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Mohammad Abdul Malek Visiting PhD Fellow, Institute of Microfinance (InM), Bangladesh

and PhD Candidate, The United Graduate School of Agricultural Sciences, Tottori

University, Japan

Participation of micro-credit borrowing households in household based enterprises in rural Bangladesh

Institute of Microfinance (InM), PkSF Building, Agargoan, Dhaka

22 October 2009.

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Contents of the presentation

• Introduction: Motivation, research questions, contributions, etc.

• Framework of the study: Definitional context, description of the data, model specification, empirical regressions and variables

• Results and discussion

• Conclusions

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Motivation of the research…

• In rural Bangladesh, the livelihood diversification is a major challenge.

•Poor households do not have fund except their inheritance or thrifty savings for establishing a tiny or micro-scale household based enterprises or household enterprises (HEs).

•So, credit is critical for the poor.

•For two-thirds of enterprises operating households, insufficient access to credit is the major constraint (World Bank 2002).

In this context, the micro credit programs (MCPs) emerge.

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• A good number of studies focus on the effect of micro-credit involvement on household income and production. For example, Hossain (1988), Khandker (1998), Kerr (2009).

•Few studies find the positive effects of micro credit on entrepreneurship and employment among individuals and households. For example, Pitt and Khandker (1998), Hashemi et al. (1996), Nussbum (1995).

•Other researches show that a vast majority of BRAC and GB participants profit from self-employments because of the credit that is made available to them (McKernan, 2002).

•By definition, the MCPs give attention to building HEs for the poor for moving them away from the stagnant agricultural sector.

Motivation…continued…

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•Thus, it might be argued that a group of micro credit borrowing households allocate their credit in meeting their current unproductive consumption, crisis coping or other emergencies, though, by definition, they are supposed to participate in HEs. Even micro credit borrowing households participate in HEs, some invests more than others. Some invests in farm based HEs, others in different nature of non-farm based HEs. Accordingly, some profits more in farm based HEs, others in non-farm based in HEs.

•However, the benefits of micro-credit may only be experienced by the segments of the poor (Khandker (1998).

•Another strand of literatures support the fact that the micro credit raises household consumption (Roodman and Morduch, 2009; Rahman, Mallik and Junankar, undated; Islam, 2009).

Motivation

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Research questions

Question 1

Which micro credit borrowing households participate inHEs?

Question 2

2.1 Which factors motivate households to participate in overall HEs? Which factors contribute to gain more profits?

2.2 Which factors motivate them to participate either in farm based or different non-farm based HEs? Which factors contribute to gain more profits either from farm based or non-farm based HEs?

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Contribution of the study

This study can identify a set of characteristics that contribute incidence and extent of participation of micro credit borrowing households in overall and different nature of HEs.

Data and methodology

I use PKSF-InM census data collected for ‘overlapping of micro credit’ study from Pathrail Union of Tangail district and follow standard methodology (double hurdle econometric regression, as pioneered by Cragg, 1973) for estimating two-stage participation decisions of micro credit borrowing households in HEs.

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Agriculture sector

Non-farm based HEs

Household consumption

Backward linkage (B/L)

Forward linkage (F/L)

Consumption linkage (C/L)

Counted for analysis analysis

Non-farm sector

HEs: Mostly marginal family enterprises in all primary, secondary and tertiary sectors of production and consumption.

Farm based HEsPrimary production of crop,

livestock, poultry and fisheries.

Definitional context

Non-farm based HEsSecondary and tertiary sectors

Of production and consumption

Fig:1 Nature of non-farm based HEs

Incidence of participationIf household member/members invest in HE/HEs.Level of participationYearly profit gained from HE/HEs.

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Data description..

-PKSF-InM jointly conducted a census of micro credit borrowers at Pathrail union in Delduar Upazila of Tangail district in 2007.

-This district was chosen for census, as this is one of the seasoned places in Bangladesh where MCP began as early as in the late 1970s.

-Module 4 (Household questionnaire) data is used for this study. -

10On an average, one household operates 1.49 enterprises (with S.D. =.98).

No. of enterprises Frequency Percent Cum. percent

0 925 20.57 20.57

1 1,589 35.34 55.92

2 1,144 25.44 81.36

3 597 13.28 94.64

4 182 4.05 98.69

5 51 1.13 99.82

6 6 0.01 99.96

7 2 .00 1

Total 4496 100.0

Table 1 Frequency distribution of HEs among micro credit borrowing households in Pathrail Union of Tangail district in 2007 (N=4496)

Data description..

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Characteristics

All HEs Farm based Non-farm based Non-farm basedB/L F/L C/L

Mean S.D.1 Mean S.D. Mean S.D. Mean S.D. Mean S.D. Mean S.D.

Incidence of participation (%)

79 40 55 50 51 50 5 0.22 6 24 43 50

Hired labor used (%)

51 50 58 49 52 50 72 45 44 50 51 50

Yearly working capital3 (BDT)

202,823

11,13,191 116,406 402,213 310,935

13,81,600 389,174 960,421

196,329 520,990 318,597 14,60,687

Yearly gross income (BDT)

256,445 12,30,191 157,553 480,256 389,192 15,23,599 464,372 10,30,592

264,932 627,098 399,730 16,13,368

Yearly profit4

(BDT)

53,622

326,778 41,147 175,840 78,257 406,888 75,197 196,919

68,602 300,725 81,132 438,080

Table 2 Scale of operations of household enterprises among microcredit borrowing households in Pathrail Union of Tangail district in 2007 (N=4496)

1 S.D.: Standard deviation, 2As of 2006-07, US$ 1.00 = BDT (Bangladeshi Taka) 69.03 (GOB, 2008), 3Expenses for raw materials, wage and others, 4deducting working capital from gross incomes.

Data description..

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Pattern of participation

All HEs Farm based HEs

Non-farm based HEsOverall B/L F/L C/L

Non-participation

925(20.57) 2,017(44.86) 2,220(49.38) 4,272(95.02) 4,226(93.99) 2,566(57.07)

Zero-profit 942(21.95) 2,025(45.04) 2,231(49.62) 4,274(95.06) 4,227(94.02) 2,577(57.32)Non-zero profit

3,554(79.05) 2,471(54.96) 2,265(50.38) 222(4.94) 269(5.98) 1,919(42.62)

Total 4,496(100.00) 4,496(100.00) 4,496(100.00) 4,496(100.00) 4,496(100.00) 4,496(100.00)

Table 3 Pattern of participation of micro credit borrowing households in HEs in Pathrail Union of Tangail district (N=4496)

Zero-profit observation matters

Data description

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Model specification..

-The presence of zero observations in cross-sectional studies raises several methodological questions (Amemiya, 1984; Vuong, 1989; Green, 1990).

-Like cross-sectional consumption data, the presence of zero observation in household enterprises (profit) data is attributed to (i) corner solution (ii) true non-gaining profit or non-participation and (iii) infrequency of gaining profit (Pudney, 1989).

-The presence of too many zero observations rules out the use of ordinary least squares (OLS) as a vehicle for estimation (Amemiya, 1984).

-Previous related studies employed various limited dependent variable models.

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•The Cragg double hurdle model (1973) is a parametric generalization of the tobit model, in which the decision to participate (first stage/selection) and the level of gaining profits (second stage/outcome) are determined by two separate stochastic processes.

• In our data, additional zero-observations are evident in the second stage as compared to first stage. The reasons for these additional zero-observations are not clear.

• In such a case, Double hurdle is more appropriate than any other limited dependent variable model.

Model specification..

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Empirical regressions

Dependent variables:

Selection equation(Probit version): Extent of participation (1 if individual household invests in HE/HEs, 0 otherwise)

Outcome equation(Tobit version):Level of participation (yearly profit gained from HE/HEs).

We estimate the above two equations for following cases:•Overall HEs• Farm based HEs• Overall non-farm based HEs - B/L HEs - F/L HEs - C/L HEs

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Table 4 Definition of the selected explanatory variables

Variables Definitions Demographics and human capital

Hhh_gen Gender of the household head (1 if female) agehh Age of the household head fmsize Household family size (no.) female Females of the household (no.) no_child Children at 6-14 yrs (no.) pc_edn Per capita education (schooling years) Physical assets, remittance/transfer and lumpy expenditures land Landholding owned(decimal) remtrans Remittance, rentals and other transfers(BDT) tpurchase Asset purchased after being member with NGO-MFI (BDT) Lumpy_expre Household lumpy/infrequent expenditures (BDT)Credit related variables

hhovldum Household overlapping (1 if more than one member are associated with NGO-MFI)

memovldum Membership overlapping (1 if any member is associated with more than one NGO-MFI) memduration Membership duration with NGO-MFI (years) loanamount Amount of loan at 2007 (BDT) totdeposit Current deposit at 2007 with NGO-MFI (BDT)HE nature specific dummies (only for overall HEs` outcome equation)

hfam_based1 1 if individual household member/members invest in farm based HEs

hBL_farm1 1 if individual household member/members invest in backward linkage HEs

hFL_farm1 1 if individual household member/members invest in forward linkage HEs

hcons_lin1 1 if individual household member/members invest in consumption linkage HEs

Local economy specific variablesdispakarasta Distance to the paved road(km) disbazar Distance to the bazaar (km) 22 village dummies

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Results and discussion

Variables Total(n=4496) Participating sample (n=3,571)

Non-participating sample(n=925)

Stat. sig. test

Pr(|T| > |t|) Mean S.D. Mean S.D. Mean S.D.

Demographics and human capitalhhh_gen .07 .26 .06 .24 .10 .31 0.000agehh 44 12 45 12 41 13 0.000fmsize 4.74 2.02 4.90 2.10 4.14 1.51female 2.32 1.26 2.39 1.30 2.04 1.04 0.000no_child .99 .96 1.02 .97 .87 .92 0.000pc_edn 3.11 2.37 3.26 2.36 2.52 2.31 0.000Physical assets, remittances /transfers and lumpy expendituresland 35 74 40 80 14 36 0.000remtrans 19,553 50,459 20,674 52,742 15,226 40,187 0.000tpurchase 88,783 202,993 99,823 223,450 46,161 72,505 0.000Lumpy_expre 6,779 35,493 7,346 37,082 4,591 28,453 0.035 Credit related variableshhovldum .59 .49 .61 .49 .48 .50 0.000memovldum .30 .46 .32 .46 .26 .44 0.000memduration 7.25 6.06 7.61 6.20 5.88 5.29 0.000loanamount 13,248 21,413 14,379 23,413 8,886 9,408 0.000totdeposit 976 2,689 1,075 2,957 596 1,101 0.000Local economy specific variablesdispakarasta .29 .43 .30 .44 .25 .38 0.005disbazar 3.59 2.36 3.57 2.42 3.63 2.09 0.518

Table 5 Descriptive statistics of selected explanatory variables

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Item of expenditures

Total(n=4496) Participating sample (n=3,571)

Non-participating sample(n=925)

Mean S.D. Mean S.D. Mean S.D.

Illness/accidents 449 3,482 455 3,590 428 3,033 Marriage 1,172 9,339 1,102 9,120 1,446 10,142

Education expense in urban areas

138 5,381 173 6,037 0 0

Sending abroad 5,020 33,879 5,617 35,502 2,718 26,593

Table 6 Lumpy expenditures of micro credit borrowing households in Pathrail Union of Tangail district in 2007 (n=4496)

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Question 1:

Which microcredit borrowing households participate in overall HEs?

Answer:

The micro credit borrowing households which are better positioned in demographics, human capital, physical assets, remittance and transfers, more actively associated with NGO-MFI, and even spending more in lumpy expenditures are investing in overall HEs.

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Question 2-1: Which factors motivate households to participate in overall HEs? Which factors contribute to gain more profits?

-To answer these questions I estimate selection equation (for incidence of participation) and outcome equation (for level of participation) for overall HEs.

-Diagnostic statistics show that the regression results as a whole are statistically significant.

Table 7 Participation of microcredit borrowing households in overall HEs in Pathrail Union of Tangail district in 2007: Double hurdle regression results

Variables Overall HEsSelection equation Outcome equation1 Outcome equation2

hhh_sex .-25.(***09 ) 42,865(**19374) 56,583(***19436)Agehh .81.(***15) 26,686(31640 ) 225(31778)agehh2 .-73.(***15) -25,801(31529) -630(31618)Fmsize .01.(05) 5,258(9060) 2,996(9052)female07 .07.(*04) -3,429(7999) - 6,348(7995)no_child1 .01.(03) -2 (5600) -669(5593 )pc_edn .28. (***06) 33,086(**14199) 24,010(*14223)Pc_edn2 .-25.(***07 ) -25,674(*14019 ) - 19,235(14037)

Land . 31.(***05) 3,697(5415 ) - 339(5502)Tpurchase .35.(***07) 17,488(***5083) 15,663(***5076)Remtrans .-11.(***03) -10,083(**5313) - 6,171(5308)hh_lumpy_e~e .03.(03) 7,471(4693) 7,317(4683)Hhovldum .17.(***05) 11,936(10689) 4,628(10714)Memduration . 23.(***09) -10,928(18300) -15,924(18320)Memduration2 .-18.(**09) 10,002(17744) 13,398(17750)Loanamount .07.(09) 7,691(8687) 1,320(8707)Loanamount2 .23.(35) -7,201(7992) - 2,588(7993)Totdeposit .08.(*04) 8,994(*5439) 7,541(5430)Disbazar .12.(*07 ) 1,164(10838) -484(10838)hfarm_based1 .. .. 53,256(***11042)hFL_farm1 .. .. 32,592( *20174)hcons_lin1 .. .. 93,451(***10228)

Notes: 1) Regressions as a whole are statistically significant. 2) Village dummies and constant are not reported. 3) Numbers for the explanatory variables are coefficients and standard errors (in parenthesis). 3) Statistical significance: *** at 1%, **at 5%, * at 10% levels, respectively. 4) Variables are standardized. 5) Variables indicated by the parenthesis (...) are not considered.

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Factors for incidence of participation in overall HEs.

-Household age, members schooling, physical assets, membership duration and overlapping.-Gender of household head (if female) and remittance/transfers.

Factors for extent of participation in overall HEs-Gender of household head (if female), members schooling, assets purchased-Remittance/transfers -C/L HEs have stronger positive effects on the HEs’ profits followed by farm based and F/L HEs, respectively.

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Question 2-2

Which factors motivate households to participate either in farm based or different non-farm based HEs? Which factors contribute to gain more profits either from farm based or non-farm based HEs?

-To answer these two questions I estimate selection equation (for incidence of participation) and outcome equation (for level of participation) for farm and non-farm based HEs (overall and three nature specific).

-Diagnostic statistics show that the regression results as a whole are statistically significant.

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Table 8 Summary of nature specific HEs’ double hurdle regression results

Variables Farm based HEs Non-farm based HEsOverall B/L F/L C/L

1 2 1 2 1 2 1 2 1 2hhh_sex .. .. ***+ ***- ***- ***- ***- ***- ***- ***-Agehh ***+ ***+ .. *+ .. .. ***+ ***+ ***+ ..agehh2 ***- ***- .. *- .. .. ***- ***- ***- ..Fmsize .. *** .. ***+ .. .. .. +* **+ ***+Female07 .. .. .. .. .. .. .. .. +* ..no_child1 *+ .. *+ .. .. .. .. .. .. ..

pc_edn ***+ ***+ .. ***+ ***+ ***+ .. .. ***+ +***Pc_edn2 ***- ***- .. **- ***- ***- .. .. *-Land ***+ ***+ ***- ***+ ***+ ***+ **+ +*** *- ..Tpurchase *- ****+ *+ ***+ *+ ***+ **- ***+ ***+Remtrans .. ***- ***- ***- **+ .. .. .. ***- ***-hh_lumpy_e~e .. .. .. .. .. .. .. .. .. ..Hhovldum .. .. ***+ ***+ .. .. .. .. +*** ***+Memduration ***+ .. .. .. .. .. .. .. .. ..Memduration2 ***- .. **+ .. .. .. .. .. .. ..

Loanamount .. ***+ .. ***+ ***+ ***+ ***+ +*** +*** +***Loanamount2 .. ***- .. -

*****- **- **- -*** ***- *-

totdeposit .. .. .. .. .. .. .. .. .. ..hfarm_based1hFL_farm1hcons_lin1Disbazar *+ .. .. .. .. .. +*** +*** .. ..

Notes: 1: selection equation, 2: outcome equation, B/L: backward linkage, F/L: forward linkage, C/L: consumption linkage, +: positively significant, -: negatively significant, ..: not significant, Statistical significance: *** at 1%, **at 5%, * at 10% levels, respectively.

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Factors for incidence of participation in nature specific HEs

-Farm based HEs: household head age, landholdings, membership duration and members schooling. -Non-farm based HEs (overall): household overlapping, number of children, assets purchased, gender of household head, and remittance/transfers.-B/L HEs: household members schooling, amount of loan, physical assets (both), remittance/transfers, gender of household head. -F/L HEs: household head age, amount of loan, distance to the bazar, landholdings, gender of household head.-C/L HEs: household head age, overlapping, members schooling, amount of loan, family size, number of female, remittances/transfers and landholdings.

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Factors for level of participation in nature specific HEs

Farm based HEs: household head age, landholdings, members schooling, amount of loan, family size, remittance/transfers. Non-farm based HEs (overall): household head age, members schooling, overlapping, amount of loan, physical assets (both), gender of household head.B/L HEs: household members schooling, amount of loan, physical assets (both), gender of household head. F/L HEs: household head age, amount of loan, distance to the bazar, landholdings, family size, gender of household head.C/L HEs: overlapping, members schooling, amount of loan, asset purchased, family size, gender of household head, remittance transfers.

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Conclusion-The micro credit borrowing households which are better positioned in demographics, human capital, physical assets, remittance and transfers, more actively associated with NGO-MFI, and even spending more in lumpy expenditures are investing in overall HEs.

-Overall, gender and age of household head, physical assets (landholdings and asset purchased), members schooling, remittance/transfers, credit related factors (overlapping and amount of loan) are relatively more important determinants for incidence of participation in overall and different nature of HEs; while gender of household head, household members schooling, amount of loan, assets purchased, landholdings, family size and remittance/transfers are important for extent of participation.

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-Even if gender of household head (if female) decreases participation in overall HEs and all three nature specific non-farm based HEs (except overall non-farm based HEs), it contributes relatively more profits in overall HEs than any other factors. Therefore, massive social mobilization programs for increasing female participation in HEs could enhance more productive use of micro credit.

-Remittance/transfers decreases participation in non-farm based HEs as a whole and C/L HEs in particular. However, it increases participation in B/L HEs, while relatively large capital is required. Thus, productive use of remittance/transfers in HEs deserves special attention.

Conclusion

-The diminishing rate of profits with respect to amount of loan is realized for farm and non-farm based HEs (overall and all three specific nature). However, relative profit of loan amount is higher in overall non-farm based HEs followed by B/L and C/L HEs. Therefore, MFI-NGO can enhance their finance in non-farm based HEs as a whole and B/L and C/L HEs in particular. More effective utilization of micro- credit loan also deserves special attention.

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Thanks for patience hearing.