111 EAAE-IAAE Seminar ‘Small Farms: decline or persistence’ University of Kent, Canterbury, UK 26th-27th June 2009 Title E...
 
Problem statement… <ul><li>In Bangladesh, due to limited scope of employments in farming and urban manufacturing sector, t...
<ul><li>To fill-up such knowledge gaps, this case study, therefore, aims at investigating following issues: </li></ul><ul>...
Conceptual Framework of the Study Where EP is education poverty, qi is per capita education of the household i, n is total...
Definitions of the exogenous variables   Availability of government primary schools within 1 km reach (1 if yes) primy_s P...
Analytical models at a glance.. Do Do 1 if individual participates in non-farm wage employments Do Do Farm wage employment...
Analytical models at a glance Tobit, OLS Principle physical capital, non-farm worker,  income  diversification, farm incom...
Study location- Study  upazila  Basic characteristics of Comilla Sadar  Upazila , 2005 Source: Focus group discussion with...
Donpur Moddo Bijoypur Raicho Joshpur Study location- Study villages  Urban area in Comilla Sadar Upazila
Sample of the study Sampling method : 2-stage sampling Population  : 1148 (The hhs whose income sources are not only farmi...
Participation and income share of small household workers by economic activities  in Comilla Sadar  Upazila  2007(N=377) R...
Notes: 1) Numbers in parenthesis are standard errors; 2) Statistical significance: *** at 1%, **at 5%,  * at 10% levels, r...
  Notes: 1) Numbers in parenthesis are standard errors; 2) Statistical significance: *** at 1%, **at 5%, * at 10% levels, ...
Notes: 1) Numbers in parenthesis are errors; 2) Statistical significance: *** at 1%, **at 5%, * at 10% levels, respectivel...
Notes: 1) Numbers in parenthesis are standard errors; 2) Statistical significance: *** at 1%, **at 5%, * at 10%   levels, ...
Notes: 1) Numbers in parenthesis are standard errors; 2) Statistical significance: *** at 1%, **at 5%, * at 10% levels, re...
Notes: 1) Numbers in parenthesis are standard errors; 2) Statistical significance: *** at 1%, **at 5%, * at 10% levels res...
Poverty levels of small households in Comilla Sadar  Upazila , 2007 24.3 10.8 2.0 1.1 Poverty severity (%) 29.3 19.0 5.2 3...
Characteristics of small households by poverty incidence in Comilla Sadar Upazila, 2007 Notes: 1) Statistical significance...
Notes: 1) Numbers in parenthesis are standard errors; 2) Statistical significance: *** at 1%, **at 5%, * at 10% levels, re...
<ul><li>Result Summary of analytical models related with the Effects of NFEs </li></ul><ul><li>All models are as a whole s...
<ul><li>Conclusions…. </li></ul><ul><li>The NFAs are no longer marginal among small households and their workers. </li></u...
<ul><li>Conclusions </li></ul><ul><li>More small households are education-poor than income-poor. Education-poverty levels ...
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Updated Eaae Uk Presn On Ref. No. 047

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This is one of my PhD works titled &quot;Effects of Non-farm Employments on Poverty among Small Households in Developed Villages of Bangladesh: A Case of Comilla Sadar Upazila&quot; presented at
111 EAAE-IAAE Seminar ‘Small Farms: decline or persistence’ University of Kent, Canterbury, UK dated
26th-27th June 2009

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Updated Eaae Uk Presn On Ref. No. 047

  1. 1. 111 EAAE-IAAE Seminar ‘Small Farms: decline or persistence’ University of Kent, Canterbury, UK 26th-27th June 2009 Title Effects of Non-farm Employments on Poverty among Small Households in Developed Villages of Bangladesh: A Case of Comilla Sadar Upazila Authors Mohammad Abdul Malek The United Graduate School of Agricultural Sciences, Tottori University, Japan. Email: [email_address] And Koichi Usami, PhD Professor, Faculty of Agriculture, Yamaguchi University, Japan.
  2. 3. Problem statement… <ul><li>In Bangladesh, due to limited scope of employments in farming and urban manufacturing sector, the livelihood diversification in rural areas has become one of the major challenges. </li></ul><ul><li>The Government of Bangladesh has already identified the non-farm sector (NFS) as a “leading sector” in the rural economy. </li></ul><ul><li>But in practice the NFS is not getting due attention like the farm sector, despite such neglect may be socially costly (Hazell and Haggblade, 1993). </li></ul><ul><li>The NFS expands quite rapidly in response to the farm sector development (Arif, et al., 2000) and therefore merits special attention in designing poverty reduction strategy. </li></ul><ul><li>It is envisaged that the non-farm employments (NFEs) have significant impacts on household production (farm and non-farm) and consumption (food and non-food) as because the NFS has been important since the early 90s. The latter effects (consumption) are realized in reducing poverty (for example, food adequacy, income poverty) and human poverty, respectively, in short-term and long-term. </li></ul><ul><li>But the empirical evidence in estimating such comprehensive effects is greatly scares. </li></ul>
  3. 4. <ul><li>To fill-up such knowledge gaps, this case study, therefore, aims at investigating following issues: </li></ul><ul><li>1. What are the participating factors for NFEs among poor household workers? </li></ul><ul><li>2. How do the NFEs affect household farm and non-farm production ? </li></ul><ul><li>3. Do the NFEs really affect household food adequacy? Do the NFEs affect income </li></ul><ul><li>poverty only? Do the NFEs affect human poverty also? </li></ul>Problem statement <ul><li>The poverty/non-farm literatures till to date documented income poverty effects only. </li></ul><ul><li>In rural Bangladesh context, the studies conducted so far shed light on the importance of NFS in income poverty reduction (Nargis and Hossain, 2006; Hossain, 2005). </li></ul><ul><li>In other developing countries` contexts, numerous similar studies were conducted. However, only Ruben and Den Berg (2001) estimated the effects of NFEs on farm production and food consumption. </li></ul><ul><li>But neither study contributed to estimate the comprehensive effects of NFEs as mentioned earlier. </li></ul>
  4. 5. Conceptual Framework of the Study Where EP is education poverty, qi is per capita education of the household i, n is total sample households, O is number of education poor households, p is household education poverty line, and β is degree of aversion to education inequality among the poor. Education poverty <ul><li>Where IP is income poverty, yi is per capita income of the household i, n is total households, q is number of income poor households, z is income poverty line, and α is degree of aversion to income inequality among the poor. </li></ul><ul><li>When β = 0, IP is income-poor head-count ratio; When β = 1, IP is income poverty gap ratio; and When β = 2, IP is squared income poverty gap ratio or severity of income poverty. </li></ul>FGT Income poverty Household economy C alorie intake adequacy ratio is calculated by dividing household calorie intake by household requirements for a day ( Production Economy ) ( Consumption Economy ) Decision on expenditures Community factors Household factors Household income increase Participation in non-farm employments Decision on economic activities Community factors Individual and household factors Economic well-being Poverty, stress and shocks Production effects Consumption effects Improvement Non-farm enterprises Farming Enlargement Intensification Human poverty (education poverty) Basic needs (income poverty) Calorie adequacy Poverty reducing
  5. 6. Definitions of the exogenous variables Availability of government primary schools within 1 km reach (1 if yes) primy_s Proximity to school Availability of growth centers, market or bazar within 1 km reach (1 if yes) Growth_c Proximity to business center Community level Is household receiving any technical assistance/advice from formal sources? (1 if yes) Acc_ta Is household receiving any credit from formal sources? (1 if yes) Acc_credit Is household receiving any assistance from friends, relatives, neighbors? (1 if yes) Acc_frn Is household involved with any GO/NGO/Cooperative/business association etc.? (1 if yes) acc_org Social capital Household non-farm entrepreneurs` education (schooling years) Hhentr_edn Entrepreneur's education Per capita education squared (schooling years squared) hpc_edu2 Per capita education Per capita education (schooling years) hpc_edu hhh education (schooling years) Hhh_edu hhh education Dependents-workers ratio depwkrs Children-female adults ratio child_fem Female adults (numbers) femadult Economic dependency Household head (hhh) gender (1 if male) Hhh_gen Gender Household members (numbers) HH_size Size Household calorie needs (k.cal/day) hcaln Food requirement Household other unearned (rentals, pensions, interest, gifts, etc.) income (BDT) Oundinc Household out-country remittance employment income (BDT) Outc_remit Household in-country remittance employment income (BDT) iinc_remit Household non-farm wage-employment income (BDT) nfwempinc Household non-farm self-employment income (BDT) nfsempinc Household farm wage income (BDT) Famwaginc Sector-wise NFIs Household NFI total (BDT) hnfinc_t Non-farm income (NFI) total Household farm income (BDT) Farminc Farm income Household per capita income (BDT) Hhpc_inc Per capita income A household is diversified if a single income source contributes less than 75% of its income (1 if yes) divsn Income diversification At least one worker employed in out-country remittance employments (1 if yes) owoutcre At least one worker employed in in-country remittance employments (1 if yes) owincrem At least one worker employed as salaried employments in the local formal sector (1 if yes) owfors At least one worker employed as relatively high-productive non-farm enterprises (1 if yes) owhrnfe Strengths to non-farm labor market (SNFLM) No of working persons involved in HNFEs hnfel No of working persons involved in household farming Farm_l Production workers Whether household uses hired farm worker (1 if yes) hired_l Whether household owns poultry, fisheries and livestock (1 if yes) whhpl Share of principal (rice) crops (%) Sprincrp Landholdings under cultivation squared (acres squared) Land_culn2 Landholdings under cultivation (acres) Land_cultn Farm production structures Landholdings owned (acres) landhold Principle physical capital Household level Individual income sources (numbers) I_insc Multiple Income sources Individual education squared (schooling years squared) I_edu2 Individual education (schooling years) I_edu Education Individual age squared (years squared) I_age2 Individual age (years) I_age Age Individual gender (1 if male) I_gen Gender Individual level Description Var_names Characteristics Levels
  6. 7. Analytical models at a glance.. Do Do 1 if individual participates in non-farm wage employments Do Do Farm wage employment income Do Do Out-country remittance income Do Do In-country remittance income Do Do Non-farm wage employment income Do Do 1 if individual participates in in-country remittance employments Do Do 1 if individual participates in out-country remittance employments Do Do 1 if individual participates in farm wage employments Double hurdle (Censored Tobit) Do Non-farm self-employment income Level of participation in sector-wise NFEs Double hurdle (Probit) Do 1 if individual participates in non-farm self-employments Participation in sector-wise NFEs Double hurdle (Censored Tobit) Do Individual income gained from all NFEs Level of participation in overall NFEs Participation in overall NFEs Double hurdle (Probit) Individual level (gender, age, education, multiple income sources), household level (principal physical capital, income diversification, SNFLM, size, economic dependency, hhh education, social capital), community level (proximity to business center) 1 if individual participates Participating factors Methods Explanatory variables Dependent variables Models
  7. 8. Analytical models at a glance Tobit, OLS Principle physical capital, non-farm worker, income diversification, farm income, sector-wise NFIs, gender, size, entrepreneur's education, social capital, proximity to growth center Do Model 2 Tobit, OLS Farm production structures, farm production workers, income diversification, Farm income, sector-wise NFIs, gender, size, education, social capital, proximity to business center Do Model 2 Model 1 Model 1 Education poverty severity Education poverty gap Education poverty incidence Income poverty severity Income poverty gap Income poverty incidence Model 2 Model 1 OLS Do Squared education poverty gap ratio OLS Do Education poverty gap Probit Per capita income, Income diversification, SLFLM, gender, size, economic dependency, principal physical capital, proximity to school 1 if household is education poor, 0 Otherwise Education poverty (2considerations) OLS Do Squared Income poverty gap ratio OLS Do Income poverty gap Probit Income diversification, SLFLM, gender, size, per capita education, economic dependency, principal physical capital, proximity to growth center 1 if household is income poor, 0 Otherwise Income poverty 2SLS Food requirements, income diversification, farm income, sector-wise NFIs, farm production structures, size, economic dependency, education, proximity to growth center Do 2SLS Food requirements, income diversification, farm income, NFI total, farm production structures, size, economic dependency, education, proximity to growth center Log of calorie adequacy ratio for a household/day Calorie adequacy Consumption effects (Poverty) Tobit, OLS Principle physical capital, non-farm production worker, income diversification, farm income, NFI total, gender, size, entrepreneur's education, social capital, proximity to growth center Working capital at non-farm enterprises Non-farm Tobit, OLS Farm production structures, farm production workers, income diversification, farm income, NFI total, gender, size, hhh education, per capita education, social capital, proximity to business center C ash value of external inputs at household f arming Farm Production effects Methods Explanatory variables Dependent variables Models for the effects of NFEs
  8. 9. Study location- Study upazila Basic characteristics of Comilla Sadar Upazila , 2005 Source: Focus group discussion with key informants (2006) Being assigned “Export Processing Zone (EPZ)” at Comilla. 7) Others Farm input business, farm products trade and agro-processing, transport and construction business, restaurants, handicrafts and cottage industries, grocery, etc. 6)Major trade and commerce About 30 rural markets and 7 growth centers. 5) Rural markets/ Growth centers Connected by national highway and national railway with Dhaka and Chittagong. 4) Modes of transport Full-time farm households (10%), part-time farm households (70%), full-time non-farm households (20%). 3)Level of dependency   on farming 75% 2) Literacy level Comilla district in the Division of Chittagong. About 100 km southwest of the capital city Dhaka. 1) Location Characteristics Item
  9. 10. Donpur Moddo Bijoypur Raicho Joshpur Study location- Study villages Urban area in Comilla Sadar Upazila
  10. 11. Sample of the study Sampling method : 2-stage sampling Population : 1148 (The hhs whose income sources are not only farming) Survey duration ◊ August 17–September 20 2006 ◊ 6 April-18 May 2008 Data collection Method Structured questionnaire, focus group discussion, and researcher's own observation. Sampling unit Sample size Small Households (landless 16%, marginal 54%and small landholdings 30%) 175
  11. 12. Participation and income share of small household workers by economic activities in Comilla Sadar Upazila 2007(N=377) Results and discussion 100.0 100.0 Total 2.11 33.8 16.0 Out-country remittance employments 1.02 4.3 4.2 In-country remittance employments 0.77 18.0 23.4 Non-farm wage employments 1.61 29.2 18.1 Non-farm self-employments 0.46 2.5 5.4 Farm wage employments 1.31 87.8 67.0 NFAs as a whole 0.37 12.3 33.0 Own farming Income share/ participation ratio Income share (%) Participation (%) Activities
  12. 13. Notes: 1) Numbers in parenthesis are standard errors; 2) Statistical significance: *** at 1%, **at 5%, * at 10% levels, respectively; and 3) Variables are standardized Determinants of individual participation and level of participation in overall NFEs among small household workers in Comilla Sadar Upazila in 2007 : Probit and Censored Tobit estimates (N=377) 61 .. Left censored obs at <=0 0.0000 0.0000 Prob> chi 2 141.31 155.41 LR Chi 2 (23) -50666.48(17683.64) .023(.512) Constant 20560.47 ** (9154.19) .657 ** (.286) Growth_c -2660.32(11178.22) .068(.370) Acc_frn 9664.95(11380.25) 1.032 ** (.472) Acc_ta -8743.68(9505.44) .198(.363) Acc_credit 15865.85 * (9062.29) -.689 ** (.331) acc_org 14305.28 *** (4569.36) .059(.148) landhold 531.80(13904.56) -.094(.564) hpc_edu2 -11818.68(14653.85) -.207(.561) hpc_edu 1562.08(3627.32) .274(.183) depwkrs 4357.31(5360.1) -.013(.190) child_fem 8301.28(6167.97) -.025(.220) femadult -5359.76(5751.48) -.017(.175) HH_size 18064.28 * (9832.62) -.686 ** (.342) owoutcre 29255.05 ** (13250.48) .239(.453) owincrem 26775.35 *** (10743.65) 1.284 ** (.617) owfors 34976.27 *** (7954.42) .367(.290) Owhrnfe -19188.29 *** (7466.60) .027(.247) divsn 5586.08(3891.13) 1.419 *** (.373) I_insc 3078.94(7764.44) 1.653 ** (.719) I_edu2 16864.39 * (9095.27) -.749(.511) I_edu -67344.26 *** (19770.99) -.758(.607) I_age2 56251.68 *** (19229.27) .1575(.624) I_age 60178.92 *** (11689.5) 1.553 *** (.309) I_gen Level of participation Participation Exogenous variables
  13. 14.   Notes: 1) Numbers in parenthesis are standard errors; 2) Statistical significance: *** at 1%, **at 5%, * at 10% levels, respectively; 3) Variables under the dot mark (..) are dropped and 3) variables are standardized. Determinants of individual participation in sector-wise NFEs among small household workers in Comilla Sadar Upazila in 2007 : Probit estimates 0.0000 0.0000 0.0000 0.0000 0.0000 Prob> chi 2 243.97 97.15 183.76 84.30 195.85 LR Chi 2 377 377 377 256 377 Observations -4.429(.971) -8.119(2.710) -.484(.453) -1.711(.832) -3.047(.637) Constant -.292(.414) -.718(.711) .215(.243) -1.071 ** (.439) .632 ** (.325) Growth_c -.103(.382) 1.807 ** (1.0557) .292(.314) .680(.520) -.383(.327) Acc_frn -.231(.445) .597(1.344) .693 ** (.325) .. .015(.314) Acc_ta .053(.402) -.470(.787) -.055(.259) .244(.535) .145(.280) Acc_credit -.498(.326) -.152(.702) .104(.262) -1.283 ** (.547) .332(.291) acc_org -.130(.166) -.188(.263) -.352 ** (.151) -1.453 *** (.524) -.030(.161) landhold -.100(.748) 2.246 ** (1.210) -.013(.392) -.005(1.083) .266(.423) hpc_edu2 -.123(.852) -2.012 * (1.234) -.053(.386) -.704(.809) -.132(.433) hpc_edu .043(.214) -.041(.257) -.078(.089) .354 ** (.153) -.109(.115) depwkrs -.044(.197) 1.020 ** (.445) .067(.146) -.922 *** (.326) .217(.175) child_fem .468 * (.268) 1.241 ** (.650) -.180(.174) -.707 ** (.361) .321 * (193) femadult -.466(.240) -.910 ** (.477) .273(.173) .552(.412) -.253(.198) HH_size 3.937 *** (.731) .740(.998) -1.719 *** (.288) -.468(.537) -.110(.291) owoutcre .076(.608) 4.102(1.109) -1.166 *** (.366) .. .231(.475) owincrem .692(.523) .331(.739) .309 *** (.264) .. -.154(.338) owfors -.282(.326) -1.265 ** (.706) -1.172 *** (.243) -1.605 *** (.546) 2.00 *** (.242) owhrnfe -.151(.289) 2.010 ** (.065) -.019(.204) 1.441 *** (.428) .027(.236) divsn -.977 *** (.267) .205(.273) .238 ** (.099) .441 *** (.165) .612 *** (.116) I_insc -.311(.261) -1.868 ** ( 1.040) -.129(.237) .162(.385) -.054(.416) I_edu2 .707 ** (.627) 2.281 ** ( 1.122) .362(.238) -.007(.408) -.153(.322) I_edu -1.968 * (1.103) .9175(1.072) .552(.520) -1.198(1.033) -.942 * (.509) I_age2 1.304(1.045) -.890(1.063) -.905 ** (.492) 1.433(1.048) 1.236 ** (.532) I_age .894 * (.503) 2.197(1.695) .357(.276) .567(.580) .520 * ( .321) I_gen Out-country In-country Non-farm wage employments Farm wage employments Non-farm self- employments Remittance employments Local employments Exogenous variables
  14. 15. Notes: 1) Numbers in parenthesis are errors; 2) Statistical significance: *** at 1%, **at 5%, * at 10% levels, respectively; and 3) Variables are standardized. Determinants of individual level of participation in sector wise NFEs among small household workers in Comilla Sadar Upazila (2007): Censored Tobit estimates 297 358 269 349 288 Left-censored observations 0.0000 0.0000 0.0000 0.0000 0.0000 Prob> chi 2 252.38 102.36 170.43 111.36 171.12 LR Chi 2 377 377 377 377 377 Observations -260214.2(53590.54) -497713.7(179386.6) -28022.76(16729.19) -35157.4(16805.26) -346445.3(61779.43) Constant -4626.06(24288.85) 27252.84(39657.93) 2624.53(8521.54) -20736.37 ** (8689.97) 78960.5 *** (29844.1) Growth_c 7922.92(22918.18) 73612.45(47528.4) 9768.04(11415.66) 14538.17(10650.11) -38005.16(30168.14) Acc_frn 25072.5(25496.95) -33605.39(97130.92) 19072.65 * (10431.73) -165600.3(..) -18986.17(29529.17) Acc_ta -32308.47(23176.43) -1875.25(34494.97) -3961.14(8727.86) 5150.15(10949.38) 21820.92(25607) Acc_credit 6413.30(19140.89) 9766.96(33842.47) 13353.45(8829.40) -23432.25 ** (11186.77) 34243.58(26445.13) acc_org 16111.58 * (9455.61) -19466.47(17092.71) -8856.47 * (5046.51) -29060.1 *** (10297.09) 8889.86(14100.36) landhold -63932.28(44947.95) 49744.04(46191.5) 4855.79(14268.3) 7647.44(18763.84) 27680.9(36295.61) hpc_edu2 57454.42(49314.95) -59157.88(50126.08) -6693.16(14202.47) -21738.73(14691.36) -34181.09(38427.12) hpc_edu -2.59(11975.36) 32055.59 * (16865.99) -2007.31(3190.87) 5472.16 * (2903.02) -8597.13(9479.95) depwkrs -9313.13(11942.55) 57907.97 ** (25290.74) 4745.04(5116.07) -15132.63 ** (6267.49) 31850.5 *** (15756.14) child_fem 14943.56(14233.59) 85618.1 ** (39969.08) -3241.67(5910.93) -10880.41(7103.46) 36045.12 *** (18185.18) femadult -19544.7(12826.07) -89477.52 ** (39249.11) 4337.10(5915.48) 10843.62(8030.96) -18060.18(17709.74) HH_size 181745.2 *** (29181.36) 129407.8 ** (65837.21) -57298.95 *** (10579.69) -2917.19(10692.72) 3509.91(27910.79) owoutcre -57976.51 * (35564.77) 297359.3 *** (96496.13) -59249.15 *** (15926.12) -177436.4(..) 98810.57 *** (38476.27) owincrem 5310.16(27434.65) 27256.18(35448.63) 47672.38 *** (8870.44) -147714.4(..) 11695.06(31917.09) owfors 7097.78(19196.87) -94126.36 * (53101.54) -44849.15 *** (8785.68) -33721.95 *** (11816.52) 180936.1 *** (22844) owhrnfe -33516.67 ** (17175.56) 81262.04(52258.47) 272.65(7118.65) 27075.55 *** (8563.25) 8510.48(22362.45) divsn -59954.59 *** (16714.74) 1184.24(14104.37) 5321.29( 3416.552 8463.38 *** (3316.71) 39629.5 *** (10049.06) I_insc -26023.82 * (14351.78) -68277.88(47049.03) -6211.09(9123.58) 6976.12(7851.36) 17595.7(22980.53) I_edu2 62397.54 *** (22765.61) 109629.1 ** (53068.17) 15839.15(8960.71) -9503.72(7931.24) -9440.99 (24216.3) I_edu -152470.9 ** (63225.54) 63097.51(54764.18) 7743.55(18658.96) -12836.82(17390.58) -109583.1 ** (50714.9) I_age2 99628.29 * (58085.32) -51363.5(53894.57) -13999.63(17545.27) 14591.32(17783.38) 141463.8 *** (52429.36) I_age 78441.46 *** (33959.63) -884.70(39040.42) 17495.18 * (10262.6) 6349.92(10150.88) 40643.71(33028.23) I_gen Out-Country In-country Non-farm wage employments Farm wage employments Non-farm self employments Remittance employments Local employments Exogenous Variables
  15. 16. Notes: 1) Numbers in parenthesis are standard errors; 2) Statistical significance: *** at 1%, **at 5%, * at 10%   levels, respectively; 3) Variables are standardized; and 4) Variables under the dot (..) mark are not used Effects of NFEs on farm production among small households of Comilla Sadar Upzila in 2007 (endogenous variable= cash value of external inputs at household own farming) .. .. 43 43 Left-censored observations 132 132 175 175 Observations 0.6590 0.6592 0.0840 0.0826 Pseudo R 2 (for Tobit)/ Adj R 2 (for OLS) 0.0000 0.0000 0.0000 0.0000 Prob > chi 2 12.51 15.91 261.51 257.25 LR chi 2 (for Tobit)/ F (for OLS) 22 17 22 17 Exogenous variables 10131.16(6208.38) 8819.28(6139.75) 791.58(5206.52) 147.08(5250.81) cons -3238.78(2784.08) -2190.66(2677.41) -2690.37(2404.89) -2152.36(2353.93) Growth_c 2362.51(3348.19) 1848.47(3300.19) 2102.13(2910.04) 1591.85(2896.59) acc_frn 60.24(4434.35) 1385.59(4369.68) 1369.36(3515.63) 1433.01(3556.22) acc_ta 3605.48(2976.03) 4617.42(2904.84) 3504.25(2565.08) 4279.57*(2542.17) acc_cr -102.80(2949.02) -217.47(2888.13) 400.91(2545.05) 121.75(2535.44) acc_org 139.77(1249.48) -38.49(1214.16) 576.98(1090.47) 310.78(1078.54) hh_size 2247.30(1782.16) 1691.57(1719.08) 2526.39*(1555.36) 1825.52(1514.06) Hpc_edu -4082.33**(1711.61) -3484.70**(1629.60) -3553.13**(1457.16) -3188.57 **(1410.76) hhh_edu 8628.36(5440.50) 9504.60*(5350.02) 9643.35**(4634.35) 9831.37**(4650.87) hhh_gen 368.65(1189.24) .. -67.65(1800.57) .. Oundinc -539.57(1570.94) .. -1591.64(1255.19) .. Outc_remit -818.92(1189.33) .. -1839.25(1346.34) .. iinc_remit 240.19(1301.16) .. -502.39(1122.01) .. nfwempinc 2309.07(1374.72) .. 1084.04(1724.64) .. nfsempinc 334.25(1197.10) .. 469.44(1005.26) .. Farmwaginc .. 414.86(1414.862) .. -944.68(2126.30) Nfinc_t 6716.04***(1605.32) 7266.92***(1571.37) 7288.44***(1373.94) 7676.54 ***(1368.74) Farminc -2189.10(2547.35) -2335.03(2445.50) -3879.32*(2183.28) -3631.93*(2116.09) Divsn 770.27(1191.82) 1237.72 (1144.72) 4353.01***(1216.15) 4797.50***(1197.31) Farm_l 1777.28(4105.38) (p-vallue:0.666) 1138.017(3923.11) (p-value:0.772) -198.97(3505.49) (p-value: 0.955) -148.87(3458.07) (p-value: 0.966) hired_l 3368.68(3928.56) 3066.56(3812.53) 6472.35*(3392.68) 6089.28*(3361.38) Sprincrp -3059.73(3630.85) -3777.06(3507.09) -5161.34*(2915.31) -5243.21*(2911.68) Land_culn2 12868.49***(4226.35) 12967.49***(4055.12) 15533.28***(3630.01) 14741.87***(3627.19) Land_cultn Coefficient (Std. Error) Coefficient (Std. Error) Coefficient (Std. Error) Coefficient (Std. Error) Regression 2 Regression 1 Regression 2 Regression 1 OLS Tobit Exogenous variables
  16. 17. Notes: 1) Numbers in parenthesis are standard errors; 2) Statistical significance: *** at 1%, **at 5%, * at 10% levels, respectively. 3) Variables are standardized. 4) Variables under the dot (..) mark are not used. Effects of NFEs on non-farm production among small households in Comilla Sadar Upzila in 2007 (endogenous variable= working capital at HNFEs) .. .. 103 103 Left-censored observations 72 72 175 175 Observations 0.9325 0.8232 0.1315 0.0812 Pseudo R 2 (for Tobit)/ Adj R 2 (for OLS) 0.0000 0.0000 0.0000 0.0000 Prob > chi 2 51.94 24.27 276.82 171.01 LR chi 2 (for Tobit)/ F (for OLS) 19 14 19 14 Exogenous variables 169701.1(63766) 236147.5(97015.43) -41767.05(45842.5) -62741.64(78283.58) cons -53920.41**(25368.48) -75989.71**(39747.09) -38689.06*(21570.84) -19597.2(32906.4) Growth_c 27934.55(21120.08) 34993.97(33983.02) 25953.63(20221.34) 42583.76(32344.38) acc_frn -18194.84(21933.88) -44714.12(34963.08) -33978.55(21752.53) -63130.98*(35355.92) acc_ta 24392.48(19659.07) 26276.13(30508.06) 36168.2**(18537.99) 25105.3(29336.94) acc_cr -39455.02*(21519.45) -29599.58(33093.65) -25190.82(19884.95) -305.03(30667.49) acc_org 14857.37(12730.8) 9307.22(19604.65) -1996.24(4007.16) -13863.28**(5877.90) hh_pcedu -5694.17(13050.68) -29899.96(18490.43) 12848.16(9838.59) 54270.25***(13430.42) hhentr_edu -1394.83(10094.55) -35393.61***(13321.95) 3288.35(8238.35) -20255.06*(11728.47) hh_size -37810.15(55479.14) -86844.69 (81699.75) -19083.61(40883.45) 46385.95 (71751.79) hhh_gen 169648.5***(22920.44) .. 63541.74***(11614.38) .. Oundinc 4084.58(5422.92) .. 7063.38(9856.84) .. Outc_remit 4735.78(11385.2) .. -2635.06(11163.6) .. iinc_remit -19820.33**(9493.71) .. -72192.59***(12607.84) .. nfwempinc 66472.45***(21404.85) .. 105283.7***(11730.5) .. nfsempinc -6542.46(9231.30) .. -39453.71***(14203.43) .. Farmwaginc .. 222160.7***(15319.71) .. 145953.1***(11327.63) Nfinc_t 4742.67(10478.27) 950.42(14691.63) -25089.24**(10760.24) -15440.51(15206.38) Farminc -9570.72(22776.79) -19193.62(27272.87) 63464.44***(18688.78) 42992.65*(24876.9) Divsn 11826.64(8638.36) 30861.94***(13304.1) 32273.21***(8801.21) 50185.17***(12985.01) hnfl -4844.32(11174.05) 18542.76(17242.89) -12605.7 (11605.7) -5100.72(17684.31) Landhold Coefficient (Std. Error) Coefficient (Std. Error) Coefficient (Std. Error) Coefficient (Std. Error) Regression 2 Regression 1 Regression 2 Regression 1 OLS Tobit Exogenous variables
  17. 18. Notes: 1) Numbers in parenthesis are standard errors; 2) Statistical significance: *** at 1%, **at 5%, * at 10% levels respectively; 3) Variables under the dot (..) mark are not used. 2SLS estimation of calorie adequacy at the small households in Comilla Sadar Upzila in 2007 (N=175) (endogenous variable= log of calorie adequacy ratio for small households for a day) 0.2149 0.1219 Adj R 2 0.0002 0.0080 Prob> F 2.85 2.25 F( 20, 154) 7.405(2.556) 7.719(2.651) cons .187***(.071) .212***(.071) Growth_c -.033(.024) -.008(.024) Dep_wkr -.061(.046) -.011(.047) Child_fem -.117(.113) -.026(.114) lfem_adults 1.119***(.403) .911**(.416) lhh_size .107(.075) .070(.076) lhpcpedu -.029(.038) -.030(.040) lhhh_edu -.123(.083) - .195**(.087) Acc_frn .116(.074) .094(.077) Hh_lpf .017(.029) -.002(.028) lLand_culn .009(.064) .068(.063) acc_credit .002(.007) .. lOueand_inc -.030***(.008) .. lOutc_remit -.034***(.012) .. linc_remit -.010(.007) .. lnfwe_inc -.010(.006) .. lnfse_inc -.022**(.009) .. lfarmwaginc .. .004(.054) Lnfinc_t .026*(.016) .036**(.017) lfarm_inc .092(.073) .012(.068) Diversfn -1.034***(.344) -1.084***(.359) lhcaln Coefficient (Standard Error) Coefficient (Standard Error) Regression 2 Regression 1 Exogenous variables
  18. 19. Poverty levels of small households in Comilla Sadar Upazila , 2007 24.3 10.8 2.0 1.1 Poverty severity (%) 29.3 19.0 5.2 3.1 Poverty gap (%) 44.0 42.3 20.0 12.6 Poverty incidence (%) Education poverty (for up to 20 years members) Education poverty (for all household members) Income poverty (by upper poverty line) Income poverty (by lower poverty line) Poverty measures
  19. 20. Characteristics of small households by poverty incidence in Comilla Sadar Upazila, 2007 Notes: 1) Statistical significance: *** at 1%, ** at 5%, **at 10% levels, respectively; 2) ‘ns’ stands for statistically insignificant; 3) ‘nr’ stands for ‘not reported’. 37.80 82.86 76.4 92.8 -.21 *** 62.85 87.86 -.27 *** primy_s 41.73 77.71 nr nr ns 68.6 80.0 -.11 * Growth_c .65 .51 .31 .81 -.37 * .15 .59 -.28 *** landhold 1.53 2.14 2.27 1.95 .10 * 3.23 1.87 .36 *** dep_wkrs 1.13 1.21 nr nr ns 1.97 1.02 .34 *** child_fe 47.0 32.57 19.81 52.17 -.34 *** 0 40.7 -.35 *** owoutcre 26.30 7.43 4.72 11.59 -.13 ** nr nr ns owincrem 34.49 13.71 6.60 24.64 -.26 *** 2.86 16.42 -.16 ** owfors 46.32 30.86 nr nr ns 17.14 34.28 -.15 ** owhrnfe 49.91 45.14 40.56 52.1 -.11 * 25.7 50.0 -.20 *** divsn 2.80 5.23 3.43 7.99 -.80 *** 3.57 5.64 -.30 *** Hpc_edu 25,001.63 26,116.28 21,196.83 29,318.56 -.31 *** 24,042.51 34,411.36 -.35 *** Hhpc_inc S.D. Mean Poor Non-poor Correlations Poor Non-poor Correlations Total sample Education poverty incidence Income poverty incidence Characteristics
  20. 21. Notes: 1) Numbers in parenthesis are standard errors; 2) Statistical significance: *** at 1%, **at 5%, * at 10% levels, respectively; 3) Variables under two dots (..) are not used; 4) Variables under three dots (…) are dropped. Effects of NFEs on poverty among small households in Comilla Sadar Upazila in 2007 (N=175) 0.16 0.35 .. 0.11 .38 .. Pseudo R 2 (for Probit)/Adj R 2 (for OLS) 0.000 0.000 0.000 0.006 0.000 0.000 Prob >chi 2 2.87 6.21 66.58 2.19 6.92 67.66 LR chi 2 (for Probit)/ F (for OLS) 18 18 18 18 18 17 Exogenous variables .002(.001) .003(001) 1.146(.847) -.029(.103) -.007(.005) -.533(.952) Cons .000(.001) -.001(.001) -.460(.406) .. .. .. primy_s .. .. .. .042(.039) -.002(.002) -.242(.344) growth_c -.000(.001) -.000(.001) -.135(.355) -.032(.048) .001(.002) -.342(.479) acc_frn .001(.001) .000(.001) .217(.379) -.021(.052) -.004 * (.003) -.068(.589) acc_ta .001(.001) -.001(.001) -.281(.327) -.026(.044) .001(.002) -.044(.466) acc_cr .000(.001) -.000(.000) -.243(.308) .048(.041) -.002(.002) .202(.446) Acc_org -.000(.000) -.002(.000) .320(.251) .066(.029) .001(.002) -.284(.298) whhpl .001 *** (.000) -.002 *** (.001) -.741 *** (.220) .066 ** (.029) -.006 *** (.001) -1.445 ** (.662) land hold .. .. .. -.002(.007) .000(.0003) -.065(.073) hpcapedn .000( .000) .000(.000) -.001(.104) -.0144( .0125) .001 ** (.001) .269 *** (.109) dep_wkrs -.001 ** (.000) .000(.000) -.031(.175) .023(.023) .001( .001) .417 ** (.231) child_fe -.001 ** (.000) .000(.000) .105(.230) .000(.031) .002(.002) .1425(.313) femadult .001( .001) -.000(.000) .052(.095) -.007(.013) .000(.001) .021(.122) hhsize .000(.001) -.001 *** (.001) -1.015 *** (.317) .195(.065) -.008 *** (.002) … owoutcre .002 ** (.001) -.002 ** (.001) -.785(.496) .195 *** (.065) -.009 *** (.003) -.347(.716) owincrem … -.003 *** (.001) -1.318 *** (.371) .015(.050) -.004(.003) -.968 * (.596) owfors -.000(.000) -.001(.000) -.324(.268) .075 ** (.036) -.006 *** (.002) -1.088 *** (.400) owhrnfe .000(.001) -.000(.001) -.215(.523) .007(.070) .000(.003) -.547(.720) hhh_gen -.000(.000) .001(.000) .289(.249) -.009(.033) .002.002 ) -.023(.332) divsn 0.000(0.000) 0.000(0.000) .000(0.000) .. .. .. hpc_inc Poverty severity: OLS Poverty gap: OLS Poverty incidence: Probit estimate Poverty severity: OLS Poverty gap: OLS Poverty incidence: Probit estimate Education poverty Income Poverty Exogenous variables
  21. 22. <ul><li>Result Summary of analytical models related with the Effects of NFEs </li></ul><ul><li>All models are as a whole statistically significant except education poverty effects for 20 yrs below household members. </li></ul><ul><li>However, the following variables of key interests are statistically significant: </li></ul>divsn(+),Farminc(-),Famwaginc(-),nfsempinc(+), nfwempinc(-),Oundinc(+) Model 2 divsn(-),Farminc(+) Model 2 Model 1 Model 1 Education poverty severity Education poverty gap Education poverty incidence Income poverty severity Income poverty gap Income poverty incidence Model 2 Model 1 owfors(+),owincrem(+) owfors(-),owincrem(-),owoutcre(-) owfors(-),outcre(-) Education poverty owhrnfe(+),owincrem(+) owhrnfe(-),owincrem(-),owoutcre(-) owhrnfe(-),owfors(-), owoutcre(-) Income poverty lfarm_inc(+),lfarmwaginc(-),linc_remit(-),lOutc_remit(-), lfarm_inc(+) Calorie adequacy Consumption effects (Poverty) divsn(+),nfinc_t(+) Non-farm divsn(-),Farminc(+) Farm Production effects Significant variables of key interests Models
  22. 23. <ul><li>Conclusions…. </li></ul><ul><li>The NFAs are no longer marginal among small households and their workers. </li></ul><ul><li>Individual gender, multiple income sources and household strength to local formal sector increase participation in overall NFEs, while out-country remittance strength variable decreases. Income diversification tends to decrease rather increase in overall NFI. However, all SNFLM variables contribute positively in overall NFI increase. The household workers' decisions differ quantitatively in terms of sector-wise participations and NFIs. </li></ul><ul><li>Income diversification is negatively related with farm production, while positively with non-farm production. The NFI either overall or sector-wise does not contribute significantly in farm production. </li></ul><ul><li>The overall NFI has significant positive effect on non-farm production. Among various NFIs, the non-farm self-employment income and other unearned incomes have significant positive effect on non-farm production, while wage employment incomes (both local and remittance) do not have any significant positive effect on it. </li></ul><ul><li>The effect of income diversification and overall NFI on food adequacy is not significantly positive; while only farm income has significant positive effect on food adequacy. All NFIs, especially remittance incomes, reduce food adequacy. Since remittance incomes do not contribute in household production either farm or non-farm and food adequacy, these must be spent on non-food consumption. Thus, the impact of NFIs especially remittance income on household consumption patterns requires detail investigation. </li></ul>
  23. 24. <ul><li>Conclusions </li></ul><ul><li>More small households are education-poor than income-poor. Education-poverty levels (gap and severity) are worse than income poverty levels (gap and severity) among small households. Such a picture of education poverty seems to be alarming. </li></ul><ul><li>Income diversification is not significantly important for reducing income poverty and education poverty (at all levels) as well. All SNFLM variables are significantly important for reducing income poverty (almost at all levels), while three formal SNFLM variables are for education poverty. </li></ul><ul><li>Thus, the increasing NFI is reducing some income poverty, but it is yet to realize in reducing household education poverty; however, access to formal sector employments by the small household workers is significantly reducing education poverty. Therefore, qualitative diversification of the poor household workers and productive use (preferably in education and HNFEs) of household remittance incomes deserve special attention. </li></ul>
  24. 25. Feedback from The Chair and Distinguished Participants

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