In order to provide a benchmark for the obtained results of the case study of Puerto Rican migration to the US a similar analysis has been carried out on a different sample of migrants. Due to the preferential position of Puerto Ricans on the US labor market, i.e. lack of formal restriction to labor market participation, the closest to migration from Puerto Rico to the US was internal migration across US states.
The dataset used was the same one as that in the above Puerto Rican study, yet only its US part, i.e. the 5-year 2010 American Community Survey (Ruggles et al., 2010). The variable used to differentiate movers from stayers (MIGRATE1) reports whether an individual had changed his or her residence since a reference point one year ago. The state in which the person was enumerated (STATEFIP) was compared to the state of residence one year ago (MIGPLAC). If the distance between the capital cities of those states was larger than 1000 miles (the distance from San Juan, Puerto Rico to Miami, Florida), the person was considered a mover. If one declared he or she did not change the state of residence within the past year, they were considered a stayer. The basic descriptive statistics for both groups are presented in the Appendix.
As the self-employed are of main interest to this study, only self-employed movers and self-employed stayers were compared. The propensity score related to being a mover
was computed for each individual in the final sample. The model used to estimate the propensity score was identical to that applied in the Puerto Rican study. Due to the fact that that migration was restricted to mobility exceeding the distance of 1000 miles and to persons who were self-employed, the proportion of movers was very small, 0.45%. Because of this and the fact that the distributions of propensity scores in both samples were similar, one-to-one matching was applied rather than techniques which weighed ev- ery observation in the control population. One-to-one matching is a version of nearest neighbor (NN) matching. The method matches an individual from the treated group to an individual from the comparison group that is closest in terms of the propensity score. Only one nearest neighbor, the default, has been considered for the base case compari- son5. The procedure can be carried out in a way that already matched individuals from
the comparison group can be used more then once (matching with replacement) or not (matching without replacement). Replacement was allowed, what lead to increased qual- ity of matching and smaller bias across the samples of movers and stayers. The test of the balancing properties of the predictor variables are presented in Table 4.7. In all cases remaining bias is negligible.
After establishing a good control group for internal migrant entrepreneurs, the earnings of the two groups were compared. Analogously to the Puerto Rican study, both hourly and yearly income was considered. The estimated differences in returns to self-employment for people who are internally mobile and those who are not are presented in Table 4.8. In order to test for the robustness of this result one additional attempt has been made to understand the gains to self-employment in case of internal migration in the US. The lower section of Table 4.8 also presents the results obtained by the same method as the one applied above, but where the outcomes to be compared were normalized incomes, rather than absolute incomes from self-employment. Every individual’s income from self- employment has been divided by the mean earnings from self-employment in the state if his or her residence.
Table 4.8 indicates that both in the case of hourly and yearly earnings internal migrants (the treated group) earn significantly less, than their immobile statistical twins. In case of hourly earnings the difference is USD 3.5, while in terms of yearly income the difference amounts to over USD 10,000. This finding contradicts the intuition that people move to improve their material status.
5A matching procedure taking into account 5 nearest neighbors has also been applied as a form of
sensitivity analysis. The results were similar in terms of sign and significance, but smaller in magnitude. The balancing properties were satisfied.
Table 4.7: Balancing properties, internal migration (after one-to-one matching)
Mean t-test Variable Sample Treated Control %bias bias t p>t age 20-30 Unmatched 0.27 0.17 22.5 13.84 0.00 Matched 0.27 0.27 -0.3 98.7 -0.11 0.91 age 30-40 Unmatched 0.23 0.28 -13.2 -7.20 0.00 Matched 0.23 0.23 -0.2 98.4 -0.09 0.93 age 40-50 Unmatched 0.20 0.30 -24.5 -12.99 0.00 Matched 0.20 0.20 0.1 99.4 0.06 0.95 age 50-60 Unmatched 0.11 0.17 -18.1 -9.41 0.00 Matched 0.11 0.11 0.2 99.0 0.08 0.94 gender-female Unmatched 0.40 0.36 8.3 4.72 0.00 Matched 0.40 0.40 0.1 98.4 0.05 0.96 education-primary Unmatched 0.02 0.03 -5.6 -2.93 0.00 Matched 0.02 0.02 0.2 96.4 0.09 0.93 education-secondary Unmatched 0.30 0.39 -20.7 -11.36 0.00 Matched 0.30 0.30 -0.2 99.0 -0.08 0.94 education-tertiary Unmatched 0.68 0.57 22.4 12.31 0.00 Matched 0.68 0.68 -0.1 99.7 -0.03 0.98 married-yes Unmatched 0.54 0.72 -37.5 -22.45 0.00 Matched 0.54 0.55 -0.3 99.1 -0.13 0.90 family 2-3 Unmatched 0.45 0.51 -12.9 -7.31 0.00 Matched 0.45 0.45 -0.1 99.0 -0.05 0.96 family 4-5 Unmatched 0.17 0.26 -22.7 -11.96 0.00 Matched 0.17 0.17 -0.1 99.7 -0.03 0.97 mining Unmatched 0.04 0.05 -6.0 -3.17 0.00 Matched 0.04 0.04 0.3 94.8 0.13 0.89 construction Unmatched 0.00 0.00 1.2 0.72 0.47 Matched 0.00 0.00 0.7 44.7 0.26 0.80 manufacturing Unmatched 0.15 0.18 -7.6 -4.18 0.00 Matched 0.15 0.15 0.1 98.9 0.03 0.97 transport/communications Unmatched 0.04 0.04 -2.1 -1.17 0.24 Matched 0.04 0.04 0.2 92.7 0.06 0.95 wholesale Unmatched 0.04 0.05 -0.6 -0.31 0.76 Matched 0.04 0.04 0.0 100.0 -0.00 1.00 retail Unmatched 0.02 0.03 -2.0 -1.09 0.28 Matched 0.02 0.024 0.2 90.0 0.08 0.94 finance/insurance Unmatched 0.10 0.11 -3.4 -1.90 0.06 Matched 0.10 0.10 -0.2 94.2 -0.08 0.94 business/repair services Unmatched 0.09 0.08 1.7 0.96 0.33 Matched 0.09 0.09 -0.3 80.2 -0.13 0.90 personal services Unmatched 0.13 0.11 5.5 3.20 0.00 Matched 0.13 0.13 -0.1 98.2 -0.04 0.97 entertainment Unmatched 0.08 0.09 -0.7 -0.41 0.68 Matched 0.08 0.08 -0.1 84.6 -0.05 0.96 professional services Unmatched 0.02 0.01 8.7 5.93 0.00 Matched 0.02 0.02 0.5 94.6 0.17 0.87 public administration Unmatched 0.27 0.21 13.6 8.02 0.00 Matched 0.27 0.27 -0.1 99.5 -0.03 0.98 technician, salesman, administrative worker Unmatched 0.21 0.22 -0.5 -0.30 0.76 Matched 0.21 0.21 0.3 43.1 0.12 0.90 service worker Unmatched 0.13 0.14 -2.9 -1.64 0.10 Matched 0.13 0.13 -0.2 93.7 -0.07 0.94 farmer etc. Unmatched 0.04 0.09 -18.5 -9.09 0.00 Matched 0.04 0.04 0.5 97.2 0.25 0.80 precision or repair worker, craftsman Unmatched 0.12 0.15 -9.1 -4.93 0.00 Matched 0.12 0.12 -0.1 99.0 -0.04 0.97 operator, fabricator, laborer Unmatched 0.07 0.08 -4.0 -2.20 0.03 Matched 0.07 0.07 -0.2 94.1 -0.10 0.92
Table 4.8: Average treatment effect on the treated, internal migration
Before matching After matching
Variable Treated Controls Treated Controls Difference S.E. T-stat Income as is hourly income 21.29 25.13 21.29 24.75 -3.45 1.53 -2.25 yearly income 40,269 51,115 40,269 50,725 -10,456 3,163 -3.31 Normalized income hourly income 0.85 1.03 0.85 1.00 -0.15 0.06 -2.50 yearly income 0.91 1.16 0.91 1.13 -0.22 0.07 -3.13
The above results confirm that internally mobile entrepreneurs earn significantly less than stayers who are self-employed. Both in case of hourly and yearly earnings movers
earn below the average, while stayers earn at (hourly income) or above (yearly income) the average. The earnings of movers are 85% of those of the stayers in hourly terms, and 80% in case of yearly earnings. This contradicts the findings of Kennan and Walker (2011), who found significant gains to internal migration in the US.
Two reasons may underlie this conclusion. First, the data limits the tome horizon in which mobility is observed to one year. This implies that the self-employment observed among movers is very new. It is easy to imagine that in a new start-up the owners earnings are relatively small to what all those who did not move in that past year, thus on average have well established businesses, earn. Second, in case of internal migration we cannot exclude motives other than financial gains.
The US Current Population Survey6 provides information on the motives of internal
mobility. In 2012, among movers of all ages, 23.23% of the 3 million long-distance intra- U.S. mobility (exceeding 500 miles) was due to family-related reasons. 52.04% (1.5 million persons) moved for employment-related reasons, including new job or job transfer, to look for work or lost job, to be closer to work/easier commute or becoming retired. 22.30% of movements were predominantly for housing-related reasons, 2.42% was mobility for other reasons. Thus, it might be the case that for a significant proportion of the mobile population (circa 50%) self-employment is a by-product of their move, rather than its objective. In this sense it may be considered as a necessity rather than an opportunity. Such businesses are likely to be less profitable. Neither of these reasonings can be tested on the data used in this study. Nevertheless, they constitute interesting avenues for future research and possibly could be considered on the basis of the Current Population Survey.