THE WELFARE GAINS OF FINANCIAL LIBERALIZATION: CAPITAL ACCUMULATION AND HETEROGENEITY

Research has shown that, absent individual heterogeneity and under complete markets, the welfare impact of financial openness is quantitatively limited. Not only are inequalities in wealth and labor productivity a feature of most societies, but also financial markets suffer from many well‐known frictions. This paper demonstrates that, when households face borrowing constraints and uninsurable idiosyncratic shocks to income, the welfare implications of financial liberalization are considerable. For instance, the average increase in welfare of a typical emerging market economy that switches from a closed capital market to perfect capital mobility is equivalent to a permanent increase in average consumption of roughly 5.4%. This is about 3.9 times more than the welfare gains of the same policy under a complete markets environment without any individual heterogeneity. We show that individual heterogeneity accounts for two thirds of this additional increase in the average welfare gain; market incompleteness accounts for the remaining third. In our calibration, the median household in capital‐scarce countries is in favor of international financial integration. However, if the pivotal voter is wealthy enough then such reform might not be implemented, since richer households have a vested interest in capital market closedness.     

Business Angel Investment as an Informal Learning Process: Does Experience Matter?

This paper addresses the question of how the informal learning processes of angel investors shape the way in which they assess new investment opportunities. Previous research has suggested that angels learn from their previous investment activity. However, the measurement of investment experience as a function of years of investing and number of investments made does not take into account how, and to what extent, learning occurs and how it impacts investors’ decision-making. To address this deficiency, we suggest a dynamic model of informal learning which incorporates the informal learning typology developed by Schugurensky and extended by Bennett, as a conceptual framework to understand how learning experiences impact angel investment decisions. Based on interviews and verbal protocol analysis with 30 investors, we demonstrate how learning impacts learning processes and outcomes as angels assess an investment opportunity. The findings highlight the importance of supporting angel learning with support for angel groups and angel training programmes.     

Positive coparenting previous to the COVID-19 pandemic can buffer regulatory problems in infants facing the COVID-19 pandemic

Coparenting can be a development-enhancing or risk-promoting environment for infant regulatory capacity, mainly in the presence of adversity. This study aimed to analyze the association between positive and negative coparenting previous to the COVID-19 pandemic and infant regulatory capacity in the presence of the COVID-19 pandemic, an adverse condition. A sample of 71 first-born infants and their mothers and fathers from a longitudinal cohort in Portugal were assessed at 2 weeks postpartum before the COVID-19 pandemic and again at 6 months postpartum, before (n = 35) or during the COVID-19 pandemic (n = 36). Parents completed measures of positive and negative coparenting and infant regulatory capacity in both assessment waves. Results revealed that the presence of the COVID-19 pandemic moderates the association between mothers' and fathers' positive coparenting previous to the COVID-19 pandemic and infant regulatory capacity at 6 months. The association between positive coparenting and regulatory capacity was stronger in infants facing the COVID-19 pandemic, than in infants who did not face the COVID-19 pandemic. Positive coparenting previous to the COVID-19 pandemic may be a development-enhancing environment for infant regulatory capacity in the presence of the COVID-19 pandemic. Positive coparenting may buffer regulatory problems in infants facing adverse conditions.     

Accounting for animal density gradients using independent information in distance sampling surveys

Distance sampling is extensively used for estimating animal density or abundance. Conventional methods assume that location of line or point transects is random with respect to the animal population, yet transects are often placed along linear features such as roads, rivers or shorelines that do not randomly sample the study region, resulting in biased estimates of abundance. If it is possible to collect additional data that allow an animal density gradient with respect to the transects to be modelled, we show how to extend the conventional distance sampling likelihood to give asymptotically unbiased estimates of density for the covered area. We illustrate the proposed methods using data for a kangaroo population surveyed by line transects laid along tracks, for which the true density is known from an independent source, and the density gradient with respect to the tracks is estimated from a sample of GPS collared animals. For this example, density of animals increases with distance from the tracks, so that detection probability is overestimated and density underestimated if the non-random location of transects is ignored. When we account for the density gradient, there is no evidence of bias in the abundance estimate. We end with a list of practical recommendations to investigators conducting distance sampling surveys where density gradients could be an issue.     

The Rate of Convergence of some Asymptotic Expansions for Distribution Approximations via an Esseen Type Estimate

Some asymptotic expansions not necessarily related to the central limit theorem are studied. We first observe that the smoothing inequality of Esseen implies the proximity, in the Kolmogorov distance sense, of the distributions of the random variables of two random sequences satisfying a sort of general asymptotic relation. We then present several instances of this observation. A first example, partially motivated by the the statistical theory of high precision measurements, is given by a uniform asymptotic approximation to (g(X + μ _n )) _n∈?, where g is some smooth function, X is a random variable and (μ _n ) _n∈? is a sequence going to infinity; a multivariate version is also stated and proved. We finally present a second class of examples given by a randomization of the interesting parameter in some classical asymptotic formulas; namely, a generic Laplace's type integral, randomized by the sequence (μ _n X) _n∈?, X being a Gamma distributed random variable.