing.

4 Data Description

4.1 Data

This paper uses three data sets: (1) The China Development Bank, (2) The Chinese Industry Census (CIC), and (3) The Zechen and Baidu Encyclopedia Database. CDB data contain both city- and province-level loan data. At the city level, they record yearly aggregate CDB outstanding loan amounts and loan issuances to both infrastructure projects and industry SOEs from 1998 to 2010, across 310 cities in China21. City-level data were collected by a CDB internal survey in 2010, on which branch managers at the province level manually categorized projects into various cities. At the province level, the CDB data set contains monthly aggregate CDB outstanding loan amounts and loan issuances in 95 industries for each of the 31 provinces22 from 1998 to 2013. The industries include infrastructure sectors (e.g., road, air, rail transportation, water supply, public facilities, etc.) and industry sectors (e.g., agriculture, tobacco, software, oil refining, textile, etc.). In total, there are 27 infrastructure sectors and 68 industry sectors. Table 1 Panel B lists some large infrastructure sectors such as road construction, railway, water systems, and telecommunications. City-level loan data do not include province-level projects such as highways. CDB has only provincial branches, and each branch is required to report project information to headquarters at the end of each month. A CDB central server compiles the provincial data and updates them monthly. I use annual data during analysis. City- and province-level economic variables (e.g., GDP, income per capita, total employment, and fiscal income) are from the China

21They do not include Beijing, Shanghai, Tianjing, and Chongqing, which are classified as provinces. 22In China, there are 27 provinces plus Beijing, Shanghai, Tianjing, and Chongqing that are under direct control of the central government. Among these 95 industries, CDB added 11 new industries in 2005 and doesn’t have data before 2005.

12 Statistical Yearbook. The Chinese National Bureau of Statistics (NBS) collected the Chinese Industry Census (CIC) data, including all manufacturing firms in China with annual sales over 5 million RMB (about $700,000) from 1998 to 2009. It has detailed annual accounting data and firm characteristics such as number of workers, industry categories, locations, registration types, political hierarchies, government subsidies, wages, etc. In total, there are 711,892 firms in China. CIC appears to be the most detailed database on Chinese manufacturing firms, and the content and quality of the database are sufficient. Using firm registration type from CIC data, I classify firms as SOE, private, or foreign firms. Location data in CIC is an 11-digit number that locates the firm at the street level. I cut the first 4 digits to identify the city. Industry codes are the standard 4-digits, and I cut the first 2 to match CDB industry codes. There was a change in industry codes by Chinese National Bureau of Statistics in 2002, so I adjusted the industry codes to 2002 standard. Regarding the data set from politician profiles, I manually collected it from the Zechen Database, which records all mayors and secretaries of municipal committees in each city from 1949 through 2013. I collected a name list of mayors and secretaries, and their terms in office, at the monthly level. I also collected data for members of provincial committees of the Communist Party of China. These data cover all the 334 cities and 31 provinces in China. Based on the name list, I searched politicians’ profiles from the Baidu Encyclopedia database, a Chinese-language, collaborative, web-based encyclopedia provided by the Chinese search engine Baidu. The Encyclopedia is the best Chinese online encyclopedia, and generally provides clear profiles of prominent people, better than official public profiles of politicians. However, the quality of politicians’ profiles varies among cities, especially for small cities. To compensate, I uses Xinhua News23 as a supplementary source to crosscheck data from the Baidu Encyclopedia. Final profile data includes 1,227 city secretaries and 97 provincial governors. Each profile had a politician’s gender, age, and birthplace. Some politicians had the same name, which is common in China. To overcome this limitation, I conducted a thorough double check for politicians with the same name, and distinguished them with a separate

23Xinhua News is the official press agency of the People’s Republic of China, and the largest center that collects information and press conferences in China. It is also the largest news agency in the country.

13 ID number. When I merged the CDB city-level data with politicians’ profiles, there were 310 cities in total (the remaining 24 cities did not have CDB loans).

4.2 Summary Statistics

Table 1 presents summary statistics, and Table A1 in the Appendix contains a detailed definition and construction of each variables. CDB city-level loan data include 310 cities from 1998 to 2010. The data separate loans into two categories: infrastructure and industry. The top 2 panels of Figure 1 show that the total city-level, outstanding loan amount increased from 321 billion RMB ($40 billion) in 1998 to 2,811 billion RMB ($433 billion) in 2010. Total loans for infrastructure increased from 27.4 billion RMB in 1998 to 1,143 billion RMB in 2010. Industry loans increased from 293.6 billion RMB in 1998 to 1,659 billion RMB in 2010. Overall, industry loans are bigger than infrastructure loans at the city level, but infrastructure loans grew faster. The bottom two panels of Figure 1 shows the ratio of city level infrastructure loan and industry loan to total loan amount respectively. For infrastructure loan, the ratio was almost 0 from 1998 to 2000 which is consistent with the top 2 panels of Figure 1. Moreover, the gap between top and bottom quartiles enlarged from 1999 to 2003 and closes a bit after 2003. It means that different cities have very different combinations of infrastructure and industry SOE loans. One reason is that city secretaries tend to help the local SOEs to borrow from CDB. I find industry loan amounts are significantly higher in cities with more SOEs. These cities with big state-owned sector borrow relatively less in infrastructure. For new loan issuances at the city level, the amount increased from 98 billion RMB in 1998 to 1,066 billion RMB in 2010. On average, annual loan issuances were about one-third of the outstanding loan amount. One feature of CDB loans is that both cash inflows and outflows are huge because investment projects are huge, and loans are often rolled over. CDB province-level loan data covers 31 provinces and 95 industries from 1998 to 2013. The middle 2 panels of Figure 1 show that total province-level outstanding loan amounts increased from 474 billion RMB ($59 billion) in 1998 to 5,888 billion RMB ($935 billion) in 2013. Total loans for infrastructure increased from 124 billion RMB in 1998 to 3,569 billion RMB in 2013, and total

14 loans for industry increased from 326 billion RMB in 1998 to 2,205 billion RMB in 2013. Overall, industry loans were larger than infrastructure loans in 1998, but infrastructure loans grew much faster and surpassed industry loans. For new loan issuances, the total amount increased from 142 billion RMB in 1998 to 1,796 billion RMB in 2013. There was a drop in loan issuances in 2012. In comparison to city-level loans, province-level loans were larger than infrastructure projects. If a loan was for a provincial project such as highway construction, CDB did not break it down and assign it to various cities. Instead, it recorded it at the province level. From middle right panel of Figure 1, there was a large jump in infrastructure new loan issuances from 2008 to 2009, due to a four trillion RMB ($586 billion) stimulus package beginning in November 2008. After 2010, there was a decrease in infrastructure loan issuances, due primarily to a pull-back of the package. Chinese Industry Census data contain 711,892 firms in the manufacturing sector. Each has a unique registration number and company name. The registration number is a unique number to a company from SAIC 24. The registration number of a company that no longer exists is recycled. I use registration numbers as IDs for the companies. Unfortunately, CIC data do not record registration IDs for 2008 and 2009. I used a name-matching algorithm25 and recovered 90% of registration IDs. CIC also has registration types for the company, which depends on a company’s shareholders. I used registration type to categorize companies into 3 groups: state-owned enterprises(SOEs), private companies, and foreign companies. SOEs are defined as state-owned or collective-owned enterprises. Private firms are defined as private-owned enterprises, private partnership enterprises, private limited liability company, or private limited company. If a firm’s shareholders are mainly foreigners, it is classified as foreign firms. I exclude the firms from CIC data set if it has mixed ownerships (e.g., half private owned and half state owned). From table 1, the average ROA was 9%, and the average number of employees was 108. Average ROA for SOEs was 5.2% and the employee number was 353. In China, SOEs are less efficient and have more employees. T ax Corp is the effective annual corporate tax for each firm, missing if a firm’s income before tax was negative. T ax V AT is the effective annual value-added tax rate of each firm. CIC recorded value-added tax

24State Administration for Industry and Commerce of the People’s Republic of China 25I matched the name of each company in 2008 and 2009 with company names from 1998 to 2007 that had registration IDs. I grouped companies by city and industry to increase matching probability and accuracy.

15 in only 1998, 1999, 2000, 2003, 2005, 2006, and 2007. The average corporate tax rate was 19.41% and value-added tax rate was 15.09%, consistent with real tax rates 26. Figure 4 shows the time trend of firm number and aggregate assets for SOEs, private firms, and foreign firms. The number of private firms increased dramatically from 1998 to 2009. The number of SOEs decreased over time, due primarily to privatization. Foreign firm numbers also increased slightly. For aggregate assets, the private sector again increased most, surpassing foreign and SOE sectors. SOEs’ assets decreased from 1998 to 2002 and increased gradually after 2002. From 1998 to 2009, the private sector contributed greatly to China’s double-digit economic growth, and on average, it had smaller firm sizes than SOEs and foreign companies. After matching CDB province industry data with CIC firm data, there were 41 industries in the manufacturing sector. In China, the political leader in a municipal government is called the Secretary of Municipal Committee of the Communist Party of China (equivalent to the mayor in the U.S.). For political turnover, the top panel of Figure 2 is a histogram of city secretaries’ term lengths from 1997 to 2013, among the 334 cities. I rounded monthly turnover data into year frequency using June as the cutoff27. Forty-three percent of city secretaries left the city in their fifth year. In some cases, city secretaries left before the fifth year, and in fewer cases, they remained. The national political turnover cycle is also 5 years, and it occurs around the National Congress of the Communist Party of China (CPC). Four national CPC congresses occurred in the data from 1997 to 2013: 1997, 2002, 2007, and 2012. Since the National Congresses of the Communist Party usually occur at the end of these years, the turnover could occur the next year. The middle panel of Figure 2 is a histogram of turnover years, concentrated in years 1998, 2003, 2008, and 2013. These years are one year after the National Congress of the Communist Party. However, some special cases appeared such that turnover did not occur every 5 years, and did not occur during a National Congress. The reason is because China experienced fast urbanization, and many cities were incorporated during 1990s. A city secretary’s first tenure is usually 5 years, but the starting year might not have coincided

26The current corporate tax rate is 25%. For small businesses, it is 20%. For high-technology firms or other firms supported by the government, the corporate tax rate is 15%. Value-added tax rate varies between 13% and 17%. 27If secretaries left before June and their successors succeeded them before June, I considered the successor the city secretary for the entire year. If the secretaries left after June, I considered them the city secretary for the entire year.

16 with the national cycle. The bottom panel of Figure 2 shows the sub-sample of secretaries whose term lengths were not 5 years (e.g., 4 or 6 years). Turnover more likely occurred around National Congresses, suggesting provincial governments in China wanted to bring cities with different cycles back to the national cycle, so a few cases had 4- and 6-year terms. One explanation is that the turnover of provincial governors accorded with national cycle. New governors usually want to have their own people to be city secretaries. In the politicians’ profile data, the average age of city secretaries is 50 years; the youngest was 32 and the oldest 62. The legal retirement age for males is 60, and 55 for females. ”Promotion” is a dummy variable for whether a city secretary was promoted during turnover. 38% of city secretaries were promoted after their terms ended. Promotion is defined as being appointed to a higher political hierarchy in the government28.

5 Empirical Analysis and Results

5.1 Firms’ Response to CDB Loans

There has been a long debate on the effects of government credit, especially for the private sector. Public goods such as infrastructure help economic growth since firms benefit from the infrastructures around them. However, government credit also crowds private sectors through tax channel, interest rate channel, etc. In China, tax and interest rate channels are shut off; the cities and even the provinces cannot change tax rates or categories. Only the central government can determine taxes. Although some tax treaties are flexible and local governments use them differently, the central government sets the rules for tax treaties, and manipulation is limited. Interest rates were not liberalized until July 20, 2013, after which banks determined their own interest rates on loans, but deposit rates were still controlled by the central bank strictly. The primary channel of crowding in China comes from competition between SOEs and the private sector. Chinese SOEs are important

28City secretaries’ political hierarchies vary across cities. Secretaries from Beijing, Shanghai, Tianjing, and Chongqing are at minis- terial level. Fifteen cities in China are at vice-ministerial level: Dalian, Qingdao, Ningbo, Xiamen, Shenzheng, Haerbing, Changchun, Shenyang, Jinan, Nanjing, Hangzhou, Guangzhou, Wuhan, Chengdu, and Xian. Others are at the departmental level. I do not include ministerial-level cities during city-level analyses since they are at the same level as provinces. I define promotion based on varying political hierarchies accordingly.

17 to many industries, even after the privatization wave from 1998 to 2005. Even today, SOEs control significant shares of assets (Figure 4). The majority of CDB’s industry loans go to SOEs, and only a small share goes to the private sector, and then only to large, powerful private firms. It is rare that CDB lends money to small private firms such as entrepreneurs. I begin the analysis by OLS regressions and explore the correlations between CDB loan amount and a firm’s asset investment, employment, borrowing as well as firm’s performances such as ROA, total sales and sales per worker. I use the total outstanding loan amounts instead of new issuances because many new loan issuances are for old loans’ rollover. I match province industry outstanding loan amounts with manufacturing firms in the same province and same industry. The regression is:

Yl,k,p,t = α + β × LogLoan PIk,p,t + Controlp,t−1 + Y earF E + F irmF E + εl,t, (5.1)

where Yl,k,p,t is the dependent variables of firm l in industry k province p in year t such as logarithm of total assets, fixed assets, number of employees, total debts, ROA, sales per worker, and total sales. I control local economic condition variables, and year-fixed and firm-fixed effects. Panel A of Table 2 shows the regression results for SOEs. CDB industry firm loans had significantly positive correlations with SOEs’ total assets, employment and debts. Panel B of Table 2 shows the regression results for private firms. CDB industry firm loans had significantly negative correlations with private firms’ total assets, fixed assets, employment, total sales and sales per worker. These results show that CDB industry loan amounts may have positive impacts on SOEs’ asset investment, employment and borrowing, and have negative impacts on private firms’ asset investment, employment and sales. The CDB industry loans may crowd out private sector and crowd in SOEs within the same industry. Moreover, I also study the infrastructure loans. Panel C of Table 2 shows the regression results for city-level infrastructure loans. It shows that private firms’ total assets, fixed assets, debt, total sales and sales per worker are positively correlated with CDB infrastructure loans. However, there is a potential endogeneity concern that CDB credit allocations are endogenous. CDB lends to infrastructure projects and SOEs via local governments. The local governments

18 with better relationships with CDB may borrow more. Furthermore, CDB is a policy bank with a mandate to provide credit supports to infrastructure and pillar industries, especially in undeveloped areas in China. In order to fix this problem, I use municipal government turnover timing as the instrument for CDB loans.

5.2 Instrument: City Secretary’s Turnover Timing

I first test the exogeneity of city secretaries’ turnover timing. Mentioned above, city secretaries’ terms are 5 years, and each city has its own turnover cycle. Although the types of turnover might be endogenous such as promotion, the timing of turnover is exogenous. I only use the variation from turnover timing. I use the Cox proportional hazard model, which includes politicians’ demographics, economic performance, and time of turnover. The hazard rate of turnover is given by:

h(t) = h0(t) exp(β1x1 + β2x2 + ··· + βkxk), (5.2)

where x1...xk are politician’s ages and gender, economic performance in the city such as GDP, income per capita, city government fiscal income, and city employment. I also include dummy variable NationalCycle to account for whether it a National Congress Party year. I have both time-varying and time-invariant variables, and I assume the time-varying variables are constant throughout a year. I followed Wooldridge(2002) to construct a hazard model, and report the coefficients and hazard ratios from the estimation in Table 3. I include previous CDB outstanding loans to test whether the timing of turnover is affected by existing CDB loans. In column 1 to 4 of Table 3, NationalCycle had a positive effect on city secretaries’ turnover, consistent with patterns in Figure 2 since many city turnovers are in the national turnover years. Column 5 of Table 3 reports hazard ratios, and on average, the turnover probability increased by 39.3% during national turnover years. Age of a secretary had positive effects on the timing of turnover because when city secretaries get older, they are more likely to retire. The timing of turnover did not depend on a city’s past economic performance and CDB loans. I include economic variables and city loans in two-year lags and find nothing significant. In columns 1 and 3 of Table 3, I exclude Age and

19 Gender since they have some missing values. Again, timing of turnover did not depend on a city’s economic performance and CDB loans. Then, I explore borrowing patterns over various periods of a city secretary’s term. The CDB’s primary lending method is to coordinate with local governments and support both infrastructure projects and industry firms, which are usually SOEs. The city secretary is the top-ranking politician in the city, and usually plays a large role during the lending process. The regression is:

LogLoanj,t = α + β × P oliticianY eari,j,t + Controlj,t−1 + Y earF E + cityF E

+SecretaryF E + εj,t, (5.3)

where LogLoanj,t is the CDB loan variables in city j in year t. I used the logarithm of CDB outstanding loan amounts and new issuance for infrastructure, industry, and total loans as dependent variables. P oliticianY eari,j,t is the number of years that secretary i stayed in city j in year t.

Controlj,t−1 are variables for economic conditions such as GDP, urban income per capita, fiscal income, and the working population. I also include city fixed effects, year fixed effects, and secretaries’ personal fixed effect since cities have varying situations and secretaries have their own investment styles. Standard errors are clustered at the city level. In Table 4 Panel A, the coefficient of P oliticianY ear was -0.364 at 1% significance in column 1; on average, if a city secretary stayed one more year, borrowing from CDB decreased by 36.4%. If I break loans into infrastructure and industry loans, the effects are stronger for industry loans. In columns 3 and 5 of Table 4 Panel A, the coefficient of P oliticianY ear was -0.122 for infrastructure outstanding loans and -0.338 for industry loan. Instead of using P oliticianY eari,j,t, I use Y ear 1i,j,t, ..., Y ear 6i,j,t, which are dummy variables for years that secretary i stayed in city j in year t. Y ear 1i,j,t equals 1 if it was the first year secretary i stayed in city j and zero otherwise. Y ear 2i,j,t equals 1 if it was the second year secretary i stayed in city j and zero otherwise. Y ear 3i,j,t to Y ear 6i,j,t were constructed similarly.

20 LogLoanj,t = α + β1 × Y ear 1i,j,t + β2 × Y ear 2i,j,t + β3 × Y ear 3i,j,t

+β4 × Y ear 4i,j,t + β5 × Y ear 5i,j,t + β6 × Y ear 6i,j,t

+Controlj,t−1 + Y earF E + cityF E + SecretaryF E + εj,t (5.4)

Results are shown in Table 4 Panel B. Y ear 1 is the missing category. Consistent with results in Panel A, Y ear 2’s coefficient was -0.386 at 1% significance on total CDB outstanding loan; on average, city secretaries borrowed 38.6% less during their second years than first years. In column 1 of Table 4 Panel B, 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 -1.9. Borrowing from CDB decreased monotonically when a city secretary stayed longer in a city. In column 3 of Table 4 Panel B, CDB loan amount in a secretary’s second year was 10.9% less than the first year, and decreased monotonically over time. This pattern was also true for infrastructure and industry loans, confirming results in Table 4 and suggesting that city secretaries borrowed more soon after they took the office, and slowed borrowing monotonically over their terms. This pattern was also true for infrastructure and industry loans. I exclude cities with the same turnover cycles as the national cycle, and perform regressions with equations (5.3) and (5.4) again. Table A2 in the Appendix shows that for these off-national-cycle cities, city secretaries had the same borrowing patterns; they borrowed more in the first year and decreased monotonically over time. For these cities, the secretaries also borrowed more during early terms. The variation was not only from 5-year national turnovers, but also from these off-cycle cities. I find the similar patterns in CDB loan new issuances. Moreover, in Figure 3, I plot the average logarithm of CDB city total loan amounts after taking out the year fixed effects, city fixed effects and politician fixed effects. There are 3 national turnover cycles during my sample period: 1998 to 2002, 2003 to 2007, and 2008 to 2010. I cluster the cities by P oliticianY ear for these 3 cycles respectively and calculate the average logarithm of CDB city total loan amounts for each bin in P oliticianY ear. Figure 3 shows the “Zig-Zag” pattern during these 3 cycles. On average, city secretaries borrowed significantly more during their first year in

21 office and monotonically decreased the borrowing over time. When a new city secretary came in, the borrowing spiked again. It verifies the results in Table 4. I also look at the borrowing pattern in each city. Most of them follow this “Zig-Zag” pattern. It alleviates the concern that certain cities with extreme values drive the results in Table 4. The next logical questions are: why city secretaries intended to borrow more during the early periods of terms? What are the incentives behind these patterns? In China, promotion is one of the most important career aspects to a politician. It is known well that city secretaries’ and mayors’ promotions in China depend heavily on local GDP growth. One way to boost up local GDP in short term is to borrow from CDB and invest as early as possible. See section 5.7 for more detailed discussions.

5.3 Second Stage: CDB City Level Loan Analysis

In the second-stage regressions, I begin by studying city secretaries’ turnover effects on firm decisions. To study heterogeneous impacts on various types of firms, I separate firms into 2 major categories: SOEs and private firms. SOEs’ primary shareholders are state and collective owners. Private firms’ shareholders are private investors such as individuals and institutions. Moreover, there is another type of firms with shareholders from foreign countries. These foreign firms can be considered as the subgroup of the private sector. Foreign firms in China usually have weaker connections with government than domestic private firms, and may suffer more from government credit than private firms. I match city secretaries’ turnover data with manufacturing firms in the same city. The regression is:

Yl,t = α + β1 × Y ear 1j,t + β2 × Y ear 2j,t + β3 × Y ear 3j,t

+β4 × Y ear 4j,t + β5 × Y ear 5j,t + β6 × Y ear 6j,t

+Controlj,t−1 + Y earF E + F irmF E + SecretaryF E + εl,t, (5.5)

where Yl,t is the dependent variables of firm l in year t such as logarithm of total assets, fixed assets, number of employees, total debts, etc. Y ear 1j,t, ..., Y ear 6j,t are dummy variables for years

22 secretaries stayed in city j in year t. For example, Y ear 2j,t equals 1 if it was the second year that a secretary stayed at city j and zero otherwise. Controlj,t−1 are variables for economic conditions such as GDP, urban income per capita, fiscal income, and working population. I control the year fixed effects, firm fixed effects, and politician personal fixed effects. In the regressions, Y ear 1 is the missing category. Table 5 Panel A shows results for SOEs. In column 1, assets of SOEs, on average, were 1.9% smaller in the second year of a city secretary’s term versus the first year. The coefficients for year 2 to year 6 were also negative, and decreased monotonically. This pattern is also true for fixed assets and debts (column 2 and 4). In column 5, in comparison to a city secretary’s first year, ROA increased by 1% in the city secretary’s second year, and monotonically in later years. In column 6 Table 5 Panel A, LogNumber is the logarithm of the total number of SOEs in each city annually, and controls for a year fixed effect, city fixed effects, and a secretary’s personal fixed effect. There was no clear pattern of SOE numbers in various years during city secretaries’ terms. In short, SOEs had more assets and debts in a city secretary’s first year, and decreased overtime. Table 5 Panel B is for private firms. In column 1, assets of private firms, on average, were 1.5% smaller during the second year of a city secretary’s term versus the first year. Coefficients for years 3 through 5 were negative, and decreased monotonically. This pattern was also true for debts in column 4. However, the number of private firms was smaller in the first year of a city secretary’s term. CIC data contain all manufacturing firms with annual sales greater than $700,000. If a firm was dropped from the data, it either went bankrupt or had annual sales smaller than $700,000. Results in Table 5 Panel B suggest that, on average, private firms were crowded during the earlier periods of city secretaries’ terms, but remaining firms grew. These different effects from turnover might suggest that different types of CDB credit might have different effects on private firms. CDB credit might also affect private firms differently at different levels of the supply chain. To explore which private firms were crowded, I separate firms into two group: those characterized by low and high ROAs. The dummy variable LowROA equaled 1 if a firm’s ROA was below the median. In column 1 Table A3, the interaction term P oliticianY earLowROA had a negative coefficient, which means low ROA private firms increased assets more than efficient private firms during the early stages of city secretaries’ terms. In columns 2 and 3, P oliticianY earLowROA had

23 negative coefficients, which means low ROA private firms also increased fixed assets and employment more than efficient private firms during the early stages of city secretaries’ terms.

To link city secretaries’ borrowing patterns and CDB loans’ economic impacts, I use Y ear 1i,j,t,...,

Y ear 6i,j,t as instrumental variables to measure CDB outstanding loan amounts, and perform 2 two-stage, least-squares regressions. I include economic control variables such as GDP, urban income per capita, fiscal income, and working population, and firms’ fixed effects, year fixed effects, and politicians’ fixed effect as in the first-stage regressions. Table A4 shows 2SLS regression results. Consistent with previous evidence, CDB loans increased the assets, employment, and debts of SOEs, and decreased foreign firms’ assets and employment. For SOEs, doubling CDB outstanding loans increased SOEs’ fixed assets by 6.2%, increased employment by 5.8%, and increased debts by 7%29. For foreign firms, doubling CDB outstanding loans decreased the fixed asset by 11.8%, and decreased employment by 8.6%. From Table 5 Panel B and C, there were fewer private and foreign firms when city secretaries were in their early terms. To explore changes to firm numbers further, I calculate new firm entry and old firm exit for SOEs, private-sector firms, and foreign-sector firm. Again, exit does not necessarily mean a firm went bankrupt. It could be that the firm’s annual sales were lower than 5 million RMB($700,000), so CIC did not record it. Enter does not necessarily mean the firm is new. It could be that the firm’s annual sales were higher than 5 million RMB($700,000). Table A6 shows 2SLS regression results on number of firms that exited or entered CIC data by using P oliticianY ear as the instrument for CDB loans. Columns 1 to 2 show that private firms exited more and entered less when CDB loans increased. Columns 3 to 4 show that foreign firms also exited more and entered less when CDB loans increased. Columns 5 to 6 show that SOEs exited less and entered more when CDB loans increased, which provides more evidence that private and foreign firms are crowded by CDB loans, and SOEs become stronger. However, various types of government credit should have different effects on private and foreign sectors. I separate CDB loans into infrastructure- and industry-firm loans. I perform 2SLS, and use Y ear 1i,j,t, ..., Y ear 6i,j,t as instrumental variables for both CDB infrastructure and industry

29In Table A4, coefficients in columns 2 to 4 are 0.089, 0.083, and 0.101, respectively. Doubling CDB city loans means Log(Loan City) increases by Log(2), which equals 0.693. I multiply these coefficients by 0.693 to calculate the percentage increase of the dependent variables. All following analysis are based on the same calculation.

24 loans. Table 6 shows 2SLS regression results. Interestingly, CDB infrastructure loans helped private and foreign sectors by increasing their assets, and industry loans crowded both private and foreign sectors. In column 1 to 7 in Table 6 Panel A, for private firms, CDB infrastructure loan increases increased assets, fixed assets, number of workers, debts, sales per worker, and total sales. CDB industry loan increases decreased assets, fixed assets, and sales per worker. In column 1 to 7 in Table 6 Panel B, for foreign firms, CDB infrastructure loan increases increased assets, debts, and sales per worker. CDB industry loan increases decreases assets, fixed assets, debts, and sales per worker of foreign firms. From these results, infrastructure loans supplemented both private and foreign sectors, but industry loans, which usually go to SOEs, hurt private and foreign firms. In sum, city secretaries borrowed more during early years of their terms, and during these years, SOEs increased their assets and debts, and became less efficient. For the private sector, there were fewer private firms, and the efficient ones were crowded. Moreover, infrastructure loans helped all SOEs, private firms, and foreign firms. Industry loans crowded both private and foreign sectors. Foreign firms decreased their assets and employment. The number of foreign firms also decreased during early years of secretaries’ terms. Foreign firms can be viewed as the subgroup of the private sector but with weaker connections. It may explain why the crowding out effect is bigger on the foreign firms. In China, it is debatable whether political turnover correlates with other macro variables that influence local economies. The variation I use is from the varying political cycles among cities. I control year fixed effects to remove effects from the national turnover cycle. Moreover, I find disparate CDB loans’ effects on private and SOEs from 2SLS regressions. It helps mitigate the concern that turnover timing of a city secretary correlates with demand changes or investment opportunities in a city. Disparate effects between infrastructure and industry loans in Table 6 further support the credit story instead of demand story in the city.

25 5.4 Politician’s Other Channels to Affect Local Economy

Another concern is whether CDB loans are the only way a city secretary can affect the economy; does the exclusion condition hold that local political turnover only affects the economy through CDB borrowing? When new city secretaries come to their cities, they usually have their own plans or preferences to develop local economies, and they have several tools to do it. For example, a secretary can build business districts to attract investment, speed approvals of city projects, provide better government services, etc. However, the biggest constraint is limited fiscal income. Local governments in China only share 20% to 30% of the tax revenue, and are responsible for infrastructure buildup. City secretaries have many good projects piled on the desks, but they require financial resources. There are three common ways to raise money: borrowing from CDB, selling more land, and asking for more transfers. In China, there are many pro-economic policies that are determined by the central government such as export tax rebates, corporate tax breaks for foreign companies and export companies, etc. City secretaries enforce these policies disparately. For example, they can simply give tax breaks to more firms. To rule out these channels, I regress the variable P oliticianY ear on developed land, export amounts, fiscal income, fiscal transfers, average effective corporate taxes, and average effective value-added tax for each city to explore correlations between political turnover and these variables. Table A7 shows there were no correlations. However, it might take some time for a politician to act and effect local economies. To address this concern, I use a lagged P oliticianY ear instead and find no correlations, suggesting city secretaries do not sell more land to raise money, encourage exportation, increase fiscal income, ask for more fiscal transfers, or lower tax rates30 during their early terms. I plot the time trend of SOEs’ fixed assets and employment to demonstrate the effect of political turnover. The top 2 panels of Figure 5 examine SOEs’ fixed-asset patterns for high CDB industry loan cities and low CDB industry loan cities. I define high and low categories as the top and bottom quartile of CDB city-level outstanding industry loan distributions among 310 cities annually. The

30Export tax rebates offer a huge benefit for export firms in China, and the government gives part of the value-added tax or sales taxes back to firms. This is reflected by T ax V AT in Table A7

26 plots show a clear effect of political turnover cycles on SOEs’ assets in high CDB loan cities, but not in low CDB loan cities. SOE assets were higher during early periods of city secretaries’ terms (i.e., 1 to 3 years) than during late periods (i.e., 4 to 6 years) in high CDB loan cities after 2003. In low CDB loan cities, this pattern is not evident, consistent with 2SLS regression results, which show that CDB loans increase SOE assets. One reason there are no differences in SOE assets between early and late periods of a secretary’s term in high CDB loan cities before 2003 is that before 2003, overall CDB lending size was very small in comparison to later years (Figure 1). Even in these high CDB loan cities, CDB did not lend much before 2003. The middle two panels of Figure 5 show stratification on high and low average effective corporate tax rates in the cities. I define high and low categories as the top and bottom quartile of average effective corporate tax rate distributions among 310 cities annually. SOE fixed assets have no clear difference between early and late periods of city secretaries’ terms in either high or low corporate tax cities, disqualifying the channel with which city secretaries can offer more tax breaks to influence local economies. Another way to influence a local economy is through selling land. Land reserves play two roles in China. First, new business districts require land on which to build. Second, revenue from selling land goes to local governments whose fiscal income is limited. Selling land is a common way for a city to raise money, supplement fiscal income, and support local developments. The bottom two panels in Figure 5 examine SOE fixed-asset patterns for cities with large and small land developments. I define large and small categories as the top and bottom quartile of developed land distributions among 310 cities annually. The reason I do not use total land is that only developed land can be used to develop new districts or be sold. There is much deserted land in the western part of the country, and I exclude it during analysis. The bottom two panels of Figure 5 show no clear difference in SOE fixed assets between early and late periods of city secretaries’ terms in the cities that have either large or small amounts of developed land. Figure 6 shows results from the same analysis as in Figure 5 but for number of employees, another important variable. Only the top panel for high CDB loan city shows a clear pattern that SOEs had higher fixed assets during early periods of city secretaries’ terms (i.e., 1 to 3 years) than during late periods (i.e., 4 to 6 years) after 2003. All other panels do not have similar clear patterns.

27 This confirms the exclusion condition of 2SLS. Third, I explore various effects of turnover timing in cities with different CDB loan levels. In Panel A of Table A5, I interact P oliticianY ear with dummy variable HighCDB for whether a CDB loan amount was above the median of all CDB loans in 310 cities from 1998 to 2009. I regress them on SOE variables. The interaction term P oliticianY ear and HighCDB had negative coefficients with total asset, fixed assets, employment, debts, and sales per worker, suggesting turnover timing had larger effects when CDB loans were high. The main effects of P oliticianY ear did not have significant coefficients, so most of the effects of turnover timing were from high CDB loan areas. Panel B in Table A5 is restricted to SOEs with zero CDB loans in the city. There are no significant coefficients with P oliticianY ear in these zero CDB loan areas. These results further support the exclusion conditions of turnover timing.

5.5 CDB Industry Loan Analysis

5.5.1 Industry loan’s effect on firms in the same industry

Section 5.1 and 5.3 shows that at the city level, CDB loans for infrastructure help private and foreign firms grow. However, industry firm loans crowd private and foreign sectors. This section uses province-industry level loan data and explores more industry loan effects. The data offer the advantage of analyzing the effects of government credit on industries, and effects on related industries. Most extant studies are based on aggregate government credits or spending. Most CDB industry firm loans go to SOEs; approximately, 90% of total industry loans are for them. I regress the lagged aggregate assets of SOEs and private firms on CDB loans in the same province and industry. Table A8 Panel A shows that only SOEs’ assets had positive coefficients. In column 1, the coefficient for LagAsset of SOEs was 0.058 at 1% significance. The unit of LagAsset and Loan PI are both thousands of RMB; when a province has 1000 RMB more total assets of SOEs, the industry in this province can have 58 RMB more CDB outstanding loans the following year. Private-sector assets did not have significant coefficients, verifying that CDB industry firm loans usually go to SOEs. In Table A8 Panel B, I regress aggregate lagged assets of SOEs and

28 private firms on aggregate infrastructure loans. Columns 1 and 2 of Table A8 Panel B are for province-level infrastructure loans, and columns 3 and 4 of Table A8 Panel B are for city-level infrastructure loans. Lagged SOE assets had no effects, but lagged, private-sector assets had positive effects on infrastructure loans, verifying that industry loans go primarily to SOEs. This disqualifies the alternative explanation that industry loans go to places with more investment opportunities, which happen to have more SOEs because infrastructure loans do not depend on aggregate SOEs’ assets. Infrastructure loans tend to be lent to places with more private firms. The reason could be that areas with more private firms are often big cities or provinces that also require greater infrastructure investments. In China, cities in the same province usually focus on different industries, especially for SOEs that adjust slower than those in the private sector. I aggregate total assets of SOEs at the city- industry level each year and pick out the largest industry in each city. On average, only two cities in the same province focused on the same industry, and the city usually stuck to the same industry over time. Among 310 cities, 42% did not change industries from 1998 to 2009. 40% changed once, 14% twice, and only 3% more than twice. Based on these findings, I again use city-level turnover as a shock to CDB industry loans at the province level. I mark each city with its largest SOE industry annually. If city secretaries were in their earlier terms and based on previous conclusions, the city usually borrowed more industry firm loans for SOEs from CDB. I consider it a shock to a province-level CDB loan on the industry that is the largest SOE industry in the city. For example, city A in province B focuses on industry C. If city A’s current secretary is in an earlier term, I consider it a shock to CDB loans of industry C in province B that year. Since cities in the same province usually focus on different industries and the largest SOE 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 in the industry. Formally, the regression is:

29 LogLoan PIk,p,t = α + β1 × F irstk,p,t + β2 × Secondk,p,t + β3 × T hirdk,p,t

+β4 × F ourthk,p,t + β5 × F ifthk,p,t + β6 × Sixthk,p,t

+Controlp,t−1 + Y earF E + provinceF E + IndustryF E + εk,p,t, (5.6)

where LogLoan PIk,p,t is the logarithm of CDB outstanding loan amount in industry k, province p in year t. F irstk,p,t is a dummy variable that equals 1 if there is a city that focuses on industry k in province p that has a secretary who is in her first year. Secondk,p,t is a dummy variable that equals 1 if a city focuses on industry k in province p that has a secretary who is in her second year. T hirdk,p,t to Sixthk,p,t are defined similarly. Controlp,t−1 includes lagged GDP, urban income per capita, fiscal income, and working population in province p. I control year, province, and industry fixed effects. Regression results are shown in Table A9. In the first column of Table A9, CDB industry loans were larger if a city that focused on this industry had a secretary who was in her early term; F irstk,p,t , Secondk,p,t, and T hirdk,p,t had positive coefficients. I also combine the first two or three years and create dummy variables F irst − Secondk,p,t and F irst − T hirdk,p,t. In columns 2 and 3 of Table A9, these two variables also had positive coefficients. On average, a province borrowed 33.2% more from CDB for an industry that focused on cities that had secretaries in the first three years of their terms. City secretaries borrowed more for a city’s top SOE industry during their early terms, consistent with previous results that suggest secretaries borrowed more during earlier term.

Next, I use F irstk,p,t to Sixthk,p,t to instrument LogLoan PIk,p,t and perform 2SLS. I include all control variables such as economic variables Controlp,t−1, year-fixed effects, and firm-fixed effects during first-stage regressions. The second-stage regression is:

Yl,k,p,t = α + β × LogLoan\ PIk,p,t + Controlp,t−1 + Y earF E + F irmF E + εl,t, (5.7)

where Yl,k,p,t is the dependent variables of firm l in industry k province p in year t such as

30 logarithm of total assets, fixed assets, number of employees, total debts, ROA, sales per worker, and total sales. Again, I control local economic condition variables, and year-fixed and firm-fixed effects. Panel A of Table 7 is the 2SLS regression results for private firms. In columns 1 to 4 and 6 to 7, CDB industry firm loans had negative effects on private firms’ total assets, fixed assets, employment, debts, sales per worker, and total sales. When industry loans doubled, private firms in the same industry, on average, decreased fixed assets by 14.8%, decreased employment by 9.7%, decreased debts by 20.1%, decreased sales per worker by 17.4%, and decreased total sales by 26.3%. In column 5 of Table 7 Panel A , CDB industry firm loans had positive effects on private firms’ ROAs. Panel B of Table 7 is 2SLS regression results for SOEs. Again, industry loans helped SOEs increase total assets, fixed assets, employment, debts, sales per worker, and total sales. When industry loans doubled, SOEs in the same industry, on average, increased fixed assets by 19.3%, increased employment by 13.0%, increased debts by 30.4%, increased sales per worker by 2.3%, and increased total sales by 12.6%. In sum, CDB industry loans make SOEs grow larger and sell more. Contrarily, private firms shrink in size and sell less. Since CDB industry firm loans usually go to SOEs, it is unsurprising that SOEs become stronger and crowd the private sector. The results from 2SLS are consistent with the OLS results in Table 2.

5.5.2 Industry loan’s effect on firms in the related industries

It is well known that China’s economy grew dramatically during the last two decades, and the private sector was the primary driver of this growth(Figure 4). Although government credit crowds the private sector in the same industry, it might complement the private sector in related industries. CDBs strategy is to aid basic industries such as energy and mining to help related industries. For this sector, I use an input-output matrix to identify inter-industry relationships and study spillover effects of government credit. I use the national input-output matrix from 2007 from the National

Bureau of Statistics of China to define upstream and downstream industries31. The input-output matrix has 42 industries, and CDB classifies its loans into 95 industries, which is more detailed. I

31I also use other years’ input-output matrices to double check the definition of upstream and downstream industries and assess the same inter-industry relationships that do not change much over time.

31 match these two industry classifications by combining CDB industries. For each industry, I pick the largest intermediate input from other industries as the upstream industry. At the firm level, I match each firm with its upstream industry CDB loan in the same province. After the merger, there were 25 industries in the manufacturing sector. Again, I use city-level turnover dummy variables to instrument UpstreamLoan and perform 2SLS. The first-stage regression is:

LogUpstreamLoanl,k0,p,t = α + β1 × F irstk0,p,t + β2 × Secondk0,p,t + β3 × T hirdk0,p,t

+β4 × F ourthk0,p,t + β5 × F ifthk0,p,t + β6 × Sixthk0,p,t

+Controlp,t−1 + Y earF E + F irmF E + εl,t, (5.8)

where LogUpstreamLoanl,k0,p,t is the logarithm of CDB outstanding loan amount in the upstream

0 industry of firm l in industry k, province p in year t. k indexes the upstream industry of k. F irstk0,p,t is a dummy variable that equals 1 if there was a city that focused on industry k0 in province p and that had a secretary who was in the first year. Secondk0,p,t to Sixthk0,p,t are defined similarly. The second-stage regression is:

Yl,k,p,t = α + β × LogUpstreamLoan\ k0,p,t + Controlp,t−1 + Y earF E + F irmF E + εl,t, (5.9)

where Yl,k,p,t are dependent variables of firm l at year t, which is in industry k, province p.

0 0 LogUpstreamLoan\ k0,p,t is the estimated CDB loan to upstream industry k . k indexes the upstream industry of k. Panel A of Table 8 Panel A shows results for private firms. Generally, CDB loans to firms’ upstream industry helped the private sector. In columns 1, 2, 4, 6, and 7 of Panel A Table 8, the upstream CDB industry firm loan had positive effects on private firms’ total assets, fixed assets, debts, sales per worker, and total sales. When the upstream industry loan doubled, private firms in the downstream, on average, increased fixed assets by 9.4%, increased debts by 3.2%, increased sales per worker by 4.4%, and increased total sales by 4.9%. However, there was

32 no change in employment or ROA. Moreover, in Table 9, I interact the LogUpstreamLoan with dummy Connected for whether the private firms’ political hierarchy is above the city level or not. In China, every firm(include private firm) has a political hierarchy. It defines which level of the government the firm needs to report to. In another word, it determines which level of the government the firm is affiliated with. For example, city level firms means that the firms are under city governments and report to city governments. From the regression results in Table 9, the private firms with better political connections can benefit significantly from the upstream loans. It means that although the CDB upstream loans have positive effects on downstream private firms but the connected private firms can benefit significantly more. Table 8 Panel B shows results for SOEs. Generally, CDB loans to firms’ upstream industry also helped SOEs. In column 1 to 4 and 7 of Table 8 Panel B, the upstream CDB industry firm loans had positive effects on SOEs’ total assets, fixed assets, employment, debts, and total sales. When the upstream industry loan doubled, SOEs in the downstream, on average, increased fixed assets by 9.2%, increased employment by 8.7%, increased debts by 13.2%, and increased total sales by 5.8%. However, SOEs’ sales per worker decreased. In column 5 and 6 of Table 8 Panel B , ROA and sales per worker both decreased. Although both the private sector and SOEs grew, unlike private firms, SOEs hired more workers and experienced lower efficiency.

5.6 Overall Effects of the Government Credit from CDB

From the analyses above, CDB infrastructure loans help all types of firms: SOEs, private, and foreign. CDB industry loans that usually go to SOEs expand SOEs but crowd private firms in the same industry. Upstream CDB industry loans help private firms in downstream industries. What is the overall effects of government credit from CDB? One component of CDB’s mandate is to help basic industries. Although CDB industry loans crowd the private sector in the same industry, they help private firms in downstream industries grow. I use estimated coefficients and various types of CDB credits to study overall effects. In column 1 of Table 6 Panel A, the coefficient of CDB city-level infrastructure loans on private firms’

33 total assets was 0.246, which means one unit increase in the logarithm of CDB city infrastructure loans increases the logarithm of each private firm’s assets by 0.246 in the same city. For industry loans, column 1 of Table 7 Panel A shows that one unit increase in the logarithm of CDB province industry loans decreases the logarithm of each private firm’s assets by 0.246 in the same province and industry. Column 1 of Table 8 panel A shows that one unit increase in the logarithm of CDB province upstream industry loans increases the logarithm of each private firm’s assets by 0.092 in the same province and industry. Based on these coefficients and changes to CDB loans in infrastructures and industries, I find that, on average, total CDB credit increased private firms’ assets by 3.4% annually from 1998 to 2009. During the same period, the average annual growth rate of private firms’ assets was 15.4%. Therefore, CDB loans contributed about 22% to private-sector growth from 1998 to 2009. On average, a $1 million increase in CDB total outstanding loan amount led to a $0.33 million increase in private firms’ total assets. If I exclude infrastructure loans, then from 1998 to 2004, CDB industry loans increased private firms’ assets from 2% to 7% annually and a $1 million increase in CDB outstanding industry loan amount led to a $0.43 million increase in private firms’ total assets. From 2005 to 2009, CDB industry loans decreased private firms’ assets from 2% to 5% annually and a $1 million increase in CDB outstanding industry loan amount led to a $0.89 million decrease in private firms’ total assets. Overall, from 1998 to 2009, a $1 million increase in CDB outstanding industry loan amount led to a $0.52 million decrease in private firms’ total assets. Sales per worker is an important efficiency measurement in China, especially in the manufacturing sector. An abundant labor supply made labor costs cheaper in China, one of the most important reasons for dramatic growth in exports and the economy as a whole. Most manufacturing firms in China are labor intensive. Higher sales per worker means a firm can do more with fewer workers. From results in Tables 6, 7, and 8, CDB infrastructure loans increased sales per worker for private firms, and CDB industry loans decreased sales per worker for private firms in the same industry. CDB loans to upstream industries increased sales per worker for private firms in downstream industries. Based on these coefficients, I find that, on average, total CDB credits increased private firms’ sales per worker by 10.8% annually from 1998 to 2009. During the same period, the average

34 sales per worker annual growth rate of private firms was 20%. Therefore, CDB loans contributed more than 50% to the growth of the private sector’s sales per worker from 1998 to 2009. If I exclude infrastructure loans, then from 1998 to 2004, CDB industry loans increased private firms’ sales per worker from 2% to 9% annually. From 2005 to 2009, CDB industry loans decreased private firms’ assets from 6% to 9% annually. I further explore the reasons behind disparate credit effects during various periods. Figure 7 plots total CDB loan issuances from 1998 and 2010 for the top 10 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 transportation equipment manufacturing, all of these were upstream industries. Manufacturing firms were usually direct downstream industries. The dominant weight on upstream industries can have large positive spillover effects on downstream industries, one reason industry credit can have positive effects on the private sector during earlier years. In 2010 and after 12 years, the top industries of CDB loans changed. Electric power supply is still the top industry of CDB loan issuances. However, three of the top 10 industries are in manufacturing sectors. Electronic equipment manufacturing ranks third, which was not included in the top 10 industries back to 1998. Special equipment manufacturing32 ranks fourth, and transportation equipment manufacturing ranks sixth. This could lead to bigger crowding effects in manufacturing industries, and smaller spillover effects for downstream industries. This might explain why CDB industry loans had positive effects on private sectors during earlier years, and negative effects during later years. During the past 20 years, China had dramatic GDP growth, and there were many shortages such as energy supply and mining. CDB loans in upstream industries helped solve these demand constraints, possibly explaining why CDB industry loans can help the private sector grow faster and become more efficient during earlier years. However, in later years, CDB focused less on basic industries, shifting to other industries such as electronic equipment. CDB loans to infrastructure always helped a firm grow and improve efficiency. Although there are increasingly new, modern cities in China, urbanization is far from over.

32Special equipment includes equipment for mining, agriculture, medical, clothing, etc.

35 5.7 Politicians’ Incentives Behind the Borrowing Patterns

Section 5.2 shows that city secretaries borrow significantly more during their first year in office and decrease the borrowing monotonically overtime. The next logical questions are: why city secretaries intended to borrow more during the early periods of terms? What are the incentives behind these patterns? The political view suggests politicians had personal goals that did not necessary accord with social goals. In China, promotion is one of the most important career aspects to a politician. Li and Zhou (2005) find that the likelihood of promotion of provincial leaders increased with economic performance in China between 1979 and 1995. It is known well that city secretaries’ and mayors’ promotions in China depend heavily on local GDP growth. Although other determinants also matter such as political background and personal connections, GDP is one of few aspects that can be quantified. To verify the hypothesis, I calculated GDP growth from various periods and performed the regression:

pomotioni,j = α + β1 × GP D Increaset,i,j + β2 × relationi,j

+β3 × agei,j + β4 × genderi + εi (5.10)

33 Each observation represents one city secretary in one city , where pomotioni is a dummy variable for whether city secretary i in city j got promoted during turnover. GP D Increaset,i,j is the GDP increase from secretary i ’s first year in city j in year t. I set t = 1, 2, 3, 4, 5 to examine effects from GDP increased in various stages of a city secretary’s term. relationi,j is a dummy variable for whether city secretary i in city j was from the same hometown as provincial governor. agei,j is the age of secretary i in city j during the turnover year. genderi is a dummy variable for whether a city secretary i was female. Standard errors clustered at the city level. Table A10 shows probit regression results. GDP increases increased city secretaries’ promotional chances. GDP increases during the first couple of years of a city secretary’s term had larger effects. In column

33 Some cases exist such that the same city secretaries were in different cities. The observation is at the city/secretary level. For example, if one city secretary stayed in two cities, there are two observations for that secretary.

36 1 of Table A10 Panel A, the coefficient for GDP increases in the first year of a city secretary’s term was 0.975. It decreases monotonically when I included more years (column 2 to 5). age had negative coefficients because older secretaries were more likely to retire and a rule states that to get promoted, a city secretary’s age should be lower than 55 years. I also study CDB loan increase impacts on promotion. The probit model becomes:

pomotioni,j = α + β1 × Loan Increaset,i,j + β2 × relationi,j

+β3 × agei,j + β4 × genderi + εi, (5.11)

where Loan Increaset,i,j is the logarithm of CDB outstanding loan increase from secretary i’s first year in city j in year t. I set t = 1, 2, 3, 4, 5 again. Table A10 Panel B shows that CDB loan increases had positive effects on promotion. This effect is primarily from loan increases during the first two years of secretaries’ terms. In column 1 and 2 of Table A10 Panel B, coefficients for loan increases were 0.102 and 0.087, both significant. When I included later years(column 3 to 5), the coefficients are lower and less significant. relation had positive coefficients (column 1 to 5 of Table A10 Panel B), which makes sense since promotion decisions were usually made by provincial governors. Again, age has negative effects on promotion. In short, GDP growth had positive effects on city secretaries’ promotions when their terms ended and earlier years’ GDP growth had bigger effects. CDB loans also had positive effects on promotion probability, and loan increases during first two years of secretaries’ terms had larger impacts on promotions. In China, borrowing from CDB is the primary method city secretaries use to boost local GDP during the last 15 years, and the loans take time to help the economy, so city secretaries should intend to borrow from CDB as early as possible. One concern is that politicians in China are assigned by the Communist Party instead of elected by voters. More-connected politicians can be assigned to better cities and can borrow more from CDB. Consequently, these politicians have greater chances of promotion. To deal with this, I focus on borrowing patterns from the timing of turnover. Most city secretaries’ terms are 5 years, and based on duration model results, the timing

37 of the turnover is unaffected by economic conditions and CDB loan amounts. Second, I control city secretaries’ personal fixed effects during regression to remove politicians’ personal time-invariant effects. The borrowing patterns and promotions of a city secretary accord with the hypothesis that promotion is an important goal for city secretary, and promotions depend heavily on local GDP growth. One way to increase short-term GDP growth is to borrow from CDB and invest, explaining why city secretaries tend to borrow significantly more during earlier terms and slow borrowing later. However, there are several alternative explanations for this pattern. First, it could be that a city secretary’s goal is to maximize social welfare, and the secretary wants to explore investment opportunities as early as possible during a term. Since city secretaries invest in good projects, GDP increases and they are promoted. Second, more-connected city secretaries have greater chances to get promoted and assigned to a better city with more economic growth potential. To separate the hypothesis from these alternatives, I include a dummy variable for city secretaries whose expected terms were fewer or equal to two years when they became secretaries. The bottom panel of Figure 2 shows a tendency to bring city turnover cycles back to national cycle. A city secretary would have known that there was a greater probability of turnover during these national turnover years. For example, if a city secretary began her term in 2011, she knew that the next turnover could be in 2013, a national turnover year, and there was only two years remaining. Retirement age is another cause. In China, the retirement age for male city secretary is 60, and 55 for female. I made dummy variable ShortT erm equal to 1 if national turnover or retirement was within two years when secretaries begin their terms. These secretaries expected their terms to be fewer than or equal to two years. If the secretaries’ goal was to maximize social welfare, they should have borrowed and invested regardless of how long they expected to stay in the city. If their goal was promotion, they had less incentive to borrow if there were only two years remaining. In Table A11 columns 1 and 2, ShortT erm had negative effects on both CDB outstanding loan amounts and new issuances. The coefficient for CDB outstanding loans was -2.064. On average, for city secretaries who expected their terms to be less than or equal to two years from the beginning of their terms, they borrowed 50% less. If a city secretary wanted to explore good investment

38 opportunities as soon as possible, they should still have borrowed and invested even if they expected short tenure. Second, I include dummy variable Y ounger for whether city secretaries were younger than 50 years when their terms began. In China, if city secretaries are more than 55 years old, they do not get promoted during turnover, and are usually assigned to a same-level, though less important, position until retirement. The hypothesis is that if city secretaries know there is a large chance to turnover again in the near future (e.g., 2 years), they have fewer incentives to borrow since a loan requires time to operate, and it will not help their promotions much. However, if city secretaries care about social welfare, they want to invest in good projects even if they will not help with promotions. Table A11 columns 3 and 4 show that if city secretaries were from the same hometown as provincial governors, they borrowed 400% more. Again, if city secretaries wanted to explore good investment opportunities as soon as possible, they should still have borrowed and invested even if they were older and had few chances at promotions. Third, I include dummy variable relation for whether a city secretary was from the same hometown as a provincial governor. Provincial governors make promotional decisions for city secretaries, and it is difficult identifying personal relationships between them. City secretaries are not necessarily from the same cities they rule, and neither are provincial governors. In the data, 94.64% of city secretaries were from other cities. The assumption here is that if a city secretary and provincial governor are from the same hometown, they are more likely to know each other, to have worked together previously, or for the governor to bring a city secretary with him/her during turnover. This cronyism is common in China. In the data, 8.42% of city secretaries were from the same hometowns as their governors. Table A11 column 5 shows that if city secretaries came from the same hometowns as the governors, they borrowed 18.9% more from CDB. If city secretaries or provincial governors cared about social welfare, they should have borrowed money based on project quality rather than relationships between them. A question that follows naturally is what prevented city secretaries from borrowing as much as possible during later terms? Although CDB loans during city secretaries’ later terms do not help promotions, they can still borrow as much as they can unless there is a cost of doing so. Distress

39 costs from high leverage is an obvious possibility. If a city already had many loans when a secretary began a term, the secretary should have known approximately the total borrowing during the term. Were the city constrained, the secretary had more incentives to move borrowing forward, and vice versa. To test this hypothesis, I use CDB debt-to-GDP ratio to measure constraints. City average CDB debt-to-GDP ratio was 2.4%, and I define a city as contained if the ratio were more than 6.9%(top quartile). Overall debt-to-GDP appeared low, but in China, a city shares about 20% to 30% of tax revenue. The fiscal income they can use to repay a loan is even smaller. In the top 25% of cities with a debt-to-GDP ratio greater than 6.9%, the median CDB debt-to-fiscal income ratio was 219%; these cities are constrained. I used dummy variable Constrained for whether a city had a CDB debt-to-GDP ratio greater than 6.9% before the city secretary began a term. I also used the interaction of Constrained and P oliticianY ear, with results in Table A12. P oliticianY ear still had negative coefficients from columns 1 to 6. Constrained had positive coefficients on CDB outstanding loans for both infrastructure and industry, which is mechanical. For CDB loan new issuances (columns 2, 4, and 6), Constrained did not have an effect, meaning constrained cities did or could not borrow more to invest or repay old loans. The interaction term P oliticianY earConstrained had negative coefficients. For example, in Table A12 column 1, the coefficient for P oliticianY earConstrained was -0.167 and the main effect of P oliticianY ear was -0.274, suggesting that for constrained cities, secretaries tended to move more CDB borrowing forward. This supports the hypothesis that CDB loans are costly, and if city secretaries know they can borrow only a limited amount during their terms, they tend to borrow as early as possible. In sum, city secretaries borrow significantly more during early terms, and they borrow less if the expected turnover is in fewer than or equal to two years. City secretaries who are older than 50 when they begin their terms borrow significantly less. A secretary who is from the same place as a provincial governor borrows significantly more. These results support the hypothesis that city secretaries want to borrow as early as possible to increase GDP and enhance promotion chance. Results are also against the hypothesis that city secretaries want to invest in good projects as soon as possible. However, instead of promotions, there could be other explanations for why short-term secretaries borrow less and younger secretaries borrow more. Perhaps short-term secretaries just

40 want to live a quiet life and do not want to make huge changes during their short terms. Young secretaries are more ambitious and might have different educations than older generations. I do not disqualify these alternatives, but instead offer evidence that is consistent with the political view. These alternatives do not affect my identifications in the primary results.

6 Conclusion

This paper explores the different effects of various types of government credit (infrastructure vs. industry credit). It traces the effect of government credit across different levels of the supply chain. Using unique industry, detailed loan data from China Development Bank, I find that government credits to infrastructure help firms expand in size, debt, employment, and sales per worker. Government credits to industries, which usually go to SOEs, help SOEs expand, but crowd private firms in the same industry. However, these industry loans help private firms in downstream industries. Overall, government credit from CDB supplemented the private sector with a contribution of approximately 22% to private sector growth from 1998 to 2009. These results shed light on the prior mixed empirical findings. I use municipal politicians’ turnover timing as an instrument for loans from the CDB. I find that city secretaries borrowed more during their early terms, and I provide evidence that accords with the hypothesis that promotion is the incentive behind politicians’ borrowing patterns. Although direct costs of government credit is essential (Lucas and Moore (2010), Lucas (2012a)) to evaluating government credit programs, they are beyond the scope of this paper. In future research, it is important to value the costs of these government loans to evaluate overall costs and benefits. Local-government indebtedness has a direct impact on housing prices and the shadow banking system in China. How does government credit affect households in China? What is the relationship between government credit and China’s shadow banking system? Answers to these questions will elucidate China’s government credit and the larger picture of the country’s economy.

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48 Table 1 Summary Statistics Data Panel A: City Data

Variable N Mean S.D. Min Max Loan City 3,605 31.97 69.01 0.00 1,268.20 Issuance City 3,605 11.31 26.76 0.00 428.79 Loan INF City 3,605 10.92 30.37 0.00 593.74 Issuance INF City 3,605 4.20 12.21 0.00 195.03 Loan IND City 3,605 21.34 51.54 0.00 847.77 Issuance IND City 3,605 7.13 18.77 0.00 284.22 GDP 3,587 559.46 782.80 0.00 10,604.48 AvgIncome 3,568 10,799.02 8,047.79 0.00 139,574.00 FiscalIncome 3,587 30.43 60.98 0.00 1,138.31 Employment 3,579 1,108.81 18,000.38 0.00 329,858.30 PoliticianYear 3,605 2.49 1.32 1.00 6.00 Age 3,325 50.11 4.22 32.00 62.00 Gender 3,605 0.01 0.10 0.00 1.00 Relation 3,158 0.08 0.28 0.00 1.00 Promotion 3,605 0.38 0.48 0.00 1.00 Panel B: Province Data

Variable N Mean S.D. Min Max Loan PI 44,733 8.09 43.33 0.00 1,369.09 Issuance PI 44,733 2.81 16.23 0.00 1,004.12 Loan Road 496 154.18 163.63 0 744.87 Loan Rail 496 51.44 102.32 0 1260.99 Loan Water 496 12.60 23.95 0 203.29 Loan Tel 496 8.55 41.03 0 419.93 Panel C: Firm Data

Variable N Mean S.D. Min Max LogAsset 2,949,514 9.72 1.48 0.00 20.16 LogFixAst 2,933,323 8.46 1.77 0.00 19.11 LogWorker 2,944,543 4.69 1.17 0.00 12.58 LogDebt 2,930,818 8.98 1.73 0.00 19.32 ROA 2,949,502 0.09 0.20 -0.76 2.44 Log(Sales/Worker) 2,918,158 5.25 1.24 -8.12 17.38 LogSales 2,931,478 9.95 1.46 0.00 19.24 Tax Corp 1,520,597 19.41 10.24 0.00 61.54 Tax VAT 1,356,623 15.09 13.92 0.00 86.91

Note. Panel A restricted to 310 cities from 1998 to 2010. Loan City is the city level total CDB outstanding loan amount. Issuance City is the city level total CDB loan new issuance amount. Loan INF City is the city level total CDB outstanding loan amount for infrastructure. Issuance INF City is the city level total CDB loan new issuance amount for infrastructure. Loan IND City is the city level total CDB outstanding loan amount for industry firms. Issuance IND City is the city level total CDB loan new issuance amount for industry firms. The unit of Loan City to Issuance IND City is 100 million RMB. GDP , AvgIncome, F iscalIncome and Employment are city level GDP, income per capita, fiscal income and total employment respectively. Age is the age of the city secretary. Gender is the dummy for whether the city secretary is a female. Relation is the dummy for whether the city secretary is born at the same place as the province governor. P romition is the dummy for whether the city secretary is promoted or not at the end of the term. The mean of P romition is 0.38 which means 38% of the city secretaries are promoted in the end of their terms.

Panel B restricted to 31 provinces from 1998 to 2013 among 95 industries. Loan PI is the province level CDB industry outstanding loan amount. Issuance PI is the province level CDB industry loan new issuance. There are 31 provinces and 95 industries in total. The total observation number is 44,733 which covers all 95 industries. Among these 95 industries, CDB added 11 new industries in 2005 and doesn’t have data before 2005. Among these 95 industries, I select 4 infrastructure sectors. Loan Road is the province level CDB industry outstanding loan amount for road construction. Loan Rail is the province level CDB industry outstanding loan amount for railway construction. Loan W ater is the province level CDB industry outstanding loan amount for water system. Loan T el is the province level CDB industry outstanding loan amount for Telecommunication infrastructure. The number of observations is 496 which covers 31 provinces from 1998 to 2013. The unit is in 100 million RMB.

Panel C restricted to all manufacturing firms in Chinese Industry Census data from 1998 to 2009. LogAsset is the logarithm of each firm’s total asset. LogF ixAst is the logarithm of each firm’s fixed asset. LogW orker is the logarithm of each firm’s number of employees. LogDebt is the logarithm of each firm’s total debt. Log(Sales/W orker) is the logarithm of each firm’s sales per worker. LogSales is the logarithm of each firm’s total sales. ROA is each firm’s return on assets. T ax Corp is the corporate tax rate of each firm every year. T ax V AT is the value-added tax rate of each firm every year.For more detailed variable definition and construction, please see Appendix Table A1.

49 Table 2 CDB Loan’s Effect on Firms: Evidence from OLS Regressions

Panel A: Industry Loan on SOEs (1) (2) (3) (4) (5) (6) (7) Dependent Variable LogAsset LogFixAst LogWorker LogDebt ROA Log(Sales/Worker) LogSales

Log(Loan PI) 0.006*** 0.003 0.010*** 0.017*** -0.002*** -0.010*** -0.001 (0.002) (0.003) (0.002) (0.003) (0.001) (0.003) (0.003) GDPt−1 -0.055*** -0.117*** -0.046*** -0.106*** 0.011*** -0.010 -0.060*** (0.014) (0.020) (0.013) (0.018) (0.003) (0.015) (0.018) AvgIncomet−1 0.002 0.001 0.001* 0.002* 0.000 0.000 0.001 (0.001) (0.001) (0.001) (0.001) (0.000) (0.001) (0.001) FiscalIncomet−1 0.156 0.393* 0.309** 0.215 -0.174*** -0.570*** -0.311 (0.161) (0.220) (0.140) (0.203) (0.038) (0.174) (0.197) Employmentt−1 0.001 -0.001* -0.001 0.000 -0.000 0.002* 0.001 (0.001) (0.001) (0.001) (0.001) (0.000) (0.001) (0.001) Year Fixed Effect Yes Yes Yes Yes Yes Yes Yes Firm Fixed Effect Yes Yes Yes Yes Yes Yes Yes Observations 231,682 230,283 232,003 229,696 231,682 225,214 227,342 R-squared 0.093 0.040 0.028 0.054 0.008 0.189 0.152

Panel B: Industry Loan on Pri- vate Firms (1) (2) (3) (4) (5) (6) (7) Dependent Variable LogAsset LogFixAst LogWorker LogDebt ROA Log(Sales/Worker) LogSales

Log(Loan PI) -0.003** -0.007*** -0.007*** 0.003* -0.004*** -0.016*** -0.022*** (0.001) (0.002) (0.001) (0.002) (0.000) (0.001) (0.001) GDPt−1 -0.013** -0.049*** 0.001 -0.015* -0.001 0.025*** 0.025*** (0.006) (0.009) (0.005) (0.008) (0.001) (0.006) (0.007) AvgIncomet−1 0.000 -0.002*** 0.000 0.002*** -0.000 -0.001** -0.000 (0.000) (0.001) (0.000) (0.001) (0.000) (0.000) (0.000) FiscalIncomet−1 -0.324*** -0.423*** -0.306*** -0.036 -0.209*** -1.197*** -1.499*** (0.062) (0.086) (0.054) (0.079) (0.012) (0.058) (0.067) Employmentt−1 0.001** 0.000 0.001** 0.002*** 0.000 0.000 0.001** (0.000) (0.001) (0.000) (0.001) (0.000) (0.000) (0.000) Year Fixed Effect Yes Yes Yes Yes Yes Yes Yes Firm Fixed Effect Yes Yes Yes Yes Yes Yes Yes Observations 756,826 753,444 757,033 752,871 755,947 756,820 755,687 R-squared 0.219 0.107 0.013 0.081 0.033 0.225 0.273

Panel C: Infrastructure Loan on Private Firms (1) (2) (3) (4) (5) (6) (7) Dependent Variable LogAsset LogFixAst LogWorker LogDebt ROA Log(Sales/Worker) LogSales

Log(Loan INF City) 0.013*** 0.012*** -0.010*** 0.009** 0.002*** 0.041*** 0.032*** (0.003) (0.004) (0.002) (0.004) (0.001) (0.003) (0.003) GDPt−1 -0.046*** -0.041*** 0.004 -0.063*** -0.014*** -0.139*** -0.137*** (0.006) (0.008) (0.005) (0.008) (0.002) (0.006) (0.007) AvgIncomet−1 -0.000 -0.001*** -0.000 0.000 -0.000 0.000 -0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) FiscalIncomet−1 -0.123** -0.294*** -0.350*** 0.078 -0.136*** -0.290*** -0.627*** (0.055) (0.078) (0.050) (0.072) (0.012) (0.055) (0.059) Employmentt−1 0.694 2.804*** 1.295** -0.360 1.092*** 3.445*** 4.630*** (0.688) (0.912) (0.544) (0.945) (0.156) (0.627) (0.702) Year Fixed Effect Yes Yes Yes Yes Yes Yes Yes Firm Fixed Effect Yes Yes Yes Yes Yes Yes Yes Observations 1,022,595 1,017,890 1,022,954 1,016,623 1,022,586 1,021,059 1,021,245 R-squared 0.215 0.108 0.034 0.091 0.058 0.245 0.281

Note. In Penal A and B, Loan PI is at province× industry× year level. It is the CDB industry loan (to SOEs) amount at each of the 31 provinces and 41 manufacturing industries. In Panel A, data is restricted to SOEs in CIC data from 1998 to 2009. It shows the OLS regression results by estimating equation 5.1. In Panel B, data is restricted to private firms in CIC data from 1998 to 2009. GDP , AvgIncome, F iscalIncome and Employment are province level GDP, income per capita, fiscal income and total employment respectively. All columns are controlled by year fixed effect and firm fixed effect. Standard errors are clustered at firm level. In Panel C, Loan INF City is the CDB infrastructure loan amount at city × year level. Data is restricted to private firms in CIC data from 1998 to 2009 among 310 cities. GDP , AvgIncome, F iscalIncome and Employment are city level GDP, income per capita, fiscal income and total employment respectively. All columns are controlled by year fixed effect and firm fixed effect. Standard errors are clustered at firm level. 50 Table 3 Exogeneity of City Secretary Turnover Timing (1) (2) (3) (4) (5) Coefficient Coefficient Coefficient Coefficient Hazard Ratio

NationalCycle 0.366*** 0.340*** 0.363*** 0.332*** 1.393 (0.061) (0.064) (0.063) (0.065) Age 0.019** 0.018** 1.019 (0.008) (0.008) Gender 0.123 0.133 1.142 (0.293) (0.297) GDPt−1 -0.058 -0.065 0.087 0.044 1.045 (0.064) (0.067) (0.462) (0.459) GDPt−2 -0.164 -0.118 0.888 (0.536) (0.537) AvgIncomet−1 -0.001 -0.001 0.002 -0.000 1.000 (0.003) (0.004) (0.011) (0.011) AvgIncomet−2 -0.003 -0.000 1.000 (0.012) (0.012) FiscalIncomet−1 0.807 0.769 2.167 2.755 15.722 (0.837) (0.857) (4.034) (3.936) FiscalIncomet−2 -1.658 -2.457 0.086 (4.720) (4.634) Employmentt−1 0.000 0.000 -0.008 -0.006 0.994 (0.001) (0.001) (0.010) (0.009) Employmentt−2 0.009 0.007 1.007 (0.010) (0.009) Log(Loan Cityt−1) 0.002 0.001 0.028 0.033 1.033 (0.018) (0.019) (0.043) (0.048) Log(Loan Cityt−2) -0.025 -0.030 0.970 (0.041) (0.045)

Observations 3,012 2,775 2,878 2,652 2,652 Chi Squared 36.84 38.07 36.51 34.91 34.91

Note. The regressions are estimated at city × year level. Data restricted to 1,106 city secretaries among 310 cites from 1998 to 2011. Table shows the results from Cox proportional hazard regression by following Wooldridge(2002). Estimated coefficients are reported in column 1 to 4. Age is the city secretary’s age. Gender is the dummy for whether the city secretary is female or not. NationalCycle is the dummy for whether it is the year of national congress party(1998, 2003 and 2008). GDP , AvgIncome, F iscalIncome and Employment are city level GDP, income per capita, fiscal income and total employment respectively. Log(Loan City) is the logarithm of CDB city level total outstanding loan amount. Column 1 and 2 only controls GDP , AvgIncome, F iscalIncome and Employment one year ago. Column 3 and 4 controls GDP , AvgIncome, F iscalIncome and Employment in the last two years. The time-varying variables are Age, NationalCycle, GD,AvgIncome, F iscalIncome, Employment and Log(Loan City). I assume these time-varying variables are constant during one year. Gender is time-invariant. Column 2 and 4 includes all the time-varying and time-invariant variables. Column 1 and 3 excludes Gender and Age of the city secretaries since these two variables has many missing values. Column 5 is the estimated hazard ratio from column 4. Standard errors are clustered at city secretary’s level.

51 Table 4 City Secretary Turnover Timing’s Effect on Borrowing from CDB (First Stage)

Panel A Total Loan Loan for Infrastructure Loan for Industry Firms (1) (2) (3) (4) (5) (6) Dependent Variable Log(Loan City) Log(Issuance City) Log(Loan City) Log(Issuance City) Log(Loan City) Log(Issuance City)

PoliticianYear -0.364*** -0.308*** -0.122*** -0.165*** -0.338*** -0.252*** (0.020) (0.029) (0.013) (0.025) (0.028) (0.037) GDPt−1 -0.269* 0.018 -0.222* 0.091 -0.156 -0.011 (0.138) (0.167) (0.123) (0.296) (0.145) (0.205) AvgIncomet−1 -0.004** -0.001 0.004 -0.001 -0.005** -0.004 (0.002) (0.007) (0.004) (0.007) (0.002) (0.007) FiscalIncomet−1 0.874 -1.040 -0.449 -2.549 0.837 1.552 (1.197) (1.428) (0.817) (2.250) (1.309) (2.031) Employmentt−1 -0.954 -11.682** -2.004 3.287 -9.066 -30.014*** (5.875) (5.870) (5.254) (6.935) (6.758) (10.539) Year Fixed Effect Yes Yes Yes Yes Yes Yes City Fixed Effect Yes Yes Yes Yes Yes Yes Politician Fixed Effect Yes Yes Yes Yes Yes Yes Observations 3,127 2,795 2,449 2,227 2,727 2,323 R-squared 0.853 0.685 0.884 0.588 0.756 0.597

Panel B Total Loan Loan for Infrastructure Loan for Industry Firms (1) (2) (3) (4) (5) (6) Dependent Variable Log(Loan City) Log(Issuance City) Log(Loan City) Log(Issuance City) Log(Loan City) Log(Issuance City)

Year 2 -0.386*** -0.357*** -0.109*** -0.167*** -0.330*** -0.327*** (0.031) (0.057) (0.028) (0.063) (0.044) (0.081) Year 3 -0.749*** -0.660*** -0.240*** -0.298*** -0.703*** -0.540*** (0.042) (0.074) (0.035) (0.070) (0.064) (0.099) Year 4 -1.071*** -0.912*** -0.351*** -0.509*** -1.001*** -0.731*** (0.063) (0.089) (0.041) (0.082) (0.086) (0.123) Year 5 -1.429*** -1.318*** -0.467*** -0.691*** -1.341*** -1.238*** (0.108) (0.168) (0.081) (0.172) (0.151) (0.216) Year 6 -1.900*** -1.581*** -0.691*** -0.870*** -1.668*** -1.337*** (0.144) (0.196) (0.110) (0.218) (0.204) (0.240) GDPt−1 -0.264* 0.022 -0.223* 0.087 -0.154 -0.014 (0.138) (0.167) (0.123) (0.300) (0.145) (0.202) AvgIncomet−1 -0.004** -0.001 0.004 -0.001 -0.005** -0.004 (0.002) (0.007) (0.004) (0.007) (0.002) (0.007) FiscalIncomet−1 0.859 -1.089 -0.439 -2.556 0.856 1.461 (1.196) (1.432) (0.836) (2.277) (1.316) (2.011) Employmentt−1 -1.205 -11.934** -1.976 3.167 -8.969 -30.738*** (5.867) (5.917) (5.271) (6.973) (6.773) (10.516) Year Fixed Effect Yes Yes Yes Yes Yes Yes City Fixed Effect Yes Yes Yes Yes Yes Yes Politician Fixed Effect Yes Yes Yes Yes Yes Yes Observations 3,127 2,795 2,449 2,227 2,727 2,323 R-squared 0.854 0.685 0.884 0.588 0.756 0.599

Note. The regressions are estimated at city × year level. Data restricted to CDB city level loan to 310 cites from 1998 to 2010. Panel A shows the results from OLS regression from equation 5.3. P oliticianY ear is the number of years that the secretary has been staying in the city. Column 1 and 2 are the regressions on the logarithm of total CDB loan outstanding amount and total issuance respectively. Column 3 and 4 are the regressions on the logarithm of CDB infrastructure loan outstanding amount and new issuance respectively. Column 5 and 6 are the regressions on the logarithm of CDB industry firm loan outstanding amount and new issuance respectively. GDP , AvgIncom, F iscalIncome and Employment are city level GDP, income per capita, fiscal income and total employment respectively. All columns are controlled by year fixed effect, city fixed effect and politician personal fixed effect. Standard errors are clustered at city level. Panel B shows the results from OLS regression from equation 5.4. Y ear 2 is the the dummy for whether it is the second of the secretary in the city. Y ear 3 is the the dummy for whether it is the third of the secretary in the city. Y ear 4 to Y ear 6 are defined in the same way. The dummy for year 1 is the missing category. All columns are controlled by year fixed effect, city fixed effect and politician personal fixed effect. Standard errors are clustered at city level.

52 Table 5 City Secretary’s Turnover Effects on Firms

Panel A: SOEs

(1) (2) (3) (4) (5) (6) Dependent Variable LogAsset LogFixAst LogWorker LogDebt ROA LogNumber

Year 2 -0.019*** -0.023*** -0.006 -0.028*** 0.010*** 0.019 (0.007) (0.009) (0.006) (0.010) (0.002) (0.015) Year 3 -0.032** -0.042** -0.021* -0.043** 0.017*** 0.056*** (0.013) (0.017) (0.012) (0.019) (0.003) (0.018) Year 4 -0.047** -0.056** -0.025 -0.065** 0.024*** 0.076*** (0.020) (0.026) (0.017) (0.028) (0.005) (0.024) Year 5 -0.075*** -0.098*** -0.031 -0.097** 0.028*** 0.035 (0.027) (0.035) (0.023) (0.038) (0.007) (0.049) Year 6 -0.091*** -0.115*** -0.044 -0.128*** 0.035*** 0.070 (0.034) (0.044) (0.030) (0.048) (0.008) (0.050) GDPt−1 -0.021 -0.095*** -0.022* -0.081*** 0.003 -0.035 (0.013) (0.019) (0.013) (0.018) (0.004) (0.094) AvgIncomet−1 0.000 0.000 0.000 0.000 -0.000 0.004** (0.001) (0.001) (0.001) (0.001) (0.000) (0.002) FiscalIncomet−1 -0.243** 0.031 0.197* -0.105 -0.107*** 1.813 (0.112) (0.160) (0.112) (0.151) (0.027) (1.278) Employmentt−1 -0.008** -0.009 -0.002 -0.010 -0.001 -0.003*** (0.004) (0.007) (0.002) (0.006) (0.002) (0.001) Year Fixed Effect Yes Yes Yes Yes Yes Yes Firm Fixed Effect Yes Yes Yes Yes Yes No Politician Fixed Effect Yes Yes Yes Yes Yes Yes City Fixed Effect No No No No No Yes Observations 382,317 379,632 381,720 378,567 382,318 3,250 R-squared 0.108 0.0523 0.0642 0.0647 0.0437 0.963

Panel B: Private Firms (1) (2) (3) (4) (5) (6) Dependent Variable LogAsset LogFixAst LogWorker LogDebt ROA LogNumber

Year 2 -0.015*** -0.008 -0.004 -0.012* -0.003*** 0.085*** (0.005) (0.006) (0.004) (0.007) (0.001) (0.019) Year 3 -0.032*** -0.011 -0.005 -0.038*** -0.003 0.144*** (0.009) (0.012) (0.008) (0.013) (0.002) (0.021) Year 4 -0.058*** -0.022 -0.013 -0.070*** 0.002 0.177*** (0.014) (0.018) (0.011) (0.019) (0.003) (0.030) Year 5 -0.096*** -0.060** -0.025* -0.095*** 0.006 0.212*** (0.019) (0.025) (0.015) (0.025) (0.004) (0.056) Year 6 -0.081*** -0.008 0.002 -0.111*** 0.008 0.283*** (0.024) (0.031) (0.019) (0.032) (0.005) (0.053) GDPt−1 -0.047*** -0.039*** 0.003 -0.064*** -0.008*** -0.091 (0.006) (0.008) (0.005) (0.008) (0.001) (0.115) AvgIncomet−1 -0.000 -0.001*** -0.001** 0.000 -0.000 0.003 (0.000) (0.000) (0.000) (0.000) (0.000) (0.004) FiscalIncomet−1 -0.132*** -0.257*** -0.269*** 0.072 -0.128*** 2.394 (0.051) (0.073) (0.047) (0.067) (0.012) (1.690) Employmentt−1 -0001 0.001 -0.000 0.000 -0.001* -0.001** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Year Fixed Effect Yes Yes Yes Yes Yes Yes Firm Fixed Effect Yes Yes Yes Yes Yes No Politician Fixed Effect Yes Yes Yes Yes Yes Yes City Fixed Effect No No No No No Yes Observations 1,171,115 1,165,459 1,171,449 1,163,749 1,171,106 3,233 R-squared 0.234 0.117 0.0317 0.103 0.0625 0.974

53 Table 5(Continue) City Secretary’s Turnover Effects on Firms

Panel C: Foreign Firms (1) (2) (3) (4) (5) (6) Dependent Variable LogAsset LogFixAst LogWorker LogDebt ROA LogNumber

Year 2 0.020*** 0.017* 0.009 0.008 -0.001 0.063*** (0.007) (0.010) (0.007) (0.011) (0.002) (0.020) Year 3 0.028** 0.034* 0.039*** -0.001 -0.001 0.120*** (0.014) (0.019) (0.013) (0.022) (0.003) (0.027) Year 4 0.043** 0.056* 0.043** -0.001 0.000 0.127*** (0.021) (0.029) (0.019) (0.032) (0.005) (0.040) Year 5 0.059** 0.075* 0.081*** -0.003 0.008 0.141* (0.029) (0.039) (0.026) (0.044) (0.007) (0.083) Year 6 0.110*** 0.164*** 0.141*** 0.018 0.016** 0.311*** (0.036) (0.050) (0.033) (0.055) (0.008) (0.064) GDPt−1 -0.019*** 0.000 0.047*** -0.059*** -0.001 -0.093 (0.007) (0.010) (0.007) (0.012) (0.002) (0.116) AvgIncomet−1 0.000 -0.000 0.001* 0.001 0.000* 0.001 (0.000) (0.001) (0.000) (0.001) (0.000) (0.001) FiscalIncomet−1 -0.485*** -0.652*** -0.262*** -0.334*** -0.121*** 1.952 (0.062) (0.085) (0.064) (0.094) (0.016) (1.597) Employmentt−1 -0.002 -0.003 -0.002 -0.002 -0.001 -0.003*** (0.007) (0.008) (0.005) (0.009) (0.001) (0.001) Year Fixed Effect Yes Yes Yes Yes Yes Yes Firm Fixed Effect Yes Yes Yes Yes Yes No Politician Fixed Effect Yes Yes Yes Yes Yes Yes City Fixed Effect No No No No No Yes Observations 256,887 255,976 256,652 255,513 256,887 2,976 R-squared 0.175 0.0541 0.0673 0.0601 0.0292 0.968

Note. Data restricted to manufacturing firms in Chinese Industry Census data from 1998 to 2009 among 310 cities. Y ear 2 to Y ear 6 are at the city × year level. Panel A is for SOEs. Panel B is for private firms and Panel C is for foreign firms. Column 1 is the logarithm of total asset of the firm. Column 2 is the logarithm of fixed asset of the firm. Column 3 is the logarithm of total number of workers of the firm. Column 4 is the logarithm of total debt of the firm. Column 5 is the ROA of the firm. Y ear 2 is the the dummy for whether it is the second of the secretary in the city where the firm locates. Y ear 3 is the the dummy for whether it is the third of the secretary in the city. Y ear 4 to Y ear 6 are defined in the same way. The dummy for year 1 is the missing category. GDP , AvgIncome, F iscalIncome and Employment are city level GDP, income per capita, fiscal income and total employment respectively. Column 1 to 5 are controlled by year fixed effect, firm fixed effect and politician personal fixed effect. Standard errors are clustered at firm level. Column 6 is the logarithm of total number of firms in each city every year. Column 6 is controlled by year fixed effect, city fixed effect and politician personal fixed effect. Standard errors are clustered at firm level.

54 Table 6 Opposing Effects of CDB City Infrastructure Loan and Industry Loan (Use Secretary Year in Office as Instrument)

Panel A: Private Firms

(1) (2) (3) (4) (5) (6) (7) Dependent Variable LogAsset LogFixAst LogWorker LogDebt ROA Log(Sales/Worker) LogSales

Log(Loan IND City) -0.073*** -0.165*** -0.021 0.005 0.001 -0.134*** -0.146*** (0.016) (0.022) (0.014) (0.023) (0.005) (0.018) (0.019) Log(Loan INF City) 0.246*** 0.236*** 0.052* 0.161*** -0.023** 0.544*** 0.576*** (0.033) (0.046) (0.028) (0.047) (0.009) (0.037) (0.039) GDPt−1 0.019* 0.026* 0.021** -0.027* -0.017*** 0.013 0.028** (0.010) (0.014) (0.009) (0.015) (0.003) (0.011) (0.012) AvgIncomet−1 -0.001*** -0.003*** -0.001*** -0.000 0.000 -0.002*** -0.002*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) FiscalIncomet−1 -0.191*** -0.434*** -0.366*** 0.137** -0.148*** -0.526*** -0.869*** (0.050) (0.070) (0.045) (0.066) (0.011) (0.052) (0.055) Employmentt−1 0.814 3.296*** 1.358** -1.474 1.434*** 3.664*** 4.944*** (0.725) (0.959) (0.579) (0.972) (0.157) (0.707) (0.784) Year Fixed Effect Yes Yes Yes Yes Yes Yes Yes Firm Fixed Effect Yes Yes Yes Yes Yes Yes Yes Politician Fixed Effect Yes Yes Yes Yes Yes Yes Yes Observations 851,720 847,260 852,039 846,406 851,712 850,374 850,546 Wald F-stat 474.3 472.1 477.2 471.5 474.4 478.0 475.8

Panel B: Foreign Firms

(1) (2) (3) (4) (5) (6) (7) Dependent Variable LogAsset LogFixAst LogWorker LogDebt ROA Log(Sales/Worker) LogSales

Log(Loan IND City) -0.104*** -0.146*** 0.038* -0.100*** -0.011 -0.109*** -0.070** (0.023) (0.034) (0.022) (0.036) (0.007) (0.026) (0.029) Log(Loan INF City) 0.062** 0.059 -0.176*** 0.092* -0.001 0.167*** -0.011 (0.031) (0.046) (0.032) (0.052) (0.009) (0.040) (0.040) GDPt−1 0.015 0.033 0.009 -0.021 -0.001 -0.022 -0.014 (0.014) (0.020) (0.014) (0.024) (0.004) (0.018) (0.017) AvgIncomet−1 -0.000 -0.001 0.000 0.000 0.000* 0.002*** 0.003*** (0.000) (0.001) (0.000) (0.001) (0.000) (0.000) (0.001) FiscalIncomet−1 -0.404*** -0.574*** -0.230*** -0.285*** -0.148*** -0.459*** -0.680*** (0.073) (0.101) (0.076) (0.109) (0.019) (0.080) (0.091) Employmentt−1 -3.892*** -3.104* -2.718** -2.293 0.312 3.241** 0.339 (1.169) (1.670) (1.156) (1.944) (0.316) (1.471) (1.492) Year Fixed Effect Yes Yes Yes Yes Yes Yes Yes Firm Fixed Effect Yes Yes Yes Yes Yes Yes Yes Politician Fixed Effect Yes Yes Yes Yes Yes Yes Yes Observations 194,732 194,108 194,545 193,752 194,732 194,313 194,583 Wald F-stat 288.4 284.1 280.6 283.4 288.4 282.4 289.1

Note. Table 6 are the two stage least squares results by using Y ear 1 to Y ear 6 as instrumental variable for logarithm of CDB loan for infrastructure and industry SOEs at city×year level. Log(Loan INF City) is the logarithm of the aggregate loan for infrastructure at city × year level. Log(Loan IND City) is the logarithm of the aggregate loan for industry firms at city × year level. Panel A restricted to the private firms in CIC from 1998 to 2009 among 310 cities. Panel B restricted to the foreign firms in CIC from 1998 to 2009 among 310 cities. In column 5, LogSales/W orker is the logarithm of Sales per worker. GDP , AvgIncome, F iscalIncome and Employment are city level GDP, income per capita, fiscal income and total employment respectively. All columns are controlled by year fixed effect, firm fixed effect and politician personal fixed effect. Standard errors are clustered at firm level. Cragg-Donald Wald F-statistics for weak identification tests are reported.

55 Table 7 CDB Province Industry Loan’s Effect on Firms within Same Industry (2SLS)

Panel A: Private Firms

(1) (2) (3) (4) (5) (6) (7) Dependent Variable LogAsset LogFixAst LogWorker LogDebt ROA Log(Sales/Worker) LogSales

Log(Loan PI) -0.246*** -0.213*** -0.140*** -0.290*** 0.020** -0.251*** -0.380*** (0.034) (0.044) (0.027) (0.047) (0.009) (0.037) (0.042) GDPt−1 -0.040*** -0.073*** -0.016*** -0.043*** 0.003*** 0.014*** -0.002 (0.006) (0.007) (0.004) (0.007) (0.001) (0.005) (0.006) AvgIncomet−1 0.001*** -0.001*** 0.001*** 0.002*** -0.000 -0.000 0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) FiscalIncomet−1 0.475*** 0.317** 0.168** 0.803*** -0.256*** -0.511*** -0.370*** (0.097) (0.125) (0.077) (0.131) (0.024) (0.099) (0.116) Employmentt−1 0.000 -0.000 0.000 0.001 0.000* -0.000 0.000 (0.001) (0.001) (0.000) (0.001) (0.000) (0.000) (0.001) Year Fixed Effect Yes Yes Yes Yes Yes Yes Yes Firm Fixed Effect Yes Yes Yes Yes Yes Yes Yes Observations 693,782 690,071 693,966 689,685 693,774 692,560 692,874 Wald F-stat 52.15 52.71 51.31 51.22 52.11 52.58 52.39

Panel B: SOEs

(1) (2) (3) (4) (5) (6) (7) Dependent Variable LogAsset LogFixAst LogWorker LogDebt ROA Log(Sales/Worker) LogSales

Log(Loan PI) 0.196*** 0.279*** 0.187*** 0.304*** -0.001 0.033* 0.181*** (0.017) (0.022) (0.016) (0.024) (0.004) (0.019) (0.020) GDPt−1 -0.050*** -0.109*** -0.045*** -0.097*** 0.011*** -0.011 -0.059*** (0.012) (0.017) (0.011) (0.016) (0.003) (0.012) (0.015) AvgIncomet−1 0.001 0.001 0.001 0.002* 0.000 -0.000 0.000 (0.001) (0.001) (0.001) (0.001) (0.000) (0.001) (0.001) FiscalIncomet−1 0.027 0.207 0.197 0.024 -0.173*** -0.562*** -0.397** (0.138) (0.188) (0.121) (0.175) (0.030) (0.139) (0.166) Employmentt−1 0.002 0.001 0.000 0.002 -0.000 0.002** 0.002** (0.001) (0.001) (0.001) (0.002) (0.000) (0.001) (0.001) Year Fixed Effect Yes Yes Yes Yes Yes Yes Yes Firm Fixed Effect Yes Yes Yes Yes Yes Yes Yes Observations 230,336 228,818 229,774 228,127 230,335 222,680 225,486 Wald F-stat 88.78 87.91 91.84 87.17 88.23 84.03 81.87

Note. Loan PI is the outstanding loan amount at province × industry × year level. In Panel A, data is restricted to private firms in CIC data from 1998 to 2009. Panel A shows the two stage least squares regression results by using F irsth to Sixth as instrumental variable for logarithm of CDB province level outstanding loan amount in 41 manufacturing industries and 27 provinces (exclude Beijing, Shanghai, Tianjing, and Chonqing). GDP , AvgIncome, F iscalIncome and Employment are province level GDP, income per capita, fiscal income and total employment respectively. All columns are controlled by year fixed effect and firm fixed effect. Standard errors are clustered at firm level. Cragg-Donald Wald F-statistics for weak identification tests are reported. In Panel B, data is restricted to SOEs in CIC data from 1998 to 2009. Panel B shows the two stage least squares regression results by using F irst to Sixth as instrumental variable for logarithm of CDB province level outstanding loan amount in 41 manufacturing industries and 27 provinces (exclude Beijing, Shanghai, Tianjing, and Chonqing). Loan PI is the outstanding loan amount at province- industry level every year. GDP , AvgIncome, F iscalIncome and Employment are province level GDP, income per capita, fiscal income and total employment respectively. All columns are controlled by year fixed effect and firm fixed effect. Standard errors are clustered at firm level. Cragg-Donald Wald F-statistics for weak identification tests are reported.

56 Table 8 Upstream Industry Loan’s Effect on Firms in Downstream Industry (2SLS)

Panel A: Private Firms

(1) (2) (3) (4) (5) (6) (7) Dependent Variable LogAsset LogFixAst LogWorker LogDebt ROA Log(Sales/Worker) LogSales

Log(Upstream Loan) 0.092*** 0.136*** 0.007 0.046*** -0.001 0.064*** 0.071*** (0.011) (0.015) (0.009) (0.015) (0.003) (0.012) (0.012) GDPt−1 -0.011** -0.034*** 0.005 -0.019*** 0.004*** 0.037*** 0.042*** (0.005) (0.006) (0.004) (0.006) (0.001) (0.004) (0.005) AvgIncomet−1 0.000 -0.002*** 0.000 0.002*** 0.000 -0.001*** -0.001*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) FiscalIncomet−1 -0.394*** -0.670*** -0.337*** 0.051 -0.235*** -1.333*** -1.672*** (0.057) (0.077) (0.047) (0.073) (0.013) (0.053) (0.060) Employmentt−1 0.001*** 0.001 0.001*** 0.002*** 0.000** 0.001** 0.001*** (0.000) (0.001) (0.000) (0.000) (0.000) (0.000) (0.000) Year Fixed Effect Yes Yes Yes Yes Yes Yes Yes Firm Fixed Effect Yes Yes Yes Yes Yes Yes Yes Observations 804,718 800,058 804,853 799,002 804,709 803,296 803,712 Wald F-stat 581.3 573.7 581.3 569.9 580.0 580.2 579.0

Panel B: SOEs

(1) (2) (3) (4) (5) (6) (7) Dependent Variable LogAsset LogFixAst LogWorker LogDebt ROA Log(Sales/Worker) LogSales

Log(Upstream Loan) 0.122*** 0.132*** 0.125*** 0.191*** -0.009*** -0.033** 0.084*** (0.012) (0.015) (0.011) (0.017) (0.003) (0.014) (0.015) GDPt−1 -0.067*** -0.109*** -0.047*** -0.106*** 0.006** -0.023** -0.073*** (0.012) (0.015) (0.010) (0.015) (0.003) (0.012) (0.014) AvgIncomet−1 0.001 0.001 0.001 0.003*** 0.000 -0.001 -0.000 (0.001) (0.001) (0.001) (0.001) (0.000) (0.001) (0.001) FiscalIncomet−1 0.091 0.209 0.227** 0.181 -0.137*** -0.496*** -0.347** (0.135) (0.174) (0.115) (0.171) (0.030) (0.135) (0.155) Employmentt−1 0.002** -0.000 -0.000 0.002 -0.000 0.001 0.000 (0.001) (0.001) (0.001) (0.001) (0.000) (0.001) (0.000) Year Fixed Effect Yes Yes Yes Yes Yes Yes Yes Firm Fixed Effect Yes Yes Yes Yes Yes Yes Yes Observations 285,544 283,591 283,980 282,245 285,544 275,484 279,226 Wald F-stat 620.1 613.0 607.8 602.5 617.9 577.8 590.6

Note. Upstream Loan is the loan amount in firm’s most related upstream industry at province × industry × year level. In Panel A, data is restricted to private firms in CIC data from 1998 to 2009. Panel A shows the two stage least squares regression results by using F irst to Sixth as instrumental variable for logarithm of CDB province level outstanding loan amount in 25 manufacturing industries (collapsed from 41 CIC manufacturing industries) and 27 provinces (exclude Beijing, Shanghai, Tianjing, and Chonqing). Upstream Loan is the outstanding loan amount in firm’s most related upstream industry. GDP , AvgIncome, F iscalIncome and Employment are province level GDP, income per capita, fiscal income and total employment respectively. All columns are controlled by year fixed effect and firm fixed effect. Standard errors are clustered at firm level. Cragg-Donald Wald F-statistics for weak identification tests are reported. In Panel B, data is restricted to SOEs in CIC data from 1998 to 2009. Panel B shows the two stage least squares regression results by using F irst to Sixth as instrumental variable for logarithm of CDB province level outstanding loan amount in 25 manufacturing industries (collapsed from 41 CIC manufacturing industries) and 27 provinces (exclude Beijing, Shanghai, Tianjing, and Chonqing). Data restricted to SOEs in CIC data from 1998 to 2009. Upstream Loan is the outstanding loan amount in firm’s most related upstream industry. GDP , AvgIncome, F iscalIncome and Employment are province level GDP, income per capita, fiscal income and total employment respectively. All columns are controlled by year fixed effect and firm fixed effect. Standard errors are clustered at firm level. Cragg-Donald Wald F-statistics for weak identification tests are reported.

57 Table 9 What Private Firms Benefit from CDB Upstream Industry Loans (2SLS)

(1) (2) (3) (4) (5) (6) (7) Dependent Variable LogAsset LogFixAst LogWorker LogDebt ROA Log(Sales/Worker) LogSales

Log(Upstream Loan) 0.091*** 0.135*** 0.006 0.045*** -0.000 0.066*** 0.072*** (0.011) (0.015) (0.009) (0.015) (0.003) (0.011) (0.012) Log(Upstream Loan)*Connected 0.073*** 0.059** 0.067*** 0.120*** -0.026*** -0.050*** 0.016 (0.019) (0.023) (0.013) (0.024) (0.003) (0.014) (0.019) GDPt−1 -0.011** -0.034*** 0.005 -0.019*** 0.004*** 0.037*** 0.043*** (0.005) (0.006) (0.004) (0.006) (0.001) (0.004) (0.005) AvgIncomet−1 0.000 -0.002*** 0.000 0.002*** 0.000 -0.001*** -0.001*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) FiscalIncomet−1 -0.390*** -0.666*** -0.333*** 0.059 -0.237*** -1.340*** -1.674*** (0.057) (0.077) (0.046) (0.073) (0.013) (0.053) (0.060) Employmentt−1 0.001*** 0.001 0.001*** 0.002*** 0.000** 0.001** 0.001*** (0.000) (0.001) (0.000) (0.000) (0.000) (0.000) (0.000) Year Fixed Effect Yes Yes Yes Yes Yes Yes Yes Firm Fixed Effect Yes Yes Yes Yes Yes Yes Yes Observations 804,718 800,058 804,853 799,002 804,709 803,296 803,712 Wald F-stat 1451 1441 1459 1431 1456 1457 1454

Note. Data is restricted to private firms in CIC data from 1998 to 2009. Table 9 shows the two stage least squares regression results by using F irst to Sixth as instrumental variable for logarithm of CDB province level outstanding loan amount in 25 manufacturing industries (collapsed from 41 CIC manufacturing industries) and 27 provinces (exclude Beijing, Shanghai, Tianjing, and Chonqing). Upstream Loan is the outstanding loan amount in firm’s most related upstream industry. Connected is the dummy for whether the private firm’s political hierarchy is above the city(province level and national level) or not. In order to include the interaction term Log(Upstream Loan) ∗ Connected in 2SL, I follow Wooldridge (2002). I first regress instrumental variables(F irst to Sixth) on Log(Upstream Loan) with all exogenous variables and get fitted value Log(Upstream\ Loan). Second, I use Log(Upstream\ Loan) and Log(Upstream\ Loan) ∗ Connected as instrumental variables for Log(Upstream Loan) and Log(Upstream Loan) ∗ Connected, and perform the standard 2SLS again. GDP , AvgIncome, F iscalIncome and Employment are province level GDP, income per capita, fiscal income and total employment respectively. All columns are controlled by year fixed effect and firm fixed effect. Standard errors are clustered at firm level. Cragg-Donald Wald F-statistics for weak identification tests are reported.

58 Appendix

Table A1 Variables’ Definition and Construction Loan City Outstanding total CDB city level loan from 1998 to 2010. It covers 310 cities in China. The unit is 100 million RMB. Issuance City Total CDB new city level loan issuance from 1998 to 2010. It covers 310 cities in China. The unit is 100 million RMB. Loan INF City Outstanding CDB city level loan for infrastructure projects from 1998 to 2010. It covers 310 cities in China. The unit is 100 million RMB. Issuance INF City Total CDB city level loan issuance for infrastructure projects from 1998 to 2010. It covers 310 cities in China. The unit is 100 million RMB. Loan IND City Outstanding CDB city level loan for industry firms from 1998 to 2010. It covers 310 cities in China. The unit is 100 million RMB. Issuance IND City Total CDB city level loan issuance for industry firms from 1998 to 2010. It covers 310 cities in China. The unit is 100 million RMB. GDP City GDP amount from 1998 to 2010. It covers 310 cities in China. The unit is 100 million RMB. Data is from National Bureau of Statistics of China. AvgIncome Income per capita in the city from 1998 to 2010. It covers 310 cities in China. The unit is RMB. Data is from National Bureau of Statistics of China. FiscalIncome City fiscal income amount from 1998 to 2010. It covers 310 cities in China. The unit is 100 million RMB. Data is from National Bureau of Statistics of China. Employment City total number of workers from 1998 to 2010. It covers 310 cities in China. The unit is 100 thousand. Data is from National Bureau of Statistics of China. PoliticianYear Number of years that the city secretary has been staying in the city. It covers 310 cities in China from 1998 to 2010. Age The age of the city secretary. It covers 310 cities in China from 1998 to 2010. Gender Dummy for whether the city secretary is a female or not. It covers 310 cities in China from 1998 to 2010. Relation Dummy for whether the city secretary is born at the same city as the province governors. It covers 310 cities in China from 1998 to 2010. Promotion Dummy for whether the city secretary is promoted to a higher position in the government or not in the end of her term. It covers 310 cities in China from 1998 to 2010. In China, different cities have different political hierarchy. For example, Shanghai, Beijing, chonqing, and Tianjing are in the ministry level (same level as province). I take this into account when define ”Promotion” Loan PI Outstanding total CDB province loan for each of the 95 industries from 1998 to 2013. It covers all 31 provinces in China. The unit is 100 million RMB. Issuance PI Total CDB province loan new issuance for each of the 95 industries from 1998 to 2013. It covers all 31 provinces in China. The unit is 100 million RMB. LogAsset Logarithm of the total asset of the firm. The unit of asset is in thousand RMB. It covers all 711,892 firms in CIC data from 1998 to 2009. LogFixAst Logarithm of the fixed asset of the firm. The unit of fixed asset is in thousand RMB. It covers all 711,892 firms in CIC data from 1998 to 2009. LogWorker Logarithm of the employee number of the firm. It covers all 711,892 firms in CIC data from 1998 to 2009. LogDebt Logarithm of the total debt of the firm. The unit of the total debt is in thousand RMB. It covers all 711,892 firms in CIC data from 1998 to 2009. ROA Contemporaneous ROA. It is calculated by dividing a firm’s annual earnings by its total asset in the same year. It covers all 711,892 firms in CIC data from 1998 to 2009. Log(Sales/Worker) Logarithm of the sales per employee. It covers all 711,892 firms in CIC data from 1998 to 2009. LogSales Logarithm of the total sales. The unit of the total sales is in thousand RMB. It covers all 711,892 firms in CIC data from 1998 to 2009. Tax Corp The corporate tax rate of each firm by dividing corporate tax payable amount by the income before tax every year. T ax Corp is a missing value when income before tax is negative. It covers all 711,892 firms in CIC data from 1998 to 2009. Tax VAT The value-added tax rate of each by dividing value-added tax payable amount by the production value-added every year. T ax V AT is a missing value when production value-added is negative. It covers all 554,882 firms in CIC data in 1998, 1999, 2000, 2003, 2005, 2006 and 2007. CIC data doesn’t record the value-added tax payment in 2001, 2002, 2004, 2008 and 2009.

59 Table A2 City Secretary’s Turnover and Borrowing from CDB(off national cycle cities)

Panel A Total Loan Loan for Infrastructure Loan for Industry Firms (1) (2) (3) (4) (5) (6) Dependent Variable Log(Loan City) Log(Issuance City) Log(Loan City) Log(Issuance City) Log(Loan City) Log(Issuance City)

PoliticianYear -0.078** -0.190*** -0.124*** -0.161*** -0.099** -0.242*** (0.031) (0.023) (0.017) (0.032) (0.041) (0.033) GDPt−1 -0.234 0.062 -0.228* 0.200 -0.180 -0.121 (0.157) (0.194) (0.135) (0.379) (0.201) (0.233) AvgIncomet−1 -0.005*** -0.003 0.003 -0.004 -0.007*** -0.005 (0.002) (0.008) (0.004) (0.007) (0.002) (0.008) FiscalIncomet−1 0.023 -1.655 -0.652 -3.266 -0.535 1.678 (1.302) (1.604) (0.965) (2.811) (1.634) (2.265) Employmentt−1 -1.083 -1.815 0.539 5.085 -6.179 -23.345*** (7.736) (8.707) (3.062) (6.218) (9.291) (6.817) Year Fixed Effect Yes Yes Yes Yes Yes Yes City Fixed Effect Yes Yes Yes Yes Yes Yes Politician Fixed Effect Yes Yes Yes Yes Yes Yes Observations 1,686 1,542 1,387 1,278 1,495 1,293 R-squared 0.809 0.639 0.855 0.553 0.706 0.514

Panel B Total Loan Loan for Infrastructure Loan for Industry Firms (1) (2) (3) (4) (5) (6) Dependent Variable Log(Loan City) Log(Issuance City) Log(Loan City) Log(Issuance City) Log(Loan City) Log(Issuance City)

Year 2 -0.086* -0.174** -0.119*** -0.161* -0.043 -0.257** (0.047) (0.073) (0.042) (0.095) (0.063) (0.111) Year 3 -0.137** -0.350*** -0.228*** -0.261*** -0.179** -0.472*** (0.063) (0.067) (0.045) (0.083) (0.083) (0.097) Year 4 -0.181** -0.527*** -0.358*** -0.537*** -0.207* -0.644*** (0.091) (0.085) (0.055) (0.114) (0.119) (0.130) Year 5 -0.314** -0.891*** -0.487*** -0.624*** -0.415* -1.229*** (0.154) (0.181) (0.095) (0.201) (0.217) (0.242) Year 6 -0.579*** -1.050*** -0.728*** -0.791*** -0.611** -1.316*** (0.195) (0.194) (0.133) (0.279) (0.278) (0.231) GDPt−1 -0.224 0.063 -0.224 0.196 -0.169 -0.122 (0.162) (0.201) (0.137) (0.384) (0.210) (0.234) AvgIncomet−1 -0.005*** -0.003 0.003 -0.004 -0.007*** -0.005 (0.002) (0.008) (0.004) (0.007) (0.002) (0.009) FiscalIncomet−1 -0.096 -1.815 -0.676 -3.229 -0.737 1.385 (1.327) (1.612) (1.022) (2.852) (1.727) (2.283) Employmentt−1 -1.193 -11.330 0.616 5.065 -5.658 -23.103*** (7.917) (9.092) (3.043) (6.169) (9.528) (6.509) Year Fixed Effect Yes Yes Yes Yes Yes Yes City Fixed Effect Yes Yes Yes Yes Yes Yes Politician Fixed Effect Yes Yes Yes Yes Yes Yes Observations 1,686 1,542 1,387 1,278 1,495 1,293 R-squared 0.810 0.640 0.856 0.554 0.707 0.517

Note. Data restricted to CDB city level loan from 1998 to 2010. I exclude the city secretaries whose turnover are at the same time as the national turnover cycle(year 1998, 2003 and 2008). The remaining observations are for the cities with different turnover cycles than the national cycle. Panel A shows the results from OLS regression from equation 5.3. P oliticianY ear is the number of years that the secretary has been staying in the city. Column 1 and 2 are the regressions on the logarithm of total CDB loan outstanding amount and total issuance respectively. Column 3 and 4 are the regressions on the logarithm of CDB infrastructure loan outstanding amount and new issuance respectively. Column 5 and 6 are the regressions on the logarithm of CDB industry firm loan outstanding amount and new issuance respectively. GDP , AvgIncome, F iscalIncome and Employment are city level GDP, income per capita, fiscal income and total employment respectively. All columns are controlled by year fixed effect, city fixed effect and politician personal fixed effect. Standard errors are clustered at city level. Panel B shows the results from OLS regression from equation 5.4. Y ear 2 is the the dummy for whether it is the second of the secretary in the city. Y ear 3 is the the dummy for whether it is the third of the secretary in the city. Y ear 4 to Y ear 6 are defined in the same way. The dummy for year 1 is the missing category. All columns are controlled by year fixed effect, city fixed effect and politician personal fixed effect. Standard errors are clustered at city level.

60 Table A3 What Firms are Crowded Out in Private Sector (1) (2) (3) (4) Dependent Variable LogAsset LogFixAst LogWorker LogDebt

PoliticianYear -0.015*** -0.004 0.002 -0.018** (0.005) (0.007) (0.005) (0.008) Low ROA 0.148*** 0.089*** -0.052*** 0.187*** (0.004) (0.006) (0.004) (0.006) PoliticianYear*Low ROA -0.011*** -0.010*** -0.010*** -0.003 (0.001) (0.002) (0.001) (0.002) GDPt−1 -0.052*** -0.045*** -0.002 -0.067*** (0.006) (0.009) (0.006) (0.008) AvgIncomet−1 -0.000 -0.001*** -0.000 0.000 (0.000) (0.000) (0.000) (0.000) FiscalIncomet−1 -0.248*** -0.317*** -0.299*** -0.091 (0.053) (0.077) (0.050) (0.070) Employmentt−1 -0.001 0.001 -0.000 0.001 (0.001) (0.001) (0.001) (0.002) Year Fixed Effect Yes Yes Yes Yes Firm Fixed Effect Yes Yes Yes Yes Politician Fixed Effect Yes Yes Yes Yes Observations 899,857 895,698 900,086 893,378 R-squared 0.261 0.133 0.0436 0.117

Note. Data restricted to private firms in Chinese Industry Census data from 1998 to 2009. Column 1 is the logarithm of total asset of the firm. Column 2 is the logarithm of fixed asset of the firm. Column 3 is the logarithm of total number of workers of the firm. Column 4 is the logarithm of total debt of the firm. P oliticianY ear is the number of years that the secretary has been staying in the city. GDP , AvgIncome, F iscalIncome and Employment are city level GDP, income per capita, fiscal income and total employment respectively. All columns are controlled by year fixed effect, firm fixed effect and politician personal fixed effect. Standard errors are clustered at firm level.

Table A4 CDB Loan’s Effect on Firms (Use Secretary Year in Office as Instrument)

SOEs Foreign Firms (1) (2) (3) (4) (5) (6) (7) (8) Dependent Variable LogAsset LogFixAst LogWorker LogDebt LogAsset LogFixAst LogWorker LogDebt

Log(Loan City) 0.069** 0.089** 0.083*** 0.101** -0.106*** -0.170*** -0.124*** -0.051 (0.031) (0.042) (0.029) (0.044) (0.033) (0.046) (0.030) (0.048) GDPt−1 -0.013 -0.096*** -0.007 -0.064*** -0.015* -0.001 0.056*** -0.057*** (0.011) (0.016) (0.012) (0.015) (0.008) (0.011) (0.008) (0.013) AvgIncomet−1 0.001 0.001 0.000 0.001 -0.000 -0.001** 0.000 0.001 (0.001) (0.001) (0.001) (0.001) (0.000) (0.001) (0.000) (0.001) FiscalIncomet−1 -0.333*** -0.088 0.109 -0.250* -0.491*** -0.706*** -0.272*** -0.367*** (0.099) (0.141) (0.098) (0.134) (0.072) (0.100) (0.075) (0.107) Employmentt−1 -0.583 1.249* -2.325*** -1.626** -1.133 0.381 -2.809*** -0.093 (0.519) (0.748) (0.634) (0.744) (0.943) (1.299) (0.889) (1.444) Year Fixed Effect Yes Yes Yes Yes Yes Yes Yes Yes Firm Fixed Effect Yes Yes Yes Yes Yes Yes Yes Yes Politician Fixed Effect Yes Yes Yes Yes Yes Yes Yes Yes Observations 305,516 303,351 304,861 302,458 236,430 235,517 236,222 235,144 Wald F-stat 307.8 300.5 302.2 308.0 801.2 789.3 795.8 797.3

Note. Table A4 are the two stage least squares regression results by using Y ear 1 to Y ear 6 as instrumental variable for logarithm of CDB city level outstanding loan amount. Column 1 to 4 are based on SOEs in CIC from 1998 to 2009. Column 5 to 8 are based on foreign firms in CIC from 1998 to 2009. GDP , AvgIncome, F iscalIncome and Employment are city level GDP, income per capita, fiscal income and total employment respectively. All columns are controlled by year fixed effect, firm fixed effect and politician personal fixed effect. Standard errors are clustered at firm level. Cragg-Donald Wald F-statistics for weak identification tests are reported.

61 Table A5 Turnover Timing’s Effects on SOEs under Different CDB Loan Amount Level

Panel A: All Cities

(1) (2) (3) (4) (5) Dependent Variable LogAsset LogFixedAst LogWorker LogDebt Log(Sales/Worker)

PoliticianYear -0.007 -0.013 0.000 -0.018* 0.014* (0.007) (0.009) (0.006) (0.010) (0.008) PoliticianYear*HighCDB -0.013*** -0.010*** -0.012*** -0.007** -0.010*** (0.003) (0.003) (0.003) (0.004) (0.004) HighCDB 0.025*** 0.023** 0.055*** 0.006 0.013 (0.009) (0.011) (0.009) (0.012) (0.012) GDPt−1 -0.008 -0.082*** -0.007 -0.074*** -0.029* (0.013) (0.019) (0.014) (0.018) (0.017) AvgIncomet−1 0.000 0.000 0.000 0.000 0.000 (0.001) (0.001) (0.001) (0.001) (0.001) FiscalIncomet−1 -0.237** 0.031 0.181 -0.094 -0.365** (0.111) (0.160) (0.112) (0.151) (0.148) Employmentt−1 -0.008** -0.009 -0.002 -0.010 -0.004 (0.004) (0.007) (0.002) (0.006) (0.005) Year Fixed Effect Yes Yes Yes Yes Yes Firm Fixed Effect Yes Yes Yes Yes Yes Politician Fixed Effect Yes Yes Yes Yes Yes Observations 382,317 379,632 381,720 378,567 369,479 R-squared 0.108 0.0523 0.0643 0.0647 0.178

Panel B: Cities with Zero CDB Loan

(1) (2) (3) (4) (5) Dependent Variable LogAsset LogFixedAst LogWorker LogDebt Log(Sales/Worker)

PoliticianYear -0.073 0.003 -0.182 -0.025 0.130 (0.157) (0.220) (0.142) (0.230) (0.210) GDPt−1 -0.062 0.121 0.505** -0.385 0.828*** (0.214) (0.287) (0.228) (0.317) (0.319) AvgIncomet−1 0.000 -0.001 0.003* -0.001 -0.001 (0.001) (0.003) (0.002) (0.002) (0.002) FiscalIncomet−1 2.092 -0.288 0.225 3.216 -4.682 (1.946) (2.731) (2.091) (2.701) (3.084) Employmentt−1 -0.007 -0.009 -0.002 -0.010 -0.002 (0.005) (0.009) (0.003) (0.007) (0.006) Year Fixed Effect Yes Yes Yes Yes Yes Firm Fixed Effect Yes Yes Yes Yes Yes Politician Fixed Effect Yes Yes Yes Yes Yes Observations 78,512 77,919 78,261 77,643 75,212 R-squared 0.0391 0.0237 0.0635 0.0305 0.0534

Note. Panel A is restricted to SOEs in CIC data from 1998 to 2009. P oliticianY ear is the number of years that the secretary has been staying in the city. HighCDB is the dummy for whether the city’s CDB loan amount is above the median of all CDB loan in 310 cities from 1998 to 2009. GDP , AvgIncome, F iscalIncome and Employment are city level GDP, income per capita, fiscal income and total employment respectively. All columns are controlled by year fixed effect, firm fixed effect and politician personal fixed effect. Standard errors are clustered at firm level. Panel B is restricted to SOEs in CIC data from 1998 to 2009 with 0 CDB loan amount in the city. P oliticianY ear is the number of years that the secretary has been staying in the city. GDP , AvgIncome, F iscalIncome and Employment are city level GDP, income per capita, fiscal income and total employment respectively. All columns are controlled by year fixed effect, firm fixed effect and politician personal fixed effect. Standard errors are clustered at firm level.

62 Table A6 CDB Loan and Firm Exit&Enter (Use Secretary Year in Office as Instrument)

Private Firm Foreign Firms SOEs (1) (2) (3) (4) (5) (6) Dependent Variable LogExit LogEnter LogExit LogEnter LogExit LogEnter

Log(Loan City) 0.383*** -0.270*** 0.321*** -1.123*** -0.405*** 0.310*** (0.097) (0.067) (0.067) (0.150) (0.117) (0.106) GDPt−1 0.427 0.082 0.593* -0.046 -0.290 0.306** (0.271) (0.114) (0.318) (0.251) (0.185) (0.129) AvgIncomet−1 -0.027 0.002 -0.047 -0.006 0.008 0.012*** (0.025) (0.004) (0.042) (0.003) (0.006) (0.002) FiscalIncomet−1 -1.673 2.075 -1.361 3.169 1.853 1.390 (2.393) (1.583) (2.237) (2.225) (1.451) (1.107) Employmentt−1 14.465 9.460*** -18.981 18.686 28.998*** 17.325 (11.330) (3.505) (11.957) (19.411) (10.529) (10.925) Year Fixed Effect Yes Yes Yes Yes Yes Yes City Fixed Effect Yes Yes Yes Yes Yes Yes Politician Fixed Effect Yes Yes Yes Yes Yes Yes Observations 2,152 2,632 1,521 2,076 2,434 2,444 Wald F-stat 62.43 68.35 155.2 58.34 40.16 65.00

Note. Table A6 are the two stage least squares regression results by using P oliticianY ear as instrumental variable for logarithm of CDB city level outstanding loan amount. Data restricted to manufacturing firms in CIC data from 1998 to 2009. GDP , AvgIncome, F iscalIncome and Employment are city level GDP, income per capita, fiscal income and total employment respectively. All columns are controlled by year fixed effect, firm fixed effect and politician personal fixed effect. Standard errors are clustered at firm level. Cragg-Donald Wald F-statistics for weak identification tests are reported.

63 Table A7 Politician’s Other Channels to Affect Local Economy

Panel A: Current Turnover

(1) (2) (3) (4) (5) (6) Dependent Variable Developed Land Export Fiscal Income Fiscal Transfer Tax Corp Tax VAT

PoliticianYear 0.139 -0.298 0.473 -0.411 -0.066 0.019 (0.092) (0.383) (0.715) (0.368) (0.050) (0.078)

Observations 3,401 3,588 3,587 3,378 3,263 1,873 R-squared 0.001 0.000 0.000 0.000 0.001 0.000

Panel B: Lagged Turnover

(1) (2) (3) (4) (5) (6) Dependent Variable Developed Land Export Fiscal Income Fiscal Transfer Tax Corp Tax VAT

PoliticianYeart−1 0.157 -0.270 0.280 -0.349 -0.069 -0.012 (0.110) (0.356) (0.998) (0.341) (0.049) (0.079)

Observations 3,110 3,279 3,277 3,091 2,956 1,601 R-squared 0.001 0.000 0.000 0.000 0.001 0.000

Note. Data restricted to 310 cites from 1998 to 2010. In column 1, Developed Land is the total developed land in each city every year. In column 2, Export is the total export amount in each city every year. In column 3, F iscal Income is the total tax fiscal income amount in each city every year. In column 4, F iscal T ransfer is the total central government transfer amount in each city every year. In column 5, T axC orp is the average manufacturing firm effective corporate tax rate in each city every year.In column 6, T axV AT is the average manufacturing firm effective value-added tax rate in each city every year. This table mainly explore the correlations between city secretary turnover and other channels that city secretary can influence the local economy. Panel A is for current turnover P oliticianY ear and Panel B is for the lagged turnover P oliticianY earLag. Standard errors are clustered at city level.

64 Table A8 Where Does the CDB Industry Loan Go?

Panel A: Industry Loan (1) (2) (3) (4) (5) (6) Dependent Variable Loan PI Issuance PI Loan PI Issuance PI Loan PI Issuance PI

Asset SOEt−1 0.058*** 0.016*** 0.051*** 0.013*** (0.009) (0.002) (0.008) (0.002) Asset privatet−1 0.003 0.002 0.003 0.002 (0.007) (0.003) (0.007) (0.003) Year Fixed Effect Yes Yes Yes Yes Yes Yes Province Fixed Effect Yes Yes Yes Yes Yes Yes Industry Fixed Effect Yes Yes Yes Yes Yes Yes Observations 7,334 7,334 6,518 6,518 6,230 6,230 R-squared 0.687 0.372 0.640 0.344 0.735 0.386

Panel B: Infrastructure Loan (1) (2) (3) (4) Dependent Variable Loan INF Prov Issuance INF Prov Loan INF City Issuance INF City

Asset SOEt−1 0.015 0.007 0.009 0.009 (0.042) (0.019) (0.011) (0.007) Asset privatet−1 0.063*** 0.025*** 0.035** 0.017** (0.014) (0.005) (0.015) (0.009) Year Fixed Effect Yes Yes Yes Yes Province Fixed Effect Yes Yes No No City Fixed Effect No No Yes Yes Observations 297 297 3,257 3,257 R-squared 0.905 0.843 0.619 0.561

Note. In Panel A,data restricted to CDB province level industry loan data from 1998 to 2009 in 31 provinces and 41 manufacturing industries. Loan PI is the outstanding loan amount at province-industry level every year. Issuance PI is the loan new issuance amount at province-industry level every year. The unit is 1000 RMB. Asset SOE is the total asset of SOEs in each province and industry every year. Asset private is the total asset of private firms in each province and industry every year. The unit is in 1000 RMB. All columns are controlled by year fixed effect, province fixed effect and industry fixed effect. Standard errors are clustered at province level. In Panel B, column 1 and 2 are based on province level aggregate infrastructure loan data from 1998 to 2009 in 31 provinces. Loan INF P rov is the outstanding infrastructure loan amount at province level every year. Issuance INF P rov is the infrastruc- ture loan new issuance amount at province level every year. The unit is 1000 RMB. Asset SOE is the total asset of SOEs in each province every year. Asset private is the total asset of private firms in each province every year. The unit is in 1000 RMB. Column 1 and 2 are also controlled by year fixed effect and province fixed effect. Standard errors are clustered at province level. Column 3 and 4 in Panel B are based on city level aggregate infrastructure loan data from 1998 to 2009 in 310 cities. Loan INF City is the outstanding infrastructure loan amount at city level every year. Issuance INF City is the infrastructure loan new issuance amount at city level every year. The unit is 1000 RMB. Asset SOE is the total asset of SOEs in each city every year. Asset private is the total asset of private firms in each city every year. The unit is in 1000 RMB. Column 3 and 4 are also controlled by year fixed effect and city fixed effect. Standard errors are clustered at city level.

65 Table A9 CDB Province Industry Level Loan and City Secretary Turnover (First Stage) (1) (2) (3) Dependent Variable Log(Loan PI) Log(Loan PI) Log(Loan PI)

First 0.268*** (0.090) Second 0.366*** (0.099) Third 0.251*** (0.086) Fourth 0.128 (0.082) Fifth 0.057 (0.140) Sixth -0.097 (0.120) First-Second 0.339*** (0.103) First-Third 0.332*** (0.099) GDPt−1 -0.022 -0.021 -0.020 (0.022) (0.022) (0.022) AvgIncomet−1 0.190** 0.184** 0.181** (0.083) (0.083) (0.083) FiscalIncomet−1 0.005 -0.000 -0.011 (0.199) (0.199) (0.200) Employmentt−1 0.953 0.916 0.977 (2.094) (2.101) (2.074) Year Fixed Effect Yes Yes Yes Province Fixed Effect Yes Yes Yes Industry Fixed Effect Yes Yes Yes Observations 4,197 4,197 4,197 R-squared 0.538 0.535 0.536

Note. The regressions are estimated at province× industry × year level. Data restricted to CDB province level industry loan data from 1998 to 2009 in 31 provinces and 41 manufacturing industries. Log(Loan PI) is logarithm of the outstanding loan amount at province-industry level every year. F irst is the dummy for whether the city secretary is in the first year of the term and the firm is in the city’s largest SOE industry. Second is the dummy for whether the city secretary is in the second year of the term and the firm is in the city’s largest SOE industry. The dummies from T hird to Sixth are defined in the same way. GDP , AvgIncome, F iscalIncome and Employment are province level GDP, income per capita, fiscal income and total employment respectively. All columns are controlled by year fixed effect, province fixed effect and industry fixed effect. Standard errors are clustered at province level.

66 Table A10 City Secretary’s Promotion and GDP Performance

Panel A: Promotion and GDP

(1) (2) (3) (4) (5) Dependent Variable Promotion Promotion Promotion Promotion Promotion

GDP Increase in 1st year 0.975** (0.481) GDP Increase in 1st-2nd year 0.597*** (0.215) GDP Increase in 1st-3rd year 0.407*** (0.148) GDP Increase in 1st-4th year 0.348*** (0.126) GDP Increase in 1st-5th year 0.318*** (0.121) Relation 0.270 0.295 0.299 0.299 0.303 (0.196) (0.194) (0.194) (0.194) (0.194) Age -0.079*** -0.081*** -0.080*** -0.079*** -0.079*** (0.011) (0.011) (0.011) (0.011) (0.011) Gender 0.185 0.191 0.198 0.203 0.204 (0.333) (0.333) (0.331) (0.330) (0.330) Observations 1,027 1,034 1,028 1,024 1,023 Chi squared 59.73 64.86 64.12 63.65 62.48

Panel B: Promotion and CDB Loan

(1) (2) (3) (4) (5) Dependent Variable Promotion Promotion Promotion Promotion Promotion

Log(Loan Increase in 1st year) 0.102*** (0.035) Log(Loan Increase in 1st-2nd year) 0.087** (0.034) Log(Loan Increase in 1st-3rd year) 0.055* (0.033) Log(Loan Increase in 1st-4th year) 0.062* (0.032) Log(Loan Increase in 1st-5th year) 0.057* (0.033) Relation 0.430* 0.457** 0.431** 0.394* 0.426** (0.237) (0.218) (0.211) (0.207) (0.205) Age -0.084*** -0.078*** -0.074*** -0.074*** -0.074*** (0.013) (0.013) (0.012) (0.012) (0.012) Gender 0.617 0.663* 0.477 0.410 0.407 (0.388) (0.388) (0.364) (0.381) (0.381) Observations 691 758 792 810 812 Chi squared 54.58 48.75 42.96 43.85 43.70

Note. Data restricted to 1,066 city secretaries among 310 cites from 1998 to 2010. Panel A shows the results from Probit regression. P romition is the dummy for whether the city secretary is promoted or not at the end of the term. GDP Increase in 1st year is the GDP increase in the first year of the city secretary’s tenure. GDP Increase in 1st-2nd year is the GDP increase in the first two years of the city secretary’s tenure. The unit of GDP is 100 billion RMB. Age is the age of the city secretary in the end of the term. Standard errors are clustered at city level. Panel B shows the results from Probit regression with CDB loan. Log(Loan Increase in 1st year) is the logarithm of CDB outstanding loan amount increase in the first year of the city secretary’s tenure. Log(Loan Increase in 1st-2nd year) is the logarithm of CDB outstanding loan amount increase in the first two years of the city secretary’s tenure. Age is the age of the city secretary in the end of the term. Standard errors are clustered at city level.

67 Table A11 City Secretary’s Characteristics and Borrowing from CDB (1) (2) (3) (4) (5) (6) Dependent Variable Log(Loan City) Log(Issuance City) Log(Loan City) Log(Issuance City) Log(Loan City) Log(Issuance City)

PoliticianYear -0.364*** -0.308*** -0.366*** -0.300*** -0.380*** -0.299*** (0.020) (0.029) (0.021) (0.030) (0.023) (0.032) ShortTerm -2.064*** -1.919*** (0.086) (0.142) Younger 4.042*** 2.026*** (0.298) (0.212) Relation 0.189*** 0.085 (0.072) (0.144) GDPt−1 -0.269* 0.018 -0.283** -0.007 -0.271** -0.002 (0.138) (0.167) (0.138) (0.170) (0.133) (0.169) AvgIncomet−1 -0.004** -0.001 -0.004** -0.002 -0.004*** -0.002 (0.002) (0.007) (0.002) (0.007) (0.001) (0.007) FiscalIncomet−1 0.874 -1.040 0.954 -1.126 1.006 -1.344 (1.197) (1.428) (1.189) (1.433) (1.181) (1.453) Employmentt−1 -0.953 -11.682** -1.802 -10.212* -2.728 -10.223* (5.875) (5.870) (5.659) (5.803) (5.179) (5.817) Year Fixed Effect Yes Yes Yes Yes Yes Yes City Fixed Effect Yes Yes Yes Yes Yes Yes Politician Fixed Effect Yes Yes Yes Yes Yes Yes Observations 3,127 2,795 2,921 2,644 2,782 2,523 R-squared 0.853 0.685 0.851 0.676 0.857 0.671

Note. The regressions are estimated at city × year level. Data restricted to CDB city level loan to 310 cites from 1998 to 2010. Column 1 to 6 are the regressions on the logarithm of total CDB loan outstanding amount and total issuance respectively. P oliticianY ear is the number of years that the secretary has been staying in the city. ShortT erm is the dummy for whether the secretaries expect their terms to be less or equal than 2 years when they begin their terms. Y ounger is the dummy for whether the city secretaries are younger than 50 years old when they begin the terms. Relation is the dummy for whether the city secretaries are from the same place as the province governors. GDP , AvgIncome, F iscalIncome and Employment are city level GDP, income per capita, fiscal income and total employment respectively. All columns are controlled by year fixed effect, city fixed effect and politician personal fixed effect. Standard errors are clustered at city level.

68 Table A12 City Secretary’s Borrowing from CDB and City’s Debt Level

Total Loan Loan for Infrastructure Loan for Industry Firms (1) (2) (3) (4) (5) (6) Dependent Variable Log(Loan City) Log(Issuance City) Log(Loan City) Log(Issuance City) Log(Loan City) Log(Issuance City)

PoliticianYear -0.274*** -0.283*** -0.112*** -0.154*** -0.234*** -0.203*** (0.025) (0.039) (0.015) (0.029) (0.034) (0.053) Constrained 0.726*** 0.125 0.203* -0.039 0.806*** 0.243 (0.128) (0.179) (0.119) (0.218) (0.161) (0.250) PoliticianYear*Constrained -0.167*** -0.137*** -0.051 -0.041 -0.158*** -0.189*** (0.031) (0.048) (0.036) (0.060) (0.041) (0.067) GDPt−1 -0.311** -0.083 -0.234* 0.059 -0.205 -0.171 (0.142) (0.180) (0.126) (0.296) (0.146) (0.223) AvgIncomet−1 -0.004* -0.001 0.004 -0.000 -0.006** -0.005 (0.002) (0.007) (0.004) (0.007) (0.003) (0.007) FiscalIncomet−1 1.561 0.000 -0.181 -2.209 1.410 2.811 (1.178) (1.462) (0.856) (2.259) (1.303) (2.043) Employmentt−1 -2.665 -12.442** -2.304 3.272 -11.028* -31.956*** (5.189) (5.926) (5.299) (6.911) (6.127) (11.404) Year Fixed Effect Yes Yes Yes Yes Yes Yes City Fixed Effect Yes Yes Yes Yes Yes Yes Politician Fixed Effect Yes Yes Yes Yes Yes Yes Observations 3,127 2,795 2,449 2,227 2,727 2,323 R-squared 0.859 0.689 0.884 0.588 0.764 0.604

Note. Data restricted to CDB city level loan to 310 cites from 1998 to 2010. Table shows the results from OLS regression from equation 5.3. Column 1 and 2 are the regressions on the logarithm of total CDB loan outstanding amount and total issuance respectively. Column 3 and 4 are the regressions on the logarithm of CDB infrastructure loan outstanding amount and new issuance respectively. Column 5 and 6 are the regressions on the logarithm of CDB industry firm loan outstanding amount and new issuance respectively. P oliticianY ear is the number of years that the secretary has been staying in the city. Constrained is the dummy for whether CDB debt to GDP ratio is at the top quartile among 310 cities. GDP , AvgIncome, F iscalIncome and Employment are city level GDP, income per capita, fiscal income and total employment respectively. All columns are controlled by year fixed effect, city fixed effect and politician personal fixed effect. Standard errors are clustered at city level.

69 Figure 1: CDB Outstanding Loan Amount and New Issuance

CDB Total City Loan Amount CDB City Loan New Issuance 3000 1000 800 2000 600 Billion RMB Billion RMB 400 1000 200 0 0

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Year Year

Total Outstanding Loan Loan for Infrastructure Total Issuance Issuance for Infrastructure Loan for Industry Firms Issuance for Industry Firms

CDB Total Province Loan Amount CDB Province Loan New Issuance 2000 6000 1500 4000 1000 Billion RMB Billion RMB 2000 500 0 0

1997 1999 2001 2003 2005 2007 2009 2011 2013 1997 1999 2001 2003 2005 2007 2009 2011 2013 Year Year

Total Outstanding Loan Loan for Infrastructure Total Issuance Issuance for Infrastructure Loan for Industry Firms Issuance for Industry Firms

Ratio of Infrastructure Loan to Total Loan Amount Ratio of Industry Loan to Total Loan Amount 1 1 .8 .8 .6 .6 Ratio Ratio .4 .4 .2 .2 0 0

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Year Year

Top 25 Percentile Median Ratio Top 25 Percentile Median Ratio Bottom 25 Percentile Bottom 25 Percentile

Figure 1 includes the plots of CDB outstanding loan amount and new issuance. The loans can be separated into infrastructure loan and the loan for industry firms. Infrastructure includes transportation(e.g. road, railway, airport, bridge, tunnel), water supply, energy supply(e.g. gas,electric), telecommunication and public service(e.g. Sewage discharge) The top two panels are the loans at city level from 1998 to 2010. City level loan doesn’t include the province level projects even though part of the project is located in the city such as high way. The middle 2 panels are the loans at province level from 1998 to 2013. The unit is billion RMB. The bottom 2 panels are ratios of infrastructure loan and industry loan to the total city level loan amount respectively. The solid lines are median ratio among 310 cities and dash lines are top and bottom quartile of ratios among 310 cities each year.

70 Figure 2: Political Turnover in China

Histogram of City Secretaries' Tenure .4 .3 .2 Fraction .1 0 1 2 3 4 5 6 7 8 9 10 Length of Tenure

Histogram of City Secretaries' Turnover Year .2 .15 .1 Fraction .05 0 1997 1999 2001 2003 2005 2007 2009 2011 2013 Turnover Year

City Secretaries' Turnover Year(Tenure is not 5 years) .2 .15 .1 Fraction .05 0 1997 1999 2001 2003 2005 2007 2009 2011 2013 Turnover Year

Figure 2 includes the plots of city level political turnover in China. The top panel is the histogram of city secretaries term length from 1997 to 2013 among 334 cities and 1,227 city secretaries. I round the monthly turnover data into year frequency by using June as the cutoff. 43% of the city secretaries leave the city in their fifth year. The middle panel is the histogram of the turnover year of all city secretaries from 1997 to 2013 among 334 cities and 1,227 city secretaries. The bottom panel is the histogram of the turnover year of the sub sample of city secretaries whose term is not 5 years. It is also from 1997 to 2013 among 306 cities and 689 city secretaries. The bottom penal shows that if the tenure is not 5 years (e.g, 4 or 6 years), the turnover usually happens in national turnover years.

71 Figure 3: Local Government Borrowing

City Secretary's Borrowing Pattern 5 .1 4 .05 3 2 PoliticianYear 0 1 LoanAmount w/o Fixed Effects 0 -.05 0 5 10 15 Cycle

PoliticianYear Loan Amount w/o Fixed Effects

Figure 3 is the plots of logarithm of CDB city total loan amounts after taking out the year fixed effects, city fixed effects and politician fixed effects. The horizontal axis is the cycle of city secretary turnover. There are 3 cycles in total and every cycle has 5 years. From 1998 to 2010, there are 3 national turnover cycles: 1997 to 2002, 2003 to 2007, and 2007 to 2012. The left vertical axis P olitianY ear is the number of years that city secretary has been staying in the city, which is from 1 to 5 years. For example, the first cycle (1 to 5 on horizontal axis) is from 1998 to 2002. I cluster the cities by P olitianY ear(1 to 5 years) from 1998 to 2002 and plot the average CDB city loan amounts for each bin of P olitianY ear. I do the same thing for the second cycle from 2003 to 2007 which is from 6 to 10 in the horizontal line. For the third cycle is from 2008 to 2010 which is from 11 to 15 in the horizontal line.

72 Figure 4: Manufacturing Firms in China

Number of Firms in Chinese Manufacturing Sector 300 200 100 Number of Firms(in thousand) 0

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Year

SOEs Number Private Firm Number Foreign Firm Number

Aggregate Assets in Chinese Manufacturing Sector 20 15 10 5 Aggregate Assets(Trillion RMB) 0

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Year

SOEs Asset Private Firm Asset Foreign Firm Asset

Figure 4 includes the plots of aggregate firm assets and firm numbers in manufacturing sector of China. The data is the Chi- nese Industry Census(CIC) from 1998 to 2009 which includes all the manufacturing firms with annual sales more than 5 million RMB($ 700,000). The top panel is the firm numbers of SOEs, private firms and foreign firms from 1998 to 2009. The unit is thousand. The bottom panel is the aggregate firm assets of SOEs, private firms and foreign firms from 1998 to 2009. The unit is trillion RMB.

73 Figure 5: SOEs’ Fixed Asset Patterns

Logarithm of Fixed Asset, High CDB Loan Logarithm of Fixed Asset, Low CDB Loan 9.5 9.4 9.2 9 9 8.8 8.5 8.6 8.4 8

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Year Year

Early Years(1 to 3) Later Years(4 to 6) Early Years(1 to 3) Later Years(4 to 6)

Logarithm of Fixed Asset, High Corp Tax Rate Logarithm of Fixed Asset, Low Corp Tax Rate 10 9.5 9.5 9 9 8.5 8.5 8 8

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Year Year

Early Years(1 to 3) Later Years(4 to 6) Early Years(1 to 3) Later Years(4 to 6)

Logarithm of Fixed Asset, Large Developed Land Logarithm of Fixed Asset, Small Developed Land 10 9.5 9.5 9 9 8.5 8.5 8 8

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Year Year

Early Years(1 to 3) Later Years(4 to 6) Early Years(1 to 3) Later Years(4 to 6)

Figure 5 includes the plots of patterns of average SOEs’ fixed assets from 1998 to 2009 in the cities where secretaries are in the early terms and late terms. The vertical axis is the average logarithm of SOEs’ fixed assets. The top 2 panels are stratified on CDB loan amount. The left panel is the pattern for high CDB industry loan cities and the right panel is the pattern for high CDB industry loan cities. ”high” and ”low” categories are defined as the top and bottom quartile of CDB city level outstanding industry loan amount distribution among 310 cities each year. The middle 2 panels are stratified on average effective corporate tax rate in the city. The left panel is the pattern for high effective corporate tax rate cities and the right panel is the pattern for low effective corporate tax rate cities. ”high” and ”low” categories are defined as the top and bottom quartile of average effective corporate tax rate distribution among 310 cities each year. The bottom 2 panels are stratified on city developed land amount. The left panel is the pattern for cities with high developed land and the right panel is the pattern for the cities with low developed land. ”high” and ”low” categories are defined as the top and bottom quartile of developed land amount distribution among 310 cities each year. The dash lines are for the cities where secretaries are in the early terms(1 to 3 year). The solid lines are for the cities where secretaries are in the late terms(4 to 6 year).

74 Figure 6: SOEs’ Employment Patterns

Logarithm of Employment, High CDB Loan Logarithm of Employment, Low CDB Loan 5.2 5.1 5 5 4.9 4.8 4.8 4.6 4.7 4.4 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Year Year

Early Years(1 to 3) Later Years(4 to 6) Early Years(1 to 3) Later Years(4 to 6)

Logarithm of Employment, High Corp Tax Rate Logarithm of Employment, Low Corp Tax Rate 5.2 5.2 5 5 4.8 4.8 4.6 4.6 4.4 4.4 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Year Year

Early Years(1 to 3) Later Years(4 to 6) Early Years(1 to 3) Later Years(4 to 6)

Logarithm of Employment, Large Developed Land Logarithm of Employment, Small Developed Land 5.2 5.2 5 5 4.8 4.8 4.6 4.6 4.4 4.4 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Year Year

Early Years(1 to 3) Later Years(4 to 6) Early Years(1 to 3) Later Years(4 to 6)

Figure 6 includes the plots of patterns of average SOEs’ number of employees from 1998 to 2009 in the cities where secre- taries are in the early terms and late terms. The vertical axis is the average logarithm of SOEs’ employee numbers. The top 2 panels are stratified on CDB loan amount. The left panel is the pattern for high CDB industry loan cities and the right panel is the pattern for high CDB industry loan cities. ”high” and ”low” categories are defined as the top and bottom quartile of CDB city level outstanding industry loan amount distribution among 310 cities each year. The middle 2 panels are stratified on average effective corporate tax rate in the city. The left panel is the pattern for high effective corporate tax rate cities and the right panel is the pattern for low effective corporate tax rate cities. ”high” and ”low” categories are defined as the top and bottom quartile of average effective corporate tax rate distribution among 310 cities each year. The bottom 2 panels are stratified on city developed land amount. The left panel is the pattern for cities with high developed land and the right panel is the pattern for the cities with low developed land. ”high” and ”low” categories are defined as the top and bottom quartile of developed land amount distribution among 310 cities each year. The dash lines are for the cities where secretaries are in the early terms(1 to 3 year). The solid lines are for the cities where secretaries are in the late terms(4 to 6 year).

75 Figure 7: Shifts of CDB Industry Loan Over Time

Figure 7 are plots of top 10 industries which have the biggest loan issuance from CDB. Data is restricted to CDB province level industry loan data. There are 41 manufacturing industries in total among 31 provinces in China. The top penal shows the CDB’s biggest 10 industries in 1998. The amount of each industry is the sum of all CDB loan issuance amounts from all 31 provinces in China. The bottom penal shows the CDB’s biggest 10 industries in 2010. The amount of each industry is the sum of all CDB loan issuance amounts from all 31 provinces in China. The unit is billion RMB.

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