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The milk addiction paradox refers to an empirical finding in which commodities that are typically considered to be non addictive, such as milk, appear instead to be addictive. This result seems more likely when there is persistence in consumption and when using aggregate data, and it suggests that the AR(2) model typically used in the addiction literature is prone to produce spurious result in favor of rational addiction. Using both simulated and real data, we show that the milk addiction paradox disappears when estimating the data using an AR(1) linear specification that describes the saddle-path solution of the rational addiction model. The AR(1) specification is able to correctly discriminate between rational addiction and simple persistence in the data, to test for the main features of rational addiction, and to produce unbiased estimates of the short and long-run elasticity of demand. These results hold both with individual and aggregated data, and they suggest that, for testing rational addiction, the AR(1) model is a better empirical alternative than the canonical AR(2) model.

The rational addiction model is usually tested by estimating a linear second-order difference Euler equation, which may produce unreliable estimates. We show that a linear first-order difference equation is a better alternative. This empirical specification is appropriate under the reasonable assumption that people are uncertain about the time of their death, it is based on the same structural assumptions used in the literature, and it retains all policy implications of the deterministic rational addiction model. It is also empirically convenient because it is simple, it allows using efficient estimation strategies that do not require instrumental variables, and it is robust to the possible non-stationarity of the data. As an application we estimate the demand for smoking in the US from 1970 to 2016, and we show that it is consistent with the rational addiction model.

This paper investigates the role of higher order beliefs in the formation of exchange rates. Our model combines a standard macroeconomic dynamics for the exchange rates with a microeconomic specification of agents' heterogeneity and their interactions. The empirical analysis relies on a state space model estimated through Bayesian methods. We exploit data on macroeconomic fundamentals in a panel of subjective forecasts on the euro/dollar exchange rate. The equilibrium strategy on the optimization process of the predictors shows that higher order beliefs is the relevant factor in performing individual forecasting. Moreover public information, namely past exchange rates and fundamentals, plays a crucial role as a coordination device to generate expectations among agents on the basis of their forecasting abilities.

We study the underground economy within a dynamic and stochastic general equilibrium framework. Our model combines limited tax enforcement with an otherwise standard two- sector neoclassical stochastic growth model. The Bayesian estimation of the model based on Italian data provides evidence in favor of an important underground sector in Italy, with a size that has increased steadily over the whole sample period. We show that this pattern is due to a steady increase in taxation. Fiscal policy experiments suggest that a moderate tax cut, along with a stronger effort in the monitoring process, causes a sizeable reduction in the size of the underground economy and provides a positive stimulus for the regular economy. Both of these effects jointly increase total fiscal revenues.

We study the Lemons Problem when workers have private information on both their skills and their intrinsic motivation for the job offered by firms in the labor market. We first show that, when workers are motivated, inefficiencies due to adverse selection are mitigated. More interestingly, depending on the association between productivity and motivation, higher salaries affect the pool of candidates in three possible ways: they can attract (i) more skilled but less motivated applicants, as expected; (ii) more skilled and more motivated applicants; (iii) less skilled and less motivated applicants. The last two counterintuitive effects can only occur when a positive correlation exists between productivity and motivation. Our results are relevant in the policy debate on whether it is possible to improve the quality of workers in vocational markets by changing their wage rate and reconcile the different empirical evidence provided so far on motivated workers such as public servants, teachers, health professionals and, politicians.

We study the Lemons Problem when workers have private information on both their skills and their intrinsic motivation for the job offered by firms in the labor market. We first show that, when workers are motivated, inefficiencies due to adverse selection are mitigated. More interestingly, depending on the association between productivity and motivation, higher salaries affect the pool of candidates in three possible ways: they can attract (i) more skilled but less motivated applicants, as expected; (ii) more skilled and more motivated applicants; (iii) less skilled and less motivated applicants. The last two counterintuitive effects can only occur when a positive correlation exists between productivity and motivation. Our results are relevant in the policy debate on whether it is possible to improve the quality of workers in vocational markets by changing their wage rate and reconcile the different empirical evidence provided so far on motivated workers such as public servants, teachers, health professionals and, politicians.