Heterogeneous Impact of Livelihood Diversification: Cross-Country Evidence from Sub-Saharan Africa
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CGIAR Research Program on Policies, Institutions, and Markets Workshop on Rural Transformation in the 21st Century (Vancouver, BC – 28 July 2018, 30th International Conference of Agricultural Economists). Presentation by Solomon Asfaw, GCF-IEU. Co-authors: Antonio Scognamillo, Gloria Di Caprera, Ada Ignaciuk, and Nicholas Sitko.
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Heterogeneous Impact of Livelihood Diversification: Cross-Country Evidence from Sub-Saharan Africa
- 1. Heterogenous Impact of Livelihood Diversification: Cross-Country Evidence from Sub-Saharan Africa Co-authors: Antonio Scognamillo, Gloria Di Caprera, Ada Ignaciuk, and Nicholas Sitko PIM Pre-ConferenceWorkshop: RuralTransformation in the 21st century ICAE 2018 –Vancouver 28 July 2018 Solomon Asfaw, GCF-IEU
- 2. Motivation and research questions • Managing risk & income variability • Adapting to changing weather condition • Diversification provides safety-net Push factors • Off-farm opportunities • Higher wage rates & higher returns to entrepreneurial activities • Economies of scope Pull factors Lead to lower, though more stable, welfare levels Impact on welfare Increase welfare, but not necessarily more stability Research questions: What are the linkages of weather events and diversification choices? What are the implications of crop and income diversification choices for household welfare? Assess heterogeneity of impact across income distribution?
- 3. Crop and income diversification • We use the Gini Simpson index ranging from 0 to 1 to measure household-livelihood diversification - Cropland diversification index: based on number of crop types planted and the area allocated - Income diversification index: crop, livestock and fishery incomes, wage from agricultural and non-agricultural activities and other incomes received through transfers and remittances
- 4. Country Year Type of data Data Sources Climatic Variables Malawi 2010 - 2013 Panel Malawi Integrated Household Panel Survey (IHPS) Africa Rainfall Climatology (ARC 2), 1983-2016 Niger 2011 - 2014 Panel Niger National Survey on Living Conditions and Agriculture (ECVM/A) Africa Rainfall Climatology (ARC 2), 1983-2016 Zambia 2012 - 2015 Panel Rural Agriculture Livelihood Survey (RALS) Africa Rainfall Climatology, (ARC 2), 1983-2016 Data: three countries comparison
- 5. NIGER ZAMBIA MALAWI Standard Precipitation Index (SPI)
- 6. Empirical strategy Dit1 = β0 + Xit1βi + ai + uit1 Dit2 = β0 + Xit2βi + ai + uit2 with i=1…N t=1, …, T; • Dit1, and Dit2 indicate, respectively, crop and income diversification for the household i at time t . • Xit is vector of explanatory variables affecting the degree of diversification • ai representing the unobserved individual-level effects. • uitj represents the observation-specific error in the equation 𝑗. Estimate the drivers: Seemingly-unrelated regression (SUR) model
- 7. Malawi Niger Zambia Crop Income Crop Income Crop Income Climatic variables Long-term negative rainfall shocks 0.488** -0.037 0.096 0.156 0.683*** 0.223*** Peer effects % of crop div. within the EA 0.387*** - 0.590*** - 0.534** - % of income div. within the EA - 0.366*** - 0.297*** - 0.275*** Human, natural and physical capital Household head level of education -0.045 -0.502*** 0.016 -0.222** -0.285*** 0.535*** Land size 10.404*** 0.383 0.097*** 0.066 0.134** -0.604*** Wealth Index 12.631*** 9.795*** -3.107 18.073** -4.085*** 13.678*** Institutions and infrastructures Agriculture Extension officer 2.697** 0.686 3.830*** -1.141 1.337*** 1.245*** Market access (km) -0.132* 0.062 -0.017* -0.004 0.016*** -0.037*** Road access (km) 0.089 -0.022 -0.001 0.003 0.011** 0.003 HH socio demographic YES YES YES YES YES YES Year and region dummy YES YES YES YES YES YES Random effects YES YES YES YES YES YES Simultaneous estimates YES YES YES YES YES YES Observations 1556 2938 10889 Drivers and constraints for diversification strategy Results
- 8. Empirical strategy Local Average Treatment Effect – Impact of diversification Dcit = β0 + β1 𝐙kcjt + βiXit + ai + ucit1 Yit = β0 + β1 𝐃cit + βiXit + ai + uit2 • Yit represents the total income of household i at the time 𝑡; • Dcit represents our endogenous variables • 𝐙kcjt is a vector of 𝑘 instruments Identification strategy - IV • Probability of suffering a negative rainfall shock (Di Falco and Veronesi, 2013) • Percentage of households in the community adopting the considered diversification strategy (Townsend 1994)
- 9. Malawi Niger Zambia RE FE CRE IV-FE RE FE CRE IV-FE RE FE CRE IV-FE Crop diversification 0.006*** 0.009*** 0.010*** 0.013* 0.002* -0.002 -0.002 -0.017*** 0.001*** 0.003*** 0.003*** -0.074*** Regional dummies YES YES YES YES YES YES YES YES YES YES YES YES Individual effects YES YES YES YES YES YES YES YES YES YES YES YES Observations 1601 872 2938 2594 10583 10146 Income diversification 0.004*** 0.007*** 0.007*** 0.020* 0.014*** 0.014*** 0.014*** 0.026*** 0.006*** 0.007*** 0.007*** 0.068*** Regional dummies YES YES YES YES YES YES YES YES YES YES YES YES Individual effects YES YES YES YES YES YES YES YES YES YES YES YES Observations 1581 836 3059 2780 10583 10146 Welfare mean impact of diversification: cross-country results Results
- 10. Malawi Niger Zambia QFE QFE-IV QFE QFE-IV QFE QFE-IV Crop diversification Q10% 0.011*** 0.011*** 0.005*** 0.004*** 0.007*** 0.009*** Q25% 0.008*** 0.008*** 0.000 -0.002*** 0.006*** 0.007*** Q50% 0.006*** 0.006*** 0.002*** -0.001 0.004*** 0.001* Q75% 0.003*** 0.006*** 0.002*** 0.001 -0.002*** -0.006*** Q90% 0.001 -0.002 0.005*** -0.010** -0.005*** -0.005*** AEZ dummies YES YES YES YES YES YES Individual fixed effects YES YES YES YES YES YES Observations 1601 2938 10583 Income diversification Q10% 0.012*** 0.012*** 0.022*** 0.018*** 0.006*** 0.007*** Q25% 0.008*** 0.003*** 0.017*** 0.016*** 0.009*** 0.008*** Q50% 0.004*** 0.002*** 0.015*** 0.015*** 0.008*** 0.005*** Q75% -0.001 0.000 0.011*** 0.008*** 0.006*** 0.007*** Q90% -0.002 -0.004*** 0.000 -0.004 0.005** 0.005*** AEZ dummies YES YES YES YES YES YES Individual fixed effects YES YES YES YES YES YES Observations 1581 3059 10583 Heterogeneous impacts: quantile treatment effect QTE results
- 11. Marginal treatment effect of crop and income diversification in Malawi QTE results
- 12. Marginal treatment effect of crop and income diversification in Niger QTE results
- 13. Marginal treatment effect of crop and income diversification in Zambia QTE results
- 14. Conclusions • Drivers are country and strategy-specific. • Land size, wealth, information availability and the proximity to diversified farmers are common determinants. • On average income diversification is a welfare enhancing strategy in all the countries • On average, crop diversification increases the household income in Malawi but the impact turns to be negative in Niger and Zambia. • The QTE is always positive for the poorest and decreases (or even turns negative in the case of crop diversification in Niger and Zambia) moving toward the upper end of the income distribution.
Notas do Editor
- The use of the SPI presents some advantages with respect to other methods. First, in order to identify climate anomalies such as drought or excessive rainfalls, only time-series data on precipitation are required. Moreover, the SPI is an index based on the probability of recording a given amount of precipitation. Since the probabilities are standardized, a value of zero indicates the median precipitation amount, thus the index is negative for drought, and positive for wet conditions. As the dry or wet conditions become more severe, the index becomes more negative or positive, ranging within a commonly-used scale from -2.5 and +2.5 (WMO, 2012). The characteristic of being standardized thus provides a straightforward interpretation and allows for a fully indexed comparison over time and space. In addition, the SPI can be computed for several time scales, ranging from one to 24 months, capturing various scales of both short-term and long-term anomalies. In order to compute our climate shock variables, we first calculate the SPI at 12 months for the reference year 2011. Once the long-run climate anomalies are detected by using the interpretation table provided in WMO (2012), we identify drought and rainfall shocks with dummy variables corresponding to SPI values ranging from less than -2 to more than +2, respectively. Thus, a SPI value of -2.0 or less signals a drought shock while values of +2.0 or more indicates extremely wet conditions. SPI index calculated using historical series of precipitation during the three months preceding the peak of the rainfall season in each country. Since we excluded the current year 𝑡 from the SPI calculation, the index is expected to be correlated with the diversification index at the household level but to not be correlated with the source of unobserved heterogeneity and to not have any direct effect on current household income. In our framework, we consider only the dry shocks such as droughts. According to McKee et al. (1993, 1995), an extreme dry episode is identified when the SPI index is smaller than -1.5.
- The analysis is based on such a restrictive specification to ensure cross-country comparability. Robustness checks using a wider set of controls according to the data availability in each country produce similar results and are available upon request.
- Targeting the poorest farmers who do not have access to other instrument to face external shocks is crucial; A wrong targeting will have no impact or even reduce the welfare of the farmers who would have benefit more from specialization