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ournal of Indian Business Researchmerald Article: Does financial outreach engender economic growth?vidence from Indian states

aibal Ghosh

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Does financial outreach engendereconomic growth? Evidence

from Indian statesSaibal Ghosh

Reserve Bank of India, Mumbai, India

Abstract

Purpose Employingdata on 14 major Indian states during 1973-2004, this paper aims to investigatethe hypothesis that economic growth is affected by financial outreach.

Design/methodology/approach The paper employs univariate tests as well as advancedpanel regression techniques to examine whether financial outreach matters for state-level economicgrowth.

Findings The analysis suggests that improvements in financial outreach led to a perceptible rise inpercapita growth. In terms of magnitudes, a rise in demographic outreach by 10 percent raisesstate percapita growth by 0.3 percent; in case of geographic outreach, the increase is lower. Finally, the analysissupports the hypotheses thatstates withhigher manufacturing sharetend to growfaster and thequalityof state-level institutions and infrastructure exert a significant bearing on growth.

Research limitations/implications Although the definitions of financial outreach are based oninternational best practice, they focus only on banks and are driven by the availability of data onrelevant variables.

Practical implications The article belongs to the broad strand of literature which examines thefinance-growth nexus.

Social implications Financial outreach is presently an avowed objective of policymakers, bothin India and elsewhere. The article examines which sets of economic/policy variables impactfinancial outreach. The analysis can provide policymakers with feedback as regards the feasibilityof the strategies pursued to improve financial outreach and thereby, how best to redesign andfine-tune them.

Originality/value To theauthorsknowledge, this is presumably thefirst study in India to examinethe financial outreach-growth nexus in a systematic manner at the sub-national level.

Keywords Economic growth, Banking, Financial economics, India

Paper type Research paper

1. IntroductionThe extent of financial outreach is often regarded as a critical factor in making financialproducts andservicesavailableto a wider segment of the population. This is all the more

relevant in emerging economies where such facilities typically tend to exclude vastsegments of the population, especially the underprivileged sections of society.Cross-country data suggest that in several African economies, there is less than onebank branch per 100,000 people, while in developed economies, these numbers are quitehigh (Beck et al., 2007).

The current issue and full text archive of this journal is available atwww.emeraldinsight.com/1755-4195.htm

The author would like to thank, without implicating, the Area Editor of the journal and,especially, an anonymous referee for the comments and insights on an earlier draft thatimproved the exposition and analysis. Needless to state, the views expressed and the approachpursued in the paper are strictly personal.

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Journal of Indian Business ResearchVol. 3 No. 2, 2011

pp. 74-97q Emerald Group Publishing Limited1755-4195

DOI 10.1108/17554191111132206


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In large federal structures, an additional dimension is introduced by the existence ofcomponent federal states with their democratically elected governments. The latter,in effect, provides a convenient anchor for studying sub-national dimensions of policyactions. Since the nation comprises of several states with not only differential growthpatterns, but also differential abilities to respond to macro policies, it would therefore beof interest to understand the extent of such reactions at the sub-national level[1].

Towards this end, the paper chooses India as a case study and explores the impact offinancial outreach on state-level economic growth. Borrowing from the recent literaturein this area (Beck et al., 2007), it utilizes a consistentset of indicatorsof financial outreachand explores their empirical association with state-level economic growth, aftercontrolling for other state-level determinants of growth.

The results indicate that improvements in financial outreach, as measured in terms ofexpansion and use of financial services, have a salutary impact on per capita economicgrowth. In fact, it is the high income, coastal states that exhibit relatively higher growth,which suggestsa gradual divergence in growth patternsacrossregions. Besides, growth ishigher in states that have bigger(in termsof size) stategovernments.As well,the quality ofstate-level institutionsand infrastructure arefound to be importantfactors which influencestate-level economic growth. Although preliminary, the analysis also suggests thegrowing role of technology as an important factor in promoting greater financial outreach.

The paper makes several contributions to the literature on financial outreach andeconomic growth. First, the paper is linked to a large body of literature that emphasizestheimportance of financein influencing economic growth. Cross-country studies(Levineand Zervos, 1998; Aghion et al., 2005a, b) and also evidence at the industry (Rajan and

Zingales, 1998) and firm level (Demirguc-Kunt and Maksimovic, 1998) offer persuasiveevidence that various measures of financial development have a positive effect oneconomic growth (see Levine (2005) for a survey). Even within a country, local financialdevelopment matters for growth because it engenders increased entry of firms andgreater growth of existing ones (Guiso et al., 2004). Judged thus, the paper complementscross-country studies with an in-depth country study.

Second,the paper augments theevolving literature on financial outreach.In emergingmarkets, owing to a lack of adequate institutional and informational infrastructure,imperfections in financial markets are quite pervasive. The net effect of suchasymmetries is felt particularly by small entrepreneurs with limited collateral backing.As a consequence, the importance of well-developed financial systems for economicgrowth and poverty alleviation can hardly be over-emphasized. Access to finance canalso have important effects on technological progress and the creation of ideas; with anabsence of financing ideas, the incentive for innovation is much lower (King and Levine,

1993). Finally, outreach is also intricately linked to human development. Financialliteracy is a critical ingredient of financial outreach, since it enables individuals to makeinformed decisions about their financial choices. The sub-prime crisis, in large part, isbelieved to have been the result of inadequate awareness on the part of consumers tounderstand the nuances of complex financial products (Bernanke, 2008). To the extentthat better education feeds into better health (Grossman, 1972) and vice versa, the neteffect of higher outreach is reflected in better human development[2]. The index offinancial outreach developed by Honohan (2007) appears to corroborate this point.

Third and moregenerally, thepaper belongs to theclass of literature thatexplores theissue of financing constraints. Most studies exploit the World Business Environment

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Survey a cross-sectional firm-level survey data covering 80 developed and developingcountries to examinethis issue. Clarke et al. (2001), for example, investigatethe impactof foreign bank penetration on SME lending. They find that foreign bank penetrationincreases the share of financing from banks and lowers financing obstacles as perceivedby firms, particularly in case of large firms. Love and Mylenko (2003) explore howcredit-reporting institutions affect financing constraints. Their research appears tosuggest that private credit registries relax financing constraints and increase bankfinancing, particularly for SMEs. Beck et al. (2003), study the impact of bankconcentration on firms financing obstacles and access to credit. They find that incountries with low levels of institutional development, bank concentration leads tohigher financing obstacles and a lower share of bank financing, particularly for SMEs.Finally, Galindo and Micco (2005) study the effect of several measures of creditorprotection on the share of financing from banks and find that creditor rights improveaccess to financing for SMEs.

Finally, the paper belongs to the literature that explores the sub-national effects ofgreater financial outreach and to a wider literature which examines the impact ofeconomic policies on sub-national economies, both for India (Sachs et al., 2002; Besleyand Burgess, 2004; Aghion et al., 2003, 2005a, b; Mitra and Ural, 2007; Hasan et al., 2007;Bhandari, 2009) and elsewhere (Carlino and Defina, 1998; Arnold and Vrugt, 2002;Wachtel et al., 2006). Many of these studies have identified important effects of variedlegislations on sub-national growth performance. Although certain studies focus on thefinance-growth interface (Burgess and Pande, 2005; Topalova, 2008), few studies assessthe effects of financial outreach on sub-national economic growth. Even more

importantly, none have examined the effects of different measures of financial outreachon sub-national growth and this is one of the major concerns of the paper.The choice of sub-national governments is premised on three major considerations.

First, like the USA and other emerging economies, such as Argentina and Brazil, India isa federal polity comprising of states with their own governments and a measure of policyautonomy. Over time, these states have developed distinct characteristics, driven by acombination of their geographical location and economic policies pursued. In thisprocess, the feasibility of the strategies pursued to increase financial outreach canprovide practitioners with useful leads to redesign and fine-tune their strategies. Second,the data comparability issues are less serious within a country than across countries.While the comparison of institutional and financial characteristics across countries canbe difficult owing to wide divergences in their historical experiences, institutionalcharacteristics and the legal environment,sub-national data can control for such contextsand thereby reduce the unobserved heterogeneity at the cross-section level. Third,

India is one of the few important emerging economies for which a comprehensive andreliable state-level database is available. The cross-sectional and time-varying nature ofthe data makes it amenable to rigorous statistical analysis. To the extent that suchstudies bypass the limitations inherent in cross-country studies (Rodrik, 2005),it provides an ideal laboratory to explore the effects of financial outreach on sub-nationaleconomic growth and could be representative of such association in other emergingmarkets.

The paper comprises of six sections after this introduction. Section 2 presentsan overview of the literature with reference to India. This is followed by a descriptionof the stylized facts for the Indian states with emphasis on financial outreach.

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The data and variables and the regression specification are detailed in the subsequenttwo sections. Section 6 discusses the results along with some robustness tests, followedby the concluding remarks.

2. Financial inclusion: overview and Indian experienceThis paper is related to an emerging literature on access to financial services. Extantresearch analyzes access to financial services at the firm (Beck et al., 2006b) and banklevel(Beck etal., 2006a).More recently,Beck etal. (2007),present aggregate cross-countrydata on banking sector outreach (such as branch and ATM penetration, deposits per

capita, and loans per capita) and show that these indicators closely track more difficultand costly to collect micro-level statistics of household and firm use of banking services.A recent study by the European Commission (2008) classifies the 25 EU economies in

terms of their level of financial exclusionand findsseveraltransition economies(Hungary,Poland, Lithuania, and Latvia) experiencing maximum financial exclusion. The reportedfigures for the USA are much higher, with anywhere between 9.5 and 20 percent ofhouseholds having no access to bank accounts (His Majestys Treasury, 2004).

In the Indian case, the foundation for broad-basing the institutional credit structureand promoting greater financial access can be traced to the findings of the All-IndiaRural Credit Survey (RBI, 1954). Thefindingsof thesurvey indicated that, out of thetotalborrowings of farmers in 1951-1952 estimated at Rs 7,500 million, commercial banksprovided less than 1 percent, while moneylenders provided 70 percent. The distributionof bank branches was also highly skewed, with nearly 38 percent of the bank branchesbeing located in urban and metropolitan/port town locales. The distribution of bank

credit was also highly skewedwith an overwhelming proportion of credit being corneredby private corporate business.

Theseand several other disquieting featuresin theallocation of bankcredit eventuallyculminated in the process of bank nationalization in July 1969. In essence, the state tookcontrol of the banking sector and made it a tool for promoting social objectives. Salientelements of the process included control over interest rates and dovetailing of lendingtowards priority sectors. A critical ingredient of this strategy entailed the imposition ofthe 1:4 license rule in 1977, wherein, banks could open a branch in a location with one ormore branches only if it had opened four in a location with no branches (unbankedlocation). Thus, over the period 1969-1991, over 50,000 new bank branches were built,predominantly in rural locales (Table I). As Burgess and Pande (2005) demonstrate, byimproving access to cheap formal credit for the rural poor, this redistributive nature ofbranch expansion strategy made a significant dent on rural poverty.

The second phase of public policy towards promoting greater financial access can be

tracedto the inception of financial sector reforms. Salient features of this period includeda higher allocation of credit to the private sector, moving away from administered tomarket-determined interest rates both for commercial and government borrowing,increased competitiveness, and liberal entry of foreign banks (Chairlone and Ghosh,2008). In a sense, the period demonstrated that policies for inclusive banking have toexist concurrently with encouraging strong and efficient financial institutions.

Three important features of the strategy towards promoting inclusive banking duringthis period deserve mention. The first was the initiation of the Self Help Group Banklinkage program in 1992. After a tepid beginning, the program gathered momentumthereafter with increasing outreach both in coverage and bank loan disbursement.

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Thesecond wasthe establishment of theRural Infrastructure Development Fund during1995-1996 towards completion of on-going projects of rural infrastructure. This wascomplemented by strengthening the capital base of the National Bank for Agricultureand Rural Development (NABARD)[3].

Furthermore, a systemof Kisan Credit Cards (Kisan, meaning farmer)was initiated in1999 to provide adequate and timely financial support in a flexible and cost-effectivemanner from the banking system to farmers for cultivation needs and purchases.

On a broader plane, the Reserve Bank has adopted a two-pronged strategy to expandthe reach of banking services. Thefirst strategy, termed empowerment, entails inculcatingawareness among the masses about financial products through financial education alongwith supporting advisory mechanisms (e.g. credit counseling). The second one, termedprotection,envisages a comprehensive codeof conduct for minimum standardsof bankingservices to be offered by banks with closer and continuous regulatory oversight.

3. Financial outreach in India: data and summary statisticsWe begin the analysis by providing an overview of financial outreach across states.Following from Beck et al. (2007), we utilize the following indicators of banking sectoroutreach at the state level:

(1) Geographic outreach: number of bank branches per 1,000 km2.

(2) Demographic outreach: number of bank branches per 100,000 people.

(3) Loan accounts per capita: number of loan accounts per 1,000 people.

(4) Deposit accounts per capita: number of deposit (aggregate of savings, term, andcurrent) accounts per 1,000 people.

(5) Loan-income ratio: average size of loans to per capita net state domestic product(PCNSDP).

(6) Deposit-income ratio: average size of deposits to PCNSDP.

IndicatorsJune1973

December1980

March1991

March1998

March2004

Number of commercial banks 83 154 272 300 290Of which: regional rural banks (RRBs) 107 196 196 196

Number of bank offices 15,362 34,594(4,471)

60,570(14,519)

66,408(14,471)

69,170(14,446)

Of which: rural/semi-urban branches 11,282 23,227 46,115 47,130 47,766Annual growth rate (%) of rural/semi-urbanbranches 15.1 8.9 0.31 0.22

Population per bank office (000s) 36 16 14 15 16Deposits of commercial banks (Rs billion) 92 404.4 2,011.9 6 ,054 15,044Per capita deposit (Rs) 167 738 2,368 6,270 14,089Credit of commercial banks (Rs billion) 64 250.8 1,218.7 3,241 8,408Per capita credit (Rs) 117 457 1,434 3,356 8,273Deposits/national income (%) 24 36 48.1 46.4 60Total Assets (Rs billion) 110 710.8 3,275.2 5,215.4 11,516.2

Note: Figures in parentheses are branches of RRBsSource: RBI (various years (a)(b))

Table I.Commercial bankingin India: progresssince 1973

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In particular, we focus exclusively on banking outreach for two major reasons. First, in amajority of countries including India, thebankingsectorintermediates most of the fundsin the economy. Second, the statistical information for this sector is easier to obtain ascompared to other non-bank service providers.

Indicators(1) and(2) measure theoutreach of the financial sector in terms of access tobanks physical outlets. These measures, however, have limitations as indicators ofaccess to physical banking outlets. More importantly, these measures implicitly assumea uniform distribution of bank outlets within a countrys area and across its population.In reality, bank branches and ATMs could be concentrated across population groups,delimiting its utility in certain cases. To overcome this drawback, indicators (3) through

(6) measure the use of banking services. We focus on bank deposits and loans becausethese are the main services offered by banks for which information is available.

Table II reports the correlations of growth in PCNSDP with these various measuresdescribed above. What is striking is the fact that the correlation of the growth indicatorwith measures of financial outreach has, in fact, weakened over the reform period.By way of example, the correlation of PCNSDP growth with bank office per 100,000people was 54 percent for the entire period and 56 percent in the pre-reform regime.In contract, the correlation was insignificant in the post-reform era. What this indicatesis a possible weakening of the growth-financial outreach nexus, especially in thepost-reforms era.

We also examine the extent of financial outreach across states. For expositionalsimplicity, we classify the states by three-fold criteria: income, region and geography.In our subsequent analysis, we employ dummies to examine differences in financialoutreach across these classifications. Specifically, high-income states are as defined bythe The World Bank (2005) and corroborate the earlier classification to this effect bySachs et al. (2002) and Ahluwalia (2002). Likewise, states have also been classifiedaccording to regions, following RBI (2008) and finally, as coastal or land locked(Government of India, 2008a)[4].

The evidence indicates significant differences in both geographic and demographicoutreach across high- and low-income states; similar evidence is also manifest in thecaseof deposit and loan accounts. There is also evidence to suggest limited use of depositservices in the low-income states (Beck et al., 2007). By way of example, the meandeposit/income ratio in the low-income state is 3.78 as compared to 2.54 in thehigh-income states. The difference is statistically significant at the 0.05 level.

In terms of regional divergence, the differences in demographic outreach and depositaccounts per capita are most substantial. Illustratively, the number of bank offices

Financial outreach indicator1972-1973 to

2003-20041972-1973 to

1991-19921992-1993 to

2003-2004

Bank office/1,000 km2 0.321 * 0.368 * 0.037Bank office/100,000 people 0.535 * 0.562 * 0.242Number of loan accounts per capita 0.568 * 0.661 * 0.276 * * *

Number of deposits accounts per capita 0.588 * 0.618 * 0.189Loan income ratio 20.554 * 20.663 * 0.032Deposit income ratio 20.641 * 20.544 * 20.026

Note: Significance at: *1, * *5 and * * *10 percent levels

Table II.Correlations of financialoutreach measures with

PCNSDP growth

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per 100,000 people is the highest at 7.67 in the southern region as compared with 4.73 inthe eastern region. This difference is statistically significant at the 0.01level. Differencesin loan accounts per capita are significantly higher in the southern as compared to otherregions; both deposit-income ratio and loan-income ratio exhibits limited divergenceacross regions. The evidence is consistent with recent findings which reports significantregional variation in the provision of financial services (Basu, 2006; Government ofIndia, 2008b). Finally, across most indicators, coastal states exhibit higher levelsof financial outreach; only in the case of loan accounts per capita was the differencestatistically significant (Table III).

The differences in the financial outreach for the pre- and post-reform

period are particularly striking. The evidence indicates a significant improvementin both geographic and demographic outreach; as well, the values of both deposit- andloan-income ratios have declined, signifying the greater outreach of banking services(Beck et al., 2007).

4. Data and variablesFor the analysis, we use state-level data for the 30-year period, 1973-2004. We selectedthis timeperiod for several reasons. First, it seems sufficiently long to allow the long-runinfluences to play out. Second, the period coincides with the availability of consistentdata on theconcerned variables at the state level to clearly discern the impact of financialoutreach on state level growth. And finally, the period of analysis is the post-banknationalization period, which helps to clearly isolate the impact of economic policiesuncontaminated by other confounding influences.

The data include14 statesin India, in linewiththe standardpractice of comparing theeconomic performance of states that treats smaller or north-eastern states differently(Ahluwalia, 2002; Sachs et al., 2002; Nachane et al., 2002)[5]. Therefore, we exclude fromthe study the special category states that receive exceptionally generous grantsfrom the Federal Government on account of their specific institutional characteristics(Raoand Singh, 2007). In the financial year 2003-2004, these statesaccounted for roughly80 percentof Indias land area, over 70 percent of her population and nearly 75 percent ofthe domestic product.

Many of the variables in the model vary less over time. Therefore, rather than usingannual data, following earlier analysis in this area (Grier and Tullock, 1989; Barro, 1997),we grouped the data into eight, non-overlapping, four-year time intervals. Therefore,we have eight sets of observations on each of the 14 states, yielding a total of112 observations.

The dependent variable is change in logarithm of real PCNSDP, which represents

value added originating in each state. A similar variable was employed by Rodrik andSubramanium (2004) in their analysis of Indias growth trajectory during the last fivedecades.

The level of initial per capita income is included to test the convergence hypothesis:if there is convergence, stateswith higher income levelswill tend to grow at a slowerrate.The vector of state-level controls includes the ratio of manufacturing to NSDP (toexamine the existence of interest rate channel). Rodrik and Subramanium (2004), forinstance, find a significant role for registered manufacturing in explaining inter-stategrowth rates. Furthermore, the state-level literacy rate is included to control for thequality of educational attainment in the state.

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Variables

Bank

office/

1,00

0km

2

Bankoffice/100,000

people

Deposit/

income

Loan/income

Depositaccount/1,000

people

Loanaccount/1,000

people

No.obs

Panel

A:

income

Highincome

28.77(19.56)

7.15(2.28)

2.54(1.99)

16.45(22.21)

399.92(18.36)

61.75(36.10)

72

Lowincome

14.70(9.81)

5.39(1.91)

3.78(3.55)

16.85(25.10)

195.48(10.29)

37.97(27.33)

40

t-testfordifference

5.06*

4.36*

2

2.04**

2

0.08

7.55*

3.92*

Panel

B:reg

ion

Northern

23.75(15.63)

7.48(2.35)

2.29(1.84)

17.79(21.39)

390.77(24.56)

41.83(22.12)

24

Southern

32.69(23.94)

7.67(2.02)

2.27(1.62)

18.03(16.56)

397.69(17.42)

84.63(32.51)

32

Western

23.29(5.25)

5.92(2.06)

3.04(2.27)

24.90(24.77)

364.26(12.63)

36.39(16.22)

16

Eastern

13.30(15.71)

4.73(1.67)

4.26(4.11)

20.78(33.78)

214.49(12.49)

45.37(35.16)

24

Central

16.95(9.87)

6.06(1.96)

3.49(2.77)

17.42(23.58)

220.21(11.30)

36.33(31.14)

16

t-test

for

difference

Northernvssouthern

1.69***

2

0.31

0.06

2

2.17**

2

0.11

2

5.85*

Northernvswestern

3.04*

2.22**

2

1.09

2

0.93

0.44

0.89

Northernvseastern

0.09

4.68*

2

2.14**

2

0.36

3.12*

2

0.41

Northernvscentral

1.69***

2.07**

2

1.54

0.05

2.96*

0.61

Southernvswestern

4.38*

2.81*

2

1.21

2

2.69*

0.75

6.86*

Southernvseastern

1.77***

5.97*

2

2.24**

2

1.83***

4.57*

4.27*

Southernvscentral

3.21*

2.66*

2

1.64

2

1.57

4.25*

4.99*

Westernvseastern

2

2.89*

1.92***

2

1.21

0.44

3.68*

2

1.09

Westernvscentral

2

1.31

2

0.21

2

0.51

0.87

3.39*

0.07

Easternvscentral

1.57

2

3.21*

0.70

0.37

2

0.14

0.85

Panel

C:

location

Coastal

25.63(20.71)

6.57(2.25)

2.75(2.13)

14.83(20.93)

349.82(16.53)

64.64(37.24)

64

Landlocked

21.23(13.41)

6.46(2.41)

3.30(3.32)

18.94(25.89)

296.35(21.04)

38.08(25.05)

48

t-testfordifference

1.36

0.24

2

1.01

2

0.89

1.46

4.51*

Panel

D:re

forms

Pre-reforms

19.41(15.64)

6.04(2.45)

4.15(2.83)

24.42(26.39)

257.54(16.81)

43.57(33.14)

70

Post-reforms

30.97(19.51)

7.32(1.79)

1.06(0.48)

13.54(11.57)

442.52(15.89)

69.40(32.45)

42

t-testfordifference

2

3.26*

2

3.15*

8.93*

6.59*

2

5.83*

2

4.05*

Notes:Significanceat:*1,**

5and***10percentlevels;standarddeviationwithinparentheses

Table III.Financial outreach across

state characteristics:mean and standard

deviation

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Several studies in the Indian context highlight the importance of infrastructure inimpacting state output (Nagaraj et al., 2000; Ahluwalia, 2002; Kochhar et al., 2006).Kochhar et al. (2006), point out that the transmission and distribution (T&D) losses ofstate electricity boards, to the extent it is the outcome of state-level decisions, reflect onboth the quality of its institutions (lack of viability of state electricity boards) as also itsinfrastructure (high T&D losses reflect low-power quality which directly impingeson manufacturing) and could end up dampening economic growth.

Earlier studies on India do not control for state-level fiscal policy. Studies havedocumented that large government sectors tend to impede growth, ceteris paribus(Barro, 1991; Easterly and Levine, 2002). We control for this factor by incorporating the

ratio of state budget deficit to NSDP.State-level income data are derived from the Economic and Political Weekly States

Database (EPW Research Foundation, 2003). Using these data, we construct an annualseries on real net per capita income and the shares of agriculture, manufacturing andservices, by appropriately splicing the three base-year series. Data on state-level fiscalvariables are hand coded from various issues of annual reports on state finance,supplemented by the Handbook of Statistics on State Government Finances (RBI, 2004).Infrastructural variables are obtained from the Statistical Abstract, supplemented withannual reports of state electricity boards as available on the web site of the IndianPlanning Commission. The data on the banking variables are culled from the BasicStatistical Returns, a yearly central bank publication which provides detailed state-levelinformation on deposits and credit of commercial banks. The Appendix providesa description of the variables, data sources and summary statistics.

5. Regression analysisThe univariate tests do not control for factors that might systematically impact stateeconomic growth. For one, we do not account for state-level controls. The pace ofeconomic activity could also be an important consideration. Another major concern isthe possibility of endogeneity: one must determine that correlations between output andfinance are due to output responding to finance and not the other way around. Takingthese aspects on board, we estimate the effect of financial outreach on state-leveleconomic growth, employing regressions of the following form:

Gr_PCNSDPs;t fInitial PCNSDP; Inclusion Control variables;

Year dummies error1

where s indexes state and tdenotes year.

In equation (1), the dependent variable (PCNSDP) is assumed to be a function ofstate-level controls (Control variables) including measures of its institutional structure,size, educational attainment and infrastructure. Year dummies are included to controlfor shocks to the state economy. The variable of interest is Inclusion. Under thehypothesis, that greater financial outreach improves growth, we would expect thecoefficient of this variable to be positive.

The outreach of lagged dependent variable renders static panel estimation ofequation (1) inconsistent. To address this concern, and also to overcome the potentialendogeneity of other regressors, we employ panel GMM estimations. More specifically,we employ the system GMM estimator due to Blundell and Bond (1998). We employ

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the system GMM as compared to its competing rival, the difference GMM estimator,since the latter may suffer from weak instruments problems (Blundell and Bond, 1998).

The reliability of the GMM estimation procedure depends on the validity of theinstruments. We consider the validity of the instruments by presenting the Sargan teston over-identifying restrictions. It is asymptotically distributed as x2 and tests the nullhypothesis of validity of the (over-identifying) instruments. The p-values report theprobability of incorrectly rejecting the null hypothesis, so that a p-value above0.05implies that the probabilityof incorrectly rejecting thenull is above 0.05. As a result,a higher p-value makes it more likely that the instruments are valid.

The consistency of the estimates also depends on the absence of serial correlation in

the error terms. This will be the case if the differenced residuals display significantnegative first-order serial correlation and no second-order serial correlation. We presenttests for first- and second-order serial correlations related to the estimated residuals infirst differences. The test statistics are asymptotically distributed as standard normalvariables. The null hypothesis relates to insignificance, so that a low p-value for the teston first-order serial correlation and a high p-value for the test on second-order serialcorrelation suggest that the disturbances are not serially correlated.

6. Results and discussion6.1 Baseline resultsThe results, reported in Table IV, show that we are not able to reject the Sargan test.Moreover, we are not able to reject the null hypothesis of no second-order serialcorrelation. In other words, the GMM model is well specified.

In all cases, we run themodel with andwithout the control variables. In specification (1),the coefficient on initial per capita income is 0.63, which suggests that initially poor statesgrow faster than rich ones. In other words, absent controls for differences in state policiesand economic structure, the speed of absolute convergence occurs slowly. The rate ofconvergence is 1.4 percentper annum, which implies that it takes roughly 50 years to closehalf the gap (also called as half life)[6] between a states initial PCNSDP and itssteady-state level of income. This convergence rate is comparable to those obtained incross-country regressions (Barro and Sala-i-Martin, 1995). In the Indian case, theconvergence rate has been estimated at anywhere between 7 and 48 percent (Nosbusch,1999; Nagaraj et al., 2000) and more recently, at around 5.3 percent (Trivedi, 2006).Accountingforcontrols,thehalflifeissmalleracrossallmodels,rangingfrom24to46years.

We first discuss the control variables. States with greater dependence onmanufacturing are observed to grow faster. Earlier evidence for India (Nagaraj et al.,2000) and the USA (Carlino and Defina, 1998) also suggests a positive coefficient on

manufacturing, which can be interpreted as evidence in favor of an interest rate channelof monetary policy. The coefficient on literacy exhibits mixed signs. Although thepositive sign is consistent with the growth-enhancing effects of human capital aspopularized in the endogenous growth literature (Lucas, 1988), the negative sign couldbe picking up standard conditional convergence effects, whereby states with lowerinitial human capital grow faster, as observed by Barro and Sala-i-Martin (1999) in theircross-country studies. Contextually, studies for India (Ahluwalia, 2002) as alsocross-country regressions (Islam, 1995) confirm a negative coefficient on the literacyvariable[7]. The coefficient on size is negative, suggesting that the higher this ratio,the greater is the states reliance on deficit financing of outlays, which tends

Financialoutreach

83


12/25

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Ln(initialpcincome)2

0.63

(0.35)**

20

.55

(0

.32)**

2

0.39

(0.14)*

2

0.36

(0.15)**

2

0.41

(0.24)

2

0.39

(0.21)***

2

0.33

(0.14)**

2

0.27

(0.15)***

2

0.67

(0.19)*

2

0.59

(0.26)**

2

0.44

(0.24)***

2

0.41

(0.20)**

Geographic

0.004

(0.003)

0.0

02

(0.0

01)***

Demographic

0.0

32

(0.0

15)**

0.0

29

(0.0

14)**

Deposita/cpc

2

0.003

(0.003)

0.0

02

(0.0

008)**

Loana/cpc

0.001

(0.0007)

0.001

(0.0008)

Deposit/income

2

0.033

(0.023)

2

0.0

61

(0.0

27)**

Loan/income

2

0.0

09

(0.0

04)*

2

0.009

(0.019)

Controlvariables

Ln(Mfg.)

0

.371

(0

.125)*

2

0.058

(0.073)

0.198

(0.148)

0.063

(0.038)***

0.064

(0.026)*

2

0.092

(0.069)

Ln(Literacy)

0

.204

(0

.082)*

0.035

(0.115)

0.373

(0.167)**

0.029

(0.056)

2

0.606

(0.267)**

2

0.030

(0.015)**

Ln(Size)

20

.069

(0

.026)*

2

0.073

(0.044)***

2

0.131

(0.098)

2

0.084

(0.044)**

2

0.173

(0.048)*

2

0.186

(0.105)***

Ln(T&Dlosses)

20

.061

(0

.035)***

2

0.010

(0.029)

2

0.049

(0.026)***

0.003

(0.032)

2

0.376

(0.162)**

2

0.029

(0.096)

dy_Merger

0.014

(0.033)

20

.111

(0

.044)*

2

0.030

(0.027)

2

0.065

(0.040)***

2

0.007

(0.023)

2

0.049

(0.067)

2

0.059

(0.037)***

2

0.053

(0.021)*

0.023

(0.017)

2

0.351

(0.137)*

2

0.027

(0.022)

2

0.117

(0.033)*

Constant

2

0.600

(0.267)**

21

.447

(0

.568)*

2

1.105

(0.389)***

2

0.365

(0.358)

2

1.147

(1.022)

2

1.867

(1.004)***

2

0.164

(0.170)

2

0.319

(0.249)

2

0.791

(0.202)*

2

0.717

(0.302)*

2

0.411

(0.183)**

2

0.578

(0.395)

Yearcontrols

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

States(no.obs)

14,97

14,97

14,97

14,97

14,97

14,97

14,97

14,97

14,97

14,97

14,97

14,97

x2:Hansenover-id

test(p-value)

0.504

0

.545

0.197

0.190

0.205

0.288

0.253

0.122

0.334

0.685

0.590

0.329

AR(2):p-value

0.581

0

.256

0.698

0.587

0.443

0.460

0.858

0.302

0.770

0.618

0.767

0.717

Endogenous

variablesusedas

instruments

Initialpc

income,

literacy,T&D

loss

Initialpc

income,

literacy,T&D

loss

Initialpc

income,

literacy,T&D

loss

Initialpc

income,

literacy,T&D

loss

Initialpc

income,

literacy,T&D

loss

Initialpc

income,

literacy,T&D

loss

Initialpc

income,

literacy,T&D

loss

Initialpc

income,

literacy,T&D

loss

Initialpc

income,

literacy,T&D

loss

In

itialpc

in

come,

literacy,T&D

lo

ss

Initialpc

income,

literacy,T&D

loss

Initialpc

income,

literacy,T&D

loss

Lagsofendogenous

variablesusedin

instrumentation

UR

UR

UR

UR

UR

UR

UR

UR

UR

UR

UR

UR

Notes:Significanceat:*1,**5and***10percentlevels;UR,unrestricted;robuststandarderro

rsarereportedinparentheses;AR(2)isatestofsecond-orderserialcorrelationandfollowsN(0,1);

detailsoftheGMMprocedure,withall

thechoices,reportedinthelastrowsofthetable

Table IV.Regression estimation:system GMM results

JIBR3,2

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to dampen growth. For the US states, Razzolini and Shughart (1997) also uncoveredevidence thatgovernmentsize had a significant and negative effect on economic growth.Across several specifications, the coefficient on T&D losses is negative, indicating thatstates with better power network, are typically more attractive investment destinations.

All specifications control for the impact of the merger of several of the states duringthe post-reform period. Wherever significant, the coefficient is negative, suggesting thatthe net effect of mergers has been a lowering in economicgrowth in the concernedstates.

Our coefficient of interest is outreach. Across all models, the coefficient exhibitsexpected signs, although it is significant in six (out of the 12) specifications. Themagnitude of the effect is however, economically small. To understand the economic

significance of this variable, take for instance, specification (4), where the coefficient ondemographic outreach is 0.03. Consider a state with demographic outreach at 0.31, theminimum value for the sample; the corresponding growth rate in PCNSDP being0.89 percent. An increase in demographicoutreach to 1 (over 200 percent increase) wouldraise its per capita growth to nearly 0.95 percent, a rise of about 7 percent. Likewise,in specification (10), the coefficient on deposit-income ratio is 20.06. Therefore, greateroutreach of banking services as reflected in a decline in this ratio by roughly 86 percentfrom its median value of 1.92-1.03, the value obtaining at the 25th percentile, wouldimprove the per capita growth by roughly 5 percentage points. In sum, financialoutreach seems to exert a salutary effect on economic growth.

6.2 Does financial technological outreach matter?[8]Table V reports cross-sectional regressions of measures of financial technological

outreach on economic growth, after accounting for various controls. These resultsprovide further insights into the role of outreach in influencing state-level economicgrowth. We obtained state-level data on the number of ATMs of public sector banks for2002[9]. Following from Beck et al. (2007), we computed the two measures:ATMs/100,000 people (demographic technological outreach) and ATMs/1,000 km2

(geographic technological outreach). We did not have sufficient time-series dataavailable for the above explanatory variable. Therefore, we estimated a basiccross-sectional OLS model with robust standard errors for 2002. Given the limitednumber of observations, the model specification is fragile and many variables cannot besimultaneously employed in order to avoid multicollinearity problems. We run theregressions with and without the state-level variables, although we controlfor state areaacross all models[10]. We find thatdemographic technological outreach has a significantimpact on PCNSDP. In contrast, however, the impact of geographic technologicaloutreach on PCNSDP appears limited. The result is significant not only statistically, but

economically as well. In model (2), the estimates reveal that a rise in demographictechnological outreach by one standard deviation results in 0.528 percentage pointhigher NSDP. Although not directly comparable, it appears that the potential oftechnological outreach to advance economic growth is much higher, since in absoluteterms, the magnitude on the ATM-related variables (the measures of technologicaloutreach) far outweighs the other indicators discussed earlier.

To examine the differences in technological outreach across states, we interactedthe outreach variables with a dummy, depending on whether a state is high income.A similar exercise was conducted to examine the differences in technological outreachof costal versus landlocked states. The results indicate significant differences

Financialoutreach

85


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(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

ATMs/100,000people

0.3

28(0

.09

4)*

0.2

33(0

.078)*

2

0.023(0.161)

0.171(0.111)

ATMs/1,000km

2

0.039(0.031)

0.061(0.077)

0.0

22(0

.006)*

0.039(0.025)

Interact

ionterms

ATMs/100,000

people*highincome

0.1

90(0

.100)***

ATMs/100,000

people*coastal

0.047(0.080)

ATMs/1,000km

2*high

income

0.0

72(0

.005)*

ATMs/

1,000km

2*coastal

0.018(0.023)

Lnmfg.

0.309(0.191)

0.652(0.112)*

0.461(0.263)

0.337(0.196)

0.544(0.059)*

0.639(0.061)*

Lnunregisteredmfg.

2

0.046(0.221)

2

0.221(0.137)

2

0.126(0.279)

2

0.035(0.203)

2

0.227(0.057)*

2

0.214(0.089)**

Lndeficit

2

0.144(0.079)***

2

0.239(0.130)***

2

0.104(0.057)***

2

0.213(0.131)***

2

0.249(0.050)*

2

0.400(0.332)

Lncrime

2

0.393(0.149)**

2

0.706(0.131)*

2

0.309(0.135)**

2

0.467(0.256)***

2

0.638(0.048)*

2

0.877(0.311)*

Constant

9.508(1.255)*

13.268(1.202)*

8.982(2.373)*

12.105(0.635)*

12.597(1.338)*

13.935(1.636)*

12.022(0.315)*

13.994(2.022)*

Controlforstatearea

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

14

14

14

14

14

14

14

14

R2

0.632

0.882

0.294

0.93

0

0.910

0.888

0.973

0.935

Notes:Significanceat:*1,

**5and

***10percentlevels;robuststandarderrorsinparen

theses

Table V.Financial technologicaloutreach and economicgrowth: cross-sectionalresults for 2002

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86


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in technological outreach across these two types of categories. Consider for instance,model (7). A rise in the number of ATMs per 1,000 km2 by 10 percent improvesPCNSDP by 0.2 percent. For high-income states, there is an increase of an additional0.7 percent. The fit of the model is fairly high: inclusive of state controls, all modelsexplain over 85 percent of the variation in the dependent variable.

Together, these results suggest that efforts at improving technological outreachhave a perceptible effect on state per capita incomes.

6.3 Sensitivity testsFollowing from the earlier discussion, we consider several robustness tests of thebaseline results pertaining to geographic outreach (Panel A), demographic outreach(Panel B), deposit accounts per capita (Panel C) and deposit-income ratio (Panel D).In particular, we consider the impact of these variables on state per capita growth onthe basis of a priori classification such as income and geography as also the pre- andpost-reform period. The results are highlighted in Table VI.

The results suggest that high-income states exhibited high levels of geographicexclusion as compared to low-income ones. Illustratively, an increase in geographicoutreachby 10 percent wouldentail a rise in percapita incomeby 0.1percent. Ascomparedto this, per capita growth in the high-income states is 80 percent higher for the samemagnitude of improvement in banking outreach. It is a similar story in the case of coastalstates. In terms of magnitudes, a 10 percent increase in geographic outreach raises theper capita income of coastal states by 33 percent more as compared to landlockedstates. The final two specifications introduces three-way interaction variables, high

income*coastal*geographic and high income*pre-1990s*geographic. In specification (4),the coefficient on the interaction term is positiveand significant at the 0.05 level, indicatingthat high income, coastal states with higher levels of geographical outreach typicallyexhibit higher per capita income growth. The evidence also indicates that high income,coastal states with higher levels of geographical outreach typically grow faster. To theextent that the analysis categorizes states based on certain inherent characteristics,it offers a rationale for the high growth of certain states/regions vis-a-vis others[11].

The remaining categories focus on the three financial outreach measures which wereobserved to be significantin the baseline analysis. Thus, the evidence suggests thathighincome states with higher demographic outreach exhibited higher per capita growthin the pre-1990s (specification (5)). Likewise, by extending the outreach of bankingservices, higher deposit accounts per capita in the pre-1990s regime was an importantfactor in raising per capita growth in high-income states (Panel C, specification (5)).Finally, the evidence emanating from Panel D appears to indicate that, as compared to

low-income states, the use of banking services in high-income states has been lower bynearly 80 percent[12].

7. Concluding remarksThe paper makes a systematic attempt to ascertain the nexus between financeand growth at the sub-national level for an emerging economy. Borrowing fromthe literature, we employ measures of financial outreach that capture both the outreachand also the use of banking services. We subsequently examine the impact of thesemeasures of financial outreach on per capita economic growth, using data on majorstates in India covering the period 1973-2004.

Financialoutreach

87


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(1)

(2)

(3)

(4)

(5)

Panel

A

Geographic

0.0

10(0

.003)*

0.003(0

.001)**

2

0.0005(0.001)

0.0

04(0

.001)*

2

0.0003(0.001)

Highincome*geographic

0.0

08(0

.003)**

Coastal*geographic

0.001(0

.009)**

Pre-1990s*geographic

2

0.002(0.001)

Highincome*coastal*geographic

0.0

02(0.0

01)**

Highincome*pre-1990s*geographic

0.002(0.002)

Groups(no.obs)

14,83

14,83

14,83

14

,83

14,83

x2

(Hansenover-idtest)

0.832

0.333

0.433

0.370

0.507

AR(2)test

0.191

0.301

0.401

0.246

0.637

Endogenousvariablesusedas

instruments

Lagsofendogenousvariables

usedin

instrumentation

UR

UR

UR

UR

UR

Exogenousinstrumentingvariable

Region

Region

Region

Re

gion

Region

Panel

B

Demographic

0.029(0.032)

0.0

11(0

.006)***

0.005(0.013)

0.002

(0.006)

0.0

04(0

.002)***

Highincome*demographic

0.013(0.010)

Coastal*demographic

0.003(0.002)

Pre-1990s*demographic

0.031(0.028)

Highincome*coastal*demogra

phic

0.0003(0.002)

Highincome*pre-1990s*demog

raphic

0.0

01(0

.0006)***

(continued)

Table VI.Regression estimation:system GMM results:sensitivity tests

JIBR3,2

88


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(1)

(2)

(3)

(4)

(5)

Groups,N.Obs

14,83

14,83

14,83

14

,83

14,83

x2

(Hansenover-idtest)

0.166

0.413

0.850

0.648

0.735

AR(2)test

0.816

0.780

0.416

0.805

0.868


instruments


usedin

instrumentation

UR

UR

UR

UR

UR


Region

Region

Region

Re

gion

Region

Panel

C

Deposita/cpc

0.0

009(0

.0001)*

0

.0004(0.0008)

2

0.0001(0.0002)

0.0008

(0.0007)

2

0.0007(0.0010)

Highincome*deposita/cpc

2

6.8

102

6

(0.00005)

Coastal*deposita/cpc

6.6

102

6

(0.0003)

Pre-1990s*deposita/cpc

0.0

003(0

.0001)***

Highincome*coastal*deposita

/cpc

2

0.000

1(0.0002)

Highincome*pre-1990s*deposita/cpc

0.0

001(0

.00008)***

Groups,N.Obs

14,83

14,83

14,83

14

,83

14,83

x2

(Hansenover-idtest)

0.655

0.796

0.900

0.941

0.923

AR(2)test

0.920

0.549

0.134

0.998

0.165


instruments


usedin

instrumentation

UR

UR

UR

UR

UR


Region

Region

Region

Re

gion

Region(continued)

Table VI.

Financialoutreach

89


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(1)

(2)

(3)

(4)

(5)

Panel

D

Deposit/income

2

0.1

03(0

.051)**

2

0.030(0.038)

2

0.021(0.027)

2

0.00

2(0.009)

2

0.005(0.009)

Highincome*deposit/income

0.0

59(0

.032)***

Coastal*deposit/income

0.004(0.012)

Pre-1990s*deposit/income

0.015(0.061)

Highincome*coastal*deposit/income

2

0.00

6(0.007)

Highincome*pre-1990s*deposit/

income

2

0.008(0.007)

Groups,N.Obs

14,83

14,83

14,83

14

,83

14,83

x2

(Hansenover-idtest)

0.955

0.247

0.277

0.632

0.618

AR(2)test

0.132

0.831

0.752

0.732

0.463


instruments


usedin

instrumentation

UR

UR

UR

UR

UR


Region

Region

Region

Re

gion

Region

Notes:Significanceat:*1,**5and***10percentlevels;UR,unrestricted;inallspecifications,thedependentvari

ableisgrowthinPCNSDP;all

specificationscontrolforstate-levelvariables,buttheyarenotreportedtosavespace;robuststandarderrorsarereported

inparentheses;AR(2)isatestof

second-orderserialcorrelation

andfollowsN(0,1);detailsoftheGMMprocedurealongwiththechoicesarereportedin

thelastrowsofthetable

Table VI.

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The analysis indicates significant regional divergences in financial outreach acrossstates, and also in terms of their income characteristics. As well, while indicators offinancial outreach have exhibited improvements, their relationship with growth appearsto have become tenuous, especially over the reforms period.

More importantly, the multivariate regressions that take on board the state-levelcontrols indicate a significant impact of financial outreach on economic growth.In particular, efforts to improve outreach of the financial sector appear to have led to aperceptible improvementin economic growth. In terms of magnitude, a 10 percent rise indemographic outreach raises per capita growth by roughly 0.3 percent, all other thingsequal. In the case of geographic outreach, the magnitude is distinctly lower with percapita growth rising by only 0.02 percentfor a 10 percent branch expansion. In addition,the results also suggest that the efforts at raising economic growth via the use ofbanking services operate primarily through the deposit side. The study points to analternative impact of the social banking experiment: the positive impact of financialoutreach on state economic growth. In a sense, we confirm the findings of Burgess andPande (2005).

The evidence also supports the fact that, as compared to low-income states,high-income states with higher geographic outreach levels have higher economicgrowth. Furthermore,high-income coastal stateswith higher geographic outreach levelshave higher per capita income growth. The analysis therefore offers a rationale for thehigh growth of certainregions and states in India based on their intrinsiccharacteristics.We, therefore, substantiate the findings of the growth literature in India which findsunequal growth across regions and states (Bajpai and Sachs, 1999; Ahluwalia, 2002;

Nagaraj et al., 2000; Kochhar et al., 2006).The findings appear to indicate that financial outreach as measured by demographicoutreach has a significant impact on PCNSDP. In addition, high-income and coastalstates have higher technological outreach levels as compared to their counterparts.

It seems that the innate advantages of high-income states are compounded bythe fact that the financial system has been redirecting financial savings towardsthe better-performing states, as the analysis would testify. A reorientation of the policyframework in these states coupled with appropriate utilization of public investment tobuild economic and social infrastructure in the low-income states assumes relevance inthis regard.

In sum, the results are also a pointer to the fact that the social banking strategypursued in India was influential in raising state per capita growth, through theeffects of expanding the outreach and use of financial services. Given the potential forfinancial technological outreach to promote economic growth, the analysis suggests a

role for technology to reach out to larger segments of the populace. Analysis of suchinterlinkages between growthand technological outreach using comprehensivedatasetsconstitutes elements for future research.

Although the present analysis represents perhaps the first systematic attempt toanalyze financial outreach at the state-level, it is not without its limitations either. First,the focus is primarily on banking exclusion; other dimensions of financial exclusion,such as price exclusion, marketing exclusion, and self-exclusion have not beenaddressed (FSA, 2000). Additionally, the measures of financial exclusion consideredsuffer from certain shortcomings. An individual or firm may receive more than oneloanor have multiple deposit accounts, so that the number of loans and deposits accounts

Financialoutreach

91


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is far from being a perfect proxy of the number of people using these services within astate. As well, the average size of loans and deposits to PCNSDP might not berepresentative of the value of services that a typical individual might receive.Accordingly, the results emanating from the analysis would need to be interpreted withcare and caution.

Notes

1. The terms state and sub-national are used interchangeably.

2. Kremer and Miguel (2001) report that kids in schools treated with de-worming drugs (drugsagainst hookworm, roundworm, whipworm, and schistosomiasis) display reducedabsenteeism by one quarter (with gains being especially large among the youngestchildren). A second effect of education on health operates through the Beckerianquality-quantity trade-off (of children). Parents know that their children are very likely toexpireearly will tend to have many kids in order to endup with some adult descendants.Witha binding budget constraint, the amount of resources devoted to each child consequentlydeclines, and as a result, each child ends up with lower education and human capital. Parents,therefore, substitute quality of children for quantity of children. A third effect of health oneducationoperatesthrough incentives: the rate of return to educationis the present discountedvalue of all future wages that an educated person receives. Low life expectancy tends toreduce the rate of return and, as a result, the incentives to educate and accumulate humancapital.

3. NABARD is the apex institution concerning policy, planning and operations in the field ofcredit for agriculture and other economic activities.

4. High-income states are in alphabetical order: AP, GUJ, HAR, KARN, KER, MAH, PUNJ, TNand WB. Likewise, coastal states are AP, GUJ, KARN, KER, MAH, ORIS, TN and WB(See footnore [6]).

5. These states, in order, are regional location, are Andhra Pradesh (AP), Karnataka (KARN),Kerala (KER) and, Tamil Nadu (TN) in Southern region, Haryana (HAR), Punjab (PUNJ) andRajasthan (RAJ) in the Northern region, Bihar (BIH), Orissa (ORIS), and West Bengal (WB) inthe Eastern region, Gujarat (GUJ) Maharashtra (MAH) in the Western region and MadhyaPradesh (MP) and Uttar Pradesh (UP) in the central region.

6. The half-life means the half time for a state to converge to its steady state. It is computedas 2ln2=ln1 r, where r is the convergence coefficient.

7. Using the gross enrolment ratio (6-11 years) did not significantly alter the results.

8. I am grateful to the referee for the observations on this point.

9. Obtained from the statistical web site, www.Indiastat.com. The financial technological

outreach numbers are likely to be under-estimates, since we do not have information of thenumber of ATMs for private and foreign banks.

10. We also ran the regressions with PCNSDP growth as dependent variable for all models. Theresults (not reported) suggest that neither of the outreach variables is significant across thespecifications.

11. To see this, note that the common set of states that are both high income and coastal includeAP, GUJ, KARN, KER, MAH, TN and WB. Without loss of generality, the high income,coastal states are those belonging to the Western and Southern regions.

12. To see this, note that, the coefficient for high income states 20.044 ( 20.103 0.059)is higher by 77 percent as compared to the coefficient on deposit/income.

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Further reading

Ghosh, S. (2010), Determinants of banking outreach: an empirical assessment of Indian states,Journal of Developing Areas (in press).

Government of India (2002), National Human Development Report, Planning Commission,Government of India, New Delhi.

Government of India (various years (a)), Economic Survey, Government of India, New Delhi.

Government of India (various years (b)), Statistical Abstract of India, Government of India,New Delhi.

Mohan, R. (2006), Economic growth, financial deepening and financial outreach, addressdelivered at the Annual Bankers Conference, Hyderabad, available at: www.rbi.org.in

About the authorSaibal Ghosh is working as an Officer in the Reserve Bank of India, the Indian Central Bank, at

its headquarters in Mumbai, India. He has over 12 years of experience in central banking indiverse areas including banking, international finance, monetary policy, and financial stability.Prior to the present job, Dr Saibal Ghosh worked with the Industrial Development Bank of India,an erstwhile development bank. Dr Saibal Ghosh has worked extensively in the areas of bankingand finance and has published papers in leading international journals including ManchesterSchool(2010), Journal of Economics and Business (2009), Economic Systems (2008), InternationalJournal of Auditing (2007) and Review of Financial Economics (2006). Saibal Ghosh can becontacted at: [email protected]

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Appendix

Notation

Var

iable

D

atasource

N.Obs

Mean

SD

Gr_PCNSDP

Firs

tdifferenceofnaturallogofpercapitanet

stat

edomesticproduct(PCNSDP)

E

PW

statesdatabase

112

0.005

0.559

Financia

loutreach

indicators

Geographic

ban

kbranchesper1,000km

2

N

umeratorisfromStatist

ica

lTa

bles

Relating

to

Ban

ks

inIndia(STB).Denominatorisfrom

W

ikipediawebpagewww.en.wikipedia.org

112

23.74

18.01

Demographic

ban

kbranchesper1lacpeople

N

umeratorisobtainedfromSTB

112

6.52

2.31

Loana/cpc

Loa

naccountsper1,000people

N

umeratorisobtainedfromBasic

Statist

ical

R

eturnso

fSchedu

ledCommercia

lBan

ks

(BSR)

112

53.26

35.03

Deposita/cpc

Dep

ositaccountsper1,000people

N

umeratorisobtainedfromBSR

112

326.91

18.70

Loan/income

Ave

ragesizeofloans/PCNSDP

D

ataondepositsisfromBSR.PCNSDPis

fromEPW

Statesdatabase

112

16.59

23.17

Deposit/income

Ave

ragesizeofdeposits/PCNSDP

D

ataoncreditisfromBSR

112

2.99

2.70

State-levelcontro

ls

Manufacturing(mfg.)

Sha

reofmanufacturinginNSDP(1980-1981

pric

es)

E

PW

StatesdatabaseandRBI(2007)

112

0.155

0.029

Literacy

Lite

racyrate

S

tatisticalabstract

112

0.493

0.175

Size

Gro

ssfiscaldeficit/NSDP

R

BI(2006)supplementedbyRBIBu

lletin,

v

ariousyears

112

0.050

0.023

T&Dlosses

Tra

nsmissionanddistributionlossesofState

ElectricityBoards

P

lanningCommissionwebsite

97

0.23

0.06

Dummyvaria

bles

dy_Merger

Dum

my

1fortheyears2001onwardsfor

stat

esfromwhichnewstateswerecarvedout

112

0.027

0.162

dy_Coastal

Dum

my

1forthecoastalstates,else0

112

0.571

0.497

dy_Highincome

Dum

my

1,forhigh-incomestates,else0

112

0.643

0.481

dy_Northern

Dum

my

1fortheNorthernregion,else0

112

0.214

0.412

dy_Southern

Dum

my


112

0.286

0.454

dy_Eastern

Dum

my


112

0.214

0.412

dy_Western

Dum

my


112

0.143

0.351

dy_Central

Dum

my


112

0.143

0.351

Table AI.Variables, data source,

and summary statistics

Financialoutreach

97

Documents

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