School attendance and economic shocks: Evidence from rural Zimbabwe

ABSTRACT

Unpredicted shocks such as weather, pests or price changes affect agricultural households negatively or positively. The shocks have two opposite effects (income and substitution) on parents’ investments in the human capital of their children, and it is not predictable from theory whether the income effect or the substitution effect of a shock has a greater impact on the investments. Therefore, it is unknown whether human capital investments (i.e. sending children to school rather than having them work) are procyclical or countercyclical. In this paper we show how hyperinflation may affect investments in the education of children by their parents using three data-sets from Zimbabwe. We find that human capital investments are countercyclical (the substitution effect dominates) in rural areas of Zimbabwe during a shock. Therefore, policymakers in Zimbabwe need to be worried about decreased schooling of children during positive shocks in the rural areas.

KEYWORDS:

• Children
• education
• Africa
• economic shock

JEL Codes:

• J13
• I21
• N37
• E31

Acknowledgements

We are very thankful to Nicolaus Tideman, Suqin Ge and Sudipta Sarangi from Virginia Polytechnic Institute and State University for their comments during this research.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Ali Reza Oryoie http://orcid.org/0000-0002-5232-5291

Notes

1 In most less developed countries, the main source of income for rural households stems from agriculture. The majority of rural children are involved in agriculture, frequently being employed on their parents’ farms (Edmonds & Pavcnik, 2005a). Agriculture is susceptible to high uncertainty. Agricultural households are exposed to negative/positive shocks such as drought and heat stress, good rain falls, price variability and agricultural pests. Due to credit/insurance market failures, formal risk management is complicated. Informal-risk sharing strategies and mechanisms may exist, but such mechanisms tend to be inadequate in the face of widespread covariate shocks. Absence of coping tools contributes to a large variance of income and consumption and households may respond to shocks by removing their children from school.

2 Consider that the 2007/8 ICES survey was not conducted from January 2008, and also the data collection of the 2001 ICES survey began in January, while the data collection of other years was started from June. Because time order is important in our analysis, we have to drop the observations which were collected from January to May in 2001 and 2011 in order to be able to compare the behaviour of the farmers after harvest across different years during the same period of time.

3 Harvest time in Zimbabwe depends on the pattern of movement of the weather and type of agricultural products. The weather travels from west to east, so harvest time of similar products is earlier in the west, and some of the agricultural products are harvested sooner than others. Furthermore, some products are harvested only in the west and some only in the east because of regional differences in agro-ecological characteristics. Because of these complications we cannot determine a specific month as harvest time in Zimbabwe, but most of the main products are harvested from May to June. Remember that we have dropped the months January–May, therefore only June is considered as harvest time. But, we consider July as harvest time as well. This is because, first, due to weather changes, harvest can be postponed in a given year, and second, it takes some time until farmers sell their products and realize their real income, so it takes some time until they make their decisions about their children. That is, we cannot see their behaviour immediately at the end of June, therefore July is considered as harvest time as well.

4 http://www.fao.org.

5 Prices might be measured poorly in 2007 due to the crisis, but we are not worried about the conclusion made about the relative price. This is because: first, the difference between the inflations in the CPI and PPI is very large, so even if there is any mismeasurement, it cannot discount the huge gap between the inflations. Second, the process of price collection is the same for both the PPI and CPI, so if the CPI is biased in a direction, then the PPI should be biased in the same direction.

6 We control the unobservable regional fixed effects at three different geographical levels of province, district and subdistrict. Consider that after district, the smallest unit of census areas is the ward. There are a few households by ward in the sample who have a child aged 7–14. The relatively small sample size by ward causes our dependent variable not to vary within many wards. Consequently, after running regressions, all of those households who are in the wards are omitted because of collinearity. Therefore we need to merge those wards such that our dependent variable varies in each ward. To this end, we have divided each district into 2 or 3 parts based on the closeness of wards to each other and these parts are called subdistricts in this paper.

7 Consider this limitation: that household is nested in subdistrict, subdistrict is nested in district, and district is nested in province. We clustered errors at higher levels as well, and got similar results, but we prefer to cluster at the level of household since agro-ecological conditions change considerably between different areas of Zimbabwe.

8 Remember that we dropped the households who were surveyed from January to May.

9 Table 1 shows that the probability is about 10% during the shock, and it is about 7% in normal conditions.

10 Recall that the income effect of a negative shock leads to a decrease in child schooling, and the substitution effect induces an increase in child schooling.