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Purpose: This study aims to examine the influence of voluntary disclosure on bank value in Pakistan, considering the moderating effect of corporate governance characteristics: ownership control, board independence and board size. Design/methodology/approach: The study uses data from 20 listed Pakistani banks for the period 2011–2021. The estimation contains robust fixed effect and its assumptions, and a model of standard error with panel corrections. Findings: The findings revealed a weak positive impact of voluntary disclosure on bank value. However, the increase in the number of independent directors strengthens the positive impact of voluntary disclosure on a bank's value. Conversely, increasing the ownership concentration, and board size (other than independent directors) may strongly decrease the impact of voluntary disclosure on a bank's value in Pakistan. Research limitations/implications: The study's limitations include its exclusive focus on the Pakistani banking industry. Future research should take into account newer contexts and data. The findings suggest that future research should investigate the topic in various contexts, including a comparison of Islamic and conventional banks. Practical implications: The practical implications for Pakistani banks emphasize transparency, board composition and ownership structure. In terms of managerial implications, using independent directors, aligning ownership interests and addressing disclosure challenges are highlighted. Originality/value: Focusing on independent directors, ownership concentration and board size, this study enhances knowledge of the impact of voluntary disclosure on bank value in Pakistan. It contributes to agency theory and the literature in this domain. [ABSTRACT FROM AUTHOR]

Abstract.To facilitate semiparametric estimation of general discrete-choice, censored, sample selection, and other complex panel data models, we study identification and estimation of nonseparable multiple-index models in panel data with correlated random effects and a fixed number of time periods. The parameter vectors of interest are shown to be identified up to multiplicative constants and the average marginal effects are identified under the assumption that the distribution of individual effects depends on the explanatory variables only through their averages across time. Under this assumption, we propose to estimate the unknown parameters by the generalized method of moments based on the average and outer product of the difference of derivatives of the regression function. The rate of convergence and asymptotic distribution is established both for the proposed parameter estimates and the average marginal effects. We conduct Monte Carlo simulation studies to assess finite-sample performance of the proposed estimator. We also use it to model dynamic earnings of women, and using our general identification results, we find a negative effect of the number of children not only on the selection into employment as usual, but also on the average earnings. [ABSTRACT FROM AUTHOR]

AbstractIn this study, we consider 2- and 3-step estimators for censored quantile regression models with fixed effects. In the first step of these estimators, the informative subset is determined through the estimation of censoring probability. While previous studies often employ symmetric link functions for estimating the censoring probability, these may not accurately identify the best subset when the distribution of the censoring probability is skewed. Given the prevalence of asymmetrically censored data in empirical analyses, we propose using an asymmetric link function for more accurate subset determination. We conduct Monte Carlo simulations and empirical analysis to assess our proposed methods. Our Monte Carlo simulations and empirical analysis confirm that an asymmetric link function offers a better fit for asymmetrically censored data. For symmetrically censored data, the symmetric link function performs better in moderate and large samples, while the asymmetric link function performs slightly better in small samples. These findings underscore the importance of considering both the censoring probability distribution and sample size in panel quantile regression. [ABSTRACT FROM AUTHOR]

Cohesion Policy is one of the core policy areas of the European Union and one of the largest expenditure items in its budget. Its impact is the subject of intense academic and policy debate, but its effectiveness is an empirical question still open. This article contributes to this debate by performing a region-specific investigation of the direct and indirect impact of Structural Funds expenditure. We employ a heterogeneous panel data model, which accommodates the spatial dependence in a heterogeneous setting and measures heterogeneous spatial spillover effects, both inward and outward. We also consider different channels of spatial spillovers with respect to standard geographical closeness. In particular, the introduction of new measures of proximity that explicitly consider economic linkages between regions is a novel contribution to the literature. The results clearly indicate that the impact of the Cohesion Policy, while positive overall, is highly heterogeneous and the Structural Funds expenditure generates substantial heterogeneous spatial spillovers: the positive effects do not remain confined to the target regions. Finally, strong spillover effects are channelled by value-added trade connections, particularly in regions belonging to the so-called 'Central European Manufacturing Core'. [ABSTRACT FROM AUTHOR]

Our confidence set quantifies the statistical uncertainty from data‐driven group assignments in grouped panel models. It covers the true group memberships jointly for all units with pre‐specified probability and is constructed by inverting many simultaneous unit‐specific one‐sided tests for group membership. We justify our approach under N,T → ∞ asymptotics using tools from high‐dimensional statistics, some of which we extend in this paper. We provide Monte Carlo evidence that the confidence set has adequate coverage in finite samples. An empirical application illustrates the use of our confidence set. [ABSTRACT FROM AUTHOR]

Purpose: China is a significant energy consumer with increasingly severe resource constraints and environmental problems, requiring low-carbon energy transformation and encouraging high-quality energy development (HED). Green finance significantly affects the effect on HED as a cutting-edge financial strategy to support environmental improvement and encourage green development. Design/methodology/approach: Using panel data from 30 provinces from 2007 to 2019 and the system-generalized method of moments method, this paper investigates the impact of green finance on HED, and further explores their threshold effect, heterogeneous and asymmetry analysis. Findings: The main results indicate that: (1) green finance positively affects HED in China; in other words, a 1% increase in the green finance index will boost HED by an average of 0.767%; (2) as the economy improves, the positive impact of green finance on HED will be even more significant and (3) the contribution of green finance to HED is more significant in the northern provinces and areas with lower HED levels. Originality/value: This paper puts forward relevant policy suggestions to further improve the construction of the green financial system. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to identification of the semiparametric multinomial choice model with fixed effects. The framework employed is the semiparametric version of the traditional multinomial logit with the fixed‐effects model (Chamberlain (1980)). This semiparametric multinomial choice model places no restrictions on either the joint distribution of the random utility disturbances across choices or their within group (or across time) correlations. We show that a novel within‐group comparison leads to a set of conditional moment inequalities. Our main finding shows that the derived conditional moment inequalities yield the sharp identified set for the random utility covariate index, while avoiding the incidental parameter problem. Specializing this result to the binary choice case shows that Manski (1987)'s conditional moment inequalities still lead to sharp bounds without restrictions on covariates. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to estimate the housing wealth effect on non-durable consumption using data from the Spanish Survey of Household Finances (Encuesta Financiera de las Familias, SHF) for the period 2002–2017. Design/methodology/approach: The authors aim at identifying the effect of anticipated and unanticipated housing wealth changes on consumption with the sample of homeowners, following Paiella and Pistaferri (2017). Findings: Results of this study lead us to conclude that there exists a strong housing wealth effect on consumption for the Spanish households. Originality/value: The authors provide evidence against the permanent income model. They also analyse how the results change with income expectations, age and the household indebtedness rate. Finally, they detect a strong excess sensitivity to income. [ABSTRACT FROM AUTHOR]

We investigate the effects of automation on total factor productivity (TFP). Using industry-level panel data for nine countries, we find that more intensive use of industrial robots has a significantly positive effect on TFP. Specifically, an increase of one standard deviation in the robot intensity is associated with more than 6% higher TFP. Moreover, we find that the robot intensity increases with Chinese import competition and that automation is associated with higher wages and unchanged or higher employment. [ABSTRACT FROM AUTHOR]

We consider fixed‐effects binary choice models with a fixed number of periods T and regressors without a large support. If the time‐varying unobserved terms are i.i.d. with known distribution F, Chamberlain (2010) shows that the common slope parameter is point identified if and only if F is logistic. However, he only considers in his proof T = 2. We show that the result does not generalize to T ≥ 3: the common slope parameter can be identified when F belongs to a family including the logit distribution. Identification is based on a conditional moment restriction. Under restrictions on the covariates, these moment conditions lead to point identification of relative effects. If T = 3 and mild conditions hold, GMM estimators based on these conditional moment restrictions reach the semiparametric efficiency bound. Finally, we illustrate our method by revisiting Brender and Drazen (2008). [ABSTRACT FROM AUTHOR]

We use a stochastic frontier analysis (SFA) approach to model the propagation of the COVID-19 epidemic across geographical areas. The proposed models permit reported and undocumented cases to be estimated, which is important as case counts are overwhelmingly believed to be undercounted. The models can be estimated using only epidemic-type data but are flexible enough to permit these reporting rates to vary across geographical cross-section units of observation. We provide an empirical application of our models to Spanish data corresponding to the initial months of the original outbreak of the virus in early 2020. We find remarkable rates of under-reporting that might explain why the Spanish Government took its time to implement strict mitigation strategies. We also provide insights into the effectiveness of the national and regional lockdown measures and the influence of socio-economic factors in the propagation of the virus. [ABSTRACT FROM AUTHOR]

National income calculations may not include knowledge that directly concerns the socioeconomic stance in an economy. Knowledge plays a significant role in promoting the economic growth of a country; however, its socio-economic role has received little attention in the literature. Total factor productivity, which is the main source of long-term economic prosperity, expresses the increase in productivity in all production factors. This paper attempts to quantify the effects of total factor productivity growth on economic prosperity. The analysis is conducted for the 2007-2020 period and 18 Central Eastern Europe countries. The "Legatum Prosperity Index" is utilized in the econometric analysis, in line with the purpose of the study. Results of the study with panel ordinary least squares, panel fixed-effects, panel random effects, panel-corrected standard errors (robustness check), and system-generalized method of moments (robustness check) confirm that total factor productivity growth positively correlated with economic prosperity. [ABSTRACT FROM AUTHOR]

This paper provides estimation and inference methods for conditional average treatment effects (CATE) characterized by a high‐dimensional parameter in both homogeneous cross‐sectional and unit‐heterogeneous dynamic panel data settings. In our leading example, we model CATE by interacting the base treatment variable with explanatory variables. The first step of our procedure is orthogonalization, where we partial out the controls and unit effects from the outcome and the base treatment and take the cross‐fitted residuals. This step uses a novel generic cross‐fitting method that we design for weakly dependent time series and panel data. This method "leaves out the neighbors" when fitting nuisance components, and we theoretically power it by using Strassen's coupling. As a result, we can rely on any modern machine learning method in the first step, provided it learns the residuals well enough. Second, we construct an orthogonal (or residual) learner of CATE—the lasso CATE—that regresses the outcome residual on the vector of interactions of the residualized treatment with explanatory variables. If the complexity of CATE function is simpler than that of the first‐stage regression, the orthogonal learner converges faster than the single‐stage regression‐based learner. Third, we perform simultaneous inference on parameters of the CATE function using debiasing. We also can use ordinary least squares in the last two steps when CATE is low‐dimensional. In heterogeneous panel data settings, we model the unobserved unit heterogeneity as a weakly sparse deviation from Mundlak's (1978) model of correlated unit effects as a linear function of time‐invariant covariates and make use of L1‐penalization to estimate these models. We demonstrate our methods by estimating price elasticities of groceries based on scanner data. We note that our results are new even for the cross‐sectional (i.i.d.) case. [ABSTRACT FROM AUTHOR]

We model the empirical relationship between the within-establishment union membership rate and wage inequality in Norway. The data set is a panel of 2,285 private-sector establishments observed in the period 2000–18. The statistical model represents joint feedback between wage inequality and union membership. Dynamic panel data models are estimated for different wage inequality measures, with gini as the reference measure. The results show a negative relationship between union membership and wage inequality, which is robust with respect to different inequality measures and estimation method. The strength of the relationship increases with the permanency of the shift in union membership. We find evidence that union membership is a more important explanatory variable for the lower part of the wage distribution than for the upper part. Furthermore, the impact of union density on wage inequality is conditional on the presence of a collective agreement. [ABSTRACT FROM AUTHOR]

This study investigates the determinants of coffee prices received by growers in Costa Rica, paying attention to the impact of environmental, regional, quality, and international aspects in a panel data set for the period 2008–2016. We identify three groups of variables that affect domestic coffee prices. Some of them are external to the control of the coffee growers, such as the international price of green coffee or the power of multinationals; others, such as the altitude where the coffee is harvested or the berries' yield, are related to coffee quality but difficult to modify by coffee growers. The focus of our study is on the third group, which refers to differentiation strategies related to environmental certifications. More specifically, we consider two particularly relevant certifications, which are Fairtrade mills and organic coffee. We find that organic coffee berries received higher prices, but Fairtrade mills report lower average prices than other, non-certified, buyers. [ABSTRACT FROM AUTHOR]

We propose a framework for estimation and inference when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We construct estimators whose mean squared error is minimax in a neighborhood of the reference model, based on one‐step adjustments. In addition, we provide confidence intervals that contain the true parameter under local misspecification. As a tool to interpret the degree of misspecification, we map it to the local power of a specification test of the reference model. Our approach allows for systematic sensitivity analysis when the parameter of interest may be partially or irregularly identified. As illustrations, we study three applications: an empirical analysis of the impact of conditional cash transfers in Mexico where misspecification stems from the presence of stigma effects of the program, a cross‐sectional binary choice model where the error distribution is misspecified, and a dynamic panel data binary choice model where the number of time periods is small and the distribution of individual effects is misspecified. [ABSTRACT FROM AUTHOR]

This study investigates the boomerang phenomenon among adult children in Thailand. We estimate the effect of having children on co-residence between parents and adult children using Socio-Economic Survey panel data. We find that adult children who have moved out tend to move back in with their parents after having children to save time and money on childcare. The presence of young children increases the likelihood of intergenerational co-residence by over 30%. This study is the first to provide empirical evidence of alternative boomerang kids in an Asian context, which is distinctive compared with Western countries. The relationship between intergenerational co-residence and the maternal labor supply is also examined using the instrumental variable approach based on the cross-sectional Labor Force Survey, which has data covering over 30 years. Our results show that co-residence increases the female labor supply by 21% and also extend women's working hours by 10 hours. [ABSTRACT FROM AUTHOR]

Wine investment returns can come from overall market trends or price increases with age. Because of the short wine price histories available, market and maturation effects are difficult to separate. Consequently, researchers often obtain dramatically different estimates of investment returns. We find that data sample bias may be the hidden cause of the disparate estimates. In wine auction data, the sample bias refers to a shift in the distribution of which wines are traded as a function of their age. Such sample bias in panel data sampled across many different wine labels can distort the estimation of price increases versus age and consequently impact the estimation of market trends. This analysis shows that segmenting the analysis such that the data panels contain wine labels with similar trading characteristics can lead to a more stable estimation. The analysis here looks at data from Bordeaux, Italy, Australia, and California. An Age-Period-Cohort (APC) analysis is applied to data panels from each region. Then the data in each region is segmented by a measure of popularity in order to reduce sampling bias. Data thus segmented is then re-analyzed to demonstrate the difference in estimating price appreciation lifecycles and market trends. [ABSTRACT FROM AUTHOR]

Using firm-level panel data we analyze the misallocation of capital and labor for the Netherlands in the period 2001–2017. We use the dispersion in marginal revenue products of capital and labor to measure the extent of misallocation. Compared to a counterfactual efficient allocation we find that misallocation has had a sizable negative impact on aggregate productivity of around 14 percentage points in the period 2001–2017. Especially capital misallocation has increased over time. Exploiting a panel data error components model we find that capital misallocation has a much more permanent character than labor misallocation. Moreover, it is the permanent component of capital misallocation that has increased over time. Finally, we show that in our sample the measurement of misallocation is largely insensitive to capital adjustment costs and alternative specifications of the production function. The contribution of heterogeneous markups to observed misallocation, however, is non-negligible. [ABSTRACT FROM AUTHOR]

In panel experiments, we randomly assign units to different interventions, measuring their outcomes, and repeating the procedure in several periods. Using the potential outcomes framework, we define finite population dynamic causal effects that capture the relative effectiveness of alternative treatment paths. For a rich class of dynamic causal effects, we provide a nonparametric estimator that is unbiased over the randomization distribution and derive its finite population limiting distribution as either the sample size or the duration of the experiment increases. We develop two methods for inference: a conservative test for weak null hypotheses and an exact randomization test for sharp null hypotheses. We further analyze the finite population probability limit of linear fixed effects estimators. These commonly‐used estimators do not recover a causally interpretable estimand if there are dynamic causal effects and serial correlation in the assignments, highlighting the value of our proposed estimator. [ABSTRACT FROM AUTHOR]

The relationship between temperature and agriculture outcomes in Brazil has been widely explored, overlooking the fact that most of the country's labor force is employed in non-agriculture sectors. We use monthly individual-level panel data spanning the period from January 2015 to December 2016 to ask whether temperature shocks impact non-agriculture wages in formal labor markets. Our results show that additional days in a month that fall within high-temperature ranges have significant adverse effects on real wages. Assuming a uniform climate change scenario where the daily temperature distribution shifts by 2 $^{\circ }$ C, we calculate income losses for formal workers in non-agriculture markets equivalent to 0.12 per cent of 2015 GDP. [ABSTRACT FROM AUTHOR]

This paper aims to analyze the determinants of cash usage in a selection of European Union (EU) countries over the 2003–2016 period, based on a set of technological, socioeconomic, and socio-cultural indicators and cost components. Our results reveal the existence of both common and region-specific determinants for the EU advanced and Central and Eastern European (CEE) countries. In both groups cash usage is determined by payment system characteristics. Additionally, in the EU advanced countries cash usage can also be explained by the level of economic development and income inequalities and proliferation of Internet. In contrast, cash usage in CEE countries is negatively associated with consumer confidence and is inversely related to the technological progress, expressed in terms of mobile users. [ABSTRACT FROM AUTHOR]

The panel approach with fixed effects and nonlinear weather effects has become a popular method to uncover weather impacts on economic outcomes, but its ability to capture long‐run climatic adaptation remains unclear. Building upon a framework proposed by McIntosh and Schlenker (2006), this paper identifies empirical conditions under which the nonlinear panel approach can approximate a long‐run response to climate. When these conditions fail, the obtained relationship may still be interpretable as a weighted average of underlying short‐run and long‐run responses. We use this decomposition to revisit recently published climate impact estimates. For spatially large panels, the estimated temperature–outcome relationship mostly reflects the long‐run climatic response; this is not so for precipitation. We find some evidence of long‐run climatic adaptation for crop yield outcomes in the United States and France. [ABSTRACT FROM AUTHOR]

This work aims to investigate scale, scope and nature of spatial dependence of agricultural total factor productivity (TFP) by using farm‐level survey data. TFP is measured using transitive index numbers, and the spatial properties of TFP are assessed within a dynamic spatial panel data model designed to separate production fundamentals from productivity spillovers. Because of the statistical issues that typically affect spatial analyses based on survey data, a Bayesian model selection procedure is used to inspect the spatial properties of TFP at different aggregation levels and to search for the most appropriate spatial scale to conduct the investigation. The application concerns Italian FADN farm‐level data over the period 2008–2015 then aggregated at the NUTS3 level. Results suggest that agricultural productivity spillovers significantly occur though over a limited spatial range. The cumulated effects of the estimated diffusion mechanism are described through a set of spatial indicators and presented graphically. [ABSTRACT FROM AUTHOR]

We explore inference on regression coefficients in semiparametric multinomial response models. We consider cross‐sectional, and both static and dynamic panel settings where we focus throughout on inference under sufficient conditions for point identification. The approach to identification uses a matching insight throughout all three models coupled with variation in regressors: with cross‐section data, we match across individuals while with panel data, we match within individuals over time. Across models, we relax the Indpendence of Irrelevant Alternatives (or IIA assumption, see McFadden (1974)) and allow for arbitrary correlation in the unobservables that determine utility of various alternatives. For the cross‐sectional model, estimation is based on a localized rank objective function, analogous to that used in Abrevaya, Hausman, and Khan (2010), and presents a generalization of existing approaches. In panel data settings, rates of convergence are shown to exhibit a curse of dimensionality in the number of alternatives. The results for the dynamic panel data model generalize the work of Honoré and Kyriazidou (2000) to cover the semiparametric multinomial case. A simulation study establishes adequate finite sample properties of our new procedures. We apply our estimators to a scanner panel data set. [ABSTRACT FROM AUTHOR]

This study analyzes efficiency in the reduction of air pollutants and the associated healthcare costs using a stochastic frontier cost function panel data approach. For the empirical analysis, we use monthly data covering 25 districts in the Seoul metropolitan city of South Korea observed over the period January 2010 to December 2017. Our results show large variations in air pollution and healthcare costs across districts and over time and their efficiency in reducing air pollutants. The study concludes that efforts are needed to apply the World Health Organization's air quality standards for designing and implementing location-specific customized policies for improving the level of air quality and its equal distribution, provision of health services, and improved efficiency in improving air quality standards. The study identifies a number of determinants of air pollutants and efficiency enhancement which provide useful pointers for policymakers for addressing the current environmental problems in South Korea. [ABSTRACT FROM AUTHOR]

This article investigates how entrepreneurial risk-taking in family firms is shaped by CEO tenure, generational involvement, and CEO founder status. We used a dynamic panel data model with a large sample of Italian family firms over the period 2003–2012. Consistent with the theoretical framework of Hambrick and Fukutomi. Academy of Management Review, 16:719-742 (1991), we showed that CEO tenure has a concave relationship with entrepreneurial risk-taking. We step forward to understand whether this relationship is shaped by the number of family generations simultaneously involved in the firm and CEO founder status. We provided evidence that the impact of CEO tenure on risk-taking is strengthened if generational involvement in the business is high, and is inverted if the CEO is also the founder of the firm. [ABSTRACT FROM AUTHOR]

Machine learning has dramatically expanded the range of tools for evaluating economic panel data. This paper applies a variety of machine-learning methods to the Boston housing dataset, an iconic proving ground for machine learning. Though machine learning often lacks the overt interpretability of linear regression, methods based on decision trees score the relative importance of dataset features. In addition to addressing the theoretical tradeoff between bias and variance, this paper discusses practices rarely followed in traditional economics: the splitting of data into training, validation, and test sets; the scaling of data; and the preference for retaining all data. The choice between traditional and machine-learning methods hinges on practical rather than mathematical considerations. In settings emphasizing interpretative clarity through the scale and sign of regression coefficients, machine learning may best play an ancillary role. Wherever predictive accuracy is paramount, however, or where heteroskedasticity or high dimensionality might impair the clarity of linear methods, machine learning can deliver superior results. [ABSTRACT FROM AUTHOR]

We study potential impacts of future climate change on U.S. agricultural productivity using county‐level yield and weather data from 1950 to 2015. To account for adaptation of production to different weather conditions, it is crucial to allow for both spatial and temporal variation in the production process mapping weather to crop yields. We present a new panel data estimation technique, called mean observation OLS (MO‐OLS) that allows for spatial and temporal heterogeneity in all regression parameters (intercepts and slopes). Both forms of heterogeneity are important: We find strong evidence that production function parameters adapt to local climate, and also that sensitivity of yield to high temperature declined from 1950–89. We use our estimates to project corn yields to 2100 using 19 climate models and three greenhouse gas emission scenarios. We predict unmitigated climate change will greatly reduce yield. Our mean prediction (over climate models) is that adaptation alone can mitigate 36% of the damage, while emissions reductions consistent with the Paris targets would mitigate 76%. [ABSTRACT FROM AUTHOR]

This research aims to explore the existence of a new concept known as "environmental Phillips curve" (EPC) developed by the authors. Taking annual data of 30 countries for 26 years, a panel data estimation method is applied. The invented function shows an inverse relationship between pollution and unemployment. In most of the cases, the industrialized countries show that the relationship is valid. The notion is proved effective in every format of investigation. It seems that curbing pollution in the world is only possible at the cost of human employment. Therefore, if countries want to curb environmental pollution without affecting the generation of employment and reducing poverty, they should contemplate both innovation and enforcement alternative technologies that would be less polluting but employment friendly. Moreover, this research also suggests that if a country can treat pollution efficiently, it can increase the national income without a deteriorating unemployment level. [ABSTRACT FROM AUTHOR]

In the past decades, renewable energy consumption has grown considerably because of environmental degradation caused by non-renewable energy consumption. This research aims to find the causal link between renewable and non-renewable energy consumption, human capital, and non-renewable energy price for the 53 most renewable energy-consuming countries worldwide (hydroelectric) during the period 1990–2017. We use data collected from the World Bank (http://data.worldbank.org/data-catalog/world-development-indicators, 2018) and Statistical Review of World Energy (https://www.bp.com/, 2018). We test simultaneously two types of regressions in order to measure the degree of elasticity of the two types of energy by using econometric techniques for panel data. The results of the GLS models indicate that human capital has a stronger significant effect on renewable energy consumption at the global level, in the middle high-income countries and low-middle income countries, compared with non-renewable energy consumption. Besides, at the global level, there is a positive and statistically significant relationship between the non-renewable energy price and the two types of energy consumption. There is a long-run consumption of both types of energy. On the other hand, the one-way relationship between human capital and non-renewable energy price and renewable energy consumption is stronger than the relationship with non-renewable energy consumption. The policy implications derived from this study should be designed to promote human capital development in order to promote renewable energy consumption and increase the investment in renewable energy sources to guarantee their access to lower prices that reduce non-renewable energy consumption. [ABSTRACT FROM AUTHOR]

This paper incorporates technological interdependence into a neoclassical regional growth framework with imperfect factor mobility, leading to a convergence equation with spatial effects. The empirical analysis is based on the estimation of a spatial Durbin panel data model and the implementation of multiple imputation techniques. Our results show that taking into account both unobserved heterogeneity and spatial dependence increases the estimated regional convergence rate. This provides an explanation for puzzling findings in the related literature. We also obtain evidence of heterogeneity across country groups regarding the regional speed of convergence and the degree of diffusion of technology. [ABSTRACT FROM AUTHOR]

This study has empirically tested the relation between leverage and market concentration using a balanced panel data on 1469 firms over 26 manufacturing industries during 2001–2016 in the context of India. Our regression results indicate that competition has an overall negative effect on leverage. The negative effect of competition on leverage is intensified with larger firm size and larger growth opportunities. Finally, we examine whether the relationship between leverage and competition is non-monotonic. Our results show no important departure from linearity while using HHI but support the cubic relationship while using Tobin's q. Different tests of robustness support our results. [ABSTRACT FROM AUTHOR]

The aim of this paper is to explain the determinants of longevity in 20 European Union countries for which comparable data are available for the period 1990–2016. We use a health equation to explain life expectancy at age 65 for total population considering socioeconomic factors, population structure, health resources, lifestyles and environment as the main determinants of health status. Panel estimation techniques are implemented to estimate the health equation with lagged explanatory variables to attenuate the endogeneity problems of regressors. Our evidence shows that per capita income, education and pharmaceutical expenditures positively affect life expectancy at the age of 65. As expected, risky lifestyles and air pollution have a significant negative impact on health. The interaction between population age structure and pharmaceutical expenditures proves also to be important for explaining longevity. Our evidence reinforces the idea that investing in education and health care provision is the way to achieve healthier longevity allowing a more active participation of the elderly in society. [ABSTRACT FROM AUTHOR]

In this paper, we develop a very flexible and comprehensive state-space framework for modeling time series data. Our research extends the simple canonical model usually employed in the literature, into a panel-data time-varying parameters framework, combining fixed (both common and country-specific) and varying components. Under some specific circumstances, this setting can be understood as a mean-reverting panel time-series model, where the mean fixed parameter can, at the same time, include a deterministic trend. Regarding the transition equation, our structure allows for the estimation of different autoregressive alternatives, and include control instruments, whose coefficients can be set-up either common or idiosyncratic. This is particularly useful to detect asymmetries among individuals (countries) to common shocks. We develop a GAUSS code that allows for the introduction of restrictions regarding the variances of both the transition and measurement equations. Finally, we use this empirical framework to test for the Feldstein–Horioka puzzle in a 17-country panel. The results show its usefulness for solving complexities in macroeconomic empirical research. [ABSTRACT FROM AUTHOR]

This study develops cluster robust inference methods for panel quantile regression (QR) models with individual fixed effects, allowing for temporal correlation within each individual. The conventional QR standard errors can seriously underestimate the uncertainty of estimators and, therefore, overestimate the significance of effects, when outcomes are serially correlated. Thus, we propose a clustered covariance matrix (CCM) estimator to solve this problem. The CCM estimator is an extension of the heteroskedasticity and autocorrelation consistent covariance matrix estimator for QR models with fixed effects. The autocovariance element in the CCM estimator can be substantially biased, due to the incidental parameter problem. Thus, we develop a bias‐correction method for the CCM estimator. We derive an optimal bandwidth formula that minimizes the asymptotic mean squared errors, and propose a data‐driven bandwidth selection rule. We also propose two cluster robust tests, and establish their asymptotic properties. We then illustrate the practical usefulness of the proposed methods using an empirical application. [ABSTRACT FROM AUTHOR]

Starting with a reduced form derived from standard urban economics theory, this paper estimates the possible job-shortfall across UK and EU regions using a time-space dynamic panel data model with a spatial moving average random effects structure of the disturbances. The paper provides a logical rational for the presence of spatial and temporal dependencies involving the endogenous variable, leading to estimates based on a state-of-the-art dynamic spatial generalized moments estimator proposed by Baltagi et al. (Reg Sci Urban Econ, 2018. 10.1016/j.regsciurbeco.2018.04.013). Given reliable interregional trade estimates, the simulations are based on a linear predictor which utilizes different regional interdependency matrices according to assumptions about interregional trade post-Brexit. The results indicate that heightened barriers to trade will evidently cause job-shortfalls both in the UK and across the EU, but it is also shown that there is a considerable amount of asymmetry in the outcomes across regions and sectors. [ABSTRACT FROM AUTHOR]

The main focus of this paper is on the role of the past in budgetary policy and the impact of a multitude of relevant political characteristics on the local budget balance in a dynamic context. Although the importance of past policy was stated approximately 6 decades ago by Lindblom (Public Adm Rev 19(2):79–88, 1959), dynamic studies are relatively underexposed in recent literature on the budget balance at the local government level. The studies that do take into account the dynamics surprisingly do not focus on the past but treat it as a control variable. This paper exhibits the theoretical background of introducing past performance into the analysis. It is also noticeable that the dynamic studies thus far only test the partisan and the political budget cycle hypotheses. In the empirical part of this study, we add government fragmentation and government power to the dynamic analysis, and we look for both short- and long-term effects on the budget balance. In doing so, we test the impact of multiple political characteristics simultaneously on a dataset of all Flemish local governments for the period 1995–2017. The results from the dynamic panel analysis show that past performance is of importance, as are the electoral budget cycle and the power of the government. The importance of the government power for the budget balance is a new insight in dynamic research. As we focus on the long-term impact of the explanatory variables due to the dynamics, we avoid the underestimation of the impact of politics on the budget balance in the long run. [ABSTRACT FROM AUTHOR]

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A strategy for MCMC estimation of a family of models involving multiple simultaneous dependence parameters is set forth that is capable of producing rapid estimates for problems involving a large number of observations. Simultaneous dependence parameters arise when dependence exists between dependent variable observations, with spatial and cross-sectional dependence being specific examples. The approach taken is to express the joint conditional distribution of the dependence parameters as a quadratic form, where the dependence parameters are outer vectors of the quadratic form and the inner matrix expressions of the quadratic form involve only sample data that allow these to be pre-calculated. During MCMC estimation, multiple evaluations of the joint conditional distribution of the dependence parameters can be carried out rapidly, allowing a block-sampling scheme. Block sampling of the dependence parameters is useful for imposing stability restrictions that arise for these parameters. In addition, a Taylor series approximation to the log-determinant expression that arises in the joint conditional distribution for the dependence parameters can be used to rapidly evaluate this term. The joint conditional distribution for the dependence parameters is obtained by analytically integrating out other model parameters, allowing Monte Carlo integration of the log-marginal likelihood, which can be used for model comparison. Timing results are discussed along with Monte Carlo evidence regarding performance as well as applied illustrations involving large sample sizes. [ABSTRACT FROM AUTHOR]

This paper estimates the possible job-shortfall across Italian and EU regions using a time–space dynamic panel data model with a spatial moving average random effects structure of the disturbances. The paper is a companion paper to an earlier prediction exercise regarding Brexit. The model includes spatial and temporal dependencies involving the endogenous variable, leading to estimates based on a new dynamic spatial generalized moments estimator proposed by Baltagi et al. (Reg Sci Urban Econ, 2018. 10.1016/j.regsciurbeco.2018.04.013). The predictions use modified interregional trade estimates, assuming a reduction in trade flows between Italian and EU regions due to Italexit, to simulate the impact on employment across Italian regions and the wider EU. Comparisons are made between Italexit and Brexit. [ABSTRACT FROM AUTHOR]

This paper analyses the differences in labour demand between family and non‐family managed firms. These firms seem to have better employment performance than non‐family controlled companies. Therefore, this study applies a treatment model for panel data controlling for endogeneities of being a family managed firm or not. The results of the estimations indicate that labour demand is possibly larger because of family members joining the firms as extra employees. Moreover, labour turnover is lower, supporting the assumption that family firms offer some kind of implicit labour contracts. However, in opposite to previous results, it seems that only small family managed firms show different employment behaviour. [ABSTRACT FROM AUTHOR]

This study uses the Hamilton function method to explore the dynamic relationships among fiscal decentralization, economic growth, and environmental pollution decoupling under the framework of endogenous growth theory. Furthermore, the study uses a partial derivative method, the results of which reveal that fiscal decentralization and haze pollution decoupling display an inverse U-shaped relationship and that the economic growth rate has a negative effect on haze pollution decoupling. This study uses the panel data of 30 provinces and municipalities of China to perform an empirical investigation. Our findings demonstrate empirical results, which verify the correctness of the theoretical results. This study also uses the threshold regression method to conduct a grouping experimental research and compares the regression results of each group. Our findings demonstrate the degrees of fiscal decentralization and the years that correspond to their turning points have differences. Finally, policies and recommendations are proposed. [ABSTRACT FROM AUTHOR]

This paper analyses and looks more closely at the empirical debate regarding the Income Elasticity Hypothesis postulated in relation to the Environmental Kuznets Curve, and the impact of corruption on the relationship between growth and environmental impact, measured in terms of soil sealing, as proxied by the number of Building Permits issued by public authorities. It postulates that when current private rent is high compared to perceived social costs and a large enough minority benefits from the rules, the EKC does not emerge and public intervention fails to perceive social optimality. To validate this hypothesis, we run a panel data regression model based on data from all Italian regions. Results confirmed the hypothesis evidencing a U-shaped relationship, i.e. a detrimental relationship between income and environmental impact/quality. It also demonstrated that this is contingent on the level of corruption. The latter affects the position and the shape of the inverted EKC, speeding up the exploitation process. It shows that the higher the income, the higher the effect of corruption on the environment. Therefore, it advocates caution against any simplistic inference from EKC. [ABSTRACT FROM AUTHOR]

This paper considers identification and estimation of the Quantile Treatment Effect on the Treated (QTT) under a straightforward distributional extension of the most commonly invoked Mean Difference in Differences Assumption used for identifying the Average Treatment Effect on the Treated (ATT). Identification of the QTT is more complicated than the ATT though because it depends on the unknown dependence (or copula) between the change in untreated potential outcomes and the initial level of untreated potential outcomes for the treated group. To address this issue, we introduce a new Copula Stability Assumption that says that the missing dependence is constant over time. Under this assumption and when panel data is available, the missing dependence can be recovered, and the QTT is identified. We use our method to estimate the effect of increasing the minimum wage on quantiles of local labor markets' unemployment rates and find significant heterogeneity. [ABSTRACT FROM AUTHOR]

This paper presents identification and estimation results for a flexible state space model. Our modification of the canonical model allows the permanent component to follow a unit root process and the transitory component to follow a semiparametric model of a higher‐order autoregressive‐moving‐average (ARMA) process. Using panel data of observed earnings, we establish identification of the nonparametric joint distributions for each of the permanent and transitory components over time. We apply the identification and estimation method to the earnings dynamics of U.S. men using the Panel Survey of Income Dynamics (PSID). The results show that the marginal distributions of permanent and transitory earnings components are more dispersed, more skewed, and have fatter tails than the normal and that earnings mobility is much lower than for the normal. We also find strong evidence for the existence of higher‐order ARMA processes in the transitory component, which lead to much different estimates of the distributions of and earnings mobility in the permanent component, implying that misspecification of the process for transitory earnings can affect estimated distributions of the permanent component and estimated earnings dynamics of that component. Thus our flexible model implies earnings dynamics for U.S. men different from much of the prior literature. [ABSTRACT FROM AUTHOR]