IMPACT OF SMALL-SCALE IRRIGATION ON POVERTY IN RURAL MALAWI

Zephania Bondera Nyirenda,

Introduction

• The aftermath of SAPs & droughts in the 1990s • The 2002 food crisis renewed Govt and donor

interest to reinvest in irrigation agriculture.– A number of investments to spur irrigation

agriculture btwn 2006 and 2014• “Have these irrigation initiatives and resources

helped in moving rural farming households out of poverty?”

Statement of the Problem

• Other researchers have argued that irrigation has helped in reducing poverty in Malawi including Mangisoni, (2008) and Nkhata, (2014).

• However, poverty in Malawi is still rampant and productivity has predominantly remained low (GoM, 2011).

• Review of literature on the impact of irrigation in Malawi leads to four important points:1. All the studies in Malawi have been scheme-based except

for a few that had a bigger geographical coverage.

Statement of the Problem

2. Proxied poverty with food security situation of households or asset ranking &/or farmers’ own assessments of what they consider as symbols of wealth.

importance of using standard poverty metrics in analyzing poverty impacts of irrigation for comparison of impacts.

3. Non-standard impact analysis with no treatment and a control group for comparison.

4. None has employed a double difference method It is from this background that the current study was

undertaken

Justification

• Contributes to the overall strategy for the country (MGDS II- and the ASWAp-section 5.3.5) on improving agricultural productivity through evidence based policy planning, monitoring and evaluation.

• Will inform policy makers on the impacts that irrigation agriculture has had on the livelihoods of rural Malawians

• Will help in identifying gap to be filled to enhance performance and hence, yield more positive results.

Research Question

• What is the impact in terms of poverty, crop productivity, crop income and food security of recent efforts to improve small-scale irrigation in Malawi?

Conceptual Framework

Data Source

• The Third Integrated Household Survey (IHS3) data from National Statistical Office (NSO) was used in the study. Collected information from a representative sample of 12,271 households between March 2010 and March 2011.

Analytical Framework

• Selection bias, spill-over effect and data measurement errors confounding factors in non-experimental evaluation techniques. (Ravallion, 2005)

• Selection bias originates from sample selection and/self-selection (endogeneity).

• The second problem is the spill-over effect

Analytical Framework

• To address these problems, researchers have used1. The IV regression technique2. Regression Discontinuity Design (RDD)3. Others have employed endogenous switching

regression technique• Based on the identified weaknesses in these

techniques, Propensity Score Matching combined with DD method was employed

Household Demographic and Social Economic Characteristics

Variable All Irrigating Households Non-irrigating households P-value

Mean or proportion Household size 4.7

(0.02)5.1(0.06)

4.6(0.02)

1.0000

Household head age 43.1(0.2)

41.6(0.4)

43.4(0.2)

0.0003

Household head gender 0.75(0.4)

0.82(0.4)

0.74(0.4)

0.000

Prop of household heads with primary education.

10.0(0.003)

11.7(0.009)

9.8(0.003)

0.0158

Total cultivated land size (ha) 0.4(0.003)

0.5(0.02)

0.4(0.003)

0.0000

Real expenditure per capita (MK) 47,170.18(405.86)

47,964.50(1078.77)

47057.32(437.46)

0.7696

Poverty Incidence 50.8(0.5)

45.9(1.5)

51.5(0.5)

0.002

Ultra-poverty 23.1(0.4)

18.7(1.1)

23.7(0.5)

0.0001

Poverty gap 18.1(0.2)

15.1(0.6)

18.6(0.3)

0.0000

Poverty severity 8.6(1.1)

6.9(0.4)

8.9(0.2)

0.0000

Crop productivity 1,444.4(28.5)

1,823.25(147.5)

1,421.8(28.8)

0.0006

Crop income 6,222.4(419.7)

10,200.4(865.0)

5,655.3(463.1)

0.0002

Food security situation 1.65(0.005)

1.71(0.013)

1.64(0.005)

0.0000

Access to extension advisory services 0.04(0.002)

0.03(0.009)

0.04(0.003)

0.8570

Determinants of Participation in Small-scale Irrigation

In the first stage, a Probit model was used to determine factors that influence participation in irrigation for smallholder farmers.Treatment Coefficient dy/dxhhsize 0.031657*** 0.006011***head_age -0.00441*** -0.00084***head_gender 0.173416*** 0.031257***head_edlevel -0.04909* -0.00932*AccesstoExt 0.028696 0.005497TotalLandPlatd 1.316772*** 0.250028***rexpaggcap 6.82E-07 1.29E-07_cons -1.41912***Number of obs. 9437 LR chi2(7) 486.43 Prob>chi2 0.000 Log Likelihood -3303.6196 Pseudo R2 0.0686

Impact of Irrigation on Poverty

ATT and ATE Estimates for Various Poverty Measures

Variable Sample Treated Controls Difference S.E. T-stat p>t

Poverty ATT 45.87234 52.30114 -6.4288 1.560869 -4.12 0.001Incidence ATE -4.49379

Ultra-poor ATT 18.7234 24.29239 -5.56898 1.236536 -4.5 0.000

ATE -4.52937

Poverty Gap ATT 15.11859 18.98272 -3.86412 0.681431 -5.67 0.000

ATE -3.02399

Poverty Severity ATT 6.916881 9.116383 -2.1995 0.411087 -5.35 0.000

ATE -1.74662

Impact of Irrigation on Crop Productivity

Variable Sample Treated Controls Difference S.E. T-stat p>|t|Maize Crop ATT 1823.25 1407.19 416.07 150.6 2.72 0.001

productivity ATE 501.67

ATT and ATE of Irrigation on Maize Productivity

A double difference fixed effects model showed that the targeted population had a higher crop productivity as compared to non-targeted population at 10% level of significance

In conclusion, irrigating farmers had statistically significantly higher mean maize productivity as compared to non-irrigating famers.

Impact of Irrigation on Crop Income

Variable Sample Treated Controls Difference S.E. T-stat p>|t|

MaizeCropIncome ATT 10200.40 6214.09 3986.31 988.39 4.03 0.0003

ATE 3573.64

ATT and ATE of Irrigation on Maize Crop Income

A fixed effect model of the double difference showed that the targeted population had significantly higher maize crop income as compared to the un-targeted population

In conclusion, irrigating farmers had statistically significantly higher maize crop income as compared to non-irrigating farmers.

Impact of Irrigation on Food Security

Variable Sample Treated Controls Difference S.E. T-stat p>tFood security ATT 1.7130 1.6660 0.0471 0.01391 3.38 0.000

ATE 0.0506

Fixed effects model of Food security outcomes of targeted and non-targeted population indicated that the targeted population was more food secure as compared to the untargeted population

However, almost equal proportions of households from both groups reported experiencing food shortages within a 12 months recall period (p-value=0.3460).

Conclusion

• Ample evidence that irrigation has positive and significant impacts on poverty reduction, crop productivity, crop income and food security of participating households. – However, the impact on poverty reduction is not

overwhelmingly profound even though it is statistically significant.

– It translates into minimal poverty reduction.

Implications on Policy

1. Famers who have access to water for irrigation should be encouraged to participate in irrigation agriculture

2. Large scale irrigation schemes/commercial agriculture

3. There is need to increase cultivable area under irrigation or increase the number of times a crop is grown in a year for food security