Equalisation Transfers Based on Spending Needs and Fiscal Capacity of State Governments in India

Abstract

This study addresses an important policy issue pertaining to the determination of equalisation transfers to Indian states. It empirically estimates the effect of transfers on the expenditures of 29 Indian states using panel data methodology. It also determines transfers based on the spending needs and fiscal capacities of states. The results indicate a strong crowding-in effect of transfers on the public spending of states and the presence of the flypaper effect. Fiscal transfers relate positively to revenue expenditures in 13 out of 18 general category states and in 8 out of 11 special category states. The estimated amounts of equalisation transfers for all 29 states in four alternative scenarios, based on alternative benchmarks of fiscal capacities and spending needs, range between ₹555 billion and ₹16,048 billion. We believe these results will help policymakers and other stakeholders to design appropriate fiscal transfer strategies such that all citizens can avail a standard level of public services in India.

JEL Codes: H77, H73, H72, C23

Keywords

Fiscal Transfers Fiscal Equalisation Expenditure Needs Fiscal Capacity Indian States Panel Data Methods

1. Introduction

In multi-tier governments, an asymmetry exists in assigning resources and expenditure responsibilities between the Centre and the subnational (or state) governments which results in vertical and horizontal imbalances. As regional or state governments may differ in their fiscal capacity or ability to raise revenues, the rich states are usually better equipped to finance public service provisions than the poorer ones. On the expenditure side, states may also differ in their spending needs. Even states, with the same fiscal capacity, may face different costs in providing a standard bundle of public services due to differences arising from factors such as price levels, demographic profiles, geographical and climatic conditions, incidence of poverty and level of unemployment. Under such circumstances, states’ expenditure needs differ, and this hampers their ability to provide a comparable minimum standard of public services to ensure equity and efficiency in governance.

Intergovernmental transfers are important policy instruments to address such issues (Oates, 1999). Buchanan (1950) and other proponents of equalisation transfers (Boadway, 1980; Boadway & Flatters, 1991) argue that by permitting equal fiscal treatment of identical persons in a federation, such transfers can promote ‘equity’. By discouraging fiscally induced migration and enabling states to provide certain minimum comparable standards of public services, they can reduce barriers to factor mobility, and thereby enhance economic ‘efficiency’ (Shah, 1994).¹ Thus, equalisation transfers are consistent with normative considerations of equity and efficiency (Munoz et al., 2016).

Countries such as Australia, Canada, Germany and Switzerland have developed their own equalisation strategies with varying implications for equity, incentives and distribution (Bahl et al., 1992; Blair, 1992; Boadway, 2004; Ladd & Yinger, 1994; Ma, 1997; Ridge, 1992). Among these, the Canadian and Australian systems are well-established systems of equalisation: the former focuses on fiscal capacity equalisation, while the latter focuses on both fiscal capacity and expenditure equalisation (Rangarajan & Srivastava, 2004).

In India, the Constitution (1950) provided for a two-tier federal system of government, namely the Centre and the state (or subnational).² It assigned separate tax sources and spending responsibilities to them. As in most other federal nations, it allocated almost all broad-based and buoyant taxes to the Centre and more expenditure responsibilities to the states. States’ fiscal capacity, however, varied due to their economic base, geographical location, etc., which led to vertical and the horizontal imbalances. To mitigate these, the Constitution provided for the transfer of resources from the Centre to the states.

The Finance Commission (FC) of India was constitutionally assigned the task of determining transfers to all states, including to the larger or general category states (GCSs) and the smaller or special category states (SCSs) in the form of tax devolution (shared taxes) and grants, including revenue grants. These transfers were supplemented by Planning Commission grants (until 2014) and grants under various Centrally Sponsored Schemes (CSS) since 1950. Central transfers have played an important role in state governments’ budgets. During 2011–2012 to 2018–2019, the share of Central transfers in the total revenue receipts of the (29) states ranged between 38.3 per cent (2013–2014) and 47.1 per cent (2016–2017).³ The majority of the transfers were unconditional, with a small portion being conditional/specific. Generally, the approach pursued by the FC has been to equalise content in designing tax devolution by incorporating fiscal capacity distance. It considers to some extent the expenditure side of equalisation in the revenue deficit grant. But the procedure is not robust and consistent. There are sharp differences in the level of federal transfers to states in different years. For instance, among the states, Haryana had the lowest per capita transfers of ₹5,434 in 2018–2019 and Arunachal Pradesh had the highest per capita transfers of ₹90,124. Such differentials exist because allocations are made not based on full equalisation.

Wide temporal and spatial disparities exist in other fiscal indicators too. In 2018–2019, Bihar had the lowest per capita own revenues of ₹2,824 and the lowest per capita revenue expenditures of ₹10,515. Goa had the highest per capita own revenues of ₹40,532 and Sikkim had the highest per capita revenue expenditures of ₹79,197. Such vast variations in fiscal indicators should progressively lessen in order to achieve equity in public services. In this context, the following important questions emerge: (a) whether the existing transfers system has an incentive or disincentive effect on state government expenditures; (b) whether the incentive or disincentive effect of transfers occurs in GCSs and/or in SCSs; (c) whether there is a need to reform transfers so that the goal of horizontal fiscal equalisation can be achieved; (d) whether different equalisation principles are needed for GCSs and SCSs; and (e) whether additional resources are required to achieve equalisation across states in India. This study attempts to answer these questions on fiscal equalisation transfers in India using data for 29 states from 2005–2006 to 2018–2019 and a panel data methodology based on a model closer to the Australian transfer mechanism. Specifically, it empirically determines the panel regression-based expenditure needs for each expenditure category for each state and determines the excess fiscal capacity of states using their actual (own) revenues and benchmark revenues.⁴ Then it determines fiscal equalisation transfers as the difference between ‘spending needs’ and ‘excess fiscal capacity’.

The main contributions of this study are as follows. First, although there are several studies on the merits and standards of equalisation for various countries, studies on how to practically equalise spending needs are few (Maarten & Lewis, 2011). This study contributes to this sparsely researched area. Second, studies designing equalisation transfers in the developing country context are very rare. One such is Munoz et al. (2016), which estimates for 10 Latin American countries the effects of transfer systems to identify which transfer equalises in greater or lower degree the own revenues of subnational governments. Saraf and Srivastava (2009) applies the Canadian approach in calculating fiscal capacity equalisation and the Australian approach in calculating the expenditure need-based equalisation only for education and health in India. The present study is the first one to design equalisation transfers based on revenue, capital and total expenditures needs with due consideration of the fiscal capacity of GCSs and SCSs. Third, it also empirically examines states in which transfers have had an incentive or disincentive effect or no effect on public spending.⁵ These state-specific results could be useful for policy makers to design appropriate strategies to achieve a horizontal balance. Finally, while this study provides policy suggestions based on the Indian experience, these may be relevant for other similar federal nations.

The study proceeds as follows. Section 2 briefly reviews the literature on the study topic. Section 3 explains the empirical model, data and estimation technique employed. Section 4 presents and discusses the empirical results, and Section 5 provides the concluding remarks for the study.

2. Brief Review of the Literature

Tiebout (1956), Musgrave (1959) and Oates (1972) developed the ‘First-Generation Theory’ (FGT), which considers equalisation transfers as a necessary tool to prevent relatively richer jurisdictions attracting more investments at the expense of poorer ones. The ‘Second-Generation Theory’ (SGT), which emerged recently, strongly argues for own revenue powers of subnational governments. It suggests the importance of horizontal competition among subnational governments for economic efficiency and a refrainment of the federal government from intervening in subnational taxation and spending decisions. To the SGT theorists, the Centre’s fiscal intervention is distortionary and creates incentive compatibility problems by inducing subnational spending, amassing unsustainable deficits and perpetuating states’ dependence on the Centre for support.

Despite these contrary views, there is a large volume of literature on transfers addressing horizontal fiscal inequalities. These studies argue that it is impossible to provide a comparable level of public services at a comparable level of taxation in all jurisdictions. The jurisdictions may have dissimilar needs and the costs of public service provisions vary among them. On the revenue side, jurisdictions with strong tax bases are better able to finance public service provisions than jurisdictions with poor tax bases. These are the main justifications for net fiscal benefits in the form of equalisation which are essential to rectify inefficiency and inequality.⁶

Buchanan (1950, 1952), Boadway (1980) and Boadway and Flatters (1991) have proposed equalisation transfers as the remedy. In theory, such transfers from the federal government can discourage fiscally induced migration and ensure that every subnational government is capable of providing a standard level of public services at standard tax rates. However, Scott’s (1952) and Courchene’s (1978) view is that equalisation transfers may introduce inefficiency in the regional allocation of resources, because they discourage outmigration of labour to high income jurisdictions where it would be more productive.

Other opponents like Shah (1988) argue that in the presence of full capitalisation, there may not be an efficiency and equity basis for fiscal equalisation transfers, because people in jurisdictions with fiscal surpluses pay relatively more for private services and less for public services, and vice versa for jurisdictions with fiscal deficiencies. Since net benefits are capitalised into property values, a capital gain or loss on account of the local public sector is realised at the time of a property sale. As a result, Tiebout’s prescription that a system of local governments would ensure optimal levels of local public services is not guaranteed. Despite these limitations, as equalisation transfers provide a way to estimate expenditure needs and fiscal capacity as accurately as possible, many procedures have emerged (Munoz et al., 2016).

Measuring Fiscal Capacity: There are various methods to measure the fiscal capacity of a subnational government, which is its ability to raise revenues from its own tax base. The simplest one uses the current or past year’s revenue collections. But this raises many issues: (a) while the potential ability to raise revenue is not directly affected by the tax rates, actual revenues are affected by fiscal effort and tax payer compliance; (b) use of current revenue may provide an incentive for regional governments to impose a lower tax or make less effort in order to realise a higher compensation; and (c) the use of past collection may have time inconsistency because subnational governments expect that current increases in revenue obtained by rates or collection efforts would reduce future transfers (Vaillancourt & Bird, 2005). Another method to measure fiscal capacity is to use a macroeconomic indicator like GDP at the regional level, but this may not be a good indicator because it indicates maximum capacity. A third one is the representative tax system (RTS), which measures the amount of revenue that could be raised by a regional government if it uses the standard tax bases and rates. A fourth method uses the regression method to project fiscal capacity with reference to benchmark revenues chosen from the sample.

Expenditure Needs: The simplest approach is to use historical expenditure patterns (Boex & Martinez-Vazquez, 2004). Although this is simple, there is no guarantee that past expenditures accurately reflect spending needs. Further, due to changing expenditure norms and priorities, past expenditure on a particular service may not reflect current policy objectives (Vaillancourt & Bird, 2007). A second approach assumes that all jurisdictions have identical expenditure needs, so each is allocated the same amount. While this is simple to apply, it leads to a significant gap in per capita resource availability.

A third approach regresses actual expenditures on need indicators and other determinants of regional spending. The coefficients of the need indicators are used to build an allocation formula while keeping the effect of non-need expenditure determinants constant (Ladd, 1994). However, this approach requires data on appropriate regional characteristics that influence regional spending. Further, it is applicable only when actual expenditures are good indicators of spending needs. Another approach is the representative expenditure system (RES) method. This measures a subnational government’s per capita spending needs as the sum of its workload for each category of service weighted by the average spending on each unit of service, divided by the population. Thus, it provides an estimate of how much a jurisdiction would spend per capita given an average service level, its workload and the cost of providing services. However, this approach requires data on various categories of expenditures, workload, etc. (Maarten & Lewis, 2011).

Interestingly, many countries have designed their own equalisation methods. For instance, the Australian model considers both revenue and expenditures. It prepares a ‘standard budget’ for each service based on an all state average of expenditures as well as revenues, so that the system reflects average efficiency. Germany and Switzerland also consider expenditure needs in fiscal equalisation. In Germany, average nation-wise tax revenue per capita is used as the proxy for expenditure of each subnational government. In Switzerland, the calculation of the expenditure needs of cantons considers population density, mountain zones, productive area, etc. The Canadian system uses an elaborate RTS approach where each tax or revenue source is considered individually and the average or representative tax effort is applied to the difference between the standard revenue base and the actual base (see Hansjörg & Claire, 2008; Ma, 1997; Vaillancourt & Bird, 2007 for the main features of fiscal equalisation schemes in selective countries).

Most empirical literature considers both income and transfers as two important economic factors determining public spending. Various hypotheses have been put forward to analyse the effect of these factors on public spending: (a) Wagner’s hypothesis posits a long-run positive relationship between public spending and income (GDP); (b) the Veil hypothesis suggests that as unconditional transfers, like lump sum transfers, can be spent on any combination of public goods and services or used to provide tax relief to residents, they do not affect relative prices (so there is no substitution effect). Therefore, they are no different from the effect of distributing lump sum funds directly to local residents. In theory, a US$1 increase in local resident’s income should have exactly the same impact on local spending as the receipt of US$1 of transfers (Bradford & Oates, 1971); (c) the disincentive effect: Scott (1952) argues that most subnational governments will distribute transfers as a lowering of taxes, and this will crowd out local spending; and (d) the flypaper effect hypothesis, which posits that unconditional transfers to a community have a greater stimulatory effect on spending than an equivalent increase in the income of the median voter. That is, ‘money sticks where it hits’.

Large volumes of empirical studies have emerged to analyse the more general redistributive effects of transfers systems. Most studies focus on individual countries, examining the equalisation capacity of existing systems and suggesting alternatives to reduce horizontal imbalances. Some of them are as follows: ACIR (1986 and 1988) for the USA; Martínez-Vázquez and Boex (1999) for the Russian Federation; and Ruggeri and Yu (2000) for Canada. Regional studies include an analysis of five federal countries: Germany, Australia, Canada, Spain and Switzerland (Hierro et al., 2007); of fiscal disparities in East Asian countries: China, Indonesia, Philippines, Thailand and Vietnam (Hofman & Guerra, 2005); and of OECD countries (OECD, 2014).

A few studies have emerged on this topic that are related to India. For instances, Lalvani (2002) and Panda (2015) have empirically tested and showed that the flypaper effect hypothesis is vindicated in the Indian context. Panda and Velan (2013) examine the incentive effects of fiscal transfers on the spending of 22 states during 1980–1981 to 2004–2005 and find that on average transfers are positively and significantly associated with states’ aggregate expenditures, revenue expenditures and capital expenditures. Panda (2017) examines the impacts of federal transfers on the expenditure of 22 states from 1980–1981 to 2007–2008, and shows that Central transfers stimulate states’ revenue expenditures, capital disbursements and aggregate expenditures. Saraf and Srivastava (2009) estimate the equalisation transfers for health and education by allowing for both revenue and expenditure disabilities in a panel framework.

3. Empirical Model, Data and Estimation

This study employs a framework closer to the Australian transfer mechanism. It involves four steps: (a) identify the expenditure categories; (b) specify and estimate category-wise expenditure equations; (c) estimate the expenditure needs state-wise for each expenditure category, utilising the estimated model and standard benchmark; and estimate the fiscal capacity of each state using per capita own revenues and its standard benchmark; and (d) determining the equalisation transfers for each state based on its estimated expenditure needs and fiscal capacity. As states spend on various areas such as education, health and sanitation specifying and estimating sector-specific expenditure equations would be difficult. Following past studies which have broadly classified aggregate expenditures into revenue and capital expenditures, this study specifies the following linear panel data expenditure model:⁷

\begin{matrix} E_{i t} = β_{0} + β_{1} F T_{i t} + β_{2} O R_{i t} + β_{3} G S D P_{i t} + β_{4} N P_{i t} + \\ β_{5} P D_{i t} + β_{6} U R_{i t} + β_{7} R L_{i t} + β_{8} P C_{i t} + λ_{i} + μ_{t} + ε_{i, t}, \end{matrix}

(1)

where E_it is the annual real per capita (revenue or capital or total) expenditure of the ith state in year t; FT_it is real per capita central fiscal transfers, OR_it is real per capita own tax and own non-tax revenues (as a proxy for states’ own revenue efforts), GSDP_it (gross state domestic product) is real per capita income, NP_it is the non-primary sector share in total GSDP, PD_it is population density, UR_it is the urban ratio, RL_it is road length and PC_it is per capita power consumption. λ_i is the region (state)-specific effect which captures the impact of unobserved heterogeneity in the model, μ_t captures the time-specific impact and ε_it is the stochastic error term.⁸

Basically NP, PD, UR, RL and PC are included to control for economic and social infrastructure variations across states.⁹ A high share of NP indicates a higher level of industrialisation or a less-agrarian state, so the state can spend less on revenue but more on capital investment. A longer road length also indicates higher prosperity, and the state can spend less on revenue and more on capital items. High population density and a high urban ratio may have a negative or positive effect on state expenditures. Power consumption may indicate prosperity. Therefore, the state can spend less, but if power is constrained, then the state needs to spend more to procure from others and supply. In that case, it may have a negative impact.

The data sources for per capita GSDP and the share of the non-primary sector in total GSDP for 29 states from 2005–2006 to 2018–2019 (in 2011–2012 prices) are the NSO and EPW Research Foundation. The data sources for revenue expenditures, capital expenditures, total expenditures, transfers to each state and own revenues are the Comptroller and Auditor General (CAG) of India Audit Reports and the Finance Accounts of the state governments. Using the GSDP deflator and population of the respective states, we compute the real values of the fiscal variables. We extrapolate the population density data using Census 2001 and 2011. We obtain the projected urban ratios from the Office of the Registrar General and Census Commissioner (2006) until 2010–2011 and from the National Commission on Population (2019) after 2010–2011. The data source for the two infrastructure variables, the road length and per capita power consumption, is the RBI’s Handbook of Statistics on the State Economy. The data used is a balanced panel data with (29 x 14 =) 406 observations.

The sample states are as follows: (a) GCS: Andhra Pradesh, Bihar, Chhattisgarh, Gujarat, Haryana, Jharkhand, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Tamil Nadu, Telangana, Uttar Pradesh, Uttarakhand and West Bengal; and (b) SCS: Arunachal Pradesh, Assam, Goa, Himachal Pradesh, Jammu and Kashmir, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim and Tripura.¹⁰ Evidences indicate that the share of the 11 SCS is approximately 4.8 per cent of the total own revenue of all 29 states, 13.8 per cent of transfers, 9 per cent of expenditures and 5 per cent of GSDP. The state expenditure equations in Equation (1) can be estimated using the standard (static) panel data estimation techniques: the fixed effects (FE) or the random effects (RE) method. The Hausman statistics can help us to choose the right model.

As GCSs and SCSs differ in their characteristics, we analyse them separately (split sample). Finally, to analyse the effect of fiscal transfers on the expenditure categories of each state, we allow the fiscal transfer variable to interact with state dummies in an alternative specification of the model. If the estimated coefficient of transfers, β₁, is negative (positive), then there is disincentive (incentive) effect. If it is larger than the income coefficient β₃, there is a flypaper effect. If it is equivalent to the income coefficient, then the Veil hypothesis is valid. If the income coefficient is positive, then the Wagner law holds.

The spending needs of each state is computed as follows. The estimated values from Equation (1) represent how much state i spends (in per capita terms on a given category of expenditure j = revenue or capital or total), conditional on its needs and cost profiles and revenues at period t, on each category, $\hat{E_{i j t}}$ . From these values for a given year, we can establish a benchmark or standardised per capita expenditure (which may be either all states’ average or average of top three states) for each category, $\hat{E_{i j t}^{*}}$ . Then, we calculate the real per capita expenditure need for each state i for each category j as follows:

$E N_{i j t} = \hat{E_{i j t}^{*}} - \hat{E_{i j t}}$ . Multiplying EN_ijt for each state by its population and by the GSDP deflator will give the expenditure needs in nominal terms for a given category j for state i in year t, $E_{i j t}^{*}$ . For states spending more than the benchmark, $E_{i j t}^{*}$ is negative and we assign a zero expenditure needs value.¹¹ Adding the revenue spending and capital spending needs gives the total spending needs of each state, $E_{i j t}^{*}$ .

To determine the fiscal capacity of each state (see footnote 6), first a benchmark per capita own revenue (nominal) $O R_{i j t}^{*}$ is chosen; this may be all states’ averages or the average of the top three states. This is multiplied with the respective population to give the fiscal capacity of each state. The difference between the fiscal capacity and the actual own revenue is the excess fiscal capacity of the state, $E_{i j t}^{*}$ . Then we determine the fiscal equalisation transfer for each state as: $T_{i t} = E_{i j t}^{*} - E_{i j t}^{*}$ . If the difference is negative, the equalisation transfer is zero.

Table 1 presents the descriptive statistics of the study variables. The average real per capita total expenditure of SCSs is 2.4 times higher than that of GCSs. While the average real per capita own revenue of SCGs is 1.1 times higher than that of GCSs, the average real per capita transfers to SCSs is almost five times higher than the average transfers to GCSs. The GCSs and SCSs have different geographical characteristics, indicating that they need separate treatment in determining transfers, so the common benchmark cannot serve the purpose. The correlation analysis (not shown) indicates that a few independent variables such as real per capital own revenue and real per capita income are highly correlated but not perfectly (and so there is no perfect multicollinearity issue).

Table 1

Means and Standard Deviations of the Study Variables

Variables	General Category States		Special Category States
Variables	Mean	SD	Mean	SD
Real per capita revenue expenditure (₹)	10541.29	4215.56	23835.43	11717.81
Real per capita capital expenditure (₹)	1803.26	1079.63	5719.55	3901.75
Real per capita total expenditure (₹)	12914.72	5506.09	30447.98	16809.65
Real per capita transfers (₹)	3957.67	1887.79	19810.71	12213.79
Real per capita own revenues (₹)	6375.82	2985.82	7026.92	7746.26
Real per capita income (GSDP) in ₹	82258.00	39207.13	93324.94	69174.83
Non-primary sector share in GSDP (%)	77.05	7.53	75.67	9.10
Population density	488.11	294.05	177.02	134.81
Urban ratio (%)	32.56	11.30	29.53	15.68
Road length (Km)	198705.0	125496.2	48803.2	77548.1
Per capita power consumption (Kwh)	1069.31	514.11	726.88	577.42
Sample size	252		154

Source: The authors.

4. Empirical Results

4.1 Estimation Results of State Government Expenditures

(1) General Category States: Column 1 of Table 2 reports the two-way RE estimation results of the real per capita revenue expenditure equation (1). As expected, the coefficient of per capita transfers (β₁) is positive and statistically significant at the 1 per cent level, indicating a strong incentive (or crowding-in) effect. This means that revenue expenditures increase with higher transfers. This result confirms that transfers directly affect expenditure. The coefficient of per capita income is also positive and statistically significant at the 1 per cent level, confirming the Wagner hypothesis. Further the transfers’ coefficient is higher than the income coefficient indicating the presence of the flypaper effect.

It is expected that states may also distribute transfers in the form of lower taxes. This will affect the own revenue effort, which will in turn will affect expenditure levels negatively, that is, it will crowd out local spending (Scott, 1952). But per capita own revenue is positively and significantly related to per capita revenue expenditure, implying that states with greater fiscal capacity incur higher revenue expenditure. Both population density and urban ratios have positive parameters, which are statistically significant at the 1 per cent level. As expected, both the non-primary sector share and road length are negatively and significantly associated with per capita revenue expenditure.

Column 2 of Table 2 shows the one-way RE results of the capital expenditures equation. The own revenue, urban ratio, road length variables and per capita power consumption are not included as they are not statistically significant even at the 10 per cent level. Both transfers and income have positive and statistically significant coefficients and the magnitude of the former is higher than that of the latter, indicating the presence of the flypaper effect. The coefficient of population density is negative and statistically significant at the 1 per cent level. As expected, the non-primary sector has a positive and significant impact on capital expenditures.

Table 2
Panel Model Estimation Results of Real Per Capita Expenditures of GCSs and SCSs (2005–2006 to 2018–2019)

Variables	General Category States (GCSs)			Special Category States (SCSs)
	Two-way RE	One-way RE	Two-way RE	One-way FE	Two-way RE	One-way FE
	Revenue Exp.	Capital Exp.	Total Exp.	Revenue Exp.	Capital Exp.	Total Exp.
	(1)	(2)	(3)	(4)	(5)	(6)
Constant	2459.010 (2.22)**	–2444.159 (–2.66)	6341.309 (3.51)*	–	–113.053 (–0.16)	–
Per capita transfers	0.880 (11.79)*	0.317 (9.36)*	1.108 (8.43)*	0.629 (17.7)*	0.236 (9.28)*	1.099 (10.33)*
Per capita own revenues	0.512 (7.76)*	–	0.699 (5.40)*	0.558 (5.69)*	–	–
Per capita income	0.047 (9.61)*	0.005 (2.03)**	0.061 (6.14)*	0.019 (2.19)**	0.023 (5.72)*	0.067 (3.97)*
Non-primary sector share	–59.265 (–3.69)*	38.989 (2.98)*	–111.417 (–3.88)*	82.609 (2.27)**	–	–
Population density	1.645 (2.99)*	–0.821 (–2.59)*	1.573 (2.22)**	39.956 (2.68)*	–	–
Urban ratio	52.481 (4.77)*	–	44.761 (2.15)**	136.75 (3.44)*	–	–
Road length	–0.002 (–2.17)**	–	–0.004 (–3.34)*	–	–	–
Per capita power consumption	–	–	–	2.889 (2.15)**	–	–
R-square	0.961	0.548	0.935	0.9963	0.7671	0.9769
Hausman statistics	23.83	1.28	5.76	20.73	3.55	11.28
State dummies	Included	Included	Included	Included	Included	Included
Time effect	Included	Not Included	Included	Not Included	Included	Not Included

Source: The authors.

Note: t-values are in parentheses. * and ** indicate 1 per cent and 5 per cent levels of significance, respectively.

Column 3 shows the two-way RE results of the total spending equation. Both transfers and income positively and significantly influence per capita total expenditures and the magnitudes of their parameters confirm the presence of the flypaper effect. An INR1 increase in per capita transfers leads to an ₹1.11 increase in per capita total expenditures. Own revenues, population density and urban ratios influence positively and significantly, while the non-primary sector share affects negatively and significantly.

(2) Special Category States: Columns 4–6 of Table 2 present the estimation results for SCSs. Both transfers and income have a positive and statistically significant effect and the transfers coefficient is higher than the income coefficient in all three equations, indicating the presence of the flypaper effect. Per capita own revenue, the share of the non-primary sector, population density, urban ratios and power consumption have a positive and significant impact on per capita revenue expenditures.

(3) Results of Expenditure Equations with Transfers Interaction: Table 3 depicts the estimation results of the alternative specifications of expenditures equations which allow interaction between the state dummies and transfers for GCSs. In all the GCSs except Bihar, Gujarat, Maharashtra, Uttar Pradesh and West Bengal, the effect of transfers on revenue expenditures is positive and significant. The impact of transfers is positive and significant on capital expenditures in all the GCSs except Haryana, Kerala, Maharashtra, Punjab, and Tamil Nadu. The effect of transfers on total expenditures is not significant in Bihar, Gujarat, Jharkhand, Madhya Pradesh, Maharashtra, Tamil Nadu, Uttar Pradesh and West Bengal, but is positive and significant in the remaining 10 GCSs. The effects of other variables are more or less the same as in Table 2 except for the following changes: (a) income, the share of the non-primary sector, and road length turn out to be insignificant in the total expenditures equation; and (b) road length is not significant in the revenue expenditures equation.

Table 4 shows the estimation results of the expenditure equations allowing for the interaction of transfers with state dummies for the SCSs. The effects of other variables are more or less the same as in Table 2, except that urban ratio and power consumption turn out to be insignificant in the revenue expenditures equation. The effect of transfers on revenue expenditures is positive and significant in all SCSs except Goa, Meghalaya and Sikkim. Apart from Assam, the effect of transfers is positive and significant on capital expenditures in all the SCSs; and apart from Assam, Manipur and Sikkim, transfers influence positively and significantly the total expenditures of all the SCSs.

Table 3

Panel Model Estimation Results for GCSs with Transfer Interaction

Variables	Two-way RE		One-way RE		Two-way RE
	Revenue Exp.		Capital Exp.		Total Exp.
	Coefficient	T-ratio	Coefficient	T-ratio	Coefficient	T-ratio
Constant	2292.936	0.82	–1567.412	–1.34	–1108.875	–0.48
Per capita own revenues	0.456	8.74*	–	–	0.531	4.36*
Per capita income	0.026	3.05*	0.012	2.69*	0.006	0.29
Non-primary sector share	–30.992	–2.22**	26.254	1.64***	30.474	0.93
Population density	3.215	6.61*	–1.212	–2.19**	3.251	2.86*
Urban ratio	48.552	4.71*	–	–	46.381	1.93***
Road length	0.001	1.30	–	–	0.002	0.71
Per capita power consumption	–	–	–	–	–	–
R-square	0.988		0.902		0.961
General Category States
Andhra Pradesh* Per capita transfers	0.753	8.41*	0.197	2.70*	1.437	6.87*
Bihar* Per capita transfers	–0.349	–1.58	0.463	4.08*	–0.697	–1.35
Chhattisgarh* Per capita transfers	0.633	7.41*	0.194	3.85*	0.654	3.28*
Gujarat* Per capita transfers	0.086	0.56	0.338	2.18**	0.484	1.35
Haryana* Per capita transfers	0.896	4.95*	0.291	1.59	1.483	3.51*
Jharkhand*Per capita transfers	0.252	1.77***	0.267	5.27*	0.150	0.45
Karnataka* Per capita transfers	0.401	3.44*	0.203	1.78***	0.617	2.27**
Kerala* Per capita transfers	0.646	4.80*	0.064	0.50	0.558	1.77***
Madhya Pradesh* Per capita transfers	0.297	2.26**	0.275	4.36*	0.367	1.20
Maharashtra* Per capita transfers	0.064	0.33	–0.026	–0.15	–0.004	–0.01
Odisha* Per capita transfers	0.475	5.67*	0.259	5.77*	0.530	2.71*
Punjab* Per capita transfers	0.739	5.20*	–0.008	–0.07	1.266	3.81*
Rajasthan* Per capita transfers	0.701	5.90*	0.190	2.53**	0.809	2.91*
Tamil Nadu* Per capita transfers	0.349	2.58*	0.133	1.03	0.484	1.53
Telangana* Per capita transfers	0.472	4.06*	0.369	3.21*	1.160	4.27*
Uttar Pradesh* Per capita transfers	–0.243	–1.22	0.455	5.31*	–0.486	–1.05
Uttarakhand* Per capita transfers	0.788	11.83*	0.234	3.66*	1.050	6.75*
West Bengal* Per capita transfers	–0.149	–0.80	0.316	2.97*	–0.445	–1.02
State effect	Included		Included		Included
Year effect	Included		Not included		Included

Source: The authors.

Note: *, ** and *** indicate 1 per cent, 5 per cent and 10 per cent levels of significance, respectively.

Table 4

Panel Model Estimation Results of SCSs with Transfer Interaction

Variables	One-way FE		Two-way RE		One-way FE
	Revenue Expenditure		Capital Expenditure		Total Expenditure
	Coefficient	T-ratio	Coefficient	T-ratio	Coefficient	T-ratio
Constant	–	–	325.251	0.30	–	–
Per capita own revenues	0.341	3.40*	–	–	–	–
Per capita income	0.059	4.72*	0.009	3.06*	0.093	2.89*
Non-primary sector share	151.136	3.36*	–	–	–	–
Population density	139.800	3.94*	–	–	–	–
Urban ratio	71.501	1.01	–	–	–	–
Road length	–	–	–	–	–	–
Per capita power consumption	0.212	0.15	–	–	–	–
R-square	0.997	–	0.851	–	0.979	–
Special Category States
Arunachal Pradesh* Per capita transfers	0.599	13.83*	0.284	10.05*	1.246	8.71*
Assam* Per capita transfers	–1.680	–2.71*	0.214	1.01	1.174	1.17
Goa* Per capita transfers	0.090	0.56	0.727	5.08*	1.031	1.86***
Himachal Pradesh* Per capita transfers	0.358	2.73*	0.201	2.67*	0.923	2.19**
Jammu & Kashmir* Per capita transfers	0.404	2.01**	0.313	3.79*	1.231	2.01**
Manipur* Per capita transfers	0.513	2.78*	0.270	3.71*	0.809	1.38
Meghalaya* Per capita transfers	0.216	1.12	0.205	2.06**	1.189	2.45**
Mizoram* Per capita transfers	0.579	10.44*	0.212	6.23*	0.812	4.48*
Nagaland* Per capita transfers	0.869	6.23*	0.197	3.93*	1.359	4.57*
Sikkim* Per capita transfers	0.174	1.51	0.288	9.23*	0.446	1.26
Tripura* Per capita transfers	0.135	0.49	0.296	3.39*	1.077	1.92***
State effect	Included		Included		Included
Year effect	Not included		Included		Not included

Source: The authors.

Note: *, ** and *** indicate 1 per cent, 5 per cent and 10 per cent levels of significance, respectively.

4.2 Spending Needs of Indian States

As explained in Section 3, we use the predicted values of real per capita revenue, capital and total expenditures from the results given in Table 2 to determine the expenditure needs for each GCS and SCS in the respective expenditure category. We use two alternate benchmarks for each expenditure category: average of top 3 states (benchmark 1) and average of all states (benchmark 2). The expenditure need is the difference between the benchmark expenditure and predicted value of expenditure of state i. Notice that we use separate benchmarks for GCS and SCS. Table 5 shows the estimated expenditure needs for GCS and SCS for the year 2018–2019.¹²

Uttarakhand, Kerala and Haryana are the top three states in revenue expenditures. States that are spending above the benchmark get a negative expenditure need value and are assigned a zero need value. In Table 5, the spending needs of Kerala and Uttarakhand are negative and are assigned a zero, using benchmark 1 for the revenue expenditures of GCSs. Haryana’s revenue expenditure needs are the lowest, while Uttar Pradesh has the highest revenue expenditure need of about ₹4,000 billion, followed by Bihar and West Bengal. As per benchmark 1, the total revenue spending needs of GCSs is about ₹13,147 billion. With the average benchmark 2, only 8 out of 18 GCSs have revenue spending needs. The total estimated revenue spending need based on benchmark 2 is about ₹5,295 billion. Similarly, the total revenue expenditure need estimated for all SCSs is about ₹2,996 billion using benchmark 1 and ₹1,250 billion using benchmark 2. Thus, the total revenue expenditure need for all 29 states is about ₹16,143 billion (with benchmark 1) and ₹6,545 billion (with benchmark 2). The total capital expenditures need for all 29 states is about ₹3,911 billion (benchmark 1) and ₹1,244 billion (benchmark 2) and the total expenditures need is about ₹20,017 billion (benchmark 1) and ₹8,585 billion (benchmark 2).

Table 5
Expenditure Needs of State Governments, 2018–2019 (₹1 Crore = ₹10 Million)

States Revenue Expenditure Needs
Capital Expenditure Needs
Total Expenditure Needs

Top 3 Average Average Top 3 Average Average Top 3 Average Average

General Category States

Andhra Pradesh 26,227 0 9,620 254 20,162 0

Bihar 236,680 142,871 40,574 18,547 283,569 176,707

Chhattisgarh 21,373 153 2,972 0 23,689 0

Gujarat 35,690 0 5,527 0 33,310 0

Haryana 6,517 0 4,980 0 7,290 0

Jharkhand 53,484 26,010 8,083 1,632 62,184 30,887

Karnataka 25,940 0 2,881 0 26,558 0

Kerala 0 0 7,651 1,288 2,038 0

Madhya Pradesh 127,444 56,515 24,350 7,695 144,498 63,698

Maharashtra 59,877 0 22,558 1,921 76,199 0

Odisha 32,650 1,243 3,411 0 36,300 523

Punjab 15,312 0 8,197 2,874 18,748 0

Rajasthan 85,159 25,677 20,183 6,217 100,787 33,028

Tamil Nadu 30,585 0 13,904 593 33,357 0

Telangana 15,584 0 4,564 0 9,550 0

Uttar Pradesh 399,649 215,697 66,511 23,317 483,321 273,772

Uttarakhand 0 0 0 0 0 0

West Bengal 142,488 61,340 35,140 16,086 182,437 89,997

All GCS Total 1,314,659 529,505 281,107 80,424 1,543,998 668,613

Special Category States

Arunachal Pradesh 0 0 0 0 0

Assam 173,265 87,359 62,540 30,294 262,746 131,525

Goa 0 0 207 0 492 0

Himachal Pradesh 19,586 1,866 8,825 2,174 30,962 3,895

Jammu & Kashmir 51,720 15,449 19,029 5,414 80,910 25,507

Manipur 15,951 6,889 5,078 1,676 23,078 9,235

Meghalaya 15,101 6,281 5,735 2,425 23,216 9,745

Mizoram 685 0 568 0 1,426 0

Nagaland 6,609 558 2,699 428 10,055 813

Sikkim 129 0 0 0 430 0

Tripura 16,552 6,562 5,300 1,550 24,428 9,169

All SCS Total 299,598 124,965 109,980 43,960 457,743 189,888

All States 1,614,257 654,470 391,087 124,384 2,001,741 858,501

States	Revenue Expenditure Needs	Capital Expenditure Needs	Total Expenditure Needs
General Category States
Andhra Pradesh	26,227	0	9,620	254	20,162	0
Bihar	236,680	142,871	40,574	18,547	283,569	176,707
Chhattisgarh	21,373	153	2,972	0	23,689	0
Gujarat	35,690	0	5,527	0	33,310	0
Haryana	6,517	0	4,980	0	7,290	0
Jharkhand	53,484	26,010	8,083	1,632	62,184	30,887
Karnataka	25,940	0	2,881	0	26,558	0
Kerala	0	0	7,651	1,288	2,038	0
Madhya Pradesh	127,444	56,515	24,350	7,695	144,498	63,698
Maharashtra	59,877	0	22,558	1,921	76,199	0
Odisha	32,650	1,243	3,411	0	36,300	523
Punjab	15,312	0	8,197	2,874	18,748	0
Rajasthan	85,159	25,677	20,183	6,217	100,787	33,028
Tamil Nadu	30,585	0	13,904	593	33,357	0
Telangana	15,584	0	4,564	0	9,550	0
Uttar Pradesh	399,649	215,697	66,511	23,317	483,321	273,772
Uttarakhand	0	0	0	0	0	0
West Bengal	142,488	61,340	35,140	16,086	182,437	89,997
All GCS Total	1,314,659	529,505	281,107	80,424	1,543,998	668,613
Special Category States
Arunachal Pradesh		0	0	0	0	0
Assam	173,265	87,359	62,540	30,294	262,746	131,525
Goa	0	0	207	0	492	0
Himachal Pradesh	19,586	1,866	8,825	2,174	30,962	3,895
Jammu & Kashmir	51,720	15,449	19,029	5,414	80,910	25,507
Manipur	15,951	6,889	5,078	1,676	23,078	9,235
Meghalaya	15,101	6,281	5,735	2,425	23,216	9,745
Mizoram	685	0	568	0	1,426	0
Nagaland	6,609	558	2,699	428	10,055	813
Sikkim	129	0	0	0	430	0
Tripura	16,552	6,562	5,300	1,550	24,428	9,169
All SCS Total	299,598	124,965	109,980	43,960	457,743	189,888
All States	1,614,257	654,470	391,087	124,384	2,001,741	858,501

Source: The authors.

4.3 Determining Fiscal Equalisation Transfers

Expenditure need (EN*) is the sum of revenue and capital expenditure needs from Table 5. It is noted that the estimated EN* of each state slightly varies from the total expenditure need of the state in Table 5. As indicated in Section 3, the difference between the fiscal capacity and actual own revenue is the excess fiscal capacity for each state i in t, EF*_it. Table 6 indicates expenditure needs and excess revenue capacity based on two benchmarks for each state. The fiscal equalisation transfer for a state is: EN* – EF*. We consider four scenarios: (a) excess fiscal capacity and expenditure needs, both based on the top three average benchmarks (Scenario 1); (b) excess revenue capacity based on the all states’ average benchmark and expenditure needs based on the top three average benchmarks (Scenario 2); (c) excess fiscal capacity based on the top three average benchmarks and all states’ average expenditure needs (Scenario 3); and (d) excess fiscal capacity and expenditure needs both based on all states’ average benchmarks (Scenario 4).

Table 6
Equalisation Transfers for All States in 2018–2019 (in ₹ Crore)

States Exp. Need (EN)
Excess Revenue (EF)
Transfers

Top 3 Average All States’ Average Top 3 Average All States’ Average Top 3 EN, Top 3 EF Top 3 EN, All States’ Average EF All States’ Average EN, Top 3 EF All States’ Average EN, All States’ Average EF

General Category States

Andhra Pradesh 35,847 254 32,472 0 3,375 35,847 0 254

Bihar 277,254 161,418 186,010 109,330 91,244 167,924 0 52,088

Chhattisgarh 24,345 153 24,181 5,562 164 18,783 0 0

Gujarat 41,217 0 31,339 0 9,878 41,217 0 0

Haryana 11,497 0 1,579 0 9,918 11,497 0 0

Jharkhand 61,567 27,642 45,713 21,711 15,854 39,856 0 5,931

Karnataka 28,821 0 18,981 0 9,840 28,821 0 0

Kerala 7,651 1,288 1,649 0 6,002 7,651 0 1,288

Madhya Pradesh 151,794 64,210 88,336 35,557 63,458 116,237 0 28,653

Maharashtra 82,435 1,921 21,626 0 60,809 82,435 0 1,921

Odisha 36,061 1,243 35,988 7,843 73 28,218 0 0

Punjab 23,509 2,874 17,441 0 6,068 23,509 0 2,874

Rajasthan 105,342 31,894 65,271 15,937 40,071 89,405 0 15,957

Tamil Nadu 44,489 593 19,909 0 24,580 44,489 0 593

Telangana 20,148 0 0 0 20,148 20,148 0 0

Uttar Pradesh 466,160 239,014 264,219 119,471 201,941 346,689 0 119,543

Uttarakhand 0 0 5,138 0 0 0 0 0

West Bengal 177,628 77,426 117,548 54,004 60,080 123,624 0 23,422

All GCS Total 1,595,765 609,930 977,399 369,414 623,503 1,226,351 0 252,525

Special Category States

Arunachal Pradesh 0 0 2,529 321 0 0 0 0

Assam 235,805 117,653 64,977 18,185 170,828 217,620 52,676 99,468

Goa 207 0 0 0 207 207 0 0

Himachal Pradesh 28,411 4,040 8,603 0 19,808 28,411 0 4,040

Jammu & Kashmir 70,749 20,863 22,542 3,264 48,207 67,485 0 17,599

Manipur 21,029 8,565 7,576 2,962 13,453 18,067 989 5,603

Meghalaya 20,836 8,706 6,883 2,103 13,953 18,733 1,823 6,603

Mizoram 1,253 0 1,920 294 0 959 0 0

Nagaland 9,308 986 4,378 1,501 4,930 7,807 0 0

Sikkim 129 0 172 0 0 129 0 0

Tripura 21,852 8,112 8,235 2,789 13,617 19,063 0 5,323

All SCS Total 409,579 168,925 127,814 31,419 285,004 378,480 55,488 138,636

All States 2,005,344 778,855 1,105,213 400,834 908,507 1,604,831 55,488 391,160

States	Exp. Need (EN*)	Excess Revenue (EF*)	Transfers
General Category States
Andhra Pradesh	35,847	254	32,472	0	3,375	35,847	0	254
Bihar	277,254	161,418	186,010	109,330	91,244	167,924	0	52,088
Chhattisgarh	24,345	153	24,181	5,562	164	18,783	0	0
Gujarat	41,217	0	31,339	0	9,878	41,217	0	0
Haryana	11,497	0	1,579	0	9,918	11,497	0	0
Jharkhand	61,567	27,642	45,713	21,711	15,854	39,856	0	5,931
Karnataka	28,821	0	18,981	0	9,840	28,821	0	0
Kerala	7,651	1,288	1,649	0	6,002	7,651	0	1,288
Madhya Pradesh	151,794	64,210	88,336	35,557	63,458	116,237	0	28,653
Maharashtra	82,435	1,921	21,626	0	60,809	82,435	0	1,921
Odisha	36,061	1,243	35,988	7,843	73	28,218	0	0
Punjab	23,509	2,874	17,441	0	6,068	23,509	0	2,874
Rajasthan	105,342	31,894	65,271	15,937	40,071	89,405	0	15,957
Tamil Nadu	44,489	593	19,909	0	24,580	44,489	0	593
Telangana	20,148	0	0	0	20,148	20,148	0	0
Uttar Pradesh	466,160	239,014	264,219	119,471	201,941	346,689	0	119,543
Uttarakhand	0	0	5,138	0	0	0	0	0
West Bengal	177,628	77,426	117,548	54,004	60,080	123,624	0	23,422
All GCS Total	1,595,765	609,930	977,399	369,414	623,503	1,226,351	0	252,525
Special Category States
Arunachal Pradesh	0	0	2,529	321	0	0	0	0
Assam	235,805	117,653	64,977	18,185	170,828	217,620	52,676	99,468
Goa	207	0	0	0	207	207	0	0
Himachal Pradesh	28,411	4,040	8,603	0	19,808	28,411	0	4,040
Jammu & Kashmir	70,749	20,863	22,542	3,264	48,207	67,485	0	17,599
Manipur	21,029	8,565	7,576	2,962	13,453	18,067	989	5,603
Meghalaya	20,836	8,706	6,883	2,103	13,953	18,733	1,823	6,603
Mizoram	1,253	0	1,920	294	0	959	0	0
Nagaland	9,308	986	4,378	1,501	4,930	7,807	0	0
Sikkim	129	0	172	0	0	129	0	0
Tripura	21,852	8,112	8,235	2,789	13,617	19,063	0	5,323
All SCS Total	409,579	168,925	127,814	31,419	285,004	378,480	55,488	138,636
All States	2,005,344	778,855	1,105,213	400,834	908,507	1,604,831	55,488	391,160

Source: The authors.

Aggregate equalisation transfers, considering both the expenditure needs and excess fiscal capacity of all 29 states, is about ₹9,085 billion (4.95% of the GSDP of these states) in Scenario 1, ₹16,048 billion (8.74%) in Scenario 2, ₹555 billion (0.3%) in Scenario 3 and ₹3,912 billion (2.13%) in Scenario 4.¹³

5. Summary and Conclusion

In this study, an attempt has been made to address an important policy issue pertaining to the determination of fiscal transfers in India guided by the equalisation principle. It has used a model closer to the Australian model to determine both spending needs and the fiscal capacity of 29 Indian states. It has also empirically analysed the effect of fiscal transfers on broad categories of expenditures for each of these states from 2005–2006 to 2018–2019, using standard panel data methodology. As fiscal attributes vary between smaller and hilly states and the larger or GCS, the study has applied separate benchmarks for these two groups of states.

The empirical results indicate a strong incentive or a crowding-in effect of transfers on the revenue, capital and total expenditures of state governments in India. The Wagner hypothesis holds, as income has a positive and significant impact on all three categories of public spending. Further, the transfers’ coefficient is greater than the income coefficient. Thus, the flypaper effect hypothesis is vindicated in the Indian context. These results are consistent with results in past studies.

The results also indicate that in all GCSs, except Bihar, Gujarat, Maharashtra, Uttar Pradesh, and West Bengal and in all SCSs except Goa, Meghalaya and Sikkim, fiscal transfers significantly and positively contribute to revenue expenditures. Except in Haryana, Kerala, Maharashtra, Punjab and Tamil Nadu, fiscal transfers significantly increase the capital expenditures of all the GCSs, and significantly increase the capital expenditures of all SCSs, except Assam. While the effect of transfers on total expenditures is not significant in Bihar, Gujarat, Jharkhand, Madhya Pradesh, Maharashtra, Tamil Nadu, Uttar Pradesh, and West Bengal, in the remaining 10 GCSs, it is positive and significant. Apart from Assam, Manipur, and Sikkim, transfers influence positively and significantly the total expenditures of all SCSs.

As per the all states’ average benchmark, the total amount of expenditure (revenue + capital) needs for all 29 states was about ₹7,790 billion. Using the top 3 states’ average norms for both GCSs and SCSs, the total expenditure needs of all states was ₹20,053 billion. The total amount of excess revenue for all states was ₹11,050 billion using the top 3 average benchmark and ₹4,008 billion using the all states’ average benchmark.

The study determines fiscal equalisation transfers under four alternative scenarios. Total transfers for all 29 states: (a) under Scenario 1, which considers excess fiscal capacity and expenditure needs based on the top three states’ average benchmarks was ₹9,085 billion; (b) in Scenario 2, which uses all states’ average fiscal capacity and expenditure needs based on the top three average benchmarks, was ₹16,048 billion; (c) under Scenario 3, which considers excess fiscal capacity based on the top three average benchmarks and all states’ average expenditure needs, was ₹555 billion; and (d) under Scenario 4, which considers excess fiscal capacity and expenditure needs based on all states’ average benchmarks, was ₹3,112 billion (this is consistent with the Australian approach which equalises with respect to average benchmarks).

In 2018–2019, the Centre’s actual gross revenue receipts (GRR) was ₹25,679 billion, while actual transfers to the 29 states was ₹11,933 billion (i.e., 46.47% of GRR). It could be possible for the Centre to fully or mostly equalise these transfers. To start with, it could consider Scenario 4, under which 17 of the 29 states would receive additional transfers. In the longer run, the Centre could aim at reaching Scenario 1. Thus, our analyses broadly indicated the relevance of the FGT which suggest the importance of equalisation transfers.

To our knowledge, this is the first empirical study to show the state-specific effects of transfers on the expenditures of Indian states and provides estimates of spending needs, fiscal capacity and equalisation transfers for GCSs and SCSs in India. Nevertheless, the study is not free from limitations. First, the results may suffer from econometric issues such as endogeneity of transfers and misspecification of expenditure equations. Second, it has computed spending needs based on the assumption that each unit of public services delivery cost is equal for all states. However, as discussed above, the cost of public service delivery differs across states. Third, it computes spending needs using the levels of expenditure incurred. This may provide an adverse incentive or favour the gap-filling approach. Fourth, in the Australian model, capital expenditure needs are supplemented by an elaborate framework of loan distribution for states, which is not taken into account here. Fifth, this study uses the average per capita revenue benchmark to denote fiscal capacity, but the fiscal capacities of states may not be equal, and revenue mobilisation may dependent on tax compliance and tax effort. Lastly the results are sensitive based on benchmarks.

Despite these limitations, we hope that the findings of this study are useful to policy makers, international agencies and other researchers to take appropriate strategies to design effectively equalisation transfers to Indian states such that all citizens can avail of a comparable standard level of public services.

Footnotes

Acknowledgements

We are thankful to Dr D. K. Srivastava, the Chief Policy Advisor, EY India, for his valuable suggestions on an early version of this article. We are also thankful to anonymous referees of the journal for their useful comments and suggestions.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Notes

References

ACIR. (1986). Measuring state fiscal capacity: Alternative methods and their, uses (Information Reports No. M–150). Advisory Commission on Intergovernmental Relations.

ACIR. (1988). State fiscal capacity and effort (Information Reports No. M–170). Advisory Commission on Intergovernmental Relations.

Bahl

, Martinez-Vazquez

, & Sjoquist

D. L.

(1992). Central city-suburban fiscal disparities. Public Finance Quarterly, 20, 420–432.

Blair

(1992). Financial equalisation between local and regional authorities in European countries. Local Government Studies, 18, 7–27.

Boadway

(1980). Intergovernmental fiscal transfers in Canada. Canadian Tax Foundation.

Boadway

(2004). The theory and practice of equalisation. CESifo Economic Studies, 50, 211–254.

Boadway

, & Flatters

(1991). Federal-provincial relations revisited: Consequences of recent constitutional and policy developments. In McMillan

(Ed.), Provincial public finances: Plaudits, problems, prospects (pp. 87–121). Canadian Tax Foundation.

Boadway

, Sandra

, & Shah

(1993). The reform of fiscal systems in developing countries: A federalism perspective [Paper presentation]. Conference on fiscal reform and structural change, New Delhi.

Boex

, & Martinez-Vazquez

(2004). Designing intergovernmental equalisation transfers with imperfect data: Concepts, practices, and lessons (Working Paper No. 04–21). Georgia State University.

10.

Bradford

D. F.

, & Oates

W. E.

(1971). The analysis of revenue sharing in a new approach to collective fiscal decisions. Quarterly Journal of Economics, 85, 434–448.

11.

Buchanan

J. M.

(1950). Federalism and fiscal equity. American Economic Review, 40, 583–599.

12.

Buchanan

J. M.

(1952). Federal grants and resource allocation. Journal of Political Economy, 60, 208–217.

13.

Courchene

T. J.

(1978). Avenues of adjustment: The transfer system and regional disparities. In Walker

(Ed.), Canadian confederation at the crossroads: The search for federal-provincial balance (pp. 145–186). The Fraser Institute.

14.

Hansjörg

, & Claire

(2008). Fiscal Equalisation. OECD Economic Studies, 44, 1–22.

15.

Hierro

, Atienza

, & Patiño

(2007). Inequality and progressiveness in the distribution of revenues of the states in federal countries: A comparative study (Working Paper No. 07–03). Andrew Young School of Policy Studies, Georgia State University.

16.

Hofman

, & Cordeira Guerra

(2005). Fiscal disparities in East Asia: How large and do they matter? In World Bank (Ed.), East Asia decentralizes: Making local government work (pp. 67–83). The World Bank.

17.

Ladd

H. F.

(1994). Measuring disparities in the fiscal condition of local governments. In Anderson

J. E.

(Eds.), Fiscal equalisation for state and local government finance (pp. 21–54). Praeger.

18.

Ladd

, & Yinger

(1994). The case of equalisation aid. National Tax Journal, 47, 211–224.

19.

Lalvani

(2002). Flypaper effect: Evidence from India. Public Budgeting & Finance, 22(3), 67–88.

20.

(1997). Intergovernmental fiscal transfers in nine countries: Lessons for developing countries (Policy Research Working Paper No. WP 1822). World Bank.

21.

Maarten

, & Lewis

(2011). Equalizing spending needs of sub-national governments in a developing country: The case of Tanzania. Environment and Planning: Government and Policy, 29, 487–501.

22.

Martínez-Vázquez

, & Boex

(1999). Fiscal decentralization in the Russian federation during the transition (Working Paper No. 3). Andrew Young School of Policy Studies, Georgia State University.

23.

Martínez-Vázquez

, & Searle

(2007). Fiscal equalisation: challenges in the design of intergovernmental transfers. Springer Science & Business Media.

24.

Munoz

A. F.

, Radics

G. X.

, & Bone

(2016). Sub-national fiscal disparities and intergovernmental transfers in LAC. Review of Public Economics, 219(4), 35–66.

25.

Musgrave

(1959). Theory of public finance: A study in public economy. McGraw-Hill.

26.

Oates

W. E.

(1972). Fiscal federalism. Harcourt Brace Jovanovich Inc.

27.

Oates

(1999). An essay on fiscal federalism. Journal of Economic Literature, 37(3), 1120–1149.

28.

OECD. (2014). Fiscal federalism: Making decentralization work.

29.

Panda

P. K.

(2015). Fly-paper effect of fiscal transfers in India: A dynamic panel analysis. The Empirical Econometrics and Quantitative Economics Letters, 4(2), 69–79.

30.

Panda

P. K.

(2017). Budgetary impacts of central fiscal transfers in India: Evidence from state level data. VISION: Journal of Indian Taxation, 4(2), 20–38.

31.

Panda

P. K.

, & Nirmala

(2013). Central fiscal transfers and states’ spending in India: An analysis of incentive effect. Economics Bulletin, 33(2), 1229–1246.

32.

Rangarajan

, & Srivastava

D. K.

(2004). Fiscal transfers in Australia: Review and relevance to India. Economic and Political Weekly, 39, 3709–3722.

33.

Ridge

(1992). Local government finance and equalisation: the case of Ireland. Fiscal Studies, 13, 54–73.

34.

Ruggeri

, & Yu

(2000). Federal fiscal balances and redistribution in Canada: 1992–1997. Canadian Tax Journal, 48(3), 626–655.

35.

Saraf

, & Srivastava

D. K.

(2009). Determining general and specific purpose transfers: An integrated approach (Working Paper No. 40). Madras School of Economic.

36.

Scott

A. D.

(1952). Federal grants and resource allocation. Journal of Political Economy, 60, 534–538.

37.

Shah

(1988). Capitalization and the theory of local public finance: An interpretive essay. Journal of Economic Surveys, 2(3), 209–243.

38.

Shah

(1994). A fiscal needs approach to equalisation transfers in a decentralized federation (Policy Research Working Paper No. 1289). World Bank.

39.

Spahn

(2007). Equity and efficiency aspects of interagency transfers in a multi-government framework. In Boadway

& Shah

(Eds.), Intergovernmental fiscal transfers-principles and practice. The World Bank.

40.

Tiebout

C. M.

(1956). A pure theory of local expenditures. Journal of Political Economy, 64(5),416–424.

41.

Vaillancourt

, & Bird

(2005). Expenditure-based equalisation transfers (Paper No. 0512). ITP.

42.

Vaillancourt

, & Bird

(2007). The interregional incidence of central budgets in federation: Some evidence from Canada. Public Budgeting and Finance, 27(1), 1–19.

Equalisation Transfers Based on Spending Needs and Fiscal Capacity of State Governments in India

Abstract

Keywords

1. Introduction

2. Brief Review of the Literature

3. Empirical Model, Data and Estimation

4.1 Estimation Results of State Government Expenditures

Table 2 Panel Model Estimation Results of Real Per Capita Expenditures of GCSs and SCSs (2005–2006 to 2018–2019)

Footnotes

Acknowledgements

Declaration of Conflicting Interests

Funding

Notes

References

Table 2
Panel Model Estimation Results of Real Per Capita Expenditures of GCSs and SCSs (2005–2006 to 2018–2019)