them to seek out risky sexual behaviors. These effects would lead to a bias in the coefficient β1 in equation (1) estimated with a probit model.

Unfortunately, the data at hand does not offer any source of exogenous variations for the mining status. We attempted to address endogeneity with an instrumental variable approach, using the road distance between the DHS cluster locations and the cross border points with South Africa as an instrument for the probability of becoming a migrant miner. However, we were not confident that the exclusion restriction would be satisfied. Coefficients on miners and miners’ partners should therefore be interpreted with caution and as associations rather than causal effects. In table 7 below, we attempt to identify possible controls capturing omitted variables both correlated with the likelihood of being a miner and with HIV infection. In particular, the control on which we will rely most is the presence of the husbands/partners in the household. This might allow us to proxy individual preferences to spend time away from the household.

# **5. Main results**

## **5.1. HIV status**

We next move to multivariate analysis to examine the association between HIV prevalence and mining activity. We first analyze the association between migrant miners and HIV infection for Lesotho and Swaziland. In these two countries, miners are always migrants since, as highlighted in section 2, Lesotho and Swaziland do not have a local mining industry. We will then focus on the case of Zimbabwe, where miners typically work within the country boundaries. The idea is to attempt to disentangle the effect of migration towards the mining sites versus the effect of mining activities per se on HIV prevalence.

## [Insert table 3]

Table 3 shows pooled estimates for Lesotho and Swaziland in which the dependent variable is HIV status (zero for HIV negative and one for HIV positive individuals). In columns 1-4, we report results for the sample of males, while in columns 5-6 we consider women only. Marginal effects of probit coefficients are reported together with robust standard errors corrected for clustering of the residuals at the cluster level. Summary statistics of the variables used in the regressions can be found in table 2.

According to the estimates in column 1, being a miner is not significantly associated with HIV infection, when we control for additional socio-demographic characteristics. The coefficient loses its statistically significance once we include age and age squared among the regressors, suggesting that variation in HIV status is mainly explained by the age of the respondent. More precisely, the likelihood of being HIV positive first increases and then decreases with age, showing that individuals in the middle stages of their life are those most affected by HIV. In Lesotho and Swaziland, men who reached tertiary education are less likely to be HIV positive compared to those without education, while primary and secondary school does not seem to be an important factor in reducing HIV infections. This finding is particularly interesting because the question of the effect of education on HIV infection has different answers depending on the countries and the settings analyzed. For example, an analysis of five Demographic and Health Surveys in Burkina Faso, 12