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Journal of Development Studies,
Vol. 42, No. 4, 640–661, May 2006

The Political Determinants of Fiscal
Policies in the States of India:
An Empirical Investigation
KAUSIK CHAUDHURI* & SUGATO DASGUPTA**
*Indira Gandhi Institute of Development Research, Mumbai, India, **Jawaharlal Nehru University,
New Delhi, India

Final version accepted April 2005

ABSTRACT Using data from the 14 major states of India, we investigate whether state
governments’ fiscal policy choices are tempered by political considerations. Our principal findings
are twofold. First, we show that certain fiscal policies experience electoral cycles: state
governments raise less commodity tax revenue, spend less on the current account, and incur larger
capital account developmental expenditures in election years than in all other years. Second, we
show that coalition state governments raise less own non-tax revenues and spend less on the
current account than state governments that are more cohesive in composition. In sum, the
dispersion of political power affects government size.

I. Introduction
What determines the fiscal policies of a government? While the traditional public
finance literature answers this question from alternative perspectives, it is always
assumed that the government is a benevolent social planner, interested in maximising
the representative citizen’s welfare. In contrast, the recent literature on political
economy emphasises the institutional constraints under which policies are
formulated. It argues that policy-makers are typically political parties or politicians.
Naturally, the fiscal policies that are undertaken are tempered by political factors.
This paper tests some of the political economy theories of government behaviour
in the context of a developing country. Specifically, we examine the 14 major states
of India over 21 financial years (1974–75 to 1994–95) and ask the following question:
Does the proximity of a state legislative assembly election or the extent of
government cohesion affect fiscal policies in the states of India?

Correspondence Address: Kausik Chaudhuri, IGIDR, Gen. Vaidya Marg, Goregaon (East), Mumbai
400 065, India, Email: kausik@igidr.ac.in
Sugato Dasgupta, Centre for Economic Studies and Planning, Jawaharlal Nehru University, New Delhi
110 067, India, Email: sugatodasgupta@rediffmail.com

ISSN 0022-0388 Print/1743-9140 Online/06/040640-22 ª 2006 Taylor & Francis
DOI: 10.1080/00220380600682116

Political Factors and Fiscal Policies in Indian States 641

Our focus on India is interesting for two reasons. First, there is a vast literature
that uses data from developed countries to test for the presence of electoral and other
political considerations in government behaviour. This literature has scarcely been
extended to developing countries (Schuknecht (2000), Block (2002), Shi and
Svensson (2002a, b), and Khemani (2004) are recent and notable exceptions). There
is an obvious reason for the lacuna – democratic institutions have only very recently
taken root in developing countries. India, on the other hand, has been a democracy
since its independence in 1947, and periodic elections to the national and state
legislative assemblies have taken place since 1952. Therefore, the Indian experience
sheds light on political economy models in the context of a developing country with
long-standing democratic traditions.1 Second, it is commonly maintained that voters
in India do not condition their votes on economic variables; rather, voting behaviour
is exclusively based on caste allegiances. If this is true, political economy models,
which invariably hinge on economic factors, are doomed to fail in the Indian
context. Evidence to the contrary will indicate that voters in India, at least at the
margin, are not blind devotees of primordial caste ties.
Our use of state level data yields tangible benefits as well. Most studies that seek to
establish electoral cycles in fiscal policies proceed one country at a time. Even when a
relatively long country-specific history is observed, the number of election years
remains small.2 We, on the other hand, obtain information on an aggregate of 68
state legislative assembly elections, thereby circumventing the degrees-of-freedom
problem.3 In addition, the cross-section units in our data set (that is, the states of
India) are not too disparate entities. Similar arguments also apply when the
relationship between government cohesion and fiscal policies is examined.
We now briefly outline the models that provide the theoretical rationale for our
empirical investigation. Beginning with Nordhaus (1975), several theoretical papers
have explored the interactions between election timing and governments’ fiscal policy
choices. We test the predictions of two influential models in this genre. In Rogoff and
Sibert (1988), an opportunistic incumbent government seeks to remain in power. In
an election year, it lowers tax revenues and raises public expenditures to signal its
competency to the electorate.4 In an interesting variant, Rogoff (1990) allows the
government to incur expenditures of two sorts: public consumption and public
investment. Since the effects of public investment are imperfectly observed by voters,
election-year signalling shifts expenditures in favour of consumption and against
investment.5
A separate theoretical strand of the political economy literature emphasises the
connection between the dispersion of political power and budget deficits. Alesina and
Tabellini (1990) consider a polity with two political parties that differ in their pre-
ferences over the composition of public spending. The party in power, fearing an
electoral defeat, issues public debt to constrain the spending capacity of its successor.
Hence, the intertemporal sharing of political authority generates budget deficits.6
Alesina and Drazen (1993) explore the case of a coalition government that must raise
taxes to balance its budget. Each coalition partner wishes to avoid its share of the tax
burden; the ensuing war of attrition generates rising public debt. Hence, the contem-
poraneous sharing of political authority facilitates the creation of budget deficits.7,8
Instead of focusing on the government budget deficit, we consider its two main
components: expenditures incurred by the government and tax revenues collected.

642 K. Chaudhuri & S. Dasgupta

At least two plausible theories have been proposed linking government characteristic
(coalition or single-party) to expenditure and tax policies. First, in a coalition
government, each coalition partner normally caters to a narrow constituency of core
supporters. A coalition partner therefore has an incentive to demand targeted
programmes that are inefficiently large relative to financing costs, which are
customarily borne by the citizens in aggregate.9 In sum, coalition governments spend
more than single-party governments. If government spending is roughly matched
by tax revenue, coalition governments also raise more taxes than single-party
governments. An alternative, albeit informal, argument (see, for example, Dutta
(1996a) and Harrinvirta and Mattila (2001)) runs as follows: Coalition governments
are frequently unstable; hence, the future consequences of current actions are
severely discounted. This general neglect of the future coupled with a desire to buy
more time in office from supporters leads to lower taxes and higher expenditures. We
shall argue (see Section II for the theoretical framework) that the instability
associated with coalition governments may actually dampen public spending! When
indivisibilities force the benefits of public spending in a given year to be conferred on
a subset of the coalition partners, the excluded parties in the government will be
willing to pay up their share of the financing costs only if there are compensating
future gains to be had. The possibility of a government breakdown reduces the
expected value of future benefits and therefore leads to a bargaining impasse.10
Our study contributes to the small but growing literature that uses data from
India to test political economy models of government behaviour. A branch of this
literature explores the links between the timing of elections to the national legislative
assembly and central governments’ economic policies in India. Chibber (1995) shows
that central governments’ food subsidies are higher in election years than in non-
election years. Karnik (1990) demonstrates that central government expenditure,
aggregated over current and capital accounts, increases when an election is
impending. Sen and Vaidya (1996) fail to uncover electoral cycles in central
governments’ current account expenditure, capital account expenditure and current
account receipt; however, deficit in the current account is shown to be higher in
election years than in non-election years. In a slightly different vein, Chowdhury
(1993) detects political surfing by central governments – that is, central governments
strategically call for national elections when the economy’s output growth turns out
to be high. Besley and Burgess (2000, 2002), on the other hand, focus on the behaviour
of state governments in India. Besley and Burgess (2000) demonstrate that party
ideology affects public policy. Specifically, the cumulative land reforms passed in a
given state-year depends on the four-year lagged state legislative assembly seat shares
of different political groups. Besley and Burgess (2002) show that state governments
are more responsive to falls in food production and crop damage through floods where
newspaper circulation is higher and electoral accountability is greater.
Our work is directly linked to two papers that we discuss below. Dutta (1996a)
analyses state level data from India to determine whether the tax and expenditure
policies of coalition governments are markedly different from those of single-party
governments. Coalition governments are shown to have higher levels of current
account expenditure and lower levels of non-tax revenue; on the other hand, the
levels of tax revenue and surplus (deficit) in the current account budget are
unaffected by government fragmentation. Khemani (2004) examines the impact of


state legislative assembly elections on the fiscal policies of state governments in India.
Her findings are in striking contrast to the predictions of Rogoff and Sibert (1988)
and Rogoff (1990). During election years, Khemani concludes that state govern-
ments do not provide tax rate cuts on products that are widely consumed; rather, tax
breaks are given to small groups of producers. Furthermore, the proximity of state
elections leaves state governments’ total expenditure unaffected. Instead, the
composition of spending changes during election years, away from public
consumption and in favour of investment spending. All of this is viewed as evidence
of election-year manipulation of fiscal policies to favour a narrow constituency of
pivotal voters. Our study differs from these two papers in three ways. First, we
examine a substantially larger set of policy variables; the ensuing analysis is therefore
more detailed. Second, we provide a framework in which the policy effects of
government characteristic and election timing are jointly studied. It turns out that
both of the political variables impact on state governments’ fiscal policies. Third, the
econometric model that we specify differs somewhat from that of Dutta (1996a) and
Khemani (2004).11 Empirical findings that survive these differences can therefore be
deemed to be reasonably robust.
The principal findings of this paper are threefold. (1) State governments collect less
commodity tax revenue in election years than in non-election years. (2) Spending on
the current account, which has a substantial government consumption component, is
lower in election years than in non-election years. In contrast, developmental
spending on the capital account, which has a large physical investment component,
experiences an election-year spurt. Results (1) and (2) are broadly consistent with the
findings of Khemani (2004). (3) Coalition governments raise less own non-tax
revenues and spend less on the current account than governments that are more
cohesive in composition. In other words, the dispersion of political power affects
governments’ fiscal policy choices.
The remainder of the paper is structured as follows. Section II presents a
theoretical framework to demonstrate that the political instability associated with
coalition governments can actually dampen public spending. Section III provides a
description of the data set used in our analysis. Section IV presents both the
econometric procedures that we employed and the empirical results obtained. Section
V briefly considers the robustness of the empirical results. Section VI concludes.

II. A Theoretical Framework
In this section, we present a simple model to show that the political instability of
coalition governments may actually dampen government spending. Consider, to this
end, a two-period discrete-time model in which a government forms in the first
period (that is, period 1). The government consists of two political parties, A and B,
each political party representing exactly one quarter of the population of a nation.
In period 1, the government has the option to undertake a new project with the
following features: (1) the total financing cost of the project is c (each political party
c
in power has to pay up 4), and (2) aggregate benefits from the project are V, which
are evenly distributed over both the time periods. In contrast to Baron (1991, 1998),
project benefits within a given period are however not divisible. Specifically, should
the project be undertaken, the period-1 benefit from the project is V to one of the two
2


political parties in power. Without loss of generality, let party A be this period-1
beneficiary.
Once period 1 concludes, two possibilities arise. First, for exogenous reasons the
government terminates with probability p; post-collapse, the project is scrapped and
the continuation payoffs of the two erstwhile coalition members are normalised to 0.
Second, if government collapse is averted, the project remains in place and party B
receives period-2 benefits of V. 2
Suppose sanctioning of the project requires consent by both parties A and B. Then,
for a fixed V, when is the project undertaken? Notice that if party B agrees to finance
the project, its expected benefit, assuming no discounting, is V Â ð1 À pÞ while its cost
2
c
is 4. Hence, the project goes through only if c is weakly less than the threshold value of
2V 6 (1 7 p). As government instability (that is, p) increases, the threshold value
decreases. In other words, the overspending associated with pork barrel politics is
muted when government instability is enhanced. The intuition behind the result is
transparent: party B agrees to finance the project in period 1 only because current
period cooperation leads to gains in the future. When government breakdown is
imminent, the future is heavily discounted and cooperation becomes difficult to
sustain.12
We close the discussion of our model with a final remark. Our model is written in
terms of public investment. A slight reinterpretation of the model allows us to
consider public consumption as well. Here, we can think of the political parties as
representing distinct geographical regions, the government potentially engaged in
hiring a fixed number of workers at an above-market wage, and certain
indivisibilities in the hiring process.

III. The Data
The data set for our study consists of annual observations. It spans 21 financial years
(1974–75 to 1994–95) and covers the 14 major states of India. India comprises 25
states and seven union territories. In the financial year 1994–95, the aforementioned
14 states accounted for 83.1 per cent of India’s land area, 93.2 per cent of her
population, and 92.6 per cent of the net domestic product.
The variables that we consider partition into three distinct categories: (1) Data on
the fiscal (revenue and expenditure) policies of state governments were assembled
from various volumes of the Reserve Bank of India Bulletin, published by the central
bank of India. (2) State economic and demographic characteristics, which served as
control variables in the empirical analysis, were compiled as follows. The National
Accounts Statistics (published by the Central Statistical Organisation) provided
information on state domestic product and the proportion of state domestic product
derived from the primary sector; state literacy rate data were obtained from the
Census of India and the National Sample Survey rounds (both published by the
Government of India); while Press in India (published by the Ministry of
Information and Broadcasting) recorded the per capita newspaper circulation data
for each state-year.13 (3) The political variables were gleaned from two sources. First,
the dates of all state legislative assembly elections were taken from the book India
Decides. Thereafter, for each state-year, the ‘nature’ of the state government (details


given below) was determined. This information was extracted from the publication
Encyclopaedia of India and Her States.
How do we measure the ‘nature’ of state governments? The theory sketched in
Section II maintains that fiscal policy choices are systematically influenced by the extent
of government cohesion. At first blush, this suggests a distinction between single-party
and coalition governments. Such a distinction misses an important point: coalition
governments per se do not affect fiscal policy choices; rather, what matters is the
bargaining between strategically important coalition partners with disparate interests.
Following Dutta (1996a, b), we posit that a coalition partner is strategically important
only if it is a pivotal member of the coalition; that is, if its withdrawal from the
government transforms a hitherto winning coalition (with majority support) into a
nonviable one. A coalition government, in turn, is presumed to distort fiscal policy
choices should it contain two or more pivotal parties. Henceforth, we shall refer to such
a government as a fragmented coalition.14
For each of the 14 major states of India, column 1 of Table 1 shows the incidence
of fragmented coalition governments. The construction of column 1 proceeds as
follows. First, from the 21-year-long electoral history of each state (financial year
1974–75 to 1994–95), we calculated the number of months during which the
government had multiple pivotal parties. Second, the aggregate months were
converted into equivalent years. For example, the state of Bihar witnessed
governments of the aforementioned type for a sum total of 22 months. This is the
equivalent of 1.83 years. Column 1 reveals an interesting finding: most of the 14
chosen states have had some exposure to fragmented coalition governments.
Table 2 shows the summary statistics of state governments’ fiscal policy variables
analysed in this study. Notice that these variables are of three kinds: revenue

Table 1. Incidence of fragmented coalition governments and left-wing governments in the
states of India

States Fragmented coalition govt. (in years) Left-wing govt. (in years)

Andhra Pradesh 0.00 0.00
Bihar 1.83 0.00
Gujarat 5.08 0.00
Haryana 5.08 0.00
Karnataka 1.92 0.00
Kerala 16.25 5.83
Madhya Pradesh 0.00 0.00
Maharashtra 2.08 0.00
Orissa 0.17 0.00
Punjab 2.75 0.00
Rajasthan 0.67 0.00
Tamil Nadu 0.00 0.00
Uttar Pradesh 1.83 0.00
West Bengal 0.00 17.83

Note: The sample period covered is financial year 1974–75 to 1994–95. ‘Fragmented coalition
govt. (in years)’ shows the total number of years during which the government of the state was
a fragmented coalition. ‘Left-wing govt. (in years)’ shows the total number of years during
which a communist party headed the government of the state.


Table 2. Summary statistics of fiscal policy variables: (constant 1960–61 rupees per capita)

Variables # Obs. Mean Std. dev.

Revenue
Own tax revenue 21 35.94 18.50
Commodity tax revenue 21 32.15 16.85
Own non-tax revenue 21 13.26 8.29
Current account expenditure
Total current acct. expenditure 21 74.89 30.58
Non-developmental expenditure 21 23.14 11.36
Developmental expenditure 21 50.89 20.41
Grants to local bodies 21 0.85 0.80
Economic services expenditure 21 22.03 11.53
Social services expenditure 21 28.99 10.77
Capital account expenditure
Developmental expenditure 21 10.83 5.26
Economic services expenditure 21 9.51 5.11
Social services expenditure 21 1.38 1.00
Education expenditure 21 0.19 0.20

Note: The sample period considered is financial year 1974–75 to 1994–95. All of the variables
are measured per capita in 1960–61 rupees. ‘# Obs.’ is the number of state-specific
observations for the variable. ‘Mean’ (‘Std. dev.’) computes the average (standard deviation)
of the variable over the sample state-years.

variables, and variables related to expenditure on the current and capital accounts.
Below, we provide a brief overview of the fiscal structure of state governments.
The total revenue of a state government has two components: total tax revenue
and total non-tax revenue. Averaged over the sample state-years, total tax revenue
accounts for 67.8 per cent of total revenue. A state government’s total tax revenue is,
in turn, decomposed into two parts: its share in the tax revenue of the central
government (31.5 per cent), and revenue raised through state taxes (68.5 per cent).
State governments levy taxes on agricultural income, property, and commodities.
Commodity taxes yield 88.9 per cent of states’ own tax revenue. A state
government’s non-tax revenue derives from two sources: grants from the central
government and own non-tax revenue (the three most important subcategories being
interest receipts from loans issued by the state government, dividends and profits
from public sector undertakings owned by the state government, and revenues from
state lotteries). Our study pays special attention to the own tax revenue and own
non-tax revenue components of total revenue.
Expenditures incurred by state governments are on either the current account (71.1
per cent) or the capital account (28.9 per cent). Current account expenditure is of three
types: developmental spending (68.3 per cent), non-developmental spending (30.6 per
cent), and grants to local bodies and Panchayati Raj institutions (1.2 per cent).
Developmental expenditure, which is largely devoted to the maintenance of existing
assets, involves spending on social services (56.7 per cent) and economic services (43.3
per cent). Non-developmental expenditure principally comprises interest payments on
accumulated debt and outlays on fiscal and administrative services.
State governments’ capital account expenditure mainly entails the discharge of
internal debt, the repayment of loans to the central government, and the provision of


loans for assorted purposes. However, 39.8 per cent of capital account expenditure is
devoted to the creation of physical assets. This developmental component, in
turn, involves spending on social services (12.2 per cent) and economic services
(87.8 per cent). Our study focusses on capital account developmental expenditure.

IV. Empirical Results
Do political considerations (namely, the proximity of a state election and the extent
of government cohesion) affect the fiscal policy choices of state governments? To
answer this question, we consider estimating a fixed-effects error-components model
of the form:

Pst ¼ as þ dt þ bxst þ gFragmentst þ oElecst þ est ðs ¼ 1; . . . ; S; t ¼ 1; . . . ; TÞ ð1Þ

where Pst denotes a particular fiscal policy variable (for example, the log of per
capita own tax revenue) in state s during financial year t, as is a state fixed effect, dt is
a year effect, xst is a (k61) vector of explanatory variables that capture economic
and demographic characteristics of state s in financial year t, Fragmentst measures
the proportion of financial year t during which the government of state s had more
than one pivotal party, Elecst (henceforth, referred to as the election year dummy) is a
zero-one variable that equals one if financial year t is an election year in state s, and
est is the error term, presumed to be orthogonal to all of the regressors. The political
theories that we test deal exclusively with the significance, sign and magnitude of the
estimates of g and o.
The treatment of elections in equation 1 merits scrutiny. First, some rule must
specify when financial year t is deemed to be an election year in state s. Following
Alesina et al. (1993) and Reid (1998), financial year t is called an election year in
state s if a state legislative assembly election is held in the second half of financial
year t or in the first half of the next financial year.
A more serious problem pertains to the potential endogeneity of elections.15 The
constitution of India mandates that a state legislative assembly have a normal term
of five years from the date appointed for its first sitting. Accordingly, we classify a
state legislative assembly election as scheduled if it is held when the current assembly
is at least four years of age. In our data set, the 14 states have experienced an
aggregate of 68 state legislative assembly elections; 47 of these elections are classified
as scheduled. What accounts for the remaining 21 mid-term elections?
Three circumstances lead to mid-term elections. First, a state government may lose
the confidence of a majority in the state legislature. The governor of the state, upon
verifying that no claimant can form an alternative government commanding majority
support, conventionally calls for fresh elections. Second, the president of India, upon
receipt of a report by the governor of a state or otherwise, may be satisfied that
constitutional breakdown has occurred at the state level. This leads to the temporary
imposition of President’s Rule and, eventually, fresh elections. Third, a state government
may voluntarily petition the governor of the state to hold mid-term elections.
The third reason for mid-term elections is especially problematic. If the incumbent
state government strategically holds elections for electoral gains, then election timing


Table 3. Time duration between successive state legislative assembly elections

# elects.; # elects.; # elects.; # elects.; # elects.;
States 0–1 yr. 1–2 yrs. 2–3 yrs. 3–4 yrs. !4 yrs.

Andhra Pradesh 0 0 1 0 4
Bihar 0 0 1 0 4
Gujarat 0 0 0 1 4
Haryana 0 0 0 1 3
Karnataka 0 0 1 0 4
Kerala 0 0 2 0 3
Madhya Pradesh 0 0 2 0 3
Maharashtra 0 0 1 0 4
Orissa 0 0 1 1 3
Punjab 0 0 1 0 3
Rajasthan 0 0 2 0 3
Tamil Nadu 0 0 2 0 3
Uttar Pradesh 0 1 2 1 2
West Bengal 0 0 0 0 4
Column Totals 0 1 16 4 47

Note: The sample period considered is financial year 1974–75 to 1994–95. For each of the 14
states, the variable ‘# elects.; 0–1 yr.’ computes the number of state legislative assembly
elections for which the elapsed time since the immediately preceding state legislative assembly
election is weakly less than 12 months. The other variables are constructed analogously.

may be correlated with shocks to fiscal policies. Table 3 reports on the time duration
between successive state legislative assembly elections. Table 3 is constructed as
follows. We proceeded one state at a time. For each state legislative assembly
election held between financial years 1974–75 and 1994–95, we calculated the number
of months that had elapsed between the election under scrutiny and the one directly
preceding it. Thereafter, we computed the total number of elections for which the
elapsed time is less than 12 months, between 12 months and 24 months, and so on.
Table 3 reveals two patterns of interest: (1) In aggregate, there are 47 scheduled
elections and 21 mid-term elections in our sample. (2) Out of the 21 mid-term
elections, 17 experienced an elapsed time less than 36 months. Yet, even this
clustering of mid-term elections near the beginning of a government’s term does not
necessarily preclude a strategic accounting of such elections.16
Following Khemani (2004) and Shi and Svensson (2002a, b), we therefore estimate
equation 1 with Elecst equal to 1 only if a scheduled election takes place in state-year
(s, t). In other words, our empirical work differentiates scheduled election years from
all other years.17 This estimation strategy does not guarantee that o measures the
causal effect of scheduled elections on public policy. If governments that last for the
full term of five years are distinct from governments that dissolve rapidly, then o
could simply be reflecting policy differences between the different government
types.18 Such concerns about survivorship bias are partially allayed since we include
Fragment (a government characteristic variable) as a regressor in equation 1. In
addition, we add an extra regressor, called Match Dummy, to account for the extent
of political affiliation between governments at the centre and the state. Specifically,
Match Dummyst is a dummy variable that assumes the value 1(0) if the government


in state s is politically affiliated with the central government for more (less) than six
months during financial year t.
We conclude this section with an important caveat. In equation 1, the following
four variables are included in the vector xst: the log of per capita domestic
product in state-year (s, t), the proportion of the domestic product in state-year
(s, t) that is derived from the primary sector, the literacy rate in state-year (s, t),
and the per capita newspaper circulation in state-year (s, t). These variables are
intended to control for state specific factors that affect both state governments’
fiscal policy choices and government characteristics. Yet, concerns about the
interpretation of g remain warranted. Specifically, g measures the causal effect of
government fragmentation only if there is no omitted and time-varying state
specific variable that drives both government fragmentation and public policy.
Since the above requirement is not guaranteed, our results should be viewed with
caution.19

Empirical Results for State Governments’ Revenues
In this subsection, we analyse the political determinants of state governments’ own
revenue. Recall that state governments’ own revenue is the sum of own tax revenue
and own non-tax revenue. Columns 1 and 3 of Table 4 present the regression results
for, respectively, state governments’ per capita own tax revenue and own non-tax
revenue.20 Note that in neither case is the coefficient of the election year dummy
statistically significant at conventional levels. Averaged over the state-years in our
sample, 88.9 per cent of state governments’ own tax revenue is obtained from taxes
levied on commodities. Given this, we next investigate whether political considera-
tions influence state governments’ commodity tax revenue. Column 2 of Table 4
presents the regression results. Here, the dummy variable for election year is

Table 4. Impact of political factors on revenue

Dependent variables
(1) (2) (3)
Own tax Commodity tax Own non-tax
revenue revenue revenue

Share primary sector 70.32 (71.16) 70.37 (71.56) 70.72 (70.86)
State domestic product 0.54 (5.32) 0.56 (6.65) 1.23 (3.28)
Literacy rate 0.01 (0.82) 0.00 (0.22) 70.04 (71.01)
Newspaper circulation 70.12 (70.20) 0.51 (0.90) 0.30 (0.22)
Match dummy 70.01 (70.35) 70.00 (70.18) 70.09 (72.05)
Election year dummy 70.01 (70.68) 70.02b (71.78) 70.04 (71.44)
Government fragmentation 70.01 (70.29) 70.00 (70.35) 70.11b (71.84)
R-squared 0.98 0.99 0.84
Number of observations 294 294 294

Note: Each of the dependent variables is measured per capita in 1960–61 rupees. All of the
regressions include state fixed effects and year dummies. The t-ratios, which are
heteroskedasticity-robust and corrected for within-state clustering of the error term, are in
parentheses; b ¼ significance at the 0.10 level (two-tailed test).


correctly signed and statistically significant at the 0.10 level; per capita commodity
tax revenue collected by state governments is approximately 2 per cent lower in
scheduled election years than in all other years.21 The electoral cycle in commodity
tax revenue is a robust feature of the Indian experience; Khemani (2004) detects
this cycle as well despite differences in model specification and a somewhat different
data set.
How does one interpret the electoral cycle in commodity tax revenue? State
governments’ commodity tax revenue derives from three sources: state excise duties
on the production of alcoholic liquor, a producer tax on inter-state trade of goods,
and state sales tax. Khemani (2004: 139–40) finds that scheduled elections lower state
governments’ revenues from state excise tax and trade tax, but leave revenues from
the regressive sales tax unaffected. Since the first two tax categories have a narrow
base, the evidence is viewed as inconsistent with the broad-based tax reductions
predicted by Rogoff and Sibert (1988); instead, the targeted tax relief for a small set
of individuals is regarded as pointing towards ‘a story of political purchase of
campaign support’. Our evidence is more ambiguous. We ran separate regressions
for state governments’ per capita sales tax revenue and the sum of state governments’
per capita excise and trade tax revenue. In both regressions, the coefficient of the
election year dummy is negatively signed but not significant at conventional levels
(the t-statistics are 71.34 and 71.28, respectively).
The government fragmentation variable in Table 4 merits scrutiny as well. In each
of the three regressions reported, the coefficient of Fragment is negative; statistical
significance at the 0.10 level obtains for state governments’ own non-tax revenue.
How large in magnitude is this effect? Contrast state-years of two sorts: one type
witnesses 12 months of fragmented coalition government, the other type experiences
12 months of single-party rule. Per capita own non-tax revenue is 11 per cent lower
in a state-year of the first kind than in a state-year of the second kind. Taken in total,
our evidence suggests that fragmented coalition governments raise less revenue than
single-party governments.
In summary, political factors influence the revenue collected by state governments.
The commodity tax revenue is reduced when state legislative assembly elections are
impending. Furthermore, own non-tax revenue declines when the state government
is a fragmented coalition rather than a single party.

Empirical Results for State Governments’ Current Account Expenditures
This subsection explores the political determinants of state governments’ expenditure
on the current account. To this end, column 1 of Table 5 reports the regression
results for state governments’ per capita total current account expenditure. Contrary
to the predictions of Rogoff and Sibert (1988), there is no election year increase in
public spending. Indeed, the coefficient of the election year dummy is negative and
statistically significant at the 0.10 level; per capita total current account expenditure
by state governments is approximately 4 per cent lower in scheduled election years
than in all other years.
How does one interpret the above result? Khemani (2004) also finds that
scheduled election years are associated with a decrease in state governments’ total
current account expenditure. This, she argues, represents an election-year shift in

Table 5. Impact of political factors on current account expenditure

Dependent variables
(1) (2) (3) (4) (5) (6)
Current Non-developmental Grants to Developmental Social Economic
account exp. exp. local bodies exp. services exp. services exp.

Share primary sector 0.17 (0.32) 0.92 (1.95) 1.92 (0.98) 70.08 (70.13) 70.59 (72.06) 70.29 (70.36)
State domestic product 0.33 (1.91) 70.00 (70.07) 1.74 (1.30) 0.35 (1.84) 0.62 (5.22) 0.37 (1.32)
Literacy rate 70.01 (70.47) 0.01 (0.64) 0.17 (3.97) 70.02 (71.79) 70.01 (70.47) 70.02 (71.30)
Newspaper circulation 1.08 (1.08) 2.66 (1.62) 4.58 (1.04) 0.14 (0.88) 70.83 (71.56) 71.59 (71.65)
Match dummy 70.02 (70.84) 70.03 (71.05) 0.11 (0.66) 70.02 (70.49) 70.07 (72.79) 0.00 (0.09)
Election year dummy 70.04b (71.66) 70.05b (71.72) 70.16a (72.37) 70.03 (71.26) 70.01 (70.27) 70.01 (70.39)
Government fragmentation 70.04b (71.84) 0.01 (0.16) 70.21 (70.63) 70.06b (71.88) 70.02 (71.00) 70.10 (71.53)
R-squared 0.94 0.93 0.75 0.93 0.96 0.91
Number of observations 294 294 294 294 294 294

Note: Each of the dependent variables is measured per capita in 1960–61 rupees. All of the regressions include state fixed effects and year dummies.
The t-ratios, which are heteroskedasticity-robust and corrected for within-state clustering of the error term, are in parentheses; a ¼ significance at the
0.05 level (two-tailed test), and b ¼ significance at the 0.10 level (two-tailed test).


the composition of spending, away from public consumption and towards
physical investments. However, what drives the electoral cycle in total current
account expenditure? Columns 2, 3, 4, 5 and 6 of Table 5 report the regression
results for, respectively, state governments’ per capita non-developmental
expenditure on the current account, per capita grants to local bodies and
Panchayati Raj institutions, per capita developmental expenditure on the current
account, and the two components of developmental expenditure (that is, per
capita expenditure on social services and economic services). Our results show
that the electoral cycle in total current account expenditure is based on the
electoral cycles in non-developmental current account expenditure and grants to
local bodies. In other words, during a scheduled election year, the incumbent
government does not tinker with subsidies or wages to public sector employees
(these are items in the developmental expenditure category); rather, current
account expenditure is pruned at the expense of local bodies and by organising
the functioning of government at a lower cost.22
The government fragmentation variable in Table 5 elicits two observations. First,
the coefficient of Fragment is negative and statistically significant at the 0.10 level for
per capita total current account expenditure. The magnitude of the coefficient
indicates that a state-year with 12 months of fragmented coalition government
experiences 4 per cent lower spending on the current account than a state-year with
12 months of single-party rule. Second, the effect of government fragmentation on
total current account expenditure works through the developmental component.
Column 4 shows that per capita developmental expenditure on the current account is
6 per cent lower in a state-year with 12 months of fragmented coalition government
than in a state-year with 12 months of single-party government.
In summary, the current account expenditure of state governments is influenced by
political factors. In striking contrast to the predictions of Rogoff and Sibert (1988),
current account spending by state governments is lower in scheduled election years
than in all other years. There is also some evidence that government fragmentation
reduces total current account expenditure and developmental expenditure on the
current account.

Empirical Results for State Governments’ Capital Account Expenditures
As mentioned in Section III, state governments’ per capita total capital account
expenditure consists of several items (for example, the discharge of internal debt, the
repayment of loans to the central government, and the provision of loans for assorted
purposes) that are not readily manipulable. Accordingly, we emphasise here the
manipulable component of per capita total capital account expenditure – namely, per
capita capital account developmental expenditure. Column 1 of Table 6 reports the
regression results for state governments’ per capita capital account developmental
expenditure. Notice that in sharp contrast to the predictions of Rogoff (1990), the
coefficient of the election year dummy is positively signed and statistically significant at
the 0.10 level; per capita capital account developmental expenditure by state governments
is approximately 6 per cent higher in scheduled election years than in all other years.
The above finding elicits two observations. First, Khemani (2004) detects an
electoral cycle in state governments’ per capita total capital account expenditure and

Table 6. Impact of political factors on capital account expenditure

Dependent variables
(1) (2) (3) (4)
Developmental exp. Social services exp. Economic services exp. Education exp.

Share primary sector 70.13 (70.12) 0.64 (0.47) 70.31 (1.20) 0.64 (0.36)
State domestic product 0.60 (1.21) 0.66 (1.21) 0.63 (0.60) 2.23 (4.41)
Literacy rate 70.02 (70.41) 70.09 (71.68) 70.01 (70.16) 70.12 (72.35)
Newspaper circulation 0.10 (0.08) 1.72 (1.32) 70.17 (70.11) 7.77 (1.64)
Match dummy 0.11 (1.49) 70.03 (70.41) 0.12 (0.08) 70.11 (70.72)
Election year dummy 0.06b (1.69) 70.02 (70.33) 0.07 (1.61) 0.15a (2.09)
Government fragmentation 70.09 (70.81) 0.01 (0.09) 70.11 (70.73) 70.27a (71.97)
R-squared 0.75 0.73 0.72 0.74
Number of observations 292 293 292 292

Note: Each of the dependent variables is measured per capita in 1960–61 rupees. All of the regressions include state fixed effects and year dummies.
The t-ratios, which are heteroskedasticity-robust and corrected for within-state clustering of the error term, are in parentheses; a ¼ significance at the
0.05 level (two-tailed test), and b ¼ significance at the 0.10 level (two-tailed test).


argues that the flexibility of public investment spending allows the incumbent party
to provide election-year targeted benefits to a select group of pivotal voters. If this
conjecture is accurate, then an electoral cycle should perforce crop up in the
developmental component of total capital account expenditure. We have shown that
this is indeed the case. Second, in a panel study of 24 developing countries for the
1973–92 period, Schuknecht (2000) finds prominent electoral cycles in public
investment. Our study extends this finding to a different setting.
Capital account developmental expenditure has two components: spending on
social services and spending on economic services. Accordingly, we ran separate
regressions with state governments’ per capita social services expenditure and
economic services expenditure as regressands. For economic services expenditure,
the coefficient of the election year dummy is positive with a p-value of 0.108. In
contrast, for social services expenditure, the coefficient of the election year dummy is
negative and far from achieving statistical significance. This suggests that the
electoral cycle in capital account developmental expenditure stems from the
behaviour of economic services expenditure.
We also estimated separate regressions for three expenditure subcategories of
social services (education, medical and public health, and water supply and
sanitation) and two expenditure subcategories of economic services (agriculture and
allied activities, and industry and minerals). An electoral cycle crops up only in the
case of education expenditure (column 4 of Table 6). The size of the electoral cycle in
education expenditure is large: per capita capital account education expenditure by
state governments is approximately 15 per cent higher in scheduled election years
than in all other years.
Does government cohesion affect spending on the capital account? Table 6 shows
that the coefficient of Fragment is negative and statistically significant for state
governments’ education expenditure. This effect is substantial in size. Contrast state-
years of two sorts: one type witnesses 12 months of fragmented coalition
government, the other type experiences 12 months of single-party rule. Per capita
capital account education expenditure is 27 per cent lower in a state-year of the first
kind than in a state-year of the latter variety.
We summarise the findings of Table 6 as follows. First, in contrast to the
predictions of Rogoff (1990), there is no election-year decline in capital account
expenditure. Indeed, we observe election-year spurts in capital account develop-
mental expenditure and capital account education expenditure. Second, fragmented
coalitions spend less on education than single-party governments.

V. Robustness of the Results
In a seminal paper, Hibbs (1977) modelled political parties as partisan rather than
opportunistic. In other words, political parties were assumed to have intrinsic
preferences defined on a policy space. Once in office, these political parties simply
implemented their favoured policies.23 We, on the other hand, have allowed no role
for government ideology as a determinant of fiscal outcomes (see equation 1). This is
because most political parties in India are organised around linguistic, regional,
religious and caste affiliations (Weiner, 1989); they simply do not have natural
locations on a conventional left–right scale.24


The above difficulties notwithstanding, we did experiment briefly with government
ideology. Specifically, we distinguished between a left-wing government (that is, a
government headed by a communist party) and all other government types.
Column 2 of Table 1 details the incidence of left-wing governments in the 14 major
states of India. Column 2 is constructed as follows. From the electoral history of
each state, we first computed the number of months during which a left-wing
government was in place. Subsequently, the aggregate months were converted into
equivalent years. Column 2 indicates that only two of the 14 states, Kerala and West
Bengal, have witnessed left-wing state governments.
To study the impact of government ideology on fiscal policies, we constructed a
variable, LeftWingst, that measures the proportion of financial year t during which
the government of state s was a left-wing government. Inclusion of this variable as a
regressor yielded two conclusions. First, out of the 13 policy variables in Tables 4
through 6, LeftWingst is statistically significant at conventional levels in three
cases. Specifically, the coefficient of LeftWingst is 70.09 (t-statistic equals 71.95),
70.52 (t-statistic equals 72.58) and 70.80 (t-statistic equals 74.25) for,
respectively, non-developmental expenditure on the current account, social services
expenditure on the capital account, and education expenditure on the capital
account. Second, while the above findings are perhaps contrary to expectation, it
turns out that the results for Elecst and Fragmentst are entirely insensitive to the
inclusion of the ideology variable.
In Section IV, the electoral cycle in expenditure variables was estimated by
considering scheduled election years only. The logical justification for this
approach has previously been given. What happens, however, when the regressions
are re-run without differentiating between scheduled and mid-term elections?25
Three observations are relevant here. First, the effects of government fragmenta-
tion are robust and do not depend on how the election year dummy is coded.
Second, for the current account expenditure categories, there is no hint of an
electoral cycle: the t-statistic corresponding to the election year dummy ranges
from a high of 0.99 (developmental expenditure) to a low of 0.01 (social services
expenditure). In other words, as in Section IV, our findings continue to violate the
predictions of Rogoff and Sibert (1988). Third, in Section IV, we had noted
election-year spurts in capital account developmental expenditure and education
expenditure, thereby contradicting the predictions of Rogoff (1990). Now, the
electoral cycle in capital account developmental expenditure disappears. Summing
up, insofar as expenditure variables are concerned, our evidence lends scant
support to Rogoff and Sibert (1988) and Rogoff (1990) regardless of how the
election year dummy is coded.
Finally, to allay concerns regarding the potential endogeneity of the (scheduled)
election year dummy, we estimated panel regressions instrumenting Elecst in
equation 1. Our instrument takes the value of 1 in the fifth year after every state
legislative assembly election (mid-term or scheduled), and is 0 otherwise.26,27 Our
findings can be summarised as follows. First, the effects of government fragmenta-
tion do not change when Elecst is instrumented. Second, consider the revenue results
in Table 4. The dummy for election year remains negatively signed for own tax
revenue and commodity tax revenue. However, statistical significance is no longer
obtained for commodity tax revenue. Third, consider the current account


expenditure results in Table 5. The election year dummy remains negatively signed
for all of the expenditure categories; statistical significance at the 0.05 level obtains
for total current account expenditure and current account non-developmental
expenditure. Fourth, consider the capital account expenditure results in Table 6.
Here, the election year dummy turns up positively signed for developmental
expenditure and education expenditure; however, in neither case is statistical
significance obtained. While our results undoubtedly deteriorate when Elecst is
instrumented, note that we continue to find no support for the predictions of Rogoff
and Sibert (1988) and Rogoff (1990).

VI. Conclusion
Using state level data from India, we have studied whether the fiscal policies of state
governments are systematically affected by the proximity of scheduled state elections
and the extent of government cohesion. Our findings are twofold. First, state
governments raise less commodity tax revenue, spend less on the current account,
and incur larger capital account developmental expenditures in scheduled election
years than in all other years. Consistent with Khemani (2004), we detect no election-
year splurges in current account expenditure (as predicted by Rogoff and Sibert
(1988)) and no election-year contractions in capital account spending (as predicted
by Rogoff (1990)). Second, our evidence suggests that fragmented coalition
governments are smaller than their single-party counterparts. Relative to single-
party governments, fragmented coalitions have lower levels of own non-tax revenue
and current account expenditure.
We note that many empirical questions remain to be explored. In this study,
government expenditure was analysed. Variations in expenditure do not necessarily
translate into corresponding movements in the provision of public goods. Hence,
there is a need to estimate the impact of political factors on the supply of various
public goods.
Frey and Schneider (1978a, b) and Schultz (1995) have independently suggested an
amendment to the politico–economic models of government policy. Since policy
manipulation is presumed to be costly (for example, there may be a loss in
reputational capital), the extent of government manoeuvring depends on the value of
the marginal vote. Thus, safe elections should not witness policy distortions. Indian
state level data provide an ideal testing ground for this intriguing observation (see
notes for details).28
Finally, we have presented but one half of the complete story. Specifically, while
governments’ fiscal policy choices were analysed, voter behaviour was unmodelled.
Does the electorate condition its vote on fiscal variables? Some evidence, employing
US and OECD data, already exists. Peltzman (1992) and Lowry et al. (1998) find
clear fiscal policy effects in the vote data for US presidential, senatorial and
gubernatorial elections. Besley and Case (1995b) show that US governors whose tax
hikes are less than in adjacent states have a higher probability of re-election. Alesina
et al. (1998) use OECD data to demonstrate that government fiscal restraint is not
systematically followed by a popularity decline. Comparable work with Indian data
is non-existent. In sum, the analysis of voter behaviour in India remains a fruitful
and unexplored research topic.


Acknowledgements
The authors would like to thank seminar participants at Indira Gandhi Institute of
Development Research, Indian Statistical Institute (Delhi Centre), and University of
Sydney for helpful comments. Thanks are especially due to Lekha Chakraborty,
Pinaki Chakraborty, Saumen Chattopadhyay, Bhaskar Dutta, Shubhashis
Gangopadhyay, Jyotsna Jalan, Kirit Parikh and Arijit Sen for numerous helpful
discussions. Two anonymous referees suggested changes that greatly improved the
quality of the paper. Of course, the usual disclaimer applies. S. Dasgupta’s research
was supported by a grant from PPRU.

Notes
1. Khemani (2004) makes this point as well.
2. For example, Alesina and Sachs (1988) study ten US presidential elections; Andrikopoulos et al.
(1998) study ten national elections in Greece; Chowdhury (1993) studies seven national elections in
India; Heckelman and Berument (1998) study ten national elections in Japan; while Schultz (1995) and
Serletis and Afxentiou (1998) study, respectively, nine and twenty national elections in Great Britain and
Canada.
3. To alleviate the degrees-of-freedom problem, recent studies have examined cross-country evidence.
Thus, Alesina and Roubini (1992), Alesina et al. (1993), De Haan and Sturm (1997), De Haan et al.
(1999), Harrinvirta and Mattila (2001), and Perotti and Kontopoulos (2002) consider panels of OECD
countries.
4. Since this article repeatedly refers to Rogoff and Sibert (1988), it may be worthwhile to provide a brief
sketch of the mechanism at work in that paper. The set-up of the Rogoff-Sibert model is as follows. An
incumbent government, which may be intrinsically competent or incompetent, provides certain fixed
services to citizens. To pay for these services, revenues must be raised through any combination of
non-distortionary taxes (that voters observe immediately) and distortionary taxes (that voters observe
with a lag). To provide the given services, competent governments naturally require less aggregate tax
revenue than incompetent governments.
Suppose the incumbent government is competent. In an election year, it convinces the electorate of
its competency by lowering the level of visible non-distortionary tax revenues. Why does an
incompetent government not mimic the behaviour of a competent government, thereby enhancing its
electoral prospects as well? This is because mimicking forces the incompetent government to raise very
large revenues through distortionary taxes; the consequent damage to voter welfare serves as an
effective deterrent. The argument for electoral cycles in expenditures, omitted for brevity, is identical
in spirit to that for the tax revenues case.
5. Several studies furnish empirical support for the signalling models. Besley and Case (1995a), for
example, examine the behaviour of governors in the US; state taxes are shown to be lower when the
incumbent governor stands for re-election.
6. De Haan and Sturm (1994) provide corroborating evidence: for member countries of the European
Community, the growth of government debt is positively related to the frequency of government
changes.
7. Empirical evidence for this theory is mixed. Alt and Lowry (1994) and Poterba (1994, 1996) examine
state governments in the United States; adjustments to fiscal shocks are demonstrated to be slower
when a state government is ‘divided’ (that is, the executive and legislative branches are controlled by
different political parties). Roubini and Sachs (1989a, b) study the fiscal histories of several OECD
countries; budget deficits are shown to be correlated with the extent of government fragmentation.
However, De Haan and Sturm (1994, 1997), De Haan et al. (1999), Volkerink and De Haan (2001),
and Perotti and Kontopoulos (2002) argue persuasively that the Roubini-Sachs conclusions are
fragile.
8. While Alesina and Tabellini (1990) and Alesina and Drazen (1993) explore the link between budget
deficits and the fragmentation of political power, we alert the reader to a complementary strand of
work (see, for example, Alesina et al. (1999), Harrinvirta and Mattila (2001), Perotti and Kontopoulos


(2002)) that uses cross-country panels to show that budget institutions (for example, ex ante spending
limits or borrowing constraints) robustly predict budget deficits and government spending.
9. Weingast et al. (1981) were the first to argue that in a system wherein geography forms the basis for
political representation, there is an inherent tendency for programmes to exceed efficient scale. We
adapt their insights to the problem at hand. To make matters concrete, consider a single-party
government that represents exactly one half of the population of a nation. This government will
implement a targeted programme should its benefit–cost ratio exceed 0.5. Consider a two-party
government in which each coalition partner represents one quarter of the population of a nation. A
coalition partner will now seek to implement a targeted programme should its benefit–cost ratio
exceed 0.25. Hence, the fragmentation of a government leads to increased public spending (see also
Perotti and Kontopoulos (2002) for an extended discussion).
10. Boix (1997) analyses the privatisation experiences of various countries and hints at a similar treatment
of coalition governments. In contrast to single-party governments, coalition governments are posited
to generate gridlock effects: ‘parties within the coalition are prone to veto each other’s projects
whenever the resulting policies are believed to result in significant costs for their corresponding
constituencies’.
11. Dutta (1996a), for example, does not include state fixed effects in his model and uses state specific time
trends instead of year dummies. The set of controls in Khemani (2004) (state net domestic product,
share of agriculture in state net domestic product, and proportion of state population that is rural) is
different from ours, the set of states considered differs as well (she includes Assam in her list of 14
states while we include Haryana instead), and so on.
12. Our model imposes period-by-period budget balance. Since unstable coalition governments spend less
than stable governments, they raise less revenues as well. This revenue result will persist in a less
restrictive model so long as expenditures and revenues move together (that is, are positively
correlated). In our data set, the correlation coefficient between state governments’ own tax revenue
and current account developmental expenditure is 0.86, while that between state governments’ own tax
revenue and capital account developmental expenditure is 0.27.
13. For certain years, state literacy rate data were not available from either the Census of India or the
National Sample Survey rounds. For these years, we have interpolated the data using a simple growth
rate formula.
14. In the Indian context, it is not uncommon to find coalition governments with one pivotal party. For
instance, the state of West Bengal has been ruled by a coalition called the Left Front since 1977. The
Communist Party of India (Marxist), which is a member of this coalition, has won an absolute
majority of legislative assembly seats in each of the state elections since 1977.
15. The possibility of election endogeneity in a parliamentary system has been recognised by several
researchers: Cargill and Hutchison (1991) explore this issue in the case of Japan, Chowdhury (1993)
and Khemani (2004) focus on India, Heckelman and Berument (1998) study both Japan and the
United Kingdom, while Reid (1998) looks at the Canadian situation.
16. The authors thank an anonymous referee for emphasising this point.
17. Section V discusses the sensitivity of the regression results to an alternative coding of Elecst.
18. Khemani (2004) makes this point as well.
19. The authors thank an anonymous referee for emphasising this point.
20. The regressands are, in fact, the log of state governments’ per capita own tax revenue and per
capita own non-tax revenue. For expositional ease, we henceforth suppress the qualifier ‘log of.’
21. States’ own tax revenue consists of commodity tax revenue and revenue from taxes levied on
agricultural income and property. There is an electoral cycle for commodity tax revenue. Why does
this cycle disappear for the broader category of own tax revenue (compare columns 1 and 2 of Table
4)? For many states, the direct tax component of own tax revenue has been left untouched for years.
Thus, the Report of the Eleventh Finance Commission (p. 28) observes: ‘There are a few other taxes
which the states can levy, but remain unexploited or under-exploited. Taxation of agricultural incomes
is one of them and profession tax is another. The land revenue which has traditionally been the
principal mode of taxing agriculture in the country has fallen into disuse’. At any rate, since one
component of own tax revenue does not respond at all to electoral considerations, precise estimation
of the electoral cycle for own tax revenue becomes difficult. Economists at the National Institute of
Public Finance and Policy (a government-funded institute dealing with public finance issues) provided
two reasons for why state governments show little interest in manipulating the direct tax component of


own tax revenue: (1) the cost of administering such taxes is exceptionally high; and (2) states perceive
their share of centrally-levied direct taxes to be the principal source of direct tax revenue.
22. Non-developmental current account expenditure consists mainly of interest payments on accumulated
debt and outlays on fiscal and administrative services. It is surprising that an electoral cycle crops up
for non-developmental current account expenditure since its components appear a priori to be difficult
to manipulate. To explore this issues further, we ran a regression where the regressand was the sum of
per capita expenditure on fiscal services and per capita expenditure on administrative services. The
coefficient of the election year dummy was negative (70.09) and statistically significant (t-statistic
equal to 72.24).
23. Alesina (1987) demonstrates that the partisan assumption does not rule out election-year effects on
macroeconomic variables. Hibbs (1992) provides an in-depth review of the political economy literature
with partisan political parties.
24. The difficulty in assigning ideological locations to political parties is a common feature of developing
country experiences. We are unaware of a single paper that uses data from developing countries and
considers fiscal policy as partially determined by government ideology (see, for example, Schuknecht
(2000), Block (2002), Shi and Svensson (2002a, b), and Khemani (2004)).
25. Now, Elecst equals 1 if an election (scheduled or mid-term) takes place in state s during the second half
of financial year t or during the first half of the next financial year.
26. Khemani (2004) briefly experiments with this instrument as well.
27. Following Davidson and MacKinnon (1993: 241–42), we also conducted a test to determine whether
the (scheduled) election year dummy, Elecst, is endogenous in India. The exogeneity of Elecst could not
be rejected.
28. The electoral history of India divides into two distinct phases: a pre-1967 period during which state
legislative assembly elections were dominated by the Congress party, and a post-1967 era that beheld
lively inter-party competition for assembly seats. Electoral cycles in fiscal variables – shown, in this
study, to be sometimes present in the post-1967 data – should disappear completely during the years of
Congress party hegemony. More generally, a contrast of the two electoral phases can shed light on the
benefits and costs of political competition.

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