All previous models have excluded government employees on the grounds that migrants
do not compete with government employees. As a robustness test, re -estimate our wage
model using (regression-adjusted) government wages as the dependent variable. A nonzero
coefficient on migrant intensity would suggest that our estimation strategy is flawed
(Angrist and Krueger 2001). The estimated coefficient, shown in column 4, turns out to
be indistinguishable from zero.
Finally, we apply the same approach to testing whether immigration has induced
compensatory migration by Thais. Unlike the labor force variables, our data for the
migration variables come from the 20 percent sample of the 1990 and 2000 censuses,
which provide sufficiently large samples to allow migration variables to be constructed at
the district level11.We calculate the percentage of each district’s Thai population (other
than government employees) who stated that they had migrated into the district for work
within the previous two years. The mean value for 1990 is 0.85 percent and for 2000 is
1.00 percent. We also calculate a second version of this variable that includes only Thai
migrants with 6 years of schooling or less. We use district-level differences between 1990
and 2000 as our outcome variable; differencing should eliminate biases due to fixed
characteristics that make some districts less attractive or more attractive to Thai migrants.
We assume that immigrant flows during the 1990s followed a similar geographical
pattern to that of 2004 and treat a positive coefficient on distance to the Myanmar border,
or a negative coefficient on a border dummy, as evidence that Thais have avoided
migrating into districts receiving large numbers of immigrants. The results are shown in
columns 5 and 6 of Table 4. Neither column provides support for the idea that
immigration has affected internal migration by Thais.