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The purpose of this paper is to analyze the changes in wealth and consumption inequality in Spain and estimate the consumption effects of housing and financial wealth.

The estimations are made using micro-data from the Spanish Survey of Household Finances (2002–2014) applying cross-section, panel and interquartile techniques.

The findings of this paper suggest that there was an increase in wealth inequality during the period under analysis and a reduction in consumption inequality. Also, the authors find a significant positive effect of wealth on consumer expenditure. Disaggregating by asset type, the value of the main residence is the category with the highest estimated effect on consumption, whereas the remaining types of assets, although still positive and generally significant, have more modest effects on consumption. However, the estimated coefficients and their significance can change substantially depending on the phase of the economic cycle and the position of the household in the income distribution.

These results provide new empirical evidence on the effects of household wealth changes on their consumption behavior, the differences depending on the household's position in the distribution and the fluctuations of these estimated coefficients throughout a period of profound economic upheavals.

The purpose of this paper is to examine the effect of bank capital on liquidity creation. Especially, the authors test two competing hypotheses: the “risk absorption” hypothesis and the “financial fragility-crowding out” hypothesis that describe such association in the context of UK and French banking industry.

The authors use data collected from Bankscope for commercial banks pertaining to the aforementioned countries. The sample period ranges from 2000 to 2014. Liquidity creation was measured using a novel approach proposed by Berger and Bouwman (2007). This study uses the quantile regression (QR) and the instrumental variables QR, along with classical ordinary least squares (OLS) and panel regression, to deal with the mixed results reported by previous papers.

Using OLS and panel regression, the authors first find that bank capital negatively affects liquidity creation which supports risk absorption hypothesis. Second, the result from QR confirms the negative association between the aforementioned variables and shows that the effect is homogenous across quantiles of liquidity creation distribution. The result remains unchanged when using the QR with instrumental variables to address the potential problem of endogeneity.

This paper sheds more lights on the relationship between bank capital and liquidity creation by using a novel estimation approach based on the QR methodology.

This paper investigates the impact of institutional factors on financial deepening and its implications on bank credit in Africa.

The paper employs different panel econometric models to examine the heterogeneity of 50 African countries from 2000 to 2019. The estimators include panel corrected standard errors, system generalized method of moments, quantile and threshold regressions.

The results show that rule of law, regulatory quality, government effectiveness, voice and accountability, control of corruption and political stability significantly influence financial deepening in Africa. However, government effectiveness has a higher effect on middle- and high-income countries, while other indicators have a high impact on low-income countries. All institutional indicators have stronger effects, almost double, at higher financial depth levels than for countries with lower levels. Government effectiveness and regulatory quality impact financial deepening more for countries with strong institutions than weak ones. Thus, the relationship between institutional qualities and credit provided by banks is non-monotonic.

The findings suggest that strengthening appropriate institutional factors based on country heterogeneity may effectively stimulate debt financing in Africa, the primary source of financing for small and medium-sized enterprises and entrepreneurs.

The novelty of this paper is that previous studies did not sufficiently scrutinize the heterogeneity of the structure of African economies – i.e. differences in institution, credit and income levels.

The rising concern for the accuracy of residential valuations in Nigeria has created the need for key stakeholders in the residential property markets in the study areas to know the level of accuracy of valuations in order to make rational residential property transactions, amongst other purposes.

A blend of descriptive and causal designs was adopted for the study. Data were collected via structured questionnaire administered to 179 estate surveying and valuation (ESV) firms in the study areas using census sampling technique. Analytical techniques such as median percentage error (PE), mean and relative importance index (RII) analysis were employed in the analysis of data collected for the study.

The study found that valuation accuracy is greater in the residential property market in Abuja than in Minna, with inappropriate valuation methodology as the most significant cause of valuation inaccuracy.

The practical implication of this study is that a reliable databank should be established for the property market to provide credible transaction data for valuers to conduct accurate valuations in these cities. Strict enforcement of national and international valuation standards by the regulatory authorities as well as retraining of valuers on appropriate application of valuation approaches and methods are the recommended corrective measures.

No study has comparatively examined the accuracy of valuations in two extremely different residential property markets in the country using actual valuation and transaction prices.

We study the statistical properties of Pearson correlation coefficients of Gaussian ranks, and Gaussian rank regressions – ordinary least-squares (OLS) models applied to those ranks. We show that these procedures are fully efficient when the true copula is Gaussian and the margins are non-parametrically estimated, and remain consistent for their population analogs otherwise. We compare them to Spearman and Pearson correlations and their regression counterparts theoretically and in extensive Monte Carlo simulations. Empirical applications to migration and growth across US states, the augmented Solow growth model and momentum and reversal effects in individual stock returns confirm that Gaussian rank procedures are insensitive to outliers.

While several studies have reported a relationship between chronic daily headache (CDH) and different dietary patterns, no study has investigated the association between CDH and the dietary inflammatory index (DII). This study aims to hypothesize that a higher DII score (proinflammatory diets) is associated with higher odds of CDH.

This cross-sectional study was performed using the baseline data of the Dena PERSIAN cohort study, including demographic information, body mass index, medical history, laboratory tests, sleep duration and blood pressure. The DII was computed based on the data collected by a valid 113-item food frequency questionnaire and a 127-item indigenous food questionnaire. The association between CDH and DII score was analyzed by simple and multiple logistic regression.

Out of 3,626 people included in the study, 23.1% had CDH. The median DII was −0.08 (interquartile range = 0.18). People in the third and fourth quartiles of DII (proinflammatory diet) had a 20% (odds ratio: 0.80; 95% confidence interval: 0.65–1) and a 25% (odds ratio: 0.75; 95% confidence interval: 0.61–0.94) lower chance of having CHD than those in the first quartile, respectively. After adjustment for confounding variables, this association did not remain statistically significant (p > 0.05).

Although the analysis conducted without adjustment for medical history showed a significant association between proinflammatory diet and reduced CDH, considering the diverse etiology of different types of headaches and the paucity of studies in this area, further studies are needed to investigate the DII score of patients by the type of headache, its severity and duration.

This study aims to assess the association of Healthy Eating Index (HEI) with levels of fasting blood sugar (FBS) and lipid profile in normoglycemic and elevated FBS patients.

This case-control study was conducted on 144 participants, namely, 72 normoglycemic subjects (FBS < 100 mg/dl) and 72 high-glycemic patients (FBS ≥ 100 mg/dl) aged 20–60 years of age, who were selected from the nutrition and diet clinics in Tehran city. The dietary intake was collected by using a validated food frequency questionnaire to determine the HEI score.

The mean±SD age and body mass index of participants were 47.1 ± 12.7 years and 29.6 ± 6.0 kg/m², respectively. The median (interquartile range) of HEI scores in the normoglycemic group and the high-glycemia group were 19.34 (15.24–24.31) and 16.53 (13.35–24.07), respectively. In the overall population, the findings of the multi-variable linear regression model indicated a positive association between the HEI score and high-density lipoprotein-cholesterol (HDL-C) (ß = 0.34; 95%CI: 0.05–0.64, P = 0.01). However, there is no significant association between HEI and HDL-C in normoglycemic (ß = 0.19; 95%CI: −0.31, 0.69, P = 0.45) and hyperglycemic subjects (ß = 0.28; 95%CI: −0.10–0.66, P = 0.15). Furthermore, the association of HEI with levels of FBS, triglycerides (TGs) and low-density lipoprotein-cholesterol (LDL-C) was not significant in any of the analyzed groups, including the total population, normoglycemic individuals and hyperglycemic subjects.

This study was the first study to assess the role of HEI and its components with levels of FBS and lipid profile in normoglycemic and hyperglycemic individuals in Iran. The findings suggested that higher adherence to HEI may be associated with an increase in the HDL-C level. However, HEI could not predict FBS, TGs and LDL-C levels in the adult population.

Several approaches have been proposed to evaluate treatment effect, relying on matching methods propensity score, quantile regression, influence function, bootstrap and various combinations of the above. This paper considers two of these approaches to define the quantile double robust (DR) estimator: the inverse propensity score weights, to compare potential output of treated and untreated groups; the Machado and Mata quantile decomposition approach to compute the unconditional quantiles within each group – treated and control. Two Monte Carlo studies and an empirical application for the Italian job labor market conclude the analysis. The paper aims to discuss these issue.

The DR estimator is extended to analyze the tails of the distribution comparing treated and untreated groups, thus defining the quantile based DR estimator. It allows us to measure the treatment effect along the entire outcome distribution. Such a detailed analysis uncovers the presence of heterogeneous impacts of the treatment along the outcome distribution. The computation of the treatment effect at the quantiles, points out variations in the impact of treatment along the outcome distributions. Indeed it is often the case that the impact in the tails sizably differs from the average treatment effect.

Two Monte Carlo studies show that away from average, the quantile DR estimator can be profitably implemented. In the real data example, the nationwide results are compared with the analysis at a regional level. While at the median and at the upper quartile the nationwide impact is similar to the regional impacts, at the first quartile – the lower incomes – the nationwide effect is close to the North-Center impact but undervalues the impact in the South.

The computation of the treatment effect at various quantiles allows to point out discrepancies between treatment and control along the entire outcome distributions. The discrepancy in the tails may differ from the divergence between the average values. Treatment can be more effective at the lower/higher quantiles. The simulations show the performance at the quartiles of quantile DR estimator. In a wage equation comparing long and short term contracts, this estimator shows the presence of an heterogeneous impact of short term contracts. Their impact changes depending on the income level, the outcome quantiles, and on the geographical region.

To date, the literature on quantile regression and least absolute deviation regression has assumed either explicitly or implicitly that the conditional quantile regression model is correctly specified. When the model is misspecified, confidence intervals and hypothesis tests based on the conventional covariance matrix are invalid. Although misspecification is a generic phenomenon and correct specification is rare in reality, there has to date been no theory proposed for inference when a conditional quantile model may be misspecified. In this paper, we allow for possible misspecification of a linear conditional quantile regression model. We obtain consistency of the quantile estimator for certain “pseudo-true” parameter values and asymptotic normality of the quantile estimator when the model is misspecified. In this case, the asymptotic covariance matrix has a novel form, not seen in earlier work, and we provide a consistent estimator of the asymptotic covariance matrix. We also propose a quick and simple test for conditional quantile misspecification based on the quantile residuals.

We survey the literature on the Risk Augmented Mincer equation that seeks to estimate the compensation for uncertainty in the future wage to be earned after completing an education. There is wide empirical support for the predicted positive effect of wage variance and the negative effect of wage skew. We discuss robustness of the findings across specifications, potential bias from unobserved heterogeneity and selectivity and consider the core issue of students' information on benefits from education.