Mohammad Abdul Malek   Visiting PhD Fellow, Institute of Microfinance (InM), Bangladesh  and  PhD Candidate, The United Gr...
<ul><li>Contents of the presentation </li></ul><ul><li>Introduction: Motivation, research questions, contributions, etc. <...
<ul><li>Motivation of the research… </li></ul><ul><li>In rural Bangladesh, the livelihood diversification is a major chall...
<ul><li>A good number of studies focus on the effect of micro-credit involvement on household income and production. For e...
<ul><li>Thus, it might be argued that a group of micro credit borrowing  households allocate their credit in meeting their...
Research questions Question 1 Which micro credit borrowing households participate inHEs? Question 2 2.1 Which factors moti...
Contribution of the study This study can identify a set of characteristics that contribute incidence and extent of partici...
Non-farm  sector HEs: Mostly marginal family enterprises in all  primary, secondary and tertiary sectors of  production an...
Data description.. -PKSF-InM jointly conducted a census of micro credit borrowers at Pathrail union in Delduar Upazila of ...
On an average, one household operates 1.49 enterprises (with S.D. =.98). Table 1 Frequency distribution of HEs among micro...
Table 2 Scale of operations of household enterprises among microcredit borrowing households in Pathrail Union of Tangail d...
Table 3 Pattern of participation of micro credit borrowing households in HEs in Pathrail Union of Tangail district (N=4496...
Model specification.. - The presence of zero observations in cross-sectional studies raises several methodological questio...
<ul><li>The Cragg double hurdle model (1973) is a parametric generalization of the tobit model, in which the decision to p...
Empirical regressions Dependent variables: Selection equation(Probit version):  Extent of participation (1 if individual h...
Table 4 Definition of the selected explanatory variables Variables  Definitions  Demographics  and human capital Hhh_gen  ...
Results and discussion Table 5 Descriptive statistics of selected explanatory variables Variables Total(n=4496) Participat...
Table 6 Lumpy expenditures of micro credit borrowing households in Pathrail Union of Tangail district in 2007 (n=4496) Ite...
Question 1:  Which microcredit borrowing households participate in overall HEs? Answer: The micro credit borrowing househo...
Question 2-1:  Which factors motivate households to participate in overall HEs? Which factors contribute to gain more prof...
Table 7 Participation of microcredit borrowing households in overall HEs in Pathrail Union of Tangail district in 2007: Do...
Factors for incidence of participation in overall HEs. - Household  age, members schooling , physical assets, membership d...
Question 2-2 Which factors motivate households to participate either in farm based or different non-farm based HEs? Which ...
Table 8 Summary of nature specific HEs’ double hurdle  regression results Notes: 1: selection equation, 2: outcome equatio...
<ul><li>Factors for incidence of participation  in nature specific HEs </li></ul><ul><li>Farm based HEs:  h ousehold head ...
Factors for level of participation in nature specific HEs Farm based HEs:  h ousehold head age , landholdings,  members sc...
Conclusion -The micro credit borrowing households which are better positioned in demographics, human capital, physical ass...
-Even if gender of household head (if female) decreases participation in overall HEs and all three nature specific non-far...
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Malek Participation In H Es In M

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This is my presentation with InM titled &quot;Participation of micro-credit borrowing households in household based enterprises in rural Bangladesh&quot; at Institute of Microfinance (InM), PkSF Building, Agargoan, Dhaka, Bangladesh dated 22 October 2009.

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Malek Participation In H Es In M

  1. 1. 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.
  2. 2. <ul><li>Contents of the presentation </li></ul><ul><li>Introduction: Motivation, research questions, contributions, etc. </li></ul><ul><li>Framework of the study: Definitional context, description of the data, model specification, empirical regressions and variables </li></ul><ul><li>Results and discussion </li></ul><ul><li>Conclusions </li></ul>
  3. 3. <ul><li>Motivation of the research… </li></ul><ul><li>In rural Bangladesh, the livelihood diversification is a major challenge. </li></ul><ul><li>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). </li></ul><ul><li>So, credit is critical for the poor. </li></ul><ul><li>For two-thirds of enterprises operating households, insufficient access to credit is the major constraint (World Bank 2002). </li></ul><ul><li>In this context, t he micro credit programs (MCPs) emerge. </li></ul>
  4. 4. <ul><li>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). </li></ul><ul><li>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). </li></ul><ul><li>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). </li></ul><ul><li>By definition, the MCPs give attention to building HEs for the poor for moving them away from the stagnant agricultural sector. </li></ul>Motivation…continued…
  5. 5. <ul><li>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. </li></ul><ul><li>However, the benefits of micro-credit may only be experienced by the segments of the poor (Khandker (1998). </li></ul><ul><li>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). </li></ul>Motivation
  6. 6. 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?
  7. 7. 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.
  8. 8. Non-farm sector HEs: Mostly marginal family enterprises in all primary, secondary and tertiary sectors of production and consumption. Farm based HEs Primary production of crop, livestock, poultry and fisheries. Definitional context Non-farm based HEs Secondary and tertiary sectors Of production and consumption Fig:1 Nature of non-farm based HEs Incidence of participation If household member/members invest in HE/HEs. Level of participation Yearly profit gained from HE/HEs. Agriculture sector Non-farm based HEs Household consumption Backward linkage (B/L) Forward linkage (F/L) Consumption linkage (C/L) Counted for analysis analys i s
  9. 9. 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 (H ousehold questionnaire ) data is used for th is study. -
  10. 10. On an average, one household operates 1.49 enterprises (with S.D. =.98). Table 1 Frequency distribution of HEs among micro credit borrowing households in Pathrail Union of Tangail district in 2007 (N=4496) Data description.. 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
  11. 11. 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, 2 As of 2006-07, US$ 1.00 = BDT (Bangladeshi Taka) 69.03 (GOB, 2008), 3 Expenses for raw materials, wage and others, 4 deducting working capital from gross incomes. Data description.. Characteristics All HEs Farm based Non-farm based Non-farm based B/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 capital 3 (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 profit 4 (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
  12. 12. 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 Pattern of participation All HEs Farm based HEs Non-farm based HEs Overall 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)
  13. 13. 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.
  14. 14. <ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>In such a case, Double hurdle is more appropriate than any other limited dependent variable model . </li></ul>Model specification..
  15. 15. 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). <ul><li>We estimate the above two equations for following cases: </li></ul><ul><li>Overall HEs </li></ul><ul><li>Farm based HEs </li></ul><ul><li>Overall non-farm based HEs </li></ul><ul><li>- B /L HEs </li></ul><ul><li>- F /L HEs </li></ul><ul><li>- C /L HEs </li></ul>
  16. 16. 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 variables dispakarasta Distance to the paved road(km) disbazar Distance to the bazaar (km) 22 village dummies
  17. 17. Results and discussion Table 5 Descriptive statistics of selected explanatory variables 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 capital hhh_gen .07 .26 .06 .24 .10 .31 0.000 agehh 44 12 45 12 41 13 0.000 fmsize 4.74 2.02 4.90 2.10 4.14 1.51 female 2.32 1.26 2.39 1.30 2.04 1.04 0.000 no_child .99 .96 1.02 .97 .87 .92 0.000 pc_edn 3.11 2.37 3.26 2.36 2.52 2.31 0.000 Physical assets, remittances /transfers and lumpy expenditures land 35 74 40 80 14 36 0.000 remtrans 19,553 50,459 20,674 52,742 15,226 40,187 0.000 tpurchase 88,783 202,993 99,823 223,450 46,161 72,505 0.000 Lumpy_expre 6,779 35,493 7,346 37,082 4,591 28,453 0.035 Credit related variables hhovldum .59 .49 .61 .49 .48 .50 0.000 memovldum .30 .46 .32 .46 .26 .44 0.000 memduration 7.25 6.06 7.61 6.20 5.88 5.29 0.000 loanamount 13,248 21,413 14,379 23,413 8,886 9,408 0.000 totdeposit 976 2,689 1,075 2,957 596 1,101 0.000 Local economy specific variables dispakarasta .29 .43 .30 .44 .25 .38 0.005 disbazar 3.59 2.36 3.57 2.42 3.63 2.09 0.518
  18. 18. Table 6 Lumpy expenditures of micro credit borrowing households in Pathrail Union of Tangail district in 2007 (n=4496) 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
  19. 19. 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.
  20. 20. 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.
  21. 21. Table 7 Participation of microcredit borrowing households in overall HEs in Pathrail Union of Tangail district in 2007: Double hurdle regression results Note s : 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) V ariables indicated by the parenthesis (...) are not considered. Variables Overall HEs Selection 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)
  22. 22. 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 - R emittance/transfers - C/L HEs have stronger positive effects on the HEs’ profits followed by farm based and F/L HEs, respectively.
  23. 23. 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.
  24. 24. Table 8 Summary of nature specific HEs’ double hurdle regression results 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. Variables Farm based HEs Non-farm based HEs Overall B/L F/L C/L 1 2 1 2 1 2 1 2 1 2 hhh_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_based1 hFL_farm1 hcons_lin1 Disbazar +* .. .. .. .. .. +*** +*** .. ..
  25. 25. <ul><li>Factors for incidence of participation in nature specific HEs </li></ul><ul><li>Farm based HEs: h ousehold head age , landholdings, membership duration and members schooling . </li></ul><ul><li>Non-farm based HEs (overall): household overlapping, number of children, assets purchased, gender of household head, and remittance/transfers. </li></ul><ul><li>B/L HEs: household members schooling, amount of loan , physical assets (both), remittance/transfers, gender of household head. </li></ul><ul><li>F/L HEs: household head age, amount of loan , distance to the bazar, landholdings, gender of household head. </li></ul><ul><li>C/L HEs: household head age, overlapping, members schooling, amount of loan, family size, number of female, remittances/transfers and landholdings. </li></ul>
  26. 26. Factors for level of participation in nature specific HEs Farm based HEs: h ousehold 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.
  27. 27. 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 (land holdings 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.
  28. 28. -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. Th erefore , 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.
  29. 29. Thanks for patience hearing.

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