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Objective Method Results Conclusions Entringer et al. used longitudinal data from a German panel study to examine reciprocal causal effects between personality and religiosity, along with cultural moderators of these effects. The current paper examines the robustness of the original effects to alternative model specifications.We reanalyzed the same four‐wave data spanning 12 years (total N = 46,316), first replicating the original cross‐lagged panel analyses and then extending these analyses in three ways: Using a random‐intercept cross‐lagged panel model, using observed rather than latent variables, and modeling each trait individually rather than simultaneously.Correlations between personality and religiosity were all small in size, even when aggregating over 12 years. Lagged effects were very small, and none was robust across all model specifications. Cultural moderators also depended on model specifications.The very small size of these reciprocal effects, along with their sensitivity to model specifications, suggest that conclusions about causal effects of personality and religiosity should be drawn very cautiously. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of specialization, urbanization, and diversification externalities on the dynamics of tourism employment in Brazilian microregions. We use a dynamic panel data model for the 2006–2019 period. The location quotient, population density and the inverse of the Hirschman–Herfindahl index, proxies for specialization, urbanization, and diversification, respectively, positively affect tourism employment in the long run. Based on the estimated long‐run elasticities, the specialization externality produces the strongest influence on tourism employment after a permanent increase of one standard deviation. [ABSTRACT FROM AUTHOR]

The Korean government initiated a multi‐tiered benefits system for older people, but the relative poverty rate of the older population remains the highest among developed economies. This paper employed the Shapley decomposition and four efficiency criteria to evaluate the anti‐poverty efficacy and efficiency of major public transfer benefits with the Korea Welfare Panel Study 2006–2018. The results present that the Korean public pension exerted the greatest efficacy among the programmes. The efficacy of the Basic Pension, operating as a means‐tested benefit, significantly increased when implemented in 2008 and expanded in 2014. In terms of efficiency, the contributory public pension supported only 40% of older adults in poverty. However, the generous coverage of the Basic Pension since 2008 might have led to more than half of recipients not experiencing absolute poverty. Therefore, this paper concludes with suggestions for more financial support targeting the economically vulnerable. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of Supplemental Nutrition Assistance Program (SNAP) benefit disbursement on intramonthly household level purchases made from a supermarket retailer. We find that spending, the likelihood of shopping, the bulk expenditure share and the national brand expenditure share increase by $2, 1.5, 2, and 0.6% points, respectively, on the day that SNAP benefits are disbursed. We also compare and contrast estimates that use variation in the indicator for benefit receipt to estimates that utilize variation in the probability of SNAP benefit receipt. We find substantial differences between the two approaches for the outcome of spending. [ABSTRACT FROM AUTHOR]

Quantile regression, known as a robust alternative to linear regression, has been widely used in statistical modeling and inference. In this paper, we propose a penalized weighted convolution‐type smoothed method for variable selection and robust parameter estimation of the quantile regression with high dimensional longitudinal data. The proposed method utilizes a twice‐differentiable and smoothed loss function instead of the check function in quantile regression without penalty, and can select the important covariates consistently using the efficient gradient‐based iterative algorithms when the dimension of covariates is larger than the sample size. Moreover, the proposed method can circumvent the influence of outliers in the response variable and/or the covariates. To incorporate the correlation within each subject and enhance the accuracy of the parameter estimation, a two‐step weighted estimation method is also established. Furthermore, we prove the oracle properties of the proposed method under some regularity conditions. Finally, the performance of the proposed method is demonstrated by simulation studies and two real examples. [ABSTRACT FROM AUTHOR]

Accepting defeat in the aftermath of elections is crucial for the stability of democracies. But in times of intense polarization, the voluntary consent of electoral losers seems less obvious. In this paper, I study whether affective and perceived ideological polarization amplify the winner–loser gap in political support. Using multilevel growth curve modelling on pre and post-election panel data from the British Election Study Internet Panel collected during the 2015 and 2019 UK general elections, I show that the winner–loser gap is indeed more pronounced amongst voters with higher levels of affective and perceived ideological polarization. Moreover, the results illustrate that polarized voters experience a stark decrease in their support for the political system following their electoral loss. Given the high and, in some Western democracies, rising polarization levels, these findings have important implications for losers’ consent and the stability of democracies in election times. [ABSTRACT FROM AUTHOR]

Are political attitudes a stable feature of individuals or a rational response to changing circumstances and contexts? This question has long been a feature of political science and underpins our theories of how political attitudes are formed and what their consequences might be. In this paper, we explore this perennial question with a focus on the case of political trust, a fundamental indicator of democratic legitimacy and a long-standing topic of debate. Theoretically, we devise a framework that highlights how different theories of political trust assume different levels of stability or volatility and the implications that this has for those theories and their normative consequences. Empirically, we study within-individual stability of political trust using six panel studies that cover five countries between 1965 and 2020. Our results consistently point to trust being stable in the long term, with potential for short-term volatility in response to changing political contexts, and for substantial changes between people’s formative years and their adulthood. Even over a period of 19 years, most people’s responses to trust questions are remarkably similar between surveys and significant life events such as unemployment and going to University do not significantly influence trust. Changes in the political environment, like incumbent government turnover, have larger effects but these appear to return to equilibrium in a few years. The exception to this general finding is individuals who are first surveyed when they are under the age of 18, who appear much more likely to change their trust levels in subsequent waves. Overall, our results complement previous research on attitude stability, indicating that trust is approximately as stable as other attitudes, such as towards immigration and redistribution. These findings have fundamental implications for our understanding of the nature of political trust and attitude formation more broadly. [ABSTRACT FROM AUTHOR]

Religiosity influences economic behavior in various domains, but what determines religiosity? Using data from the representative Socio‐Economic Panel Study for Germany (SOEP), this paper shows that religious activity increases after a family member's death. The life event's effect is larger after a partner's death when compared to a parent's death. Our results indicate that the influence of a family member's death on religious activity is temporary and are consistent with the religious coping hypothesis. [ABSTRACT FROM AUTHOR]

Considering that public debt is a priority for policymakers in Mexico, the aim of this paper is to examine the convergence or divergence in debt per capita and main drivers of debt in the 32 Mexican states in the period 2006–2021. The results support significant spatial correlation of debt/cap across states. Cross‐section and spatial dynamic regressions are used to analyse absolute and conditional beta‐convergence in debt per capita, and the divergence assumption is documented. The panel data models based on the method‐of‐moments quantile models and pooled mean group estimators indicate that poverty is the main determinant of debt, whereas foreign direct investment is insufficient to support the reduction in debt in the long run. These results provide evidence for a set of policy recommendations to ensure a sustainable debt path in Mexico. [ABSTRACT FROM AUTHOR]

Random coefficient (RC) models are commonly used in clinical trials to estimate the rate of change over time in longitudinal data. Trials utilizing a surrogate endpoint for accelerated approval with a confirmatory longitudinal endpoint to show clinical benefit is a strategy implemented across various therapeutic areas, including immunoglobulin A nephropathy. Understanding conditional power (CP) and information fraction calculations of RC models may help in the design of clinical trials as well as provide support for the confirmatory endpoint at the time of accelerated approval. This paper provides calculation methods, with practical examples, for determining CP at an interim analysis for a RC model with longitudinal data, such as estimated glomerular filtration rate (eGFR) assessments to measure rate of change in eGFR slope. [ABSTRACT FROM AUTHOR]

This paper examines household earnings arrangements and parenthood. Previous research has shown that parenthood is associated with a motherhood wage penalty with women withdrawing from the labour market or reducing their work hours. But few studies have examined within‐couple relative earnings and breadwinning arrangements across the transition to parenthood. We identify three types of households—"female‐breadwinner households" (where women earn more than 60 per cent of the couple's annual labour income); "male‐breadwinner households" (where women earn less than 40 per cent of the joint income); and "equal‐earner households" (where women earn 40 per cent to 60 per cent of the joint income). Using longitudinal data from the Household Income and Labour Dynamics in Australia (HILDA) Survey and fixed effects models, we find a substantial decrease in the percentage of couples in equal‐earner households in the year prior to and after parenthood that is largely replaced by an increase in the percentage in male‐breadwinner households. We observe little return to pre‐parenthood earnings arrangements for equal‐earner and male‐breadwinner households. For female‐breadwinner households, we observe a gradual return to pre‐parenthood arrangements. These results provide evidence that parenthood is a major milestone contributing to gender inequality and highlight the importance of policies for reducing the impact of parenthood on women's earnings. [ABSTRACT FROM AUTHOR]

The cultural diversity brought by migrants has attracted widespread interest in recent years. This paper investigates the relationship between migrant cultural diversity and regional innovation by constructing panel data of provincial migrant cultural diversity and combining them with invention applications data from China's State Intellectual Property Office. The empirical results show that the migrant cultural diversity, measured by migrant's hukou and dialect respectively, has a positive impact on regional innovation. The effect still holds after a series of robustness tests and addressing endogeneity issues. Furthermore, the further analysis implies that the effects of the migrant cultural diversity in workers aged from 30 to 50, engaged in an innovative occupation, working in high market‐competitiveness or inclusive provinces are more pronounced. Our study supplements the current research on the relationship between migrant cultural diversity and regional innovation. [ABSTRACT FROM AUTHOR]

Outliers are common in longitudinal data analysis, and the multivariate contaminated normal (MCN) distribution in model‐based clustering is often used to detect outliers and provide robust parameter estimates in each subgroup. In this paper, we propose a method, the mixture of MCN (MCNM), based on the joint mean‐covariance model, specifically designed to analyze longitudinal data characterized by mild outliers. Our model can automatically detect outliers in longitudinal data and provide robust parameter estimates in each subgroup. We use iteratively expectation‐conditional maximization (ECM) algorithm and Aitken acceleration to estimate the model parameters, achieving both algorithm acceleration and stable convergence. Our proposed method simultaneously clusters the population, identifies progression patterns of the mean and covariance structures for different subgroups over time, and detects outliers. To demonstrate the effectiveness of our method, we conduct simulation studies under various cases involving different proportions and degrees of contamination. Additionally, we apply our method to real data on the number of people infected with AIDS in 49 countries or regions from 2001 to 2021. Results show that our proposed method effectively clusters the data based on various mean progression trajectories. In summary, our proposed MCNM based on the joint mean‐covariance model and MCD of covariance matrices provides a robust method for clustering longitudinal data with mild outliers. It effectively detects outliers and identifies progression patterns in different groups over time, making it valuable for various applications in longitudinal data analysis. [ABSTRACT FROM AUTHOR]

Recent research suggests that emotions are a central motivation for radical right voting. One emotion that has gained particular interest is nostalgia: Radical right politicians use nostalgic rhetoric, and feeling nostalgic is associated with radical right support. However, while nostalgia is widely and frequently experienced, previous work differentiates personal contents of nostalgia (e.g., childhood) from group‐based contents (e.g., traditions) and suggests that only the latter is related to the radical right. But why does nostalgia, and specifically its group‐based content, matter? In the present paper, I argue that nostalgia evokes implicit comparisons between the past and the present. Using relative deprivation theory, I posit that group‐based nostalgia makes people subjectively evaluate society's present as worse than its past. In turn, this temporal group‐based relative deprivation is associated with attempts to restore the past through radical right voting. Personal nostalgia, instead, does not evoke equivalent experiences of personal relative deprivation and is, therefore, unrelated to radical right support. In preregistered analyses of representative panel data from the Netherlands, I show that group‐based nostalgia is more consistently related to radical right support than personal nostalgia. In subsequent exploratory analyses, I test the relative deprivation argument and find that group‐based relative deprivation does indeed mediate the relationship between group‐based nostalgia and radical right voting: People who long for the group‐based past are more likely to feel dissatisfied with the government and, in turn, consider voting for the radical right. In studying this mechanism, I connect recent work on emotional and relative deprivation explanations to radical right voting. [ABSTRACT FROM AUTHOR]

The environmental Kuznets curve (EKC) hypothesis assumes the nonlinear relationship between production and its environmental effects, suggesting that the negative effect of production on the natural environment is reversed at higher levels of development. In contrast to the majority of the studies, this paper considers the possibility of non‐linear effects of per capita output on agricultural land use (cropland), as one of the natural resource stocks, and additionally examines the influence of demographic and productivity variables on the agricultural land use. The study draws on panel data from 143 economies over the 1990–2019 period and employs a series of panel econometrics techniques (including panel unit root and cointegration tests, panel ordinary least squares, generalized method of moments, and panel quantile models). The results unequivocally indicate the inverted U‐shaped relationship between the GDP per capita and cropland use, as per EKC hypothesis. The results are robust with respect to employing different techniques, the identified turning points correspond to low and low‐middle‐income levels, and the effects of agricultural productivity and population density on land use are respectively negative and positive. [ABSTRACT FROM AUTHOR]

This paper advocates the wider use of the spatial autoregressive (AR) panel data model with spatial moving average (MA) errors, individual and time effects, and different spatial weight matrices for each spatial lag. We demonstrate the practical relevance of this model, derive and investigate the asymptotic properties of a simple quasi maximum likelihood within estimator when N$$ N $$ is large and T$$ T $$ is finite, and provide an empirical example explaining military expenditures in 144 countries over the period 1993–2007. [ABSTRACT FROM AUTHOR]

For decades, scholars have argued that low and declining political trust affect citizens' support for democratic and undemocratic reform. While some theorized that low political trust induces alienation and support for non‐democratic decision making, others argued that it pushes critical citizens to support reforms aimed to reinvigorate democracy. Yet, empirical tests of these expectations remained sparse and inconclusive. This paper employs panel data from the Netherlands (covering 3 waves in 3 years) to test these diverging theories simultaneously. We employ the random effects within‐between (REWB) model to differentiate between the effects of structurally low and declining political trust. Our results suggest that low and declining trust both diminish support for representative democracy, enhance support for direct democratic decision making and do not affect support for authoritarianism. These findings cast doubt on the understanding of political distrust as a determinant of political alienation. Rather, they support theories of critical citizenship and stealth democracy. [ABSTRACT FROM AUTHOR]

This paper used longitudinal data from five studies conducted in Bangladesh, Bhutan, Cambodia, Ethiopia, and Rwanda to examine the links between family stimulation and early childhood development outcomes (N = 4904; Mage = 51.5; 49% girls). Results from random‐effects and more conservative child‐fixed effects models indicate that across these studies, family stimulation, measured by caregivers' engagement in nine activities (e.g., reading, playing, singing), predicted increments in children's early numeracy, literacy, social‐emotional, motor, and executive function skills (standardized associations ranged from 0.05 to 0.11 SD). Study‐specific models showed variability in the estimates, with null associations in two out of the five studies. These findings indicate the need for additional research on culturally specific ways in which caregivers may support early development and highlight the importance of promoting family stimulation to catalyze positive developmental trajectories in global contexts. Research Highlights: Research on the links between family stimulation and early childhood development in low‐and‐middle‐income countries (LMICs) is limited.We used longitudinal data from studies conducted in five LMICs to examine the links between family stimulation and early childhood development outcomes.Results suggest that family stimulation predicted increments in children's numeracy, literacy, social‐emotional, motor, and executive function skills.We found variability in the observed estimates, with null associations in two out of the five studies, suggesting the need for additional research in LMICs. [ABSTRACT FROM AUTHOR]

Whether regional integration can promote the urban agglomeration to attract foreign direct investment and expand the opening‐up is a question worthy of in‐depth discussion. This paper constructs a quasi‐natural experiment from the perspective of regional integration expansion in the Yangtze River Delta, using the panel data of 208 cities in China from 2005 to 2017. The main purposes are to examine and analyze the common trends and regional differences regarding the influences of regional integration on FDI in entire cities, incumbent cities, and new cities. Next, the internal mechanisms of expansion on FDI are explored. The results show that the regional integration exerted a significant positive effect on the FDI of the entire Yangtze River Delta. For different regions, the effect of regional integration on FDI in the incumbent cities was higher than that in the new cities. In terms of mechanisms, regional integration could affect FDI through industrial division mechanism and market unification mechanism, yet the role of economic connection mechanism exhibited regional differences. The conclusions of this paper bear important enlightenment for promoting the construction of regional integration in China and the high‐quality growth of FDI in the Yangtze River Delta. [ABSTRACT FROM AUTHOR]

Given the recent changes in the supply and demand of dairy products, many opportunities arise for exporting and importing countries. This paper examines determinants of dairy‐product trade by applying the Poisson Pseudo‐Maximum Likelihood (PPML) method to the gravity model using panel data on 49 exporting and 235 importing countries for the 17 years from 2000 to 2016. The gravity model is estimated using both interval data and dynamic analyses. The results show that domestic subsidies have a modest, but significant, impact on dairy‐product trade across the models. For example, a 1% increase in subsidies leads to a roughly 0.02% increase in trade for an average country. Memberships in trade agreements, market size factors, and government institutions also positively affect dairy‐product trade. However, tariffs are insignificant in the main model specification. Results from the lag‐policy analysis show that the impact of subsidies disappears after the second year of distribution; whereas for the lead‐policy analysis, results suggest at least 3 years of anticipatory effects on domestic subsidies. [ABSTRACT FROM AUTHOR]

This paper considers an alternate dimension of government institutions associated with the separation of powers between government and its central bank. A more independent central bank is consistent with greater institutional quality and constraints on government. We propose that central bank independence influences the prevalence of the shadow, or underground, economy. Using cross‐country panel data for over 100 nations over the period 1991 to 2012, the results from instrumental variables techniques show that central bank independence curbs underground economic activity. Furthermore, considering different dimensions of independence, we find that independence related to central bank CEO, policy formation, and central bank lending are effective at checking the shadow sector. Overall, these findings are robust to a different measure of the underground economy, correcting for the potential influence of outliers, controlling for the impact of additional factors, accounting for heterogeneity related to the level of development, and considering the heterogeneity related to the prevalence of the shadow sector. The main implication of the results is that nations seeking to reduce the prevalence of the underground economy would benefit from policies that promote central bank independence. [ABSTRACT FROM AUTHOR]

In medical studies, composite indices and/or scores are routinely used for predicting medical conditions of patients. These indices are usually developed from observed data of certain disease risk factors, and it has been demonstrated in the literature that single index models can provide a powerful tool for this purpose. In practice, the observed data of disease risk factors are often longitudinal in the sense that they are collected at multiple time points for individual patients, and there are often multiple aspects of a patient's medical condition that are of our concern. However, most existing single‐index models are developed for cases with independent data and a single response variable, which are inappropriate for the problem just described in which within‐subject observations are usually correlated and there are multiple mutually correlated response variables involved. This paper aims to fill this methodological gap by developing a single index model for analyzing longitudinal data with multiple responses. Both theoretical and numerical justifications show that the proposed new method provides an effective solution to the related research problem. It is also demonstrated using a dataset from the English Longitudinal Study of Aging. [ABSTRACT FROM AUTHOR]

Likelihood‐based methods ignoring missingness at random (MAR) produce consistent estimates provided that the whole likelihood model is correct. However, the expected information matrix (EIM) depends on the missingness mechanism. It has been shown that calculating the EIM by considering the missing data pattern as fixed (naive EIM) is incorrect under MAR, but the observed information matrix (OIM) is valid under any MAR missingness mechanism. In longitudinal studies, linear mixed models (LMMs) are routinely applied, often without any reference to missingness. However, most popular statistical packages currently provide precision measures for the fixed effects by inverting only the corresponding submatrix of the OIM (naive OIM), which is effectively equivalent to the naive EIM. In this paper, we analytically derive the correct form of the EIM of LMMs under MAR dropout to compare its differences with the naive EIM, which clarifies why the naive EIM fails under MAR. The asymptotic coverage rate of the naive EIM is numerically calculated for two parameters (population slope and slope difference between two groups) under various dropout mechanisms. The naive EIM can severely underestimate the true variance, especially when the degree of MAR dropout is high. Similar trends emerge under misspecified covariance structure, where, even the full OIM may lead to incorrect inferences and sandwich/bootstrap estimators are generally required. Results from simulation studies and application to real data led to similar conclusions. In LMMs, the full OIM should be preferred to the naive EIM/OIM, though if misspecified covariance structure is suspected, robust estimators should be used. [ABSTRACT FROM AUTHOR]

As rural decline sweeps the world and draws public attention, it becomes necessary to consider the relationship between urbanization and rural development. The paper investigates this topic in China for the period 1989–2018. The research results find positive contribution of urbanization to rural economy in the research period but with a decreasing tendency. Compared to less developed areas, rural economy of developed regions has become less dependent on cities and can flourish in a larger context with the support of modern transportation and telecommunication technologies. However, those peripheral rural areas tend to face continuous and large‐scale rural–urban migration which has aggravated rural decline. Rural economic growth helps to decrease rural outmigration and people will stay in their hometown with available job opportunities to raise their families and maintain livelihood. The paper proposes balance‐friendly urbanization structure and diversified rural economy to improve rural resilience to meet the challenge of rural decline. [ABSTRACT FROM AUTHOR]

The existing literature mainly focuses on the impact of information communication technologies on carbon emissions, but little attention has been paid to the role of the digital economy in transporting carbon emissions. This paper calculates the index of the digital economy through the entropy weight Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method and constructs the panel data of 30 provinces in China from 2008 to 2017. By extending the Stochastic Impacts by Regression on Population, Affluence and Technology model, this study explores the potential linear and non‐linear relationship between digital economy and carbon emissions in the transport sector. Empirical results show that the digital economy has the alleviating effect on carbon emissions in the transportation sector, and a change in the digital economy of one unit standard deviation resulted in a 6.14% reduction in carbon emissions. In terms of sub‐regions, the digital economy has a significant negative impact on transport carbon emissions in the eastern and central regions, while it is insignificant in the western regions. This paper further investigates the threshold effect of urbanization on the relationship between the digital economy and transportation‐related carbon emissions. The digital economy accelerates the transport sector's carbon emissions in the low urbanization stage, while it reduces the carbon emissions in the high urbanization stage. [ABSTRACT FROM AUTHOR]

In this paper, we present a detailed and comprehensive analysis of the location patterns of manufacturing industries in Brazil using a distance‐based measure and micro‐geographic panel data for a 10‐year period. Our results show that 89.9% and 91% of manufacturing at the 3‐digit level have statistically significant localization for 2006 and 2015, respectively, and that these patterns remain high when we consider 4‐digit classification. High‐tech industries have location patterns at short distances, being localized mainly in large urban areas, while low‐tech industries are also localized at long distances. We also present evidence that agglomeration economies affect the degree of spatial concentration. [ABSTRACT FROM AUTHOR]

Due to externalities and market failure, innovation activities are facing insufficient investment. Venture capital guidance funds (VCGFs) are an important policy implemented by China's government to guide private capital to participate in innovation and entrepreneurship activities, and it can have an important impact on scientific and technological innovation. Based on the panel data of 285 cities in China, this paper empirically analyzed the impact of VCGFs on innovation and its mechanism. VCGFs can significantly promote urban innovation and this role is sustained for three years following VCGF establishment. Further research shows that VCGFs do not have a direct impact on innovation but promote innovation by promoting the accumulation of innovative talent and capital, and the capital accumulation effect of VCGFs is the main path through which they promote urban innovation. Corruption is also a factor that restricts the effectiveness of an innovation policy. Thus, China's top‐down anticorruption campaigns can strengthen the ability of VCGFs to promote urban innovation. This paper opens the black box to show the mechanism through which VCGFs impact urban innovation, affirms the important role of the government in the regional innovation system, and clarifies the debate on government innovation policy. [ABSTRACT FROM AUTHOR]

In this paper, we estimate the effect of the homicides by the Camorra, the Neapolitan Mafia, on housing prices in Naples. The study develops on a unique panel data set at the administrative district level for the period 2002–2018 of geo‐localized homicides involving innocent victims (denoted as IVH), which are treated as exogenous shocks that negatively affect housing demand. We find that the occurrence of such homicides causes a decrease in housing prices in the range of 2.5–3.8 percentage points. This effect decreases with the distance from an IVH and over time. These results are robust to the utilization of different econometric specifications and to the considerations of possible confounding factors such as other types of Camorra homicides. [ABSTRACT FROM AUTHOR]

Weak institutions and high levels of corruption are issues of great concern in West Africa because of their adverse effects on the economic growth of the region. While a significant portion of extant literature has focused on the determinants of corruption, empirical investigations of the effect of institutional quality on corruption are still limited, especially in Africa. This paper provides empirical evidence, which shows that improvement in the quality of governmental institutions is an effective means of controlling corruption in West Africa. Furthermore, the paper reveals that improvements in terms of the ability of governmental institutions to meet the economic needs of the people make the most impact on the ability to curb corruption in West Africa. Annual panel data series for14 ECOWAS countries on corruption control and governance quality, obtained from the Worldwide Governance Indicators and Ibrahim Index of African Governance online databases, for the period 2000–2015, were utilized in the study. [ABSTRACT FROM AUTHOR]

The present study is concerned about the relationship between firm reputation and managerial entrenchment in listed firms on the Tehran Stock Exchange. In other words, this paper attempts to figure out whether a firm reputation contributes to managerial entrenchment. The multivariate regression model is used for this study. Research hypotheses also examined using a sample of 768 listed observations on the Tehran Stock Exchange during 2012–2017 and by using the multivariate regression model based on panel data technique and fixed effects model. The obtained results show a positive and significant relationship between firm reputation and managerial entrenchment, such that an increase in firm reputation during the time has led to an increased managerial entrenchment. Moreover, the present paper figures out that firm reputation would also lead to narcissism and overconfidence of managers. This paper utilized the exploratory factor analysis for measuring managerial entrenchment and firm reputation, which is a modern method for calculating these two indices. Six indices of board compensation, CEO tenure, CEO duality, reverse distributed earnings, and excessive investment was used for managerial entrenchment. Further, for measuring CEO narcissism by using the exploratory factor analysis, two indices of CEO signature magnitude and cash compensation were converted to a single index of CEO narcissism for the first time which is used for calculating narcissism. This paper is the first study on this topic in the emerging markets, so it provides the users, analysts, and legal institutions with useful information about intrinsic and acquired features of management that contribute significantly to the comparability of financial statements. [ABSTRACT FROM AUTHOR]

Summary: Data collected in the context of usual care present a rich source of longitudinal data for research, but often require analyses that simultaneously enable causal inferences from observational data while handling irregular and informative assessment times. An inverse‐weighting approach to this was recently proposed, and handles the case where the assessment times are at random (ie, conditionally independent of the outcome process given the observed history). In this paper, we extend the inverse‐weighting approach to handle a special case of assessment not at random, where assessment and outcome processes are conditionally independent given past observed covariates and random effects. We use multiple outputation to accomplish the same purpose as inverse‐weighting, and apply it to the Liang semi‐parametric joint model. Moreover, we develop an alternative joint model that does not require covariates for the outcome model to be known at times where there is no assessment of the outcome. We examine the performance of these methods through simulation and illustrate them through a study of the causal effect of wheezing on time spent playing outdoors among children aged 2–9 years and enrolled in the TargetKids! study. [ABSTRACT FROM AUTHOR]

Summary: Joint modeling and landmark modeling are two mainstream approaches to dynamic prediction in longitudinal studies, that is, the prediction of a clinical event using longitudinally measured predictor variables available up to the time of prediction. It is an important research question to the methodological research field and also to practical users to understand which approach can produce more accurate prediction. There were few previous studies on this topic, and the majority of results seemed to favor joint modeling. However, these studies were conducted in scenarios where the data were simulated from the joint models, partly due to the widely recognized methodological difficulty on whether there exists a general joint distribution of longitudinal and survival data so that the landmark models, which consists of infinitely many working regression models for survival, hold simultaneously. As a result, the landmark models always worked under misspecification, which caused difficulty in interpreting the comparison. In this paper, we solve this problem by using a novel algorithm to generate longitudinal and survival data that satisfies the working assumptions of the landmark models. This innovation makes it possible for a "fair" comparison of joint modeling and landmark modeling in terms of model specification. Our simulation results demonstrate that the relative performance of these two modeling approaches depends on the data settings and one does not always dominate the other in terms of prediction accuracy. These findings stress the importance of methodological development for both approaches. The related methodology is illustrated with a kidney transplantation dataset. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of simultaneous variable selection and estimation in the random‐intercepts model with the first‐order lag response. This type of model is commonly used for analyzing longitudinal data obtained through repeated measurements on individuals over time. This model uses random effects to cover the intra‐class correlation, and the first lagged response to address the serial correlation, which are two common sources of dependency in longitudinal data. We demonstrate that the conditional likelihood approach by ignoring correlation among random effects and initial responses can lead to biased regularized estimates. Furthermore, we demonstrate that joint modeling of initial responses and subsequent observations in the structure of dynamic random‐intercepts models leads to both consistency and Oracle properties of regularized estimators. We present theoretical results in both low‐ and high‐dimensional settings and evaluate regularized estimators' performances by conducting simulation studies and analyzing a real dataset. Supporting information is available online. [ABSTRACT FROM AUTHOR]

Summary: As a result of advances in data collection technology and study design, modern longitudinal datasets can be much larger than they historically have been. Such "intensive" longitudinal datasets are rich enough to allow for detailed modeling of the variance of a response as well as the mean, and a flexible class of models called mixed‐effects location‐scale (MELS) regression models are commonly used to do so. However, fitting MELS models can pose computational challenges related to the numerical evaluation of multi‐dimensional integrals; the slow runtime of current methods is inconvenient for data analysis and makes bootstrap inference impractical. In this paper, we introduce a new fitting technique, called FastRegLS, that is considerably faster than existing techniques while still providing consistent estimators for the model parameters. [ABSTRACT FROM AUTHOR]

This paper studies the impact of urban density, city government efficiency, and medical resources on COVID‐19 infection and death outcomes in China. We adopt a simultaneous spatial dynamic panel data model to account for (i) the simultaneity of infection and death outcomes, (ii) the spatial pattern of the transmission, (iii) the intertemporal dynamics of the disease, and (iv) the unobserved city‐specific and time‐specific effects. We find that, while population density increases the level of infections, government efficiency significantly mitigates the negative impact of urban density. We also find that the availability of medical resources improves public health outcomes conditional on lagged infections. Moreover, there exists significant heterogeneity at different phases of the epidemiological cycle. [ABSTRACT FROM AUTHOR]

A growing body of literature highlights the importance of export sophistication for economic development. Given the newness of the literature on export sophistication, its determinants are under‐exploited. As a pioneer study, this paper attempts to fill this gap in the literature by examining the effect of infrastructure development on export sophistication. Based on a panel of 45 African countries over the period 2003–2016, the results of the different estimations show that infrastructure, including transport, electricity, ICT and access to water and sanitation, improves export sophistication in Africa. Our results also show that the effect of infrastructure varies at different intervals of the export sophistication distribution. Therefore, improved infrastructure would allow African countries to not only improve their export structure but also achieve sustainable and durable growth. [ABSTRACT FROM AUTHOR]

This paper considers the feature screening method for the ultrahigh dimensional semiparametric linear models with longitudinal data. The C‐statistic which measures the rank concordance between predictors and outcomes is generalized to the longitudinal data. On the basis of C‐statistic and the score equation theory, we propose a feature screening method named LCSIS. Based on the smoothed technique and the score equations, the proposed estimating screening procedure is easy to compute and satisfies the feature screening consistency. Furthermore, Monte Carlo simulation studies and a real data application are conducted to examine the finite sample performance of the proposed procedure. [ABSTRACT FROM AUTHOR]

In many psychological studies, in particular those conducted by experience sampling, mental states are measured repeatedly for each participant. Such a design allows for regression models that separate between‐ from within‐person, or trait‐like from state‐like, components of association between two variables. But these models are typically designed for continuous variables, whereas mental state variables are most often measured on an ordinal scale. In this paper we develop a model for disaggregating between‐ from within‐person effects of one ordinal variable on another. As in standard ordinal regression, our model posits a continuous latent response whose value determines the observed response. We allow the latent response to depend nonlinearly on the trait and state variables, but impose a novel penalty that shrinks the fit towards a linear model on the latent scale. A simulation study shows that this penalization approach is effective at finding a middle ground between an overly restrictive linear model and an overfitted nonlinear model. The proposed method is illustrated with an application to data from the experience sampling study of Baumeister et al. (2020, Personality and Social Psychology Bulletin, 46, 1631). [ABSTRACT FROM AUTHOR]

How to realize the ecological protection and high‐quality development (HQD) of the Yellow River Basin (YRB) has become a great challenge for China. Based on panel data from 100 prefecture‐level cities in the YRB from 2006 to 2018, this innovative paper employs the System Generalized Method of Moments (SYS‐GMM) panel model and takes environmental regulation as a situational factor to empirically examine the influence mechanism of industrial agglomeration on HQD in the YRB. Our findings reveal that: (i) overall, a "U‐shaped" relationship exists between industrial agglomeration and HQD in the YRB, and environmental regulation plays a vital role in augmenting their relationship. (ii) Presently, the industrial agglomeration degree prevails on the left side of the threshold value, and the upsurge of industrial agglomeration degree still limits HQD. (iii) From the perspective of river basins, the influence of industrial agglomeration on HQD in the upstream is not significant, and the strengthening effect of environmental regulation is not noticeable. We find a "U‐shaped" relationship between industrial agglomeration and HQD in the midstream, along with notable differences in the correlation between industrial agglomeration and different dimensions of HQD in downstream. This paper helps in further understanding the influence of industrial agglomeration, environmental regulation, and HQD, as well as establishes efficient policies to promote HQD in the YRB and other similar river basins through industrial agglomeration in the context of environmental constraints. [ABSTRACT FROM AUTHOR]

Automation risk prevails less in large cities compared to small cities but little is known about the drivers of this emerging urban phenomenon. A major challenge is that automation risk is quantified by work‐related tasks that allows for measurement through occupation, which is in turn implicitly related to local economic structure and to individual career paths. This paper examines the role of working in cities on changes in automation risk through individual career mobility. Using panel data on Swedish workers, we show that the metropolitan effect of reducing automation risk is mainly induced through inter‐firm job mobility. Separate estimates for different groups show that this effect accrues mostly to native, high‐skilled and male workers. [ABSTRACT FROM AUTHOR]

This paper proposes a slacks‐based global DEA for Chinese urban green economic growth (GECO) and a global Malmquist index for decomposing its changes into three components, technology change (GTEC), scale efficiency change (GSEC), and pure technical efficiency change (GPEC). On this basis, using China's 286 city‐level panel data for the period 2006–2018, this paper then analyses the key driving factors of GECO and the regional heterogeneous effects of resource abundance on GECO. Results show that: (a) during the sample period, the city‐level GECO of China remains at a low level. GTEC and GSEC promote GECO, but much of this growth effect is offset by a reduction in GPEC; (b) there is negative effect of resource abundance on Chinese urban GECO. Specifically, in eastern, central, and western cities, the negative effect on GECO by adversely affecting GSEC and GPEC, while in northeast cities, the main transmission is GTEC; (c) because the key factors which affect the decompositions show different characteristics from regional heterogeneity perspective, this paper takes further step to formulate specific strategies and effective implementation of policies for the improvement of GECO in China's city level. [ABSTRACT FROM AUTHOR]

In short‐term nonlife (e.g., car and homeowner) insurance, policies are renewed yearly. Insurance companies typically keep track of each policyholder's claims per year, resulting in longitudinal data. Efficient modeling of time dependence in longitudinal claim data will improve the prediction of future claims needed for routine actuarial practice, such as ratemaking. Insurance claim data usually follow a two‐part mixed distribution: a probability mass at zero corresponding to no claim and an otherwise positive claim from a skewed and long‐tailed distribution. This two‐part data structure leads to difficulties in applying established models for longitudinal data. In this paper, we propose a two‐part D‐vine copula model to study longitudinal mixed claim data. We build two stationary D‐vine copulas. One is used to model the time dependence in binary outcomes resulting from whether or not a claim has occurred. The other studies the dependence in the claim size given occurrence. Under the proposed model, the prediction of the probability of making claims and the quantiles of severity given occurrence is straightforward. We use our approach to investigate a dataset from the Local Government Property Insurance Fund in the state of Wisconsin. [ABSTRACT FROM AUTHOR]

Theoretical arguments and empirical evidence have been provided in the literature for the role of fairness in wellness. In this paper, we explore the role of two potential mediating variables: autonomous human choice and social capital. Using aggregated panel data across countries belonging to the Organisation for Economic Co‐operation and Development (OECD), we compared the OECD Social Justice Index (SJI) with data on life satisfaction to test whether fairness has direct and indirect effects on wellness. Results from a series of Manifest Path Analyses with time as fixed effect, support the hypothesis that the OECD SJI is directly linked to country‐level life satisfaction, additionally revealing that its indirect effect operates primarily through people's autonomous choices in life and their country's level of social capital. Our results contribute to two distinct bodies of knowledge. With respect to community psychology, the findings offer empirical evidence for the synergistic effect of personal, relational, and collective factors in well‐being. With respect to the impact of economic inequality on wellness, we extend the literature by using social justice as a more comprehensive measure. Limitations and recommendations for future studies are discussed. [ABSTRACT FROM AUTHOR]

This paper examines the impact of access to electricity on internal conflicts in Africa. Using income inequality as a transition variable and a sample of 36 countries, we estimate a panel data model by several regressions, including fixed effects, random effects, generalized least squares and the Driscoll–Kraay method from 1990 to 2017. Our findings show that regardless of the considered dimension, access to electricity reduces the risk of internal conflicts in Africa. However, its effects differ depending on whether a given area is urban or rural. The findings also suggest that income inequality is crucial to reducing internal conflicts in Africa. [ABSTRACT FROM AUTHOR]

This study investigates the development of vulnerable employment in 43 sub‐Saharan African countries by using yearly data for 1996–2018 period. The paper first outlines the main factors and discusses the issue from the perspective of developing countries, and then constructs a panel data elasticity model. The model expands the economic growth framework further and assesses the effects of rurality, sectoral composition, demography, and the quality of economic and political institutions. Prais–Winsten robust estimation results for the expanded model show nonindustrial employment to be the main source of vulnerable employment. Additionally, the increase in urban population share is found to have a positive impact, while nonemployed population is negatively associated. The results also show that upper‐middle income countries differ from low and lower‐middle income groups with regard to the significance of economic and political institutions. These findings clarify the prominence of the Sustainable Development Goals policy agenda and gradual reform strategies beginning with economic quality toward improvement of political institutions. At the end, we highlight several policy implications. [ABSTRACT FROM AUTHOR]

Poverty is a challenge faced by all countries across the world. How to effectively reduce poverty has always been an issue of scholars and governments, and understanding poverty traps and the causes is the basis for understanding poverty governance. Current research has no longer only attributed poverty to "lack of capital formation," but put more emphasis on the significance of spatial factors. By using China's county‐level large sample panel data, this paper identifies poverty traps in rural China from three dimensions: aggregation, persistence and reinforcement. Research in this paper shows that from 2006 to 2015, there was nearly no change in spatial pattern of poverty in rural China, and the spatial distribution of poverty is not synchronised with economic growth. Distribution of poverty‐stricken counties is mainly related to topographic factors (slope and elevation). In addition, rural poverty in China is persistent, and there is not only a low‐level equilibrium of income in poverty‐stricken counties, but also a "club convergence" of income between poverty‐stricken and non‐poverty counties. Further research has also found that income in poverty‐stricken counties has a "spatial spillover" effect, so poverty also manifests itself as spatial self‐reinforcement. Research in this paper reveals the relationship between spatial externalities and rural poverty. It not only provides empirical evidence for correctly assessing the relationship between development and poverty, it also offers support for reasonably formulating a regional anti‐poverty targeting mechanism. [ABSTRACT FROM AUTHOR]

Objectives: This study empirically investigates the effects of income inequality and income redistribution policy on country‐level subjective well‐being. Methods: The paper uses panel data of 134 countries from 2005 to 2017. The fixed effect model with time and country dummies is employed. Results: We find that a higher inequality level significantly deteriorates happiness in a country with high income inequality, while it does not affect happiness in a country with a low level of inequality. In addition, stronger redistributive policies improve subjective well‐being of countries that have high income inequality, while the policies do not have significant effect on subjective well‐being in countries with relatively low‐income inequality. Furthermore, this paper finds that people are affected by relative levels of income inequality as income grows: people in developed countries may have higher standard for income equality compared with those in developing countries. Conclusions: The policy implication of this paper is that an active income redistribution policy can improve the country level subjective well‐being if a country's income inequality level is higher than its peer group, regardless of whether it is a developed or developing country. [ABSTRACT FROM AUTHOR]

This paper examines the impact of China's export trade on smog pollution. Based on provincial panel data from 2006 to 2016 in China, we use spatial econometric methods to empirically investigate the smog pollution effects of China's export trade from two dimensions, export scale and export structure. It has been found that smog pollution in various regions of China has obvious spatial correlation; China's export trade has a significant positive impact on smog pollution, whether in terms of export scale or export structure. After establishing different forms of spatial weight matrices and considering alternative indicators of smog pollution, the results of this paper are still robust. [ABSTRACT FROM AUTHOR]

Summary: This paper conducts a robustness analysis of the impact of diasporas on institutional quality in home countries. Using the Database on Immigrants in OECD Countries (DIOC), we attest that the home country institutional development role of diasporas found in the literature is robust to modifications in terms of the dataset, the measurement of diasporas and the instrumental variable procedures used in our cross‐sectional and panel analyses. The novelty of this paper is that we take the heterogeneity of diasporas into account in terms of their distribution across host countries, their duration of stay, and the level of development of their home countries. As in earlier literature, we find robust evidence that diasporas enhance institutional quality in home countries. We also find that the intensity of the diffusion of advanced institutions from developed host countries to home countries through the international migration channel is weaker with diasporas characterized by shorter average duration of stay and with diasporas from developing home countries. However, this diffusion effect is not significantly related to the distribution of diasporas across OECD host countries. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to analyse the impact of health on growth, assuming that it is a fundamental component of the human capital of a region along with education. Various measures of population health status are used, together with a health index generated by principal component analysis. Potential endogeneity between health and growth is controlled for using instrumental variable regressions and dynamic panel data. The results show a positive effect of a change in health status on regional output and a negative effect of proxy variables for health limitations on regional performance. This corroborates the importance of investing in health along with education with the aim of improving not only the well‐being of individuals but the human capital and growth of a region. [ABSTRACT FROM AUTHOR]