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Measuring Municipal Fiscal Condition: The Applicationof U.S.-Based Measures to the Context of ThailandWeerasak Krueathep aa Public Administration, Faculty of Political Science , Chulalongkorn University , Bangkok,ThailandPublished online: 05 Apr 2010.

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International Journal of Public Administration, 33:223–239, 2010Copyright © Taylor & Francis Group, LLCISSN: 0190-0692 print / 1532-4265 onlineDOI: 10.1080/01900690903405550

LPAD

Measuring Municipal Fiscal Condition: The Application of U.S.-Based Measures

to the Context of ThailandMeasuring Municipal Fiscal Condition Weerasak Krueathep

Public Administration, Faculty of Political Science, Chulalongkorn University, Bangkok, Thailand

This research attempts to apply U.S.-based measures in order to examine municipal fiscalconditions in Thailand. Fourteen municipal governments located in the central and easternregions of the country are explored, utilizing data from FY 2001 to 2006. The findings showthat the selected measures of revenue-raising capacity and expenditure provide a sensiblepicture of Thai municipal fiscal conditions when compared to U.S. cities during past decades.Large, highly populous central cities as well as semi-rural, residential areas are fiscally weak.By contrast, suburban and industry-based cities are fiscally healthy. This study provides afoundation for the design of intergovernmental transfer systems that takes into account thelocal fiscal conditions and helps to extend the external validity of existing analytical tools.

Keywords: fiscal health, fiscal condition, decentralization in Thailand, local government

INTRODUCTION

The study of municipal fiscal health is still a prominenttopic of discussion today.1 Fundamentally, it is an attemptto assess whether a city government possesses adequate fis-cal resources relative to its service obligations as demandedby constituents (Hendrick, 2004; Ladd & Yinger, 1989;Mead, 2001; Yilmaz et al., 2006). Past studies attempted tomeasure the magnitude of fiscal capacity from a set of com-parable cities and tried to make sense of it (e.g., Bradburyet al.,1982; Clark & Ferguson, 1983; Dearborn et al., 1992;Kamer, 1983; Ladd & Yinger, 1989). To date, they haveuncovered some of the dynamics of city fiscal strain andprovided essential information for formulating municipalfiscal policy.

Notwithstanding, current knowledge in this area ismainly based within the American context. Much is left

unknown about local fiscal conditions in less developedinstitutions. Is the analysis of local fiscal conditions relevantto developing nations? What if the measures of fiscal capac-ity that has been developed for use in American cities areapplied to a developing country like Thailand? Would theseAmerican-born indices still provide a sensible picture ofThai municipal fiscal conditions when compared to the U.S.experience? As Carmeli (2003: 1428) once argued, “wehave limited knowledge about the sources of fiscal andfinancial crises between countries”. Hence, comparativelocal public finance research is essential in order to helpresearchers more deeply understand the dynamics thatgive rise to fiscal strain as well as the strengths and limita-tions of existing analytical measures, if any, from a differ-ent angle.

Thailand, like other developing nations, has long beenunder a highly centralized administration. The past twodecades have seen many attempts to promote local fiscalautonomy and the devolution of service responsibilitiesin the hands of local governments (International Bank forReconstruction and Development, 2005; Nelson, 2002;Suwanmala, 2007; Varanyuwatana, 2003). Later, theDecentralization Plan and Process Act of 1999 waspromulgated in order to institutionalize needed policymeasures and detailed plans for decentralization. If theincreased fiscal capability of local authorities to take ondevolved responsibilities is a key element for successful

1Recent studies have appeared in several leading journals such asPublic Budgeting and Finance, Public Administration Review, Journal ofPublic Administration Research and Theory, International Review ofAdministrative Sciences, and Administration and Society. These articlesare, for instance, Zafra-Gomez et al. (2009), Wang et al. (2007); Coe(2007, 2008); Hou and Moynihan (2008); Carmeli (2008); and the wholeissue of International Journal of Public Administration, 26 (13) in 2003.

Correspondence should be addressed to Weerasak Krueathep, Chula-longkorn University, Faculty of Political Science, Henry Dunant Road, Patum-wan District, Bangkok 10330, Thailand. E-mail: weerasak.k@chula.ac.th

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decentralization (Tannenwald, 1998; Varanyuwatana,2003; Warner, 1999), then one important questionevolves: Are Thai localities fiscally capable of taking onservice obligations as demanded by constituents? Thisquestion is of utmost importance since this question hasnot yet been thoroughly examined in academic research.2

Therefore, this research attempts to measure municipalfiscal condition in the context of Thai devolution.

Thailand is a focal point since its changing environmentscall for the study of local fiscal conditions as one of the keyinputs in order to help create a smooth path towards decen-tralized fiscal administration, and, since its moderate levelof political, administrative, and economic developmentmake the country a typical decentralization model fromwhich other developing nations can learn (e.g., Ichimura &Bahl, 2009; International Bank for Reconstruction andDevelopment, 2005; Kim, 2003; Shah, 2007).

The approach used in this article is to select a set ofU.S.-based measures of fiscal conditions based on theoreti-cal and practical justifications, and then apply it to a sampleof 14 Thai municipalities. This approach has one importantadvantage in that it provides a known baseline for assessingthe present research findings. This study hypothesizes thatthe same fiscal condition experiences faced by U.S. citiesover the past few decades hold true for Thai local authori-ties. Not only does this study provide a framework forfuture municipal finance research in Thailand and otherdeveloping countries alike, but it also serves as an externalvalidity test to U.S.-based measures when applied to a con-text outside the U.S. origin.

This article begins with an overview of municipal reve-nue and expenditure structures in Thailand. It then discussesthe analytical measures that fit the context of Thai munici-pal finance. Procedures for data collection are elaborated ina subsequent section. Next, the results of Thai municipalfiscal conditions are presented and discussed. Finally, thearticle concludes with some policy implications and recom-mendations for future research.

MUNICIPAL GOVERNMENT IN THAILAND

Institutional Arrangements

Local authorities in Thailand are self-governing bodies sim-ilar to those of other nations, although they are viewed ashaving restricted roles and are less autonomous by westernstandards (e.g., White & Smoke, 2005). Local council mem-bers and executive bodies are popularly elected to serve fora four-year term. At present, there are 1,619 municipalitiesgoverning mostly urbanized and suburban areas; 6,157townships, officially referred to as Tambon (Sub-district)Administrative Organization (TAO), governing a rural, less-populated area; and 75 Provincial Administrative Organiza-tions (PAOs) (comparable to a county government in theUnited States) operating as an upper-tier local governmentunit.3 While the municipality and TAO are responsible fordirect service provisions, the PAO is responsible for work-ing in large-scale service deliveries and in developmentalprojects that need collaboration across municipalities and/orTAOs within a provincial jurisdiction.

This study focuses on the fiscal conditions of the munici-pal form of local governments, not on the TAO or the PAO.Thus, the following discussions are confined to the munici-pality. Municipal government is the focal point because it isthe most well established, both politically and administra-tively, among the three general forms of local authority inThailand (Suwanmala, 2002; Weist, 2001). It has long beenestablished (since 1930s) and has served a significant por-tion of population in the nation (about 38 percent of totalpopulation), with publicly recognized local taxation and ser-vice provisions. TAO and PAO, by contrast, have beenmore recently incorporated (since 1994 for the TAO and1999 for the PAO). Their service responsibilities and/or fis-cal information might not be well institutionalized. As aresult, conducting fiscal condition analysis of the municipalgovernment is of policy significance as well as a practicalchoice.

The decentralization movement in the past two decadeshas significantly shaped the municipal operations and insti-tutional arrangements in several ways. First, all municipali-ties are now governed by a mayor-council form. Theexecutive and the council members are both directly electedby local constituents. A city mayor is the chief politicalhead of a local executive body and is held accountable tothe local council and to the residents. The executive is alsoresponsible for preparing local development policies andannual budgets which are ultimately reviewed and officiallyadopted by the council. In addition, municipal operationsare run by permanent civil servants. Very few of them areprivatized or contracted out (e.g., construction of infra-structures and capital assets and garbage collection). The

2To date, three empirical studies conducted by Thai scholars cameclose to the research question posited above. Notwithstanding, none ofthem provided any complete, dependable answer. First, Suwanmala(1998) examined the fiscal capacity of four small localities in Thailandusing the main data from municipal budgets. However, his researchfocused specifically on the local ability to collect taxes as forecasted(tax collection ability), instead of tax-raising capacity, and on the verti-cal fiscal imbalance between local and national governments. Secondly,Thumkosit (2002) employed financial ratio analysis to study the fiscalcapacity of one small town. Notwithstanding, his analysis focused prin-cipally on the internal financial management, thereby, leaving mostparts of local service responsibilities unexamined. Furthermore, his useof unaudited financial reports did not yield reliable results. Finally,Patamasiriwat (2006) estimated the revenue raising capacity of Thailocalities based on a set of explanatory variables using survey data.However, the analysis of local spending needs was not incorporated.Thus, his work fell short of explaining the local fiscal condition in ameaningful sense.

3Data from the Department of Local Administration, Ministry ofInterior, as of August 15, 2008 (the latest data).

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monitoring of local operations and finance is the responsi-bility of the Department of Local Administration (DoLA)4

and of the Office of Auditor General.In terms of fiscal administration, municipalities across

the nation have been assigned uniform taxes and spendingprograms. The Decentralization Act defines certain tax/revenue bases and upper-limit tax rates for municipalities,with a clear-cut separation of tax bases and service respon-sibilities between municipalities, the upper-tier local gov-ernments (PAOs), and, to some extent, the centralgovernment. In effect, these uniform fiscal institutionalarrangements allow for the direct comparison of fiscal con-ditions across municipalities with few adjustments needed(these will be discussed later).

Revenue Structures

Thai municipalities have revenues from three majorsources:

1. locally collected taxes/revenues;

2. local taxes/revenues collected by central governmentagencies; and

3. intergovernmental transfers.

Table 1 exhibits an overview of municipal revenues fromFY 2001 to 2006. Of these totals, municipal revenues col-lected by central agencies constituted about 54.0 percent inFY 2006. Revenues from intergovernmental transfers wereabout 31.6 percent, whereas the locally collected amountwas merely 14.4 percent in the same fiscal year. Note thatthe figures in Table 1 do not include proceeds from debtfinancing and revenues from municipal enterprises (e.g.,pawnshop, water-supply services, etc.). Enterprise revenuesappear in separate (proprietary) funds and are designated forspecific uses. Thus, they do not reflect the taxing capacityof municipalities to finance general services and, therefore,are not included in the analysis that follows.

Value-added taxes (VAT) and sales taxes (item 2.1), landand real estate transfer fees (item 2.5), and excises and alco-hol taxes (item 2.3) are three major sources of revenues,respectively, for Thai municipalities. They constitute morethan 50 percent of total municipal revenues. On the otherhand, commercial and land building taxes (item 1.1) andfees and charges (item 1.5) are the major owned source ofmunicipal revenues. Since these five major revenues constitute

4Articles 62, 69 to 70, and 71 to 75 of the Municipal Act of B.E.2496(1953), (12th Amendment B.E.2546 (2003))

TABLE 1 Revenue Structures of Thai Municipalities, Fiscal Year 2001 to 2006

(million Thai baht, current prices)

Municipal revenues 1 2006 2005 2004 2003 2002 2001

1 Own-source revenues 9,910.4 9,226.6 8,374.7 7,132.6 6,755.3 6,012.61.1 Commercial land and building taxes2 4,386.6 4,039.7 3,629.9 2,736.2 2,601.7 2,513.11.2 Land development taxes 198.7 199.2 178.3 140.8 133.8 138.81.3 Signboard taxes 572.9 521.8 479.1 352.3 330.2 334.51.4 Animal slaughter taxes 68.3 55.9 56.6 80.7 62.6 58.21.5 Fees and charges, permits, fines2 1,889.1 1,802.1 1,584.2 1,165.2 1,107.9 1,074.31.6 Revenues from properties 998.3 906.6 868.2 1,677.9 1,585.9 1,663.91.7 Miscellaneous 1,796.6 1,701.3 1,578.4 979.6 933.1 229.7

2 Taxes collected by national government agencies and revenue sharing

37,167.9 36,540.1 35,251.9 26,126.5 22,361.8 19,963.9

2.1 VAT and sales taxes2 25,306.4 25,240.2 26,146.6 18,311.2 11,090.5 10,206.82.2 Specific business taxes 278.9 258.6 142.1 117.1 139.5 132.42.3 Excises and alcohol taxes2 5,289.7 5,425.2 4,660.0 3,261.0 3,097.1 2,830.82.4 Motor vehicle taxes 0.0 0.0 0.0 2,434.9 6,222.1 4,635.42.5 Land and real estate transfer fees2 6,068.8 5,402.4 4,140.2 1,882.2 1,700.1 2,044.22.6 Others 224.1 213.7 163.0 120.0 112.5 114.2

Subtotal (1+2) 47,078.3 45,766.6 43,626.5 33,259.1 29,117.1 25,976.4(68.4%) (73.0%) (73.9%) (71.5%) (69.9%) (64.2%)

3 Intergovernmental transfers 3 21,774.7 16,895.6 15,372.7 13,260.6 12,554.6 14,505.9(31.6%) (27.0%) (26.1%) (28.5%) (30.1%) (35.8%)

4 Total (1 + 2 + 3) 68,853.0 62,662.2 58,999.2 46,519.7 41,671.7 40,482.3

Source: Office of the Decentralization Commission, the Office of Prime Minister’s Office of Thailand.1 Figures include the revenues of all municipalities. Figures in parentheses are proportional to the total municipal revenues.2 These taxes/revenues are used in the analysis of revenue-raising capacity.3 Transfers are of general-purpose only. They do not include special-purpose grants due to the unavailability of data at the national accounts.

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more than 90 percent of the total municipal revenues,excluding transfers, and have been levied uniformly acrossmunicipalities as stipulated by the Municipal Act of B.E.2496 (1953), 12th Amendment B.E.2546 (2003), they arethus selected as bases for estimating revenue-raising capac-ity (RRC). The inclusion of shared taxes and nationally col-lected local taxes should not distort the analysis ofmunicipal taxing capacity since these taxes are reimbursedto municipalities based on the source-generation principle.As scholars once argued, it would not make any differenceif revenues are centrally collected for tax administrationpurposes, as long as municipal governments can tap thoseresources (Olowu & Smoke, 1992).

Service Responsibilities

This research focuses on service responsibilities of munici-palities appearing in a general fund.5 They are classifiedinto six categories:

1. public safety;2. education;3. healthcare and health services;4. housing, public works, and community services;5. social and public welfare; and6. general administration.6

Most of these services are provided across municipali-ties in accordance with budget guidelines from the DoLA.Table 2 depicts major programs and service functions ofThai municipalities.

As for the purpose of this research, one major differencein service responsibilities across 14 municipalities occurs ineducational services and has been accounted for in the anal-ysis of expenditure need. With the exception of Pakkred, allcities operate their own public schools. Education expendi-tures of the sampled cities account for approximately 30percent of municipal budgets as appeared in the general

fund. However, those of Pakkred City account for merely12 percent, and most of them subsidize schools run by cen-tral government agencies. To take this difference intoaccount, different weights for service responsibilities wereassigned, especially to Pakkred City (see Appendix 3 forfurther explanation).

SELECTING ANALYTICAL MEASURES FOR THAI MUNICIPALITIES

Scholars have provided several definitions of local fiscalconditions. Ladd and Yinger (1989: 8) explain that “localfiscal health is the difference between revenue-raisingcapacity and expenditure need, expressed as a percentage ofcapacity”. Mead (2001: 71) states that “fiscal capacity is theability of [government] to raise resources to finance the pro-vision of services its constituency demands”. In a similarvein, Yilmaz et al. (2006: 1) reason that “fiscal condition is theratio of city’s revenue capacity relative to its expenditureneed; where revenue capacity is the potential revenue rais-ing ability of the city, and expenditure need is the city’sneed for public expenditures.”

In short, this article defines local fiscal condition gener-ally as the fiscal ability of a municipal government to meetits service obligations. The analysis of fiscal condition con-sists of two interconnected elements:

1. the revenue side, or otherwise referred to as revenue-raising capacity (RRC), and

2. the expenditure side, otherwise referred to as expendi-ture need (EN).

A city’s RRC is its ability to raise taxes/revenues if itwere to apply the average tax effort to its tax/revenue bases.On the other hand, a city’s EN is the amount the city has tospend in order to provide a standard quality of serviceswithin its jurisdiction. Cross-city comparison will be usedas a major means in estimating the RRC and the EN. Then,when a city’s relative difference between the RRC and theEN is sufficiently large, say the former is greater than thelatter, it can be said that a city’s fiscal condition is relativelystrong or healthy. By contrast, a city is under fiscal stress ifits RRC is far behind the EN.

The literature suggests that any analytical measure offiscal condition should adequately capture institutionalarrangements and environmental factors that give rise to thelocal fiscal condition (Hendrick, 2004). Thus, challenges tothe current research lie in the choices of fiscal conditionmeasures that satisfactorily take into account the fiscal insti-tutional arrangements and socioeconomic conditions ofThai municipalities. However, most, if not all of theexisting measures are advanced for use in countries withwell-developed fiscal institutions and with a comprehen-sive system of fiscal and socioeconomic statistics. As Thai

5Arguably, the focus on general funds might distort the fiscal conditionanalysis as contended by Wang et al. (2007). Unlike most cities in theUnited States, where a substantial portion of general services are listed inseparate public funds, the general fund of Thai municipalities constitutes alarge portion of municipal activities. Thus, the use of general fund accountswould not cause significant bias of municipal expenditure needs.

6Thai municipalities are very passive in performing economic develop-ment and industrial services, except for Pattaya and Maptaput cities, whichare the specialty cities for tourism and heavy manufacturing industries.Budget allocations for this service program were about 1.2 percent of totalgeneral fund expenditures for all the studied cities, excluding Pattaya andMaptaput, during FY2001 and FY2006. Therefore, the economic andindustrial services function was dropped from the analysis. Otherwise, itwould potentially underestimate the expenditure needs of Pattaya andMaptaput cities, and would overestimate those of the other 12 cities. Thecomparison of common services is not unusual as it has been utilized inprevious research (e.g., Clark & Ferguson, 1983; Gramlich, 1976; Ladd &Yinger, 1989; Rafuse, 1990).

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fiscal institutions are distinct and the data availability is rel-atively limited as compared to other well developed nations,several analytical measures are not directly applicable to theanalysis of Thai municipal finance.

Such limitations are also prevalent in many developingsocieties (Martell & Guess, 2006; Martinez-Vazquez &Boex, 1997a, 1997b). For example, in Thailand, data ontotal taxable resources and personal income at the municipallevel are not yet available.7 This is because none of them,especially the assessed-value property taxes, have ever beenutilized as a basis of municipal taxation. In this respect, thefiscal condition measures that require these pieces of infor-mation, e.g. the representative tax/revenue system (RTS/RRS)as developed by ACIR (1962, 1988), Clark and Ferguson’s(1983) city wealth index, and Ladd and Yinger’s (1989)income-with-tax-exportation approach to the estimation ofRRC are not good candidates for examining the municipaltaxing capacity in Thailand.

Furthermore, the majority of Thai municipalities usecash-basis accounting and most of the time their financialreports are incomplete and lack external auditing(Varanyuwatana, 2003).8 As a result, there is virtually noreliable financial information for the analysis of financialratios; such as the financial trend monitoring system(FTMS) (Groves & Valente, 1994), the ten-point test(Brown, 1993), or the more comprehensive indicators asdeveloped by Wang et al. (2007) and Kloha et al. (2005).Moreover, bond markets have not yet existed for Thaimunicipal governments (Varanyuwatana, 2003). As a result,the credit ratings approach to financial condition analysis assuggested by Lipnick et al. (1999), Mercer and Goldberg(1984), Marquette et al. (1982), and J. Petersen (1980) is notapplicable. Likewise, the gross city product (GCP) approach assuggested by Aten (1986) is practically restricted since alarge portion of GCP does not constitute local tax bases,particularly personal and corporate incomes. The use ofGCP would easily overstate the Thai municipal fiscal condi-tion. Here, the alternative context-relevant measures of Thaimunicipal fiscal health are needed.

Fortunately, Martinez-Vazquez and Boex (1997a,1997b) and Dye (1984) have provided an alternative mea-sure for analyzing revenue-raising capacity (RRC) for usewith few data requirements, namely regression-based RRC,as compared to other analytical measures. Regression analy-sis to RRC allows for the computation of potential munici-pal revenues based on statistical relationships with a set ofsocioeconomic factors and other proxies of a city’s tax base.The predicted amount of revenue that follows from theregression represents the potential municipal revenue thateach municipality would collect under the average tax

effort. Details of the procedures for estimating the RRC areshown in Appendix 2.

The major advantage of using the regression-based RRCis that it is flexibly adjustable to fit the Thai local fiscal con-text and can include as many revenue sources upon whichThai localities rely. It also allows for the inclusion of manysocioeconomic factors that presumably determine the sizeof local tax bases in the regression model, albeit with theadditional cost of data requirements (Martinez-Vazquez &Boex, 1997a). Additionally, results from the analysis mightbe more comprehensible to local administrators since thefactors constituting the regression model are clearlyvisible to them, although some statistical literacy is required.

On the other hand, for the analysis of expenditure need(EN), the measure that has been developed by Ladd andYinger (1989) and Ladd (1994), the so-called regression-based cost approach to EN, seems to fit the Thai municipalfiscal framework. Unlike the representative expendituresystem (RES) as developed by Rafuse (1990), Ladd andYinger’s approach has one crucial aspect for estimating themunicipal expenditure need: it takes into account servicecost differentials in estimating the expenditure need. Forexample, a city with unsupportive socioeconomic condi-tions or with some difficulties in providing public services(e.g., high poverty rate, high density) may need to provide ahigher level of spending than the average city. This costadjustment component is very crucial since it is the maindeterminant of variations in municipal spending across cit-ies (Bradbury et al., 1984; Ladd, 1994). Estimation proce-dures for the EN are elaborated in Appendix 3.

Like the regression-based RRC, the estimation of theEN requires a multiple regression analysis. It helps eval-uate the impact of each of the cost factors on the varia-tion of municipal spending. The predicted amount ofexpenditures that follow from the regression, includingthe service cost adjustment, represents the potentialmunicipal expenditure that each municipality would pro-vide under the average quality of public services (Ladd &Yinger 1989).

Overall, the proposed measures of RRC and EN haveseveral advantages. Compared with the representative sys-tem (ACIR, 1962, 1988; Rafuse, 1990) and the credit rat-ings approach (Lipnick et al., 1999; Marquette et al., 1982;Mercer & Goldberg, 1984), the selected measures aremore flexible and less data demanding. In effect, they arequite suitable to many developing societies since existingdata for the fiscal condition analysis are somewhatrestricted.

Additionally, both selected measures take into accountsocioeconomic variables in estimating local fiscal condi-tions. Unlike the often-used financial ratio analysis(Brown, 1993; Groves & Valente, 1994), which focusesmainly on internal management aspects of fiscal condi-tions, the proposed measures incorporate the external,structural factors & service cost differentials in estimating

7This problem also prevailed in the Russian Federation as discussed inMartinez-Vazquez & Boex (1997a).

8Presently, the DoLA is transforming local accounting practices to anapplied accrual accounting basis.

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the local fiscal condition. In turn, this property wouldenable the utilization of fiscal condition findings for theformulation of policy measures in order to cope well withthe municipal structural deficits as argued by researcherselsewhere (Bradbury et al., 1982; Ladd & Yinger, 1989;Zafra-Gomez et al., 2009 ).

As multiple regression analysis is an important part ofthe analyses, brief discussions about the estimator areessential. Regression analyses were conducted separatelyfor each of the five major taxes/revenues and for each ofthe six service functions by a random effect (RE) estima-tor. Generally, the analysis of fiscal condition examinesfiscal strengths across cities (or between-city variations).The RE estimator would recognize the heterogeneity oflocal fiscal conditions across cities induced by city-specific, unobservable effects (Halaby, 2004; Nielsen &Alderson, 1995; Owusu-Gyapong, 1986; Wooldridge,2000). By contrast, using fixed effect (FE) or first-differencing (FD) estimators would throw away anybetween-unit variations. In this regard, the RE estimatoris preferable.9

Additionally, the RE estimator allows for explanatoryvariables that are constant over time to be included in themodel (Wooldridge, 2000), e.g., city’s geographical area,street-kilometer, and like variables. This is a strong rea-son to use the RE estimator since these time-invariantfactors are of theoretical importance in explaining acity’s revenue-raising capacity and/or expenditure needsas suggested by the literature (Bradbury et al., 1984;Ladd & Yinger, 1989; Wasylenko & Yinger, 1988).

Finally, a Hausman Test between the FE and RE estima-tors indicates that, for most of the regression models (10out of 11), it fails to reject the null hypothesis at 95percent confidence level, meaning that there is no evi-dence to argue that the FE estimator provides betterresults than the RE10.

In sum, whether or not these two selected measures sen-sibly depict the fiscal condition of Thai municipalities issubject to empirical investigation. The subsequent sectionsexplain how the research was conducted as well as its find-ings. Later, the paper discusses how fit the present researchfindings are as compared to the past fiscal condition experi-ences of U.S. cities.

DATA

This research focuses on the fiscal condition analysis ofmajor cities (with population sizes over 10,000) located inthe vicinity of the capital of Thailand, Bangkok Metropolitan,and in the eastern region.11 This is because most cities inthese two regions, when compared with the rest of the nation,

9It is also reasonable to assume in this study that unobservable, city-specific effects are exogenous of other explanatory variables. For instance,the unobservable factors might be local political and administrativecultures that are not necessarily correlated with local economic andgeographical factors. Thus, presumably, the basic assumptions of the REare not violated. That is, RE is still BLUE (best linear unbiased estimator)under the assumption that E(uit | Xit ) = 0. See further discussions fromBaltagi (1995) and Wooldridge (2000).

TABLE 2 Programs and Service Functions of Thai Municipalities

Programs Service Functions

Public safety Management of public orders, fire fighting, disaster managementEducation (K-12) Elementary and secondary schools, early childhood development center, vocational schools, public libraries,

educational and career guidancePublic health Primary care units, community hospital and health center, sanitary services, disease controlHousing and community services Housing, public works, garbage collection and waste disposal, sewage treatment, public parks, local road

construction and maintenance, lights and traffic utility systemsSocial and public welfare Sports and welfare facilities, supports for senior, low-income people, children, and the disabled, recreational

activities, cultural promotion, historic place preservationEconomic and industrial services Economic development, agriculture and fishery promotion, local trade and commerce promotionGeneral administration Municipal ordinance and regulation, personnel administration, budgeting, finance, and planning, statistics and

household registration, contingency management, debt service

10Heteroscedasticity by White’s heteroscedasticity test and the autocor-relation of order one were detected in most of the regressions. Thus, the REestimator with cluster robust standard errors was employed. This techniquehas been demonstrated elsewhere for being effective in solving the dualheteroscedasticity and serial correlation problem (Bertrand et al., 2004;M. Petersen, 2007). In this study, the standard errors are clustered on thepanel identifier - a municipal government.

11Access to data and time limitations resulted in the selection of thesetwo specific regions, instead of a nationwide sample. Cities are located in

1. Bangkok metropolitan: Nonthaburi, Patumthani, Samutprakarn,Samutsakorn, and Nakornpatom; and in

2. eastern provinces: Chacheongsao, Chantaburi, Chonburi, Prachinburi,Rayong, Sakaew, and Trad.

Due to the limited availability of secondary data, as provided byresponsible agencies, expanding the scope of this study to cover muchbroader geographical areas would be difficult for this specific exploratoryresearch.

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MEASURING MUNICIPAL FISCAL CONDITION 229

vary considerably and have experienced rapid social andeconomic changes in recent decades. Thus, they are a goodcandidate for the fiscal condition analysis. In the early 1980s,Thailand had attempted to minimize urban-rural disparitiesand to boost up regional economic development through theEastern Seaboard project.12 The Fifth (1982–1986) andSixth (1987–1991) National Economic and Social Devel-opment Plans institutionalized long-term strategies inorder to modernize several secondary cities outside theBangkok Metropolitan areas and designated several east-ern cities as the hubs for economic and industrial develop-ment.13 As will be seen shortly, these developmentinitiatives resulted in significant population and economicchanges to the cities in recent years.

This scope of study constituted 22 municipalities in asample. Preliminary analysis showed that they did not dif-fer from other major cities throughout the country in termsof the population size (F statistic =1.4, p-value = .239).The research began with sending an invitation letter tomayors of all 22 cities in order to invite them to participatein the study, of which 14 cities agreed to join. This was animportant step since most of the needed data were avail-able at the municipal level. The sample showed that itspopulation size and density as well as its economic andpopulation growth rates were comparable to the nationalaverages (see Table 3), suggesting a non-biased represen-tation of Thai cities in general.

One may consider this small sample size as a majorweakness. Notwithstanding, the sample satisfactorily repre-sents cities of diverse socioeconomic types as commonlybeing the locus in municipal finance research. The samplecomprises cities of diverse socioeconomic activities (indus-trial, commercial, tourism and services, and residential), ofdifferent population sizes (from slightly above 10,000 toabout 270,000), and, most important, of varying economicand social changes (rapidly growing, moderately growing,and declining). These diverse city characteristics help illu-minate the comparative perspective of municipal fiscal con-ditions, which will be evident momentarily.

Data for this research were compiled mostly from pri-mary sources (city profiles, city’s medium-term strategicplan, annual financial reports, tax and revenue collectiondatabases, budget documents, etc.) owing to the limited

availability of secondary data.14 Numerical data incorpo-rated municipal revenues and expenditures, socioeconomicvariables, and the like, covering the period from FY 2001 toFY 2006. This resulted in the maximum observations of 14× 6 = 84 for regression analyses. However, missing datareduced this number. These fiscal years were selected due totwo practical reasons. First, they were readily available formost of the studied cities (10 out of 14). Secondly, theexpenditure data before FY 2001 were presented in a line-item format, which were not suitable for analyzing theexpenditure need. The program budget in municipality hasonly been in place since FY2001. Descriptive statistics ofvariables used in the analyses are exhibited in Appendix 1.All monetary figures are at constant prices in 2000.

Fourteen cities vary considerably in terms of populationsize, per capital revenue and spending, and socioeconomicchanges from 2001 to 2006. Based on socioeconomic orien-tations, these fourteen cities can be classified into fourmajor groups. The first group of cities is highly urbanized,consisting of central cities within Bangkok Metropolitanarea, and has a median population of 167,464. Next, fivesuburban cities skirt the central and heavy-industrial zonesand have a median population of 55,269. Another four aresmall, residential cities located in semi-rural areas and hav-ing a median population of 18,722. The last group of citiesis unique in that they are in economically concentratedareas. Pattaya City is a major tourism city and MaptaputCity is the hub of heavy manufacturing industries (e.g.petrochemical, steel, energy and power, and the like) of thenation. The analyses that follow will rely on the city classi-fication just discussed.

Interesting pieces of information are revealed in columns8 and 9 in Table 3. There was evidence of growth anddecline in the studied cities. Central areas faced a popula-tion decline of about 1.6 percent between 2001 and 2006and their economies grew more slowly than the sample andnational averages. By contrast, the economy and populationof industry-based cities grew significantly higher than thoseof the sample and national averages. Suburbs and semiruralareas exhibited unique patterns. On average, the economy inthe suburban cities grew moderately from 2001 to 2006,while their population declined as much as 8.5 percent. Itmight be the case that those who migrated from suburbancities were low skilled or unproductive laborers so that theimpact of emigration on city economies was trivial. On theother hand, it was fascinating that semi-rural cites experienced

12This project was possible largely in part due to the 1981 discovery oflarge reserves of natural gas found in the Gulf of Thailand. In response, thenational government launched the Eastern Seaboard Development Projectto maximize the economic utility of natural gas and promoted the easternregion as a base for heavy industries (petrochemical and petroleum, steeland metal, automobile, energy and power, and the like).

13According to the Plans, central government had invested in highwaysand economic infrastructure development. Moreover, several tax incentiveswere issued in order to attract businesses to relocate to newly emergingsuburbs and industrial zones, as clearly evident in the Investment Promo-tion Act of 1977 (the Second Amendment in 1991, and the Third Amend-ment in 2001).

14To date, publicly available statistics on local government finance arenationally aggregated, compiled by the Department of Local Administration(DoLA) and the Office of Decentralization Commission, Office of thePrime Minister’s Office. Notwithstanding, such aggregate data do notallow for any meaningful analysis of local fiscal conditions as oncesuggested by Bahl (1984). Thus, the alternative strategy was to collectfiscal and related data from the primary sources by the author, resulting in asomewhat small sample size.

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MEASURING MUNICIPAL FISCAL CONDITION 231

significant growth from 2001 to 2006. On average, theireconomy and population grew 46.3 percent and 5.4 percent,respectively. Whether these socioeconomic changes have adirect relationship with municipal fiscal conditions will beexamined next.

FINDINGS

Revenue-Raising Capacity (RRC)

The analysis of municipal revenue-raising capacity fol-lowed the framework provided by Martinez-Vazquez andBoex (1997a, 1997b) and Dye (1984), as exhibited inAppendix 2. Here, major determinants of taxing capacityconsisted of wealth, demography and city economic devel-opment. Wealth included gross city product per capita,property wealth, and city areas (sq. km.); demographyincluded population density (per sq. km.) and populationgrowth rate; and the level of economic developmentincluded cost of living as measured by consumer priceindex (CPI), a portion of labor population to total city popu-lation, and a dummy for economically concentrated area.These variables were included as suggested by the literature(e.g., Ladd, 1994; Ladd et al., 1991; Wasylenko &Yinger, 1988), given the availability of the data. All butproperty wealth are self-explanatory in determiningcity’s taxing capacity and, hence, need no further elabo-ration. Attention is now given to the index of the city’sproperty wealth.

According to the design of commercial land and buildingtaxes in Thailand, the data on property’s assessed values donot exist. Thus, a proxy of the city’s property wealth isneeded. Data on the total number of properties (residential,rental housing, commercial, and industrial) in each city areavailable. Unfortunately, the figures are shown in aggre-gate. Few cities have available data on the details of eachproperty class.

As commonly known, different property classes havedifferent contributions to local taxation. For instance, inMaptaput, an industry-based city, the proportion of indus-trial properties to the total number of properties was about6 percent in 2006. On the other hand, Prachinburi, a resi-dential city, had a share of commercial properties about14 percent. It turned out that Maptaput collected 4,213.7baht per capita on the commercial and land taxes whilePrachinburi collected merely 235.0 baht per capita, or about17.9 times fewer. Thus, unless the city’s property wealth isestimated from each of the property classes, the simple useof the total number of properties would bias the estimationof the city’s taxing capacity.

To deal with this data limitation, the ratio of a city’sgross city product (GCP) per capita to that of the nationalaverage was factored into the total number of city’s proper-ties in order to reflect the relative property wealth across thecities. An underlying assumption is that a city’s propertywealth is somehow proportional to the level of GCP percapita. People living in a relatively rich city have a goodreason to invest more in commercial and industrial assets.Given this adjustment, Maptaput and Prachinburi have a rel-ative ratio of city’s property wealth of 18.6 to 1, which issomewhat close to the size of tax disparity discussed above.

The predicted values of each of the five major municipaltaxes/revenues following from the RE regression estima-tions were summed up in order to obtain the overall reve-nue-raising capacity (RRC) per capita. Generally, allregression models did a good job in explaining municipalrevenues, with the exception of excises and alcohol taxes(R2(between) were between .625 and .981, with p-values < .001for all models). Table 4 shows the estimated RRC, groupedby the city types as discussed thus far. For consideration ofspace, full regression results are not shown here, but areavailable from the author upon request.

Table 4 shows clearly that cities with relatively largeeconomic endowments, or the industry-based cities, hadabout two times higher RRC per capita than the average city

TABLE 4 Regression-based Revenue-Raising Capacity

City group

2006 2001

Average percentage changes (FY 01–06)RRC Relative to the means RRC Relative to the means

1. Central 3,457.5 82.3 1,322.4 65.8 237.22. Suburb 3,589.6 85.5 1,291.8 64.3 183.53. Semi-rural 3,028.0 72.1 1,234.4 61.5 157.14. Industry-based 8,601.1 204.8 5,991.7 298.3 43.5

Average 4,200.5 100.0 2,008.7 100.0 169.2S.D. 2,032.1 1,824.2 113.1Min 2,578.9 613.1 43.4Max 9,435.3 6,566.2 468.6

Notes: RRC was shown as Thai baht per capita (about 34 baht is equivalent to 1 USD, as of May 2009).

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232 KRUEATHEP

in FY 2006. Semi-rural cities, on the other hand, had thelowest RRC per capita throughout the period of study, orabout 62 percent to 72 percent of the average city. Thismakes reasonable sense since their socioeconomic condi-tions might not be able to yield much tax revenue given theaverage level of tax efforts. Likewise, large, highly popu-lous central cities exhibited relatively low tax capacity percapita, although their capacity had been improving greatlyduring the past five years. Overall, these figures indicatethat the central cities do not necessarily have stronger taxingcapacity than average. Still, whether the relatively low (orhigh) RRC contributes to the weak (or strong) fiscal condi-tion will be examined in the following section.

Expenditure Need (EN)

The analysis of expenditure need followed the proceduresgiven by Ladd and Yinger (1989) as elaborated in Appen-dix 3. Six service programs were included in the expendi-ture need analysis. As already discussed, these serviceprograms were uniformly provided in all studied cities,except the educational service for Pakkred City which wasaccounted for by assigning a particular service weight tothe city. Expenditure figures included operating and capi-tal. A smoothed 3-year average of capital expenditure (twoyears earlier and the current year) was used in order tocope with an uneven nature of investment decisions fromyear to year. This procedure is commonly used in previous

research (e.g., Ladd et al., 1994; Wasylenko & Yinger,1988).

Overall, regression models did reasonably well inexplaining municipal spending for most of the service pro-grams, with the exception of public health (R2(between)were between .782 and .954, with p-values < .001 for allmodels). As before, full regression results are availableupon request. The first column of Table 5 below indicateshow the dependent variables were derived, while the secondcolumn shows the cost factors used for the estimation of acity’s cost indices for municipal spending.

Next, Table 6 presents the cost indices of six service pro-grams for each of the four city groups. It reveals that large,highly populous central cities tended to have higher servicecosts for all service functions, with the exception of educa-tion. This finding is somewhat congruent with that of Ladd(1992) where higher density cities are more likely to havehigher service costs. Perhaps this is because having a largepopulation size makes it more difficult for a city govern-ment to provide public services in an efficient manner, orbecause central cities offer a significantly higher number ofservices than average (Warner & Hefetz, 2002).

In a similar vein, industry-based cities had a tendency tohave more expensive service costs than the average city, withthe exception of public safety. Recall that these services areessential for economic and human capital development inaccordance with the nature of the cities. In contrast, suburbswere better off than the average city in that they exhibited

TABLE 5 Regression-based Cost Approach to Expenditure Need Analysis

Service responsibilities (programs) Cost factors

Public safety

average current spending per capita + 3-year average capital spending

per capita average capital spending per capita

Cost of living in a city (CPI); population density (thousand per sq. km.); population density squared; labor population; and city employees (as a proxy of service demands)

Education (K-12)

average current spending per student enrollment + 3-year average capi-

tal spending per student enrollment

Cost of living in a city (CPI); youth population; and property density (per sq. km.)

Public health

average current spending per capita + 3-year average capital spending

per capita

Population density (thousand per sq. km.); proportion of aged population; and city employees (as a proxy of service demands)

Housing and community services

average current spending per capita + 3-year average capital spending

per capita

Population density (thousand per sq. km.); labor population; proportion of aged population; city employees (as a proxy of service demands); and city own street kilometer

Social welfare

average current spending per capita + 3-year average capital spending

per capita

Cost of living in a city (CPI); population density (thousand per sq. km.); poverty rate; labor population; and city employees (as a proxy of service demands)

General administration

average current spending per capita + 3-year average capital spending

per capita

Cost of living in a city (CPI); population density (thousand per sq. km.); and city employee (full-time equivalent) (as a proxy of service demands)

Note: Economic and industrial services function was dropped from the analysis since it was not a common function for the cities in this study. Only twocities (Maptaput and Pattaya) provided rigorously this service function.

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MEASURING MUNICIPAL FISCAL CONDITION 233

relatively lower cost indices for most service functions. Pre-sumably, suburb residents are financially able to affordneeded services from private markets and, thus, are lessdependent on municipal services.

The last two columns of Table 6 present expenditureneed indices of the studied cities in FY 2001 and FY 2006(as calculated by equation 2 in Appendix 3). The base valuefor the expenditure need index in FY 2006 was set equal to100. The value above the base means that a city requiresmore resources on average in order to fulfill its service obli-gations as compared to other cities in the sample. FromTable 6, it can be seen that central cities exhibited a slightlyhigher expenditure need in 2006, or about 12.7 percentabove the norm. Industry-based cities also demanded ahigher level of public services. By contrast, residents in sub-urbs tended to have a significantly lower level of serviceneeds than the average.

There are a couple of reasons that potentially account forthe expenditure need differentials. Detailed analysis sug-gested that populations in central cities were presumably thepoorest among the four city groups, as reflected in a grosscity product (GCP) per capita between 2001 and 2006.Their average GCP per capita was about 21.4 percent, 43.2percent, and 13.2 percent of that of the suburban, semi-rural, and industry-based counterparts, respectively. Hence,residents in the central areas might demand more for ser-vices and assistance provided by their respective govern-ments. Alternatively, industry-based cities demanded arelatively higher level of services which could be due tospecial needs for socioeconomic development.

The last column of Table 6 exhibits the expenditureneed index of FY 2001, relative to that of the FY 2006.The average expenditure need for all city groups in 2001was about 57.4 percent of that in 2006, indicating that theaverage spending need had increased about 74.2 percent[(100–57.4)/57.4 * 100] over the 5-year span. Significantincreases over time were evident in the suburbs and the

semi-rural cities. This might be due to past attempts to min-imize urban-rural disparities and to the 1999 decentraliza-tion movement to expand service provisions in suburb andsemi-rural areas.

City’s Fiscal Health

According to Ladd and Yinger (1989), the city’s fiscalhealth index (FHI) was quantified from a differencebetween revenue-raising capacity (RRC) and expenditureneed (EN), expressed as a percentage of capacity. Or alge-braically, the FHI is [(RRC - EN) / RRC * 100]. The FHIquantitatively identifies a city’s relative ability to finance itsservice responsibilities as compared to the sampled cities,given its city’s economic, social, and demographic charac-teristics. The index has a base value of zero. A positiveindex implies a city’s taxing capacity is greater than itsexpenditure need, suggesting that the city could have avail-able resources for increases in services or for tax cuts.

Following the Ladd and Yinger’s (1989) procedures forcalculating the FHI, a city’s EN indices first were convertedto monetary terms (Thai baht per capita) so that they couldbe compared with the RRC. The average EN was set equalto the average RRC, indicating that on average cities use upall of their capacity. The next step was to calculate city’sFHI as discussed above. Table 7 depicts the indices of FY2001 and FY 2006.

The fiscal health index (FHI) indicates that highly popu-lous, central cities were in high distress. In FY 2006, theylacked approximately 40 percent of their fiscal capacity tofulfill constituents’ needs as compared with other citygroups (see column 4). Likewise, semi-rural, residential cit-ies had relatively weak fiscal conditions in the same period.On the contrary, industry-based cities were fiscally strong inFY 2006, having about 24 percent of higher capacity thanneeded. Similarly, suburban areas also had a moderatelystrong fiscal position. In light of these findings, a concrete

TABLE 6 Public Service Cost Indices and City’s Expenditure Needs

City group

2006 Cost Indices for1:

2006 EN index1

2001 EN index2Public Safety Education (K-12) Public health

Housing & Community services

Social welfare

General Administration

1. Central 208.1 109.9 160.4 193.5 161.2 184.9 112.7 80.22. Suburb 68.5 71.5 78.9 49.6 86.3 60.1 73.9 30.63. Semi-rural 86.5 88.8 71.7 67.7 81.4 61.6 92.0 33.54. Industry-based 106.6 149.8 127.4 131.1 129.5 134.6 158.1 113.8

Average 110.7 96.4 103.5 99.5 109.1 100.7 100.0 57.4S.D. 70.9 36.3 49.8 74.1 43.5 67.5 41.9 47.4Min. 27.2 41.5 54.2 12.4 60.9 22.2 40.6 13.2Max. 304.2 197.6 240.5 288.7 207.0 248.5 204.8 169.8

1A baseline for the expenditure need index in 2006 was set equal to 100.2A baseline for the expenditure need index in 2001 was set equal to 57.4, representing the proportion to the level of expenditure need of 2006.

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answer to the current research question is evident. Munici-palities in the large central cities and in the semi-rural basedareas tend to have financial difficulties in meeting their ser-vice obligations, as compared to the suburbs and industry-based cities. Thus, some actions may be needed in order torelieve their fiscal predicaments (to be discussed below).

Uneven changes in the FHI values across four citygroups over the study period also merit further discussion.Comparisons between columns 8 and 9 of Table 3 and col-umns 4 and 5 of Table 7 reveal that the connection betweenthe socioeconomic and the FHI changes over the 5-yearspan is less clear. Indeed, the socioeconomic changes ineach city group from 2001 to 2006 were in an oppositedirection in comparison to the changes in the FHI. Forexample, central cities facing economic and populationdeclines during 2001 and 2006 had improved their fiscalconditions, although they were still relatively weaker thanother cities. By contrast, semi-rural cities had poorer fiscalconditions although they experienced significant economicand population growths. In other words, it is highly likelythat differentials in the FHI changes have been triggered bythe devolution movement in the late 1990s rather than eco-nomic and population changes.

The FHI changes depict that the decentralization move-ment has been very financially beneficial to large, urbanizedmunicipalities, where fiscal conditions were strengthened(from -182.3 percent in 2001 to -39.8 percent in 2006). Bycontrast, decentralization has weakened the fiscal positionof rural-based municipal governments. This finding indi-cates one shortcoming of past decentralization efforts: theimbalanced revenue and expenditure assignments to munic-ipalities. Service responsibilities imposed on municipalitiesin the semi-rural areas might be too overwhelming for themto take on viably. Hence, tax and expenditure re-assignments,especially for the semi-rural cities, might be essential.However, note that this interpretation should be viewed assuggestive since the FHI data for the years before the 1999

decentralization were unavailable. If the data had beenavailable, a more concrete statement might be made.

DISCUSSION AND CONCLUSION

The purpose of this research was to measure fiscal condi-tions in Thai municipal governments from a comparativeperspective because research in this area from the experi-ence of developing countries is extremely limited.Although Thailand is not representative of all developingnations, its moderate level of socioeconomic developmentmakes the country’s experience worth learning from. Inthis article, the concrete answer regarding the fiscal abilityof Thai municipalities in taking on service obligations hasbeen revealed. Evidence from 14 major cities during FY2001 and FY 2006 indicates that, as revealed by theAmerican-born measures of fiscal condition developed byMartinez-Vazquez and Boex (1997a, 1997b), Dye (1984),and Ladd and Yinger (1989), municipal governments insuburban and industrial areas had a relatively higher RRCper capita because their voluminous economic endow-ments directly enhanced municipal tax bases. As such,they experienced relatively strong fiscal conditionsthroughout the period of this study.

On the contrary, it was unfortunate for large, highly pop-ulous central cities that they not only faced relatively lowRRC per capita, but also experienced higher productioncosts and expenditure needs. In effect, the central citiesended up having a relatively weak fiscal condition, at leastduring the period of this study. The same was true, thoughto a lesser degree, for semi-rural, residential cities. This wasbecause they normally had limited access to tax bases and,thereby, low taxing capacity relative to their expenditureneeds.

How well do these selected measures depict the fiscalcondition of a Thai municipality? One way to answer this

TABLE 7 City Fiscal Health, FY 2001 and FY 2006

City group

2006 2006 2006 2001

RRC(baht) [1] EN index [2] Converted EN(baht) [3] Fiscal Health Index [4] Fiscal Health Index [5]

1. Central 3,457.5 112.7 4,734.8 −39.8 −182.32. Suburb 3,589.6 73.9 3,104.0 14.7 17.13. Semi-rural 3,028.0 92.0 3,866.0 −29.5 −5.84. Industry-based 8,601.1 158.1 6,642.3 24.3 36.0

Average 4,200.5 100.0 4,200.5 −6.6 −33.4S.D. 2,032.1 41.9 1,761.8 40.9 126.3Min. 2,578.9 40.6 1,704.6 −102.6 −301.6Max. 9,435.3 204.8 8,602.1 39.7 62.6

Notes: RRC was shown as Thai baht per capita (about 34 baht is equivalent to 1 USD, as of May 2009). EN index was converted from index values toThai bath per capita. Fiscal health index (FHI) was calculated from [(RRC – EN) / RRC * 100]. A positive FHI value indicates a relatively healthy fiscalcondition.

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MEASURING MUNICIPAL FISCAL CONDITION 235

question is to evaluate the current research findings withinthe experiences of U.S. cities. Empirical studies in 1970sand 1980s indicate that highly populous, central cities, par-ticularly those in the Northeast and Midwest of the UnitedStates, were often identified as having severe fiscal condi-tions (Bradbury et al., 1982; CBO, 1978; Dearborn et al.1992; Kamer, 1983; Ladd & Yinger, 1989; Mercer &Goldberg. 1984; J. Petersen, 1980; Stanley, 1976).15 Inaddition, the literature exhibited the existence of fiscalstress in small or rural-based cities due to their limited tax-ing capacity, small tax bases, and restricted managementcapacity (Badu & Li, 1994; Dougherty et al., 2000; Honadle& Lloyd-Jones, 1998; Koven & Koven, 1989; Ward, 2001;Weinberg, 1984). On the other hand, scholars also foundthat suburban municipalities tended to be more fiscallyhealthy than the inner city areas (e.g., Bell et al., 2004;Dluhy & Frank, 2002; Hendrick, 2004)16.

In light of past research discussed above, it is not anexaggeration to say that the current research provides a sat-isfactory picture of municipal fiscal conditions in Thailandconsistent with the U.S. experience. Although past studiesemployed different analytical methods and estimation pro-cedures from one to another (from financial ratio analysis,to socioeconomic indicators, and to bond ratings) and fromthe measures being employed in the current study, they haveobtained somewhat consistent results as contended byscholars elsewhere (e.g., Bahl, 1984; Bahl et al., 1992;Marquette et al., 1982). This demonstrates a strength of theselected measures of fiscal conditions in that they can flexi-bly be adjusted to fit the local context, while still providinga sensible picture of Thai municipal fiscal conditions ascompared to known research findings from the UnitedStates.

Because Thailand is a typical case of a developingnation, it is believed that the same analytical measures andprocedures as utilized in the current study can also beapplied to other developing societies in order to helpuncover the fiscal conditions of their localities. Still, moreevidence is needed to judge if the selected measures aregenerally applicable to other administrative contexts besideThailand. At the very least, the present study signifies sub-stantial room for future comparative research in the munici-pal fiscal condition issue.

Furthermore, this study highlights the importance of thestudy of local fiscal conditions in developing societies, par-

ticularly those currently undergoing decentralization. Thefindings of Thai municipal fiscal conditions suggest thatimbalanced transfers of fiscal resources and serviceresponsibilities, especially those happening with the smallscale, semi-rural localities, might hinder, rather thanexpedite the decentralization movement. If reform effortsare not able to adequately fund the newly devolved ser-vice functions into the hands of local territories, it islikely that the decentralization movement would struggle,which was experienced in Poland during the late 1990s(Owsiak & Owsiak, 2001). Clearly, the analysis of fiscalcondition can help generate the needed information forthis purpose.

Notwithstanding, there are some inconsistencies of thecurrent research findings with past literature that deservefurther investigation. As the data in Table 3 indicate, onaverage, the suburb areas faced population declines whiletheir economies were growing, whereas the semi-rural cit-ies experienced significant economic and populationexpansions. While the former had relatively healthy fiscalconditions, the latter experienced fiscal distress. The liter-ature often suggests that a growing economy and popula-tion will induce improving municipal fiscal conditions(Bradbury, 1982; Chernick & Reschovsky, 2001; Inman,1995; Ladd & Yinger, 1989; Kamer, 1983). However,Thai suburbs experiencing population declines hadrelatively healthy fiscal conditions while the growing ruralareas had relatively weak fiscal conditions. Suchdivergence from the literature suggests that there might besome other determining factors of municipal condition leftunexplored.

Future research might focus on the alternative explana-tion of municipal fiscal conditions in Thailand. To date, theliterature pays close attention to political and administrativeexplanations as to why local fiscal condition deviates fromthe norm (e.g., Chapman et al., 2003; Clark & Ferguson,1983; Dluhy & Frank, 2002; Lowery, 1984; Rubin, 1982;Tabb, 1982). Would it be possible that growing rural-basedcities managed themselves into fiscal strain despite theirsupportive socioeconomic environments? An understandingof political and administrative domains in municipal financemight yield additional insights from a newly decentralizingcountry like Thailand.

Beside the theoretical contributions to the literature, thisresearch also sheds some light on the articulation of munici-pal fiscal policies. Since socioeconomic conditions areexogenous to local government control, localities withunsupportive socioeconomic environments are less likely tocope with structural deficits by their own means and oftenend up with fiscal strain. Based on the current findings,fiscal assistance from external sources, e.g., central govern-ment agencies, are particularly essential for highly popu-lous, central cities and small, semi-rural areas. To date,intergovernmental transfers provided by central agencieshave been allocated to localities based on population and

15Studies from other well developed societies also exhibited the exist-ence of fiscal strain in the central cities relative to the outer areas, e.g., inEngland (Bailey, 1991), in Israel (Carmeli, 2008), and in the capital citiesof Denmark, Finland, Norway, and Sweden (Lotz, 1981).

16Additionally, this research depicts that city population size is notnecessarily beneficial to the city’s fiscal health. Indeed, the population sizetends to drive up public service costs as already shown in Table 6, andeventually heightens overall expenditure need. However, city populationsize does not directly translate to the city’s RRC. In effect, larger citiestend to be more fiscally distressed than average ones.

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236 KRUEATHEP

jurisdictional size, regardless of the city’s fiscal condition.17

Here, an alternative, stress-relief fiscal transfer, which is not yetin place in Thailand, could be provided to fiscally distressedcities in order to help eradicate their financial predicaments.

On the other hand, the centralization of some serviceresponsibilities might be needed in order to relieve munici-pal fiscal burdens, e.g., educational service, social welfareand public safety.18 This is important to the small, rural-based cities since their organizational capacity might be toolimited to provide effectively needed services. Equallyimportant is the use of fiscal condition results in municipaltax administration and service planning, which are rarelydone in developing nations, including Thailand. Once thefiscal condition is known, especially during the weak condi-tion, local policy makers are required to adjust their politicaland fiscal strategies (e.g., increasing tax efforts, cuttingexcessive services or both) that could help their cities sus-tain the provision of needed services. In short, residents inthe fiscally distressed cities should not be deprived ofneeded services because of their place of residence.

Since the practice of Thailand’s municipal finance is justbeginning, as compared with other well-developed societies,it is essential for policy makers and academic researchersalike to develop an effective monitoring system of Thaimunicipal finance. Municipal fiscal condition analysis is notjust an academic exercise, but also a subject of great interestto a variety of stakeholders. Unless we examine the dynamicsof municipal fiscal conditions, as this research has attemptedto do, we can hardly know the magnitude of fiscal problemsand then cannot go on to prescribe the right cure. It isbelieved that the experience of the Thai municipal fiscal con-dition might attract an interest in the local fiscal health analy-sis of other developing nations in the near future.

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APPENDIX 2. PROCEDURES FOR ESTIMATING REVENUE-RAISING CAPACITY (RRC)

Following the framework developed by Martinez-Vazquezand Boex (1997a, 1997b) and Dye (1984), municipal reve-nue-raising capacity will be estimated from the generalfunction of economic wealth, demographic characteristicsand the level of city economic development.

where RRCi is revenue-raising capacity for each of the ith

taxes/revenues. They are

1. value-added taxes (VAT) and sales taxes;2. land and real estate transfer fees;3. excises and alcohol taxes;4. commercial and land building taxes; and5. fees and charges.

City economic wealth includes gross city product percapita, property wealth, and city areas (sq. km.); demogra-phy includes population density and population growth rate;and the level of economic development includes cost of liv-ing as measured by consumer price index (CPI), a portion oflabor population to total city population, and some other rel-evant indicators. These variables will be included, whenavailable, as suggested by the literature (e.g., Ladd, 1994;Ladd et al., 1991; Wasylenko & Yinger, 1988).

Regression estimations were done separately for each of therevenue/tax sources and, later summed to obtain the overallRRC of each respective city. By using this analytical techniqueof the RRC, it assumes the average tax rates across the studiedcities, but each city’s tax bases. Thus, the variation of the RRCemerges depending on a city’s socioeconomic endowments.

APPENDIX 3. PROCEDURES FOR ESTIMATING EXPENDITURE NEED (EN)

Based on the works of Ladd and Yinger (1989) and Ladd(1994), expenditure need (EN) was estimated from

Where i = city ith from 1, 2, …, 14j = service program jth from 1, 2, …, 6Qj = standardized per capita spending on the jth service

program (or standardized per student spending for theeducation services)

APPENDIX 1.Descriptive Statistics for Key Variables

Variable N MeanStd. Dev. Min. Max.

Dependent variablesValue added taxes (VAT)1 84 1,402.1 1,274.0 22.0 7,651.4Commercial land and building

taxes184 635.6 1,027.6 153.9 4,833.4

Land development taxes1 84 457.4 459.7 17.4 2,220.1Excises and alcohol taxes1 84 285.4 80.6 163.6 445.7Fee and charges1 84 191.0 169.3 43.9 1,020.9Public safety expenditure1 82 190.2 127.6 39.1 765.8Education expenditure2 77 15,830.4 7,813.4 846.0 41,440.1Public health expenditure1 82 288.3 249.4 7.9 897.9Housing service expenditure1 83 1,765.1 1,423.2 377.3 6,885.1Social welfare expenditure1 83 200.1 191.2 19.8 1,027.6General administration

expenditure183 856.4 657.3 215.5 4,624.1

Explanatory variablesGross city product (thousand)1 84 315.9 248.8 66.8 1,189.9Economic growth rate (%) 84 5.4 9.5 −13.7 56.9Consumer price index (CPI) 84 104.8 5.9 98.1 121.2Property wealth index 76 75.0 75.0 6.5 301.2City population (thousand) 84 70.0 67.6 12.2 270.0Population density (thou. per

sq. km.)84 4.1 2.5 0.2 9.8

Population growth rate (%) 84 0.5 4.0 -20.3 7.5General purpose transfer1 83 1,385.8 1,506.4 0.0 6,305.2Specific purpose transfer1 83 2,151.9 3,251.9 0.0 16,343.0Labor population (%) 84 64.9 4.7 51.2 74.2Youth population (thousand) 84 25.5 2.6 20.5 32.0Aged population (thousand) 84 8.6 2.1 5.6 13.2Dependency population ratio (%) 84 34.1 1.8 30.5 38.8City area (sq. km.) 84 39.3 62.1 2.2 208.1City employees 84 554.0 400.0 107.0 2,325.0City street (km.) 84 86.8 79.9 14.4 307.2Student enrollment 78 4,285.0 3,026.0 909.0 14,118.0Poverty ratio (%) 84 3.7 3.8 0.0 14.4

Notes; 1 refers to Thai bath per capita, 2refers to Thai baht per student

RRC =

i f city economic wealth demography( , ,

)city economic development

(1)

EN Q S Ci j ij ijj= ∑ [ * * ] (2)

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MEASURING MUNICIPAL FISCAL CONDITION 239

Sij = the ith city’s index of service responsibility for the jth

spending program relative to the average of all citiesCij = cost factors, which are the ith city’s index of per

capita costs for the jth spending program relative to theaverage of all cities

Service responsibility indices were calculated from theproportion of average spending of the studied cities. Indicesfor public education = .3, housing and community services= .35, public safety = .05, public health = .05, socialwelfare = .05, and general administration = .2. These indi-ces are derived from the proportion of spending in each pro-gram of the average city. However, the indices for PakkredCity are distinct since the city itself does not operate munic-ipal schools. Instead, it provides financial assistance to schoolsoperated by other agencies. Therefore, Pakkred’s indices forpublic education = .12, housing and community services =.38, public safety = .05, public health = .20, social welfare= .05, and general administration = .2, respectively.

Then, the cost factors (C) were estimated from

and

where a is the average value of the sampled cities and i isthe value of the ith city. EXPPCSIM is the predicted value ofper capita spending given the average level of determiningfactors for spending, e.g., service demand, intergovernmen-tal aid, and citizen preferences, but the city’s own values ofcost factors. EXPPC is the predicted value of per capitaspending given the data of each respective city, and is esti-mated by multiple regression analyses (random effect esti-mator with cluster robust standard error). The estimate ofcost factor (Ci) is repeated for all jth service programs.Demand and aid variables included gross city product percapita, property wealth index, general purpose transfers percapita, and specific purpose transfers per capita. Cost fac-tors were listed in Table 5.

By this approach, the estimated EN represents the poten-tial spending level for each of the service functions, giventhe average service quality (as reflected in Qj in equation 3)and scopes of service responsibilities (as reflected in Sij inequation 3). A city’s variation in the EN occurs when itscost index is higher or lower than other comparable cities(as captured by Cij in equation 3). Like the estimation ofRRC, the estimation of six major service responsibilitieswas done separately and, later summed in order to obtainthe overall EN.

C EXPPCSIM EXPPCi i i= / (3)

EXPPCSIMi = g DEMAND AID PREF

COSTFACTOR

a a a

i

(

), , , (4)

EXPPCi = h DEMAID AID PREF COSTFACTORi i i i( ), , , (5)

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