Can Minimum Wages Effectively Reduce Poverty under Low Compliance? A Case Study from the Agricultural Sector in South Africa

ABSTRACT

What were the effects of a 52 per cent increase in the minimum wage in the agricultural sector in South Africa in 2013? We estimate the short run effects of this policy change on the income, employment, and poverty rate of farmworkers, using individual-level panel data from the Quarterly Labour Force Surveys (QLFS). Before the implementation date, 90 per cent of farmworkers were paid below the new minimum wage level. We find that the wage gain of farmworkers is strongly quadratically related to pre-implementation wages, suggesting lower compliance as the gap between the minimum and the pre-implementation wage increases. We estimate that farmworkers received a median wage increase of 9 per cent as a result of the policy, and we find no evidence of job losses. Overall, farmworkers were 7 per cent less likely to have household income per person below the poverty line. One possible explanation for these outcomes is that endogenous compliance may mitigate against unemployment effects. While the minimum wage literature is large, our paper adds to the small subset of this literature on large increases, partial compliance, and poverty effects.

KEYWORDS:

• Minimum wages
• poverty
• South Africa

JEL CODES:

• I32
• J08
• J38

This article is referred to by:

Commentary on Bassier and Ranchhod, ‘Can Minimum Wages Effectively Reduce Poverty Under Low Compliance? A Case Study from the Agricultural Sector in South Africa’

Acknowledgements

[I/we] would like to use this opportunity to acknowledge and thank the reviewers who reviewed this article and aided in its publication. We also thank participants in the SALDRU seminar for valuable comments.

Disclosure Statement

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

Notes

1 This may be reflecting lagged effects of rainfall outcomes. South Africa had very good and increasing rainfall from 2009 to 2011, and then experienced a prolonged multi-year drought beginning in 2012.

2 We included workers with missing income values as farmworkers, as the vast majority of African or Coloured workers in the agricultural sector do not earn more than R5000 per month. They are thus included in farmworker summary statistics. However, observations with missing income values are excluded from the estimation samples of the regressions, as well as any related poverty estimates.

3 An anecdote with a conference participant highlights this view. As the child of a farm owner, he had grown up on a farm and periodically spent time working there. He thought that the main reason a farm owner would raise wages in response to the minimum wage law was not to do with legal enforcement, but rather that workers would expect an increase in wages, failing which the disgruntled workers would (in ways difficult to trace) damage tools and property. The wage increase need not correspond to the law, but rather to workers’ expectations. This highlights an aspect of wage-setting under non-contractable employment, where the wage-setting is dependent on workers’ expectations which in turn depend on laws and neighboring workplace norms.

4 In terms of linearity of the relationship between the squared percentage gap and the outcomes, we rely on the result that the regression coefficients should be interpreted as providing the ‘best linear approximation to the underlying conditional expectation function, regardless of its shape’, and similarly we rely on this interpretation to justify using binary outcomes of employment and poverty outcome variables (Angrist and Pischke 2009). We use heteroskedasticity-robust standard errors in all our specifications to account for any heteroskedasticity.

5 We adjust the reference poverty line (see above) using the CPI to get R1111 per person per month in 2013, and since we are using 1.5 their poverty threshold as our reference line, our cut-off value in 2013 is equal to a value of R1667 per person per month.

6 We confirm that this result is robust to the use of standard household equivalence scales when calculating household income per capita. Specifically, we use the original OECD equivalence scale of assigning 1 to the household head, 0.7 to each additional adult household member, and 0.5 to each child. We find that the percentage change in household income per capita (equivalence adjusted), under the same regression specification as above, gives large and significant increases of 11 to 14per cent, which is just slightly higher than the main results reported above.

7 While these coefficients are statistically significant, they require very strong assumptions in order to be unbiased estimates of the effect of the law. In addition, even if one accepts that these do represent a valid causal estimate, these results would indicate a greater chance of finding employment as a farmworker two quarters after the law had come into effect.

8 The average may hide substantial dispersion, as a district could have a large proportion under the minimum wage while on average still having a relatively high wage. This leads to a potential measurement error problem, which would attenuate the estimate.

9 Some authors have tried to measure the long-run effects of minimum wage laws by including lags of the minimum wage in their employment regressions. These coefficients are often quite small, which indicates that most of the impact of minimum wages are observed in the periods immediately after a minimum wage law is passed. This suggests that the long-run effects are similar to the short-term effects. See Neumark and Wascher (1992), and Dube, Lester, and Reich (2010).