Craig, Ashley C.
Optimal income taxation with spillovers from employer learning / Ashley C. Craig
Resumen.
I study optimal income taxation when human capital investment is imperfectly observable by employers. In the model, Bayesian inference about worker productivity compresses the wage distribution, lowering the private return to human capital investment. An externality arises: given the same information, employers are more optimistic about each individual if workers are generally more productive. The significance of this externality hinges on the accuracy of employers' beliefs and the responsiveness of human capital. For the United States, taking it into account lowers optimal marginal tax rates for most workers, reducing them by a maximum of 9–13 percentage points between $50,000 and $100,000.
IMPUESTO SOBRE LA RENTA DE LAS PERSONAS FISICAS
RENDIMIENTOS DE TRABAJO
TRABAJADORES
APRENDIZAJE
PRODUCTIVIDAD
IMPOSICION OPTIMA
MODELOS ECONOMETRICOS
American Economic Journal : Economic Policy 1945-7731 v. 51, n. 2, May 2023, p. 82-125
Optimal income taxation with spillovers from employer learning / Ashley C. Craig
Resumen.
I study optimal income taxation when human capital investment is imperfectly observable by employers. In the model, Bayesian inference about worker productivity compresses the wage distribution, lowering the private return to human capital investment. An externality arises: given the same information, employers are more optimistic about each individual if workers are generally more productive. The significance of this externality hinges on the accuracy of employers' beliefs and the responsiveness of human capital. For the United States, taking it into account lowers optimal marginal tax rates for most workers, reducing them by a maximum of 9–13 percentage points between $50,000 and $100,000.
IMPUESTO SOBRE LA RENTA DE LAS PERSONAS FISICAS
RENDIMIENTOS DE TRABAJO
TRABAJADORES
APRENDIZAJE
PRODUCTIVIDAD
IMPOSICION OPTIMA
MODELOS ECONOMETRICOS
American Economic Journal : Economic Policy 1945-7731 v. 51, n. 2, May 2023, p. 82-125