effectiveness of habp program supports in ethiopia: a cross sectional study

IFPRI

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

Effectiveness of Rural Income

Diversification Program

Supports in Ethiopia: A Cross

Sectional Study

Preliminary Findings

Dereje Getu and Getaw Tadesse

September 26, 2016

Outline

Introduction

Objectives and Research Questions

Data and descriptive statistics

Estimation approach

Estimation results and Discussion

Conclusion

Int: Why for Income Diversification

Poverty reduction in Africa is best achieved if

the burden on agriculture is somehow lifted

through diversified employment both in rural

and urban areas.

Generally, diverse livelihoods are less

vulnerable to shocks than undiversified ones

(Ellis, 2000)

Lifting the burden from agriculture

»moving up ??

»moving out ??

Opportunities for rural income diversification In Africa

Expansion of rural infrastructure

» Transaction cost

» Business communication

Education

Economic Growth

Agr Productivity Growth

DD for Non agr services

Limiting factors for Income diversification in Africa

Limited business literacy

Capital or credit shortage

Cultural resistance

Risk aversion

Asymmetries in output market

Underdeveloped input supply market

Rigid land policy

HABP for Income Diversification

Launched in 2010

Rural incomes are less diversified

Self reliance and graduation from PSNP

How RIDS shall be guided and targeted

A series of Supports

Consultation meeting

Business Plan

Preparation

Credit Access

Technical and Commercial

Support

Objectives of the study

G O: to measure the effectiveness of HABP

program support for RIGA’s Success

How diversified are RIG pathways?

To what extent the program supports are

implemented?

How the supports are perceived by the

beneficiaries?

Which supports are effective

Which supports for Which IGA pathways

Data and descriptive statistics

Structured questionnaire

» 4 regions

» 8 Woredas

» 40 kebeles

» 600 HHs

» All HABP beneficiaries

Multi stage Sampling

Both descriptive and econometric analysis were

employed

Measuring Success / Performance in Rural IGA

• No profit

• No Sales

• Discontinued

Failed HHs

• made profit

• discontinuedRisk coping HHs

• Active

• No sale

• No profit

Struggling HHS

• Have sale

• Active

• No profit

Reviving HHs

• Have no sale

• Active

• Made Profit

Declining HHs

• Made profit

• Current sale

• Active

Successful HHs

• Annual sales

• Profitability and

• Sustainability

Table 1. Diversification of RIGAs in rural Ethiopia

Rural business pathways Tigray Amhara Oromiya SNNP Total

Type of IGAs per Kebele 8.5 6.1 5 2.6 5.6

Number households per

Kebele(N)

556.5 263.6 26.4 25.4 218

Beef 0.04 0.12 0.33 0.13 0.08

Dairy 0.15 0.07 0.06 0.16 0.12

Small ruminant 0.38 0.76 0.39 - 0.49

Poultry 0.15 0.01 - - 0.10

Beekeeping 0.03 - - - 0.02

Irrigation/crop 0.11 - 0.08 0.22 0.08

Petty trade 0.12 0.04 0.14 0.12 0.10

Manufacturing 0.01 0.01 - - 0.01

Rural service 0.00 0.01 - 0.37 0.01

Resource extraction/use 0.01 - - - 0.01

On-farm 0.86 0.95 0.86 0.51 0.88

Off-farm 0.14 0.05 0.14 0.49 0.12

Table 2: Participation of HHs to HABP Program Support

Program supports Dairy

Farming

Small

Ruminants

Crop

Production

Beef

fattening

Off farm Total

Proportion

participated in

consultation meeting

91.18 82.11 92.68 88.37 93.02 87.96

Business plan

prepared

94.12 82.63 90.24 94.77 87.21 89.13

Proportion received

training

72.06 63.16 80.49 72.09 77.91 71.24

Proportion received

advice

91.18 85.26 90.24 85.47 86.05 86.79

Proportion took loan 98.53 100.00 100.00 100.00 100.00 99.83

Input market linkage

created by HABP

26.47 17.37 50.00 9.30 2.33 18.39

Average loan taken

(000)

5.704478 4.713889 6.552122 4.45407 5.396326 5.101

Number of HHs 68 190 82 172 86 599

Table 3 : Participants perception to the relevance of HABP program supports

Program supports

perception

Dairy

Farming

Small

Ruminant

s

Crop

Productio

n

Beef

fattening

Off farm Total

Proportion responded

the plan as relevant

90.63 94.90 94.59 96.00 95.09 94.56

Percentage responded

training as relevant

100.00 95.83 98.48 95.97 94.03 96.48

Percentage responded

advice as relevant

98.39 96.91 97.30 98.64 98.65 97.88

Proportion responded

the loan as sufficient

58.21 65.26 40.24 29.65 43.02 47.57

Number of HHs 68 190 82 172 86 599

Table 4: Performance of IGAs

Income generation

pathways

Average

size of

investment

in 000

Proportion

households

generated

profit

Average

total

profit

Average

rate of

return

Averag

e per

capita

sales

% of

functiona

l IGAs

Dairy 8639 73.53 5097 .732 .672 39.71

Small ruminant 6493 86.84 5376 1.03 .739 40.00

Crop Production 11103 90.24 17639 2.616 2.021 71.95

Beef Fattening 7424 75.58 5180 .930 .889 34.88

Off-farm 8469 89.53 6035 .987 2.171 74.42

Total 7921 82.94 7052 1.178 1.156 47.83

Table 5: Over all Performance of IGA

Performing IGA Freq Percent

Successful 262 43.74

Un successful IGAs 337 56.26

Estimation approach

We have employed different regression models

» Simple linear regression

» Robust regression (rreg)

» LPM

Functionality, percapita sales, rate of return and

performance of IGA across each IGA

Functionality, per capita sales, rate of return and

performance of IGA for off farm and on farm

comparison

Functionality, percapita sales, rate of return and

performance of IGA of the full model

» Panel Data

Cot’d

Program Support Variables

» Participation to Consultation Meeting

» Business Plan preparation

» Access to Credit

» IGA training

» Input market linkage

» IGA advice

Controlled variables

» Social Capital

» Human Capital

» Market and Infrastructure access

Estimation Result 1: Small Ruminants

VARIABLES Fun_

Probit

Sales_

OLS

Sales_Robu

st Reg

Sales_

Tobit

RR_

OLS

RR_Robu

st Reg

RR_

Tobit

Perfo_

Probit

Consmeeting 0.160 0.432** 0.274** 0.699* 0.177 0.0330 0.161 0.162

(0.353) (0.200) (0.106) (0.386) (0.192) (0.168) (0.307) (0.359)

Business_Plan 0.573 -0.118 0.174 0.146 0.228 0.116 0.214 0.503

(0.367) (0.388) (0.108) (0.411) (0.189) (0.170) (0.315) (0.366)

IGA_training 0.488* 0.154 0.0581 0.126 -

0.00438

0.157 -0.0832 0.353

(0.273) (0.166) (0.0869) (0.321) (0.276) (0.138) (0.255) (0.273)

IGA_adivice 0.00540 0.735 0.175 0.971** 0.180 0.0552 0.246 0.102

(0.352) (0.666) (0.113) (0.415) (0.189) (0.177) (0.330) (0.363)

Input market

linkage

0.403 0.0046

8

0.0838 0.124 0.308 0.510*** 0.406 0.505*

(0.286) (0.184) (0.0908) (0.316) (0.236) (0.144) (0.261) (0.283)

lnCredit_amout -0.411* -0.254 -0.00944 -0.287 -0.256 -0.0718 -0.215 -0.344

(0.237) (0.191) (0.0667) (0.241) (0.174) (0.106) (0.193) (0.229)

Observations 188 189 188 189 189 189 189 188

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Estimation Result 2: Crop Production/ irrigation

VARIABLES Fun_

Probit

Sales_

OLS

Sales_Robust

Reg

sales_

Tobit

RR_O

LS

RR_Robus

t Reg

RR_

Tobit

Perfo_

Probit

Consmeeting -1.717 -0.681 -1.595 1.102 -0.495 1.375 -2.138**

(2.343) (0.652) (1.843) (1.910) (0.744) (2.119) (0.953)

Business_Plan 0.343 -0.823 -0.851 -0.874 0.184 -0.806 -0.673 -0.706

(0.559) (1.070) (0.591) (1.731) (1.407) (0.674) (1.934) (0.589)

IGA_training 0.709 0.497 0.430 1.952 -1.020 0.218 -0.366 1.667***

(0.501) (0.962) (0.416) (1.287) (1.182) (0.475) (1.382) (0.513)

IGA_adivice -0.790 0.808 1.016* 2.574 0.803 1.834*** 1.606 0.133

(0.733) (1.369) (0.589) (1.819) (1.470) (0.671) (1.998) (0.698)

Input_market

linkage

0.357 0.405 0.275 0.478 0.0632 -0.324 0.159 0.572

(0.462) (0.969) (0.343) (0.977) (1.125) (0.391) (1.116) (0.460)

lnCredit_amout 0.459 -2.48** -0.621** -2.51*** -1.252 -0.786** -1.201 0.632

(0.427) (1.033) (0.289) (0.798) (0.762) (0.329) (0.928) (0.409)


*** p<0.01, ** p<0.05, * p<0.1

Estimation Result 3: Beef fattening

VARIABLES Fun_

Probit

Sales_

OLS

Sales_Robust

Reg

Sales_

Tobit

RR_

OLS

RR_Robus

t Reg

RR_

Tobit

Perfo_

Probit

Consmeeting -1.717 -0.681 -1.595 1.102 -0.495 1.375 -2.138**

(2.343) (0.652) (1.843) (1.910) (0.744) (2.119) (0.953)

Business_Plan 0.343 -0.823 -0.851 -0.874 0.184 -0.806 -0.673 -0.706

(0.559) (1.070) (0.591) (1.731) (1.407) (0.674) (1.934) (0.589)

IGA_training 0.709 0.497 0.430 1.952 -1.020 0.218 -0.366 1.667***

(0.501) (0.962) (0.416) (1.287) (1.182) (0.475) (1.382) (0.513)

IGA_adivice -0.790 0.808 1.016* 2.574 0.803 1.834*** 1.606 0.133

(0.733) (1.369) (0.589) (1.819) (1.470) (0.671) (1.998) (0.698)

Input_market

linkage

0.357 0.405 0.275 0.478 0.0632 -0.324 0.159 0.572

(0.462) (0.969) (0.343) (0.977) (1.125) (0.391) (1.116) (0.460)

lnCredit_amout 0.459 -2.48** -0.621** -2.507*** -1.252 -0.786** -1.201 0.632

(0.427) (1.033) (0.289) (0.798) (0.762) (0.329) (0.928) (0.409)


*** p<0.01, ** p<0.05, * p<0.1

Estimation Result 4: Off farm

VARIABLES Fun_

Probit

Sales_

OLS

Sales_Robust

Reg

sales_

Tobit

RR_

OLS

RR_Robust

Reg

RR_

Tobit

Perfo_

Probit

Consmeeting -1.855* -11.2** 0.874 -12.16*** -2.00** -0.453* -2.2*** -2.083**

(1.090) (4.853) (0.530) (2.537) (0.865) (0.269) (0.553) (0.973)

Business_Plan 0.475 -2.774 0.617 -2.283 0.259 0.540** 0.508 -0.0743

(0.623) (2.216) (0.431) (2.259) (0.464) (0.222) (0.476) (0.605)

IGA_training 1.685*** -0.132 0.0808 0.205 -0.443 -0.256 -0.502 0.953*

(0.588) (1.584) (0.340) (1.739) (0.468) (0.180) (0.366) (0.506)

IGA_adivice -1.194* -1.337 -0.782* -1.597 -0.150 0.205 0.0157 -0.433

(0.714) (2.466) (0.394) (1.990) (0.633) (0.205) (0.432) (0.669)

Input_market

linkage

-0.989 3.083*** 2.858 -0.646 -0.231 -0.223

(2.583) (0.873) (4.983) (0.481) (0.459) (1.031)

lnCredit_amout 0.894 -1.377 0.249 -1.271 -0.210 -0.280 -0.184 0.715

(0.578) (1.475) (0.351) (1.760) (0.325) (0.183) (0.373) (0.510)


*** p<0.01, ** p<0.05, * p<0.1

Estimation Result 5 : On farm

VARIABLES Fun_

Probit

Sales_

OLS

Sales_Robust

Reg

Sales_

Tobit

RR_

OLS

RR_Robus

t Reg

RR_

Tobit

Perfo_

Probit

Consmeeting -0.0543 -0.777 0.0607 -0.758** -0.494 -0.0198 -0.542 -0.0975

(0.195) (0.500) (0.0857) (0.383) (0.777) (0.103) (0.462) (0.201)

Business_Plan 0.00125 0.190 0.0765 0.365 -0.0063 -0.0369 -0.346 -0.143

(0.228) (0.350) (0.0940) (0.429) (0.237) (0.113) (0.504) (0.231)

IGA_training 0.49*** 0.45** 0.192*** 0.703** 0.454 0.242*** 0.484 0.528***

(0.153) (0.204) (0.0652) (0.295) (0.415) (0.0781) (0.355) (0.159)

IGA_adivice 0.250 0.460* 0.0840 0.530 0.72** 0.151 1.12** 0.312

(0.199) (0.270) (0.0854) (0.385) (0.297) (0.102) (0.475) (0.208)

Input_market

linkage

0.230 0.173 0.158** 0.294 0.274 0.246*** 0.378 0.266*

(0.155) (0.204) (0.0674) (0.295) (0.296) (0.0807) (0.358) (0.155)

lnCredit_amout -0.0868 -0.536* -0.0792 -0.606** -0.161 -0.121* -0.0878 -0.0844

(0.126) (0.306) (0.0546) (0.241) (0.285) (0.0654) (0.290) (0.127)


*** p<0.01, ** p<0.05, * p<0.1

Estimation Result 6: Functionality of IGAs

VARIABLES Probit xtProbit

Consmeeting -0.0506 -0.180

(0.180) (0.186)

Business_Plan 0.0288 0.0966

(0.202) (0.338)

IGA_training 0.596*** 0.578***

(0.140) (0.140)

IGA_adivice 0.188 0.191

(0.179) (0.179)

Input_marketlinkage 0.114 0.0937

(0.149) (0.189)

lnCredit_amout -0.109 -0.114

(0.119) (0.150)

Observations 586 586

Number of IGA_5 5Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Estimation Result 7 : Percapita Sales

VARIABLES OLS Robust Reg Tobit xtReg xtTobit

Consmeeting -1.775** 0.0755 -1.860*** -1.775 -1.966***

(0.750) (0.0876) (0.520) (1.407) (0.522)

Business_Plan -0.264 0.173* 0.0619 -0.264 0.0682

(0.556) (0.0919) (0.559) (0.641) (0.557)

IGA_training 0.360* 0.191*** 0.741* 0.360* 0.685*

(0.217) (0.0648) (0.391) (0.212) (0.391)

IGA_adivice 0.106 0.0189 0.0932 0.106 0.0953

(0.382) (0.0846) (0.506) (0.365) (0.502)

Input_market linkage 0.361 0.127* 0.592 0.361 0.588

(0.270) (0.0704) (0.411) (0.258) (0.416)

lnCredit_amout -0.914** -0.0644 -0.990*** -0.914* -1.007***

(0.422) (0.0552) (0.324) (0.549) (0.323)

Observations 586 586 586 586 586

Number of IGA_5 5 5Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Estimation Result 8: Rate of Return

VARIABLES OLS Robust Reg Tobit xtReg xtTobit

Consmeeting -0.630 -0.0468 -0.711* -0.630 -0.759*

(0.717) (0.0952) (0.414) (0.814) (0.419)

Business_Plan 0.0132 0.0847 -0.177 0.0132 -0.160

(0.215) (0.0998) (0.434) (0.256) (0.435)

IGA_training 0.367 0.168** 0.404 0.367 0.402

(0.390) (0.0704) (0.308) (0.329) (0.308)

IGA_adivice 0.611** 0.155* 0.988** 0.611* 0.982**

(0.278) (0.0919) (0.411) (0.344) (0.410)

Input_marketlinkage 0.303 0.221*** 0.375 0.303** 0.312

(0.268) (0.0764) (0.328) (0.149) (0.343)

lnCredit_amout -0.222 -0.121** -0.181 -0.222 -0.186

(0.263) (0.0600) (0.257) (0.359) (0.257)

Observations 586 586 586 586 586

IGA_5 5 5

Estimation Result 9 : Over all performance of IGAs

VARIABLES Probit xtProbit

consmeeting -0.130 -0.236

(0.184) (0.226)

Business_Plan -0.129 -0.0651

(0.202) (0.323)

IGA_training 0.603*** 0.566***

(0.143) (0.148)

IGA_adivice 0.255 0.255*

(0.183) (0.141)

input_marketlinkage 0.164 0.174

(0.149) (0.182)

lnCredit_amout -0.0902 -0.0777

(0.118) (0.166)

Observations 586 586

Number of IGA_5 5Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Conclusion

The findings of the different regression outputs show that the effectiveness

of program supports varies across type of IGAs,

C-1: Training specific to an IGA is effective program support to both on-

farm and off-farm activities

C-2: while frequent technical advice positively contributes to the success of

on-farm IGAs , it adversely affects success of off-farm IGAs

C-3:Market linkage support significantly increases the probability of

success in rural income diversification specially for on-farm activities

C-4: While credit amount has no relevance for off-farm IGA success, it has

an adverse effect for on-farm IGAs

Business plan has no significant effect on all IGA’s success.

C-4 : Consultation meeting has shown an adverse impact for IGA’s Success

?????

Thank you!