The Benefit Incidence of Public Spending in Ethiopia

Agricultural Extension, Drinking Water, and the Food Security Programme

Tewodaj Mogues

Preliminary results: Comments very welcome!

Acknowledgements

• Financial support for this study:• IrishAid (through its Ethiopia country office)• World Bank (through its Ethiopia country office)

• The study draws on data, the survey work for which was financed by:• Ethiopia Strategy Support Programme II (ESSP-II)• IrishAid (country office)• World Bank (country office)• World Bank (through BNPP trust fund)

BIA in Three Programmes in Ethiopia

• The benefit incidence of local public investments in selected agricultural and rural programmes in Ethiopia• Agricultural extension• Food Security Programme• Drinking water supply

• Asks: Who benefits the most? the least? from these programmes? • To what extent does local public spending reach

different wealth groups? To what extent women versus men?

Why these three programmes

• Ag extension: recent big-push to dramatically expand extension services—how far have different segments of rural areas gained access to these services?

• FSP: A huge programme, the second-largest of its kind in Africa, seeking to target food insecure households—examine the incidence of FSP components by wealth groups and gender

• Drinking water: A service which has been identified as the most important by households themselves (IFPRI/WB 2010)—what types of households have better access to (safe) drinking water?

Benefit Incidence Analysis – What it does and doesn’t do

• What benefit incidence analysis does do:• Provides insights into how the benefits from the supply of

public services are distributed across different socio-economic groups

• E.g. can address whether public spending in certain sectors is progressive or regressive

• Allows examination of extent to which public resources equitably benefit men and women

• What benefit incidence analysis doesn’t do:• It does not examine the impact of having access to these

public services on other outcomes (e.g. household income, agricultural production, etc.)

Existing BIAs have neglected agriculture

• In studies of benefit incidence in developing countries, agricultural and rural services wholly neglected• Nearly all BIA analyses are on the health and education

sectors (e.g. Younger 2003 for Peru, Castro-Leal 1999 for S. Africa, van de Walle 1994 for Indonesia, Lanjouw et al. 2001 for Indonesia)

• In Ethiopia, the only benefit incidence analysis I am aware of is World Bank (2005) and Woldehanna and Jones (2006), both on the education sector

• Yet, important—certainly in Ethiopia, though not only here—to understand who is most reached by public investments for agricultural and rural development

Benefit incidence of public spending: Basic Methodology

• Some notation: i = public service type (index); j = quantile index; l = location index; = No. of people in quantile j and location l with access to service i; = public spending on service i in location l; Mj = No. of people in quantile j;

• Benefit incidence of public spending:• Benefit incidence (share of public spending on

service i accruing to quantile j), where:

• Amount of public spending on service i reaching

quantile j, with

• Total amount of public spending on service i

• Benefit-to-population odds ratio:

Average vs. Marginal Benefit Incidence

From: Lanjouw & Ravallion (1999)

Average vs. Marginal Benefit Incidence

• Additional notation: k = lower administrative unit index (below l)

• Average odds ratio:

• Marginal odds ratio:

• Define , i.e. the average participation rate of quantile j

• The marginal odds ratio MOj is obtained through instrumental

variables estimation of:

• where is instrumented by , the “leave-out mean” participation rate for the higher unit l, i.e. the mean participation rate in l, after removing the respondents in the jth quantile of lower unit k.

• MOj = βj , the marginal odds ratio for quantile j

Data Used in this Study

• “Demand-side” data on the use of public services: Gender and Rural Services Survey• Eight weredas in seven regions• Kebele-level surveys: Census of all kebeles in the sample

weredas• Household/individual level survey: In four randomly selected

kebeles in each sample wereda (total of 1,118 households; 1,898 respondents)

• Each questionnaire was administered separately to the head as well as spouse of each sample household

• “Supply-side” data on the provision of public services: Wereda-City Benchmarking Survey• Used wereda-level survey’s data on the sample weredas

overlapping with G & RS survey

Agricultural Extension

Public Spending Wealth Incidence of Ag. Extension (Benefit-to-Population Odds Ratio)

Public Spending Wealth Incidence of Ag. Extension (Benefit-to-Population Odds Ratio)

Concentration Curve of Extension Access0

.2.4

.6.8

1

0 .2 .4 .6 .8 1

Farm/home visit DA meetings All extension

Incidence of Ag Extension, by Gender and Headship Status

    Total Women Men* Gender gap

Total

All extension 32.93% 24.70% 42.43% 0.58Visit farm/home 22.90% 20.37% 25.82% 0.79DA meetings 19.16% 11.12% 28.44% 0.39Demo. plot/home 3.22% 1.08% 5.69% 0.19FTC 0.79% 0.59% 1.02% 0.58

Heads of households

All extension 39.44% 28.75% 42.38% 0.68Visit farm/home 25.43% 23.75% 25.89% 0.92DA meetings 26.06% 17.92% 28.29% 0.63Demo. plot/home 5.03% 2.92% 5.61% 0.52FTC 0.90% 0.42% 1.03% 0.41

Spouses of household heads

All extension 23.66% 23.45%Visit farm/home 19.31% 19.33%DA meetings 9.34% 9.02%Demo. plot/home 0.64% 0.52%FTC 0.64% 0.64%

Headship gap

All extension 0.60 0.82Visit farm/home 0.76 0.81DA meetings 0.36 0.50Demo. plot/home 0.13 0.18FTC 0.71 1.52

Public Spending Incidence of Ag Extension, by Gender and Headship Status

Benefit share B-P odds ratio

Gender

Women 41.97% 0.78

Men 58.03% 1.25

Total 100% —

Headship status

Spouse 29.98% 0.73

Head 70.02% 1.19

Total 100% —

Average and Marginal Odds Ratio for Agricultural Extension

Q1 (poorest) Q2 Q3 Q4 Q5

TotalAverage odds 1.193 1.506 0.873 0.705 0.723

Marginal odds 1.081 *** 1.080 *** 0.828 *** -0.050 1.325 ***

(0.100) (0.097) (0.150) (0.156) (0.192)WomenAverage odds 1.283 1.646 0.871 0.598 0.539

Marginal odds1.029 *** 1.070 *** 0.671 ** -0.098 1.117 **

(0.082) (0.177) (0.275) (0.079) (0.556)MenAverage odds 1.202 1.401 0.858 0.760 0.824

Marginal odds 1.225 *** 1.024 *** 0.888 *** 0.303 1.268 ***

(0.152) (0.093) (0.139) (0.336) (0.124)

Food Security Programme

Public Spending Incidence of FSP (Benefit-to-Population Odds Ratio)

Public Spending Incidence of PW (Benefit-to-Population Odds Ratio)

Public Spending Incidence of DS (Benefit-to-Population Odds Ratio)

Concentration Curve of Access to FSP

0.2

.4.6

.81

0 .2 .4 .6 .8 1

FSP Direct Support Public Works Assets

Concentration Curve of Value of FSP receipts

0.2

.4.6

.81

0 .2 .4 .6 .8 1

FSP Direct Support Public Works Assets

Public Spending Incidence of FSP, by Gender

All FSP BP odds ratio

Public Works

BP odds ratio

Direct Support

BP odds ratio

Female-headed HHs 25.34% 0.89 18.45% 0.65 79.38% 2.78Male-headed HHs 74.66% 1.04 81.55% 1.14 20.62% 0.29

Average and Marginal Odds Ratio for FSP

Q1 (poorest)

Q2 Q3 Q4 Q5

TotalAverage odds 1.454 1.360 0.658 0.699 0.831Marginal odds 1.239 *** 0.861 *** 0.647 *** 0.768 *** 0.920 ***

  (0.127) (0.135) (0.139) (0.158) (0.189)Female-Headed HHsAverage odds 0.916 1.150 0.827 1.078 1.102Marginal odds 0.746 *** 0.924 *** 0.954 *** 0.809 *** 1.089 ***

  (0.259) (0.188) (0.252) (0.171) (0.113)Male-Headed HHsAverage odds 1.765 1.459 0.647 0.644 0.758Marginal odds 1.486 *** 0.914 *** 0.585 *** 0.807 *** 0.872 ***

  (0.211) (0.143) (0.155) (0.203) (0.206)

Drinking Water

Time to Primary Water Source in Dry Season (minutes)

Time to Primary Water Source in Wet Season (minutes)

Access to Improved Water Sources in Both Seasons (%)

Gender Incidence of Water Supply

Female-headed HHs

Male-headed HHs

Gender gap (ratio)

Physical access to drinking water (minutes)

Primary source in dry season

One-way 29.0 24.3 1.20Full trip 73.5 63.0 1.17

Primary source in wet season

One-way 25.1 19.9 1.26Full trip 62.8 50.5 1.24

Use of safe drinking water (per cent)

Primary source in:

Dry season 49.51% 33.68% 1.47Wet season 48.53% 35.25% 1.38Both seasons 48.04% 32.38% 1.48

All sources used in:

Dry season 29.56% 24.77% 1.19Wet season 29.56% 25.40% 1.16Both seasons 28.08% 23.55% 1.19

Summary and Conclusions

• Agricultural extension:

• On average, greatest incidence for poorest households

• However, incidence of the least poor is highest on the margin

• Women’s incidence pronouncedly lower (only partially explained by headship status

• FSP:

• Pronouncedly pro-poor overall

• However, while DS’s incidence is highest for FHHs, benefits of DS spending accrue disproportionately to least poor

• Water supply

• Surprisingly, benefit incidence of drinking water supply is highest for the poorest households

• While FHHs’s is poorer when considering physical access to water, they consume better quality water at a higher rate than MHHs

The Benefit Incidence of Public Spending in Ethiopia

Agricultural Extension, Drinking Water, and the Food Security Programme

Tewodaj Mogues