New Evidence of Lease-Debt Relationship from China’s Interest Rate
Liberalization Reform: Estimation based on Difference-in-Differences
Model
Ting Yang
1
, Yanping Shi
2
and Ruohong Liu
3
1
North China University of Technology, School of Economics and Management, Beijing, China
2
University of International Business and Economics, School of International Trade and Economics, Beijing, China
3
University of International Business and Economics, Leasing Research Center, Beijing, China
Keywords: Finance Leases, Difference-in-Differences Model, Lease-Debt Relationship, Interest Rate Liberalization Reform.
Abstract: This paper examines the relationship of leases and debt in China using the interest rate liberalization reform
as an exogenous shock. The Difference-in-Differences results show that leases and debt are substitutes in
China. Specifically, compared with large-sized firms, small-sized firms increase more loans, especially long-
term loans, and decrease more leases after the lending-rate-floor reform. Moreover, the substitution relation-
ship of leases and debt applies to state-owned-enterprises instead of private enterprises. This paper provides
new evidence about the lease-debt relationship in emerging markets.
1 INTRODUCTION
The relationship of leases and debt has generated ex-
tensive debate in literature. Traditional financial the-
ories suggest that leases should substitute for debt be-
cause leases use up debt capacity (Beattie et al., 2000;
Yan, 2006). However, some theoretical models also
show that leases and debt can be complements. For
example, Lewis and Schallheim (1992) and Eisfeldt
and Rampini (2009) explain that leases can expand
debt capacity from the view of tax arbitrage and re-
possession ability. Therefore, the lease-debt relation-
ship has two possibilities theoretically, making em-
pirical tests necessary and important. But most previ-
ous empirical studies may suffer from an endogeneity
problem since the factors simultaneously affecting
leases and debt are hard to control. Moreover, exist-
ing empirical evidence mainly focuses on developed
countries, especially in the U.S. and Europe, while the
leasing markets in developing countries remain under
researched. In fact, during recent years, the leasing in-
dustries have been booming in several emerging mar-
kets, among which China is the most prevalent. From
2007 to 2016, China’s leasing market had a remarka-
ble growth. The leasing investment volume rose from
RMB 46 billion to RMB 1794 billion, with a com-
pound growth ratio of 50%. The international ranking
of China’s leasing investment volume rose from No.
27 to No. 2, second only to the U.S..
In this paper, we use the cancellation of the lend-
ing rate floor, a big step in the interest rate liberaliza-
tion reform in China, as an exogenous shock. In July
2013, China liberalized the lending rate floor, before
which the lending rate floor was 70 percent of the
benchmark lending rate. This reform intensified
banks’ competition for high-quality customers with
strong repayment ability (Obstfeld, 1994; He and
Wang, 2012). In order to win high-quality customers,
banks would cut the lending rate and thus earn lower
profits from these customers. To offset the lower
profits from high-quality customers, banks would of-
fer more loans for low-quality customers, who have
weaker bargaining power due to their poor repayment
ability. As a result, compared with high-quality cus-
tomers who have always been preferred by banks, the
loan availability for low-quality customers would in-
crease significantly after the lending-rate-floor re-
form.
The lending-rate-floor reform was decided by
People’s Bank of China, the central bank, and cannot
be affected by firms. So we regard this reform as a
natural experiment for the loan availability of low-
quality customers. We use the firm size to measure
the repayment ability and define large-sized firms as
high-quality and small-sized firms as low-quality. By
522
Yang, T., Shi, Y. and Liu, R.
New Evidence of Lease-Debt Relationship from China’s Interest Rate Liberalization Reform -Estimation Based on Difference-in-Differences Model.
DOI: 10.5220/0011751200003607
In Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology (ICPDI 2022), pages 522-525
ISBN: 978-989-758-620-0
Copyright
c
 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

taking small-sized firms as the treatment group and
the large-sized firms as the control group, we use the
Difference-in-Differences (DID) method to estimate
the lending-rate-floor reform’s impact on loans and
leases for the two groups.
This paper contributes to existing literature in
three aspects. First, as far as we know, this is the first
paper exploring the lease-debt relationship in China
using comprehensive hand-collected data of leasing.
Second, the DID method could solve the endogeneity
problem of leases and debt, and reach a more robust
conclusion. Third, this paper first gives advice that
leases can be used to address challenges of fintech.
2 DATA AND MODEL
Our sample includes all non-financial Chinese A-
share firms during the period 2007-2016. Data about
finance leases
1
is hand collected from the annual re-
ports of the listed firms, and data of other financial
variables comes from the China Stock Market and
Accounting Research (CSMAR) database. We delete
samples that lack relevant information about finance
leases and other variables. Finally we obtain 16386
firm-year observations. All the continuous variables
are winsorized at the 1 and 99 percentiles to reduce
outliers.
The baseline empirical specification is as follows:
𝐷𝑒𝑝
,
= 𝛽

+ 𝛽

𝐴𝑓𝑡𝑒𝑟_2013

∗𝑆𝑚𝑎𝑙𝑙−𝑠𝑖𝑧𝑒𝑑

+ 𝛽

ln

𝑎𝑠𝑠𝑒𝑡

,
+ 𝛽

𝐶𝑎𝑠ℎ_𝑓𝑙𝑜𝑤
,
+ 𝛽

𝐶𝑎𝑠ℎ_ℎ𝑜𝑙𝑑𝑖𝑛𝑔
,
+ 𝛽

𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒
,
+ 𝛽

𝑇𝑜𝑏𝑖𝑛𝑞
,
+ 𝛽

𝑇𝑎𝑥
,
+ 𝐹𝑖𝑟𝑚𝐹𝐸 + 𝑌𝑒𝑎𝑟𝐹𝐸
+ 𝜀
,
(1)
First, we explore the impact of the lending-rate-
floor reform on the loan availability for the treatment
group and the control group. The dependent variable
is Loans/total assets. After_2013 is a dummy variable
representing the lending-rate-floor reform, which
equals to 1 after 2013 and 0 otherwise. Small-sized is
a dummy representing the firm size, which equals to
1 for small-sized firms and 0 for large-sized firms.
We calculate every sample firm’s mean of asset size
before 2013, denoted as Mean_asset_2013. Firms
with the Mean_asset_2013 above the median level are
identified as large-sized firms, and firms with the
1
We ignore operating leases because they only account for a very small fraction, less than 10% in terms of total volume
(Zhang and Liu, 2020).
Mean_asset_2013 below the median level are identi-
fied as small-sized firms. The cross term Af-
ter_2013*Small-sized is the main independent varia-
ble, which captures the response difference between
the treatment group and the control group. It is worth
noting that Small-sized and After_2013 are not added
into Model (1) independently because they can be ab-
sorbed into the firm fixed effect and year fixed effect
respectively.
Next we explore the impact of the lending-rate-
floor reform on leases for the treatment group and the
control group. The dependent variables are Lease
dummy (which equals to 1 if the firm has finance
leases and 0 otherwise) and Lease assets/total assets.
Similarly, the cross term After_2013*Small-sized is
the main independent variable. Following Sharpe and
Nguyen (1995), we include a vector of control varia-
bles.
3 EMPIRICAL RESULTS
In Table 1, column (1)-(3) present the results regard-
ing the change of loans after the lending-rate-floor re-
form. The coefficients on After_2013*Small_sized
are significant and positive for total loans and long-
term loans, but not significant for short-term loans. So
we can conclude that compared with large-sized
firms, small-sized firms increase more long-term
loans instead of short-term loans significantly after
the lending-rate-floor reform.
Column (4)-(5) present the results regarding the
change of leases after the lending-rate-floor reform.
The coefficients on After_2013*Small_sized are sig-
nificant and negative in both columns, suggesting that
small-sized firms decrease more leases after the lend-
ing-rate-floor reform compared with large-sized
firms. Such results confirm the substitution relation-
ship of leases and loans. Combined with the result in
column (3), we can say that leases and long-term
loans are substitutes. This finding is consistent with
the results of Schallheim et al. (2013), which also
show that leases and long-term debt are substitutes.
Considering that state ownership would influence
Chinese firms’ leasing decisions (Zhang and Liu,
2020), we explore the change of loans and leases for
state-owned enterprises (SOEs) and private enter-
prises respectively and the results are presented in Ta-
ble 2. In column (1)-(3), we can see that loans and
New Evidence of Lease-Debt Relationship from China’s Interest Rate Liberalization Reform -Estimation Based on
Difference-in-Differences Model
523

Table 1: The change of loans and leases after the lending-rate-floor reform.
Total Loans/to-
tal assets
Short-term
L
oans/total assets
Long-term
L
oans/total assets
Lease
dummy
Lease assets/to-
tal assets
(
1
)
(
2
)
(
3
)
(
4
)
(
5
)
Af-
ter_2013*Small_sized
0.0121
***
-0.0011 0.0133
***
-0.0321
***
-0.0009
**
(
2.95
)
(
-0.31
)
(
4.84
)
(
-2.72
)
(
-2.52
)
L
n(asset) 0.0209
***
0.0012 0.0207
***
0.0332
***
0.0004
(
5.43
)
(
0.36
)
(
8.25
)
(
3.98
)
(
1.39
)
Cash_flow -0.1255
***
-0.0753
***
-0.0491
***
-0.0625
**
-0.0019
**
(
-8.28
)
(
-5.71
)
(
-5.32
)
(
-2.21
)
(
-2.22
)
Cash_holding -0.0812
***
-0.0729
***
-0.0090 0.0305 0.0013
*
(
-7.99
)
(
-8.43
)
(
-1.52
)
(
1.29
)
(
1.79
)
L
everage 0.2216
***
0.1552
***
0.0559
***
0.1246
***
0.0042
***
(
14.86
)
(
12.38
)
(
6.44
)
(
4.45
)
(
4.84
)
Tobin_q -0.0037
***
-0.0034
***
0.0000 -0.0034
**
-0.0001
*
(
-3.85
)
(
-4.22
)
(
0.06
)
(
-2.03
)
(
-1.91
)
Tax 0.0137
**
0.0053 0.0094
**
-0.0148 0.0002
(
2.52
)
(
1.12
)
(
2.40
)
(
-1.03
)
(
0.32
)
N
16386 16386 16386 16386 16386
adj.
R
2
0.744 0.690 0.679 0.469 0.423
Notes: T-values are in parenthesis, based on standard errors clustered by firm.
***
,
**
, and
*
denote significance at the 1%, 5%,
and 10% level, respectively. Firm fixed effect and year fixed effect are controlled (the same below).
Table 2. The change of loans and leases after the lending-rate-floor reform for SOEs and private enterprises.
SOEs Private enter
p
rises
Total loans/
total assets
Lease
dummy
Lease assets/
total assets
Total loans
/total assets
Lease
dummy
Lease assets/
total assets
(
1
)
(
2
)
(
3
)
(
4
)
(
5
)
(
6
)
After_2013 *Small_sized 0.0139
**
-0.0544
***
-0.0020
***
0.0089
*
-0.0091 0.0000
(
1.97
)
(
-2.64
)
(
-2.97
)
(
1.75
)
(
-0.64
)
(
0.06
)
L
n(asset) 0.0239
***
0.0451
***
0.0003 0.0178
***
0.0224
**
0.0003
(
4.07
)
(
2.99
)
(
0.62
)
(
3.60
)
(
2.36
)
(
1.07
)
Cash_flow -0.1642
***
-0.0324 -0.0014 -0.0971
***
-0.0560
*
-0.0018
*
(
-7.29
)
(
-0.60
)
(
-0.83
)
(
-4.89
)
(
-1.93
)
(
-1.94
)
Cash_holding -0.0557
***
-0.0229 0.0029
*
-0.0922
***
0.0310 0.0003
(
-2.86
)
(
-0.42
)
(
1.66
)
(
-7.39
)
(
1.37
)
(
0.41
)
L
everage 0.2274
***
0.1642
***
0.0072
***
0.2045
***
0.1023
***
0.0024
**
(
9.60
)
(
3.12
)
(
4.63
)
(
10.63
)
(
3.52
)
(
2.39
)
Tobin_q -0.0057
***
0.0029 -0.0000 -0.0016 -0.0038
**
-0.0001
*
(
-3.24
)
(
0.84
)
(
-0.03
)
(
-1.46
)
(
-2.20
)
(
-1.67
)
Tax 0.0101 -0.0443
**
-0.0005 0.0168
**
0.0120 0.0006
(
1.37
)
(
-2.09
)
(
-0.63
)
(
2.14
)
(
0.61
)
(
1.01
)
N
7255 7255 7255 9131 9131 9131
adj.
R
2
0.770 0.495 0.450 0.727 0.468 0.446
leases are substitutes for SOEs. However, the results
in column (4)-(6) suggest that compared with large-
sized private enterprises, small-sized private enter-
prises increase loans but do not decrease leases. This
implies that the substitution relationship of leases and
debt only applies to SOEs. A possible explanation is
that Chinese private enterprises suffer from financial
constraint, so they increase loans without cutting
leases.
4 ROBUSTNESS CHECK
We use two alternative measurement of leases for ro-
bustness check. The first is Lease assets/PPE, in line
with Sharpe and Nguyen (1995). The second is
SLB_dummy, which equals to 1 if the firm has a sale-
and-leaseback (SLB) transaction in year t and equals
to 0 otherwise. We choose SLB_dummy for two rea-
sons. First, SLB is the most representative leasing
ICPDI 2022 - International Conference on Public Management, Digital Economy and Internet Technology
524

transactions in China, which accounts for roughly
80% of the total leasing volume. Second, SLB could
satisfy the ceteris paribus condition, in which the as-
sets of the firm do not change because of the leasing
transaction (Schallheim et al., 2013). The robustness
check results are presented in Table 3. After changing
the measurements of leases, the coefficients on Af-
ter_2013*Small_sized are still negative and signifi-
cant, consistent with previous findings.
Table 3: Robustness check.
Lease assets/
PPE
SLB
dumm
y
(1) (2)
A
f
ter_2013*Small_sized -0.0020
*
-0.0187
**
(
-1.68
)
(
-2.36
)
L
n(asset) 0.0026
***
0.0155
***
(
2.73
)
(
3.03
)
Cash_flow -0.0082
***
-0.0329
(
-2.68
)
(
-1.64
)
Cash_holding 0.0039 0.0084
(
1.49
)
(
0.59
)
L
everage 0.0148
***
0.0459
***
(
4.99
)
(
2.73
)
Tobin_q -0.0001 0.0001
(
-0.39
)
(
0.06
)
Tax -0.0001 -0.0014
(
-0.09
)
(
-0.10
)
N
16386 16386
adj.
R
2
0.429 0.236
5 CONCLUSIONS
By taking the interest rate liberalization reform as an
exogenous shock, this paper uses DID method to ex-
plore the lease-debt relationship in China. Using com-
prehensive hand-collected data of leasing for Chinese
listed firms, we find that leases and loans, especially
long-term loans, are substitutes. The substitution
lease-debt relationship applies to SOEs instead of pri-
vate enterprises.
6 DECLARATION OF
COMPETING INTERESTS
The authors have declared no conflict of interests.
ACKNOWLEDGEMENTS
The authors are grateful to James Schallheim for
helpful comments and suggestions. This work was
supported by the research fund of North China Uni-
versity of Technology under #110051360002 and
Beijing Municipal Education Commission under
#110052972027/129.
REFERENCES
Beattie, V.A., Goodacre, A., Thomson, S., 2000. Operating
leases and the assessment of lease‐debt substitutability.
Journal of Banking and Finance, 24(3), 427-470.
Eisfeldt, A., Rampini, A., 2009. Leasing, ability to repos-
sess, and debt capacity, Review of Financial Studies.
22(4), 1621-1657.
He, D., Wang H., 2012. Dual-track interest rates and the
conduct of monetary policy in China, China Economic
Review. 23(4), 928-947.
Lewis, C.M., Schallheim, J.S., 1992. Are debt and leases
substitutes? Journal of Financial and Quantitative
Analysis. 27(4), 497-512.
Obstfeld, M, 1994. Risk-taking, global diversification and
growth, American Economic Review. 84(5), 1310-1329.
Schallheim, J., Wells, K., Whitby, R. J., 2013. Do leases
expand debt capacity? Journal of Corporate Finance.
23, 368-381.
Sharpe, S.A., Nguyen, H.H., 1995. Capital market imper-
fections and the incentive to lease. Journal of Financial
Economics, 39(2-3), 271-294.
Yan, A., 2006. Leasing and debt financing: substitutes or
complements. Journal of Financial and Quantitative
Analysis, 41(3), 709-731.
Zhang, S., Liu, C., 2020. State ownership and the structur-
ing of lease arrangements, Journal of Corporate Fi-
nance, https://doi.org/10.1016/j.jcorpfin.2020.101597.
New Evidence of Lease-Debt Relationship from China’s Interest Rate Liberalization Reform -Estimation Based on
Difference-in-Differences Model
525