We model merger waves as reallocation waves, and argue that mergers spread new technology in a way that is similar to that of the entry and exit of firms. We focus on two periods: 1890–1930, during which electricity and the internal combustion engine spread through the U.S. economy, and 1970–2000—the Information Age. As the model implies, reallocation did rise during both epochs. The model also implies that exits should lead mergers during a transition, but this seems to have happened more emphatically in the electrification epoch.

This paper uses decennial Census data to examine the residential integration of the foreign born in the United States between 1910 and 2000. Immigrant segregation declined in the first part of the century, but has been rising over the past few decades. Recent immigrants tend to hail from countries with greater cultural distinctions from U.S. natives, whether economic, racial, or linguistic. These factors explain much of the increase in segregation after 1970. Evidence also points to changes in urban form, particularly native-driven suburbanization and the decline of public transit as a transportation mode, as an explanation for the new immigrant segregation.

We investigate the impact of institutions on financial development by analyzing the financial behavior of immigrants in the United States. We find that immigrants from countries with institutions that more effectively protect private property are more likely to own stock in the United States. The effect of home-country institutions is persistent and absorbed early in life. The impact of institutions is amplified for immigrants who live in metropolitan areas with many other immigrants from the same country. These findings are robust to alternative measures of institutional effectiveness and to various methods of controlling for unobserved individual characteristics, including specifications with country fixed effects.

Researchers have increasingly realized the need to account for within-group dependence in estimating standard errors of regression parameter estimates. The usual solution is to calculate cluster-robust standard errors that permit heteroskedasticity and within-cluster error correlation, but presume that the number of clusters is large. Standard asymptotic tests can over-reject, however, with few (five to thirty) clusters. We investigate inference using cluster bootstrap-t procedures that provide asymptotic refinement. These procedures are evaluated using Monte Carlos, including the example of Bertrand, Duflo, and Mullainathan (2004). Rejection rates of 10% using standard methods can be reduced to the nominal size of 5% using our methods.