THE POLITICAL ECONOMY OF INTERGENERATIONAL INCOME MOBILITY

The intergenerational elasticity of income is considered one of the best measures of the degree to which a society gives equal opportunity to its members. While much research has been devoted to measuring this reduced‐form parameter, less is known about its underlying structural determinants. Using a model with exogenous talent endowments, endogenous parental investment in children, and endogenous redistributive institutions, we identify the structural parameters that govern the intergenerational elasticity of income. The model clarifies how the interaction between private and collective decisions determines the equilibrium level of social mobility. Two societies with similar economic and biological fundamentals may have vastly different degrees of intergenerational mobility depending on their political institutions. We offer empirical evidence in line with the predictions of the model. We conclude that international comparisons of intergenerational elasticity of income are not particularly informative about fairness without taking into account differences in politico‐economic institutions. (JEL E24, J62, J68, P16)     

Estimation in Discretely Observed Diffusions Killed at a Threshold

Abstract. Parameter estimation in diffusion processes from discrete observations up to a first‐passage time is clearly of practical relevance, but does not seem to have been studied so far. In neuroscience, many models for the membrane potential evolution involve the presence of an upper threshold. Data are modelled as discretely observed diffusions which are killed when the threshold is reached. Statistical inference is often based on a misspecified likelihood ignoring the presence of the threshold causing severe bias, e.g. the bias incurred in the drift parameters of the Ornstein–Uhlenbeck model for biological relevant parameters can be up to 25–100 per cent. We compute or approximate the likelihood function of the killed process. When estimating from a single trajectory, considerable bias may still be present, and the distribution of the estimates can be heavily skewed and with a huge variance. Parametric bootstrap is effective in correcting the bias. Standard asymptotic results do not apply, but consistency and asymptotic normality may be recovered when multiple trajectories are observed, if the mean first‐passage time through the threshold is finite. Numerical examples illustrate the results and an experimental data set of intracellular recordings of the membrane potential of a motoneuron is analysed.