Empirical Economics最新文献

This paper seeks to identify the effect of macroeconomic, industry-specific and bank-specific determinants on the profitability of the Brazilian banking sector. Profitability is measured by return on assets (ROA), return on equity (ROE) and economic value added (EVA). We incorporate in the analysis-independent variables of this sector that have not been considered in previous studies. To address profit persistence, we apply a dynamic panel data model and the GMM technique described by Arellano and Bover (1995) over the quarterly period from 2009Q1 to 2019Q4. The main results show that the macroeconomic variables of credit, activity and interest rate contribute to understanding the determining factors of bank profitability in Brazil. Regarding industry-specific and bank-specific determinants, the total operating expenses to total assets ratio and net interest margin are important determinants of the bank profitability, respectively. Thus, these new macroeconomic variables and industry-specific and bank-specific variables are important drivers to understand banking profitability in Brazil, as well as indicators to be monitored by the monetary authority to ensure the financial health of the banking system.

Multi-country surveys often aim at cross-country comparisons. A common quality standard is conducting these surveys within a common fieldwork period, across all participating countries. However, the rate the target sample is achieved within that fieldwork period in each country varies substantially. Thus, the distribution of the interview month often varies substantially in the final sample. This may lead to biased estimates of cross-country differences if the variable of interest exhibit a non-constant trend over time. We demonstrate the implications of such an asynchronous fieldwork, using physical activity measured in the European Social Survey Round 7 collected between September 2014 and January 2015. Accounting for fieldwork month, we present a set of different post-estimation predictions. Physical activity varies across interview month, with countries with more observations during autumn were upward-biased, compared to countries with more observations during winter. Our results demonstrate how comparisons between countries are affected when interview month is omitted, and how accounting for interview month in the analysis is an easy way to mitigate this problem.

Abstract

The Newcomb-Benford law states that the frequency of different leading significant digits in many datasets typically follows a specific distribution. Deviations from this law are often a sign of data manipulation. There has been no established method to test whether the non-reliability of observations depends on some potential explanatory variables. A novel method to address this issue is presented. If a leading significant digit has a higher observed frequency than implied by Benford’s distribution, such observations are particularly likely to be non-reliable. Dividing the frequency in Benford’s distribution by the observed frequency of the same leading significant digit yields an ordinal explained variable. The method is applied to bank deposit data collected in interviews. Many interviewees have provided rounded data, which may be a problem. Answers seem unreliable if the respondent belongs to the age group 51–65, has only primary education, does not live alone, and lives in a city.

Abstract

Motivated by the increasing demand for alternative assets that can contribute to reducing portfolio risk, this paper examines the volatility spillovers between collateralized loan obligations (CLOs) and various in-demand investment instruments, including equities, bonds, crude oil, commodities, gold, bitcoin, shipping and real estate. The applied methodology comprehends the time-varying parameter vector autoregressive (TVP-VAR) modification of the classical spillover approach, for the period from January 1, 2012, to August 31, 2023. The empirical findings show moderate levels of dynamic connectedness; albeit several external shocks strengthened the interconnection among the assets. Moreover, we compare the ability of CLOs for hedging, during the overall sample period and multiple subperiods, by estimating hedge ratios and optimal portfolio weights, in order to inform investors about feasible portfolio adjustments. Our results indicate that CLOs constitute an effective hedging tool, irrespective of the period covered, as the short position in their volatility provides high hedging effectiveness for investors holding long positions in the volatility of all the remaining assets.

Abstract

In this paper, we propose a hybrid Watts-MPI multidimensional poverty measure that combines the multidimensional Watts poverty index (MWPI), which can accommodate continuous poverty dimensions, with the multidimensional poverty index (MPI), which can accommodate binary poverty dimensions. Unlike the stand-alone MPI that entails total loss of dimension-specific information on both poverty intensity with respect to shortfall and inequality, the proposed hybrid Watts-MPI measure entails only partial loss of such information since poverty intensity and inequality estimates can still be obtained for the continuous poverty dimensions included in the hybrid measure. The hybrid Watts-MPI also specializes to the stand-alone MWPI and MPI when all the poverty dimensions are continuous and binary, respectively. Furthermore, formation of the hybrid Watts-MPI does not entail loss of normative properties by either the constituent MWPI or MPI. The seemingly unrelated regression approach to the estimation of the hybrid Watts-MPI is described and an empirical example demonstrating its efficacy is provided.

This study investigates the impact of significant wildfires from 2019 to 2022 on nine sectors within the US capital markets, utilizing a dataset encompassing 161 wildfires. Employing a combination of parametric and nonparametric tests, alongside regression analysis, the research scrutinizes how capital markets in distinct sectors respond to wildfire events, revealing nuanced effects. In sectors directly impacted, the insurance industry displays sensitivity to fire costs, with explicit country or event mentions correlating with sustained returns. Conversely, the real estate sector experiences diminished returns during prolonged wildfires, while the forestry and timber industry exhibits heightened sensitivity to fire costs, especially when ignited by lightning. Within indirect impact sectors, the health industry shows vulnerability to fire-related fatalities, with subsequent negative correlations with country mentions. In the food industry, fire costs contribute positively to returns, while duration and size yield negative effects. The transportation industry witnesses a gradual decline in returns, escalating with the number of fire days or associated costs. In resilience and mitigation sectors, utilities demonstrate recovery post-wildfires, contrasting with consistent declines in the energy sector. Among interconnected sectors, the travel and tourism industry sees increased returns tied to the number of victims, with events caused by human actions having a more pronounced impact. This research underscores the significance of tailored risk assessment and mitigation strategies, offering valuable insights for investors and policymakers navigating the intricate relationship between environmental events and financial markets.

Abstract

While the product and labour market imperfections reveal efficiency losses, they may influence technology adoption and its change, raising the endogeneity issue of productivity and efficiency estimates. Using a two-step approach, this work offers the endogeneity-corrected stochastic frontier for such a contemporaneous relation and accounts for efficiency and productivity losses due to market imperfections. A modified frontier function, defined as the residue per capital unit, has been drawn from the Cobb–Douglas function to estimate the terms containing the product and labour market imperfections along with other factors capturing the levels of technology, scale and technical efficiency. First, a standard frontier panel model estimates technology and technical efficiency terms with a proxy function in polynomials of market imperfection terms used for the contemporaneous relation, and then a GMM approach applies to the residue to estimate the parameters containing market imperfections. The estimated results using the three-digit industries across 17 major Indian states for 2008–2016 reveal a strong presence of product and labour market imperfections and associated efficiency losses. The efficiency in the product market has been lower and has further deteriorated in most industries, but not in the labour market.

Abstract

We employ an extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model combined with the environmental Kuznets curve and machine learning algorithms, including ridge and lasso regression, to investigate the impact of institutions on carbon emissions in a sample of 22 European Union countries over 2002 to 2020. Splitting the sample into two: those with weak and strong institutions, we find that the results differ between the two groups. Our results suggest that changes in institutional quality have a limited impact on carbon emissions. Government effectiveness leads to an increase in emissions in the European Union countries with stronger institutions, whereas voice and accountability lead to a fall in emissions. In the group with weaker institutions, political stability and the control of corruption reduce carbon emissions. Our findings indicate that variables such as population density, urbanization and energy consumption are more important determinants of carbon emissions in the European Union compared to institutional governance. The results suggest the need for coordinated and consistent policies that are aligned with climate targets for the European Union as a whole.

We study the effects of expected initial wages, expected wage growth, and observed and unobserved heterogeneity in the choice of college major in a sample of American college graduates. We propose a three-stage empirical model that relates future earnings to individual choices. In the first stage, starting from revealed choices, observed wages, and life-cycle wage profiles, we estimate the expectation on initial wages and wage growth from the individual point of view, where the panel structure of the data allows us to produce estimates corrected for self-selection bias. We find substantial differences in expected real wages and expected real wage growth between majors and that both characteristics of life cycle earnings influence major choice. Our parametric models show a strong correlation between salary trends and major choice, whereas semiparametric models yield less reliable results. We interpret our results as being consistent with agents being rational and as a validation for our estimation strategy based on counterfactual imputation.

The paper discusses the results of a selection of a set of monthly indicators to be used as predictors of the quarterly index of Italian service turnover. A mixed frequency approach based on sparse temporal disaggregation is used, which outperforms the classical methods of the Chow and Lin family, allowing both a high number of regressors by the LASSO method and stable estimates. The application refers to the turnover in transport, a sector strongly affected in 2020 by the dramatic movements due to the COVID-19 pandemic and the resurgence of inflation at the end of 2021. The monthly indicators are selected from 143 time series: 56 series of business surveys in transport about both the climate and frequency of the answers; 18 series from Assaeroporti about both passengers and cargo flights split by national and international routes; 69 series of monthly turnover in industry split by both sector of economic activity and reference market. The sample spans the months from January 2010 to December 2021 for both seasonally adjusted and unadjusted data. Several aspects of the estimation are considered: the stability of selected indicators over the quarters 2017–2021; their forecasting performance; the reliability of the estimates in terms of their monthly pattern.