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Politicians and Banks: Political Influences on Government-Owned Banks in Emerging Markets

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Government ownership of banks is very common in countries other than the United States. This paper provides cross-country, bank-level empirical evidence about political influences on these banks. It shows that government-owned banks increase their lending in election years relative to private banks. This effect is robust to controlling for country-specific macroeconomic and institutional factors as well as bank-specific factors. The increase in lending is about 11% of a government-owned bank's total loan portfolio or about 0.5% of the median country's GDP per election per government-owned bank.
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Journal of Financial Economics 77 (2005) 453–479
Politicians and banks: Political influences on
government-owned banks in emerging markets
$
I. Serdar Dinc-
University of Michigan Business School, 701 Tappan, Ann Arbor, MI 48109, USA
Received 1 August 2003; accepted 24 June 2004
Available online 26 April 2005
Abstract
Government ownership of banks is very common in countries other than the United States.
This paper provides cross-country, bank-level empirical evidence about political influences on
these banks. It shows that government-owned banks increase their lending in election years
relative to private banks. This effect is robust to controlling for country-specific
macroeconomic and institutional factors as well as bank-specific factors. The increase in
lending is about 11% of a government-owned bank’s total loan portfolio or about 0.5% of the
median country’s GDP per election per government-owned bank.
r2005 Elsevier B.V. All rights reserved.
JEL classification: G21; G32; D72; D73
Keywords: Corporate governance; Political economy; Corruption; State-owned enterprises; Electoral cycle
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www.elsevier.com/locate/jfec
0304-405X/$ - see front matter r2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.jfineco.2004.06.011
$
I thank the referee, Sugato Bhattacharyya, Mark Carey, Giovanni Dell’Ariccia, Mara Faccio,
Haizhou Huang, Simon Johnson, Han Kim, M.P. Narayanan, Charlotte Ostergaard, Francisco Perez-
Gonzalez, Manju Puri, Nejat Seyhun, Andrei Shleifer, Anjan Thakor, and Ayako Yasuda as well as
seminar participants at the American Finance Association, Chicago Federal Reserve, European Finance
Association, International Monetary Fund, Massachussets Institute of Technology, and University of
Michigan for many helpful comments. Craig Brown provided outstanding research assistance.
Fax: +1 734 764 2557.
E-mail address: dincs@umich.edu.
1. Introduction
Government ownership of banks is very common outside the United States.
1
When bank assets are directly controlled by the government, the government’s role
in finance is much broader than the regulation and enforcement functions to which it
is generally limited in the U.S. In any discussion of financial systems in countries
with government ownership of banks, therefore, it is imperative to take the
government’s control of financial resources into account.
It is commonly claimed that government ownership of banks facilitates the
financing of projects that private banks are unable or unwilling to finance,
particularly projects that could help economic development. However, La Porta et
al. (2002) document that government ownership of banks is associated with lower
subsequent economic growth and argue that politicians use government-owned
banks to further their own political goals. Barth et al. (1999) provide further
empirical evidence that government ownership of banks is associated with a low level
of financial development and Beck and Levine (2002) also fail to find any positive
effect of the government ownership of banks on growth. The negative effect on
development is not the only cost of government ownership of banks. Caprio and
Peria (2000) show that government ownership of banks is associated with a higher
likelihood of banking crises. These negative effects are likely to persist because
banking is one of the very few sectors in which privatization has made very few
inroads around the world, as discussed by Megginson and Netter (2001).
Despite the accumulation of empirical evidence on the magnitude of bank
ownership by the government and its negative effects, there has been no direct, cross-
country empirical evidence of politically motivated actions by these banks. Nor is the
literature that establishes the inefficiency of government-owned enterprises relative
to private firms likely to be very helpful in this regard. Although political influences
on government-owned enterprises have long been considered a major source of
inefficiency,
2
direct, cross-country evidence of political influence on government-
owned enterprises in nonfinancial sectors has been lacking as well. Moreover, the
problem of political influence will be greater at banks than at other government-
owned enterprises for several reasons. First, the asymmetric information between
lending banks and outsiders about the quality of a specific loan makes it easy to
disguise political motivation behind a loan. Second, revealing the costs of any
politically motivated loan can be deferred until the loan maturity. Third, while a
non-bank government-owned enterprise operates in a defined industry, which can
limit the politicians’ ability to transfer resources, banks operate across the whole
economy, providing politicians with more opportunity to channel funds. Finally, the
political elite can maintain and increase its power through the control of financial
resources more easily than open entry barriers in other sectors (Rajan and Zingales,
2003).
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1
La Porta et al. (2002) study the 10 largest banks in 92 countries and find that 42% of their assets are
controlled by the government-owned banks.
2
See Shleifer and Vishny (1994) for a theory and Shleifer (1998) for a general discussion.
I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479454
This paper studies a question that arises naturally from the government ownership
of banks: Given that politicians control the government, are the actions of these
banks motivated by political concerns? Elections, in particular, might tempt the
politicians in power to use the government-owned banks for political purposes.
Thus, do government-owned banks behave differently around elections? Do they
increase their lending in election years? This paper studies these questions by
comparing the actions of government-owned banks with the actions of private banks
around general elections in major emerging markets over the period 1994–2000.
This paper provides the first cross-country, bank-level evidence of politically
motivated lending at government-owned banks in emerging markets in the form of
increased lending in election years relative to private banks. The increase is robust to
controlling for macroeconomic factors and the level of development. Despite
differences in efficiency and objectives between private banks and government-
owned banks, the methodology used in this paper is able to isolate political
influences from other confounding factors by focusing on a political event.
Although government-owned banks increase their lending in election years, the
share of loans as a fraction of total assets is not any greater in government-owned
banks across the electoral cycle on average. In fact, perhaps more strikingly, the
share of government securities in bank assets is about 50% greater in government-
owned banks in emerging markets than it is in private banks. One of the main
arguments in favor of government ownership of banks has been their ability to
finance viable projects that private banks cannot or will not finance. Yet the evidence
suggests that government-owned banks in emerging markets finance the government
itself to a greater degree than do private banks.
The evidence provided here extends the insights from single-country studies on
banking. Clarke and Cull (2002) argue that governors who belonged to a fiscally
conservative party were more likely to privatize banks in Argentina. Sapienza (2004)
finds that the interest rates charged by government-owned banks in Italy reflect the
local power of the party that controls the bank. Mian (2003a) compares private and
government-owned banks in Pakistan and demonstrates the differences in incentives
and supervision.
More generally, Kane (1996) and Kroszner and Strahan (1999) study the role of
politics in designing bank regulation, while Brown and Dinc (2004) demonstrate that
the implementation of existing regulation is also politically driven. Perotti and von
Thadden (2003) show how the distribution of human and financial capital can affect
the emergence of bank or market dominance through the political process. Pagano
and Volpin (2004) examine the role of the electoral system in the level of minority
protection.
Several recent papers study the role of political connections in finance. Fisman
(2001) shows how the news about Suharto’s deteriorating health adversely affected
the value of firms with strong connections to him. Johnson and Mitton (2003)
demonstrate that capital controls in Malaysia provided rents to politically connected
firms. Faccio (2004) finds in a cross-country study that firms with political
connections have easier access to debt financing and enjoy lower taxation. Ramalho
(2003) shows that politically connected firms in Brazil lost value during the
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I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479 455
impeachment of then-president Collor in 1992. Faccio et al. (2004) demonstrate the
role of political connections in the government’s decision to rescue a financially
troubled company. The results in this paper show that politicians can reward their
allies and punish their opponents by using their influence on government-owned
banks.
The evidence provided in this paper has policy implications that go beyond
economic development and financial stability. For example, international institu-
tions, often led by the IMF, provide emergency funds to countries experiencing a
crisis. These funds tend to be conditional on certain monetary and fiscal restrictions,
often to prevent politicians from channeling them to political uses. Yet the financial
accounts of government-owned banks are rarely part of the government’s budget.
The evidence about the political influences on these banks indicates that monetary
and fiscal restrictions placed on the local politicians are unlikely to be sufficient.
The paper is organized as follows. The next section discusses the methodology.
Section 3 describes the data. The regression analysis is presented in Section 4,
and robustness checks are discussed in Section 5. Concluding remarks follow
in Section 6.
2. Methodology
There are three major issues to consider when isolating and studying politically
motivated actions by government-owned banks. First, an event that induces
politicians to use government-owned banks for their own political aims must be
identified. Second, myriad institutional differences across countries must be
controlled for. Third, previously documented differences in efficiencies between
government-owned and private enterprises must be accounted for so that the
politically motivated actions of government-owned banks can be distinguished from
other differences between these two types of banks.
The general elections that determine the head of government are events that could
motivate politicians to use government-owned banks to increase their chances of
reelection. This does not rule out any politically motivated actions by government-
owned banks at other times, but to the extent that the elections genuinely determine
the head of government, the intensity of politicians’ use of government-owned banks
will be correlated with the electoral cycle. There is a large literature on the effect of
the political economy in general and of the electoral cycle in particular on
macroeconomic factors
3
but this is the first cross-country study of electoral cycle
effects at the firm level, to the best of my knowledge.
Controlling for many institutional differences across countries requires a firm-
level, as opposed to a country-level, analysis. By comparing banks with each other in
the same country, it is possible to control for many institutional differences across
countries. As it is virtually impossible to account for all the institutional, historical,
legal, and political differences across countries in a country-level cross-sectional
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3
See Alesina et al. (1997),Drazen (2000), and Persson and Tabellini (1999, 2003) for recent surveys.
I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479456
regression analysis, a firm-level analysis prevents assigning a false significance to a
country-specific factor, such as geography, due to an omitted variable.
Unfortunately, a firm-level analysis that can control for these country-level
differences also has the potential to increase the problems related to the inefficiencies
of government-owned enterprises in general. A mere cross-sectional comparison of
government-owned banks with private banks might only reflect a multitude of
differences between government-owned enterprises and private firms. Instead, this
paper compares the actions of government-owned banks with those of private banks
over time in a panel regression framework. More specifically, it compares the
changes in the actions of government-owned banks with those of private banks
around elections relative to other years. This ‘difference-in-differences’ methodology
isolates the actions taken by government banks due to political motivations from
other differences that also exist between government banks and private banks in
other years. The time dimension also allows for the control of country-wide factors,
such as macroeconomic factors, that change over time.
Once the time-independent and time-variant country-specific factors are con-
trolled for, the cross-country nature of the analysis strengthens the tests. For
example, elections occur in different years in different countries. In fact, countries
have different election frequencies. This prevents a spurious correlation between the
election year and some other one-time event in the world economy.
Although the focus of this study is very different from the literature on electoral
cycles and macroeconomics, which studies the role of political actions in
macroeconomics and business cycles in particular, this study’s methodology is
similar in that it uses elections as events that motivate politicians. However, it is
different in that it employs a firm-level analysis, rather than a country-level analysis.
3. Data
Emerging markets and developed countries covered weekly by The Economist in
its data section form the starting sample. These countries are augmented by members
of the OECD. Since elections play a central role in the analysis, only countries that
have free or partially-free elections in the 1994–2000 sample period according to
Freedom House are included. Three countries that did not—China, Egypt, and
Indonesia—are dropped from the sample. The resulting initial sample contains 43
countries.
The ten largest banks in each country are identified based on their book value of
assets as of 1994. Central banks and investment banks are excluded. As Bankscope
Online might drop a bank two years after it ceases its operations or is acquired by
another bank, previous CD-ROM editions of Bankscope are used in the
identification problem to avoid survivorship bias. Bankscope carries data on only
eight and four banks for Finland and Iceland, respectively; all those banks are
included in the sample.
By far, the most time- and resource-consuming task was hand-collecting the data
on the ultimate ownership of each bank for each year. Past editions of Bankscope
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I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479 457
CD-ROMs and Factiva were used heavily in this process, complemented with other
hard copy and Internet sources. Ambiguities in the ownership data were further
checked with local practitioners. Following La Porta et al. (1999), the ultimate owner
of each bank is identified and a bank is classified as government-owned if the
government controls (directly or indirectly) at least 20% of the bank.
Table 1 reports the government ownership of banks as of 1994 and confirms that
government ownership of banks is very common: 39% of all the banks in the world
(163 out of 462) are at least 20%-owned by the government. This proportion is
higher in emerging markets: 47% (99 out of 210, including India and Taiwan) of
banks are government-owned at a 20% level or higher in emerging markets while
only 30% of banks (64 out of 212) are so classified in developed economies. Overall,
42% of all the bank-years in the sample represent banks controlled by the
government at the 20% level or higher. Government-owned banks include banks
owned by local governments as well as by the central government, with the former
being especially prevalent in the developed economies of Continental Europe.
Countries differ substantially in government ownership. For example, India and
Taiwan have no private banks among their ten largest banks in 1994 while Canada,
Denmark, Japan, the U.K., and the U.S. have no government-owned banks among
the ten largest banks. As discussed in the previous section, this paper’s methodology
essentially compares the behavior of government-owned banks to private banks in
the same country. Only countries with at least one bank of each ownership type are
included in the main regression analysis, so these seven countries are dropped from
the main analysis: The resulting sample contains 36 countries with 19 emerging
markets and 17 developed economies.
Table 2 reports the number of bank-years available for regression analysis. The
biggest loss of bank-years is due to mergers, acquisitions, and, to a lesser degree,
bank closings. If Bankscope continues to use the accounts of the surviving bank for
the new entity after a merger or acquisition, the surviving bank remains in the
sample. If Bankscope starts a new account for the new entity, all the banks involved
in that merger exit the sample (When the sample with replacement is constructed, as
detailed in the Section 5, the new entity typically rejoins the sample as a new bank).
On the other hand, the loss due to bank failures is relatively small, as the typical
result of a large bank failure is the government takeover of the failing bank (Brown
and Dinc, 2004). These banks continue their operations and remain in the sample as
long as their balance sheet data are available. These banks are classified as
government-owned after the takeover.
The second most important reduction in bank-years is simply due to missing data
for the years before a bank joins the sample. The lag structure used in the regression
analysis needs balance sheet data for two previous years. To avoid any possible
selection bias, banks are included based on the magnitude of their assets in 1994
whether or not Bankscope has balance sheet data for their fiscal 1992 and 1993. This
decrease in the number of bank-years available for the regression analysis is included
in the Missing Data row in Table 2 and reflected in the final size of different samples.
Unfortunately, no loan-level data exist for these banks; hence, the analysis in this
paper is based on bank balance sheets. Table 3 presents sample statistics for selected
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I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479458
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Table 1
Bank ownership around the world in 1994
The table gives the ownership structure of the ten largest banks by assets as of 1994. Private denotes
banks with government ownership of less than 20%. GovtBank denotes the banks that are owned, directly
or indirectly, by the government at least at a 20% level.
Private GovtBank Total
Emerging markets
Argentina 6 4 10
Brazil 6 4 10
Chile 9 1 10
Colombia 5 5 10
Czech Republic 5 5 10
Hungary 2 8 10
Israel 4 6 10
South Korea 5 5 10
Malaysia 7 3 10
Mexico 6 4 10
Peru 8 2 10
Philippines 8 2 10
Poland 1 9 10
Russia 7 3 10
Singapore 8 2 10
South Africa 7 3 10
Thailand 5 5 10
Turkey 5 5 10
Venezuela 7 3 10
Total 111 79 190
Developed economies
Australia 7 3 10
Austria 4 6 10
Belgium 8 2 10
Finland 5 3 8
France 8 2 10
Germany 6 4 10
Greece 5 5 10
Iceland 2 2 4
Ireland 8 2 10
Italy 5 5 10
Luxembourg 9 1 10
Netherlands 7 3 10
Norway 5 5 10
Portugal 3 7 10
Spain 5 5 10
Sweden 6 4 10
Switzerland 5 5 10
Total 98 64 162
Countries with only private or government banks among ten largest banks in 1994
Emerging markets
India 0 10 10
Taiwan 0 10 10
Total 0 20 20
I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479 459
balance sheet items and reveals some interesting differences between private banks
and government-owned banks, although the differences are not necessarily uniform
between emerging markets and developed economies. In terms of the book value of
assets, government-owned banks are about twice as large as private banks in
emerging markets, on average, but they are smaller in developed economies. These
differences are statistically significant at the 1% level.
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Table 1 (continued )
Private GovtBank Total
Developed economies
Canada 10 0 10
Denmark 10 0 10
Japan 10 0 10
UK 10 0 10
USA 10 0 10
Total 50 0 50
Total (whole sample) 259 163 422
Table 2
The sample
The table gives the number of banks and bank-years available for regression analysis. The sample
constructed with the ten largest banks in 1994 in each country that had at least one private and one
government-owned bank among the ten largest banks in 1994. Each bank joins the sample in 1994 and is
followed until it exits or until the end of 2000. Unbalanced Panel includes the banks that exit the sample
before 2000 due to mergers, acquisitions, or closings. Banks that are taken over by the government due to
their failure but that continue their operations under government management remain in the sample but
are classified as government-owned banks after the take-over. If no balance sheet data are available for the
two years before a bank joins the sample, the number of bank-years available for regression analysis
decreases due to the lagged variables used. This loss is included in the Missing Data row and reflected in
the final size of each panel.
World (36 countries) Emerging markets (19
countries)
Developed economies
(17 countries)
Bank Bank-year Bank Bank-year Bank Bank-year
Largest possible sample 360 2520 190 1330 170 1190
Ten banks in each country
for seven years
Lost due to fewer than ten
banks in Finland and
Iceland
856— 856
Lost due to mergers,
acquisitions, closings
— 296 — 170 — 126
Missing data 110 1 93 2 17
Remaining (unbalanced)
panel
349 2058 189 1067 160 991
I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479460
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Table 3
Sample statistics
Private denotes the banks with government ownership less than 20%. GovtBank denotes the banks that are owned, directly or indirectly, by the government
at least at the 20% level. Change in Loans (t)isLoans (t)Loans (t1) and normalized by Assets (t1). Capital ratio is equity divided by total assets. All
variables are book values. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively, in a two-sided test of the mean with the
government-owned banks and the private banks.
Emerging markets Developed economies World
Private GovtBank All Private GovtBank All Private GovtBank All
Assets (in $B) Mean 8.688 15.465
***
11.355 79.000 49.552
***
68.629 43.708 30.935
***
38.935
sd. 11.785 19.672 15.724 119.917 72.265 106.511 91.995 53.533 80.046
N647 420 1067 642 349 991 1289 769 2058
Loans/assets Mean 0.564 0.548 0.558 0.519 0.549
**
0.530 0.542 0.548 0.544
sd. 0.161 0.204 0.179 0.203 0.221 0.210 0.184 0.212 0.195
N647 420 1067 642 349 991 1289 769 2058
Change in loans Mean 0.064 0.024
***
0.048 0.058 0.015
***
0.043 0.061 0.020
***
0.045
sd. 0.166 0.146 0.159 0.141 0.092 0.128 0.154 0.124 0.145
N649 420 1067 642 349 991 1289 769 2058
Treasury securities/assets Mean 0.091 0.133
***
0.108 0.117 0.114 0.116 0.103 0.125
***
0.111
sd. 0.088 0.136 0.111 0.111 0.101 0.108 0.1 0.123 0.11
N476 314 790 428 217 645 904 531 1435
Deposits/assets Mean 0.742 0.696
***
0.724 0.726 0.644
***
0.697 0.734 0.672
***
0.711
sd. 0.141 0.21 0.173 0.164 0.255 0.204 0.153 0.233 0.189
N644 414 1058 642 343 985 1286 757 2043
Operating income/assets Mean 0.016 0.004
***
0.012 0.008 0.004
***
0.007 0.012 0.004
***
0.009
sd. 0.028 0.047 0.037 0.009 0.009 0.009 0.021 0.035 0.028
N638 412 1050 628 343 971 1266 755 2021
Capital ratio Mean 0.101 0.095 0.098 0.052 0.051 0.051 0.076 0.075 0.076
sd. 0.072 0.097 0.083 0.026 0.046 0.034 0.059 0.081 0.068
N647 420 1067 642 349 991 1289 769 2058
I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479 461
The reverse pattern exists with regard to the ratio of loans to total assets. While
that ratio is lower for government-owned banks in emerging markets, it is higher in
government-owned banks in developed economies, with the latter difference being
statistically significant at the 5% level. Unfortunately, the data exist only at the bank
level; in particular, no data on the industrial or geographic distribution of these loans
are available.
The annual increase in loans relative to bank size is much higher in private banks
in both emerging markets and developed economies. In emerging markets, loans
grow by about three times as fast in private banks as in government banks, and
about 4 times as fast in developed economies. Both differences are statistically
significant at the 1% level.
Government-owned banks in emerging markets hold a larger share of their assets
in government securities. While private banks hold only 9% of their assets in
government securities, this ratio is 13% for government-owned banks in those
countries, on average; the difference is statistically significant at the 1% level. The
government ownership of banks has sometimes been justified on the grounds that
such banks can finance private projects that create positive externalities for the whole
economy but are too large or unprofitable for private banks to finance. The evidence,
however, suggests that government-owned banks take, instead, a more active role in
financing the government itself relative to private banks.
The ratio of deposits to total assets is lower in government-owned banks in both
emerging markets and developed economies, with the difference being statistically
significant at the 1% level. Annual net operating income also tends to be lower in
government banks. The ratio of income to assets is about 0.4% in government-
owned banks in both emerging and developed markets while it is 1.6% and 0.8% in
private banks in emerging markets and developed economies, respectively. The
difference is statistically significant at the 1% level. On the other hand, there is no
statistically significant difference in the capital ratio, defined as total equity
divided by total assets, of both types of banks. The differences documented
here between private and government-owned banks are, in general, consistent with
Mian (2003b).
The analysis also requires the collection of political data. It is first determined
whether the president or the prime minister is the head of government from
the constitution of each country, as provided in Maddex (2001). Then, the
dates of all the elections that decided the head of government during the sample
period are recorded using the Europa Yearbook,World Political Almanac, and
Elections around the World. Macroeconomic variables are obtained from IMF and
other sources. A detailed description of all the variables and their sources is provided
in the appendix.
4. Regression analysis
As discussed in the methodology section, the analysis compares changes in the
actions of government banks around elections with changes in the actions of private
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I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479462
banks during the same period, controlling for country-level macroeconomic factors
as well as bank-specific factors. Towards this aim, the analysis uses panel regressions
covering the years 1994–2000.
One factor that complicates the econometrics of the analysis is that loans in a
given year will affect the bank-specific factors of future years. In other words, the
dependent variable for a given year—increase in loans—will be correlated with the
bank-specific control variables for future years. For example, as an accounting
matter, loans are part of bank assets, the typical measure of bank size. Hence, when
bank size is controlled for by bank assets, this measure includes loans that were made
in previous periods but had a maturity longer than a year. Furthermore, banks are
likely to adjust their capital ratio based on their past lending. The regression
structure given below takes that correlation into account:
yit ¼b0xit þc0wit1þelectionit þelectionitngovtbankit þytþaiþuit, (1)
where the dependent variable y
it
is the change in loans normalized by the
previous year’s assets, namely, (Loans(t)Loans (t1))/Total Assets(t1); x
it
is the
vector of strictly exogenous variables such as macroeconomic variables; w
it
is the vector of sequentially exogenous variables such as bank size and bank
capital ratio; election
it
is a dummy variable that takes the value of one if it is an
election year in the country of bank i;govtbank
it
is a dummy variable that takes the
value of one if bank iis controlled by the government at least at a 20% level; ytis a
time dummy; aiis the bank fixed effect; and u
it
is the error term. The error structure
is given by
E½uit jxi1;...;xiT ¼0(2)
and
E½uit jwi1;...;wit1¼0. (3)
Notice that the error structure makes explicit the correlation between sequentially
exogenous variables with future error terms, as required. All the regressions
include bank fixed effects, which help control for time-independent differences
between government-owned banks and private banks as well as country-
specific time-independent factors. Due to sequentially exogenous variables, the
usual within estimator, which relies on subtracting the (time-series) means of
variables to eliminate the fixed effect, gives inconsistent estimates. Hence, the
fixed effects are eliminated by first differencing and the resulting system is
estimated by using the past values of sequentially exogenous variables as
instruments.
4
Finally, the standard errors are corrected for clustering at the
country level—hence, at the bank level as well—to prevent possible bias in the
standard errors while providing errors robust to bank-level autocorrelation; see
Bertrand et al. (2004).
Main regressions use the unbalanced sample, which follows all the banks until
2000 or their early exit from the sample, and are reported in Table 4. The dependent
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4
See, e.g., Wooldridge (2002, pp. 299–307) for a textbook treatment.
I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479 463
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Table 4
Elections and bank lending
The dependent variable is the increase in the total loans that year normalized by total assets from the previous year, i.e., (Loans(t)Loans (t1))/Total
Assets(t1). Total Assets/GDP is the bank’s total assets normalized by that country’s GDP; Capital Ratio is total equity divided by total assets; both variables
are as of year t1 and instrumented with their lagged values (t2). Election is a dummy variable that equals one in the year of elections; Govtbank is a dummy
variable that equals one if the bank is owned, directly or indirectly, by the government at least at the 20% level that year. Heteroskedasticity-robust standard
errors, corrected for clustering at the country level, are in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively.
F-test is a statistic to test the hypothesis that all the explanatory variables are jointly zero.
World Emerging markets Developed economies
Total Assets/GDP 0.000 0.001 0.000 0.001
*
0.001 0.001
*
0.081 0.037 0.036
(0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (1.259) (1.153) (1.148)
Capital Ratio 2.696
*
2.688
*
2.693
*
0.100 0.089 0.112 6.385
***
6.416
***
6.417
***
(1.524) (1.528) (1.525) (0.399) (0.399) (0.387) (0.527) (0.490) (0.491)
Election 0.009 0.020
*
0.009 0.031
*
0.015 0.013
(0.008) (0.010) (0.014) (0.015) (0.011) (0.015)
ElectionGovtBank 0.027
*
0.055
**
0.005
(0.015) (0.023) (0.023)
Ln (GDP per capita) 0.244
**
0.254
***
0.251
***
0.337
***
0.346
***
0.342
***
0.303 0.332
*
0.333
*
(0.094) (0.092) (0.092) (0.106) (0.100) (0.100) (0.191) (0.180) (0.182)
Bank fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes
Number of banks 349 349 349 189 189 189 160 160 160
Number of bank-years 2058 2058 2058 1067 1067 1067 991 991 991
p-value of F-test 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479464
variable is the change in loans normalized by the previous year’s total assets. All the
regressions include as explanatory variables Total Assets/GDP, equal to total assets
of the bank normalized by the GDP of the country where the bank operates to
control for bank size, and Capital Ratio as defined by the book value of equity
divided by total assets. Both variables are as of year t1 and assumed to be only
sequentially exogenous; all other explanatory variables are assumed to be strictly
exogenous and are as of year t.
The regressions are first performed for the whole sample, then for emerging
markets and developed economies separately. The size variable Total Assets/GDP
has a negative but statistically insignificant coefficient in the regressions for the
whole sample. Capital Ratio has a positive coefficient and it is statistically significant
in the regressions for the whole sample. This suggests that better-capitalized banks
increase their lending more.
The second regression includes Election, a dummy variable that equals one in
election years in the country where the bank is located; it is common to all the banks
in that country regardless of bank ownership. It has a negative and statistically
insignificant coefficient in the second regression. In other words, there seem to be no
economy-wide shocks related to elections with a common effect to all the banks. This
finding will strengthen the interpretation of any election effect due to the government
ownership of banks.
The third regression adds an interaction term ElectionGovtBank, where
GovtBank is a dummy variable that equals one if the bank is at least 20%-owned
by the government that year. If government-owned banks act differently in election
years, this interaction term can capture those differences. The interaction term has a
positive and statistically significant coefficient for whole sample, suggesting that
government-owned banks increase their lending in election years more than
private banks. However, when the sample is split between emerging markets
and developed economies, the regression results show that this finding is
driven mainly by government-owned banks in emerging markets. The interaction
term ElectionGovtBank has a positive and statistically significant coefficient for
emerging markets but has a negative and insignificant coefficient for developed
economies, although the negative sign of the interaction variable for developed
economies is not very robust and changes to positive in regressions with different
control variables.
Notice that all the regressions include bank fixed effects, which control for all the
time-independent differences between private banks and government-owned banks,
so the differences related to election years are unlikely to be due to the general
differences between private enterprises and government-owned enterprises in
operating efficiency or objectives. Bank fixed effects naturally control for
institutional differences across countries as well.
The rest of the paper focuses on the emerging markets to test the robustness of the
finding that government-owned banks in these countries increase their lending in
election years relative to private banks. Possible reasons for the differences in the
government bank behavior between emerging markets and developed economies are
discussed in the concluding section.
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I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479 465
5. Robustness
This section studies the robustness of the finding of increased lending in election
year by government-owned banks in emerging markets. As no such effect is detected
in developed economies, the tests in this section focus on emerging markets.
5.1. Macroeconomic factors
Given the literature on political macroeconomics, it is important to study the
robustness of the results to potential macroeconomic changes in election years. Five
different macroeconomic variables are studied: GDP per capita, GDP growth rate,
government budget surplus (or deficit), inflation rate, and exchange rate. Table 5,
Panel A, reports the results of regressions when macroeconomic variables are
included. Ln (GDP per capita) and GDP Growth both have positive and statistically
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Table 5
Elections and bank lending in emerging markets: controlling for macroeconomic factors
The dependent variable is the increase in the total loans that year normalized by total assets from the
previous year, i.e., (Loans(t)Loans (t1))/Total Assets(t1). Total Assets/GDP is the bank’s total assets
normalized by that country’s GDP; Capital Ratio is total equity divided by total assets; both variables are
as of year t1 and instrumented with their lagged values (t2). Election is a dummy variable that equals
one in the year of elections; Govtbank is a dummy variable that equals one if the bank is owned, directly or
indirectly, by the government at least at the 20% level that year. Budget surplus is the government budget
surplus as a percentage of GDP and takes a negative value when the government runs a deficit. Exchange
rate change is the change in the exchange rate of the domestic currency against the U.S. dollar from the
previous year; it is negative if the currency depreciates against the dollar that year. Heteroskedasticity-
robust standard errors, corrected for clustering at the country level, are in parentheses. *, **, and ***
denote statistical significance at the 10%, 5%, and 1% level, respectively. F-test is a statistic to test the
hypothesis that all the explanatory variables are jointly zero.
Panel A. Macroeconomic variables
Total assets/GDP 0.001
*
0.000 0.001 0.094 0.000
(0.001) (0.000) (0.001) (0.118) (0.001)
Capital ratio 0.112 0.158 0.249 0.126 0.146
(0.387) (0.328) (0.274) (0.332) (0.334)
Election 0.031
*
0.024 0.008 0.015 0.020
(0.015) (0.015) (0.020) (0.018) (0.018)
ElectionGovtbank 0.055
**
0.057
**
0.048
**
0.057
**
0.058
**
(0.023) (0.025) (0.022) (0.025) (0.025)
Ln (Gdp per capita) 0.342
***
(0.100)
GDP growth 0.009
***
(0.002)
Budget surplus 0.873
*
(0.460)
Inflation rate 0.042
(0.155)
Exchange rate change 0.015***
(0.004)
I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479466
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Table 5 (continued )
Panel A. Macroeconomic variables
Bank fixed effects Yes Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes Yes
Number of banks 189 189 185 189 189
Number of bank-years 1067 1067 988 1067 1061
p-value of F-test 0.000 0.000 0.000 0.000 0.000
Panel B. Macroeconomic variables interacted with the Election dummy
Total assets/GDP 0.001 0.000 0.001 0.162 0.001
(0.001) (0.000) (0.001) (0.157) (0.001)
Capital ratio 0.111 0.155 0.257 0.249 0.132
(0.385) (0.328) (0.276) (0.367) (0.331)
Election 0.176 0.027 0.004 0.033** 2.142***
(0.142) (0.019) (0.022) (0.016) (0.709)
ElectionGovtbank 0.055** 0.056** 0.050** 0.056** 0.062**
(0.023) (0.025) (0.023) (0.025) (0.022)
Ln (Gdp per capita) 0.340***
(0.099)
Ln (Gdp per capita)Election 0.017
(0.016)
GDP growth 0.008***
(0.002)
GDP growthElection 0.001
(0.004)
Budget surplus 0.868*
(0.452)
Budget surplusElection 0.220
(0.435)
Inflation rate 0.123
(0.107)
Inflation rateElection 0.462***
(0.149)
Exchange rate change 0.092**
(0.035)
Exchange rate change
Election 0.081**
(0.033)
Bank fixed effects Yes Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes Yes
Number of banks 189 189 185 189 189
Number of bank-years 1067 1067 988 1067 1061
p-value of F-test 0.000 0.000 0.000 0.000 0.000
Panel C. Macroeconomic variables interacted with government ownership
Total assets/GDP 0.001* 0.000 0.001 0.086 0.000
(0.001) (0.000) (0.001) (0.206) (0.001)
Capital ratio 0.119 0.138 0.254 0.127 0.137
(0.384) (0.321) (0.280) (0.332) (0.331)
I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479 467
significant coefficients, which is consistent with banks increasing their lending with
economic development and growth. Budget Surplus has a positive and significant
coefficient, which suggests that banks increase their loans when the government does
not have a deficit to finance. Exchange Rate also has a positive and statistically
significant coefficient, which suggests that banks increase their lending as the local
currency appreciates. Inflation, however, does not have a statistically significant
coefficient. On the other hand, the coefficient of the interaction term Elec-
tionGovtBank remains positive and statistically significant at the 5% level, which
indicates that the increased lending by the government banks in election years is
robust to controlling for macroeconomic factors.
It is possible that macroeconomic variables have a different effect in election years.
Regressions are repeated with the macroeconomic variables interacted with the
Election dummy variable. The results are reported in Table 5, Panel B. The
coefficient of the interaction term ElectionGovtBank is again positive and
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Table 5 (continued )
Panel C. Macroeconomic variables interacted with government ownership
Election 0.032** 0.025 0.007 0.015 0.021
(0.015) (0.015) (0.020) (0.018) (0.018)
ElectionGovtbank 0.057** 0.058** 0.045* 0.057** 0.060**
(0.023) (0.025) (0.022) (0.025) (0.025)
Ln (Gdp per capita) 0.342***
(0.100)
Ln (Gdp per capita)Govtbank 0.003
(0.004)
GDP growth 0.010***
(0.003)
GDP growthGovtbank 0.004
(0.002)
Budget surplus 1.001*
(0.549)
Budget surplusGovtbank 0.410
(0.485)
Inflation rate 0.042
(0.155)
Inflation rateGovtbank 0.006
(0.009)
Exchange rate change 0.015***
(0.004)
Exchange rate change
Govtbank 0.001
(0.014)
Bank fixed effects Yes Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes Yes
Number of banks 189 189 185 189 189
Number of bank-years 1067 1067 988 1067 1061
p-value of F-test 0.000 0.000 0.000 0.000 0.000
I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479468
statistically significant at the 5% level, which indicates that increased lending by
government-owned banks is not just a reflection of macroeconomic variables having
different effects in election years.
Finally, it is also desirable to verify that the results reported in the previous section
are not just a reflection of different responses by government banks to common
macroeconomic shocks that are correlated with the electoral cycle. Macroeconomic
variables interacted with the GovtBank dummy variable are included in the
regressions. If election-year lending increases are just a reflection of a different
response by government banks to common macroeconomic shocks, the interactions
of macroeconomic variables with the GovtBank dummy variable would have a
significant coefficient while the coefficient of ElectionGovtBank would be
insignificant. The results are reported in Table 5, Panel C. The coefficient of the
interaction term ElectionGovtBank is still positive and statistically significant at the
10% level or better. Hence, increased lending by government-owned banks in
election years does not appear to be merely a reflection of macroeconomic factors
but instead represents a secular increase in lending by these banks.
5.2. Different slopes for government-owned banks
Bank fixed effects control for the difference in the levels between private banks and
government banks. However, the main variable of interest is the interaction term
ElectionGovtBank, which effectively allows the Election dummy variable to have a
different slope for government banks. Since bank fixed effects cannot capture
differences in slopes, one concern is whether the ElectionGovtBank interaction term
is capturing these differences as it is the only variable allowed to have a different
slope for government banks.
To investigate this concern, each bank-level explanatory variable included in the
regressions in the previous section is allowed to have a different coefficient for
government banks. The results are reported in Table 6. Only Capital Ratio has a
statistically different (and negative) slope for government-owned banks, which
suggests that capitalization does not play as important role for these banks as for
private banks. However, ElectionGovtBank, the main variable of interest, continues
to have a positive coefficient and is statistically significant at the 5% level. In other
words, the results reported in the previous section do not reflect any different role of
size or capital ratio in the lending of government banks but instead indicate a secular
increase in the loans by government banks in election years.
5.3. Timing of elections
The main analysis takes the calendar year in which the elections take place as the
election year. However, if elections take place early in the calendar year, the election-
related increase in lending by government-owned banks might occur in the previous
calendar year. Ideally, we would need quarterly data on bank lending. Without those
data, we have to rely on different definitions of the ‘election year.’
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I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479 469
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Table 6
Elections and bank lending in emerging markets: controlling for different slopes
The dependent variable is the increase in the total loans that year normalized by total assets from the previous year, i.e., (Loans(t)Loans (t1))/Total
Assets(t1). Total Assets/GDP is the bank’s total assets normalized by that country’s GDP; Capital Ratio is total equity divided by total assets; both variables
are as of year t1 and instrumented with their lagged values (t2). Election is a dummy variable that equals one in the year of elections; Govtbank is a dummy
variable that equals one if the bank is owned, directly or indirectly, by the government at least at the 20% level that year. Heteroskedasticity-robust standard
errors, corrected for clustering at the country level, are in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively.
F-test is a statistic to test the hypothesis that all the explanatory variables are jointly zero.
Total assets/GDP 0.005 0.004 0.002 0.001 0.001 0.001
(0.003) (0.004) (0.004) (0.001) (0.001) (0.001)
(Total assets/GDP)Govtbank 0.004 0.004 0.001
(0.003) (0.003) (0.003)
Capital ratio 0.084 0.075 0.109 1.524
**
1.516
**
1.533
**
(0.408) (0.409) (0.396) (0.619) (0.617) (0.603)
Capital ratioGovtbank 1.790
***
1.788
***
1.782
***
(0.551) (0.554) (0.547)
Election 0.009 0.031
*
0.005 0.026
(0.014) (0.016) (0.014) (0.016)
ElectionGovtbank 0.055
**
0.051
**
(0.023) (0.024)
Ln (GDP per capita) 0.338
***
0.348
***
0.343
***
0.299
**
0.305
**
0.301
**
(0.107) (0.101) (0.101) (0.114) (0.109) (0.109)
Bank fixed effects Yes Yes Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes Yes Yes
Number of banks 189 189 189 189 189 189
Number of bank-years 1067 1067 1067 1067 1067 1067
Prob4F0.000 0.000 0.000 0.000 0.000 0.000
I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479470
The main regressions are first repeated for the April–March election year, which
defines year tas an election year if the elections take place between April of year t
and March of year t+1. The results are reported in the first two regressions of Table
7. The interaction term ElectionGovtBank continues to have a positive coefficient
and is statistically significant at the 5% level. The magnitude of this coefficient is
higher than that reported in Table 4 using the calendar year definition, which
suggests that this adjustment strengthens the results.
The main regressions are then repeated for the July–June election year, which
defines year tas an election year if the elections take place between July of year tand
June of year tþ1. This is a more important modification because more elections
take place in the second quarter of the year than in any other quarter. The results
are reported in the last two regressions of Table 7. The interaction term
ElectionGovtBank continues to have a positive coefficient and is statistically
significant at the 10% level. The magnitude of the coefficient is lower than the
calendar-year definition, however. This suggests that government banks concentrate
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Table 7
Elections and bank lending in emerging markets: timing of elections
The dependent variable is the increase in the total loans that year normalized by total assets from the
previous year, i.e., (Loans(t)Loans (t1))/Total Assets(t1). Total Assets/GDP is the bank’s total assets
normalized by that country’s GDP; Capital Ratio is total equity divided by total assets; both variables are
as of year t1 and instrumented with their lagged values (t2). Election is a dummy variable that equals
one in the year of elections; Govtbank is a dummy variable that equals one if the bank is owned, directly or
indirectly, by the government at least at the 20% level that year. With the April– March Election Year
convention, year tis an election year if the election takes place between April of year tand March of year
tþ1. With the July– June Election Year convention, year tis an election year if the election takes place
between July of year tand June of year tþ1. Heteroskedasticity-robust standard errors, corrected for
clustering at the country level, are in parentheses. *, **, and *** denote statistical significance at the 10%,
5%, and 1% level, respectively. F-test is a statistic to test the hypothesis that all the explanatory variables
are jointly zero.
April–March Election Year July–June Election Year
Total assets/GDP 0.001 0.001* 0.001
**
0.002
**
(0.001) (0.001) (0.001) (0.001)
Capital ratio 0.085 0.093 0.085 0.042
(0.396) (0.385) (0.400) (0.408)
Election 0.008 0.037
**
0.008 0.009
(0.012) (0.014) (0.013) (0.014)
ElectionGovtbank 0.070
**
0.040
*
(0.026) (0.023)
Ln (GDP per capita) 0.344
***
0.341
***
0.333
***
0.339
***
(0.103) (0.102) (0.103) (0.103)
Bank fixed effects Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes
Number of banks 189 189 189 189
Number of bank-years 1067 1067 1067 1067
p-value of F-test 0.000 0.000 0.000 0.000
I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479 471
their election-year lending fairly close to the elections. Although the politicians who
control the government banks do not need to wait until the campaign season to start
election-year lending, this result is consistent with accounts of political campaigns in
emerging markets suggesting that the campaign seasons in those countries are short
relative to the U.S. presidential elections.
5
5.4. Different samples
The analysis presented in the previous section uses the unbalanced panel, which
follows all the banks until 2000 or their early exit from the sample, with mergers and
acquisitions being the most important reason for an early exit. It is desirable to check
the robustness of these results to the sample construction.
A sample with replacement is constructed by replacing each exiting bank with the
largest bank that operates in the same country but is not already in the sample. This
procedure is repeated for every exiting bank except for those that survive through
1999 but exit before the end of their 2000 fiscal year; including a bank for only one
year would not allow a panel analysis. This method has the advantage of increasing
the sample size even though the theoretical limit is not attained because the lagged
variables in the regressions require data from the two years before a bank joins the
sample; those data are not always available. The main disadvantage of this method is
that it decreases the average number of years spent by each bank in the sample,
which, in turn, decreases the power of a panel analysis that relies on time-series
variation. The main regressions are repeated using this sample and reported as the
first three regressions of Table 8. The interaction term ElectionGovtBank again has
a positive coefficient but the p-value is only 0.13. This is probably due to the lower
power of the panel analysis in this sample. Indeed, when the analysis is repeated with
the balanced sample, the coefficient of ElectionGovtBank is even higher than the
one with the unbalanced panel used in Table 4 and is significant at the 5% level. This
balanced sample contains only the banks that survive to 2000 so its advantages and
disadvantages are exactly the opposite of those of the sample with replacement: the
sample size is smaller but the time series are longer on average.
The number of government-owned banks varies greatly from country to country
so the regressions are repeated with the same number of each type of bank for each
country. The five largest private banks and five largest government-owned banks as
of 1994 are selected. India and Taiwan are included in this sample. Not every
country had five banks of each type so the highest equal number of banks is chosen
for those countries. This method has the advantage of equal representation by each
type. Its main disadvantage is that some of the banks are much smaller, more
regional, and more specialized than the other banks from the same country. The
interaction term ElectionGovtBank again has a positive coefficient but has a p-value
of only 0.16. These tests suggest that the main findings are not driven by some banks
or country but the power of the differences-in-differences methodology used in the
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5
See Callahan (2000, pp. 19–37) for Thailand, Jomo (1996, p. 110) for Malaysia, and Bustani (2001) for
Brazil.
I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479472
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Table 8
Elections and bank lending in emerging markets: different samples
The dependent variable is the increase in the total loans that year normalized by total assets from the previous year, i.e., (Loans(t)Loans (t1))/Total
Assets(t1). Total Assets/GDP is the bank’s total assets normalized by that country’s GDP; Capital Ratio is total equity divided by total assets; both variables
are as of year t1 and instrumented with their lagged values (t2). Election is a dummy variable that equals one in the year of elections; Govtbank is a dummy
variable that equals one if the bank is owned, directly or indirectly, by the government at least at the 20% level that year. Sampling with Replacement replaces a
bank that exits the original sample before 2000 by a bank among the ten largest banks in that country in that year. Balanced Panel includes only banks that
remain in the original sample until 2000. The sample with the Same Number of Private and Government-Owned banks has five largest private banks and five
largest government-owned banks as of 1994; if a country does not have five private or government-owned bank, the highest equal number of banks are
included for that country. Heteroskedasticity-robust standard errors, corrected for clustering at the country level, are in parentheses. *, **, and *** denote
statistical significance at the 10%, 5%, and 1% level, respectively. Fis a statistic to test the hypothesis that all the explanatory variables are jointly zero.
Sampling with replacement Balanced panel Same number of private and government-owned bank
Total assets/GDP 0.001
**
0.001
**
0.001
**
0.001
***
0.001
*
0.001
**
0.001
*
0.001 0.001
*
(0.000) (0.001) (0.001) (0.000) (0.000) (0.001) (0.001) (0.001) (0.001)
Capital ratio 0.655 0.649 0.662 0.308 0.302 0.338 0.059 0.064 0.091
(0.577) (0.572) (0.568) (0.293) (0.292) (0.276) (0.341) (0.333) (0.319)
Election 0.005 0.020 0.013 0.043
***
0.004 0.015
(0.014) (0.017) (0.013) (0.011) (0.014) (0.019)
ElectionGovtbank 0.038 0.071
**
0.041
(0.024) (0.026) (0.028)
Ln (GDP per capita) 0.441
***
0.447
***
0.445
***
0.326
***
0.340
***
0.329
***
0.310
***
0.306
***
0.304
***
(0.091) (0.095) (0.095) (0.074) (0.071) (0.068) (0.097) (0.092) (0.091)
Bank fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes
Number of banks 231 231 231 135 135 135 156 156 156
Number of bank-years 1204 1204 1204 886 886 886 925 925 925
p-value of F-test 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479 473
analysis is weaker when the banks do not stay in the sample long enough or are not
very similar in size and scope.
5.5. Non-election years
Main regressions are repeated for the year immediately before and after the
elections. The results are reported in Table 9. The variables of interests are Pre-
election and Post-election, which are dummy variables that equal one in the year
preceding and following the elections, respectively. These variables, alone or when
interacted with the GovtBank dummy variable, do not have a statistically significant
coefficient. This implies that the election-year increase in government-owned banks
is not a reflection of a change that takes place in non-election years. In particular,
there is no evidence that private banks defer their lending until after elections due to
the uncertainties about the election results. That would imply an increase in the year
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Table 9
Bank lending in emerging markets: before and after elections
The dependent variable is the increase in the total loans that year normalized by total assets from the
previous year, i.e., (Loans(t)Loans (t1))/Total Assets(t1). Total Assets/GDP is the bank’s total assets
normalized by that country’s GDP; Capital Ratio is total equity divided by total assets; both variables are
as of year t1 and instrumented with their lagged values (t2). Pre_election and Post_election are dummy
variables that take 1 in the year preceding and following the elections, respectively. Govtbank is a dummy
variable that equals one if the bank is owned, directly or indirectly, by the government at least at the 20%
level that year. Heteroskedasticity-robust standard errors, corrected for clustering at the country level, are
in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. F-
test is a statistic to test the hypothesis that all the explanatory variables are jointly zero.
Pre-election Post-election
Total assets/GDP 0.001 0.001 0.006 0.006
(0.001) (0.001) (0.122) (0.122)
Capital ratio 0.105 0.127 0.159 0.151
(0.401) (0.395) (0.288) (0.289)
Pre-election 0.014 0.024
(0.012) (0.016)
Pre-electionGovtbank 0.021
(0.023)
Post-election 0.007 0.001
(0.012) (0.013)
Post-electionGovtbank 0.013
(0.019)
Ln (GDP per capita) 0.343
***
0.340
***
0.331*** 0.331
***
(0.104) (0.103) (0.103) (0.103)
Bank fixed effects Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes
Number of banks 189 189 189 189
Number of bank-years 1063 1063 1067 1067
p-value of F-test 0.000 0.000 0.000 0.000
I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479474
following the elections, but regressions 3 and 4 in Table 9 do not provide any
evidence of a post-election increase.
6. Conclusion
This paper provides empirical evidence about the political influences on
government-owned banks in major emerging markets in the 1990s. The paper
focuses on political events—elections—and studies their effects on bank lending
across both government-owned banks and private banks. By comparing the different
reactions of both types of bank to a political event, the analysis isolates political
influences from many other differences between private banks and government-
owned banks. It shows that government-owned banks increase their lending in
election years relative to private banks. These effects are robust to controlling for
macroeconomic and bank-specific factors. The results indicate that political
motivations influence the actions taken by government-owned banks and cannot
be attributed to other differences between private banks and government-owned
banks in efficiency and objective.
The results provided in this paper do not depend on the reasons why the
government owns banks in the first place. They are also independent from other (real
or perceived) benefits and costs of government ownership of banks, and from
macroeconomic factors that politicians might try to affect before the elections. While
the political influences on government-owned enterprises have long been thought to
be a potentially important source of distortion in the economy, these findings are the
first cross-country, firm-level evidence about the political influences on government-
owned enterprises, financial or otherwise. By demonstrating a channel through
which the negative effects of government ownership take place, this paper also
complements the findings in La Porta et al. (2002) about the association between
government ownership of banks and subsequent low economic growth in that
country.
Political influences documented in this paper also indicate how politicians can use
government-owned banks to distribute these rents to their supporters. This paper
can provide an estimate of political lending due to the elections. The election-year
lending increase per government-owned bank per election is about 11% of the total
loans of a government-owned bank, on average, or 0.5% of GDP of the median
country in 1996. However, it should be emphasized that this is very likely to be an
underestimate of the political influences on government-owned banks. First, the
analysis relies on the differences between election years and non-election years. To
the extent that politicians use their influence on these banks in non-election years,
our estimates are biased towards zero. Second, this paper focuses only on
government ownership but politicians can also use the power of government to
influence private banks. To the extent that politicians can also influence private
banks, our estimates of the differences between private and government-owned
banks are again biased towards zero. Quantifying the total cost of political influences
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I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479 475
on government-owned banks, which are rarely publicly traded, will be an important
future research topic.
The analysis fails to detect a similar election-year increase in developed economies.
While these countries often have better legal and political institutions, their
importance is not detected in an (unreported) regression analysis in which the
election-year effect in government-owned banks is interacted with measures of
institutional quality. Instead, the lack of an election-year effect in developed
economies could be due to several other factors. First, many government-owned
banks in developed countries are owned by regional or local governments and
operate locally. The private banks in those countries, on the other hand, are often
multinational banks. Hence, the power of our tests, which relies on the comparison
of private and government-owned banks, is likely to be diminished in developed
countries. Second, banks that are owned by the local governments in developed
countries would be more inclined to increase their lending not before national
elections but before local elections. Finally, most of the developed economies are
members of the European Union where there are also elections for the European
Parliament. While those elections may not be as important as the national elections,
they blur the differences between the years national elections take place and other
years.
The findings reported in this paper also have implications for studies on financial
systems and the role of banks. They demonstrate that the ownership of banks
matters in financial systems.They also suggest that the comparison of financial
systems in general and the role of banks in those systems in particular cannot be fully
understood without due regard to the political environment in which these financial
systems operate, as in Aoki (2002), who provides a general approach to comparative
institutional analysis that also incorporates the incentives of politicians and
bureaucrats.
Appendix. Data description
Variable Description
Ownership variables
GovtBank Dummy variable that is equal to one if a bank is
owned by the government, directly or indirectly, at
least at the 20% level. Data are collected for each
bank and for each year between 1994 and 2000
Sources: Bankscope Online,Bankscope CD-ROMs
(previous editions), Factiva, Internet sources, various
individual sources
Private Dummy variable that is equal to one if a bank is
owned by the government, directly or indirectly, at a
level less than 20% that year. Data are collected for
each bank and for each year between 1994 and 2000
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I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479476
Sources: Bankscope Online,Bankscope CD-ROMs
(previous editions), Factiva, Internet sources, various
individual sources
Balance sheet variables
Total Assets Total assets of a bank in that particular year
Sources: Bankscope Online,Bankscope CD-ROMs
(previous editions)
Total Loans Total loans of a bank in that particular year
Sources: Bankscope Online,Bankscope CD-ROMs
(previous editions)
Change in Loans Change in the total loans normalized by total assets
from the previous year, i.e., (Loans(t)Loans (t1))/
Total Assets(t1)
Treasury Securities Domestic Treasury bond and bill holdings of a bank
in that particular year
Sources: Bankscope Online,Bankscope CD-ROMs
(previous editions)
Total deposits Total deposits of a bank in that particular year
Sources: Bankscope Online,Bankscope CD-ROMs
(previous editions)
Operating Income Net operating income of a bank in that particular year
Sources: Bankscope Online,Bankscope CD-ROMs
(previous editions)
Capital ratio Equity divided by total assets of a bank in that
particular year
Sources: Bankscope Online,Bankscope CD-ROMs
(previous editions)
Election variables
Election Dummy variable that is equal to one if elections
that determine the head of government take place in
that country that year. Sources: Europa World Year
Book,CIA World Factbook,World Political Almanac,
and Elections Around The World
(www.electionworld.org)
Pre-Election Dummy variable that is equal to one if elections that
determine the head of government take place in that
country in the immediately following year. Sources:
Europa World Year Book,CIA World Factbook,
World Political Almanac, and Elections Around The
World (www.electionworld.org)
Post-Election Dummy variable that is equal to one if elections that
determine the head of government take place in that
country in the immediately preceding year. Sources:
Europa World Year Book,CIA World Factbook,
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I.S. Dinc-/ Journal of Financial Economics 77 (2005) 453–479 477
World Political Almanac, and Elections Around The
World (www.electionworld.org)
Macroeconomic variables
GDP per capita Gross Domestic Product (GDP) per capita in U.S.
dollars
Source: IMF International Financial Statistics
GDP Growth Gross Domestic Product (GDP) change (in percentage
points)
Source: IMF International Financial Statistics
Inflation rate Ln (1+Rate of wholesale price increase)
Source: IMF International Financial Statistics
Budget surplus Central government receipts minus government
outlays as a percentage of GDP (in percentage points).
It is negative when the government runs a deficit.
Sources: IMF International Financial Statistics,World
Bank, and Central Bank Sources.
Exchange rate change Change in the exchange rate of the domestic currency
against the U.S. dollar from the previous year; it is
negative if the currency depreciates against the dollar
that year
Source: IMF International Financial Statistics
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