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Objective Methods Results Conclusion We investigate the impact of a global health crisis on political behavior. Specifically, we assess the impact of Covid‐19 incidence rates, and the impact of temporal and spatial proximity to the crisis, on voter turnout in the 2020 Brazilian municipal elections.We use Ordinary Least Squares and Spatial Durbin Error models to evaluate sub‐national variation in municipal‐level Covid‐19 incidence and voter turnout. We include controls for political, economic, health, and state context.Ceteris paribus, increasing deaths in the month leading up to the election from 0.01 to 1 per 1000 people is associated with a 5 percentage point decrease in turnout;higher cases and deaths earlier in the pandemic are generally associated with higher turnout. Covid‐19 incidence rates in nearby municipalities affect local turnout in the same directions.Higher Covid‐19 incidence near the time of the election decreases voter turnout, while incidence farther from the election increases voter turnout. [ABSTRACT FROM AUTHOR] Copyright of Social Science Quarterly (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: This article investigates whether weekend work is associated with higher levels of work–family conflict (WFC) among parents, and whether resources like schedule control or presence of a partner mitigate this effect. Background: The 24/7 economy requires many workers to work on weekends. Nevertheless, research on the impact of weekend work on families, and on WFC in particular, is underdeveloped, with previous studies relying on cross-sectional data and small samples. Method: Associations between regular weekend work and a measure of WFC are examined using data from 14 waves of The Household, Income and Labour Dynamics in Australia (HILDA) Survey. The sample is restricted to workers aged 18–64 years with parenting responsibilities for children aged 17 or less (7747 individuals, 40,192 observations). Both pooled ordinary least squares and fixed-effects regression models are estimated. Results: Among both genders, weekend workers have significantly higher levels of WFC than those who only work weekdays. WFC is particularly high for those who work weekends and simultaneously have little control over their schedule. Furthermore, weekend work affects WFC similarly for couple and single parents and, within dual-earner families, independently of the partner's working schedule. Conclusion: Weekend work generally has a detrimental effect on workers' ability to combine employment with parenting commitments. However, work–domain resources like schedule control can buffer the impact of weekend work.

The spread of misinformation about COVID‐19 severely influences the governments' ability to address the COVID‐19 pandemic This study explores the predictors of accurate beliefs about COVID‐19 and its influence on COVID‐related policy and behavior Data from an original survey collected by Lucid in July 2020 are used Ordinary Least Squares regression (OLS) is used to predict accurate beliefs about COVID‐19 Ordered logistic regression models are estimated to examine the relationship between COVID‐19 knowledge, policy preferences, and health behavior intentions Ideology and education were found to have a positive effect on knowledge about COVID‐19 Moreover, low levels of knowledge about COVID‐19 were found to reduce support for mandatory vaccination policy and willingness to get a coronavirus vaccine when available These findings will help policymakers develop communication strategies for the public on the coronavirus vaccination [ABSTRACT FROM AUTHOR] Copyright of Social Science Quarterly (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )

Several studies have estimated effects of body mass index (BMI) on labour market outcomes, and these studies have mixed findings. A significant challenge has been to adequately control for omitted variables, selection, reverse causality, and measurement error. We examine the impact of BMI on income using genetic variants as instrumental variables for BMI. Individual-level pre-tax income from tax records was merged with health survey data containing measured height and weight, and data on genetic variants. The analyses were stratified by sex and a variety of methods were used to explore the sensitivity and validity of the instrumental variable (IV) strategy. For females we found that BMI had a negative effect on the logarithm of income. The effect estimated from the IV models (-0.02) was larger than the effect estimated from naïve ordinary least squares regressions (-0.01). For males, the coefficients for the effect of BMI on income were imprecise, and both positive and negative coefficients were estimated depending on the estimation method. Our results suggest that females are susceptible to reduced income levels following increased BMI.

Objective: This study examines how occupational resources and demands are associated with parents' childcare time. Background: Scholars recognize parental employment as important for understanding parental time use. Yet, given data limitations, we know relatively little about how strain-based demands (demands that can produce negative psychological states) are associated with parent's time with children. Method: Occupational-level data in the O*NET Database are linked to the American Time Use Survey (ATUS) 2011–2019 (n = 10,274 workday diaries from employed parents in 427 occupations). Ordinary Least Squares regression (OLS) is used to examine how occupational resources and demands are associated with parents' time with children and in childcare on workdays. Results: Mothers in occupations with greater strain-based demands—competitive pressure, aggression-conflict, monotony, and physicality-hazards—spend less time with their children and less time on physical childcare activities. For fathers, associations are weaker with monotonous jobs also associated with less time with children. Workplace conditions, however, are weakly or even positively associated with parents' time on nonworkdays, suggesting that the daily experience of work affects parents' time use at home. Autonomy, an occupational resource, is positively associated with fathers' time with children and with mothers' time in interactive care. Conclusion: Resources and strain-based demands—measured at the occupational-level—are associated with parents' time use. The O*NET Database can be linked to the ATUS to better understand families' time use.

Substantial capital outflows across Europe following the 2007/8 Global Financial Crisis and 201 European Sovereign Debt crisis raise concerns regarding potential capital outflows from the economies of Central and Eastern European countries. To shed light on country-level factors that can mitigate crisis and potential capital outflows across these countries, this article investigates which factors have influenced the evolution of their current accounts. Our analyses, using dynamic ordinary least squares, suggest that the long-run determinants of the current account have indeed changed over time, and threshold cointegrated estimates also confirm that, for each country, the parameters are dependent on thresholds for certain variables and there is significant heterogeneity across the countries. Our overall results are robust to complementary analyses, such as threshold estimation approach. We comment on some possible implications of these differences.

The study investigates the impact of foreign direct investment on the agriculture sector in Nigeria using quarterly time series data for the period 1981–2017 obtained from the Central Bank of Nigeria Statistical Bulletin. Preliminary tests on the time series data were done using the pairwise correlation test, Augmented Dickey–Fuller and Phillips–Perron unit root tests. The results of the Bounds test and Johansen test indicate the presence of cointegration in the model. The A utoregressive Distributed Lagged (ARDL) model, Fully Modified Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) were used to estimate the parameter estimates of the regression model. Results indicate that foreign direct investment has a positive and significant impact on agricultural sector output. Specifically, the ARDL result shows the impact of foreign direct investment to be more in the short run (0.0024

Background Survival average causal effect (SACE) can give valid estimates of the periodontal treatment effect on birth outcomes in randomized controlled trials when fetal losses are unequal across the treatment arms. A regression-based method to estimate SACE using ordinary least squares (OLS) regression can be biased if the treatment effect varies across the outcome distribution. In this case quantile regression may be a suitable alternative. Methods We compared OLS and quantile regression models estimating SACE to calculate the effect of periodontal treatment on birthweight and gestational age in secondary analyses of publicly available Obstetrics and Periodontal Therapy (OPT) trial data. Results Periodontal treatment tended to increase birthweight and gestational age at the lowest quantiles, remained flat in the middle quantiles, and trended to decrease both birthweight and gestational age in the highest quantiles. In quantile regression models estimating SACE the β-coefficients: 95% confidence intervals (CI) for the 5th, 50th, and 95th percentiles were 277.5: -141.0 to 696.0 g, 1.4: -107 to 110.3 g, and -84: -344 to 175.3 g for birthweight, and 0.6: -1.0 to 2.2 weeks, -0.1: -0.5 to 0.2 weeks, and -0.6: -1.0 to -0.1 weeks for gestational age. Estimates from OLS models estimating SACE were close to the null, β: 95% CI -4.7: 132.3 to 123.0 g for birthweight, and 0.03: -0.72 to 0.78 weeks for gestational age. Conclusions OLS models to evaluate SACE for periodontal treatment effects on birthweight and gestational age may be biased towards the null. Quantile regression may be a preferable alternative.

[enter Abstract Body]One of the proposals that have been brought forward regarding ways of discouraging banks from taking unnecessary risk, widely debated these days, is the introduction of taxes in the financial sector. Although recent empirical investigations document a positive effect of the bank levies (BL), it may also reduce banks’ activity. In this paper, we aim to analyse the Polish BL on assets. Poland’s interbank market might be affected by a new BL introduced in 2016, as interbank positions of the balance sheet have been also taxed. The analysis covers the panel structure of data of 209 Polish banks with unconsolidated financial statements. To evaluate the impact of the BL on the above interbank market features, we use the OLS regression with fixed effects in relation to the bank. Data has been sourced from OrbisFocus and Central Banks’ websites and covers the period 2011-2017. The result demonstrates that the BL on assets applied in Poland has a negative impact on the interbank market. Estimation results shows that the BL on assets significantly decrease the value of 1y and increases the value of 3m interbank loans. In addition, the BL on assets in the banking sector positively affects the value of government securities and negatively affects the interest income on interbank loans. Moreover, the dispersion of 1m and 3m quotations was reduced as a result of the BL introduction. In case of short-term transactions, dispersion amplifies. Additionally, the volatility of quotation decreases due to reduced trading after the BL implementation.

This article introduces a global hypothesis test intended for studies with multiple endpoints. Our test makes use of a priori predictions about the direction of the result of each endpoint and we weight these predictions using the sample correlation matrix. The global alternative hypothesis concerns a parameter, ϕ , defined as the researcher's ability to correctly predict the direction of each measure, essentially a binomial parameter. This allows for the test to include expected effects that are all positive, all negative or both while still using the cumulative information across those endpoints. A rejection of the null hypothesis ( H0:ϕ≤ϕ0 ) provides evidence that the researcher's underlying theory about the natural process provides a better prediction of the observed results relative to the null hypothesized predictive ability, thus indicating the theory is worthy of further study. We compare our test to O'Brien's ordinary least squares (OLS) test and show that for small samples and situations where the effect is not in the same direction across all endpoints our approach has better power, while if the effect is equidirectional across all endpoints the OLS test can have greater power.

This paper examines the governance-“education quality” nexus in a panel of 49 sub-Saharan African countries over the period 2000-2012. Ordinary Least Squares (OLS) and Quantile regression (QR) are employed as estimation strategies. The following findings are established. First, from the OLS, governance variables are negatively correlated with poor education quality. Second, with regards to QR, about half of the governance dynamics are not significantly correlated with poor education quality in the lowest quantile of poor education quality. With the exception of corruption-control, the other governance dynamics are negatively correlated with poor education quality in a non-monotonic pattern.

This study uses a dynamic slacks‐based measure (SBM) model in a data envelopment analysis (DEA) framework with surplus reserve and accounts receivable as desirable and undesirable indicators respectively, based on inter‐connectivity between consecutive periods to estimate the operating efficiency of 110 listed Chinese real‐estate companies in the first step analysis. The results show that 35 listed real‐estate companies in China operated on the efficient frontier by maintaining sustainable growth throughout the entire period. The analysis presents the projection of indicators to suggest a feasible solution to improve the inefficient terms. At the second step analysis, using ordinary least square (OLS) estimation, authors find a curve‐linear relationship between real‐estate company size and efficiency to suggest that operating efficiency increases as size also increases due to economies of scale but only up to an optimum point. Profitability and higher education are also identified as the other significant determinants of operating efficiency. The findings of this research present evidence that average efficiency scores vary over time and generally comove with China's economic growth rate and the average stock returns in the real‐estate sector. By providing a new framework for evaluating input, output and carry‐over indicators of real‐estate companies, the results of this study equip real‐estate companies to have an accurate perspective of their operating position and simultaneously provide investors with an avenue to evaluate real‐estate companies' development before any form of investment.

This study investigates the long run equilibrium relationship among military expenditure, financial development, energy use, economic growth and environmental degradation in Turkey for the period of 1960–2014. Ecological footprint and carbon dioxide emissions are used as separate proxies for environmental degradation. Fully modified ordinary least squares (FMOLS) estimator results suggest that military expenditure, energy use and economic growth increase the environmental degradation while financial development improves the environmental quality in Turkey. Toda Yamamoto (1995) causality test results reveal that there is a unidirectional causality running from military expenditure to CO2 emissions and ecological footprint; and a bidirectional causality between military expenditure and economic growth. The findings of the study confirm the existence of destruction theory for the case of Turkey.

Funding: MBM is supported by a University Research Fellowship from the Royal Society (London). 1. O’Hara and Kotze (2010; Methods in Ecology and Evolution 1: 118‐122) present simulation results that appear to show very poor behaviour (as judged by bias and overall accuracy) of linear models (LMs) applied to count data, especially in relation to generalised linear model (GLM) analysis. 2. We considered O’Hara and Kotze’s (2010) comparisons, and determined that the finding occurred primarily because the quantity that they estimated in their simulations of the LM analysis (the mean of a transformation of the count data) was not the same quantity that was simulated and to which the results were compared (the logarithm of the mean of the count data). We correct this discrepancy, re‐run O’Hara and Kotze’s simulations, and add additional simple analyses. 3. We found that the apparent superiority of the GLMs over LMs in O’Hara and Kotze’s (2010) simulations was primarily an artefact of divergence in the meanings of results from the two analyses. After converting results from LM analyses of transformed data to estimators of the same quantity as provided by the GLM, results from both analyses rarely differed substantially. Furthermore, under the circumstances considered by O’Hara and Kotze, we find that an even simpler implementation of LM analysis, inference of the mean of the raw data, performs even better, and gives identical results to the GLM. 4. While the analysis of count data with generalised linear models can certainly provide many benefits, we strongly caution against interpreting O’Hara and Kotze’s (2010) results as evidence that simpler approaches are severely flawed. Postprint

Most of the prior literature that investigated the nexus between corporate sustainability disclosures (CSD) and firm financial performance (FFP) has largely been ignored the potential problem of endogeneity. The omitted variable(s), measurement error, and reverse causality, which are the known causes of endogeneity, may also be the possible reasons for the indecisive and inconsistent relationship between CSD and FFP. Accordingly, this study reinvestigates the relationship by addressing the problem of endogeneity through applying the two‐stage least squares (2SLS) estimator to data collected from the annual reports of the top 10 Pakistani banks through content analysis from 2013 to 2017. The results show a positive impact of the CSD and social sustainability on FFP. However, environmental sustainability exerted a negative impact on FFP. The statistics for 2SLS are much different than those of the ordinary least squares (OLS), which explain the importance of addressing the endogeneity bias. The study offers a methodological procedure that identifies and addresses the endogeneity through a step‐by‐step process in Stata 13.0. Besides, the study also has a novelty to propose and validate additional instrumental variables for the research in the future. Overall, the study has many contributions and implications for different stakeholders such as academia, regulatory bodies, and practitioners in the field of corporate sustainability.

Using individuals' life‐cycle invention data, we investigate how graduate education affects inventive performance and inventors' abilities to absorb and combine diverse knowledge sources. To control for the endogeneity of educational choice, we use the status of college labor markets as an instrumental variable (IV), specifically the difference between the unemployment rate and its long‐run average rate by academic field. We find that graduate education, induced by the IV, significantly enhances inventive performance and the scope of exploited knowledge, exceeding the levels implied by ordinary least squares. Graduate education can have a significant causal effect on inventive capability and performance.

This study investigates whether women's empowerment, measured by education attainment relative to her partner, decision‐making, and domestic violence is related to nutritional status of children in Ghana. Using the 2014 Ghana Demographic and Health Survey, the study examines the effect of women empowerment on child's nutritional status at different points in its conditional distributions using ordinary least squares and quantile regression estimation technique. The study observes that women's empowerment is associated with improvement in the nutritional status of children with Z‐scores less than −4 and −3 standard deviations, for acutely and severely malnourished children respectively in the long run. The study recommends that the Ministry for Gender and Social Protection should educate male partners on the need for women participation in decision‐making at the household level as well as make formal education accessible to women.

Motivation This article provides the first comprehensive evidence on the relationship between skills and labour productivity among manufacturing firms across Africa. It makes two contributions. First, we re‐examine the relationship between skills and labour productivity using nuanced firm‐level data. Second, we examine whether the relationship between employee skills and firm productivity varies by size and age of the firm. Purpose This paper poses two questions. What is the relationship between skills and labour productivity using nuanced firm‐level data? Is the relationship between employee skills and firm productivity mediated by firm size and age? Approach and Methods We used the World Bank’s Enterprise Survey data and employed the pooled Ordinary Least Squares estimation. Owing to endogeneity concerns, we used Instrumental Variables, where country‐sector‐size averages instrument for high school and university education. Propensity Score Matching was used to estimate the effects of training. Findings We find that the effects of high school and university education are higher among small and young firms respectively. Training is positively associated with labour productivity, particularly among older and large firms. Policy implications Overall this article highlights the importance of skills development in Africa’s aspiration to boost manufacturing. Key to this is encouraging schoolchildren to complete the education cycle. It helps too if employers can identify the level of education that suits their needs for skills, given firm size and age. They need to identify skill gaps, then match training to the skills employees require.

This paper examines the relationship between state policies and bank account ownership in developing countries. While existing literatures broadly support a ‘market‐enabling’ approach, consisting of limited direct state intervention and proportionate regulation, statistical analyses frequently rely on problematic measurement techniques. This study adopts a novel and more comprehensive approach by using a modified index constructed from expert evaluations. Using ordinary least squares regression, the analysis suggests a positive relationship between a market‐enabling state policy framework and increased bank account ownership among the total population, but the relationship diminishes among key subpopulations: adult women, rural adults and the poorest 40 per cent of adults. © 2020 John Wiley & Sons, Ltd.

The importance of free trade agreements (FTAs) has been increasing as such agreements help reduce barriers to trade. This paper estimates the agricultural trade creation and export diversion effects of Australia’s free trade agreements (FTAs) at the aggregate and disaggregate levels, using the Poisson pseudo‐maximum‐likelihood (PPML) estimator. It includes 24 of Australia’s major trading partner countries comprising FTA and non‐FTA members and covers 22 years from 1996 to 2017. The heteroscedasticity robust regression error specification test (RESET) confirms the relevance of PPML over the Ordinary Least Square (OLS) estimator. Results showed that China–Australia, Korea–Australia, Australia–USA and Japan–Australia have larger trade creation effects in the agricultural sector. At the commodity level, variation in trade creation effects is estimated from the different trade agreements. Among the selected commodities, the larger effects were generated in trade in sugar and wine by the implementation of the majority of the trade agreements. Overall, the trade creation was greater than the export diversion of the FTAs. The findings of the study have implications for Australia’s future trade agreements.

This paper applies generalized autoregressive score‐driven (GAS) models to futures hedging of crude oil and natural gas. For both commodities, the GAS framework captures the marginal distributions of spot and futures returns and corresponding dynamic copula correlations. We compare within‐sample and out‐of‐sample hedging effectiveness of GAS models against constant ordinary least square (OLS) strategy and time‐varying copula‐based GARCH models in terms of volatility reduction and Value at Risk reduction. We show that the constant OLS hedge ratio is not inherently inferior to the time‐varying alternatives. Nonetheless, GAS models tend to exhibit better hedging effectiveness than other strategies, particularly for natural gas.

The aim of this article is to analyze state‐level public finances in Brazil. We examine the dynamics of governmental spending in a panel of 26 Brazilian states in search of evidence of Wagner’s Law and Fiscal Illusion Hypothesis. For the period ranging from 2002 to 2015, three methodologies are applied: dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), and pooled mean group (PMG). The main empirical results found indicate that (1) there is strong evidence of Fiscal Illusion caused by public deficit and by central government transfer grants; (2) there are possible Flypaper Effects; (3) there is no evidence in support of Wagner’s Law; (4) there is low publicness degree of local expenditures; (5) due to Fiscal Illusion, less‐developed Brazilian states tend to be stuck in a public expenditure growth mechanism, especially in expenses related to non‐public goods, which tend to benefit private interests and lobby groups.

This study examines how variations in homicide rates in Mexico are associated with the likelihood of participating in cross‐border work, that is, living in Mexico but working in the U.S. Based on Mexican census data from 2000, 2010, and 2015, and information on homicides, a series of ordinary least squares models are estimated to analyze the relationship between cross‐border commuting and homicide rates at the individual level. Fixed effects models are also estimated to study this relationship at the municipal level. The results show that from 2000 to 2010 the increase in homicide rates in northern border municipalities in Mexico reduced the likelihood of being a cross‐border worker, while from 2010 to 2015 the decrease in the homicide rate increased the probability that workers engage in cross‐border work. The decline in the number of cross‐border workers is likely in part a result of the escalation in drug‐related violence that may have led them to change their country of residence.

In modern days, economic growth is energy-dependent and vice versa. Earlier studies concentrated a bit to analyze the influence of globalization and politico-administrative factors on the energy consumption-economic growth nexus in developing economies. The motivation for the current research is to scrutinize the energy consumption-economic growth nexus while accounting for the influence of globalization and country risk indicators—the politico-administrative factors in a panel of 4 South Asian countries (Bangladesh, India, Pakistan, and Sri Lanka) during 1980–2018. To check the issues of heterogeneity and cross-sectional independence, the study employs the pooled mean group estimation method. The investigated results provide a nexus between energy consumption and economic growth where globalization - a long-run force positively affects this nexus in the long run and negatively in the short run. Besides, the politico-administrative factors have an adverse impact in the long run and an insignificant effect on this nexus in the short run. The Dumitrescu–Hurlin non-causality test establishes the feedback hypothesis concerning energy consumption-economic growth nexus in South Asian economies. The study results remain robust across the dynamic ordinary least square estimator. Therefore, this study suggests sustaining the energy-growth nexus to properly handle globalization and politico-administrative and the Covid-19 pandemic issues through institutional quality. Moreover, the objective-oriented policies are critical to strengthening the energy-growth nexus without decaying environmental quality in South Asian countries. © 2021 John Wiley & Sons Ltd.

With respect to Saudi Arabia for the period 2007–2016, this study employs linear (ordinary least square and causality tests) and nonlinear (Markov switching technique) to determine the long‐run relationship between stock market returns, in aggregate, and oil prices, as independent variable on the one hand, with sectoral returns in stock markets, and as dependent variable on the other. Results suggest that sectors in the Saudi market generally respond asymmetrically to oil prices. Overall, in the long run, the Saudi market, in aggregate, was found to be cointegrated with sectoral returns being affected more by overall stock market returns than by oil returns. When nonlinear Markov switching technique was employed, results were only significant in certain sectors namely cement, petrochemical industries, building and construction, and energy and utilities where regime switching between high and low volatilities was detected around the global financial crisis in 2008. Based on these findings, the study recommends investors to consider investing in sectors that balance between those with returns that vary directly and inversely with oil prices in an effort to create “oil‐neutral” portfolios rather than uniformly investing across all sectors in the Saudi market or consider investing in Saudi market sectors in the case of high oil prices.

The competitive yardstick role of marketing cooperatives refers to the fact that the presence of cooperatives drives the market towards competitiveness. We conduct an empirical study based on panel data from the hog industry in China to examine the existence of competitive yardstick effect of cooperatives. Data including information regarding hog producer cooperatives, farm gate prices of hogs, and other relevant factors in China's 21 provinces in the years 2009–2012 are used. Various methods, including ordinary least squares regression, random‐effect model, and fixed‐effect model, are adopted to predict the effect of cooperatives development on farm gate prices. The results confirm the role of hog producer cooperatives as the competitive yardstick of markets, that is, a positive effect of strength of cooperatives on the farm gate prices received by hog producers with various production sizes. [EconLit classifications: Q13, L16]

We show that the Alchian–Allen theorem holds in three‐good world. We empirically test the theorem by examining the relative demand for three quality‐differentiated coffee varieties sold globally and confirm that a common per‐unit charge increases the overall quality of coffee demanded. Our empirical specification employs the Poisson pseudo maximum likelihood method, which prevents sample selection bias when zero flows between country pairs occur and estimated parameter inconsistency when the data exhibit heteroscedasticity. This is a significant improvement compared to previous practice of using the ordinary least squares to estimate the log‐log formulation, which led to both biased and inconsistent parameter estimates.

This study aims to investigate the effect of board gender diversity on the transparency of environmental, social, and governance (ESG) disclosures in an emerging market such as Malaysia. Dataset is comprised of 568 firm‐year observations from 78 firms listed on the Bursa Malaysia. Ordinary least squares regression analysis of the data shows that ESG disclosure scores are significantly improved with the increasing presence of women directors on corporate boards. However, when the individual components are studied, the impact of board gender diversity varies. This study contributes to the limited but growing literature on ESG reporting quality and board gender diversity especially in emerging economies.

This study evaluates the influences of air pollution in China using a recently proposed model—multi-scale geographically weighted regression (MGWR). First, we review previous research on the determinants of air quality. Then, we explain the MGWR model, together with two global models: ordinary least squares (OLS) and OLS containing a spatial lag variable (OLSL) and a commonly used local model: geographically weighted regression (GWR). To detect and account for any variation of the spatial autocorrelation of air pollution over space, we construct two extra local models which we call GWR with lagged dependent variable (GWRL) and MGWR with lagged dependent variable (MGWRL) by including the lagged form of the dependent variable in the GWR model and the MGWR model, respectively. The performances of these six models are comprehensively examined and the MGWR and MGWRL models outperform the two global models as well as the GWR and GWRL models. MGWRL is the most accurate model in terms of replicating the observed air quality index (AQI) values and removing residual dependency. The superiority of the MGWR framework over the GWR framework is demonstrated—GWR can only produce a single optimized bandwidth, while MGWR provides covariate-specific optimized bandwidths which indicate the different spatial scales that different processes operate.

Labor‐saving technologies played a fundamental role historically in the structural transformation of agrarian economies. We focus on an emerging labor‐saving trend in Ghana, use of motorized tricycles (MTs), which provide an affordable alternative to manually transferring crops from plots to homestead. A household survey collected in 2017 in northern Ghana is used to shed light on how the time savings made available by using MTs may be converted into activities that enhance agricultural productivity and/or diversify the household away from agriculture. Detailed information collected on the agricultural production and agro‐processing behavior of 1,400 households allows us to examine the above transformative channels. Ordinary least squares and instrumental variable regressions are applied to quantify the effect of MTs on related outcomes. We find households are able to diversify their farming activities into selling processed agricultural goods with the time savings provided through MT adoption. Policies aimed to foster the diffusion of MTs elsewhere in Ghana may expedite structural change and, hence, economic growth.

Based on neo‐institutional theory, this paper examines whether social trust can influence corporate social responsibility (CSR). Using a sample of 4,209 observations selected from 788 firms listed in China between 2008 and 2015 and adopting an ordinary least square regression, we provide strong empirical evidence showing that social trust is positively associated with CSR. The result supports the view that social trust, as a kind of socially normative force, helps corporate managers safeguard stakeholders' interests by engaging in socially responsible activities. Our result holds after applying a series of robustness tests. We further find the positive relationship between social trust and CSR is more pronounced for state‐owned companies. This paper is one of the first to focus on and examine the relationship between social trust and CSR, and the findings contribute to our understanding of the determinants of CSR and highlight the influential role of social trust in improving CSR.

This study examines the subnational flow in foreign direct investment (FDI) to Mexican states, with a focus on the finished vehicle industry. Although prior studies have examined subnational FDI in Mexico, research on the automobile sector has been scant. Utilizing a framework associated with the theory of asset specificity, we examine the influence of human capital and labor conditions, infrastructure, subnational policies, and security. The study employs a data set on finished vehicle FDI across 31 states and the Federal district for the period of 2004–2014. We adopt a two‐part model. The first part, which includes all states and the Federal district, is estimated with pooled logistic and Firth logistic regressions. In the second part, we examine variation in levels of FDI among states selected for investment with pooled ordinary least squares with panel‐corrected and Driscoll Kraay standard errors. Labor conditions are significant for both state selection and levels of FDI, but education is influential only in the selection stage. In addition, states with trunk rail lines linked to the U.S. border are more likely to be selected and receive higher FDI. After controlling for these influences, murder and homicide rates, and state development have no significant effects.

The presence of unobserved heterogeneity and its likely detrimental effect on inference has recently motivated the use of factor-augmented panel regression models. The workhorse of this literature is based on first estimating the unknown factors using the cross-section averages of the observables, and then applying ordinary least squares conditional on the first-step factor estimates. This is the common correlated effects (CCE) approach, the existing asymptotic theory for which is based on the requirement that both the number of time series observations, T, and the number of cross-section units, N, tend to infinity. The obvious implication of this theory for empirical work is that both N and T should be large, which means that CCE is impossible for the typical micro panel where only N is large. In the current paper, we put the existing CCE theory and its implications to a test. This is done by developing a new theory that enables T to be fixed. The results show that many of the previously derived large-T results hold even if T is fixed. In particular, the pooled CCE estimator is still consistent and asymptotically normal, which means that CCE is more applicable than previously thought. In fact, not only do we allow T to be fixed, but the conditions placed on the time series properties of the factors and idiosyncratic errors are also much more general than those considered previously. (Less)

Background Ommatissus lybicus de Bergevin (Hemiptera: Tropiduchidae) (Dubas Bug, DB) is an insect pest attacking date palms. It occurs in Arab countries including Oman. In this paper, the logistic, ordinary least square, and geographical weighted regressions were applied to model the absence/presence and density of DB against climate factors. A method is proposed for modelling spatially correlated prorations annually over the study period, based on annual and seasonal outbreaks. The historical 2006-2015 climate data were obtained from weather stations located in nine governorates in northern Oman, while dataloggers collected the 2017 microclimate data in eight of these nine governorates. Results Logistic regression model showed the percentages of correctly predicted values using a cut-off point of 0.5 were 90%, 88% and 84%, indicating good classification accuracy. OLS and GWR models showed an overall trend of strong linear correlation between DB infestation levels and short- and long-term climate factors. The three models suggested that precipitation, elevation, temperature, humidity, wind direction and wind speed are important in influencing the spatial distribution and the presence/absence of dense DB populations. Conclusion The results provide an improved understanding of climate factors that impact DB's spread and is considered useful for managing DB infestations in date palm plantations. © 2019 Society of Chemical Industry.

This is the first article that uses panel data to investigate the impact of individuals' self‐perceived relative income on life satisfaction. Analyses show that the self‐perceived relative income has a significant impact on life satisfaction, but the impact is asymmetric. The decline in life satisfaction is much more significant due to perceiving a lower relative income in comparison to the rise in life satisfaction because of perceiving a higher relative income. Absolute income is only significantly and positively associated with life satisfaction in the pooled ordinary least squares estimations, but the association is never significantly different from zero when individual fixed effects are controlled. Household savings have a positive but small impact on life satisfaction. Among different financial‐related shocks, people's self‐perceived relative income varies the most due to changes in household net income, total savings, and employment status.

The problem of specifying and fitting a statistical model of the pricing of property catastrophe risk is addressed from a methodological perspective. Notable 21st century published efforts to do this are reviewed. The problem is framed in a business context and various strategic and tactical issues are investigated. A naive application of ordinary least squares regression is seen to have undesirable consequences. Alternative approaches are offered, including weighted least squares with weights inversely proportional to capital requirements, and alternative functional forms. Recommendations are offered.

Regularization, or shrinkage estimation, refers to a class of statistical methods that constrain the variability of parameter estimates when fitting models to data. These constraints move parameters toward a group mean or toward a fixed point (e.g., 0). Regularization has gained popularity across many fields for its ability to increase predictive power over classical techniques. However, articles published in JEAB and other behavioral journals have yet to adopt these methods. This paper reviews some common regularization schemes and speculates as to why articles published in JEAB do not use them. In response, we propose our own shrinkage estimator that avoids some of the possible objections associated with the reviewed regularization methods. Our estimator works by mixing weighted individual and group (WIG) data rather than by constraining parameters. We test this method on a problem of model selection. Specifically, we conduct a simulation study on the selection of matching-law-based punishment models, comparing WIG with ordinary least squares (OLS) regression, and find that, on average, WIG outperforms OLS in this context.

In this article, we propose a stochastic formulation of mass balances that impose physical constraints on production technologies. The estimation of the model involves a composed error term structure that is commonly applied in the literature on stochastic frontier analysis of productive efficiency. Moreover, we discuss how ordinary least squares and maximum likelihood methods can be used to estimate emission coefficients based on the proposed model and compare the results to estimates based on linear programming techniques from input–output analysis. In contrast to previous approaches, our model allows one to estimate the physical limitations to production possibilities in the presence of statistical noise and depends on substantially weaker data requirements. We apply our approach to estimate emission coefficients for sulfur dioxide and carbon dioxide using a sample of fossil‐fueled power plants in the United States.

Increasing the success rate of start‐up wineries and fostering the wine–hospitality‐tourism clusters in emerging wine regions with substantial entrepreneurial activities have attracted considerable attention among public and private stakeholders. Understanding entrepreneur's motivation to enter the industry is crucial to understand winery behaviors and industry dynamics. Some entrepreneurs enter the industry motived by passion for wine whereas others are motived by profits. Differences in motivation may contribute to the high variability in quality and quality‐adjusted prices of wines in emerging wine regions. We used ordinary least square and Tobit regression models to investigate the influence of motivation on winery pricing and quality decisions using data on 700 wines from 102 wineries in emerging US wine regions. We find that passion‐oriented owners produce higher quality wines and set higher quality‐adjusted prices relative to profit‐oriented owners. Recognizing differences in owner motivation is critical to establish appropriate policies to support the development of the wine industry in emerging regions. [EconLit citations: Q13; L83; O25].

We consider inference for the market model coefficients based on simple linear regression under a long memory stochastic volatility generating mechanism for the returns. We obtain limit theorems for the ordinary least squares (OLS) estimators of α and β in this framework. These theorems imply that the convergence rate of the OLS estimators is typically slower than √ T if both the regressor and the predictor have long memory in volatility, where T is the sample size. The traditional standard errors of the OLS‐estimated intercept (α) and slope (β), which disregard long memory in volatility, are typically too optimistic, and therefore the traditional ‐statistic for testing, say, α= 0 or β= 1, will diverge under the null hypothesis. We also obtain limit theorems (which imply slow convergence) for the estimated weights of the minimum variance portfolio and the optimal portfolio in the same framework. In addition, we propose and study the performance of a subsampling‐based approach to hypothesis testing for α and β. We conclude by noting that analogous results hold under more general conditions on long‐memory volatility models and state these general conditions which cover certain fractionally integrated exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models.

This paper examines how ISO membership impacts cost efficiency. Utilities joining ISOs can face more competition in selling electric power, possibly leading to lower profits, which can incentivize utilities to operate more cost efficiently to maintain a specified level of profits. The empirical model involves estimating a Probit model, then OLS regression, then a stochastic cost frontier. Using a 1992 to 2000 panel of 34 investor‐owned electric utilities, empirical results indicate that ISO membership contributed to higher production cost, lower cost efficiency, and ISO members subject to the 1990 Clean Air Act Amendments operated more cost efficiently than ISO members not subject to the Amendments.

The scientific needs and computational limitations of the twentieth century fashioned classical statistical methodology. Both the needs and limitations have changed in the twenty‐first, and so has the methodology. Large‐scale prediction algorithms—neural nets, deep learning, boosting, support vector machines, random forests—have achieved star status in the popular press. They are recognizable as heirs to the regression tradition, but ones carried out at enormous scale and on titanic datasets. How do these algorithms compare with standard regression techniques such as ordinary least squares or logistic regression? Several key discrepancies will be examined, centering on the differences between prediction and estimation or prediction and attribution (significance testing). Most of the discussion is carried out through small numerical examples.

This study is aimed at analyzing the effects of COVID-19 pandemic on the financial performance of Chinese listed companies. We applied pooled ordinary least square (OLS) regression as a baseline methodology to determine the effects of COVID pandemic on the financial performance of Chinese listed companies. We found that the small- and medium-sized companies are most affected by this pandemic, in addition to that our analysis has revealed that serious-impact areas and industries which were worst hit by the COVID-19 experienced a sharper decline in financial performance as compared to other industries. The findings of this study have broad implications for policymakers, as it is evident that governments, banks, regulatory bodies, and central banks must combine forces to tackle the financial and economic impacts of COVID-19 crises. They should come up with comprehensive policies to tackle the adverse impact of such crises in the future as well. Actions such as proving loans and rescheduling existing loans to worst-hit sectors such as tourism, airline industry, etc. are starting point. The disclosure of facts and figure by officials regarding the status of health care number of infections can reduce the chances of panic situations in the future.

Using longitudinal data from the Canadian Survey of Labour and Income Dynamics from 1999 to 2011, the article compares the pay and benefits of licensed and unionized workers. In a cross section of respondents and using ordinary least squares estimates, it finds a pay premium of 0.155 log points for those with an occupational licence compared to those without one; the comparable union wage premium is slightly more than half, that is 0.085 log points. Fixed‐effects estimates go in the opposite direction (0.028 and 0.046 log points for licensing and unionization, respectively), suggesting the existence of unobservable factors correlated with licensing and union status. Unionized workers are more likely to access standard benefits, such as medical insurance and pension plans, but licensed workers benefit little from their licensing status in access to benefits. Finally, union workers are significantly less likely to receive incentive pay, such as profit sharing, while the association between occupational licensing and incentive pay is close to zero and statistically insignificant.

This paper explores the interaction between venture capital and innovation in China. We focus on the difference between early, development, and expansion stages of innovation firms when they receive their first installment of venture capital. Using ordinary least squares (OLS), switching regression model and counterfactual comparison, we find that selection effect exists in all three stages, suggesting that established innovation capacity increases the possibility of innovation firms to receive venture capital. Further, the selection effect is most profound in the development stage. The treatment effect, the combined effect of financial role and value‐added service, plays an important role in promoting innovation in all three stages. However, the mechanisms of the effect vary greatly across the early, development, and expansion stages. The financial role of venture capital does not influence innovation in the early stage, but promotes innovation in the development stage, and restrains innovation in the expansion stage. Value‐added service promotes innovation in the early and expansion stages, and whether it influences innovation in the development stage needs further study.

Objectives The objective of the study was to test predictions of loss aversion and risk aversion by analyzing contestant wagers in the game show Jeopardy! Methods The primary method employed in this article is ordinary least squares regression analysis but the mean wagers of contestants in various player subcategories and stages of the game are also analyzed. Results The results support the core proposition of loss aversion in that the mean wagers of contestants who are behind in the game are greater than are the mean wagers of contestants who are ahead in the game. The results also indicate some differences in the behavior of men and women in risk taking and that contestants make use of frames of reference in making their wagers such as how far ahead or behind they are in points and how much “time” is left in the game at the time of their wager. Conclusion Given the results of this article and a review of other relevant literature, the suggestion is made that future studies of loss aversion and risk aversion continue to explore complexity and variation in the context of decision making and that future research into possible differences by sex in risk aversion explore scenarios in which choices are grounded in other‐ or community‐oriented situations rather than scenarios centering only on individual gains.