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

• Author: HONG RU
• DOI: http://doi.org/10.1111/jofi.12585
• Published date: 01 February 2018
• Date: 01 February 2018

THE JOURNAL OF FINANCE •VOL. LXXIII, NO. 1 •FEBRUARY 2018

Government Credit, a Double-Edged Sword:

Evidence from the China Development Bank

HONG RU∗

ABSTRACT

Using proprietary data from the China Development Bank (CDB), this paper exam-

ines the effects of government credit on firm activities. Tracing the effects of govern-

ment credit across different levels of the supply chain, I find that CDB industrial loans

to state-owned enterprises (SOEs) crowd out private firms in the same industry but

crowd in private firms in downstream industries. On average, a $1 increase in CDB

SOE loans leads to a $0.20 decrease in private firms’ assets. Moreover, CDB infras-

tructure loans crowd in private firms. I use exogenous timing of municipal politicians’

turnover as an instrument for CDB credit flows.

GOVERNMENT-DIRECTED LENDING PROGRAMS are pervasive around the world and

are often justified as a way to support economic development.1A central ques-

tion in the debate on government credit is whether it crowds out or crowds in

private-sector activities. The theoretical literature suggests that government

credit can have many countervailing effects. On the one hand, government

credit that supports high social return projects such as infrastructure can have

positive spillover effects (e.g., Stiglitz (1993)). On the other hand, government

credit might crowd out more productive private-sector investments (e.g., King

and Levine (1993a,1993b)). Due mainly to data limitations, previous empirical

∗Hong Ru is with Nanyang Technological University. I am indebted to my advisors Nittai

Bergman, Andrey Malenko, Robert Townsend, and especially Antoinette Schoar for their invalu-

able guidance and encouragement. I thank Jean-Noel Barrot, Stephen Dimmock, Wei Jiang, Mark

Kritzman, Chen Lin, Deborah Lucas, Eric Maskin, Stewart Myers, Tran Ngoc-Khanh, Michael

Roberts (the Editor), Stephen Ross, Zheng Song, Richard Thakor, WeiWu, an associate editor, and

two anonymous referees. This paper benefited greatly from seminar participants at Cornell, HBS,

MIT, NTU, NUS, Olin, Rotman, SMU, Texas A&M, UIUC, and Wharton for insightful comments.

I thank the discussants and participants at the CFRC, SFS Cavalcade, SEFM, and TCGC confer-

ences. I am also grateful to Gao Jian. I thank YueWu and Endong Yang for their excellent research

assistance. I thank all anonymous local government officials in China for long and engaging dis-

cussions. I thank Haoyu Gao for help on the CBRC data, Nanyang Technological University for

financial support, and the China Development Bank access to its data. To my knowledge, there is

no financial or other conflict of interest relevant to the subject of this paper. All errors are my own.

1Lucas (2014) states that the total amount of credit supported by OECD (Organisation for

Economic Co-operation and Development) governments was recently estimated at several tens of

trillions euros, and Elliott (2011) states that in 2010 the U.S. government’s outstanding commit-

ments for loans and guarantees totaled approximately $2.3 trillion, which was roughly one-third

the size of the loans of all U.S. banks combined.

DOI: 10.1111/jofi.12585

275

276 The Journal of Finance R

studies have only been able to explore the net effects of these opposing forces

and revealed mixed evidence.

Using detailed data from China Development Bank (CDB) on different types

of government credit, in this paper, I aim to separate these countervailing

channels of government credit by tracing its effects across different levels of

the supply chain. The CDB is the largest policy bank in China and lends

mainly to state-owned enterprises (SOEs) in strategic industries (e.g., energy

and mining) and to local governments for infrastructure development. The

CDB loan data contain outstanding loan amounts and issuance amounts at the

province-industry level between 1998 and 2013.2

I document two main findings. First, CDB loans to SOEs crowd out private

firms in the same industry as indicated by decreases in asset investment, em-

ployment, and sales, but they crowd in private firms in downstream industries.

More efficient private firms in downstream industries can benefit significantly

more from CDB credit to upstream SOEs. Second, CDB loans to local gov-

ernment infrastructure projects have positive (crowding-in) effects on private

firms’ activities. By disentangling the different forces of government credit

in China, this analysis sheds light on the mixed results of previous studies

regarding the net effects of aggregate government credit.

To establish the causal effects of CDB credit on firm activities, I exploit

exogenous variation in CDB credit flows using predetermined political turnover

cycles of municipalities in China, which occur every five years on average. This

allows me to alleviate the concern that the CDB endogenously targets areas

with specific economic needs for credit. For example, the CDB may maximize

spillover effects by strategically lending to industries in which downstream

private firms have high growth potential. In particular, I use the predicted

timing of municipal government turnover as an instrument for CDB loans.

In China, city secretaries are appointed and typically serve a five-year term.3

Moreover, cities in China have their own five-year turnover cycles. This allows

me to exploit variation in different five-year cycles across cities. Instead of using

actual turnover cycles, I take the first year of the secretary in the previous

term and add five years to calculate the predicted first year of the current city

secretary.4These predetermined predicted municipal turnover cycles depend

solely on past information and hence are not affected by current economic

factors (e.g., local GDP, employment, and income).

I begin the analysis by regressing the amounts that cities borrow from the

CDB on the predicted turnover cycles. I find a “zig-zag” borrowing pattern in

most cities whereby city secretaries borrow significantly more from the CDB

during their predicted first year and decrease borrowing monotonically as their

2I categorize CDB loans into two groups: industrial loans to firms and loans to infrastructure

projects. Among CDB industrial loans to manufacturing firms, approximately 95% go to SOEs.

I consider CDB industrial loans as SOE loans in this paper. See the more detailed discussion in

Section II.A.

3In China, the political leader of a municipal government is called the Secretary of the Municipal

Committee of the Communist Party of China (CPC) (equivalent to a mayor in the United States).

4I follow the strategy in Shue and Townsend (2014) of using predicted cycles as instruments.

Government Credit, a Double-Edged Sword: 277

terms progress. Borrowing rises again in the predicted first year of the next city

secretary. On average, city secretaries reduce CDB outstanding loan amounts

by 15.4% each year during their tenure. This “zig-zag” borrowing cycle is driven

primarily by career concerns of city secretaries. In China, the promotion of

local politicians depends largely on their GDP performance (e.g., Li and Zhou

(2005)). To increase local GDP quickly over the short term, city secretaries tend

to borrow from the CDB and in turn invest as much and as early as possible

during their terms.

I next investigate the heterogeneous effects of CDB loans on the private

sector across different levels of the supply chain. First, I examine the effects

of CDB industrial SOE loans on private-sector firms in the same industry.

I use city-level turnover timing as an exogenous shock to CDB credit at the

province-industry level. In particular, I identify each city’s largest SOE indus-

try (i.e., focal industry), which does not change much over time.5I then interact

dummies of the predetermined focal industry in each city with its predicted

turnover cycle. Using these interactions as instruments for CDB province-

industry loan amounts, I perform two-stage least squares (2SLS) analysis.

If the city secretary is in an early year of the term, I consider this a shock

to the province-level CDB loans in the city’s focal industry. In the first-stage

regression, I find that the province-level CDB loan amount in an industry is

41.3% higher when the corresponding city secretary is in the first two years of

the term. In the second-stage regressions, I find that, consistent with the OLS

regression results, a 100% increase in CDB SOE loan amount to the focal in-

dustry leads to a decrease in the assets, employment, and sales of private firms

in the same focal industry and province of 2%, 1.7%, and 4.1%, respectively.

By contrast, increasing CDB SOE loans leads to increases in SOEs’ activities.

Second, I examine the effects of CDB SOE loans in upstream and downstream

industries. Using an input-output matrix for China, for each focal industry I

identify its downstream industries that source the majority of their inputs from

it. On average, each focal industry has 2.3 downstream industries. I find that a

100% increase in CDB loan amount to the focal industry leads to an increase in

the assets, sales, and sales per worker of downstream private firms in the same

province of 3.4%, 2.6%, and 2.6%, respectively. The evidence also suggests that

more efficient private firms capture significantly more benefits from these CDB

upstream industrial loans. In sum, although CDB industrial SOE loans crowd

out private firms in the same industry (i.e., the focal industry), they crowd in

private firms in downstream industries. These opposing effects could explain

the mixed empirical findings in previous studies that use aggregate data on

government credit.

For the exclusion condition of the instruments, I find that other channels

through which the city secretary may influence a city’s business (e.g., borrow-

ing from other banks, selling more land, requesting fiscal transfers, and better

enforcing tax treaties) are not correlated with the turnover cycles. In particular,

5The distribution of SOE industries across cities is predetermined mainly for historical reasons

and remains stable over time. See a detailed discussion in Section III.C.