Malawi’s slowly changing employment landscape and its implications for youth

ABSTRACT

We examine Malawi’s employment landscape between 2004/05 and 2016/17, focusing on sectoral change and youth entering the workforce. Little evidence of a structural transformation in cross sectoral patterns of employment is found. The share of employment in agriculture rose slightly over the period, though the share of full time equivalent jobs declined in the sector. The analysis shows that younger youth are not participating in the limited employment growth in the service sector. Agriculture remains the sector in which most Malawians first obtain employment. Only later in their working lives are Malawian workers, particularly males, in a position to obtain work outside of agriculture. With limited structural change occurring in the economy, Malawi’s challenging employment landscape for youth is characterised by a scarcity of jobs outside agriculture and insufficient work within the sector.

KEYWORDS:

• Employment
• youth
• structural transformation
• Malawi

Acknowledgements

This research was an activity of the Malawi Strategy Support Program managed by the International Food Policy Research Institute.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 McCullough (2017) presents an empirical microeconomic assessment of gaps between sectors in labour productivity for workers in four African countries, including Malawi (using the 2010/11 IHS3). Lower productivity in agriculture in Malawi can primarily be attributed to underemployment (hours worked annually) rather than low production per hour worked. Nonetheless, on a per hour basis, labour productivity levels in the industry and service sectors are 45 percent higher than in agriculture. However, these gaps are much smaller than those estimated from macroeconomic, national accounts based analyses, such as that of de Vries et al. (2016). McCullough argues that productivity differences between agriculture and the non-agricultural sectors provide weaker incentives for workers to move out of agriculture than macroeconomic analyses would suggest. Also see Gollin et al. (2014).

2 The design of the first round of the IHS (1997/98) differs from later rounds, so was excluded from our analysis. The other rounds are quite similar, though not exactly so. IHS4 was implemented during a period of significant food insecurity for many households in southern Malawi due to poor 2015/16 harvests. Other economic conditions generally were comparable across the three survey periods.

3 Labour force surveys or population censuses typically use a recall period of the previous week to determine a respondent’s employment status. In contrast, the IHS uses varying recall periods, including the previous twelve months for farming. Nonetheless, the consistent approach over IHS survey rounds to employment categorisation permits valid comparisons to be made. However, in consequence the IHS results are not comparable to those of labour surveys or censuses for Malawi. For example, the estimate from the 2013 Labour Force Survey is that 64.1 percent of those who are of working age and are employed worked in agriculture (NSO 2014), while the estimate from the 2018 Census is 73.0 percent (NSO 2020c). Our estimate from the 2016/17 IHS4 using a broader definition of employment with longer recall periods is 87.8 percent.

4 A sharp increase is seen between IHS3 and IHS4 in the share of the working population that is not economically active and are not students. The content on agricultural time use of the Time Use and Labour questionnaire module expanded from a single question for IHS3 (and IHS2) to a maximum of 23 crop-specific questions for IHS4. We presume that some respondents who in earlier rounds might have reported having engaged in some agricultural activities did not do so for the IHS4, likely due to the detailed responses that would then be required. Consequently, we see between the IHS3 and IHS4 a drop in the share of the working age population that is employed and the share of workers engaged in agriculture and a rise in the share of the working population that is not economically active and are not students.

5 In the context of high human immunodeficiency virus (HIV) infection and associated deaths, higher mortality rates for the working age population relative to the entire population of Malawi may also be a factor in the different growth rates (Doctor 2012).

6 However, as discussed in endnote 4, a change in how data on time use was collected in the IHS4 relative to earlier rounds also contributes to this fall in younger youth engagement in agriculture.

7 Due to small sub-samples, we do not differentiate employment in the industrial sector from employment in the service sector. The small number of unemployed are also excluded from our analysis.

8 To assess changes in the determinants of participation in employment between IHS3 and IHS4, a logit analysis was done on a pooled dataset with a survey round fixed-effect variable (IHS4 = 1). The coefficient on this variable is statistically significant (p < 0.001) and negative suggesting reduced engagement in employment in 2016/17 relative to 2010/11. However the data comparability issue discussed in endnote 4 likely contributes in part to this result.

9 The validity of MNL results is predicated on the assumption of the independence of irrelevant alternatives (IIA). Based on the Small-Hsaio post-estimation test (Small & Hsaio 1985), we cannot reject the IIA assumption for the MNL model.

10 The results of this MNL analysis are not presented here, but are available from the corresponding author.

11 To assess changes in the determinants of type of employment of the employed between IHS3 and IHS4, an MNL logit analysis was done on a pooled dataset with a survey round fixed-effect variable (IHS4 = 1). The coefficient on this variable is statistically significant (p < 0.001) and positive for the ‘Agriculture and household enterprise’ model, statistically significant (p < 0.001) and negative for the ‘Agriculture and wage employment’ model, statistically not significant for the ‘Only in household enterprise in industry or service sectors’ model, and statistically significant (p < 0.01) and negative for the ‘Only in wage employment in industry or service sectors’ model. These results suggest significantly reduced engagement in wage employment in 2016/17 relative to 2010/11.

Additional information

Funding

This work was supported by Department of International Development of the government of the United Kingdom and by the Malawi mission of the United States Agency for International Development.