An important component of this identification strategy is the selection of comparison cities that can be used to estimate what would have happened in the Miami labor market absent the Mariel immigration.Ī Notes: Adapted from Card ( 1990, Tables 3 and 6). In particular, Card asks whether the Mariel immigration, which increased the Miami labor force by about 7% between May and September of 1980, reduced the employment or wages of non-immigrant groups. As in our earlier examples, the object of research on immigration is to find some sort of comparison that provides a compelling answer to “what if” questions about the consequences of immigration.Ĭard’s study used a sudden large-scale migration from Cuba to Miami known as the Mariel Boatlift to make comparisons and answer counterfactual questions about the consequences of immigration. See Friedberg and Hunt (1995) for a survey of research on this question. Anecdotal evidence for this claim includes newspaper accounts of hostility between immigrants and natives in some cities, but the empirical evidence is inconclusive. Some observers have argued that immigration is undesirable because low-skilled immigrants may displace low-skilled or less-educated US citizens in the labor market. The DD approach is explained here using Card’s (1990) study of the effect of immigration on the employment of natives as an example. An early example in labor economics is Lester (1946), who used the differences-in-differences technique to study employment effects of minimum wages.

The DD method has been used in hundreds of studies in economics, especially in the last two decades, but the basic idea has a long history. This approach, which is transparent and often at least superficially plausible, is well-suited to estimating the effect of sharp changes in the economic environment or changes in government policy. Krueger, in Handbook of Labor Economics, 1999 The differences-in-differences (DD) modelĭifferences-in-differences strategies are simple panel-data methods applied to sets of group means in cases when certain groups are exposed to the causing variable of interest and others are not.

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