income and employment effects of micro‐credit programmes: village‐level evidence from bangladesh

This article was downloaded by: [Tufts University]On: 08 October 2014, At: 14:06Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number:1072954 Registered office: Mortimer House, 37-41 Mortimer Street,London W1T 3JH, UK

The Journal ofDevelopment StudiesPublication details, including instructions forauthors and subscription information:http://www.tandfonline.com/loi/fjds20

Income and employmenteffects of micro‐creditprogrammes: Village‐levelevidence from BangladeshShahidur R. Khandker a , Hussain A. Samad b &Zahed H. Khan ca Senior economist in the Poverty Reductionand Economic Management Network , WorldBank ,b Consultant in the Poverty Reduction andEconomic Management Network ,c Urban specialist in the World Bank's ResidentMission , BangladeshPublished online: 23 Nov 2007.

To cite this article: Shahidur R. Khandker , Hussain A. Samad & Zahed H.Khan (1998) Income and employment effects of micro‐credit programmes:Village‐level evidence from Bangladesh, The Journal of Development Studies,35:2, 96-124, DOI: 10.1080/00220389808422566

To link to this article: http://dx.doi.org/10.1080/00220389808422566

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of allthe information (the “Content”) contained in the publications on our

http://www.tandfonline.com/loi/fjds20

http://www.tandfonline.com/action/showCitFormats?doi=10.1080/00220389808422566

http://dx.doi.org/10.1080/00220389808422566

platform. However, Taylor & Francis, our agents, and our licensorsmake no representations or warranties whatsoever as to the accuracy,completeness, or suitability for any purpose of the Content. Anyopinions and views expressed in this publication are the opinions andviews of the authors, and are not the views of or endorsed by Taylor& Francis. The accuracy of the Content should not be relied upon andshould be independently verified with primary sources of information.Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilitieswhatsoever or howsoever caused arising directly or indirectly inconnection with, in relation to or arising out of the use of the Content.

This article may be used for research, teaching, and private studypurposes. Any substantial or systematic reproduction, redistribution,reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of accessand use can be found at http://www.tandfonline.com/page/terms-and-conditions

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014

http://www.tandfonline.com/page/terms-and-conditions

http://www.tandfonline.com/page/terms-and-conditions

Income and Employment Effects ofMicro-credit Programmes:

Village-level Evidence from Bangladesh

SHAHIDUR R. KHANDKER, HUSSAIN A.SAMAD and ZAHED H. KHAN

Micro-credit programmes, having made their mark in providingcredit and other development services to the poor in a non-traditional way, are able to make significant changes in a ruraleconomy. This article attempts to quantify the village-level impactsof the three most important micro-credit programmes ofBangladesh, namely Grameen Bank, Bangladesh RuralAdvancement Committee (BRAC), and Bangladesh RuralDevelopment Board's (BRDB) RD-12 project. Descriptive andeconometric analyses show that these programmes have positiveimpacts on income, production, and employment, particularly inthe rural non-farm sector. Also, growth in self-employment hasbeen achieved at the expense of wage employment, which impliesan increase in rural wages. The article emphasises that an upwardshift in the labour demand curve is required for both improvedproductivity and wage gains on a sustainable basis, which can onlybe supported through a structural transformation of the ruraleconomy.

I. INTRODUCTION

Micro-credit programmes are increasingly sought as a way to enhance theincome and employment of the poor who can be self-employed in a variety

Shahidur R. Khandker is a senior economist in the Poverty Reduction and EconomicManagement Network of the World Bank; Hussain Samad is a consultant in the same network;and Zahed Khan is an urban specialist in the World Bank's Resident Mission in Bangladesh. Theauthors gratefully acknowledge the comments of two anonymous reviewers, which helped therevision. Any remaining errors and omissions are solely the authors' responsibility. Viewsexpressed in the article are those of the authors and do not reflect the views of the World Bankor any of its affiliated organisations.

The Journal of Development Studies, Vol.35, No.2, December 1998, pp.96-124PUBLISHED BY FRANK CASS, LONDON

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014

INCOME AND EMPLOYMENT EFFECTS OF MICRO-CREDIT PROGRAMMES 9 7

of informal activities [Hulme and Mosley, 1996; Yunus, 1983; World Bank,1994]. The objective is to ease the credit constraint of households or toprovide them with capital to initiate an activity, thereby increasing theirincome and consumption. Micro-credit programmes are also being used totarget the poor, especially women, to involve them in income-generatingactivities. In Bangladesh, for example, there are more than 750organisations that are working in rural areas to provide credit and non-creditservices to the target population - largely women from landless households[World Bank, 1996]. Grameen Bank and Bangladesh Rural AdvancementCommittee (BRAC) are two programmes well known all over the world.Grameen Bank achieved its fame because of its innovative group-basedlending programme specifically designed for the poor who are otherwiseexcluded from formal financial institutions. BRAC, on the other hand, isknown for its informal primary education and innovative healthprogrammes designed also for the poor. BRAC also has a micro-creditprogramme targeted at the poor; but, unlike the Grameen Bank, BRAC'semphasis is more on human capital development such as functional literacy,skill-promoting training, awareness, and so on, than on credit. Bothprogrammes provide financial services to their members, including savingsmobilisation. Unlike Grameen Bank, however, BRAC is not a bank andcannot mobilise savings from non-members. By 1994, Grameen Bank hadmobilised more than two million members, of which 94 per cent werewomen. In the same year, BRAC mobilised more than one millionmembers, of whom 88 per cent were women. In 1994 alone, Grameen Bankdisbursed US$385 million, while BRAC disbursed US$55 million. The loanrecovery rate for both programmes has been consistently more than 90 percent. Also in 1994, the savings mobilised by Grameen Bank were US$70million compared with US$12 million of B R A C

Given the success of group-based lending, the government ofBangladesh introduced group-based lending schemes in its variousprogrammes aimed at promoting rural development. One of the well-knownreplications within the government bureaucracy is the RD-12 project run bythe Bangladesh Rural Development Board (BRDB). The BRDB is thesuccessor of the well-known integrated rural development programme(IRDP) of the 1960s, which gained popularity because of its two-tier co-operative system that served as a vehicle for the delivery of modern inputssuch as high-yielding seeds, fertiliser, and credit. However, the co-operatives failed to recover loans offered to co-operative members.Evidence suggests that the co-operatives were dominated by the rural elitewho directed most of the government-provided assistance to their ownadvantage [Khan, 1971]. Recognising the limitations of the co-operativestructure in helping farmers and other groups, the government introduced a

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014

98 THE JOURNAL OF DEVELOPMENT STUDIES

micro-credit programme (the RD-12 project) within its two-tier co-operative structure, with funding from the CIDA. Similar to Grameen Bankand BRAC, the RD-12 project is group-based, targeted at the landlessfarmers, and mobilises men and women into two separate groups calledsocieties in BRDB terminology. It follows the credit delivery schemes ofGrameen Bank, and also adopted BRAC's training programmes. TheBRDB's RD-12 programme appeared to be a successful replication of thegroup-based micro-credit programme. This programme started in December1988 and by 1994 it had mobilised 452,000 members of whom 70 per centwere women, and disbursed US$17 million in the same year, and its loanrecovery rate has also been consistently above 90 per cent. The savingsmobilised by RD-12 from its members was US$5 million [Khandker, KhanandKhalily, 1995].

These three are the largest micro-credit programmes operating inBangladesh. Grameen Bank covered nearly 40 per cent of Bangladeshivillages, while BRAC and RD-12 project each covered about 20 per cent.The household survey data, on which this article is based, suggest that 60per cent of the rural households in Bangladesh meet the eligibility criterionto join such credit programmes, which says that household's non-homesteadlandholding cannot exceed 0.5 acres of land. But even in the study villages,where these micro-credit programmes have been operating for more thanthree years, only 45 per cent of the target households actually participate inmicro-credit programmes [Khandker 1998]. The low participation rate inthese villages perhaps reflects the lack of demand for micro-credit amongmany eligible households. Further, micro-credit programme participationrequires use of the credit to generate enough income to pay off the loan aswell as the opportunity cost of getting involved in the micro-credit-financedbusiness. This, in turn, requires a minimum level of entrepreneurial abilitywhich very few people among the poor may have. Thus, only those amongeligible individuals who are risk-takers and confident enough to use thecredit productively join the credit programme [Pitt and Khandker, 1996]}

A recent evaluation of these three programmes based on householdsurveys shows that participants benefit from micro-credit programmeparticipation in a number of ways [Pitt and Khandker, 1996]. Borrowingfrom a micro-credit programme such as the Grameen Bank increases percapita consumption, women's non-land assets, women's labour supply tocash income earning activities, children's school enrolment, contraceptiveuse and fertility.3 Programme participation also increases profit for self-employed rural non-farm activities, as micro-credit programmes chieflyfinance these activities in Bangladesh [McKernan, 1995].

These are, of course, the direct benefits of a programme's operation in avillage where such benefits are calculated by examining income and

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014

INCOME AND EMPLOYMENT EFFECTS OF MICRO-CREDIT PROGRAMMES 99

employment changes of programme participants only. It is also possible thatthe programmes generate externalities that affect non-participatinghouseholds in a number of ways. The full impact of programme placementis thus the sum of direct and indirect effects of programme placement.

In general, the extent of positive externalities in income andemployment depends on economic growth, as borrowing presupposeseconomic growth [World Bank, 1994]. When an economy grows, peopleborrow money and are able to pay off the principal and interest withincreased income. With increased income, they are able to save money,purchase equipment and reinvest their profits. However, if there is noeconomic growth, the only way to increase income and pay off loans is bytaking business away from competitors. Established entrepreneurs may behard hit as a result, and poverty and income change may simply be shiftedaround. In such cases, the net impact of a micro-credit programme can benegative, even if the participant-level impact is positive. Hence, programmeevaluation must include the externalities that these programmes generate atthe village level. That means, the benefits of a programme must bemeasured in terms of income and employment changes in the entire village.

In the case of Bangladesh, the economic growth rate has beenhistorically low at three to four per cent per year. In this low growthscenario, a positive micro-credit impact on income, consumption, savings,and asset growth may indicate income redistribution. This means theparticipants may have benefited at the expense of those who did notparticipate or did not qualify for participation. Even after redistribution, thequestion arises whether programme placement has a net positive impact onthe rural economy. To find this out, an evaluation of changes in income andemployment at the village-level rather than at the participant-level isessential. Ideally, measuring such external effects requires data both beforeand after programme intervention, as it is generally not possible to estimateseparately any village externality from a single cross-section of data.

Since the panel data were not available, this article follows a less thanideal method for estimating the village-level programme effect. This methodis based on data collected on programme and non-programme villages, aftercontrolling for other observable village heterogeneity. Since, programmeplacement is not entirely random, the village-level programme impact maystill capture the effect of unobservable village characteristics that attract aprogramme.4 Bearing this in mind, we attempt to assess the aggregate impactof these micro-credit programmes by using a programme placement dummy(that is, whether a village has a programme) as an explanatory variable in thevillage-level regression. Even if the method is not ideal, it can shed some lighton the basic question of whether there are net positive impacts on ruraleconomy due to micro-credit programme interventions.

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


The study is structured as follows. Section II discusses the surveymethodology and data source that has been used throughout the analysis.Section III describes the macro-picture of the Bangladesh economy in termsof the observed growth rates by sector. This section also attempts todiscover whether income redistribution is a likely outcome of micro-creditinterventions. Section four analyses the income and employment profile ofrural households to discover the role of the rural non-farm sector in the ruraleconomy of Bangladesh. Section V describes the income, employment andproduction of rural households. Section VI discusses the estimationtechnique for determining the impact of micro-credit programmes onvillage-level income and employment. Section VII presents the results onthe impacts of programmes on production, employment and income, whilethe following section presents the results of programme impacts on ruralwages. The final section summarises the results with policy implications.

II. DATA SOURCE AND SURVEY DESIGN

The analyses presented in this study are based on a household survey carriedout jointly by the World Bank and Bangladesh Institute of DevelopmentStudies (BIDS) in 1991-92. The sample comprises 29 thanas5 randomlyselected from 391 rural thanas in Bangladesh. Among these, 24 thanas haveat least one of the three programmes under study, while five have none.Three villages in each programme thana were randomly drawn from a listof villages, supplied by the programme's local office, in which theprogramme had been in operation for at least three years. Also three villagesin each nonprogramme thana were randomly drawn from the village censusof the government of Bangladesh. Twenty households were drawn fromeach village for in-depth household survey.

The number of target and non-target households in each village was 17and 3 respectively. A random sampling technique was used to draw the 17target households from non-programme villages as well as the three non-target households from both programme and non-programme villages. Thesurvey design required a sufficient number of programme participantsamong target households for the purpose of analysis. So, instead of a simplerandom technique, a stratified random sampling was used to draw targethouseholds from the programme villages, in the ratio of 12:5 for programmeparticipants and non-participants. In addition, 3 to 8 target households wererandomly drawn from each village of five programme thanas that werespecially selected for a nutrition survey. Thus a total of 1,798 householdswere drawn in which 1538 were target and 260 were non-target, and amongthe target households in programme areas 905 were found to be programmeparticipants. Because of the non-random selection of the sample,

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


appropriate weights (calculated from the village census data of actualdistribution of target, non-target, and programme participating households)were used to adjust all aggregate results.

A detailed household questionnaire was designed to collect informationon income, employment, consumption, education, health, borrowing, assets,savings, contraceptive use, and fertility behaviour for individuals (andhouseholds where applicable). In addition to household survey, a villagesurvey was also administered in each of the 87 villages. The village surveycollected information about village prices of different commodities, dailywages in farm and non-farm sectors, interest rates in informal creditmarkets, schools, health centres, infrastructures and so on. Finally, thehousehold and village survey were conducted in all three cropping seasonsduring the 1991-92 farming year in order to capture seasonal variations -Aman (November-February), Bow (March-June), and Aus (July-October).

III. GROWTH RATE BY SECTOR: IS INCOME REDISTRIBUTION ALIKELY CASE?

Although the historical growth rate of Bangladeshi economy has been quitemodest (3-4 per cent), the source of growth has shifted from farm to non-farm sector over time (Tables 1-3). Agriculture once dominated theBangladesh economy. In 1972 it explained about half of the country's GDP(Table 1). But its importance declined over time, falling to 35 per cent in1993. On the other hand, during the same period the share of industryincreased from 14 per cent to 19 per cent, and that of service sector rosefrom 37 per cent to 46.

Bangladesh's agriculture sector grew at a rate of only two per cent over1972-94, while industry and services did better during the same period,each registering a five per cent growth rate. However, the country didrelatively well during 1990-94 when agriculture and industry grew at 3.5per cent rate and 6.9 per cent rate respectively, while the service sectorremained unchanged. Within agriculture, however, growth was particularlyhigh in fisheries and livestock (5.9 per cent and 4.3 per cent respectively).

Two rounds of surveys of 62 villages, conducted by the BangladeshInstitute of Development Studies (BIDS) in 1989 and 1994, also confirmthat rural non-crop activities registered higher growth (2.5 per cent) thanthat of agriculture (1.4 per cent) as shown in Table 3. This table also showsa five per cent growth rate in the non-agriculture sector during the sameperiod. So, even within this low overall growth, which was much lower thanthat of East Asian countries (about eight per cent), there is evidence thatincreased opportunities have been created in non-crop, and especially non-farm activities.

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


TABLE 1GDP SECTORAL SHARES, 1973-74

Year

1972/731975/761980/811985/861990/911993/94

Agriculture

49.849.344.241.437.634.9

Industry

13.612.715.315.717.218.9

Services

36.738.040.542.945.246.2

Source: Bangladesh Bureau of Statistics; World Bank [1996].

TABLE 2GDP GROWTH RATE BY SECTOR (%), 1975-94

Sector 1975-79 1980-84 1985-89 1990-94 1975-94

AgricultureCropsForestryLivestock

FisheriesIndustryServices

Total GDP

1.52.05.72.4

-5.05.64.7

2.42.64.0

-1.4

3.12.83.7

0.60.11.05.0

1.44.86.0

3.53.32.54.3

5.96.94.8

2.02.03.32.6

1.45.04.8

3.2 3.0 3.6 4.7 3.6

Sources: Bangladesh Bureau of Statistics; World Bank [1996].

TABLE 3 •CHANGES IN STRUCTURE OF HOUSEHOLD INCOME

Source of income

AgricultureCropsNon-cropactivitiesWages

Non-agricultureTradeServicesOthers

Total income

Per capita income

Income per household (Tk)

1989

28,70121,2704,4143,017

18,0296,8458,3192,865

46,730

7,549

1994

30,77222,8435,0442,885

23,1418,459

11,5643,118

53,193

9,107

Growth rate (%)

1.41.42.7

-0.95.14.36.81.72.9

3.8

Contribution to totalhousehold income

1989

61.445.5

9.56.5

38.614.717.86.1

100.0

1994

57.142.4

9.35.4

42.915.721.4

5.8100.0

Source: Hossain [1995].

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


Since the micro-credit programmes gained momentum in the late 1980s,it is quite possible that the higher growth in non-crop and non-farmactivities was associated with the increased role of micro-creditprogrammes which support mostly non-farm activities in rural areas. Inother words, the induced income and employment effects of theseprogrammes are results of both income redistribution as well as theobserved growth in the rural non-farm sector. Of course, government'ssectoral interventions in promoting fisheries and livestock also contributedto the higher growth in these two activities.

IV. RURAL NON-FARM SECTOR AS A SOURCE OF INCOME ANDEMPLOYMENT FOR THE POOR

The rural non-farm sector not only registers higher economic growth, it alsoexplains a higher proportion of household income than agriculture. Ruralhousehold surveys confirm such a priori expectations. For example,according to the BIDS panel survey, more than 52 per cent of rural incomewas due to rural non-crop activities (that is, agricultural non-crop activitiesand nonagricultural activities combined) in 1994, compared with 49 percent in 1989 (Table 3). The World Bank-BIDS survey of 1991-92 alsoshows the greater role of rural non-farm sector, explaining about 55 per centof rural income (Table 4).

Agriculture is the major source of rural employment in Bangladesh.According to the 1991 labour force survey of Bangladesh, the rural non-farm sector provides a 31.5 per cent of rural employment (18.3 per cent asself-employment and 13.2 per cent as wage employment) compared with68.5 per cent for agriculture (55.7 per cent as self-employment and 12.8 percent as wage employment). This is also confirmed by the BIDS-World Banksurvey, which shows that agricultural activities (both crop and livestock)provide 65 per cent of employment (52 per cent is self-employment andonly 13 per cent is wage employment). In contrast, the rural non-farm sectorconstitutes 35 per cent of employment (17 per cent is wage employment and18 per cent is self-employment). However, the rural non-farm sectorprovides 55 per cent of rural income (24 per cent from wage and 31 per centfrom self employment), compared with 45 per cent from the agriculturesector (21 per cent from wage and 24 from farm income).

Target households (those which qualify for participation because theyhold less than or equal to 0.5 decimals of land) benefit more than non-targethouseholds from rural non-farm employment. And among targethouseholds, programme participating households benefit more than non-participating households from rural non-farm employment. Non-targethouseholds which rely more on farm income also derive a substantial

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


TABLE 4DISTRIBUTION OF INCOME AND EMPLOYMENT BY EMPLOYMENT

CATEGORY AND PROGRAMME PARTICIPATION STATUS

Activity

Agricultural activityWage employment

Employment

Income

Self-employmentEmployment

Income

Non-agricultural activityWage employment

Employment

GrameenBank

members

14.22(28.08)

20.60(29.40)

43.69(42.42)

13.37(22.25)

14.20

BRACmembers

11.41(26.43)

19.69(31.09)

42.66(44.45)

11.04(21.90)

25.08

TargetRD-12 non-

members participants

15.26(28.84)

23.32(31.67)

49.70(43.13)

18.98(27.73)

8.54

21.65(34.86)

33.60(37.27)

38.05(43.25)

9.80(20.28)

22.01

Non-targetgroup

5.46(17.58)

7.00(17.14)

70.32(39.72)

48.81(38.87)

11.99

Total

13.41(28.32)

21.19(31.77)

51.96(44.53)

23.89(33.79)

16.63

Income

Self-employmentEmployment

Income

Number of observations

(30.63) (39.41) (22.71) (37.31) (29.84) (33.45)

19.67 34.11 13.83 27.53 18.41 23.98(31.63) (39.56) (25.46) (36.87) (31.01) (34.55)

27.90 20.85 26.50 18.30 12.23 18.00(37.87) (36.35) (37.86) (34.26) (27.61) (33.60)

46.27 35.17 43.86 29.07 25.78 30.94(38.83) (41.34) (39.85) (39.16) (36.22) (38.97)

910 715 791 1,479 820 4,715

Notes: Figures in parentheses are standard deviations.Source: World Bank-BIDS survey data, 1991/92.

amount of income from rural non-farm sources, employing proportionatelyless labour than that in farm sector. Non-target households derive 44 percent of their income from rural non-farm activities, although they employonly 24 per cent of their labour in these activities. Similarly, the non-participating target households get 56 per cent of their income from non-farm activities, employing about 40 per cent of their labour. The disparitybetween employment and income in the non-farm sector is even more forprogramme participating households. BRAC households derive 69 per centof their income by employing 46 per cent of their labour, RD-12 households

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


receive 58 per cent of income by using 36 per cent of their labour, andGrameen Bank households derive 66 per cent of income by employing 42per cent of labour in rural non-farm activities.

The data suggest that programme participating households may usemore family labour in and derive more income from rural non-farm sourcesthan their non-participating counterpart because the micro-creditprogrammes have made it possible to diversify their labour portfolio moretoward non-farm activities. Credit, and hence capital, may make thedifference between participants and non-participants in seeking income andemployment from rural non-farm sources.

A structural difference in terms of (average) labour productivity is,however, worth noting. For all types of households, agriculturalself-employment yields proportionately less income than non-farm self-employment, given the same allocation of labour. For instance,self- employment in farming provides only 10 per cent of household incomefor 38 per cent of labour for target non-participants, self employment inrural non-farm activities provides 29 per cent of income for 18 per cent oflabour. This is even more pronounced for non-target households which useproportionately more labour in farming than non-farming. Farming seems tobe a high labour-cost activity; but this may not be the case if the opportunitycost of family labour is lower than the market wage rate.

Self-employment in rural non-farm activities seems more rewarding fornon-target households than for target households, irrespective of theirprogramme participation status. For example, non-target households have a2:1 income-labour ratio (26 per cent of income from self-employment in therural non-farm sector by employing only 12 per cent of labour) comparedwith a 1.6:1 income-labour ratio for non-participating households. This isbecause there may be qualitative difference in the labour used by non-targetand target households. In other words, one unit of family labour from non-target households may yield more income than one unit of labour of targethouseholds (because of better human capital endowment). For example,while an individual's average schooling is 3.2 years for non-targethouseholds rural Bangladesh, it is only 1.6 years for target households.Also, family members in non-target households command more measuredhuman capital (in terms of literacy and numeracy) than their counterpart intarget households.6 In addition, difference in capital endowments betweentarget and non-target households may be a factor. For example, capitalinvestment in non-farm self-employment is 96,000 taka for non-targethouseholds, and only 15,000 taka for target households. So, although creditis a factor in yielding higher income-labour ratio for participants among thetarget population, it is probably not enough to offset the adverse effects oflow human and physical capital.

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


TABLE 5AVERAGE HOUSEHOLD PRODUCTION, INCOME, AND EMPLOYMENT BY

VILLAGES GROUPED BY PROGRAMME INTERVENTION

Indicator

I. Annual production (taka)A. Farm production

B. Non-farm production

Total production (A+B)

II. Annual income (taka)A. Farm income

Self-employment

Wage employment

Total of farm income (A)

B. Non-farm incomeSelf-employment

Wage empioyment

Total of non-farmincome (B)

Total income (A+B)

GrameenBank

villages

9,337(4.876)82,390

(76,523)91,727

(76,806)

8,487(4.507)3,341

(2,118)11,828(5,582)

13,783(11,384)

4,374(4,172)

18,157(12,384)29,985

(12,579)

BRACvillages

8,513(5.206)89,031

(77,126)97,544

(75,043)

7,708(4,829)2,297

(1,701)10,005(6,062)

17,418(26,574)

7,189(5,832)

24,608(28,011)34,613

(27,448)

III. Individual labour force participation rate (%)A. Farm employment

• B. Non-farm employmentAll employment

65.642.177.7

53.237.369.4

IV. Monthly employment hours per householdA. Farm employment

Self-employment

Wage employment

Total of farmemployment (A)

B. Non-farm employmentSelf-employment

Wage employment

Total of non-farmemployment (B)

Total employment (A+B)

132.6(71.8)92.4

(62.5)

225.1(105.1)

119.1(77.24)

79.1(66.0)

198.1(121.0)423.2

(107.3)

97.2(66.2)55.7

(42.7)

152.9(92.8)

104.3(71.6)112.4(71.8)

216.7(118.4)369.6(85.4)

RD-12villages

12,429(6.071)

105,066(145,067)117,495

(148,888)

11,301(5,548)3,688

(2,229)14,989(6,084)

15,837(18,771)

3,213(2,456)

19,050(19,147)34,039

(21,354)

62.734.572.0

117.6(41.0)92.2

(60.7)

209.8(83.1)

105.5(56.4)48.1

(36.1)

153.7(71.4)363.5(76.1)

Allprogramme

villages

10,093(5.595)92,162

(103,600)102,255

(105,071)

9,165(5,150)3,109

(2,087)12,274(6,188)

15,680(19,675)

4,925(4,630)

20,605(20,758)32,879

(21,151)

60.538.073.0

115.8(62.0)80.1

(57.9)

195.9(97.9)

109.6(68.3)79.9

(64.8)

189.5(107.9)385.4(93.3)

Non-programme

villages

7,045(4.175)39,056

(47,428). 46,101

(46,084)

6,390(3,769)3,542

(1,320)9,932

(4,450)

6,976(6,205)5,729

(4,930)

12,705(8,823)22,637(7,891)

54.533.767.0

78.1(29.2)78.9

(25.4)

157.1(44.9)

67.2(57.8)106.6(91.4)

173.8(120.6)330.9(86.3)

Notes: Figures in parentheses are standard deviations.Source: World Bank-BIDS survey data, 1991/92.

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


Because of the higher growth potential of the rural non-farm sector andgreater role of this sector in rural income, micro-credit interventions thatfinance mostly self-employment in this sector are expected to have a netpositive impact on village level income and employment. This is the keyhypothesis to be tested with the household survey data for programme andnon-programme villages.

V. VILLAGE-LEVEL INCOME AND EMPLOYMENT OF PROGRAMMEAND NON-PROGRAMME VILLAGES: DESCRIPTIVE ANALYSIS

Descriptive comparisons of village-level average welfare indicators showthat villages perhaps benefit from programme placement. Those who do notparticipate in the programme but belong to the programme villages areexpected to benefit from the programme's induced positive effects so thatnet effects are positive. In terms of the total value of production (value ofoutput at market prices), BRDB villages registered the highest value of totalproduction, followed by BRAC, Grameen Bank, and nonprogrammevillages (Table 5). Thus, BRAC villages have 83 per cent of the totalproduction of RD-12 villages, Grameen villages have 78 per cent, and non-programme villages have only 39 per cent. A similar pattern does not holdin the case of total household income: BRAC villages have the highestaverage household income followed by RD-12, Grameen Bank, andnonprogramme villages.

Grameen Bank villages have the highest labour force participation rate(78 per cent), followed by RD-12 (72 per cent), BRAC (69 per cent) andnon-programme (67 per cent) villages. In all villages, labour forceparticipation is higher in farm than in non-farm activities. Unlike incomeand production, total monthly hours of work per household is highest inGrameen Bank villages (423 hours), followed by those in BRAC (370hours), RD-12 (364 hours), and non-programme villages (331 hours).

VI. AGGREGATE PROGRAMME IMPACTS: ESTIMATIONTECHNIQUE

The aggregate impact of programme intervention is the sum of impactsmeasured across programme participants and non-participants (where non-participants may or may not qualify to participate in any micro-creditprogramme). An increased flow of resources (credit and non-credit) tovillages through programme participants can influence resource allocationand consumption of non-participating households in two ways - throughlabour market and through demonstration.

Borrowing creates self-employment for those who were wage-employed

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


previously, so the immediate impact of borrowing is a possible reduction oflabour supply in the wage labour market. This would help increaseemployment among people who were unemployed or underemployedbefore but willing to work more at the given market wage rate. If thereduction in wage labour created by the newly self-employed households ismore than the increase in wage labour supply by the existing or new wage-employed households, the net effect would be a shortage in labour supply.This would result in higher wages. Conversely, wages would remainunchanged if the newly created vacuum in the wage market is filled byexisting or new wage-workers. On the other hand, micro-credit-financedenterprises would increase self-employment activities, which in turngenerate higher income, after meeting the interest cost of borrowing fromthese programmes. This higher income of an average programmeparticipating household may result in further changes in other householdoutcomes, such as children's schooling, per capita consumption and so on.

The labour market-induced externality may not be neutral. If thecondition of programme non-participating households remains unchangedbecause of programme placement in an area, the programme is neutral andthe net overall impact is positive. Further, the programme can displaceexisting producers of rural non-farm products by accommodating landlesshouseholds, in which case it creates a negative externality. However, the netoverall impact may be either positive or negative depending on the extent ofpositive programme participation impacts and negative externalities. Theoverall programme effect is very much conditioned by the growth of theeconomy. For example, if participating households of micro-creditprogrammes start rice trading, it will reduce the production andconsumption of village producers who are already in this business. Thiswould not occur, however, if the growth of income induces higher demandfor rice.

Another way a programme may influence behavioral outcomes of non-participating households is through a demonstration effect. For example,non-participating households may follow the lessons of skill anddevelopment training programme of a micro-credit programme in theirproduction process, which can generate additional production of a certaintype. Given the price, this may generate additional income for ruralhouseholds, which is an example of positive externality.

The full benefits of programme placement at village level are thus a sumof direct and indirect effects of programme intervention and must beevaluated in order to measure the 'net' aggregate programme effect. Anevaluation of aggregate-level impact is necessary to justify programmeinterventions because these programmes may only help distribute incomeand not contribute much to economic growth [WorldBank, 1994].

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


Aggregate benefit (B) is composed of three level impacts: benefitsaccrued to participants (Bp), benefits accrued by non-participants (Bnp) andcosts incurred to non-participants (CK;,).

7 Formally,

B = Bp + (Bnp-Cnp) (1)

Aggregate benefit (B) can exceed the sum of participant-level benefits(Bp) if Bnp > Cnp. It equals the sum of participant-level benefits (Bp) ifBnp - Cnp. Aggregate benefits (B) may even fall short of participant-levelbenefits (Bp) if Bnp < Cnp. Aggregate benefit induced by a micro-creditprogramme is measured at the village level by estimating the programmeimpact on household-level outcomes averaged at village level.

The question now is how to estimate village-level programme impacts.We can use the village-level fixed-effects method to estimate programmeparticipation impacts at the participant level. The fixed-effects methodabsorbs the village impacts that are difficult to identify. The ideal way toevaluate such an aggregate programme effect is to measure village-levelimpact before and after programme placement. It is generally not possible,however, to separately estimate any village externality from a single cross-section of data.

Alternatively, village-level programme impact can be measured bycomparing outcomes between programme villages and nonprogrammevillages from a cross-section of data. But this requires that programmeplacement in a village is randomly given, which is perhaps not the case formany programme interventions [e.g., Rosenzweig and Wolpin, 1982].Identification of programme placement independently of village attributesthat may affect programme placement is a crucial factor for estimatingvillage-level programme impacts. Assume that for village j micro-creditprogramme input (CJ) is a function of village-level characteristics (XJ)which are exogenous to the village and also some variables (ZJ) that affectonly the programme allocation of inputs measured by (CJ). Formally,

Cj = aXj+ vZj + {jo (2)

where a and v are unknown parameters to be estimated and £/ is the errorstructure which has two components

£ / = ^ + <7C (3)

where u..- is an unobserved village-specific effect and ef is a non-systematicerror uncorrelated with the other error components or the regressors. The

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


village-level outcomes (Yj) conditional upon the programme placement (C.)is given by,

where U is the error structure composed of the following

where p is the correlation coefficient and Ojy is non-systematic erroruncorrelated with regressors or other errors. Now, if p^O, then the estimatesof the programme placement (C) on Yj is inconsistent. So the challenge isto find out the Zj variables such that they must not affect the outcomes (Yj)conditional on programme placement, and they must affect the decision ofplacing a credit programme in a village (that is, p^O).

However, micro-credit programmes do not go to every village. As theseare poverty-reduction programmes, programme officials consider theoverall poverty situation of a village before deciding to place a programmethere. Micro-credit programmes define target households (which areeligible to participate in a programme) as those owning less than or equal to0.5 acres of land. This land-ownership restriction also implies thatprogramme officials keep the extent of landlessness h in mind as the keyfactor in deciding to place a programme in a village. As micro-creditprogrammes do not have resources to allocate to all eligible villages, theallocation of programme inputs is supply-driven. Assume that the dummyvariable determining whether villagey has a programme (Pj) proxies for theexogenous allocation of programme inputs. In this case, both programmeavailability and extent of landlessness in a village represent the variable Zjin equation (2). Substituting these two variables (Pj and Ij) for Zj in (2) andsubstituting the revised (2) into (3), we have the following reduced-formequation for estimation:

Yj = 80Xj + 5xPj + S2lj + £j (6)

where 8's are parameters to be estimated and £y is non-systematic errorsuncorrelated with all explanatory variables. The outcomes (Yj) analysed inthis study are village-level averages of household production, income,employment hours, individual labour force participation rate and so on. Thevillage-level exogenous variables (Xj) are dummy variables representingwhether the village has paved roads, commercial banks, electricity, and anydevelopment programmes, and a continuous variable representing thedistance of the village from the closest thana headquarter.

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


VII. RESULTS FOR PRODUCTION, INCOME, AND EMPLOYMENT

The main finding of our regression exercises is that programme villagestend to increase average household income and production which arelargely drawn from rural non-farm activities, given the fact that householdsdraw income from both farm and non-farm sources (Tables 6-9). Anotherfinding worth noting is that programme villages also promote village-levelemployment. Given the fact that they promote self-employment (againlargely non-farm self-employment) and reduce wage employment (largelyin agricultural activities), the increase in self-employment is higher than thereduction in wage employment, which results in overall higher aggregateemployment.

A semi-logarithmic regression with White's correction forheteroscedasticity is run using the natural log of the weighted average oftotal household production at the village-level (adjusted by consumer priceindex for inter-village price variations) as dependent variable against a setof exogenous village characteristics (Table 6).8 Included in this category arethe programme placement of Grameen Bank, BRAC and RD-12,availability of paved roads, commercial banks, any developmentprogramme, and electricity, distance of the village from thana headquarters,and proportion of village households with land assets of 0.5 acre or less.9 Inaddition, seasonal dummies are included in the regression to capture theimpact of seasonally on production.

All three programmes have contributed to increasing total production ofthe village economy - mainly through improving rural non-farmproduction. The proportionate increase in per household total productioncompared with that in non-programme villages is 57 per cent in BRACvillages, 56 per cent Grameen Bank villages, and 48 per cent in RD-12villages. These are programme impacts net of other exogenous villagecharacteristics. The presence of both commercial bank and ruralelectrification is also found to increase village-level production. Theproportionate increase in production is 62 per cent due to commercial bankplacement and 59 per cent due to rural electrification. The proportionateincrease in village-level production due to micro-credit programmes is thusless than that due to traditional banking and that due to rural electrification.This is perhaps because of relatively large externalities caused by ruralelectrification and commercial bank operation. After controlling for otherinfrastructures, the road network does not seem to have any significantimpact on village-level production. As for seasonality, production increaseis more in the Bom season than Aus season.

As mentioned before, gains accrue largely through expansion in the ruralnon-farm sector. The proportionate increase in rural non-farm production is

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


TABLE 6PROGRAMME IMPACTS ON VILLAGE-LEVEL VALUE OF PRODUCTION

(OLS ESTIMATES)

Log of average of households' annual

Explanatory variable Farm activities

Village has Grameen Bank?

Village has BRAC?

Village has RD-12?

Village has paved road?

Village has commercial bank?

Village has electricity?

Village distance toThana headquarters (km)

Village has anydevelopment programme?

Proportion of village householdsowning land of 50 decimalsor less

Aman season dummy

Bom season dummy

Constant

R2


0.108(0.321)0.346

(0.920)0.764

(2.065)-0.697

(-1.765)0.188

(0.324)-0.373

(-1.440)

0.011(0.471)

0.208(0.855)

0.444(0.275)0.961

(3.267)0.644

(2.070)7.110

(4.573)0.097

261

value of production in taka

Non-farm activities

0.889(2.181)0.829

(2.237)0.737

(2.192)-0.676

(-1.793)0.920

(2.886)0.876

(3.321)

-O.048(-2.621)

0.059(0.308)

-1.325(-11.528)

0.147(0.646)0.265

(1.170)10.721

(11.515)0.187

261

All activities

0.559(2.046)0.569

(2.242)0.477

(1.954)-0.342

(-1.351)0.622

(2.707)0.585

(3.114)

-0.028(-2.073)

0.038(0.277)

-1.234(-1.792)

0.158(0.864)0.336

(2.302)11.128

(16.101)0.167

261

Notes: Figures in parentheses are t-statistics.Source: World Bank-BIDS survey data, 1991/92.

89 per cent due to Grameen Bank programme placement, 83 per cent due toBRAC programnTe~placement, and 74 per cent due to RD-12 programmeplacement. In contrast, growth rate in overall farm production in the villageis significant only in RD-12 villages where it is 76 per cent. Like micro-credit programmes, rural electrification and traditional banking increaserural non-farm production. The proportionate growth rate in production is88 per cent due to electrification and 92 per cent due to commercial bankexpansion. The effects of rural electrification and commercial banks are

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


higher than those of micro-credit programmes, perhaps because of larger(positive) externalities associated with electrification and large-scale creditoperation.10

The immediate impact of a micro-credit programme, which promotesself employment in the rural non-farm sector is, of course, on the labourforce participation rate and total hours worked. This labour forceparticipation impact is shown in Table 7, where the proportion ofindividuals employed among all eligible individuals in the village (age 10and above) is regressed against all the exogenous village characteristics.Results indicate that both Grameen Bank and RD-12 have a positive impacton the overall village-level labour force participation rate. The RD-12programme increases labour force participation in farm activities (by 18.5per cent), but does not influence that in non-farm activities, resulting in anoverall increase of 9.7 per cent. By contrast, the Grameen Bank increasesthe labour force participation in both farm (10.7 per cent) and non-farm(13.4 per cent) activities, and the overall increase is 10.1 per cent. BRACincreases only the non-farm labour force participation rate (by 7.9 per cent).

Employment which is a reflection of both the supply of and demand forlabour can rise or fall depending on how much and in what waysprogrammes affect farm and non-farm production. If, for example,increased production is attained through improved technology of rural non-farm activities, then it is unlikely to have a positive and significant impacton village-level employment. In fact, technological development can reduceoverall employment by reducing the demand for labour with an increase incapital intensity. On the other hand, if production technology does notchange, an overall increase in production can be attained throughemployment expansion. Given the size of borrowing and type of activitiesundertaken by micro-entrepreneurs, it is unlikely that capital intensity hasincreased and, hence, employment is expected to rise, given the labour andcapital intensity of rural non-farm production. On the other hand, if theincome effect due to programme placement is high, it can negatively affectlabour supply of particular type (for example male labour supply, see Pittand Khandker [1996]), so that employment may decline, given the demandfor labour. Therefore, the net impact is a priori indeterminate.

However, as Table 8 confirms, overall village employment has increasedin Grameen Bank villages with a net reduction in BRAC and RD-12villages, compared with that in non-programme villages. Increases in thetotal employment (hours worked per month at the household level) inGrameen Bank villages were due primarily to the growth rate of self-employment in non-farm activities (51 per cent). Overall, however,employment in Grameen Bank villages has increased only by seven per centbecause of the decrease of wage employment in farm activities (39 per

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


TABLE 7PROGRAMME IMPACTS ON VILLAGE-LEVEL LABOUR FORCE PARTICIPATION

(OLS ESTIMATES)

Proportion of village households in

E/ApidHtiLory van a uic —Farm activities

Village has GrameenBank?

Village has BRAC?

Village has RD-12?


Village has commercial bank?


Village distance to Thanaheadquarters (km)

Village has any developmentprogramme?

Proportion of village householdsowning land of 50 decimalsor less

Aman season dummy

Boro season dummy

Constant

R2Number of observations

0.065(2.897)-0.028

(-1.178)0.112

(4.103)-0.053

(-2.131)0.048

(1.166)-0.104

(-4.667)

0.005(2.825)

0.015(0.810)

0.168(2.348)-0.008

(-0.376)0.003

(0.111)0.290

(4.541)

0.244261

Non-farm activities

0.051(3.170)0.030

(1.834)-0.019

(-1.021)0.011

(0.781)0.020

(0.896)0.071

(5.633)

-0.002(-2.230)

-0.012(-0.977)

0.217(4.746)-0.0001

(-0.010)0.003

(0.230)0.090

(1.991)

0.289261

labour force

All activities

0.074(4.442)0.008

(0.459)0.071

(3.639)-0.019

(-1.027)0.043

(1.463)-0.035

(-2.280)

0.003(2.129)

0.004(0.313)

0.352(6.343)-0.015

(-0.910)-0.005

(-0.301)0.304

(6.359)

0.239261


cent). By contrast, BRAC reduces farm (both self- and wage employment)employment, but increases only non-farm wage employment. The reductionin farm employment is high enough to offset the increase in non-farmemployment, leaving the overall employment effect as negative. Increasesin self-employment (both farm and non-farm) in RD-12 villages were notenough to offset the reduction in non-farm wage employment. Therefore,the net impact at the village level on total employment of RD-12programme was also negative."

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


TABLE 8PROGRAMME IMPACTS ON VILLAGE-LEVEL EMPLOYMENT (OLS ESTIMATES)

Explanatory variable


Village has BRAC?

Village has RD-12?


Village has commercialbank?



Village has anydevelopment programme

Proportion of villagehouseholds owning landof 50 decimals or less

Aman season dummy

Boro season dummy

Constant

Log of average of households'

Farm employment

•Self-employ-

ment

0.109(1.446)-0.312

(-3.899)0.225

(2.433)-0.180

(-2.296)

-O.014(-0.123)-0.336

(-4.993)

0.012(2.137)

-0.069(-1.101)

-1.426(-3.899)

0.311(4.432)-0.065

(-0.880)

5.532(16.550)

Wageemploy-

ment

-0.394(-2.175)-0.832

(-4.125)0.219

(1.232)-0.541

(-3.408)

0.155(0.736)-O.700

(-4.838)

0.043(3.057)

-0.030(-0.227)

-0.925(-1.211)

0.208(1.298)0.214

(1.301)

4.908(6.723)

Total offarm

employ-ment

-0.040(-0.477)-0.465

(-5.816)0.216

(2.538)-0.299

(-3.762)

0.029(0.271)-0.441

(-6.675)

0.021(3.373)

-0.070(-1.125)

-1.074(-3.015)

0.259(3.603)0.047

(0.623)

5.939(17.424)

employment hours per month

Non-farm employment

Self-employ-

ment

0.511(2.631)0.144

(0.751)0.325

(1.834)-0.101

(-0.528)

0.259(1.409)0.421

(3.208)

-0.048(-4.332)

-O.202(-1.872)

-0.615(-1.101)

0.191(1.583)0.022

(0.177)

4.679(8.143)

Wageemploy-

ment

-O.007(-O.046)

0.423(3.312)-0.717

(-4.294)0.117

(0.939)

0.365(2.352)0.452

(3.533)

-O.004(-0.400)

0.047(0.402)

1.383(2.499)0.038

(0.285)-0.146

(-1.064)

2.749(5.548)

Total ofnon-farmemploy-

ment

0.197(2.012)0.124

(1.282)-0.165

(-1.620)0.044

(0.523)

0.159(1.421)0.406

(5.580)

-0.027(-4.156)

-0.080(-1.215)

0.080(0.216)0.091

(1.236)-0.046

(-0.580)

4.907(12.990)

Total offarm andnon-farmemploy-

ment

0.068(2.387)-0.112

(-3.439)-0.067

(-1.954)-0.058

(-2.192)

0.084(2.794)0.039

(1.521)

-0.006(-2.693)

-0.044(-1.840)

-0.467(-3.330)

0.160(5.771)0.004

(0.145)

6.217(46.996)


0.385

261

0.305

261

0.450

261

0.228

261

0.236

261

0.254

261

0.278

261


Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


Since both village-level production and employment increased becauseof programme placement, at least for Grameen Bank villages, the nextquestion is: what happens to the distribution of village income, that is, whobenefits from programme placement? The total value of production is nettedout for the value of purchased inputs. Like village-level average production,different types of village level average household (self-employment)incomes were adjusted by the village CPI so that they did not reflect inter-village price variations. Various categories of average household income ina village are regressed against the same set of village-level exogenousregressors, as done in employment and production equations. As in the caseof production, a semi-logarithmic functional form is used. The estimationtechnique was OLS with White's correction (Table 9).

Unlike total production of the village, total income has been influencedby Grameen Bank and BRAC, but not by RD-12 placement. Thus, as table9 shows, average income from all sources per household is about 29 percent higher in Grameen Bank villages and 33 per cent higher in BRACvillages than that in non-programme villages, after controlling for otherobservable village characteristics. However, the sources of average incomevary by programme. Only RD-12 has increased overall farm income (by 62per cent) - with 92 per cent increase in self-employment and 38 per centincrease in wage employment. By contrast, neither Grameen Bank norBRAC has any significant impact on farm income from self-employment.Rather, they have negative impacts on farm income from wageemployment. Only BRAC has impact on overall non-farm income, with anincrease of 179 per cent, which is due mainly to its impact on wageemployment (228 per cent). On the other hand, although the Grameen Bankhas positive impact on non-farm income from self-employment (242 percent), it does not have any impact on wage income from non-farmemployment. RD-12 does not have any impact on incomes from non-farmemployment.

Like micro-credit programmes, commercial banks and ruralelectrification seem to have positive impacts on village-level income. Forrural electrification, increase in income is due to large increases in non-farmactivities (196 per cent), and for commercial banks, it is due to that in farmactivities (30 per cent). Rural electrification, in fact, reduces farm income(by 49 per cent). Overall, electrification increases income by 46 per cent,and commercial banks increase it by 45 per cent.

An important issue that is worth exploring is whether placement ofmicro-credit programmes increases income disparity among the ruralhouseholds. Here the hypothesis is that since a micro-credit programmedirectly affects the well-being of its participants they may prosper at a fasterpace than their counterpart non-participants. It is likely, therefore, that as the

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


TABLE 9PROGRAMME IMPACTS ON VILLAGE-LEVEL INCOME (OLS ESTIMATES)

Explanatory variable


Village has BRAC?

Village has RD-12?


Village has commercialbank?



Village has anydevelopment programme

Proportion of villagehouseholds owning landof 50 decimals or less

Aman season dummy

Bow season dummy

Constant

Log of average of households

Farm employment

Self-employ-

ment

0.081(0.185)0.211

(0.479)0.920

(2.038)-0.319

(-0.768)

0.459(0.829)-0.572

(-1.689)

-0.001(-0.022)

0.201(0.634)

-1.492(-0.766)

0.867(2.519)0.026

(0.068)

8.290(4.366)

Wageemploy-

ment

-0.645(-2.409)-0.975

(-2.940)0.378

(1.657)-0.703

(-2.911)

0.472(1.726)-0.686

(-3.237)

0.067(3.114)

0.010(0.053)

-1.789(-1.548)

0.312(1.245)0.358

(1.427)

8.854(8.241)

Total offarm

employ-ment

0.003(0.017)-0.068(-0.354)

0.623(3.060)-0.247

(-1.706)

0.300(1.680)-0.485

(-3.780)

0.027(2.314)

-0.121(-0.893)

-1.319(-1.920)

0.629(4.572)0.164

(1.071)

9.432(14.432)

Non-

Self-employ-

ment

2.419(2.432)0.278

(0.278)0.531

(0.517)0.491

(0.596)

-1.477(-1.346)

1.896(2.548)

-0.103(-1.573)

0.276(0.387)

-5.296(-1.332)-0.303

(-0.358)0.469

(0.588)

8.032(2.141)

' annual income in taka

farm employment

Wageemploy-

ment

0.374(0.503)2.280

(2.923)-0.907

(-1.189)0.236

(0.394)

1.372(1.498)0.651

(1.211)

-0.060(-1.249)

-0.151(-0.277)

2.731(0.863)1.001

(1.576)0.707

(1.140)

-0.238(-0.080)

Total ofnon-farmemploy-

ment

1.503(1.593)1.786

(1.966)-0.524

(-0.544)0.289

(0.387)

0.552(0.561)1.958

(3.010)

-0.179(-3.116)

0.328(0.520)

-1.653(-0.488)

0.112(0.146)0.616

(0.875)

7.669(2.397)

Total offarm andnon-farmemploy-

ment

0.294(1.861)0.327

(2.137)0.212

(1.266)-0.153

(-1.229)

0.447(3.186)0.458

(4.019)

-0.023(-1.928)

0.085(0.772)

-0.973(-1.683)

0.188(1.479)0.230

(1.924)

11.278(19.553)


0.072

261

0.236

261

0.233

261

0.094

261

0.106

261

0.131

261

0.173

261


Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


programme matures, the disparity of income or other outcomes among thehouseholds increases. Using marginal impact of programme participationon household consumption, we calculated predicted householdconsumption before programme participation, then we calculated Ginicoefficients for village households before and after programmeintervention. We did not see any noticeable variations in the consumptiondisparity among households. This suggests that income or consumptiondisparity among village households did not change as a result of programmeplacement. This is not surprising as the programmes that benefitedparticipant households also influenced the outcomes of non-participanthouseholds through the labour market and demonstration.

VIII. RESULTS ON RURAL WAGES

Micro-credit programmes mostly help the landless households which ownless than half of an acre of land by providing capital to initiate a new activityor expand old ones. Before programme participation, most of thesehouseholds would sell labour (particularly male labour) for wage in thelabour market. However, programme participation makes it possible forthem to be self-employed, for in most cases loans are given to women ofprogramme participating households. This would allow women (and alsochildren) who were unemployed (or under-employed) before or involvedonly in household chores to become self-employed in micro-credit financedsmall enterprises. Even if loans are given to women, many such smallenterprises require male labour for marketing and other purposes. In manycases, however, women get loans to finance their husbands' self-employedactivities. In any case, a significant number of men of programmeparticipating households withdraw themselves from the wage labour marketto get involved in family enterprises. Micro-credit programmes thus seem toreduce wage employment and income, but raise self-employment andcorresponding income for programme participating households. However,increases in self-employment income from farm and non-farm sources mayoutweigh reductions in wage income so the net effect of micro-credit ontotal income at the village level is still significantly positive. Similarly,although wage employment may have declined, increases in self-employment may be large enough to offset a reduction in wage employmentat the village level. For example, male and female employment in GrameenBank villages are 14 and 39 per cent higher respectively than those in non-programme areas [Rahman and Khandker, 1994]. One would expect,therefore, that a reduction of employment in wage market might increasewages, but, this may not happen because, as mentioned earlier, the wageemployment gap may be filled by previously unemployed or under-

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


employed wage workers. This, of course, depends on the slope of the labourdemand and supply curves. With elastic demand and supply curves, it ispossible to show an increase in market wages as a result of micro-creditprogramme placement, even if the demand for wage labour does notincrease.

Assume that the supply of labour to the wage market before programmeplacement is given by SQ and the demand for labour is given by Do. Giventhe supply of and demand for labour (assuming that the wage adjusts toequilibrium), the initial market-clearing wage is given by Wo and wageemployment is given by Eo (Figure 1). Now, when a programme is placedin the village, the immediate effect of withdrawing labour from the wagelabour market shifts the supply curve from So to S j , which results in a wageincrease, given the demand curve (Do). This, however, causes a reduction inwage employment from Eo to E[, and a corresponding or greater increase inself-employment.

The initial increase in wages may be offset if there is no shift in thedemand curve for labour and there is an increase in the labour supply fromothers (those who are already in the labour market or are not in the labourmarket because of higher reservation wages) due to an increase in wages.However, without a shift in the demand curve, the wage increase withreduced wage employment may be sustained if the supply curve does notshift back. However, over time, the wage increase cannot be sustainedunless the demand curve for wage labour is shifted outward, which is onlypossible with increased induced labour demand felt from the non-farm orthe farm sector as a result of a technology shift.

FIGURE 1VILLAGE-LEVEL MARKET WAGE DETERMINATION

Wage(W)

EQ Employment (E)

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


TABLE 10PROGRAMME IMPACTS ON AVERAGE DAILY VILLAGE

WAGE IN TAKA

Explanatory variable Coefficients of explanatoryvariables

Mean(of variables)

Village has Grameen Bank?

Village has BRAC?

Village has RD-12?


Village has any bank?


Distance from thana HQ (km)

Village has any development programme?

Proportions of village households owningland of 50 decimals or less

Aman season dummy

Boro season dummy

Constant

4.929(2.091)1.949

(0.899)-1.134

(-0.496)0.252

(0.115)-1.869

(-0.652)-0.624

(-0.359)-0.411

(-2.497)2.296

(1.372)

4.796(0.488)0.751

(0.379)-1.466

(-0.749)

24.481(2.596)

0.287(0.453)0.322

(0.468)0.276

(0.448)0.280

(0.450)0.100

(0.310)0.506

(0.501)8.467

(5.787)0.536

(0.500)

0.333(0.472)0.333

(0.472)

_

Adjusted R2

No. of observations

Mean of average daily village wage (taka)

0.028

261

36.62

261

Notes: Figures in parentheses are t-statistics in the middle column and standard deviations in thelast column.

Source: World Bank-BIDS survey data, 1991/92.

Table 10 presents the rural wage impact of three micro-creditprogrammes. The male aggregate wage (average of both farm and non-farm) is taken as a proxy for rural wage, because the male labour market iscompetitive, while the labour markets for women and children (under age15) are uncompetitive or incomplete (do not exist). Further, even if themarkets for women and child labour are competitive, they follow a patternsimilar to the male adult market. In other words, if the male wage increases,

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


the wage for female or child labour may also increase.12 The wages ofprogramme villages are compared with wages of nonprogramme villagesafter controlling for other exogenous observable village characteristics.

To the extent that only the male labour market is competitive, thevillage-level average wage for male is regressed against the same set ofvillage-level variables used in earlier regressions, including the presence ofGrameen Bank, BRAC and RD-12 programme in a village. The results,presented in Table 10, show that only the Grameen Bank made a positiveand significant impact on rural male wages.13 This finding is perhaps notsurprising, given that the Grameen Bank has provided the largest amount ofcredit per borrower over the years among these three micro-creditprogrammes. Even if the programme is placed in a poorer village (becausethere are 65 per cent target households in Grameen Bank villages comparedwith 52 per cent in non-programme villages), the positive wage impact onthe village is a clear sign of Grameen Bank's sustainable and viablecontribution to the village economy. However, the wage impact of theprogramme is not substantial, for the mean wage for males in villages isonly about 37 taka per day. Given the change in village-level wages due toGrameen Bank programme placement (coefficient of Grameen Bankprogramme dummy in the wage regression), this implies that the GrameenBank's credit programme has induced about a 13.5 per cent increase in therural wage. Note also that this wage increase is due to a reduction in wageemployment. This result is consistent with Figure 1, in which a reduction inwage employment is shown against an increase in wage rate.

IX. CONCLUDING REMARKS

Micro-credit programmes have brought about desirable impacts at the village-level in terms of income, employment, and production, especially in the non-farm sector. For example, programme placement of the has Grameen Bank,BRAC, and RD-12 increases average household income in the village by 29per cent, 33 per cent and 21 per cent respectively. For Grameen Bank andBRAC villages it is the tremendous growth in non-farm income whichcontributed to total income gains, while RD-12 villages benefit from thegrowth in farm income, for total production gains from non-farm sector arevital for all programme areas, although RD-12 villages enjoy gains from farmsector as well. Overall, household production is increased by 56 per cent, 57per cent, and 48 per cent by the placement of Grameen Bank, BRAC, and RD-12 respectively. Only the Grameen Bank appears to increase household laboursupply (by 7 per cent), while BRAC and RD-12 reduce it: BRAC by 11 percent and RD-12 by seven per cent. Similarly, only the Grameen Bank has apositive impact on average village wage (14 per cent). In fact, Grameen Bank,

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


being the provider of largest average loans per borrower, has managed toreduce poverty not only among the participants but also among non-participants in programme villages. The question is: what is the potential ofmicro-credit programmes in arresting poverty on a sustaining basis?

The question of whether the poverty reduction or income and employmenteffects of micro-credit programmes are sustainable depends on the type of ruralnon-farm activities that they finance, their growth and productivity potentials,and linkages with the agricultural sector. Given the fact that wage employmentwas reduced in all three programme villages with a corresponding increase inself-employment, a positive wage impact is primarily due to a backward shiftin the wage labour supply curve. Therefore, the more pertinent question is whythe demand curve has not shifted upward as a result of programme activities.This in part depends on what happens to rural non-farm activities in terms oftheir productivity increase and technological change. It appears that theGrameen Bank and other programmes have managed only to increaseemployment and production in rural non-farm activities where technology istraditional. Unless technological changes occur leading to productivity growth,a large reduction in overall rural poverty and income growth may beimpossible with this kind of intervention.

A rightward shift in the labour demand curve would take place if thereis a structural transformation of the rural non-farm economy in terms ofimproved technological adaptation in production. Unless such atransformation occurs, gains in poverty reduction will be short-lived, andprogrammes like the Grameen Bank will only be able to alleviate povertyon a short-term basis. In fact, when micro-credit programmes support thelandless workers who make a living from the rural non-farm sector which isdependent upon agricultural growth, there is not much one may expect fromsuch programmes [Osmani, 1989]. Programmes are likely to make sizeableimpacts on rural poverty reduction and income growth if they are able topromote strong backward and forward linkages between agriculture and therural non-farm sector. The possibility of such linkages is high if theprogrammes promote high productivity rural non-farm activities based onskill development among their borrowers. Moreover, as micro-creditprogrammes mostly support rural non-farm activities, the growth registeredin the rural non-farm sector (five per cent) must be accompanied by asimilar growth to be registered in the crop sector (which is now about lessthan two per cent) in order to have a large impact on income, employmentand poverty reduction in the rural sector. This suggests that concertedefforts must be made to promote crop sector growth to accompany ruralnon-farm growth.

final version received June 1998

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


NOTES

1. For details on both these programmes, see Hossain [1988]; Khandker, Khalily and Khan[1995]; Khandker and Khalily [1996]; Lovell [1992]; Wahid [1993].

2. Thus, the beneficiaries of these micro-credit programmes are those who are most able. Forexample, it has been seen that programme participants have a higher level of oral accountingability than their counterpart non-participants who are equally eligible [Khandker, 1998].This raises the question whether micro-credit programmes increase the income disparitiesamong the poor, even if they reduce the overall income disparities between the poor and thenon-poor. This will be addressed later.

3. However, programme benefits vary by the gender of programme participants. For details, seePitt and Khandker [1996].

4. The participation-level impact analysis shows that in about 25 per cent of cases, theprogramme placement endogeneity biases participant-level impact estimates [Khandker,1998].

5. A thana is the administrative centre for a number of villages.6. A battery test of literacy and numeracy skills was conducted for the members of the

households surveyed, which showed that about 19 per cent of individuals from the non-targethouseholds passed the minimum competency level, compared with only eight per cent ofindividuals from the target households [Greaney, Khandker and Alam, 1998].

7. Note that the costs incurred to the participants have been assumed to be zero. As they arevery poor and virtually landless, their opportunity cost for joining the programmes can beassumed to be zero.

8. Note that the weighted average of production for all households (both target and non-target)at the village-level is used to correct for stratified distribution of participant, non-participant,and non-target households in the village. The same is done for other outcomes, such as labourforce participation, employment and so on.

9. We could have included other village-level variables such as percentage of land area irrigatedor literacy rate. However, these variables are clearly outcomes determined by both privateand public decisions and, hence, are not included.

10. The larger impacts of electrification and commercial bank operation may also arise if thesetypes of programmes are available in agriculturally better endowed areas. If this is so, theeffects of commercial banks and rural electrification are overestimated. The ideal way toestimate the precise impacts is to use panel data which are not available to the authors.

11. The negative total employment of BRAC and RD-12 could imply the presence of reversecausality. That is, micro-credit programmes of BRAC and RD-12 may be placed in villageswith low wage employment opportunities in agriculture. Thus, farm employment (self- andwage) could be negatively associated with the presence of BRAC and RD-12. Again, onlythe use of panel data is required to eliminate such possibility of reverse causality.

12. Note that women and children receive about 50 to 70 per cent of men's wages.13. Even if programmes lend money to women, the Grameen Bank increases the wage rates of

male workers. This could be considered surprising; however, it is not surprising for thefollowing reasons: the rural labour market is active and competitive only for male labour, thelabour markets for women and child labour are incomplete and uncompetitive; labour ofdifferent kinds is an imperfect substitute. Thus, increased demand for female labour mayinduce demand for male labour, even if in an imperfect way. Although loans are given towomen (and consequently women would increase their labour supply in self-employedactivities), this would increase the demand for male labour if male labour is required infamily enterprises and, consequently, reduce the supply of male labour for market wages.

REFERENCES

Greaney, Vincent, Khandker, Shahidur R. and Mahmudul Alam, 1998, Bangladesh: AssessingBasic Learning Skills, Dhaka: University Press Ltd.

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014


Hossain, Mahabub, 1988, 'Credit for Alleviation of Rural Poverty: The Grameen Bank inBangladesh', Research Report, No.65, IFPRI, Washington, DC.

Hossain, Mahabub, 1995, 'Agricultural Growth Linkages: Implications for Rural DevelopmentStrategy and Policy in Bangladesh', paper presented at the workshop on Bangladesh'sAgriculture in the 21st Century, Bangladesh Academy for Rural Development (BARD),Comilla, 5-6 Nov.

Hulme, David and Paul Mosley, 1996, Finance Against Poverty, London and New York:Routledge.

Khan, A.A., 1971, 'Rural Credit Programme of Agricultural Cooperative Federation', Comilla:Bangladesh Academy for Rural Development (mimeo).

Khandker, Shahidur R., 1998, Fighting Poverty with Microcredit: Experience from Bangladesh,New York: Oxford University Press.

Khandker, Shahidur R., Khalily, Baqui and Zahed Khan, 1995, 'Grameen Bank: Performance andSustainability', World Bank Discussion Papers, No.306, Washington, DC.

Khandker, Shahidur R., Khan, Zahed and Baqui Khalily, 1995, 'Sustainability of a GovernmentTargeted Credit Program: Evidence from Bangladesh', World Bank Discussion Papers,No.316, Washington, DC.

Lovell, Catherine, 1992, Breaking the Cycle of Poverty: The BRAC Strategy, West Hartford, CT:Kumarian Press.

McKernan, Signe-Mary, 1995, 'The Impact of Microcredit Programs on Self-EmploymentProfits: Do Noncredit Program Aspects Matter?', Ch.1, 'Essays on Microcredit Programs:The Grameen Bank Experience', Brown University, May.

Osmani, S.R., 1989, 'Limits to the Alleviation of Poverty Through Nonfarm Credit', TheBangladesh Development Studies, Vol.17, No.1.

Pitt, Mark M. and Shahidur R. Khandker, 1996, 'Household and Intrahousehold Impacts of theGrameen Bank and Similar Targeted Credit Programs in Bangladesh', World BankDiscussion Papers, No.320, Washington, DC.

Rahman, Rushidan R. and Shahidur R. Khandker, 1994, 'Role of Targeted Credit Programmes inPromoting Employment and Productivity of the Poor in Bangladesh', The BangladeshDevelopment Studies, Vol.XXII, Nos.2 and 3, pp.49-92.

Rosenzweig, Mark R. and Kenneth I. Wolpin, 1982, 'Government Interventions and HouseholdBehavior in a Developing Country: Anticipating the Unanticipated Consequences of SocialPrograms', Journal of Development Economics (Netherlands), Vol.10, pp.209-25.

Wahid, Abu (ed.), 1993, The Grameen Bank: Poverty Relief in Bangladesh, Boulder, CO:Westview Press.

World Bank, 1994, The World Bank's Strategy for Reducing Poverty and Hunger,Environmentally Sustainable Development Studies, Monograph series No.4, Appendix, p.3,Washington, DC.

World Bank, 1996, 'Bangladesh: Rural Finance', Agriculture and Natural Resources Divisions,Country Department I, Report No. 15484 BU, 20 May.

Yunus, Muhammad, 1983, 'Group-based Savings and Credit for the Rural Poor: Grameen Bankin Bangladesh', Group-based Savings and Credit for the Rural Poor, Papers and Proceedingsof a Workshop, Bogra (Bangladesh), Geneva: ILO.

Dow

nloa

ded

by [

Tuf

ts U

nive

rsity

] at

14:

06 0

8 O

ctob

er 2

014

income and employment effects of micro‐credit programmes: village‐level evidence from bangladesh

Documents