household identifier, household number and individual line number. Based on this naive match criteria, a high non-matching rate results, as survey respondents who move out of a housing unit are replaced in the sample by those who move in and given the same unique identifiers, as well as nonresponse, mortality and migration. A naive match is then dropped if it does not match on sex, race or age criterion. Based on these criteria, the match rate is 50%, with 871,917 individuals matched, or 1,743,834 observations out of a possible 3,481,692 observations.

# H. Imputing Computer Use Rates to CPS Workers

We calculate average industry and occupation computer use rates from the October 1984, 1989, 1993, 1997, and August 2000 CPS Internet and Computer Use Supplement files. The use rate within an industry or occupation is the fraction of respondents who report using a computer or the internet at work. We impute computer use rates for the remaining years by linearizing the percent change within an occupation or industry between available years. We use the linear trend from 1984-1989 to impute computer use rates for 1982-1983, setting a lower bound of 0%. We use the linear trend from 1997-2000 to impute computer use rates for 2001-2002, setting an upper bound of 100%.

# I. Creating Price Indices by Industry

We create a set of price indices at the industry level using data from the Bureau of Labor Statistics (BLS) and the US Department of Agriculture (USDA). For most industries, a match is made to the industry-specific BLS Producer Price Index (PPI) data. For the four agricultural industries, we use an industry-specific Producer Price Index from the USDA. For 46 industries, a consistent PPI is not available across time; we instead use a product-specific Consumer Price Index from the BLS. For an additional 46 industries (mainly in the service sector), no consistent PPI or CPI is available for our entire time period; we instead use the economy-wide PPI from the BLS. For eleven of the industries, coverage begins in 1985; we freeze prior years to the 1985 level. The series are simple averages across monthly values, and are not seasonally adjusted.

We also create a series of import and export prices from BLS data. These data are downloaded at the 1, 2, and 3-digit SITC level. We then use the concordance between SITC and SIC codes received from Robert Feenstra and used in Feenstra et al. (2002) to create price series data at the SIC level. These data are then matched to our CPS industry coding scheme using a concordance created by the authors mapping each 3-digit SIC code into a corresponding CPS industry code. We then construct export and import price indices at the 1, 2 and 3-digit CPS industry codes.

A supplemental import price data series was also provided at the NAICS level generously by Robert Lawrence. We use a concordance between NAICS and SIC codes to create a series of import prices at the SIC level, which are then mapped to our CPS industry classification scheme. For many-to-one matches between the SIC code and CPS industry codes, we use the SIC code with non-missing import prices.

These data are used in our supplementary online materials and summary statistics for these variables are available upon request.

# J. Summary Statistics

In Appendix Table 1, we present summary statistics for our sample of individual CPS workers, and their assigned values for offshoring, trade, and technology measures. We present summary statistics for the entire sample (1983-2002), the 1983 sample, and the 2002 sample of workers. In Panel A, we report over 3.4 million workers in the data, with a noticeable decline in the fraction of workers who