Learn R Programming
designmatch (version 0.2.0)

germancities: Data from German cities before and after the Second World War

Description

This is part of the data used by Redding and Sturm (2008) to study the impact of market access on economic development in West German cities after the division of Germany after the Second World War. There are 119 rows corresponding to different cities and 21 columns that stand for different variables. These variables are: one treatment indicator, 15 baseline covariates, and five outcomes. Treated cities are those West German cities within 75 kilometers of the border between East and West Germany after the Second World War (see Redding and Sturm (2008) for details). The complete dataset is available at http://www.aeaweb.org/articles.php?doi=10.1257/aer.98.5.1766.

Usage

germancities

Arguments

Format

A data frame with 122 observations corresponding to 20 treated and 102 control cities. The treatment assignment indicator is the first column of the data frame: treat (1 = treated; 0 = control). The next 15 columns are the covariates:

  • log2pop, logarithm base 2 of the population in each city in 1939;

  • popgrowth1939, population growth in each city from 1919 to 1939;

  • popgrowth3339, population growth in each city from 1919 to 1939;

  • emprate, employment rates in each city in 1939;

  • indrate, industry rates in each city in 1939;

  • rubble, amount of rubble in cubic meters per capita in each city in 1939;

  • rubblemiss, missing data indicator for rubble; the missing values were imputed with the mean;

  • flats, number of destroyed dwellings in each city in 1939 as a percentage of the stock of dwelling;

  • flatsmiss, missing data indicator for flats; the missing values were imputed with the mean;

  • refugees, proportion of each city's population that identified themselves as refugees in 1939;

  • educat, categories for the employment rates in the educational sector in each city in 1939;

  • publicat, categories for the employment rates in the public administration sector in each city in 1939;

  • busiservcat, categories for the employment rates in the bussiness services sector in each city in 1939;

  • mineralcat, categories for the employment rates in the minerals sector in each city in 1939;

  • transcat, categories for the employment rates in the transport sector in each city in 1939.

    • The last five columns of the data frame are outcomes: pop50, pop60, pop70, pop80 and pop88, the populations in each city in 1950, 1960, 1970, 1980 and 1988, respectively.

    References

    Redding, S. J., and Daniel M. S. (2008), "The Costs of Remoteness: Evidence from German Division and Reunification," American Economic Review, 98, 1766-1797.