Marcel VOIA

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Empirical studies emphasise that higher financial development (FD) amplifies the output cost of banking crises. However, no study has so far investigated the effect of FD on another key dimension of banking crises, namely their duration. Using a large sample of banking crises over the 1977-2014 period, we find that higher FD is associated with a significant increase in the duration of banking crises (DBC). This result is robust to a broad range of alternative specifications and is unaffected by unobserved heterogeneity or endogeneity. Finally, we show that the effect of FD on DBC is subject to nonlinearities and varies across decades and with the level of economic development.

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The estimated average treatment effect in observational studies is biased if the assumptions of ignorability and overlap are not satisfied. To deal with this potential problem when propensity score weights are used in the estimation of the treatment effects, in this paper we propose a bootstrap bias correction estimator for the average treatment effect (ATE) obtained with the inverse propensity score (BBC-IPS) estimator. We show in simulations that the BBC-IPC performs well when we have misspecifications of the propensity score (PS) due to: omitted variables (ignorability property may not be satisfied), overlap (imbalances in distribution between treatment and control groups) and confounding effects between observables and unobservables (endogeneity). Further refinements in bias reductions of the ATE estimates in smaller samples are attained by iterating the BBC-IPS estimator.

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Abstract Zero inflation and over-dispersion issues can significantly affect the predicted probabilities as well as lead to unreliable estimations in count data models. This paper investigates whether considering this issue for German Socioeconomic Panel (1984–1995), used by Riphahn et al. (2003), provides any evidence of misspecification in their estimated models for adverse selection and moral hazard effects in health demand market. The paper has the following contributions: first, it shows that estimated parameters for adverse selection and moral hazard effects are sensitive to the model choice; second, the random effects panel data as well as standard pooled data models do not provide reliable estimates for health care demand (doctor visits); third, it shows that by appropriately accounting for zero inflation and over-dispersion there is no evidence of adverse selection behaviour and that moral hazard plays a positive and significant role for visiting more doctors. These results are robust for both males and females’ subsamples as well as for the full data sample