We now turn our attention to other observed exogenous factors, identified as �� � in Eqs. (1) and (2), which could induce a household to send a permanent work migrant. The variables in Table 5 are grouped into categories – such as prices, shocks, and access to networks – which have been previously identified as potential push and pull factors for migration. While a number of these factors have been theorized and identified in the literature, we still know comparatively little about their relative importance for migration decisions (D. McKenzie and Yang 2012).

First, in columns (1) and (2) of Table 5, we examine the relationship between lagged market prices for crops and a household’s decision to send a work migrant. We observe in col. (1) that a 1 percent increase in the average price of maize, one of Uganda’s principal agricultural crops, is associated with a 0.09 percent higher likelihood of sending a work migrant in the next panel wave. After controlling for household-level unobservables that remain constant across time, we observe in column (2) that a similar positive relationship holds between maize prices and household migration decisions. There is, meanwhile, an inverse relationship between the prices of matoke – a key staple that is also traded – and sending work migrants. These results suggest that variation in agricultural market prices can influence household migration decisions.

In cols. (3) and (4), we test whether household shocks shape future migration decisions. After controlling for household fixed effects in column (4), we uncover a positive and significant 4 percentage point relationship between a household experiencing a theft or fire and future out-migration, which suggests that migration may be an economic coping mechanism for these types of households. Agricultural shocks and the loss of a household member, meanwhile, do not induce a similar response. We also do not observe a significant relationship between conflict and violence and the decision to send a work migrant (cols. (5) and (6)). These null findings may be tied to the timing of these shock events (which can be acute but short in duration) and the four-year time gap between the first and second survey panel waves. Indeed, the relationship between fatality events and sending a work migrant is positive and significant when examining the contemporaneous relationship with household fixed effects (results available upon request).

Next, we test whether public service availability influences future household migration in columns (7) through (10). A previous analysis from Uganda found that a lack of service amenities is associated with greater out-migration (World Bank 2012). We find further evidence in support of this assertion. Having a primary school within an hour of the household is associated with a two percentage point lower likelihood (sig. at 10 percent level) of sending a work migrant in the next wave.

Liquidity and credit access can also shape household migration decisions. Panel evidence from rural South Africa, for example, suggests that relaxing the credit constraints of households via transfer schemes can boost employment through labor migration (Ardington, Case, and Hosegood 2009). We examine whether these factors matter for the Ugandan context in columns (11) through (14). Having a formal loan and a formal savings account increases the likelihood of being a migrant-sending household in the next wave (by 3 and 6 percentage points, respectively). The results in columns (11) and (13) suggest that facilitating households’ access to these savings and credit products could help them overcome liquidity constraints to migration. We explore this question in further detail in Section V.

Access to information and migration networks has also been found to play a role in migration decisions in Uganda (Muto 2012). Despite its relevance for individual-level migrant networks (see appendix, we find that the density of migrants within the household head’s ethnicity does not predict future household out11