The heterogeneous effects of adoption of climate-smart agriculture on household welfare in Pakistan

ABSTRACT

In this study, we analyse the heterogeneity in the impacts of adoption of climate-smart agricultural (CSA) practices on welfare indicators such as food and nutrition security and poverty reduction in Pakistan. We employ the marginal treatment effects (MTE) approach to estimate the treatment effects heterogeneity and policy-relevant treatment effects (PRTE). The findings show substantial heterogeneity in benefits from adoption of CSA with respect to both observed and unobserved household characteristics. In particular, the estimates show that households with higher unobserved benefits are more likely to adopt CSA practices. The empirical results show that adoption of CSA practices significantly reduces household food insecurity and increases household dietary diversity but reduces the poverty headcount and severity of poverty of the households at the lower level of unobserved resistance to adoption. The PRTE indicate that sources of climate change information and climate-resilient trainings could help to reduce rural poverty and improve food and nutrition security in Pakistan.

KEYWORDS:

• Climate-smart agriculture
• food and nutrition security
• poverty reduction
• marginal treatment effects
• Pakistan

JEL CLASSIFICATION:

• Q18
• I32
• Q54
• C36
• O33
• D13.

Acknowledgements

Authors would like to thank the journal editor Prof. David Peel and anonymous reviewer for their comments that substantially improved the article. The first author gratefully acknowledges the scholarship funding from the Higher Education Commission (HEC) of Pakistan in collaboration with German Academic Exchange Service (DAAD), Germany.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ In OLS estimates of outcome variables for non-adopters, the F-test shows that the instruments are not jointly statistically significant (see Tables A3-A6 in Appendix A). For correlation test see Table A7 in the Appendix A.

² In the interest of brevity, the equations to calculate ATE, ATT, ATU, and LATE are given below:

(a) $\mathrm{A}\mathrm{T}\mathrm{E}=E\left[y_{1i}-y_{0i}\right]=E[{\beta }_1\left(X_i\right)-{\beta }_0\left(X_i\right)$(a)

(b) $\begin{array}{rl}& ATT=E\left[y_{1i}-y_{0i}\left|A_i=1\right.\right]+E\left[U_1-U_0\left|A_i=1\right.\right]=E\left[{\beta }_1\left(X_i\right)-{\beta }_0\left(X_i\right)\left|A_i=1\right.\right]+\\ & E\left[U_1-U_0\left|A_i=1\right.\right]\end{array}$(b)

(c) $\mathrm{A}\mathrm{T}\mathrm{U}=E\left[y_{1i}-y_{0i}\left|A_i=0\right.\right]+E\left[U_1-U_0\left|A_i=0\right.\right]=E\left[{\beta }_1\left(X_i\right)-{\beta }_0\left(X_i\right)\left|A_i=0\right.\right]+E\left[U_1-U_0\left|A_i=0\right.\right]$(c)

(d) $LATE=E\left[y_{1i}-y_{0i}\left|A_{1i}>A_{0i},X_i=x\right.\right]{\beta }_1\left(X_i\right)-{\beta }_0\left(X_i\right)+E\left[U_{1i}-U_{0i}\left|A_{1i}>A_{0i},X_i=x\right.\right]$(d)

³ We used international poverty line (dollar 1.90 a day) indicated by the World Bank to calculate poverty headcount and poverty gap indices of farm households. We used the Foster–Greer–Thorbecke (FGT) (1984) indices to estimate poverty in our data sample by using the formula: $FG{T}_{\gamma }=\frac{1}{N}\sum _{i=1}^M{\left[\frac{PL-h_i}{PL}\right]}^{\gamma }$, where $\gamma$ = 0, 1. When $\gamma$ = 0 then $FG{T}_0=\frac{M}{N}$and for $\gamma$ = 1, $FG{T}_1=\frac{1}{N}\sum _{i=1}^M\left[\frac{PL-h_i}{PL}\right].$ where $N$ is the total number of people in a household, $PL$ represents the poverty line, $h_i$ represents per capita income of the ith person in a household, and $\gamma$ represents the poverty aversion parameter. When $\gamma$ = 0, $FG{T}_0$ is simply the headcount index or the proportion of people that are poor. When $\gamma$ = 1, $FG{T}_1$ is the poverty gap index, which reflects the severity or intensity of poverty defined by the mean distance to the poverty line. Thus, the poverty gap index, which captures the severity of poverty, is the average shortfall in income for the farm household, from the poverty line. Hence, $FG{T}_1$ represents the severity of poverty and reflects the extent of inequality among the poor households.

⁴ In the interest of brevity, the empirical results are reported in Appendix A (see Tables A3-A6).

⁵ In the interest of brevity, the results of robustness checks are given in Appendix B.