DETERMINANTS OF HOUSEHOLD FINANCIAL VULNERABILITY: EVIDENCE FROM SELECTED EU COUNTRIES

Household debt has been increasing in the last decades, and it poses a threat not only to the financial stability of households but is a precursor of the economic and financial crisis. A downturn caused by the coronavirus pandemic is expected to deepening inequalities, mainly due to the inability of households to repay existing debts or finance basic living needs. Understanding the determinants of household indebtedness and financial vulnerability is crucial for policymakers who process measures to prevent increasing household indebtedness. This paper investigates the determinants of household financial vulnerability in euro area countries using the Household Finance and Consumption Survey micro-dataset collected by the European Central Bank. The quantitative approach was applied using ordinary least square and quantile estimation procedures. The difference between OLS and quantile estimations showed the appropriateness of using the quantile regression approach. Performance analysis proved that only the number of elderly and the value of wealth and existence of mortgage interest tax relief statistically significant affects the level of vulnerability in all three waves. While the increasing number of elderly and greater value of household wealth lowers the vulnerability, the effect of mortgage interest tax relief differs across individual waves. All other used factors are essential and statistically significant for the financial vulnerability of households as well, but the importance and significance could differ across the distribution and individual waves. The effect of financial assets, education, and employment were found to be negative in all observations of all waves. On the other hand, the number of children and the value of households’ real assets is associated with increased financial vulnerability indicators.

Revision of Frequency Estimates of Extreme Precipitation Based on the Annual Maximum Series in the Jiangsu Province in China

Frequency estimates of extreme precipitation are revised using a regional L-moments method based on the annual maximum series and Chow’s equation at lower return periods for the Jiangsu area in China. First, the study area is divided into five homogeneous regions, and the optimum distribution for each region is determined by an integrative assessment. Second, underestimation of quantiles and the applicability of Chow’s equation are verified. The results show that quantiles are underestimated based on the annual maximum series, and that Chow’s formula is applicable for the study area. Next, two methods are used to correct the underestimation of frequency estimation. A set of rational and reliable frequency estimations is obtained using the regional L-moments method and the two revised methods, which can indirectly provide a robust basis for flood control and water resource management. This study extends previous works by verifying underestimation of the quantiles and the provision of two improved methods for obtaining reliable quantile estimations of extreme precipitation at lower recurrence intervals, especially in solving reliable estimates for a 1-year return period from the integral lower limit of the frequency distribution.

Investigating the effect of inbound tourism on FDI: The importance of quantile estimations

The current study investigates the asymmetric effect of inbound tourism on foreign direct investment (FDI) in the world’s top tourist destinations based on monthly data for the period between 1995 and 2017. The quantile-on-quantile (QQ) approach introduced by Sim and Zhou was adopted for this study, because it assesses how various quantiles of inbound tourism affect different quantiles of FDI. Thus, the QQ approach gives a more detailed explanation of the general dependence of inbound tourism and FDI than traditional approaches, such as ordinary least squares or quantile regression. Further, the test of Granger causality in quantiles proposed by Troster et al. was also applied in this study to check the causal relationship between inbound tourism and FDI. The empirical outcomes explain that the relationship between inbound tourism and FDI is mostly positive for all countries except Mexico and Russia on low and middle quantiles, although there are significant differences throughout the nations and across all quantiles of inbound tourism and FDI.

Modelling uncertainty of flood quantile estimations at ungauged sites by Bayesian networks

Prediction at ungauged sites is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. Regression models relate physiographic and climatic basin characteristics to flood quantiles, which can be estimated from observed data at gauged sites. However, some of these models assume linear relationships between variables and prediction intervals are estimated by the variance of the residuals in the estimated model. Furthermore, the effect of the uncertainties in the explanatory variables on the dependent variable cannot be assessed. This paper presents a methodology to propagate the uncertainties that arise in the process of predicting flood quantiles at ungauged basins by a regression model. In addition, Bayesian networks (BNs) were explored as a feasible tool for predicting flood quantiles at ungauged sites. Bayesian networks benefit from taking into account uncertainties thanks to their probabilistic nature. They are able to capture non-linear relationships between variables and they give a probability distribution of discharge as a result. The proposed BN model can be applied to supply the estimation uncertainty in national flood discharge mappings. The methodology was applied to a case study in the Tagus basin in Spain.