Suchergebnisse

Countries with strict immigration policies often resort to deportation measures to reduce their stocks of illegal immigrants. Many of their undocumented foreign workers, however, are not deported but rather choose to return home voluntarily. This paper studies the optimizing behavior of undocumented immigrants who continuously face the risk of deportation, modeled by a stochastic process, and must decide how long to remain in the host country. It is found that the presence of uncertainty with respect to the length of stay abroad unambiguously reduces the desired migration duration and may trigger a voluntary return when a permanent stay would otherwise be optimal. Voluntary return is motivated by both economic and psychological factors. Calibration of the model to match the evidence on undocumented Thai migrants in Japan suggests that the psychological impact of being abroad as an illegal alien may be equivalent to as large as a 68% cut in the consumption rate at the point of return.

What determines whether a temporary migrant chooses to go abroad as a documented worker or as an illegal alien? We address the question from a theoretical perspective by focusing on how immigration policies, aimed at both documented and undocumented foreign workers, influence the choice between the two modes of migration. Calibrating our model to the specific case of temporary emigration from Thailand, we provide estimates of the relative policy effectiveness. The deportation rate facing undocumented aliens is shown to be the most potent instrument, while some of the measures directed at documented contract workers are found to be more effective in influencing the choice of emigration mode than border controls and employer sanctions aimed at illegal immigrants.

Climate physics predicts that the intensity of natural disasters will increase in the future due to climate change. One of the biggest challenges for economic modeling is the inherent uncertainty of climate events, which crucially affects consumption, investment, and abatement decisions. We present a stochastic model of a growing economy where natural disasters are multiple and random, with damages driven by the economy's polluting activity. We provide a closed-form solution and show that the optimal path is characterized by a constant growth rate of consumption and the capital stock until a shock arrives, triggering a downward jump in both variables. Optimum mitigation policy consists of spending a constant fraction of output on emissions abatement. This fraction is an increasing function of the arrival rate, polluting intensity of output, and the damage intensity of emissions. A sharp response of the optimum growth rate and the abatement share to changes in the arrival rate and the damage intensity justifies more stringent climate policies as compared to the expectation-based scenario. We subsequently extend the baseline model by adding climate-induced fluctuations around the growth trend and stock-pollution effects, demonstrating robustness of our results. In a quantitative assessment of our model we show that the optimal abatement expenditure at the global level may represent 0.9% of output, which is equivalent to a tax of $71 per ton carbon.