THE RETURNS TO COMPUTER USE REVISITED: HAVE PENCILS CHANGED THE WAGE STRUCTURE TOO?* JOHN E. DINARDO AND JRN-STEFFEN PISCHKE Are the large measured wage differentials for on-the-job computer use a true return to computer skills, or do they just reflect that higher wage workers use computers on their jobs? We examine this issue with three large cross-sectional surveys from Germany. First, we confirm that the estimated wage differential associated with computer use in Germany is very similar to the U. S. differential. Second, we also measure large differentials for on-the-job use of calculators, tele- phones, pens or pencils, or for those who work while sitting down. We argue that these findings cast some doubt on the literal interpretation of the computer use wage differential as reflecting true returns to computer use or skill. 3 Interpreting the results. 1. The authors include Table II in the paper (on the next page) to demonstrate that there is a strong relationship between computer use on the job and wages in both the United States and Germany, and that the relationship has grown stronger over time. Computer use is actually more strongly related to wages than years of schooling or experience. (a) When considering whether this relationship is causal, what is an omitted variable that could potentially be related to both on-the-job computer use and wages? (b) Does that omitted variable in part (a) lead to overstating or understating the "effect" of computer use on wages? TABLE II OLS REGRESSIONS FOR THE EFFECT OF COMPUTER USE ON PAY DEPENDENT VARIABLE: LOG HOURLY WAGE (STANDARD ERRORS IN PARENTHESES) U. S. U. S. U. S. Independent variable Germany Germany Germany 19851986 19911992 1984 1989 1993 1979 Computer 0.171 0.188 0.204 0.112 0.157 0.171 (0.008) (0.008) (0.008) (0.010) (0.007) (0.006) Years of 0.068 0.075 0.081 0.073 0.063 0.072 schooling Experience (0.001) (0.002) (0.002) (0.001) (0.001) (0.001) 0.028 0.028 0.026 0.030 0.035 0.030 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Experience/ -0.043 -0.043 -0.041 -0.052 -0.058 -0.046 100 (0.002) (0.002) (0.003) (0.002) (0.002) (0.002) R? 0.444 0.448 0.424 0.267 0.280 0.336 Number of obs. 13,335 13,379 13,305 19,427 22,353 20,042