Tài liệu Fixing Market Failures or Fixing Elections? Agricultural Credit in India pptx

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e¤ects. We observe decisions made by over 45,000 public sector banks, disbursing millions
of loans. Credit varies continuosly, adjusts quickly, and repayment rates are observable.
The combination of cross-sectional and time-series analysis represents a signi…cant
methodological improvement in tools used to identify electorally-motivated redistribution.
There are several reasons, unrelated to tactical distribution, that could explain a cross-
sectional relationship between electoral outcomes and redistribution. There are other
explanations, again unrelated to political goals, that could explain time-series variation.
However, none of these reasons could explain why we would observe a cross-sectional
relationship in election years, but not in o¤-election years.
A second substantive contribution of this paper is to identify the costs of tactical
redistribution. Perhaps the threat of upcoming elections simply causes politicians to
behave more closely in line with the public interest. For example, Akhmed Akhmedov
and Ekaterina V. Zhuravskaya (2004) demonstrate that politicians pay back wages prior
to elections. If political intervention simply shifts resources from one group to another,
but both groups use resources e¢ ciently, then reducing the scope for intervention has
implications for equity, but not aggregate output. On the other hand, if the targeted
credit is not productively employed, the costs of redistribution may be substantial. A
similar question can be asked about cycles: are observed spending booms squandered
on projects with little return, or are the funds put to good use? The answers to these
questions are essential to understanding whether tactical redistribution is merely a minor
cost of the democratic process, or is so costly that it may be desirable to substantially
circumscribe the latitude of governments to intervene in the economy.
I note two limitations to the data. First, the time panel of only 8 years is shorter
than would be ideal for estimating political cycles. This drawback is mitigated to some
extent by the fact that we observe elections in 19 states, which are not synchronized with
each other. Second, the credit data are observed at the administrative district level, while
electoral competition occurs at the smaller, constituency, level.
This paper proceeds as follows. In the next section, I brie‡y describe the context of
4
banking and politics in India, including the mechanisms by which politicians may in‡uence
banks. In Section 2.3, I discuss competing theories of political redistribution, and their
testable predictions. Section 3 develops the empirical strategy and presents the main
results of political capture. In Section 4, I establish that these political manipulations
are socially costly: increases in government agricultural credit do not a¤ect agricultural
output. Finally, Section 5 concludes.
2 The Indian Context and Redistribution
2.1 Banking in India
Government planning and regulation were key comp onents of India’s post-independence
development strategy, particularly in the …nancial sector. Three government policies stand
out. First and foremost, the government nationalized many private banks in 1969 and
1980. Second, both public and private banks were required to lend at least a certain
percentage of credit to agriculture and small-scale industry. Finally, a branch expansion
policy obliged banks to open four branches in unbanked locations for every branch opened
in a lo cation in which a bank was already present.
The three policies had a substantial e¤ect on India’s banking system, making it an
attractive target for government capture. The branch expansion policy increased the
scope of banking in India to a scale unique to its level of development: in 2000, India
had over 60,000 bank branches (both public and private), located in every district across
the country. Nationalized banks increased the availability of credit in rural areas and
for agricultural uses. Robin Burgess and Rohini Pande (2005), and Burgess, Pande,
and Grace Wong (2005) show that the redistributive nature of branch expansion led to a
substantial decline in poverty among India’s rural population. However, these government
policies also made public sector banks very attractive targets for capture: public banks did
not face hard budget constraints, were subject to political regulation, and were present
throughout India.
5
Formal …nancial institutions in India date back to the 18
th
century, with the founding
of the English Agency House in Calcutta and Bombay. Over the next century, presidency
banks, as well as foreign and private banks entered the Indian market. In 1935, the
presidency banks were merged to form the Imperial Bank of India, later renamed the State
Bank of India, which became and continues to be the largest bank in India. Following
independence, both public and private banks grew rapidly. By March 1, 1969, there were
almost 8,000 bank branches, approximately 31% of which were in government hands. In
April of 1969, the central government, to increase its control over the banking system,
nationalized the 14 largest private banks with deposits greater than Rs. 500 million.
These banks comprised 54% of the bank branches in India at the time. The rationale for
nationalization was given in the 1969 Bank Nationalization Act: “an institution such as
the banking system which touches and should touch the lives of millions has to be inspired
by a larger social purpose and has to subserve national priorities and objectives such as
rapid growth in agriculture, small industry and exports, raising of employment levels,
encouragement of new entrepreneurs and the development of the backward areas. For this
purpose it is necessary for the Government to take direct responsibility for extension and
diversi…cation of the banking services and for the working of a substantial part of the
banking system.”
2
In 1980, the government of India undertook a second wave of nationalization, by
taking control of all banks whose deposits were greater than Rs. 2 billion. Nationalized
banks remained corporate entities, retaining most of their sta¤, with the exception of
the board of directors, who were replaced by appointees of the government. The political
app ointments included representatives from the government, industry, agriculture, as well
as the public.
2
Quoted in Burgess and Pande (2005).
6
2.2 Politics in India
India has a federal structure, with both national and state assemblies. The constitution
requires that elections for both the state and national parliaments be held at …ve year
intervals, though elections are not synchronized. Most notably, the central government
can declare “President’s rule”and dissolve a state legislature, leading to early elections.
Although this is meant to occur only if the state government is nonfunctional, state
governments have been dismissed for political reasons as well. Additionally, as in other
parliamentary systems, if the ruling coalition loses control, early elections are held.
The Indian National Congress Party dominated b oth state and national politics from
the time of independence until the late 1980s. Since then, states have witnessed vibrant
political competition. In the period I study, 1992-1999, a dozen distinct parties were in
power, at various times in various states. The sample I use contains 32 separate elections
in 19 states. These elections are generally competitive: over half of the elections were
decided by margins of less than 10 percent.
State governments have broad powers to tax and spend, as well as regulate legal and
economic institutions. While members of state legislative assemblies (“MLAs”) lack for-
mal authority over banks, there are several means by which they can in‡uence them. First
and foremost, the ruling state government appoints members of the “State Level Bankers
Committees,” which coordinate lending policies and practices in each state, with a par-
ticular focus on lending to the “priority sector” (agriculture and small-scale industry).
3
The committees meet quarterly, and are composed of State Government politicians and
app ointees, public and private sector banks, and the Reserve Bank of India. The com-
mittees often set explicit targets for levels of credit to be delivered. Their membership
typically turns over when the state government changes. The committees are the most
direct channel for political in‡uence, and for this reason I focus on state, rather than
federal elections.
3
See for example, “Master Circular Priority Sector Lendings,” RPCD No. SP. BC. 37, dated Sept.
29, 2004, Reserve Bank of India.
7
Governments also directly in‡uence banks. John Harriss (1991) writes of villagers in
India in 1980: “It is widely believed by people in villages that if they hold out long enough,
debts incurred as a result of a failure to repay these loans will eventually be cancelled, as
they have been in the past (as they were, for example, after the state legislative assembly
elections in 1980.”
4
A former governor of the Reserve Bank of India has lamented that the
app ointment of board members to public sector banks is “highly politicized,” and that
board memb ers are often involved in credit decisions.
5
Nor are state politicians hesitant
to promise loans during elections. For example, the Financial Express reports:
Two main contenders in the Rajasthan assembly elections are talking about
economic well-being in order to muster votes. No wonder then that easier
bank loans for farmers, remunerative earnings from agriculture on a bumper
crop as well as uninterrupted power supply appear foremost in the manifestoes
of both the parties.
6
Dale W. Adams, Douglas H. Graham, and J.D. von Pischke (1984) describe why
agricultural credit is a particularly attractive lever for politicians to manipulate: the
bene…ts are transparent, while the costs are not. This makes it hard for opposition
politicians to criticize e¤orts by those in power.
Focusing on agricultural credit makes sense within the context of India, since the
majority of the Indian population is dependent on the agricultural sector. Agricultural
lending plays a substantial role in the Indian economy: in 1996, there were approximately
20 million agricultural loans, with an average size of Rs. 11,910 (ca. $220). Although
agricultural credit comprises only about 17% of the value of public sector banks’ loan
portfolios, its importance in the share of loans is large: approximately 40% of loans made
by public sector banks are agricultural loans.
7
4
p. 79, cited in Timothy J. Besley (1995), p. 2173.
5
Times of India, June 2, 1999.
6
Financial Express, November 30, 2003.
7
“Basic Statistical Returns,” Table 1.9, Reserve Bank of India, 1996.
8
The amount of agricultural credit lent by banks is orders of magnitude larger than the
amount of money spent on campaigns in India. Each legislative constituency receives, on
average, about Rs. 50 - 80 million in credit ($1-$1.6 million). While campaign spending
is di¢ cult to measure (campaign spending limits are di¢ cult to enforce, and money spent
without authorization of a candidate does not count against the sum), the level of legal
campaign limits is informative: b etween 1992 and 1999, the legal limit ranged from Rs.
50,000 (approximately US $1,000) to Rs. 700,000 (ca. $14,000), or less than 1% of the
amount of agricultural credit. (E. Sridharan (1999)).
2.3 Theories and Tests of Redistribution
2.3.1 Political Cycles
Theories of political cycles predict politicians manipulate policy tools around elections,
either to fool voters or to signal their ability. A large literature tests for cycles in …scal
and monteary variables. Min Shi and Jakob Svensson (2006), review the literature and
o¤er new evidence, …nding that …scal cycles are more pronounced in countries in which
institutions protecting property rights are weaker and voters are less informed.
The robust relationship between elections and budget de…cits need not, however, imply
that politicians behave opportunistically. Lower tax collection or increased spending
could di¤er systematically prior to elections for other reasons. Spending increases may be
attributable to the fact that politicians, who seek to implement programs, learn on the
job. On average, a year just before an election will have politicians with a longer tenure
than a year just after an election, since the politician will have served, at a minimum,
almost an entire term in o¢ ce.
These concerns are less applicable when studying agricultural credit. Political goals
should not a¤ect the amount of agricultural credit issued by public sector banks. The
most signi…cant factor in‡uencing farmers’agricultural credit needs is almost certainly
weather, which is inarguably out of the politicians’control. Second, because I focus on
9
state elections, the possibility that state-speci…c agricultural credit moves in response to
national economic shocks (such as interest rates or exchange rate adjustments) can be
ruled out.
Of course, if there are large cycles in state government spending in India, agricultural
credit could covary with elections for reasons unrelated to government interference in
banks. Stuti Khemani (2004) tests for political budget cycles in Indian states. She …nds
no evidence of political cycles in overall spending or de…cits. She does …nd evidence of
small decreases in excise tax revenue, as well as evidence of other minor …scal manipulation
prior to Indian state elections.
2.3.2 Politically Motivated Redistribution
The literature on targeted redistribution distinguishes betwen patronage, which invovles
rewarding supporters, and tactical redistribution, which is made to acheive electoral or
political goals (Avinash K. Dixit and John B. Londregan, 1996, Snyder, 1989, and Gary W.
Cox and Matthew D. McCubbins, 1986). “Patronage” invovles awarding areas in which
the ruling party enjoys more support a disproportionate amount of resources, irrespective
of electoral goals. “Tactical redistribution” predicts resource allocation will follow one
of two patterns: resources will be targeted towards “swing” districts, or politicians will
disproportionately reward their supporters.
Empirically distinguishing between the theoretical models is di¢ cult for several rea-
sons. Data on purely tactical spending is rarely readily available, and such spending
often does not vary much over time and space. Sample sizes may be small,
8
and without
8
Matz Dahlberg and Eva Johanssen (2002) study a grant project in Sweden, in which the incumbent
government enjoyed control over which constituencies received the grant. They …nd strong evidence that
money was targeted to districts in which swing voters were located. In contrast, Anne Case (2001),
examining an income redistribution program in Albania, …nds that the program favored areas in which
the majority party enjoyed greater support. Finally, Edward Miguel and Farhan Zaidi (2003) examine
the relationship between political support and educational spending in Ghana, and …nd no evidence of
targeted distribution of educational spending at the parliamentary level. The sample sizes are 115, 47,
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a panel dimension, it is di¢ cult to rule out the possibility that omitted variables, such as
per-capita income, drive results.
This work overcomes these problems: the sample size is large, 412 districts and 32
election cycles, allowing for district …xed-e¤ects. Most importantly, the cross-sectional
and time-series component taken together allow for a much more powerful test of both
political cycles and tactical redistribution. The political budget cycle literature predicts
that politicians and voters care more about allocation of resources prior to elections,
than in other periods. Thus, observed distortions, such as patronage, or targeting swing
districts, should be larger during election years than non-election years. This test thus has
the power to distinguish between models of patronage unrelated to electoral incentives,
and models that predict a positive relationship between support and redistribution simply
as a result of electoral incentives: the former would not vary with the electoral cycle,
while the latter would. While either cycles or cross-sectional variation could be caused by
reasons other than electorally-motivated manipulation, it is very unlikely that the cross-
sectional relationships would change over the electoral cycle for any reason other than
tactical redistribution.
3 Evidence
I begin with a brief description of the data (details are available in the data appendix),
and then develop the empirical strategies, and present results for p olitical lending cycles
and tactical targeting of credit.
3.1 Data
Unless otherwise indicated, the unit of observation in this section is the administrative
district, roughly similar to a U.S. county. The data, collected by the Reserve Bank of
India (“Basic Statistical Returns”) are aggregated at the district level, and published in
and 199 units, respectively.
11
“Banking Statistics.” This aggregation is based on every loan made by every bank in
India.
9
The main outcome of interest is credit, which is available only from 1992-1999, at the
district level, for 412 districts in 19 states, yielding 3,296 observations. The credit data
are recorded as of the end of the Indian …scal year, March 31. Table 1 gives summary
statistics. Election data for state legislative elections are available at the constituency level
from 1985-1999. These data, from the Election Commission of India, include the identity,
party a¢ liation, and share of votes won, for every candidate in a state election from 1985
to 1999. Electoral constituencies are typically smaller than districts: the median district
has nine electoral constituencies.
[TABLE 1 ABOUT HERE]
I measure political outcomes in a district by using the margin of victory of the in-
cumb ent ruling party.
10
All members of parties aligned with the majority coalition were
coded as “majority.”
11
Because credit data are observed at the district level, vote shares
are also aggregated to the district level. I use as a measure of ruling party strength, M
dt
;
the average margin of victory of the state ruling party in a district. The median district
has 9 legislative assembly constituencies.
There are two important limitations to this dataset. First, the time panel is relatively
short (8 years), which is not ideal for estimating a …ve-year cycle. I focus on standard
9
Banks were allowed to report loans smaller than Rs. 25,000 (ca. $625) in an aggregated fashion until
1999, at which point loans below Rs. 200,000 (ca. $5,000) were reported as aggregates.
10
If the majority party did not …eld a candidate, I de…ne the margin of victory for the majority party
to be the negative of the vote share of the winning candidate. If the majority party candidate ran
unopposed, I de…ne the margin of victory to be 100. If no party held a majority of the seats, the ruling
coalition is identi…ed from new reports in the Times of India.
11
The theoretical models of redistribution derived below were motivated by a two-party system. Wh ile
India has many parties, I am careful to code all members of the ruling coalition as Majority Party.
Moreover, Pradeep K. Chhibber and Ken Kollman (1998) document that while India often had more
than two parties at the national level, in local elections, the political system closely resembled a two-
party system.
12
panel estimation, using log credit as the dependent variable. A large share of agricultural
credit is short-term loans, with maturation of less than a year. The median and mean
rate of real agricultural credit growth for public banks is zero over the period studied. In
a previous version of this paper (available on request) I show that the results are robust
to estimation in changes, as well as to estimation in a dynamic panel setting, using the
GMM technique developed by Manuel Arellano and Stephen R. Bond (1991). I discuss
this concern in greater detail in the next section.
Second, the data are observed at the administrative district level, while electoral con-
stituencies are typically smaller than a district. Di¤erent methods of aggreation (described
below) yield very similar results. Indeed, the district level may be the appropriate level
of analysis, as the political committees that in‡uence credit meet at the district level.
Moreover, credit itself may cross constituency b oundaries: the district of Mumbai has 34
constituencies and 1,581 bank branches.
12
3.2 Political Cycle Results
3.2.1 The Amount of Credit
The simplest approach to test for temporal manipulation is to compare the amount of
credit issued in election years to the amount issued in non-election years. I include district
…xed-e¤ects to control for time-invariant characteristics in a district that a¤ect credit. The
Reserve Bank of India divides states in India into six regions. Region-year …xed e¤ects
(
rt
) control for macroeconomic ‡uctuations.
13
Finally, I include the average rainfall in
12
Matching credit data to constituencies would require substantial e¤ort. However, identifying credit
“leakages” outside the targeted c onstituen cy would allow a test of the electoral impact of additional
credit, using a methodology similar to Steven Levitt and James M. Snyder (1997). I leave this for future
research.
13
All results presented here are robust to using year, rather than region*year …xed e¤ects. State*year
…xed e¤ects would of course be collinear with the election variables. Results are also robust to including
or excluding rainfall, which is the only time-varying variable available at the district level. Finally, results
are robust to including a district-speci…c linear time trend.
13