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Does Ethiopia’s Productive Safety Net Program improve child nutrition?


We studied the link between household membership in Ethiopia’s Productive Safety Net Program (PSNP) and short-run nutrition outcomes among children aged 5 years and younger. We used 2006 and 2010 survey data from Northern Ethiopia to estimate parameters of an exogenous switching regression. This allowed us to measure the differential impacts of household characteristics on the weight-for-height Z-scores of children in member and non-member households. The magnitude and significance of household covariates differed in samples of children from PSNP and non-PSNP households and the supply of female labour seemed to matter for promoting child health in member households. Controlling for a set of observable features of children and households we found that children in member households had weight-for-height Z-scores that were 0.55 points higher than those of children in non-member households. We conclude that the PSNP is providing positive short-term nutritional benefits for children, especially in those households that are able to leverage underemployed female labor.

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  1. We use the terms “health” and “nutrition” interchangeably here.

  2. As argued elsewhere, we do not include a longer-term measure of malnutrition (e.g. height-for-age) in this study primarily because a long-term indicator is not likely to pick up the short-term benefits of the PSNP as measured at the time of the study. Instead, height-for-age scores are more likely to reflect underlying levels of deprivation in the sample, and for this reason are likely to be positively correlated with PSNP eligibility.

  3. A child is considered stunted, wasted or underweight if the calculated Z-score for height-for-age, weight-for-height and weight-for-age, respectively, is 2 standard deviations below the median of a reference population. The Z-score is calculated as: \( \mathrm{Z}=\frac{\mathrm{observed}\ \mathrm{value}-\mathrm{median}\ \mathrm{reference}\ \mathrm{value}}{\mathrm{standard}\ \mathrm{deviation}\ \mathrm{of}\ \mathrm{reference}\ \mathrm{population}} \).

  4. In the context of our study, a Development Agent in Kihen tabia indicated that community members were informed about households that faced severe shocks and who were in need of support.

  5. Households that participated in the public works component of the PSNP did not necessarily participate in the community asset development component.

  6. Parent’s heights and weights were measured only in the 2010 survey. This precluded us from using these variables in the empirical analysis.

  7. Ideally, using panel data that follows the same children across time would give a better picture of the health status of children across time. Since we do not have such data, we opted to pool the data from the two years.

  8. We used the WHO’s STATA package (2011) to compute Z scores. When biologically implausible values were encountered, these were recoded to missing. In our dataset, 11 % of WHZ scores exceeded the WHO cutoff values.

  9. Following Duflo (2003), we focused on membership rather than participation. We tested exogeneity of membership in two ways. First, following a similar approach to that used by Yamano et al. (2005) we employed 2SLS using the deflated village average income from FFW in 1998 as our identifying instrument for PSNP membership. Although the instrument was weak, the test recommended rejecting the hypothesis that membership is endogenous in the Z-score regression. Second, we used the same instrument in an endogenous switching regression, subsequently checking the significance of the correlation between the error term in the membership equation and the error term in the Z-score regression. The estimated coefficient was not statistically different from zero, again suggesting that PSNP membership is exogenous to nutrition outcomes. Tables 3 and 4 show these results.

  10. In response to concerns that difference in significance for the effect of heads' age might point to selection bias, we looked more closely at the age structure of member and non-member households. Summary statistics and kernel-density plots show that the age structure within the two groups is broadly similar. When we repeated our regressions omitting age of the household head, the point estimates for remaining variables were similar to those in the full model. We conclude that differences in the effect of the heads' age on child outcomes can be explained by the benefits that member households receive, and that the PSNP may have had an equilibrating and compensating effect across age cohorts of households.

  11. Figure 4 shows the observed distribution of adult female labor in member and non-member households. Figure 5 shows the predicted WHZ versus number of adult female labor. It can clearly be seen that the predicted value of WHZ increases with number of adult females in member households confirming the statistically significant result in Models 1A- 1C.

  12. We use models 1A and 2A to derive predicted values and compute treatment effects.

  13. We used a more conservative approach in predicting the outcome by generating the predictions using only the significant variables. This way, we are able to take into account variables that only matter to the outcome variable in each regression. Test results using the new predictions gave an even stronger result showing children in member households have better nutritional status.

  14. The assumption of a unitary household is a strong one. However, using a bargaining household model would quickly complicate our modelling approach and would not enable us to easily show the mechanism through which PSNP affects child nutrition.


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Funding was provided by the NOMA program (NORAD’s Program for Master Studies) under the Norwegian Agency for Development Cooperation (NORAD). We thank Hosaena Gehebru and NOMA MSc students (2011) for help with data collection, cleaning and entry. Shively acknowledges support provided by the Feed the Future Nutrition Innovation Lab, which is funded by the United States Agency for International Development. The opinions expressed herein are those of the authors and do not necessarily reflect the views of the sponsoring agencies.

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Correspondence to Bethelhem Legesse Debela.

Appendix 1

Appendix 1

Theoretical Model

This section presents the theoretical model, which underpins the logic outlined in the theoretical framework. We begin by developing a multi-period dynamic model of household production and consumption in which household health evolves as a stock. As a simple starting point, we assume a unitary household in which household members make decisions, including those that affect child nutrition, jointly.Footnote 14 The representative household maximizes a discounted stream of utility, defined over consumption, subject to the technology of production and the evolution in stocks of human and physical capital. The problem can be written as:

$$ \mathrm{Max}{\displaystyle {\sum}_{t=0}^T{\beta}^t}U\left({C}_s\right) $$
$$ \mathrm{Subject}\ to\ {\mathrm{C}}_{\mathrm{t}}={\mathrm{I}}_{\mathrm{t}} - {\mathrm{S}}_{\mathrm{t}} $$
$$ {\overset{-}{L}}_b={L}_{tb}^G+{L}_{tb}^o+{L}_{tb}^F+{L}_{tb}^H $$
$$ {\overset{-}{L}}_d={L}_{td}^G+{L}_{td}^F+{L}_{td}^H $$
$$ {Q}_t=q\left({L}_{tb}^G,{L}_{td}^G,{A}_t,\eta \right) $$
$$ {I}_t={P}_t{Q}_t+{w}_t^o{L}_{tb}^F+{w}_t^F{L}_{tb}^F+{w}_t^F{L}_{td}^F $$
$$ {\mathrm{A}}_{\mathrm{t}}=\left(1+\mathrm{r}\right)\ {\mathrm{A}}_{\mathrm{t}-1} + {\mathrm{S}}_{\mathrm{t}} $$
$$ {H}_t=h\left({L}_{tb}^H,{L}_{td}^H,{C}_t\right)+{H}_{t-1} $$

where C t is a vector containing consumption of food, manufactured goods and health; It is the income of the household; and S t. represents savings. Equations (5) and (6) represent labor constraints for each gender category where subscripts b and d refer to male and female labor, respectively. \( \overline{L} \) is the total labor endowment; \( {L}_t^G \), \( {L}_t^o,{L}_t^F\ \mathrm{and}\ {L}_t^H \) represent labor allocated to agricultural production, off-farm work, food-for-work (FFW) and health, respectively. Gender disaggregation of the labor force endowment is important because household health outcomes may vary depending on whether new activities require the (re)allocation of male or female labor. As off-farm employment is generally unavailable or greatly limited for women in Tigray, we assume in equation (6) that female labor cannot be allocated to off-farm employment. Q t is an agricultural production function which is increasing in labor and stock of land and non-land productive assets (A t ) and decreasing in negative shocks that affect production (η); P t refers to the price of a composite agricultural product; \( {w}_t^o \) and \( {w}_t^F \) are wages from off-farm employment and food-for-work, respectively.

The dynamic system is governed by two equations of motion, one for physical capital (equation (8)) and one for human capital (equation (10)). A t appreciates at the rate r and can be augmented through savings. Of course, the stock of land may depreciate from degradation and the stock of animals may depreciate from disease. The household’s stock of human capital is represented as an aggregate index of health, H t , which evolves subject to previous health status (H t-1 ) and improvements in health generated through the health production function [h ()]. We assume the health production function is concave in its arguments and depends on the labor allocated to health (child care) and the current level of consumption (C t ). In subsequent modeling, we consider child health to be part of H t .

2Substitution of equation (7) into equation (8); equation (8) into equation (4) and equation (4) into the objective function yields the following fixed-horizon optimization problem:

$$ Max{\displaystyle {\sum}_{t=0}^T{\beta}^t}U\left[{P}_t\left(q\left({L}_{tb}^G,{L}_{td}^G,{A}_t,\eta \right)\right)+{w}_t^o{L}_{tb}^o+{w}_t^F{L}_{tb}^F+{w}_t^F{L}_{td}^F-{S}_t\right] $$
$$ Subject\ to:{\overset{-}{L}}_b={L}_{tb}^G+{L}_{tb}^o+{L}_{tb}^F+{L}_{tb}^H $$
$$ {L}_d={L}_{td}^G+{L}_{td}^F+{L}_{td}^H $$
$$ {\mathrm{A}}_{\mathrm{t}} - {\mathrm{A}}_{\mathrm{t}-1} = \mathrm{r}\ {\mathrm{A}}_{\mathrm{t}-1} + {\mathrm{S}}_{\mathrm{t}} $$
$$ {H}_t-{H}_{t-1}=h\left({L}_{tb}^H,{L}_{td}^H,{C}_t\right) $$

The choice variables in the problem are\( {L}_{tb}^G \),\( {L}_{td}^G \), \( {L}_{tb}^o,{L}_{tb}^F,{L}_{td}^F,{L}_{tb}^H\ \mathrm{and} \) \( {L}_{td}^H \) while the state variables are A t and H t . We assume that initial conditions for the state variables are given as A (0) = A 0 and H (0) = H 0 where A 0 =\( \overline{A} \)>0 and H 0 =\( \overline{H} \)>0. With a fixed terminal time T, transversality conditions for the state variables (with initial values \( \overline{A} \) and \( \overline{H} \)) imply that the values of physical and human capital may vary at the terminal time depending on the shadow values of increments to these stocks compared with the cost of further improvements.

Accounting for the constraints on the choice variables, the dynamic Lagrangian associated with the problem is:

$$ \begin{array}{l}\mathcal{L}={\upbeta}^{\mathrm{t}}\ \mathrm{U}\left[{\mathrm{P}}_{\mathrm{t}}\left(\mathrm{q}\left(\ {\mathrm{L}}_{\mathrm{t}\mathrm{a}}^{\mathrm{G}},\ {\mathrm{L}}_{\mathrm{t}\mathrm{b}}^{\mathrm{G}},{\mathrm{A}}_{\mathrm{t}},\upeta \right)\right)+{\mathrm{w}}_{\mathrm{t}}^{\mathrm{o}}{\mathrm{L}}_{\mathrm{t}\mathrm{b}}^{\mathrm{o}}+{\mathrm{w}}_{\mathrm{t}}^{\mathrm{F}}\ {\mathrm{L}}_{\mathrm{t}\mathrm{b}}^{\mathrm{F}}+{\mathrm{w}}_{\mathrm{t}}^{\mathrm{F}}\ {\mathrm{L}}_{\mathrm{t}\mathrm{d}}^{\mathrm{F}}-{\mathrm{S}}_{\mathrm{t}}\right]+{\uptheta}_{\mathrm{t}}\left(\mathrm{r}\ {\mathrm{A}}_{t-1}+{\mathrm{S}}_{\mathrm{t}}\right)\kern1em \\ {}\kern1em +{\updelta}_{\mathrm{t}}\left[\mathrm{h}\ \left(\overline{\mathrm{L}}\mathrm{b}-{\mathrm{L}}_{\mathrm{t}\mathrm{b}}^{\mathrm{G}}-{\mathrm{L}}_{\mathrm{t}\mathrm{b}}^{\mathrm{o}}-{\mathrm{L}}_{\mathrm{t}\mathrm{b}}^{\mathrm{F}},\overline{\mathrm{L}}\mathrm{d}-{\mathrm{L}}_{\mathrm{t}\mathrm{d}}^{\mathrm{G}}-{\mathrm{L}}_{\mathrm{t}\mathrm{d}}^{\mathrm{F}},{\mathrm{C}}_{\mathrm{t}}\right)\ \right]\kern1em \\ {}\kern1em +{\uplambda}_{\mathrm{t}}\left(\overline{\mathrm{L}}\mathrm{b}-{\mathrm{L}}_{\mathrm{t}\mathrm{b}}^{\mathrm{G}}-{\mathrm{L}}_{\mathrm{t}\mathrm{b}}^{\mathrm{o}}-{\mathrm{L}}_{\mathrm{t}\mathrm{b}}^{\mathrm{F}}-{\mathrm{L}}_{\mathrm{t}\mathrm{b}}^{\mathrm{H}}\right)+\upgamma \mathrm{t}\ \left(\overline{\mathrm{L}}\mathrm{d}-{\mathrm{L}}_{\mathrm{t}\mathrm{d}}^{\mathrm{G}}-{\mathrm{L}}_{\mathrm{t}\mathrm{d}}^{\mathrm{F}}-{\mathrm{L}}_{\mathrm{t}\mathrm{d}}^{\mathrm{H}}\right)\kern1em \end{array} $$

The first-order necessary conditions (FOC) with respect to labor allocated to health and FFW for male labor are given by equation (17) and equation (18).

$$ \begin{array}{l}\frac{\partial \mathrm{\mathcal{L}}}{\partial {\mathrm{L}}_{tb}^{\mathrm{F}}} = {\upbeta}^{\mathrm{t}}\frac{\partial \mathrm{U}}{\partial {\mathrm{C}}_{\mathrm{t}}}\frac{\partial {\mathrm{C}}_{\mathrm{t}}}{\partial {\mathrm{L}}_{tb}^{\mathrm{F}}} + {\updelta}_{\mathrm{t}}\frac{\partial \mathrm{h}}{\partial {\mathrm{L}}_{tb}^{\mathrm{F}}} - {\uplambda}_{\mathrm{t}}=0\to {\upbeta}^{\mathrm{t}}\frac{\partial \mathrm{U}}{\partial {\mathrm{C}}_{\mathrm{t}}}{\mathrm{w}}_{\mathrm{t}}^{\mathrm{F}} + {\updelta}_{\mathrm{t}}\frac{\partial \mathrm{h}}{\partial {\mathrm{L}}_{tb}^{\mathrm{F}}} - {\uplambda}_{\mathrm{t}}=0\hfill \\ {}\kern16em \to {\uplambda}_{\mathrm{t}} = {\upbeta}^{\mathrm{t}}\frac{\partial \mathrm{U}}{\partial {\mathrm{C}}_{\mathrm{t}}}{\mathrm{w}}_{\mathrm{t}}^{\mathrm{F}} + {\updelta}_{\mathrm{t}}\frac{\partial \mathrm{h}}{\partial {\mathrm{L}}_{tb}^{\mathrm{F}}}\hfill \end{array} $$
$$ \frac{\partial \mathrm{\mathcal{L}}}{\partial {\mathrm{L}}_{tb}^{\mathrm{H}}}={\updelta}_{\mathrm{t}}\frac{\partial \mathrm{h}}{\partial {\mathrm{L}}_{tb}^{\mathrm{H}}}-\kern0.5em {\uplambda}_{\mathrm{t}}=0\to {\uplambda}_{\mathrm{t}}={\updelta}_{\mathrm{t}}\frac{\partial \mathrm{h}}{\partial {\mathrm{L}}_{tb}^{\mathrm{H}}} $$

Analogously, the FOC with respect to the choice variables of interest (labor allocated to health and FFW) for female labor are presented in equation (19) and equation (20).

$$ \gamma t={\beta}^t\frac{\partial U}{\partial {C}_t}{w}_t^F+{\delta}_t\frac{\partial h}{\partial {L}_{td}^F} $$
$$ \gamma t={\delta}_t\frac{\partial h}{\partial {L}_{td}^H} $$

Solving equations (17) and (18) for male labor and equations (19) and (20) for female labor results in a pair of equations that illustrate the potential connections between a program like Ethiopia’s PSNP and nutrition outcomes. Equations (21) and (22) show these links.

$$ {\upbeta}^{\mathrm{t}}\frac{\partial \mathrm{U}}{\partial {\mathrm{C}}_{\mathrm{t}}}{\mathrm{w}}_{\mathrm{t}}^{\mathrm{F}} + {\updelta}_{\mathrm{t}}\frac{\partial \mathrm{h}}{\partial {\mathrm{L}}_{tb}^{\mathrm{F}}} = {\updelta}_{\mathrm{t}}\frac{\partial \mathrm{h}}{\partial {\mathrm{L}}_{tb}^{\mathrm{H}}}\kern0.5em \to \kern0.5em {\updelta}_{\mathrm{t}}\frac{\partial \mathrm{h}}{\partial {\mathrm{L}}_{tb}^{\mathrm{F}}} = {\updelta}_{\mathrm{t}}\frac{\partial \mathrm{h}}{\partial {\mathrm{L}}_{tb}^{\mathrm{H}}} - {\upbeta}^{\mathrm{t}}\frac{\partial \mathrm{U}}{\partial {\mathrm{C}}_{\mathrm{t}}}{\mathrm{w}}_{\mathrm{t}}^{\mathrm{F}} $$
$$ \begin{array}{lll}{\upbeta}^{\mathrm{t}}\frac{\partial \mathrm{U}}{\partial {\mathrm{C}}_{\mathrm{t}}}{\mathrm{w}}_{\mathrm{t}}^{\mathrm{F}} + {\updelta}_{\mathrm{t}}\frac{\partial \mathrm{h}}{\partial {\mathrm{L}}_{td}^{\mathrm{F}}} = {\updelta}_{\mathrm{t}}\frac{\partial \mathrm{h}}{\partial {\mathrm{L}}_{td}^{\mathrm{H}}}\hfill & \to \hfill & {\updelta}_{\mathrm{t}}\frac{\partial \mathrm{h}}{\partial {\mathrm{L}}_{td}^{\mathrm{F}}} = {\updelta}_{\mathrm{t}}\frac{\partial \mathrm{h}}{\partial {\mathrm{L}}_{td}^{\mathrm{H}}} - {\upbeta}^{\mathrm{t}}\frac{\partial \mathrm{U}}{\partial {\mathrm{C}}_{\mathrm{t}}}{\mathrm{w}}_{\mathrm{t}}^{\mathrm{F}}\hfill \end{array} $$

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Debela, B.L., Shively, G. & Holden, S.T. Does Ethiopia’s Productive Safety Net Program improve child nutrition?. Food Sec. 7, 1273–1289 (2015).

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  • Anthropometry
  • Ethiopia
  • Food security
  • Nutrition
  • Safety net

JEL codes

  • I15
  • I38