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Count data regressions are an important tool for empirical analyses ranging from analyses of patent counts to measures of health and unemployment. Along with negative binomial, Poisson panel regressions are a preferred method of analysis because the Poisson conditional fixed effects maximum likelihood estimator (PCFE) and its sandwich variance estimator are consistent even if the data are not Poisson-distributed, or if the data are correlated over time. Analyses of counts may be affected by correlation in the cross-section. For example, patent counts or publications may increase across related research fields in response to common shocks. This paper shows that the PCFE and its sandwich variance estimator are consistent in the presence of such dependence in the cross-section - as long as spatial dependence is time-invariant. In addition to the PCFE, this result also applies to the commonly used Logit model of panel data with fixed effects. We develop a test for time-invariant spatial dependence and provide code in STATA and MATLAB to implement the test.
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Historical accounts suggest that Jewish émigrés from Nazi Germany revolutionized U.S. science. To analyze the émigrés' effects on chemical innovation in the U.S. we compare changes in patenting by U.S. inventors in research fields of émigrés with fields of other German chemists. Patenting by U.S. inventors increased by 31 percent in émigré fields. Regressions that instrument for émigré fields with pre-1933 fields of dismissed German chemists confirm a substantial increase in U.S. invention. Inventor-level data indicate that émigrés encouraged innovation by attracting new researchers to their fields, rather than by increasing the productivity of incumbent inventors.
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Count data regressions are an important tool for empirical analyses ranging from analyses of patent counts to measures of health and unemployment. Along with negative binomial, Poisson panel regressions are a preferred method of analysis because the Poisson conditional fixed effects maximum likelihood estimator (PCFE) and its sandwich variance estimator are consistent even if the data are not Poisson-distributed, or if the data are correlated over time. Analyses of counts may be affected by correlation in the cross-section. For example, patent counts or publications may increase across related research fields in response to common shocks. This paper shows that the PCFE and its sandwich variance estimator are consistent in the presence of such dependence in the cross-section - as long as spatial dependence is time-invariant. In addition to the PCFE, this result also applies to the commonly used Logit model of panel data with fixed effects. We develop a test for time-invariant spatial dependence and provide code in STATA and MATLAB to implement the test.
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Historical accounts suggest that Jewish émigrés from Nazi Germany revolutionized U.S. science. To analyze the émigrés' effects on chemical innovation in the U.S. we compare changes in patenting by U.S. inventors in research fields of émigrés with fields of other German chemists. Patenting by U.S. inventors increased by 31 percent in émigré fields. Regressions that instrument for émigré fields with pre-1933 fields of dismissed German chemists confirm a substantial increase in U.S. invention. Inventor-level data indicate that émigrés encouraged innovation by attracting new researchers to their fields, rather than by increasing the productivity of incumbent inventors.
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