Urban Public Economics Review ISSN: 1697-6223 [email protected] Universidade de Santiago de Compostela España

Bandyopadhyay, Simanti A Framework for Assessing the Fiscal Health of Cities: Some Evidence from India Urban Public Economics Review, núm. 19, julio-diciembre, 2013, pp. 12-43 Universidade de Santiago de Compostela Santiago de Compostela, España

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How to cite Complete issue Scientific Information System More information about this article Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Journal's homepage in redalyc.org Non-profit academic project, developed under the open access initiative A Framework for Assessing the Fiscal Health of Cities: Some Evidence from India

Simanti Bandyopadhyay*

The paper considers big urban agglomerations and smaller cities in India to propose a two stage methodology which explains the variations in fiscal health across cities. In the first stage the expenditure need and revenue capacities are estimated. In the second stage an econometric analysis is attempted to identify the determinants of fiscal health. The nature of relationship between the determinants and the fiscal health indicator is defined by the relative strength of the ‘revenue 12 13 effect’ and the ‘expenditure effect’. We find that the role of the higher tiers of the government is important in bigger and smaller cities in their financial management. For bigger cities, own revenues can also play an important role in improving fiscal health. In smaller cities the role of the demand indicators is not that prominent but the cost indicators are found to be more effective while in case of bigger agglomerations, the demand indicators can play a role.

JEL Classification: H71, H72, H73, H76, H77, R51, R58 Keywords: Expenditure Needs, Revenue Capacity, Fiscal Gap

En este documento llevamos a cabo un estudio tanto de las grandes aglomeraciones urbanas como de ciudades más pequeñas de la India, con el fin de proponer una metodología en dos etapas, capaz de explicar la salud fiscal de las ciudades. En una primera etapa estimamos la capacidad de ingresos y la necesidad de gasto. En la segunda, introducimos un análisis econométrico que intenta identificar los determinantes de la salud fiscal. La fuerza relativa de los efectos de ingresos y gastos viene determinada por la naturaleza de la relación entre los determinantes y el indicador de salud fiscal. De los resultados obtenidos se desprende que el rol de los niveles superiores de gobierno para una buena gestión financiera es importante tanto en las

* National Institute of Public Finance and Policy. New Delhi. Urban Public Economics Review | Revista de Economía Pública Urbana of the services, crucial step in estimating expenditure need is the estima vices. Given the responsibilities ser of the local governments these to provideprovide a set to associated costs the and government local the by capacity. revenue the as to referred is effort revenue of rate dard” government is expected to raise the from resources local sourcesThe government.at a local “normal” or the - “stan of need expenditure the as to vices for those functions assigned to the urban local government is referred needed money to provide minimum basic levelsacceptable of public ser of amount The gap. fiscal or need-capacity a as to referred generally is gap This capacity. and revenue-raising needs expenditure gap between the comparingbygovernments isof conditions fiscal the assess to way One 2. Literature Review proposed in section section ; cities Indian modifications needed in the existing framework to assess fiscal health of in generalmethodologies and also with special reference these to Indian cities and of spells out the applications in difficulties the on elaborates section cities; of health fiscal the assessing on methodologies the on classes of cities. size different across conditions health fiscal in variations for minants sample and attempts to identify difference, the if any, in main the deter a as cities smaller as well as India in agglomerations urban big on draws paper The constraints. data severe of inspite identified, be can health fiscal affecting determinants main the and assessed health be can fiscal conditions which in framework a propose to main is The paper the constraints. of objective data severe to due particularly countries loping for researchers. Formulating a methodology is harder in case of the deve Assessing of health urbanfiscal local bodies has always been a challenge 1. Introduction determinantes perosíafectaelcostedelosindicadores degasto. encontramos conquelosindicadoresdedemandanoson papel importanteenlamejoradesaludfiscal.Enlas pequeñas,nos grandes, losingresospropiosyindicadoresdedemanda jueganun ciudades grandescomoenlasmáspequeñas.Para The expenditure need estimations depend on services to be provided provided be to services on depend estimations need expenditure The The paper is organised as follows. Section 3 for Indian cities; section 5 4 gives an application of the modified framework modified the of application givesan 2 gives a brief literature review concludes the paper. concludes the 3 - - - - - 14 tion of costs (Reschovsky 2007). One way to estimate a cost function of a service is to derive it from the production function which requires data on outputs of public services. Quantifying a public output is as difficult as empirically measuring it. Also, there is an element of simultaneity involved in estimating these functions empirically. Though two stage estimation methods are proposed in the literature to tackle this problem, often the data requirements to carry out such procedures are not ful- filled. Cost functions for primary and secondary education in the United States have been estimated (Duncombe and Yinger 2000; Reschovsky and Imazeki 2003; Imazeki and Reschovsky 2005). For estimating the expen- diture need the coefficients of the estimated cost function can be used to construct a cost index which is the summary measure and can be used to determine the expenditure requirements once the level of service provision 14 15 is specified. Expenditure equations in reduced form are also estimated instead of cost functions to avoid the statistical complexity and daunting data requirements of cost function estimations. The expenditure functions can be explained by a set of cost, demand and resource factors. Expendi- ture equations also can be used to derive cost indices by predicting the local government’s spending with average values for the demand and resource variables but actual values of the cost variables from the esti- mated expenditure equations and then dividing each of these predicted values by the expenditure of the local government with average costs. Bradbury et al (1984) use this methodology using data for Massachu- setts. There are two major approaches to measure revenue capacity: The Representative tax system approach and Regression or stochastic approach. The Representative Tax Approach involves three major steps, first, an appropriate base has to be identified for each category of tax. This base should not be the base which is recorded in official tax statistics, rather it should be the base that can be taken to be representative of relative taxable capacity. Second, a set of representative tax rate which can be constituted as representative tax system need to be generated. This repre- sentative rate of the tax may be derived as the average of the effective rates of that tax, where the effective rates are defined as the ratio of actual collection to the potential base. Third, the average effective rate (AER) for each source can be calculated as a weighted average of the effective rates of all the sources, weights being the share of each source. The product of AER and the potential base of a tax will indicate the revenue which the Urban Public Economics Review | Revista de Economía Pública Urbana gies proposed in the literature (Bandyopadhyay and Rao disaggregation required for assessment of fiscal health by the methodolo cities in the developing world where data is not available to the degree of This paper attempts to develop a framework for assessing fiscal health for 3. A Modified Methodological Framework method. regression is aggregate the called method is this employed in method, this its estimated ratio will overallshowAs the ULB’s effort. the tax ratiotax for a ratio tax ULB with actual of the relativea Thus capacity. comparison taxable the represent to taken be can ratio tax estimated the natively .Alter effort tax measureof the as taken be can residualvariance the of equation attributed be capacity may efforts. to tax and tax component of variance basis a the unexplained in on the be it would ratio for tax actual estimated the that and between ULB difference The analysis. regression multiple through derived be maycapacity taxable affect to identified tax the of average measure degree of the relationship the between the tax ratio that and the factors so ratio, tax of effort the government will be derived on the basis the of residuals. The on factors capacity tax factors. nent effort are tax as termed tax capacity factors and factors the affecting third the compo of governmentthe to tax. The factors affecting twofirst components are pay to taxes, the people ability of the the administration of to collect ability taxes and the the willingness on depends ratio tax actual The ables. and indicators of tax capacity and factors tax aseffort independent vari by a regression analysis where tax ratio is taken as the dependent variable explained be can jurisdiction) the of income for proxy or (GCP) Product City Gross the to jurisdiction the in levied taxes/charges all of sum as used. is concerned ULB could raise from that source if its average level of potential expenditure needs component can be estimated by econometric methods gap. fiscal mate the bring in some modifications so that we can use the data available to esti 2010 Tax effort can be measured in one of the two ways: some expression expression some ways: two the of one in measured be can effort Tax the isolate and quantify to made be can attempt Alternatively,an of In or the tax approach variation ratio (defined Regression stochastic There are two main components in measuring fiscal gap in a city. The ). Within the existing methodological framework we would like to to like would we framework methodological existing the Within ). 2009 , Krueathep , Krueathep - - - - - 16 for which city level data on consumption of local services are needed. Also, we need city level norms for these services. These requirements can- not be fulfilled in case of Indian cities. Also, apart from the expenditure on services, there are expenditures which cannot be categorized and thus cannot be specified to have norms. So it is very difficult to quantify the ideal level for a part of the expenditures which is heterogeneous in nature, but constitutes a considerable share in the expenditure of a ULB (Ban- dyopadhyay 2011, Bandyopadhyay and Rao, 2009, NIPFP (2007a, 2007b, 2007c, 2007d, 2008a). For all these difficulties we have estimated expenditure needs from expert opinion (Table 1). In India we have expert groups specifying mini- mum acceptable physical levels of these services according to city size classes to provide as physical norms. Corresponding to these physical norms, ideal levels of expenditures as financial norms for these services 16 17 are also estimated. We have used the latest HPEC (2011) norms for Indi- an cities in this paper. We have taken five major services viz water supply, sewerage/sanitation, street lighting, roads and solid waste management and have estimated the financial requirements in per capita terms on these services. We sum up the financial norms for all these services and estimate the expenditure need on these services for the ULB. The standard methodologies estimating the revenue capacities are very demanding as far as data requirements are concerned in general. Estimat- ing the representative tax base is extremely difficult in the absence of data required to the level of disaggregation and involves some amount of sub- jectivity. The applicability of these methods in case of Indian cities is restricted in particular as the data on proxies for urban tax base, for instance incomes of cities, are not available in India. The problem with regression approach is the conceptualization of the residuals as a measure of tax effort. Also, estimating a model identifying factors affecting taxable capacity becomes difficult as it involves elements of simultaneity. To overcome these methodological problems we have estimated revenue capacity by a simple procedure (Table 1). We propose to estimate the city level incomes from the data on district level domestic products. We take the ratio of own revenue to Gross City Product and propose a higher own revenue to GCP ratio as the desired rate at which revenues can be gener- ated and also which are politically feasible (Bandyopadhyay 2011, Ban- dyopadhyay and Rao, 2009). Urban Public Economics Review | Revista de Economía Pública Urbana

Table 1: Summary Results: Estimated Expenditure Needs and Revenue Capacities (INR) of Urban Local Bodies1

Population Num- Descriptive Population Expenditure Estimated Estimated Estimated Estimated Size Class ber of Statistics to Revenue Revenue Revenue Capital Total Expen- ULBs Ratio Capacity Expenditure Expenditure diture Need (Per Capita) Need Need (Per Capita) (Per Capita) (Per Capita) Below 50,000 46 Average 28,980 1.54 573 455 554 1,010 Standard Deviation 13,103 1.56 513 263 627 876 Maximum 49,569 6.81 2,883 1,955 3,133 5,088 Minimum 6,792 0.02 166 304 70 374 50,000-1 lakh 48 Average 72,586 1.39 688 972 1,214 2,186 Standard Deviation 18,454 1.21 751 1,508 2,089 3,575 Maximum 98,989 5.46 3,987 9,792 12,906 22,699 Minimum 50,038 0.4 187 549 130 680 1 Lakh -5 Lakhs 64 Average 213,827 1.20 702 829 1,705 2,535 Standard Deviation 95,544 2.28 350 388 803 1,123 Maximum 490,900 14.13 1,896 3,331 5,156 8,486 Minimum 108,242 0.03 158 556 131 687 5 Lakhs and 8 Average 4,201,750 1.32 1,317 1,347 1,893 3,241 Above Standard Deviation 4,649,833 1.13 807 920 1,407 2,301 Maximum 15,100,000 3.41 2,537 3,428 4,969 8,397 Minimum 847,093 0.07 242 577 197 1,021 1 For details of location and population of the urban local bodies see Appendix 1 18 To compensate for the lack of statistical rigor in the estimations of expenditure needs and revenue capacities, we propose a framework which analyses the ratio of expenditure needs to revenue capacity by fitting an econometric model. It is a two step method, in the first stage we estimate the expenditure need and revenue capacity separately by simple methods discussed above. In the second stage we take the ratio of expenditure need and revenue capacity as an indicator of financial performance of a ULB and fit an econometric model to explain the performance of ULBs on the basis of factors which are likely to affect the performance of the ULBs. We categorise the explanatory variables for the model into five categories viz. resource, demand, infrastructure, service and cost. The resource vari- ables are different sources of municipal revenues, the demand variables would affect the performance from the demand side of the inhabitants of the city, infrastructure indicators are those which are combined out- 18 19 comes of the efforts of the urban local bodies and the upper tiers of the government or PPP like electricity provision, banks etc, service indicators give the state of local services in the ULBs, cost indicators affect the per- formance through the cost of provision of local services. The categorization is elaborated in Bandyopadhyay (2011). Models are generated with three sets of financial ratios as the depen- dent variable viz. capital expenditure need to revenue capacity model (taking only capital expenditure needs) and revenue expenditure need to revenue capacity model (taking only revenue expenditure needs), total expenditure need to revenue capacity (taking both capital and revenue expenditure needs together). The magnitudes of ratios give an indication of what proportion of the expenditure needs can be financed once the revenue capacity is realized. A value greater than 1 would indicate that expenditure need cannot be covered even if the revenue capacity is realized in the ULB. The main advantage of this methodology is that we can not only esti- mate the expenditure needs and revenue capacities but also get an idea about the main determinants of the financial performance of the ULBs. This methodology is particularly helpful in assessing the fiscal health of cities in developing countries because it is more flexible and thus less demanding as far as data requirement is concerned. The approach is an indirect one but can bring out interesting insights explaining perfor- mances of cities. In the following section we would discuss a case study on Indian cities using this methodology. Urban Public Economics Review | Revista de Economía Pública Urbana 2 their borders with Jharkhand. A description of the ULBs according to to according ULBs the of population and location are given in Appendix description A Jharkhand. with borders their state of Jharkhand and eight adjacent districts of West Bengal which share Kolkata, Delhi, Chennai, Hyderabad viz and Pune India and the urban in local bodies agglomerations of the big five of constitutes sample Our health. of fiscal an analysis attempt to of India areas backward paratively com from cities smaller and cities metropolitan of sample a take We 4. Assessing the Fiscal Health of Indian Cities: Data and Results Cost Indicators Indicators Service Indicators Infrastructure Demand Indicators Resource Indicators Category Table 2:CategorywiseExplanatoryVariablesforPerformanceofULBs and smaller ULBs. We have models for three each class of cities. The models are fitted separately for cities in bigger urban agglomerations as the variables in the other categories are collected from Census of India. budgets from ULBsthe where of surveys the lected in course of primary ables are summarized in Table expenditure need to revenue capacity. The categories of vari explanatory is the financial performance indicator of a ULB expressed as a ratio of the The metropolitanof details citiesthe are given in Bandyopadhyay and ( Rao As we have mentioned in the previous section, the dependent variable ( ( Bohra Bandyopadhyayand in ULBs smaller the of those 2011 ). ). Size, Area (sq km), Density Population, Number of Households, Household Toilets per 1000 population (%), drainage surface closed having Households (%), water tap having Households (%), premises within water having Households population, 1000 per lights Street population, 1000 per Roads tions(%), Banks per Sq Km Non domestic connec Connections to total population, 1000 per Connections Domestic Non and Domestic population, 1000 per Electricity ing Banking Facilities and Literacy Households having No Assets, Households Avail Tax,Property Tax, Non Tax Revenue, Transfers Variables 2 . The data on resource indicators are col 1 . 2 2010 ) and Bandyopadhyayand ) 2009 - ) and and ) - - - - - 20 The principle in which the model works is very simple. All the explan- atory variables are likely to affect both the expenditure needs and the revenue capacity separately. Let us define them as the ‘expenditure effect’ and the ‘revenue effect’ respectively. Some effects are direct while some work through indirect chains. The relative strength of the two would determine the effect of the determinants on the financial ratios as per- formance indicators of ULBs. The empirical justification would come by splitting the two effects to analyse the resulting impact. We take the resource category to explain the idea. The resource vari- ables are likely to affect the revenue capacity as higher values of these variables would be associated with higher values of revenue capacities. The own revenue components would have an effect through own revenue to GCP ratio whereas the transfers would have a direct impact. This is the revenue effect. On the other hand these variables would have an 20 21 indirect impact on expenditure needs. A higher own revenues would mean that the inhabitants are capable of giving higher taxes and also the jurisdictions have a better administrative efficiency. A higher tax and nontax-paying inhabitants would likely to put pressure on the gov- ernment to provide higher and better levels of services, thus having a positive impact on expenditure needs. This is the ‘expenditure effect’. The effect of a resource variable on the financial ratio would be deter- mined by the relative strengths of these two effects. This is an empirical question. Similarly, all the categories of explanatory variables would have some impact from the demand side and some from the supply side on the two components of the ratio and end result would determine the sign and magnitude of the regression coefficients as an answer to an empirical question. In what follows we would analyse and interpret the results of the models fitted in the paper.

Smaller City models A Sample size of 88 ULBs includes all ULBs in the state of Jharkhand and those located in eight adjacent districts of the state of West Bengal. All the models are log-log models. We attempt three sets of regressions, with the total expenditure needs, capital expenditure needs and revenue expen- diture needs with the same set of explanatory variables. The keys and abbreviations for the variables used in the models are given in Appendix 2. The descriptive statistics and the results are summarized in Appendix 3 (Tables A1-A4). Urban Public Economics Review | Revista de Economía Pública Urbana have an adverse impact on fiscal health. ULBs with higher population population higher with ULBs health. fiscal on impact adverse an have to density population find denselywe cities of sample more our In areas. populated in cost lesser a at provided be can services some scale as stagedue videdto economiesof andoperation the of for service the pro services of nature the on depend would rise would needs diture tial contributors to revenues in densely populated cities. Whether expen- sample. It can cause the revenue capacity to rise because of more poten gap. fiscal lower the not necessarily would taxes raising cities In these er ratio. capacity. This health. leads to a better fiscal revenue in rise the by offset is needs expenditure on pressure the find we cities small of sample our In charges. and fees taxes, of form the in of provision. service It can alsoterms generate amounta greater of own revenues in government the on pressure a be would effect immediate The jurisdiction. its reasonspoliticalinfor peopleand attracteconomic local level the in smaller cities. level higher that fact at management fiscal in the performance better for needed to is participation indicative is This cities. fiscal smaller better a of to health lead can Partnership Private Public with or level state local the at provision service of levelcities. canhealth lead to a better fiscal better So, way. positive a in health fiscal affect can which manageable, somehow is migration in cities, smaller in facilities health and education of lack employment, of options lesser with Also, side. expenditure the on pressure less be will there hence and already met hasthe minimum provision basic standards infrastructure and has and better living service conditions higher has a which of city A performance city. the explaining in play to role positive a has premises a positive gap. role downfiscal in bringing the play grants of form the in transfers intergovernmental that find we ies, needs. Thus‘revenue the effect’ dominates. In our sample of smaller cit ment, higher revenuethe capacity littlewith or no effect on expenditure revenuecapacity ratio. to We find needs that higher expenditure the grants from total the higher tier the govern- of determinants the study We Model 1. Total expenditure need to revenue capacity model Higher density would have a negative impact in our health on fiscal However, higher per capita total tax revenue is associated with a high can which one the is growth population higher a has which ULB A Also better infrastructure provision like electricity which is done at the within water having households of proportion like indicator Service - - - - 22 densities are unlikely to perform better in terms of fiscal health. This implies that the ‘expenditure effect’ dominates the ‘revenue effect’ in this case.

Model 2. Capital expenditure need to Revenue capacity model We study the determinants of capital expenditure needs to revenue capac- ity model separately. We find that transfers and grants from the higher tier government raise the revenue capacity and reduce the ratio. Higher intergovernmental transfers can cause the ‘revenue effect’ to dominate in small cities. Service indicators like households having water within premises (%) and households having tap water (%) and infrastructure indicator like number of domestic and non domestic electricity connections per 1000 population have a negative impact on the ratio like the total expenditure 22 23 needs model. Non tax revenues play a negative impact on fiscal health defined in terms of capital expenditures. Area and population density also affect the ratio adversely. Higher the density and area, higher will be pressure on the local government expen- diture. Higher area and density can also be interpreted as higher potential for revenues. In our sample of cities the expenditure effect dominates causing a higher ratio to be associated with a bigger area and density.

Model 3. Revenue expenditure need to Revenue capacity model We study the determinants of Revenue expenditure need to revenue capac- ity ratio model separately. We find that higher the transfer and grants from the upper tier government, better the fiscal health indicators in terms of revenue expenditure needs. Service indicators like proportion of households with water sources within premises would have a positive impact on the fiscal health indica- tor. A ULB with a higher proportion of households with water sources within premises would have a lower revenue expenditure need to revenue capacity ratio in our sample. A higher population growth can lead to a better fiscal health in our sample of smaller ULBs which implies that ‘revenue effect’ dominates the ‘expenditure effect’. Higher per capita total tax revenue is associated with higher financial ratios of ULBs in our model. This implies that ‘expenditure effect’ domi- nates the ‘revenue effect’. Urban Public Economics Review | Revista de Economía Pública Urbana revenues are also a part which is generated through activities in a ULB ULB a in activities through generated is which part a also are revenues in Assigned bigger cities. the health can a ensure fiscal components better ones having better fiscal health. So a better performance in the own source which ULBs having higher revenue collections in these categories are the of result a as dominates effect’ ‘revenue the rise, revenue assigned and and transfers to lower the financial ratio. As property tax, non tax collection capita way. per In the agglomerations model, tax, bigger cities gain non both from own sources capita per tax, assigned revenue are significant and can affect in health fiscal a positive property capita per viz. indicators category resource the of components three that find Weratio. revenuecapacity to needs expenditure total the of determinants the study We Model 1. Total expenditure need to revenue capacity model are summarized arein Appendix summarized models are given in Appendix the in used variables the forabbreviations and keys The variables. atory diture needs and revenue expenditure needs with the same set of explan three sets of regressions, with the total expenditure needs, capital expen in India, viz. Delhi, Kolkata, Chennai, Hyderabad and Pune. We attempt A Sample of Agglomerations Models in any modelssignificant for of the smaller ULBs. are indicators category demand the of none that noted be to also is It model. other any in not but model capital the in significant is nections not in revenuethe model. Proportion of households water tap con with nections per total model. Also, number of domestic and non-domestic electricity con a is revenue tax non significant in variable a capital the expenditure need Similarly, model but not in the not model. is total it the but in variable, variable significant significant a is area models, capital and enue to revenue capacity model. However, arethere exceptions. In case of rev to need expenditure need expenditure are total the ones the also which affect capacity revenue capital and capacity revenue to need expenditure revenue the affect which variables the that noted be can It signs. same ‘revenuethe effect’ impact on fiscal health which implies that ‘expenditure effect’ dominates We find that across the models the same significant variables have the adverse an have density population area, like indicators cost Also, 78 1000 ULBs is considered from five major urban agglomerations population is significant in capital and total model but 4 2 (Tables A5-A8). . The descriptive statistics and resultsthe - - - - - 24 but goes to the state and comes back as a share to the ULBs. So in bigger ULBs a better performance in revenue collections can ensure better fiscal health. We also find that number of electricity connections per 1000 popula- tion can affect fiscal health in a positive way. Better infrastructure condi- tions are provided by the upper tiers of the government which in our sample of bigger cities can cause a better fiscal health of the local govern- ment. Here also, the ‘revenue effect’ dominates. We also find that demand indicators like asset possession of households can affect the fiscal health in a positive way. Proportion of households having no assets is significant with a positive sign. An increase in the households with no asset is indicative of low development indicator of the city and low standard of living of the people residing in that city. This hampers the revenue and thus revenue capacity falls causing the ratio to 24 25 rise. This also indicates less pressure on the expenditure needs as people with lower standard of living would likely to put lesser pressure on the local government for provision of quality services. In our sample of bigger cities, the ‘revenue effect’ in the negative direction seems to dominate. We can infer that the higher the proportion of people having below aver- age standard of living, lower would be the performance in terms of fiscal health. However, demand indicator like proportion of households availing banking facilities would have an adverse impact on the fiscal health ratio in our sample of bigger cities. We also find that number of toilets per 1000 population can affect the fiscal health in an adverse way. In our sample of cities the expenditure effect seem to dominate and we find that the ULBs having higher propor- tions of people with better standard of living do not perform better in terms of fiscal health indicators.

Model 2. Capital expenditure need to revenue capacity model We study the determinants of the capital expenditure needs to revenue capacity ratio for the agglomeration cities. We find that per capita prop- erty tax, per capita non tax revenue, per capita assigned revenue can play a positive role on fiscal health of cities. As property tax, non tax collection and assigned revenue rise, revenue capacity of a ULB increases and the effect dominates that on the capital expenditure needs. This reduces the ratio. We also find that number of electricity connections per 1000 popula- tion can affect fiscal health in a positive way. Urban Public Economics Review | Revista de Economía Pública Urbana fiscal health ratio defined in terms of revenue expenditure needs in a in needs expenditure revenue negative way. of terms in defined ratio health fiscal defined in terms of revenue in terms expendituredefined needs in our sample of cities. ratio health fiscal on the impact have would an adverse facilities banking cities. of these health impact fiscal on the positivehavea can households of possession asset of indicator the that find also We cities. agglomeration of sample our in revenue needs expenditureof terms in defined health fiscal on effect positive a has tion case. resource achange inthis indicators inthe with expenditure effect the dominates effect revenue the that implies This ratio. the reduces this and rises revenuecapacity the increase, variables revenuecapacity. these As revenue capacity ratio in a positive way. These are all a source of increase in enue, per capita assigned revenue can affect the revenue expenditure need to model. We revenue,tax per property per non rev capita capita tax that find We study the determinants of Revenue expenditure need to revenue capacity Model 3. Revenue expenditure need to revenue capacity model expenditure needs. reducing advantages scale of economies there are nor utilized is tage of a better fiscal health which means neither the revenue potential - advan stage of operation. In our sample of cities size of the ULB is the not and indicative services of nature the upon depending services on expenditure on impact negative or positive a have can area of coverage higher a Also, capacity. revenue the increases thus and collection tax higher to lead can area Higher model. capacity revenue to needs expenditure capital the ing sign. positive a has but in our sample of cities. ratio health fiscal on the impact have would an adverse facilities banking to rise. However, demand indicator like proportion of households ratio availingthe causing falls capacity revenue thus and revenue the affects This tor of the city and low standard of living of the people residing in the city. householdsinno the assetwith is indicative of low development indica increasepositiveAn a sign. has and significant ishaving asset,holds no We also find that number of toilets per availing households of proportion like indicator demand However, We alsonumber that find of electricity connections per Cost indicator, like area, is significant and has a positive sign in explain- Service indicator number of Service toilets per houseof proportion in Theassetpossession households,of reflected 1000 1000 population can affect the population is significant 1000 popula - - - - 26 It can be seen that the ‘total’ model is a combination of ‘revenue’ and ‘capital’ model. There is not much difference between the significant variables across the models. However, non tax is significant in only total expenditure need and revenue expenditure need model and not the capi- tal expenditure need model at 5 per cent level of significance. The resource variables in the total model behave in the same way as in the capital model. None of the cost variables are significant except area in the capi- tal model. A broad comparison between smaller ULB models (Jharkhand and West Bengal) and bigger ULB models (5 UAs) by considering only the total expenditure need model gives a few points of similarity between the mod- els. Transfers is a significant common resource variable in both the mod- els carrying a negative sign. For bigger agglomerations, it is the assigned revenue components and not the grant component which can help reduc- 26 27 ing the fiscal gap ratio. Also, number of electricity connections per 1000 population has a negative effect on the ratio in both the models. Transfers and infrastructure like electricity involve the role of the upper tiers of the government. This implies that better financial performance of the ULBs, irrespective of size, can be explained by a better performance of the upper tiers of the government in providing infrastructure or releasing grants. There are a few points of differences too. Whereas in the bigger ULB model, cities with better service indicators have higher values of expen- diture need to revenue capacity ratio, the cities with better service indica- tors would have lower expenditure need to revenue capacity ratios in smaller ULB model. This indicates that for bigger cities better service delivery creates more pressure through higher expenditure needs which dominates the contribution to additional revenue capacities. Whereas in the smaller ULBs, it is just the reverse. This difference can be explained by the magnitude and quality of in-migration in the two categories of cities in India. In bigger cities, better opportunities in employment, health and education along with better service delivery would attract a huge in- migration into the cities who cannot possibly be accommodated with the existing service provision network. In smaller cities, better service provi- sion can attract lesser magnitude of in migration which would exert less pressure on the service provision network. Also, in the smaller ULB model the total tax is significant, in case of bigger ULB model, it is not the total tax but property tax and non tax each is significant separately. In fact, in smaller ULBs higher tax collections cannot bring down the gap but widens it, whereas in the bigger ULBs Urban Public Economics Review | Revista de Economía Pública Urbana rization of the explanatory variables might have some overlap across overlap some have might variables explanatory the of rization cost indicators. the of bigger the agglomerations, demand indicators are more prominent than not that prominent but the cost indicators play an important role. In case demandrolesmaller indicatorsratio. cities is ofIn the the the fiscal the the own source revenues can also play role an in down important bringing that find we cities biggerHowever, for management. financial their in government is equally in important bigger and smaller size class of cities ferent size classes in India. We find that the role of the higher tiers of the which can give us meaningful insights. data available from health fiscal in differences the explain can we stage second the in but demanding that not are separately capacities revenue and needs expenditure the estimating in indicators. requirements data the service way This and infrastructure resource, demand, cost, graphic, explain differences the in acrosshealth fiscal cities through socio demo can which model a simple by fitting health of fiscal analysis econometric an attempts but methods simple using capacities revenue and needs ture gregation required. The present framework proposed derives the expendi developing countries of due case to non- in availability suitable of data always to not the levels are of gapsdisag fiscal the estimate to literature local urban of bodies in health developing countries. The fiscal methodologies proposed so far assessing in the for framework a offers paper The 5. Conclusion to attributable be higher qualitythe of living indicators which are highercan in bigger cities. This health. fiscal the influence to made be instrumental can factors side demand the ULBs bigger in that shows This model. ULB bigger of case in significant are assets) the of none having demand variables (households availing banking facilities and households significant effect on the ratio in case of smaller ULB models. In contrast, ULBs. bigger the than government the of tiers upper the on to help can reduce the gap. This transfers shows that smaller ULBs are intergovernmental relatively and more dependent revenues own both ULBs, intergovernmental ULBs smaller in that transfers can reduce fact the gap but own revenues the cannot. Whereas in of bigger indicative gap. is the down This bring can components revenue own the of levels higher A few limitations of studythe can be spelt out in end.the The catego The paper attempts an application a with case study citieswith of dif Another interesting finding is that none of the demand variables has - - - - - 28 categories. Some of the cost or infrastructure indicators can play a role in determining the demand for urban services. This is reflected in the regression results which we analyse and interpret intuitively but quantify- ing the impact as specific to each category might not be possible. How- ever, we have followed a conceptual framework which is clear in terms of defining these variables. Our analysis is still constrained by availability of data because of which we cannot attempt any other model apart from OLS. With limited data this paper can claim to develop a framework that can throw some light on the fiscal performance of the cities in developing countries.

28 29 Urban Public Economics Review | Revista de Economía Pública Urbana Reschovsky. A (2007) ( HPEC ( NIPFP NIPFP ( ( NIPFP NIPFP A NIPFP of Study Cities: of Pilot NIPFP,Indian Pune, (2007c) Health Fiscal the Improving Delhi, of Study Pilot A Cities: Indian of Health Fiscal the Improving (2007b) NIPFP Imazeki, J, and A Reschovsky.A and J, Imazeki,2005. 2000 Yinger J. and Duncombe,W Bandyopadhyay.(2009) RaoM.G and S Bandyopadhyay.S, and Bohra O P (2010) Bandyopadhyay S, (2011); References NIPFP ( NIPFP Krueathep W (2010) Bradbury, K, L.,Helen, F. Ladd, M Perrault, A Reschovsky, and J Yinger. 1984. India. Commission, India. NIPFP, Submitted to World Report, New Delhi, Draft Bank. NIPFP, Submitted to World Report, New Delhi, Draft Bank. NIPFP, , Submitted to World Report New Delhi, Draft Bank. New Delhi, Draft Report, Submitted to World Report, New Delhi, Draft Bank. NIPFP, ,Submitted to World Report New Delhi, Draft Bank. NIPFP, Submitted to World Report, New Delhi, Draft Bank. Texas.” from Lessons Standards: Accountability Education State eeting of Costs the mating The Case of New York State.” 151-170. Reduction and Economic Management Division, Washington DC, March No: Paper WorkingResearch of Public Finance and Policy, New Delhi. in Jharkhand, Draft Report submitted to Government of Jharkhand, National Institute School of Policy Studies, Georgia State University, May Atlanta, USA, Paper Working Program Studies International Comparisons’, State University of New Jersey, and Administration.School of Public Affairs The Rutgers, School-Newark, Graduate Thesis, PhD Thailand, In Conditions Fiscal to Offset Fiscal Disparities Across Communities,” (2007 2008 2011 2009 2008 2007 Peabody of Education Journal Rpr o Ida Ubn nrsrcue n Srie, oenet of Government Services, and Infrastructure Urban Indian on Report ) d) Improving the Fiscal Health of Indian Cities: A Pilot Study of Hyderabad, Hyderabad, of Study Pilot A Cities: Indian of Health Fiscal the Improving d) b) Improving the Fiscal Health of Indian Cities: A Synthesis of Pilot Studies, Studies, Pilot of A Synthesis Cities: Indian of Health Fiscal the Improving b) a) Improving the Fiscal Health of Indian Cities: A Pilot Study of Chennai, of Study Pilot A Cities: Indian of Health Fiscal Improvingthe a) ): Urban Property Tax Potential In India, Submitted to Thirteenth Finance Thirteenth to Submitted India, In PotentialTax Property Urban ): a) Improving the Fiscal Health of Indian Cities: A Pilot Study of Kolkata, of Study Pilot A Cities: Indian of Health Fiscal the Improving a) . Bad Luck Or Bad Budgeting A Comparative Analysis Of Municipal Compensating Local Governments for Differences in Expenditure ‘Finances of Urban Local Bodies in Jharkhand: Some Issues and Some Issues in of Jharkhand: Bodies Urban Local ‘Finances Economics of Education Review Economics of Education Review 4863 .“Financing Higher Student Performance Standards: Standards: Performance Student Higher .“Financing “Assessing the Use of Econometric Analysis in Esti in Analysis Econometric “Assessingof Use the , The World Bank, World Bank Institute, Poverty Institute, Bank World Bank, World The , 80 (3): 96–125. : Fiscal Health of Selected Indian Cities’, PolicyCities’, Indian Selected of Health Fiscal : : Functions and Finances of Urban Local Bodies National Tax Journal 37, no. 19 (4): 363–86. 11-13 , Andrew Young Andrew , 2011. “State Aid “State 2009. 2 , , (June): - 30 Needs in a Horizontal Fiscal Equalization Program, Chapter 14 in The Theory and Practice of Intergovernmental Fiscal Transfers, edited by Robin Boadway and Anwar Shah, Washington, DC, The World Bank, 2007: 397-424. Reschovsky, A, and J Imazeki. 2003. “Let No Child Be Left Behind: Determining the Cost of Improved Student Performance.” Public Finance Review 31 (3): 263–90.

30 31 Urban Public Economics Review | Revista de Economía Pública Urbana Pakaur Dubrajpur Gushkara Simdega Mihijam Chhatatanr Khunti Sonamukhi Beldanga Egra Dainhat Hussainabad Raghunathpur Jamtara Chandrakona Cantonment Barrackpur Latehar Ramjibanpur Bundu Jhalda Rajmahal Kodarma Khirpai Chakulia Jasidih Basukinath Rishra(Ct) Kharsawan Urban Local Body Appendix 1. Urban Bodies: Local Sample Seraikela Kharar Jharkhand West Bengal West Bengal Jharkhand Jharkhand Jharkhand Jharkhand West Bengal West Bengal West Bengal West Bengal Jharkhand West Bengal Jharkhand West Bengal Kolkata UA Jharkhand West Bengal Jharkhand West Bengal Jharkhand Jharkhand West Bengal Jharkhand Jharkhand Jharkhand Kolkata UA Jharkhand UA/States Jharkhand West Bengal Population Size Size Population Class (Below (Below Class 50,000) 35,644 36,029 33,236 29,282 23,606 23,030 22,558 22,203 33,981 10,952 28,021 18,538 28,570 20,575 23,441 34,224 14,325 19,082 12,270 15,697 32,173 12,159 14,129 18,143 17,246 18,519 27,378 17,977 14,137 6,792 32 Garhwa Jharkhand 36,686 Godda Jharkhand 37,008 Chirkunda Jharkhand 39,131 Gumla Jharkhand 39,761 Memari West Bengal 39,916 Murshidabad West Bengal 40,195 Chatra Jharkhand 42,020 Sainthia West Bengal 44,291 Dumka Jharkhand 44,989 Jugsalai Jharkhand 46,114 Lohardaga Jharkhand 46,196

Baruipur Kolkata UA 46,595 32 33 Madhupur Jharkhand 47,326 Dehu Road(Cb) Pune UA UA 49,019 Tmluk West Bengal 49,303 Jiaganj-Azimganj West Bengal 49,569

Population Size Urban Local Body UA/States Class (50,000 - 1 lakh) Panskura West Bengal 50,038 Katras Jharkhand 51,233 Rampurhat West Bengal 54,128 Kalna West Bengal 54,397 Chakradharpur Jharkhand 55,228 Ghatal West Bengal 55,371 Kandi West Bengal 55,999 Gayespur Kolkata UA 57,024 Jhargram West Bengal 58,971 Chaibasa Jharkhand 63,648 Bishnupur West Bengal 64,550 Suri West Bengal 65,314 Jhumri Tilaiya Jharkhand 69,503 Pujali Kolkata UA 70,455 Daltonganj Jharkhand 71,422 Urban Public Economics Review | Revista de Economía Pública Urbana Purulia Barrackpur North Bansberia Adityapur Rishra(M) Bhadreswar Baidyabati Alwal Urban Local Body Giridih Deoghar Chas Champdani Kalyani Bally(Ct) Contai Madavaram New Barrackpur Phusro Garulia Jharia Budge Budge Katwa Sahibganj Pune(Cb) Dhulian Kirkee Old Malda Sindri Konnagar Jangipur Bolpur West Bengal Kolkata UA Kolkata UA Jharkhand Kolkata UA Kolkata UA Kolkata UA Hyderabad UA UA/States Jharkhand Jharkhand Jharkhand Kolkata UA Kolkata UA Kolkata UA West Bengal Chennai UA Kolkata UA Jharkhand Kolkata UA Jharkhand Kolkata UA West Bengal Jharkhand Pune UA West Bengal Pune UA UA West Bengal Jharkhand Kolkata UA West Bengal West Bengal Class (1 lakh - 5 - lakh (1 Class Population Size Size Population 104,835 124,668 108,242 109,787 104,910 119,233 121,186 117,881 98,989 98,388 93,205 92,400 92,380 86,698 82,736 80,202 73,053 78,684 83,934 79,324 80,379 81,983 74,830 72,319 83,474 77,220 92,416 80,154 76,746 76,776 97,221 lakh) 34 Khardaha Kolkata UA 126,993 Hazaribag Jharkhand 127,269 Titagarh Kolkata UA 128,703 Halisahar Kolkata UA 128,994 Kanchrapara Kolkata UA 131,543 Uppalkalan Hyderabad UA UA 133,677 Jamuria West Bengal 134,557 Bankura West Bengal 135,176 Kharagpur West Bengal 143,684 Delhi Cantonment Delhi 145,376 Tambaram Chennai UA 148,774 Raniganj West Bengal 148,935 34 35 Barrackpur Kolkata UA 149,649 Alandur Chennai UA 153,264 Dumdum Kolkata UA 157,080 Madhyamgram Kolkata UA 160,578 Uttarpara Kotrung Kolkata UA 162,632 Mango Jharkhand 166,125 Midnapur West Bengal 167,422 Chandannagar Kolkata UA 168,048 English Bazar West Bengal 172,017 Hugli-Chinsurah Kolkata UA 176,374 Behrampur West Bengal 186,728 Bidhan Nagar Kolkata UA 188,474 Haldia West Bengal 189,697 Kapra Hyderabad UA 190,044 Serilingampally Hyderabad UA 192,160 Rajendranagar Hyderabad UA 198,680 Dhanbad Jharkhand 199,258 Uluberia Kolkata UA 209,425 Serampore Kolkata UA 210,682 Nalhati West Bengal 215,432 Secunderabad Hyderabad UA 217,919 Malkajgiri Hyderabad UA 219,999 Tiruvottiyur Chennai UA 227,455 Urban Public Economics Review | Revista de Economía Pública Urbana Delhi Municipal Corporation Chennai Municipal Corporation Corporation Municipal Chennai Kolkata Municipal Corporation Hyderabad Municipal Corporation Pune Municipal Corporation Pimpri Chinchwad Municipal Council Haora Municipal Corporation Ranchi MunicipalRanchi Corporation Jamshedpur Municipal Corporation Urban Local Body Asansol Bhatpara Dumdum South L.B.Nagar Panihati Rajpur Sonarpur Ambattur Kukatpally Quthbullapur Kulti Pallavaram Burdwan Maheshtala New Delhi Municipal Council Avadi Kamarhati Rajarhat Gopalpur Bally(M) Naihati Barasat Baranagar North Dum Dum North Delhi UA Chennai UA Kolkata UA Hyderabad UA Pune UA Pune UA Kolkata UA Jharkhand Jharkhand UA/States West Bengal Kolkata UA Kolkata UA Hyderabad UA Kolkata UA Kolkata UA Chennai UA Hyderabad UA Hyderabad UA West Bengal Chennai UA West Bengal Kolkata UA Delhi UA Chennai UA Kolkata UA Kolkata UA Kolkata UA Kolkata UA Kolkata UA Kolkata UA Kolkata UA Population Size Size Population Class (5 lakhs lakhs (5 Class and above) 15,100,000 4,506,755 2,933,950 3,896,763 1,238,597 4,167,237 923,606 490,900 306,209 330,754 305,384 245,409 348,591 239,912 245,344 281,638 234,979 334,212 308,941 344,813 612,534 274,683 227,826 457,986 367,053 847,093 258,181 457,940 361,015 302,117 347,169 36 Appendix 2. Abbreviations Used in Regression Models

Abbreviation: Name of the Variable Smaller ULBs Model logpop Log of Population loggrpop Log of Population Growth loghh Log of Number of Households logarea Log of Area (sq. Km) logpcproptax Log of Per Capita Property Tax logpcothertax Log of Per Capita Other Taxes logpctottax Log of Per Capita Total Tax Revenues logpcnon tax Log of Per Capita Non Tax Revenues 36 37 logpcownrev Log of Per Capita Own Revenue logpcrevfromgov Log of Revenue from Government (transfers) logpctotrev Log of Per Capita Total Revenue logdensity Log of Population Density logroadper1000 Log of Roads per 1000 population logliteracy Log of literacy loghhtap log of Households Having Taps logbanksperqm Log of banks Per Square Kilometers loghhwaterwithin Log of Household Having Water Within Premises logcsd Log of Household Having Closed Surface Drainage logelectper1000 Log of Electricity per 1000 population logtoiletper1000 Log of Toilet Per 1000 Population loghhnoasset Log of Households Having No Assets logexpneedctorevcap Log of Capital Expenditure Needs to Revenue Capacity logexpneedrtorevcap Log of Revenue Expenditure Need to Revenue Capacity logexpneedtorevcap Log of Total Expenditure Need to Revenue Capacity loghhsize Log of Household Size Log of Non Domestic Connections To Total Elec- lognondomtototelect tricity Connections Urban Public Economics Review | Revista de Economía Pública Urbana logpcgran logpcasnrev logTOIL1000 logAREA logDomNonDom1000 logHHTAP logHHAvailingBankingFac logHHNOASSET logSTRTLIT1000 logCSD logELECT1000 logROAD1000 logDENSITY logHH logTRANSFER logNONTAX logPROPTAX Models Agglomeration Used: Abbreviation Log of Per Grant Capita Log of Per Assigned RevenuesCapita Log of Toilet Per 1000 Population Log of Area 1000 Population Log of Domestic to Non Domestic Connections Per Taps Having Households of log Log of Households Having Banking Facility Log of Households Having No Assets Log of Street Lights Per 1000 Population perLog 1000 of Electricity population Log of Roads per 1000 population Log of Density Log of Number of Households Log of Transfer Log of Non Tax TaxLog of Property VariableName of the Log of Household Having Drainage Closed Surface 38 Appendix 3. Smaller City models

Table A1. Summary Statistics Variable Obs Mean Std. Dev. Min Max logpop 88 10.95548 .9506024 8.823501 13.64957 loggrpop 85 3.262568 .782116 1.386294 6.148468 loghh 86 9.200686 .9621989 7.094235 11.88364 logarea 88 2.680783 .8587232 1.172482 5.177223 logpcproptax 69 2.843262 1.292715 -.5798185 6.120297 logpcothertax 68 1.820533 1.624707 -2.995732 4.60517 logpctottax 72 3.247489 1.25295 -.3710637 6.196444 logpcnontax 71 2.763857 1.772648 -2.65926 5.463832 38 39 logpcownrev 73 3.903768 1.210573 -.2744369 6.393591 logpctransfer 72 5.234513 .8428414 1.922788 7.907968 logpctotrev 75 5.479287 .979284 1.922788 7.914621 logdensity 86 8.227163 .805521 6.709329 9.937987 logroadper1000 70 .3935277 .6520125 -.2629639 4.007333 logliteracy 82 4.203502 .1132699 3.637586 4.394449 loghhtap 87 3.271534 .980384 0 4.584968 logbanksper100sqkm 86 3.717921 .8535008 1.098612 5.717028 loghhwaterwithin 86 2.278777 1.191427 0 4.127134 logcsdrain 88 2.160353 .8139805 0 4.007333 logelectper1000 88 6.392644 .3094466 5.062595 6.860664 logtoiletper1000 88 6.400831 .3375505 4.682131 6.820016 loghhnoasset 66 3.261761 .3383825 2.397895 4.007333 logexpneedctorevcap 75 -.2633242 1.272061 -3.723553 1.564821 logexpneedrtorevcap 75 .2194891 .6338504 -2.249063 1.41049 lognondomtototelect 86 -1.271436 .713286 -6.216606 -.0730519 loghhsize 86 1.740954 .0983636 1.361738 2.007086 logexpneedtorevcap 75 .787879 .8147802 -2.042947 2.167597 Urban Public Economics Review | Revista de Economía Pública Urbana logelectper1000 logdensity logpcrevfromgov loghhwaterwithin logpctottax loggrpop Total Residual _cons Source Table A2.RegressionResults Model 1. Total Expenditure need to revenue capacity model: Total Residual Model Source Table A3.RegressionResults Model 2. Capital expenditure need to revenue capacity model: logexpneedtorevcap Root MSE = .32305 Adj R-squared = 0.7795 R-squared = 0.7998 Prob > F = 0.0000 F(6,59) = 39.29 Number of obs = 66 Model Root MSE = .43161 Adj R-squared = 0.8616 R-squared = 0.8763 Prob > F = 0.0000 F(7,59) = 59.69 Number of obs = 67

-.7866694 -.1739199 -.1841651 4.735461 .0854776

-.045881 .2111157 Coef. 10.9907035 88.8291524 77.8384489 30.7602262 24.6029376 6. 1572886

SS SS

.0682564 .0399846 .0528572 .6779843 .0621598 .0418632 .0260316 Std. Err.

-11.53 -1.15 -3.29 6.98 -7.07 2.04 3.40 t

0.046 0.256 0.000 0.002 0.000 0.000 0.001 P>|t| 59 66 7 59 65 df df 6

-.2362542 -.9232501 -.2796869 -.1258901 [95% Conf. Interval] .0867343 .0017094 3.378817 .104360824 .473234249 1.34589625

11.1197784 4.1004896 .18628311 -.6500888 -.0681528 6.092104 .3354971 .1692457 -.132076 MS MS .034128 40 logexpneedctorevcap Coef. Std. Err. t P>|t| [95% Conf. Interval] logpcnontax .1184747 .0378699 3.13 0.003 .0426973 .1942521 logarea .2042933 .0829394 2.46 0.017 .0383319 .3702547 loghhtap .4686725 .0858685 5.46 0.000 .2968501 .640495 logpcrevfromgov -.6160993 .0928023 -6.64 0.000 -.8017963 -.4304023 logdensity .4848385 .0957527 5.06 0.000 .2932377 .6764393 loghhwaterwithin -.3606339 .0778723 -4.63 0.000 -.5164561 -.2048117 logelectper1000 -.3115579 .034053 -9.15 0.000 -.3796977 -.2434181 _cons -.7059695 1.196781 -0.59 0.558 -3.100722 1.688783

Model 3 Revenue expenditure need to revenue capacity model: 40 41

Table A4. Regression results Source SS df MS Model 13.8988369 6 2.3164728 Residual 4.14904795 59 .070322847 Total 18.0478849 65 .277659767 Number of obs = 66 F(6,59) = 32.94 Prob > F = 0.0000 R-squared = 0.7701 Adj R-squared = 0.7467 Root MSE = .26518 logexpneedrtorevcap Coef. Std. Err. t P>|t| [95% Conf. Interval] logpctottax .0955929 .0272853 3.50 0.001 .0409951 .1501907 loggrpop -.109789 .0442319 -2.48 0.016 -.1982968 -.0212812 logarea .114944 .0530166 2.17 0.034 .0088582 .2210299 logpcrevfromgov -.6472934 .0606788 -10.67 0.000 -.7687115 -.5258754 logdensity .2081177 .0616096 3.38 0.001 .0848372 .3313983 loghhwaterwithin -.0767681 .0368665 -2.08 0.042 -.1505377 -.0029984 _cons 1.810751 .7284178 2.49 0.016 .3531906 3.268312 Urban Public Economics Review | Revista de Economía Pública Urbana logbanksperqkm logHHNOASSET logAREA logDomNnonDom1000 logHHTAP logCSDrain logSTRTLIT1000 logELECT1000 logROAD1000 logDENSITY logHH logNONTAX logPROPTAX logTRANSFER logexpneedttorevcap Root MSE = .39015 Adj R-squared = 0.8386 R-squared = 0.8609 Prob > F = 0.0000 F(8,50) = 38.68 Number of obs = 59 Total Residual Variable Table A5SummaryStatistics Appendix 4. Agglomeration models Model Source Table A6.RegressionResults Model 1. Total expenditure need to revenue capacity model:

47.1048026

7.61093867 54.7157412 Obs 70 70 66 66 63 62 60 71 71 71 71 71 71 71 71 SS

4.808025 2.720562 4.072885 3.109638 10.60082 4.755879 2.744046 4.994478 5.367033 2.847442 3.971995 6.775674 5.387311 -.040572 8.94686 Mean

.8647531 .6453858 .8025091 1.290208 1.038639 .4172806 .3312931 .8160007 .9972142 .8672876 1.024767 1.173118 .1157016 Std. Dev. .970356 1.14586 df 58 50 8

-2.302585 2.704042 -2.813411 1.589235 2.472328 .9400072 1.396245 .5128236 6.320768 3.502743 7.895436 1.175573 1.141033 2.687167 5.51986 Min

5.88810032 .943374849 .152218773 6.900731 4.468548 4.407085 8.073509 6.675325 6.494041 4.601563 6.306914 3.854818 8.116292 10.55418 4.515574 MS 14.9935 7.24229 2.16791 Max 42 logexpneedttorevcap Coef. Std. Err. t P>|t| [95% Conf. Interval] logPROPTAX -.1709645 .0721881 -2.37 0.022 -.3159586 -.0259705 logNONTAX -.1605345 .0686512 -2.34 0.023 -.2984244 -.0226446 logTRANSFER -.242977 .0738452 -3.29 0.002 -.3912995 -.0946545 logTOIL1000 1.512727 .329232 4.59 0.000 .8514453 2.174009 logHHNOASSET .6974756 .1643095 4.24 0.000 .3674503 1.027501 logAREA -.1042181 .0523371 -1.99 0.052 -.2093402 .0009041 logHHAvailing 1.662951 .209093 7.95 0.000 1.242975 2.082927 BankingFac logELECT1000 -2.041463 .5332599 -3.83 0.000 -3.112547 -.970379 _cons 3.709285 4.025003 0.92 0.361 -4.375172 11.79374

42 43 Model 2. Capital expenditure need to revenue capacity model:

Table A7. Regression Results Source SS df MS Model 62.7402252 9 6.97113613 Residual 9.94001267 49 .202857401 Total 72.6802379 58 1.25310755 Number of obs = 59 F(9,49) = 34.36 Prob > F = 0.0000 R-squared = 0.8632 Adj R-squared = 0.8381 Root MSE = .4504 logexpneedctorevcap Coef. Std. Err. t P>|t| [95% Conf. Interval] logPROPTAX -.1867966 .0853951 -2.19 0.034 -.3584046 -.0151887 logNONTAX -.1568227 .0794794 -1.97 0.054 -.3165425 .0028971 logpcasnrev -.2669624 .1134769 -2.35 0.023 -.4950027 -.0389221 logpcgran .0045838 .1033259 0.04 0.965 -.2030574 .2122249 logTOIL1000 1.552031 .3860993 4.02 0.000 .7761351 2.327926 logHHNOASSET .7951092 .1904594 4.17 0.000 .4123666 1.177852 logAREA -.1000964 .0663531 -1.51 0.138 -.2334379 .033245 logHHAvailing 1.878921 .2631109 7.14 0.000 1.35018 2.407662 BankingFac logELECT1000 -2.250674 .6323536 -3.56 0.001 -3.521437 -.9799124 _cons 3.183245 4.78162 0.67 0.509 -6.42578 12.79227 Urban Public Economics Review | Revista de Economía Pública Urbana logHHAvailing logHHNOASSET logTOIL1000 _cons logELECT1000 BankingFac logpcgran logNONTAX logpcasnrev logPROPTAX logexpneedrtorevcap Root MSE = .29946 Adj R-squared = 0.8359 R-squared = 0.8585 Prob > F = 0.0000 F(8,50) = 37.94 Number of obs = 59 Total Residual Source Table A8.RegressionResults Model 3. Revenue Expenditure need to revenue capacity model: acknowledge all the funding agencies. agencies. funding the all acknowledge funding. internal Institute’s the from and part of a project in NIPFP partly from funding of Government a of as Jharkhand undertaken were West Bengal and Jharkhand of cities smaller the in World the by Bank and Infrastructure funded Development Finance Corporation. The surveys (NIPFP) Policy and Finance Public of Institute al The in surveys five were urban agglomerations done in a project in Nation Grant na Kabiraj at work. differentfor assistance stages of the their The wouldauthor like Debrajto thank Bagchi, Aishna and Sharma Suja Acknowledgement Model

-1.358869 -.2094501 -.1072099 -.8259787 -.0302437 -.1645771 1.029622 1.639186 .5513562 Coef. 31.6985258 4.48377623 27.2147496 SS

.0630993 .0703932 3.090978 .2526708 .0527221 .0561442 .4183789 Std. Err. Err. Std. .174906 .125173

-3.25 -0.48 -2.98 -3.12 -0.27 -1.91 4.40 6.49 5.89 t t

The author would like to to like would author The P>|t| 58 50 0.004 0.000 0.000 0.002 0.790 0.000 0.062 0.003 0.634 df 8

-.1569825 [95% Conf.Interval] -.3508389 -2.199208 -.2704725 -7.034392 -.2199789 .2999388 .6783129 1.131682 .546526307 .089675525 3.4018437

-.0680612 1.380931 -.0586816 -.5185302 5.382434 .8027736 MS .005559 .096495 2.14669 - - 44