# 3 Empirical Methodology and Identification Strategy

## 3.1 Identification

The first challenge we face in estimating the causal impact of migration on household welfare is the possibility of unobserved characteristics of household heads that affect their decision to migrate also playing a role in determining their consumption level. For example, individuals with higher unobserved ability might migrate in order to have more chances of getting access to vibrant labor markets with more high-paying jobs, and earn higher income which translates into higher consumption. The same unobserved ability that makes them more likely to migrate might also have an effect on their consumption level regardless of whether they migrate or not. If the mechanisms at play were the one just described, a simple comparison of migrant and non-migrant households would overstate the gains in consumption deriving from migration. One could also argue that natural disasters, or negative shocks in general, can induce individuals to migrate and at the same time generate a drop in their consumption level, thus leading to a spurious negative correlation between migration and consumption. Therefore, ex-ante, the direction of any selectivity bias is unclear.

Following a well-established branch of migration literature, starting from Card (2001) to recent studies by Ottaviano and Peri (2005), and Rapoport and McKenzie (2011), we use historical district-level immigration rates as an exclusion restriction for the self-selection equation into migration. We derive the historical internal immigration rate using data from the 1984 Population Census and calculate the share of immigrants for each district of Ghana in 1984 as proportion of the population aged 15 and above that reported to be born in a village (or town) different from the one in which they resided at the time of the Census.

Since the variation of the instrument is limited to the district level, we cluster standard errors at that same level to allow for correlation in the error term of household heads within a district. Ghana is administratively divided into 10 regions, 6 metropolitan assemblies and 55 municipal assemblies which are subdivided into 216 districts, each one with its own district assembly. Over time the number of districts has changed: in 1984 there were 141 local authorities which were reorganized into 110 districts in 1989 within a decentralization effort of the government, 28 districts were created by splitting some of the original 110 by 2006, in February 2008 the number was brought to 170. Since then, a further 46 districts were added bringing the total to 216 districts. We were able to map all the changes occurred over time and to match the administrative units of 1984 with the 170 districts available in GLSS-6.<sup>11</sup> Intuitively, the first stage is meant to capture a simple process: immigrants tend to settle where other immigrants from the same village (or town) of origin reside, and this is remarkably more so in a multiethnic country like Ghana where there is a strong overlap between administrative regions

> 11 This number (104) is well above the threshold of 30 clusters that Cameron et al. (2008) find to be the lower bound below which standard asymptotic tests can over-reject the null.

11