20855407

The Political Determinants of FiscalPolicies 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 stategovernments’ fiscal policy choices are tempered by political considerations. Our principal findingsare twofold. First, we show that certain fiscal policies experience electoral cycles: stategovernments raise less commodity tax revenue, spend less on the current account, and incur largercapital account developmental expenditures in election years than in all other years. Second, weshow that coalition state governments raise less own non-tax revenues and spend less on thecurrent account than state governments that are more cohesive in composition. In sum, thedispersion of political power affects government size.

I. Introduction

What determines the fiscal policies of a government? While the traditional publicfinance literature answers this question from alternative perspectives, it is alwaysassumed that the government is a benevolent social planner, interested in maximisingthe representative citizen’s welfare. In contrast, the recent literature on politicaleconomy emphasises the institutional constraints under which policies areformulated. 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 statesof 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 ofgovernment cohesion affect fiscal policies in the states of India?

Correspondence Address: Kausik Chaudhuri, IGIDR, Gen. Vaidya Marg, Goregaon (East), Mumbai

400 065, India, Email: [email protected]

Sugato Dasgupta, Centre for Economic Studies and Planning, Jawaharlal Nehru University, New Delhi

110 067, India, Email: [email protected]

40651 26/4/06 20:15 FJDS_A_168181 (XML)

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

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

DOI: 10.1080/00220380600682116

Our focus on India is interesting for two reasons. First, there is a vast literaturethat uses data from developed countries to test for the presence of electoral and otherpolitical considerations in government behaviour. This literature has scarcely beenextended to developing countries (Schuknecht (2000), Block (2002), Shi andSvensson (2002a, b), and Khemani (2004) are recent and notable exceptions). Thereis an obvious reason for the lacuna – democratic institutions have only very recentlytaken root in developing countries. India, on the other hand, has been a democracysince its independence in 1947, and periodic elections to the national and statelegislative assemblies have taken place since 1952. Therefore, the Indian experiencesheds light on political economy models in the context of a developing country withlong-standing democratic traditions.1 Second, it is commonly maintained that votersin India do not condition their votes on economic variables; rather, voting behaviouris exclusively based on caste allegiances. If this is true, political economy models,which invariably hinge on economic factors, are doomed to fail in the Indiancontext. Evidence to the contrary will indicate that voters in India, at least at themargin, are not blind devotees of primordial caste ties.

Our use of state level data yields tangible benefits as well. Most studies that seek toestablish electoral cycles in fiscal policies proceed one country at a time. Even when arelatively long country-specific history is observed, the number of election yearsremains small.2 We, on the other hand, obtain information on an aggregate of 68state legislative assembly elections, thereby circumventing the degrees-of-freedomproblem.3 In addition, the cross-section units in our data set (that is, the states ofIndia) are not too disparate entities. Similar arguments also apply when therelationship between government cohesion and fiscal policies is examined.

We now briefly outline the models that provide the theoretical rationale for ourempirical investigation. Beginning with Nordhaus (1975), several theoretical papershave explored the interactions between election timing and governments’ fiscal policychoices. We test the predictions of two influential models in this genre. In Rogoff andSibert (1988), an opportunistic incumbent government seeks to remain in power. Inan election year, it lowers tax revenues and raises public expenditures to signal itscompetency to the electorate.4 In an interesting variant, Rogoff (1990) allows thegovernment to incur expenditures of two sorts: public consumption and publicinvestment. Since the effects of public investment are imperfectly observed by voters,election-year signalling shifts expenditures in favour of consumption and againstinvestment.5

A separate theoretical strand of the political economy literature emphasises theconnection between the dispersion of political power and budget deficits. Alesina andTabellini (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 anelectoral 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 raisetaxes to balance its budget. Each coalition partner wishes to avoid its share of the taxburden; 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 maincomponents: expenditures incurred by the government and tax revenues collected.

Political Factors and Fiscal Policies in Indian States 641

At least two plausible theories have been proposed linking government characteristic(coalition or single-party) to expenditure and tax policies. First, in a coalitiongovernment, each coalition partner normally caters to a narrow constituency of coresupporters. A coalition partner therefore has an incentive to demand targetedprogrammes that are inefficiently large relative to financing costs, which arecustomarily borne by the citizens in aggregate.9 In sum, coalition governments spendmore than single-party governments. If government spending is roughly matchedby tax revenue, coalition governments also raise more taxes than single-partygovernments. An alternative, albeit informal, argument (see, for example, Dutta(1996a) and Harrinvirta and Mattila (2001)) runs as follows: Coalition governmentsare frequently unstable; hence, the future consequences of current actions areseverely discounted. This general neglect of the future coupled with a desire to buymore time in office from supporters leads to lower taxes and higher expenditures. Weshall argue (see Section II for the theoretical framework) that the instabilityassociated with coalition governments may actually dampen public spending! Whenindivisibilities force the benefits of public spending in a given year to be conferred ona subset of the coalition partners, the excluded parties in the government will bewilling to pay up their share of the financing costs only if there are compensatingfuture gains to be had. The possibility of a government breakdown reduces theexpected value of future benefits and therefore leads to a bargaining impasse.10

Our study contributes to the small but growing literature that uses data fromIndia to test political economy models of government behaviour. A branch of thisliterature explores the links between the timing of elections to the national legislativeassembly and central governments’ economic policies in India. Chibber (1995) showsthat 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 isimpending. Sen and Vaidya (1996) fail to uncover electoral cycles in centralgovernments’ current account expenditure, capital account expenditure and currentaccount receipt; however, deficit in the current account is shown to be higher inelection years than in non-election years. In a slightly different vein, Chowdhury(1993) detects political surfing by central governments – that is, central governmentsstrategically call for national elections when the economy’s output growth turns outto be high. Besley and Burgess (2000, 2002), on the other hand, focus on the behaviourof state governments in India. Besley and Burgess (2000) demonstrate that partyideology affects public policy. Specifically, the cumulative land reforms passed in agiven state-year depends on the four-year lagged state legislative assembly seat sharesof different political groups. Besley and Burgess (2002) show that state governmentsare more responsive to falls in food production and crop damage through floods wherenewspaper 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 expenditurepolicies of coalition governments are markedly different from those of single-partygovernments. Coalition governments are shown to have higher levels of currentaccount expenditure and lower levels of non-tax revenue; on the other hand, thelevels of tax revenue and surplus (deficit) in the current account budget areunaffected by government fragmentation. Khemani (2004) examines the impact of

642 K. Chaudhuri & S. Dasgupta

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, taxbreaks are given to small groups of producers. Furthermore, the proximity of stateelections leaves state governments’ total expenditure unaffected. Instead, thecomposition of spending changes during election years, away from publicconsumption and in favour of investment spending. All of this is viewed as evidenceof election-year manipulation of fiscal policies to favour a narrow constituency ofpivotal voters. Our study differs from these two papers in three ways. First, weexamine a substantially larger set of policy variables; the ensuing analysis is thereforemore detailed. Second, we provide a framework in which the policy effects ofgovernment characteristic and election timing are jointly studied. It turns out thatboth of the political variables impact on state governments’ fiscal policies. Third, theeconometric model that we specify differs somewhat from that of Dutta (1996a) andKhemani (2004).11 Empirical findings that survive these differences can therefore bedeemed to be reasonably robust.

The principal findings of this paper are threefold. (1) State governments collect lesscommodity tax revenue in election years than in non-election years. (2) Spending onthe current account, which has a substantial government consumption component, islower in election years than in non-election years. In contrast, developmentalspending on the capital account, which has a large physical investment component,experiences an election-year spurt. Results (1) and (2) are broadly consistent with thefindings of Khemani (2004). (3) Coalition governments raise less own non-taxrevenues and spend less on the current account than governments that are morecohesive in composition. In other words, the dispersion of political power affectsgovernments’ fiscal policy choices.

The remainder of the paper is structured as follows. Section II presents atheoretical framework to demonstrate that the political instability associated withcoalition governments can actually dampen public spending. Section III provides adescription of the data set used in our analysis. Section IV presents both theeconometric procedures that we employed and the empirical results obtained. SectionV 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 ofcoalition governments may actually dampen government spending. Consider, to thisend, a two-period discrete-time model in which a government forms in the firstperiod (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 thefollowing features: (1) the total financing cost of the project is c (each political partyin power has to pay up c

4), and (2) aggregate benefits from the project are V, whichare evenly distributed over both the time periods. In contrast to Baron (1991, 1998),project benefits within a given period are however not divisible. Specifically, shouldthe project be undertaken, the period-1 benefit from the project is V

2 to one of the two


political parties in power. Without loss of generality, let party A be this period-1beneficiary.Once period 1 concludes, two possibilities arise. First, for exogenous reasons the

government terminates with probability p; post-collapse, the project is scrapped andthe 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 Breceives 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 financethe project, its expected benefit, assuming no discounting, is V

2 � ð1� pÞ while its costis c

4. Hence, the project goes through only if c is weakly less than the threshold value of2V6 (17 p). As government instability (that is, p) increases, the threshold valuedecreases. In other words, the overspending associated with pork barrel politics ismuted when government instability is enhanced. The intuition behind the result istransparent: party B agrees to finance the project in period 1 only because currentperiod cooperation leads to gains in the future. When government breakdown isimminent, the future is heavily discounted and cooperation becomes difficult tosustain.12

We close the discussion of our model with a final remark. Our model is written interms of public investment. A slight reinterpretation of the model allows us toconsider public consumption as well. Here, we can think of the political parties asrepresenting distinct geographical regions, the government potentially engaged inhiring a fixed number of workers at an above-market wage, and certainindivisibilities 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 25states and seven union territories. In the financial year 1994–95, the aforementioned14 states accounted for 83.1 per cent of India’s land area, 93.2 per cent of herpopulation, 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 assembledfrom various volumes of the Reserve Bank of India Bulletin, published by the centralbank of India. (2) State economic and demographic characteristics, which served ascontrol variables in the empirical analysis, were compiled as follows. The NationalAccounts Statistics (published by the Central Statistical Organisation) providedinformation on state domestic product and the proportion of state domestic productderived from the primary sector; state literacy rate data were obtained from theCensus of India and the National Sample Survey rounds (both published by theGovernment of India); while Press in India (published by the Ministry ofInformation and Broadcasting) recorded the per capita newspaper circulation datafor 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 IndiaDecides. Thereafter, for each state-year, the ‘nature’ of the state government (details


given below) was determined. This information was extracted from the publicationEncyclopaedia of India and Her States.

How do we measure the ‘nature’ of state governments? The theory sketched inSection II maintains that fiscal policy choices are systematically influenced by the extentof government cohesion. At first blush, this suggests a distinction between single-partyand coalition governments. Such a distinction misses an important point: coalitiongovernments per se do not affect fiscal policy choices; rather, what matters is thebargaining between strategically important coalition partners with disparate interests.Following Dutta (1996a, b), we posit that a coalition partner is strategically importantonly if it is a pivotal member of the coalition; that is, if its withdrawal from thegovernment transforms a hitherto winning coalition (with majority support) into anonviable one. A coalition government, in turn, is presumed to distort fiscal policychoices should it contain two or more pivotal parties. Henceforth, we shall refer to sucha government as a fragmented coalition.14

For each of the 14 major states of India, column 1 of Table 1 shows the incidenceof fragmented coalition governments. The construction of column 1 proceeds asfollows. First, from the 21-year-long electoral history of each state (financial year1974–75 to 1994–95), we calculated the number of months during which thegovernment had multiple pivotal parties. Second, the aggregate months wereconverted into equivalent years. For example, the state of Bihar witnessedgovernments of the aforementioned type for a sum total of 22 months. This is theequivalent of 1.83 years. Column 1 reveals an interesting finding: most of the 14chosen states have had some exposure to fragmented coalition governments.

Table 2 shows the summary statistics of state governments’ fiscal policy variablesanalysed in this study. Notice that these variables are of three kinds: revenue

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

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

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

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


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 revenueaccounts 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 centralgovernment (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 stategovernment’s non-tax revenue derives from two sources: grants from the centralgovernment and own non-tax revenue (the three most important subcategories beinginterest receipts from loans issued by the state government, dividends and profitsfrom public sector undertakings owned by the state government, and revenues fromstate lotteries). Our study pays special attention to the own tax revenue and ownnon-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 threetypes: developmental spending (68.3 per cent), non-developmental spending (30.6 percent), and grants to local bodies and Panchayati Raj institutions (1.2 per cent).Developmental expenditure, which is largely devoted to the maintenance of existingassets, involves spending on social services (56.7 per cent) and economic services (43.3per cent). Non-developmental expenditure principally comprises interest payments onaccumulated 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

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

Variables # Obs. Mean Std. dev.

RevenueOwn tax revenue 21 35.94 18.50Commodity tax revenue 21 32.15 16.85Own non-tax revenue 21 13.26 8.29

Current account expenditureTotal current acct. expenditure 21 74.89 30.58Non-developmental expenditure 21 23.14 11.36Developmental expenditure 21 50.89 20.41Grants to local bodies 21 0.85 0.80Economic services expenditure 21 22.03 11.53Social services expenditure 21 28.99 10.77

Capital account expenditureDevelopmental expenditure 21 10.83 5.26Economic services expenditure 21 9.51 5.11Social services expenditure 21 1.38 1.00Education expenditure 21 0.19 0.20

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


loans for assorted purposes. However, 39.8 per cent of capital account expenditure isdevoted to the creation of physical assets. This developmental component, inturn, 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 extentof government cohesion) affect the fiscal policy choices of state governments? Toanswer this question, we consider estimating a fixed-effects error-components modelof the form:

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

where Pst denotes a particular fiscal policy variable (for example, the log of percapita own tax revenue) in state s during financial year t, as is a state fixed effect, dt isa year effect, xst is a (k61) vector of explanatory variables that capture economicand demographic characteristics of state s in financial year t, Fragmentst measuresthe proportion of financial year t during which the government of state s had morethan one pivotal party, Elecst (henceforth, referred to as the election year dummy) is azero-one variable that equals one if financial year t is an election year in state s, andest is the error term, presumed to be orthogonal to all of the regressors. The politicaltheories that we test deal exclusively with the significance, sign and magnitude of theestimates of g and o.

The treatment of elections in equation 1 merits scrutiny. First, some rule mustspecify when financial year t is deemed to be an election year in state s. FollowingAlesina et al. (1993) and Reid (1998), financial year t is called an election year instate s if a state legislative assembly election is held in the second half of financialyear t or in the first half of the next financial year.

A more serious problem pertains to the potential endogeneity of elections.15 Theconstitution of India mandates that a state legislative assembly have a normal termof five years from the date appointed for its first sitting. Accordingly, we classify astate legislative assembly election as scheduled if it is held when the current assemblyis at least four years of age. In our data set, the 14 states have experienced anaggregate of 68 state legislative assembly elections; 47 of these elections are classifiedas scheduled. What accounts for the remaining 21 mid-term elections?

Three circumstances lead to mid-term elections. First, a state government may losethe confidence of a majority in the state legislature. The governor of the state, uponverifying that no claimant can form an alternative government commanding majoritysupport, conventionally calls for fresh elections. Second, the president of India, uponreceipt of a report by the governor of a state or otherwise, may be satisfied thatconstitutional breakdown has occurred at the state level. This leads to the temporaryimposition of President’s Rule and, eventually, fresh elections. Third, a state governmentmay voluntarily petition the governor of the state to hold mid-term elections.

The third reason for mid-term elections is especially problematic. If the incumbentstate government strategically holds elections for electoral gains, then election timing


may be correlated with shocks to fiscal policies. Table 3 reports on the time durationbetween successive state legislative assembly elections. Table 3 is constructed asfollows. We proceeded one state at a time. For each state legislative assemblyelection held between financial years 1974–75 and 1994–95, we calculated the numberof months that had elapsed between the election under scrutiny and the one directlypreceding it. Thereafter, we computed the total number of elections for which theelapsed 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 scheduledelections and 21 mid-term elections in our sample. (2) Out of the 21 mid-termelections, 17 experienced an elapsed time less than 36 months. Yet, even thisclustering of mid-term elections near the beginning of a government’s term does notnecessarily preclude a strategic accounting of such elections.16

Following Khemani (2004) and Shi and Svensson (2002a, b), we therefore estimateequation 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 fromall other years.17 This estimation strategy does not guarantee that o measures thecausal effect of scheduled elections on public policy. If governments that last for thefull term of five years are distinct from governments that dissolve rapidly, then ocould simply be reflecting policy differences between the different governmenttypes.18 Such concerns about survivorship bias are partially allayed since we includeFragment (a government characteristic variable) as a regressor in equation 1. Inaddition, we add an extra regressor, called Match Dummy, to account for the extentof 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

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 4Bihar 0 0 1 0 4Gujarat 0 0 0 1 4Haryana 0 0 0 1 3Karnataka 0 0 1 0 4Kerala 0 0 2 0 3Madhya Pradesh 0 0 2 0 3Maharashtra 0 0 1 0 4Orissa 0 0 1 1 3Punjab 0 0 1 0 3Rajasthan 0 0 2 0 3Tamil Nadu 0 0 2 0 3Uttar Pradesh 0 1 2 1 2West 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 14states, the variable ‘# elects.; 0–1 yr.’ computes the number of state legislative assemblyelections for which the elapsed time since the immediately preceding state legislative assemblyelection is weakly less than 12 months. The other variables are constructed analogously.


in state s is politically affiliated with the central government for more (less) than sixmonths during financial year t.

We conclude this section with an important caveat. In equation 1, the followingfour variables are included in the vector xst: the log of per capita domesticproduct 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 areintended to control for state specific factors that affect both state governments’fiscal policy choices and government characteristics. Yet, concerns about theinterpretation of g remain warranted. Specifically, g measures the causal effect ofgovernment fragmentation only if there is no omitted and time-varying statespecific variable that drives both government fragmentation and public policy.Since the above requirement is not guaranteed, our results should be viewed withcaution.19

Empirical Results for State Governments’ Revenues

In this subsection, we analyse the political determinants of state governments’ ownrevenue. Recall that state governments’ own revenue is the sum of own tax revenueand own non-tax revenue. Columns 1 and 3 of Table 4 present the regression resultsfor, respectively, state governments’ per capita own tax revenue and own non-taxrevenue.20 Note that in neither case is the coefficient of the election year dummystatistically significant at conventional levels. Averaged over the state-years in oursample, 88.9 per cent of state governments’ own tax revenue is obtained from taxeslevied on commodities. Given this, we next investigate whether political considera-tions influence state governments’ commodity tax revenue. Column 2 of Table 4presents 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 taxrevenue

Commodity taxrevenue

Own non-taxrevenue

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 0.84Number of observations 294 294 294

Note: Each of the dependent variables is measured per capita in 1960–61 rupees. All of theregressions include state fixed effects and year dummies. The t-ratios, which areheteroskedasticity-robust and corrected for within-state clustering of the error term, are inparentheses; b¼ significance at the 0.10 level (two-tailed test).


correctly signed and statistically significant at the 0.10 level; per capita commoditytax revenue collected by state governments is approximately 2 per cent lower inscheduled election years than in all other years.21 The electoral cycle in commoditytax revenue is a robust feature of the Indian experience; Khemani (2004) detectsthis cycle as well despite differences in model specification and a somewhat differentdata set.How does one interpret the electoral cycle in commodity tax revenue? State

governments’ commodity tax revenue derives from three sources: state excise dutieson 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 stategovernments’ revenues from state excise tax and trade tax, but leave revenues fromthe regressive sales tax unaffected. Since the first two tax categories have a narrowbase, the evidence is viewed as inconsistent with the broad-based tax reductionspredicted by Rogoff and Sibert (1988); instead, the targeted tax relief for a small setof individuals is regarded as pointing towards ‘a story of political purchase ofcampaign support’. Our evidence is more ambiguous. We ran separate regressionsfor 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 theelection 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; statisticalsignificance 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 typewitnesses 12 months of fragmented coalition government, the other type experiences12 months of single-party rule. Per capita own non-tax revenue is 11 per cent lowerin 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 thansingle-party governments.In summary, political factors influence the revenue collected by state governments.

The commodity tax revenue is reduced when state legislative assembly elections areimpending. Furthermore, own non-tax revenue declines when the state governmentis 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’ expenditureon the current account. To this end, column 1 of Table 5 reports the regressionresults for state governments’ per capita total current account expenditure. Contraryto the predictions of Rogoff and Sibert (1988), there is no election year increase inpublic spending. Indeed, the coefficient of the election year dummy is negative andstatistically significant at the 0.10 level; per capita total current account expenditureby state governments is approximately 4 per cent lower in scheduled election yearsthan 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’ totalcurrent account expenditure. This, she argues, represents an election-year shift in


Table

5.Im

pact

ofpoliticalfactors

oncurrentaccountexpenditure

Dependentvariables

(1)

Current

accountexp.

(2)

Non-developmental

exp.

(3)

Grants

tolocalbodies

(4)

Developmental

exp.

(5)

Social

services

exp.

(6)

Economic

services

exp.

Share

primary

sector

0.17(0.32)

0.92(1.95)

1.92(0.98)

70.08(7

0.13)

70.59(7

2.06)

70.29(7

0.36)

State

domesticproduct

0.33(1.91)

70.00(7

0.07)

1.74(1.30)

0.35(1.84)

0.62(5.22)

0.37(1.32)

Literacy

rate

70.01(7

0.47)

0.01(0.64)

0.17(3.97)

70.02(7

1.79)

70.01(7

0.47)

70.02(7

1.30)

New

spaper

circulation

1.08(1.08)

2.66(1.62)

4.58(1.04)

0.14(0.88)

70.83(7

1.56)

71.59(7

1.65)

Matchdummy

70.02(7

0.84)

70.03(7

1.05)

0.11(0.66)

70.02(7

0.49)

70.07(7

2.79)

0.00(0.09)

Electionyeardummy

70.04b(7

1.66)

70.05b(7

1.72)

70.16a(7

2.37)

70.03(7

1.26)

70.01(7

0.27)

70.01(7

0.39)

Governmentfragmentation

70.04b(7

1.84)

0.01(0.16)

70.21(7

0.63)

70.06b(7

1.88)

70.02(7

1.00)

70.10(7

1.53)

R-squared

0.94

0.93

0.75

0.93

0.96

0.91

Number

ofobservations

294

294

294

294

294

294

Note:Each

ofthedependentvariablesismeasuredper

capitain

1960–61rupees.Alloftheregressionsincludestate

fixed

effectsandyeardummies.

Thet-ratios,whichare

heteroskedasticity-robustandcorrectedforwithin-state

clusteringoftheerrorterm

,are

inparentheses;a¼significance

atthe

0.05level

(two-tailed

test),andb¼significance

atthe0.10level

(two-tailed

test).


the composition of spending, away from public consumption and towardsphysical investments. However, what drives the electoral cycle in total currentaccount expenditure? Columns 2, 3, 4, 5 and 6 of Table 5 report the regressionresults for, respectively, state governments’ per capita non-developmentalexpenditure on the current account, per capita grants to local bodies andPanchayati Raj institutions, per capita developmental expenditure on the currentaccount, and the two components of developmental expenditure (that is, percapita expenditure on social services and economic services). Our results showthat the electoral cycle in total current account expenditure is based on theelectoral cycles in non-developmental current account expenditure and grants tolocal bodies. In other words, during a scheduled election year, the incumbentgovernment does not tinker with subsidies or wages to public sector employees(these are items in the developmental expenditure category); rather, currentaccount expenditure is pruned at the expense of local bodies and by organisingthe 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 forper capita total current account expenditure. The magnitude of the coefficientindicates that a state-year with 12 months of fragmented coalition governmentexperiences 4 per cent lower spending on the current account than a state-year with12 months of single-party rule. Second, the effect of government fragmentation ontotal current account expenditure works through the developmental component.Column 4 shows that per capita developmental expenditure on the current account is6 per cent lower in a state-year with 12 months of fragmented coalition governmentthan 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 yearsthan in all other years. There is also some evidence that government fragmentationreduces total current account expenditure and developmental expenditure on thecurrent account.

Empirical Results for State Governments’ Capital Account Expenditures

As mentioned in Section III, state governments’ per capita total capital accountexpenditure consists of several items (for example, the discharge of internal debt, therepayment of loans to the central government, and the provision of loans for assortedpurposes) that are not readily manipulable. Accordingly, we emphasise here themanipulable component of per capita total capital account expenditure – namely, percapita capital account developmental expenditure. Column 1 of Table 6 reports theregression results for state governments’ per capita capital account developmentalexpenditure. Notice that in sharp contrast to the predictions of Rogoff (1990), thecoefficient of the election year dummy is positively signed and statistically significant atthe 0.10 level; per capita capital account developmental expenditure by state governmentsis 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.Im

pact

ofpoliticalfactors

oncapitalaccountexpenditure

Dependentvariables

(1)

Developmentalexp.

(2)

Socialservices

exp.

(3)

Economic

services

exp.

(4)

Educationexp.

Share

primary

sector

70.13(7

0.12)

0.64(0.47)

70.31(1.20)

0.64(0.36)

State

domesticproduct

0.60(1.21)

0.66(1.21)

0.63(0.60)

2.23(4.41)

Literacy

rate

70.02(7

0.41)

70.09(7

1.68)

70.01(7

0.16)

70.12(7

2.35)

New

spaper

circulation

0.10(0.08)

1.72(1.32)

70.17(7

0.11)

7.77(1.64)

Matchdummy

0.11(1.49)

70.03(7

0.41)

0.12(0.08)

70.11(7

0.72)

Electionyeardummy

0.06b(1.69)

70.02(7

0.33)

0.07(1.61)

0.15a(2.09)

Governmentfragmentation

70.09(7

0.81)

0.01(0.09)

70.11(7

0.73)

70.27a(7

1.97)

R-squared

0.75

0.73

0.72

0.74

Number

ofobservations

292

293

292

292

Note:Each

ofthedependentvariablesismeasuredper

capitain

1960–61rupees.Alloftheregressionsincludestate

fixed

effectsandyeardummies.

Thet-ratios,whichare

heteroskedasticity-robustandcorrectedforwithin-state

clusteringoftheerrorterm

,are

inparentheses;a¼significance

atthe

0.05level

(two-tailed

test),andb¼significance

atthe0.10level

(two-tailed

test).


argues that the flexibility of public investment spending allows the incumbent partyto provide election-year targeted benefits to a select group of pivotal voters. If thisconjecture is accurate, then an electoral cycle should perforce crop up in thedevelopmental component of total capital account expenditure. We have shown thatthis is indeed the case. Second, in a panel study of 24 developing countries for the1973–92 period, Schuknecht (2000) finds prominent electoral cycles in publicinvestment. 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 separateregressions with state governments’ per capita social services expenditure andeconomic services expenditure as regressands. For economic services expenditure,the coefficient of the election year dummy is positive with a p-value of 0.108. Incontrast, for social services expenditure, the coefficient of the election year dummy isnegative and far from achieving statistical significance. This suggests that theelectoral cycle in capital account developmental expenditure stems from thebehaviour of economic services expenditure.We also estimated separate regressions for three expenditure subcategories of

social services (education, medical and public health, and water supply andsanitation) and two expenditure subcategories of economic services (agriculture andallied activities, and industry and minerals). An electoral cycle crops up only in thecase of education expenditure (column 4 of Table 6). The size of the electoral cycle ineducation expenditure is large: per capita capital account education expenditure bystate governments is approximately 15 per cent higher in scheduled election yearsthan 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 stategovernments’ education expenditure. This effect is substantial in size. Contrast state-years of two sorts: one type witnesses 12 months of fragmented coalitiongovernment, the other type experiences 12 months of single-party rule. Per capitacapital account education expenditure is 27 per cent lower in a state-year of the firstkind 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 accountexpenditure. Indeed, we observe election-year spurts in capital account develop-mental expenditure and capital account education expenditure. Second, fragmentedcoalitions 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 thanopportunistic. In other words, political parties were assumed to have intrinsicpreferences defined on a policy space. Once in office, these political parties simplyimplemented their favoured policies.23 We, on the other hand, have allowed no rolefor government ideology as a determinant of fiscal outcomes (see equation 1). This isbecause most political parties in India are organised around linguistic, regional,religious and caste affiliations (Weiner, 1989); they simply do not have naturallocations on a conventional left–right scale.24


The above difficulties notwithstanding, we did experiment briefly with governmentideology. Specifically, we distinguished between a left-wing government (that is, agovernment 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 majorstates of India. Column 2 is constructed as follows. From the electoral history ofeach state, we first computed the number of months during which a left-winggovernment was in place. Subsequently, the aggregate months were converted intoequivalent years. Column 2 indicates that only two of the 14 states, Kerala and WestBengal, have witnessed left-wing state governments.

To study the impact of government ideology on fiscal policies, we constructed avariable, LeftWingst, that measures the proportion of financial year t during whichthe government of state s was a left-wing government. Inclusion of this variable as aregressor yielded two conclusions. First, out of the 13 policy variables in Tables 4through 6, LeftWingst is statistically significant at conventional levels in threecases. 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 servicesexpenditure on the capital account, and education expenditure on the capitalaccount. Second, while the above findings are perhaps contrary to expectation, itturns out that the results for Elecst and Fragmentst are entirely insensitive to theinclusion of the ideology variable.

In Section IV, the electoral cycle in expenditure variables was estimated byconsidering scheduled election years only. The logical justification for thisapproach has previously been given. What happens, however, when the regressionsare 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 anelectoral cycle: the t-statistic corresponding to the election year dummy rangesfrom a high of 0.99 (developmental expenditure) to a low of 0.01 (social servicesexpenditure). In other words, as in Section IV, our findings continue to violate thepredictions of Rogoff and Sibert (1988). Third, in Section IV, we had notedelection-year spurts in capital account developmental expenditure and educationexpenditure, thereby contradicting the predictions of Rogoff (1990). Now, theelectoral cycle in capital account developmental expenditure disappears. Summingup, insofar as expenditure variables are concerned, our evidence lends scantsupport to Rogoff and Sibert (1988) and Rogoff (1990) regardless of how theelection year dummy is coded.

Finally, to allay concerns regarding the potential endogeneity of the (scheduled)election year dummy, we estimated panel regressions instrumenting Elecst inequation 1. Our instrument takes the value of 1 in the fifth year after every statelegislative assembly election (mid-term or scheduled), and is 0 otherwise.26,27 Ourfindings can be summarised as follows. First, the effects of government fragmenta-tion do not change when Elecst is instrumented. Second, consider the revenue resultsin Table 4. The dummy for election year remains negatively signed for own taxrevenue and commodity tax revenue. However, statistical significance is no longerobtained for commodity tax revenue. Third, consider the current account


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

VI. Conclusion

Using state level data from India, we have studied whether the fiscal policies of stategovernments are systematically affected by the proximity of scheduled state electionsand the extent of government cohesion. Our findings are twofold. First, stategovernments raise less commodity tax revenue, spend less on the current account,and incur larger capital account developmental expenditures in scheduled electionyears 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 predictedby Rogoff (1990)). Second, our evidence suggests that fragmented coalitiongovernments are smaller than their single-party counterparts. Relative to single-party governments, fragmented coalitions have lower levels of own non-tax revenueand 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 necessarilytranslate 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 variouspublic goods.Frey and Schneider (1978a, b) and Schultz (1995) have independently suggested an

amendment to the politico–economic models of government policy. Since policymanipulation is presumed to be costly (for example, there may be a loss inreputational capital), the extent of government manoeuvring depends on the value ofthe marginal vote. Thus, safe elections should not witness policy distortions. Indianstate level data provide an ideal testing ground for this intriguing observation (seenotes for details).28

Finally, we have presented but one half of the complete story. Specifically, whilegovernments’ fiscal policy choices were analysed, voter behaviour was unmodelled.Does the electorate condition its vote on fiscal variables? Some evidence, employingUS and OECD data, already exists. Peltzman (1992) and Lowry et al. (1998) findclear fiscal policy effects in the vote data for US presidential, senatorial andgubernatorial elections. Besley and Case (1995b) show that US governors whose taxhikes are less than in adjacent states have a higher probability of re-election. Alesinaet al. (1998) use OECD data to demonstrate that government fiscal restraint is notsystematically followed by a popularity decline. Comparable work with Indian datais non-existent. In sum, the analysis of voter behaviour in India remains a fruitfuland unexplored research topic.


Acknowledgements

The authors would like to thank seminar participants at Indira Gandhi Institute ofDevelopment Research, Indian Statistical Institute (Delhi Centre), and University ofSydney for helpful comments. Thanks are especially due to Lekha Chakraborty,Pinaki Chakraborty, Saumen Chattopadhyay, Bhaskar Dutta, ShubhashisGangopadhyay, Jyotsna Jalan, Kirit Parikh and Arijit Sen for numerous helpfuldiscussions. Two anonymous referees suggested changes that greatly improved thequality of the paper. Of course, the usual disclaimer applies. S. Dasgupta’s researchwas 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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