Persistent Agricultural Shocks and Child Poverty

Abstract

AbstractThis study shows how persistent agricultural shocks in Ethiopia affect education, health and labor outcomes through a time-use study of young people aged 5-22. Leveraging five rounds of the Young Lives Study from 2002-2016, we use dynamic panel instrumental variable regressions to account for the unobserved heterogeneity and serial correlation in the estimation. Agricultural shocks significantly reduce schooling participation and time spent in schooling, deteriorate health, and increase both labor force participation and labor time. Household wealth acts as a buffer and mitigates the adverse effects of shocks on schooling. Interestingly, children from wealthier households have a higher likelihood of joining agricultural labor during shocks, but their intensity of child labor is significantly lower compared to poorer households.Keywords: agricultureshockslaboreducationwealthtime useJel Codes: D13I20I25I31 Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 For more details, see Beegle et al. (Citation2006); Singh and Vennam (Citation2016); Jensen (Citation2000); Zimmermann (Citation2012); Adhvaryu and Nyshadham (Citation2016); Andalón, Azevedo, Rodríguez-Castelán, Sanfelice, and Valderrama-González (Citation2016); Currie and Hyson (Citation1999); Buckles and Hungerman (Citation2013); Cornwell and Inder (Citation2015); Feeny, Mishra, Trinh, Ye, and Zhu (Citation2021); Agamile and Lawson (Citation2021); Giles and Yoo (Citation2007); Shah and Steinberg (Citation2017).2 Link to the report: International Labor Organization, 2020. The report states that the “agriculture sector accounts for 70 percent of children in child labor (112 million) globally”.3 Child health data is only available for the last three waves of the survey (2009, 2013 and 2016). All other health measures, except anthropometric measures, are available less frequently compared to the child’s general health status4 Although the samples are not statistically representative of the national population – as poorer regions were over-sampled – comparisons with nationally representative data sets (DHS, LSMS, etc.) show that they reflect the heterogeneity of ethnicity, religion and living standards in each of the study countries (Outes-Leon & Sanchez, Citation2008).5 The effects estimated in the paper pertain to shocks that occurred sometime over the previous four years (roughly).6 Note that we do not control for BMI when analyzing the effects of agricultural shocks on child health, as BMI is a critical determinant of child health and is strongly correlated with pre-natal conditions such as mother’s BMI, food security and obesity (Dinku, Mekonnen, & Adilu, Citation2020; Kassie & Workie, Citation2020; Kedir, Citation2009).7 The estimator assumes a constant treatment effect across groups and over time, and estimates that effect under the standard “common trends” assumption.8 See Stata’s xtdpd command for further details on the linear dynamic panel estimation.9 In Table 3, the estimated effects for a household facing agricultural shocks is compared to the average for households not facing agricultural shock.10 Link to the proclamation.11 Here, labor is the sum of children’s farming and paid work below the age of 15.12 The main child labor impact for the wealthier households is driven by farming and not paid work.13 For instance, between Rounds 1 and 3 in the younger cohort, 72 children died in Ethiopia (Barnett et al., Citation2013).14 Having the same field supervisors for the 15 years cultivated cordial and stable relations with respondents (Young Lives, Citation2018).