This paper studies the allocative efficiency in a Moscarini （2005）-type equilibrium search environment with learning. It is shown that the stationary equilib- rium is efficient if and only if the Hosios condition ho...This paper studies the allocative efficiency in a Moscarini （2005）-type equilibrium search environment with learning. It is shown that the stationary equilib- rium is efficient if and only if the Hosios condition holds no matter whether learning is about finn-specific human capital or about general human capital. However, the stationary equilibrium can never be efficient if externalities exist from unemployment. In contrast, even with externalities, the stationary equilibrium can be efficient under some modified Hosios condition if there is no uncertainty （standard Mortensen and Pissarides （1994）-type equilibrium search environment）. The key intuition is that the equilibrium can only be efficient if firm-worker matching is formed and terminated efficiently.展开更多
文摘This paper studies the allocative efficiency in a Moscarini （2005）-type equilibrium search environment with learning. It is shown that the stationary equilib- rium is efficient if and only if the Hosios condition holds no matter whether learning is about finn-specific human capital or about general human capital. However, the stationary equilibrium can never be efficient if externalities exist from unemployment. In contrast, even with externalities, the stationary equilibrium can be efficient under some modified Hosios condition if there is no uncertainty （standard Mortensen and Pissarides （1994）-type equilibrium search environment）. The key intuition is that the equilibrium can only be efficient if firm-worker matching is formed and terminated efficiently.
