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Relatively low response rates in mixed mode studies remain a concern. Whether targeting protocols to match respondents' likely mode is an effective strategy remains unclear. For those without a clear likely mode, how the details about sequencing influence response rates, mode, field work effort, and potential response bias remain important questions. This article describes a targeted sequential design implemented in a 2016 mixed mode supplement with individuals aged 30 years and older in the Panel Study of Income Dynamics, the longest running national panel study in the United States. Respondents predicted to be likely to respond by web were invited to a web study and sent a paper copy after six weeks (web-first); those likely to respond by paper were also invited to participate by web but told that a paper copy would be sent shortly (signal-andsend paper). An embedded experiment measured the impact of the two protocols among a group of respondents with no clear likely mode. More than 40 percent of individuals with no likely mode are under the age of 40 years, and the group falls between the likely web and paper groups in terms of education and internet use and includes more women and single respondents. Compared to the likely web and paper groups, those with no likely mode had lower response rates and required more fieldwork effort. Among those randomly assigned, the signal-and-send protocol increased response over the web-first protocol from weeks 4 through 7. By week 16, both protocols yielded similar response rates, field effort and distributions of respondent characteristics. Among those responding, cases randomized to web-first were more likely than those randomized to signal-and-send paper to respond by web. We discuss implications for targeted protocols in mixed mode panel surveys. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, many survey programs switched to self-administered modes of data collection, often offering respondents both web-based and paper-based questionnaires. However, there is little guidance as to when to offer which mode, especially when the aim is to recruit participants for a panel survey. This study examines the effectiveness of different mode-sequence designs by analyzing an experiment implemented in the recruitment wave of the German panel study "Family Research and Demographic Analysis." We randomly assigned 108,256 individuals aged 18–49 years to one of three mode-sequence-design conditions: concurrent, web-first including a paper-based questionnaire with the second reminder (web-first-2), and web-first including a paper-based questionnaire with the third reminder (web-first-3). A fourth, simulated group did not receive a paper-based questionnaire (web-only). We analyzed how different mode-sequence designs affected outcome rates, sample composition, response distributions, data quality, share of paper-based questionnaires, postage costs, number of postal mailings in the recruitment wave, and participation in the first regular wave. Our results show no differences in response distributions and small differences in sample composition across the four mode-sequence designs. As the web-first-2 and simulated web-only designs yielded comparatively good response and recruitment rates at reasonable costs, we recommend their use when surveying adults between 18 and 49 years old. [ABSTRACT FROM AUTHOR]

This paper provides an overview of the state of identification of left behind regions and proposes a new methodology for identifying them. Despite increased interest in rising regional inequality, the identification of left behind regions differs greatly, complicating policymaking aimed at addressing these regional challenges. Based on the substantial literature that has emerged in recent years on left behind regions, the paper argues for the importance of both time and space as key elements when identifying left behind regions. This paper applies the proposed identification method to Denmark; it uses longitudinal data from 1980 to 2018 and includes the current wave of interregional inequality and typically adopted variables for identifying left behind regions across the literature. [ABSTRACT FROM AUTHOR]

This paper studies the relationship between pre-college skills and gender differences in STEM majors. I use longitudinal data to estimate a generalised Roy model of initial major choices and subsequent graduation outcomes. I recover students' latent math ability, non-cognitive skills and math self-efficacy. High–math-ability women have lower math self-efficacy than men. Mathematical ability and self-efficacy shape the likelihood of STEM enrolment. A lack of math self-efficacy drives women's drop out from STEM majors. I find large returns to STEM enrolment for high–math-ability women. Well-focused math self-efficacy interventions could improve women's STEM graduation rates and labour market outcomes. [ABSTRACT FROM AUTHOR]

This paper, based on the panel data of 281 prefecture-level cities in China, applies the difference-in-differences (DID) approach and the propensity score matching DID (PSM-DID) approach to an empirical study on the impacts of the low-carbon pilot program on industrial structure. It concludes that the program has contributed significantly to more rational industrial structures of pilot areas. The study shows that pilot areas have more rational industrial structures than non-pilot areas, recording an increase of 0.058 units in average rational level. This conclusion holds under various robustness tests. Further heterogeneity analysis suggests that the low-carbon pilot program has notably boosted the rational levels of industrial structures of cities in central China and small cities, while less so for other parts of China or other larger cities. Mechanism test shows that low-carbon cities have realized industrial structure upgrading by facilitating urban innovation. In light of this, the low-carbon pilot program should be further put in place steadily with a focus on tailoring pilot policies to local conditions and enhancing their economic adaptability. The role of the program in optimizing industrial structure should be fully leveraged to effectively add to efforts in actively exploring green, low-carbon sustainable development of economy and pulling off the transformation of both economic structure and energy mix. [ABSTRACT FROM AUTHOR]

This paper analyses the impact of financial frictions on markup adjustments at the firm level. We use a rich panel data set that matches information on banking relationships with firm-level data. By relying on insights from recent contributions in the literature, we obtain exogenous credit supply shifters and markups that are both firm specific and time varying. We uncover new findings at this level. In particular, firms more exposed to liquidity risks tend to raise markups in response to negative bank-loan supply shocks, while less exposed firms generally reduce them. Further empirical analyses suggest that our findings are mostly consistent with models featuring a sticky customer base, where financially constrained firms have an incentive to raise markups in order to sustain liquidity without losing too many customers. Our results have important economic implications regarding the cyclicality of the aggregate markup. [ABSTRACT FROM AUTHOR]

Several studies have estimated the effects of various nonpharmaceutical interventions on the COVID-19 pandemic using a 'reduced form' approach. In this paper, I show that many different SIR models can generate virtually identical dynamics of the number of reported cases during the early stages of the epidemic and lead to the same reduced form estimates. In some of these models, policy interventions effectively reduce the transmission rate; in others, the growth of the reported number of cases slows down even though policy has little or no effect on the transmission rate. Thus, the effect of policy cannot be uniquely determined based on the reduced form estimates. This result holds regardless of whether time series or panel data is used in reduced form estimation. I also demonstrate that the reduced form estimates of the policy effect based on panel data specifications with two-way fixed effects can have the wrong sign. [ABSTRACT FROM AUTHOR]

This note discusses two errors in the approach proposed in Canay (2011) for constructing a computationally simple two-step estimator in a quantile regression model with quantile-independent fixed effects. Firstly, we show that Canay's assumption about n / T s → 0 for some s > 1 is not strong enough and can entail severe bias or even the non-existence of the limiting distribution for the estimator of the vector of coefficients. The condition n / T → 0 appears to be closer to the required set of restrictions. These problems are likely to cause incorrect inference in applied papers with large n / T , but the impact is less in applications with small n / T. In an attempt to improve Canay's estimator, we propose a simple correction that may reduce the bias. The second error concerns the incorrect asymptotic standard error of the estimator of the constant term. We show that, contrary to Canay's assumption, the within estimator has an influence function that is not i.i.d. and this affects inference. Moreover, the constant term is unlikely to be estimable at rate |$\sqrt{nT}$|⁠ , so a different estimator may not be available. However, the issue concerning the constant term does not have an effect on slope coefficients. Finally, we give recommendations to practitioners and conduct a meta-review of applied papers that use Canay's estimator. [ABSTRACT FROM AUTHOR]

Stochastic actor-oriented models (SAOMs) are a modelling framework for analysing network dynamics using network panel data. This paper extends the SAOM to the analysis of multilevel network panels through a random coefficient model, estimated with a Bayesian approach. The proposed model allows testing theories about network dynamics, social influence, and interdependence of multiple networks. It is illustrated by a study of the dynamic interdependence of friendship networks and minor delinquency. Data were available for 126 classrooms in the first year of secondary school, of which 82 were used, containing relatively few missing data points and having not too much network turnover. [ABSTRACT FROM AUTHOR]

International commodity price shocks may have large impacts on producers in developing countries. In this paper, a unique household panel data from Ethiopia is utilized to show that a decrease in international coffee price has strong pass-through to the consumption of households that rely on coffee production as a main source of livelihood. It also results in decreases in on-farm labor supply (particularly male labor supply) and induces reallocation of labor towards non-coffee fields, but has negligible effect on off-farm labor supply. The decline in consumption has significant consequences on child malnutrition: children born in coffee-producing households during low coffee price periods have lower weight-for-age and weight-for-height z -scores than their peers born in non-coffee households. [ABSTRACT FROM AUTHOR]

In this paper, we revisit the inequality–growth relationship using an enhanced panel data set with improved inequality data. We explicitly take into account the special role of transition (post-Soviet) countries and add an instrumental variable (IV) estimation to add a causal interpretation to our findings. Our analysis is based on the specification used by Forbes in her 2000 paper, but we also address functional form concerns raised by Banerjee and Duflo three years later. We arrive at three main findings: First, the significant positive association between inequality and economic growth in the full sample is entirely driven by transition countries. Second, this relationship in transition countries is not robust to the inclusion of separate time effects. Lastly, it appears that this association is not causal but rather driven by the particular timing of the transition. Results from IV estimation confirm our interpretation of the observed positive relationship in the overall sample as non-causal. [ABSTRACT FROM AUTHOR]

Given the extreme dependence of agriculture on weather conditions, this paper analyses the effect of climatic variations on this economic sector, by considering both a huge data set and a flexible spatiotemporal model specification. In particular, we study the response of N‐fertilizer application to abnormal weather conditions, while accounting for other relevant control variables. The data set consists of gridded data spanning over 21 years (1993–2013), while the methodological strategy makes use of a spatial dynamic panel data (SDPD) model that accounts for both space and time fixed effects, besides dealing with both space and time dependences. Time‐invariant short‐ and long‐term effects, as well as time‐varying marginal effects are also properly defined, revealing interesting results on the impact of both GDP and weather conditions on fertilizer utilizations. The analysis considers four macroregions—Europe, South America, Southeast Asia and Africa—to allow for comparisons among different socio‐economic societies. In addition to finding both spatial (in the form of knowledge spillover effects) and temporal dependences as well as a good support for the existence of an environmental Kuznets curve for fertilizer application, the paper shows peculiar responses of N‐fertilization to deviations from normal weather conditions of moisture for each selected region, calling for ad hoc policy interventions. [ABSTRACT FROM AUTHOR]

This paper analyses returns to task specialization using a unique panel data set of professional football players in the German Bundesliga. Based on accumulated task-specific human capital, I measure whether a player is rather a specialist in one task or a generalist able to perform several tasks. Using OLS, fixed effects, and quantile regression methods (with individual fixed effects), I analyse the impact of specialization on remuneration. Differentiating by player role in team production, I find that core team members, i.e. starting players, exhibit positive returns to specialization, which increase at higher salary quantiles. In contrast, substitutes, in particular those in the lower half of the conditional salary distribution, seem to benefit from being generalists, which renders them more attractive as substitute players for their teams. The paper discusses implications of the findings for other labour market contexts. [ABSTRACT FROM AUTHOR]

The 2020 election and its aftermath present an opportunity to understand how audiences' changing news consumption patterns within an expanded, digitized, and polarized media environment shape electoral judgments. This paper introduces measures that capture individuals' partisan slant, partisan extremity, and overall diversity of news media use to understand how people interact with the contemporary news ecology. The measures combine self-reported media use with the partisan slant of news outlets along the ideological spectrum. Using these measures, we analyze a two-wave panel survey conducted before and after the 2020 US election to investigate how slant, diversity, and extremity are related to post-election beliefs, including public confidence in the election and the acceptance of fraud claims. Our findings show that Republicans have more insulated news use behaviors in terms of slant and diversity. The analysis also reveals that the slant of people's news use is associated with post-election fraud beliefs, with right-wing news consumers more likely to believe such claims. However, a diverse news consumption style can moderate misinformation beliefs. Panel analysis points to the role of extreme-right news use in decreasing confidence in the legitimacy of the election. [ABSTRACT FROM AUTHOR]

Many different methods for evaluating diagnostic test results in the absence of a gold standard have been proposed. In this paper, we discuss how one common method, a maximum likelihood estimate for a latent class model found via the Expectation‐Maximization (EM) algorithm can be applied to longitudinal data where test sensitivity changes over time. We also propose two simplified and nonparametric methods which use data‐based indicator variables for disease status and compare their accuracy to the maximum likelihood estimation (MLE) results. We find that with high specificity tests, the performance of simpler approximations may be just as high as the MLE. [ABSTRACT FROM AUTHOR]

We propose a model‐based clustering method for high‐dimensional longitudinal data via regularization in this paper. This study was motivated by the Trial of Activity in Adolescent Girls (TAAG), which aimed to examine multilevel factors related to the change of physical activity by following up a cohort of 783 girls over 10 years from adolescence to early adulthood. Our goal is to identify the intrinsic grouping of subjects with similar patterns of physical activity trajectories and the most relevant predictors within each group. The previous analyses conducted clustering and variable selection in two steps, while our new method can perform the tasks simultaneously. Within each cluster, a linear mixed‐effects model (LMM) is fitted with a doubly penalized likelihood to induce sparsity for parameter estimation and effect selection. The large‐sample joint properties are established, allowing the dimensions of both fixed and random effects to increase at an exponential rate of the sample size, with a general class of penalty functions. Assuming subjects are drawn from a Gaussian mixture distribution, model effects and cluster labels are estimated via a coordinate descent algorithm nested inside the Expectation‐Maximization (EM) algorithm. Bayesian Information Criterion (BIC) is used to determine the optimal number of clusters and the values of tuning parameters. Our numerical studies show that the new method has satisfactory performance and is able to accommodate complex data with multilevel and/or longitudinal effects. [ABSTRACT FROM AUTHOR]

Motivated by investigating the relationship between progesterone and the days in a menstrual cycle in a longitudinal study, we propose a multikink quantile regression model for longitudinal data analysis. It relaxes the linearity condition and assumes different regression forms in different regions of the domain of the threshold covariate. In this paper, we first propose a multikink quantile regression for longitudinal data. Two estimation procedures are proposed to estimate the regression coefficients and the kink points locations: one is a computationally efficient profile estimator under the working independence framework while the other one considers the within‐subject correlations by using the unbiased generalized estimation equation approach. The selection consistency of the number of kink points and the asymptotic normality of two proposed estimators are established. Second, we construct a rank score test based on partial subgradients for the existence of the kink effect in longitudinal studies. Both the null distribution and the local alternative distribution of the test statistic have been derived. Simulation studies show that the proposed methods have excellent finite sample performance. In the application to the longitudinal progesterone data, we identify two kink points in the progesterone curves over different quantiles and observe that the progesterone level remains stable before the day of ovulation, then increases quickly in 5 to 6 days after ovulation and then changes to stable again or drops slightly. [ABSTRACT FROM AUTHOR]

In many longitudinal studies, the number and timing of measurements differ across study subjects. Statistical analysis of such data requires accounting for both the unbalanced study design and the unequal spacing of repeated measurements. This paper proposes a time‐heterogeneous D‐vine copula model that allows for time adjustment in the dependence structure of unequally spaced and potentially unbalanced longitudinal data. The proposed approach not only offers flexibility over its time‐homogeneous counterparts but also allows for parsimonious model specifications at the tree or vine level for a given D‐vine structure. It further provides a robust strategy to specify the joint distribution of non‐Gaussian longitudinal data. The performance of the time‐heterogeneous D‐vine copula models are evaluated through simulation studies and by a real data application. Our findings suggest improved predictive performance of the proposed approach over the linear mixed‐effects model and time‐homogeneous D‐vine copula model. [ABSTRACT FROM AUTHOR]

By adopting a set of panel data from thirty provinces in China from 2007 to 2017, this paper examines how regional innovation efficiency is influenced by different types of innovation policies from a specific long-range plan ('National Medium- and Long-Term Science and Technology Development Plan (2006–20)'). We incorporate public direct research and development (R&D) grants, R&D tax credits, and intellectual property rights (IPR) protection into our research. We also explore the heterogeneous influences of these factors at different stages in regional innovation development. The results indicate that public direct R&D grants have a significantly negative influence, especially in innovation-catching-up regions. Conversely, the R&D tax credit has a considerably positive impact on innovation-catching-up regions. IPR protection plays various roles according to different innovation development stages. Specifically, IPR protection has a positive impact on innovation-leading regions, while it negatively influences regional innovation efficiency in innovation-catching-up regions. [ABSTRACT FROM AUTHOR]

This paper considers canonical correlation analysis for two longitudinal variables that are possibly sampled at different time resolutions with irregular grids. We modelled trajectories of the multivariate variables using random effects and found the most correlated sets of linear combinations in the latent space. Our numerical simulations showed that the longitudinal canonical correlation analysis (LCCA) effectively recovers underlying correlation patterns between two high-dimensional longitudinal data sets. We applied the proposed LCCA to data from the Alzheimer's Disease Neuroimaging Initiative and identified the longitudinal profiles of morphological brain changes and amyloid cumulation. [ABSTRACT FROM AUTHOR]

Combining eight years of panel data with an event study approach, this study shows that rural Chinese women's labor supply falls following the birth of a child. In contrast, men's labor supply does not fall after birth. Furthermore, a woman's labor supply falls more following the birth of a son than a daughter. Following the birth of a son relative to a daughter, household cigarette consumption declines, and a mother's leisure time, her probability of school enrollment, and her participation in decision-making increase. There are no increases in other investments in boys complementary to mothers' time, such as food expenditures, breastfeeding, or immunizations. These results are consistent with the idea that mothers are rewarded more for having a son, leading them to have more leisure and work less. [ABSTRACT FROM AUTHOR]

This paper develops a spatial Durbin stochastic frontier model for panel data introducing spillover effects in the determinants of technical efficiency (SDF-STE). The model nests several existing spatial and non-spatial stochastic frontier specifications and is estimated using maximum-likelihood techniques. Estimates are shown to be unbiased even for small sample sizes and for alternative specifications of the spatial weight matrix implementing different Monte Carlo simulations. Finally, an application to the Italian accommodation sector is provided. Empirical findings suggest the relevance of the SDF-STE model in capturing labour productivity and knowledge spillover effects. [ABSTRACT FROM AUTHOR]

This paper develops a new instrumental variables estimator for spatial, dynamic panels with interactive effects under large N and T asymptotics. For this class of models, most approaches available in the literature are based on quasi-maximum likelihood estimation. The approach put forward here is appealing from both a theoretical and a practical point of view for a number of reasons. First, it is linear in the parameters of interest and computationally inexpensive. Second, the IV estimator is free from asymptotic bias. Third, the approach can accommodate endogenous regressors as long as external instruments are available. The IV estimator is consistent and asymptotically normal as |$N,T\rightarrow \infty$|⁠ , such that |$N/T \rightarrow c$|⁠ , where |$0\lt c\lt \infty$|⁠. We study the determinants of risk attitude of banking institutions. The results show that the capital regulation introduced by the Dodd–Frank Act has succeeded in influencing banks' behaviour. [ABSTRACT FROM AUTHOR]

We congratulate Prof. Jiang, Prof. Zhao and Prof. Shao for their timely and thought-provoking paper on modelling the COVID-19 infection trajectory using a piecewise linear quantile trend model. By letting HT ht for country HT ht and ignoring the covariates HT ht , the MKQR model can be used to model the COVID-19 infection curves with multiple change points. [Extracted from the article]

In this paper, we propose a functional partially linear regression model with latent group structures to accommodate the heterogeneous relationship between a scalar response and functional covariates. The proposed model is motivated by a salinity tolerance study of barley families, whose main objective is to detect salinity tolerant barley plants. Our model is flexible, allowing for heterogeneous functional coefficients while being efficient by pooling information within a group for estimation. We develop an algorithm in the spirit of the K‐means clustering to identify latent groups of the subjects under study. We establish the consistency of the proposed estimator, derive the convergence rate and the asymptotic distribution, and develop inference procedures. We show by simulation studies that the proposed method has higher accuracy for recovering latent groups and for estimating the functional coefficients than existing methods. The analysis of the barley data shows that the proposed method can help identify groups of barley families with different salinity tolerant abilities. [ABSTRACT FROM AUTHOR]

In longitudinal studies variables are measured repeatedly over time, leading to clustered and correlated observations. If the goal of the study is to develop prediction models, machine learning approaches such as the powerful random forest (RF) are often promising alternatives to standard statistical methods, especially in the context of high-dimensional data. In this paper, we review extensions of the standard RF method for the purpose of longitudinal data analysis. Extension methods are categorized according to the data structures for which they are designed. We consider both univariate and multivariate response longitudinal data and further categorize the repeated measurements according to whether the time effect is relevant. Even though most extensions are proposed for low-dimensional data, some can be applied to high-dimensional data. Information of available software implementations of the reviewed extensions is also given. We conclude with discussions on the limitations of our review and some future research directions. [ABSTRACT FROM AUTHOR]

The present paper considers a linear binary response model for panel data with random effects that differ across individuals but are constant over time, and it investigates the roles of the various assumptions that are used to establish conditions for identification. The paper also shows that even for this simple model, it is always possible—including in the logistic case—to find a distribution of the random effects given the exogenous variables, such that the slopes' parameters are arbitrarily different, but the joint distributions of the binary response variables are arbitrarily close. [ABSTRACT FROM AUTHOR]

This paper studies the estimation of latent group structures in heterogeneous time-varying coefficient panel data models. While allowing the coefficient functions to vary over cross-sections provides a good way to model cross-sectional heterogeneity, it reduces the degree of freedom and leads to poor estimation accuracy when the time-series length is short. On the other hand, in a lot of empirical studies, it is not uncommon to find that heterogeneous coefficients exhibit group structures where coefficients belonging to the same group are similar or identical. This paper aims to provide an easy and straightforward approach for estimating the underlying latent groups. This approach is based on the hierarchical agglomerative clustering (HAC) of kernel estimates of the heterogeneous time-varying coefficients when the number of groups is known. We establish the consistency of this clustering method and also propose a generalised information criterion for estimating the number of groups when it is unknown. Simulation studies are carried out to examine the finite-sample properties of the proposed clustering method as well as the post-clustering estimation of the group-specific time-varying coefficients. The simulation results show that our methods give comparable performance to the penalised-sieve-estimation-based classifier-LASSO approach by Su et al. (2018), but are computationally easier. An application to a panel study of economic growth is also provided. [ABSTRACT FROM AUTHOR]

This paper first reviews some of the approaches that have been taken to estimate the common parameters of binary outcome models with fixed effects. We limit attention to situations in which the researcher has access to a data set with a large number of units (individuals or companies, for example) observed over a number of time periods. We then apply some of the existing approaches to study fixed-effects panel data versions of entry games, like the ones studied in Bresnahan and Reiss (1991) and Tamer (2003). [ABSTRACT FROM AUTHOR]

This paper considers panel data models where the idiosyncratic errors are subject to conditonal quantile restrictions. We propose a two-step estimator based on smoothed quantile regressions that is easy to implement. The asymptotic distribution of the estimator is established, and the analytical expression of its asymptotic bias is derived. Building on these results, we show how to make asymptotically valid inference on the basis of both analytical and split-panel jackknife bias corrections. Finite-sample simulations are used to support our theoretical analysis and to illustrate the importance of bias correction in quantile regressions for panel data. Finally, in an empirical application, the proposed method is used to study the growth effects of foreign direct investment. [ABSTRACT FROM AUTHOR]

This paper analyses the effects of the 2013 Common Agricultural Policy reforms on the distribution of direct payments across the European Union (EU) member states. The differential implementation of two key redistributive policies across countries from 2014 onwards enables the use of a generalised difference-in-differences strategy to estimate their distributional effects. Using data on EU-wide direct payment recipients reveals a substantial negative impact on the largest payment recipients and significant redistribution towards smaller farmers. A disaggregated analysis suggests that of the two instruments, capping, though still an attractive option for policymakers, is less effective at redistributing subsidies towards smaller farmers than the Redistributive Payment Scheme. [ABSTRACT FROM AUTHOR]

This paper analyses whether the systematic disclosure of criminals' origins in the press affects natives' attitudes towards immigration. It takes advantage of the unilateral change in reporting policy announced by the German newspaper Sächsische Zeitung in July 2016. Combining individual-level panel data from the German Socio-Economic Panel from 2014 to 2018 with 402,819 crime-related articles in German newspapers and those newspapers' market shares, we find that systematically mentioning the origins of criminals increases the relative salience of natives' criminality and reduces natives' concerns about immigration, breaking the implicit link between immigration and crime. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between smallholder commercialisation and rural household welfare. Employing a comprehensive three-wave panel dataset of smallholder maize farmers in Ethiopia, we find a positive association between commercialisation and household income which translates to wealth endowments through asset ownership and accumulation. We also show that commercialisation is associated with poverty reduction by reducing the prevalence of income poverty and the poverty gap. Deep diving into who benefits most from commercialisation, we find that agricultural commercialisation is inclusive and benefits all households. However, the greatest welfare gains accrue to income-poor households, suggesting some potential for reducing rural inequality. These insights support existing evidence that smallholder commercialisation is pro-poor and could be a significant entry point for stirring income growth and development in rural areas. Consequently, supporting poor and marginalised farmers with resources needed to boost agricultural production and commercialisation are relevant steps in the right direction of ensuring integrated rural development. [ABSTRACT FROM AUTHOR]

This paper introduces a normative, expert-informed, time-dependent index of Social Inclusion for European administrative regions in five countries, using longitudinal data from Eurostat. Our contribution is twofold: first, our indicator is based on a non-additive aggregation operator (the Choquet Integral), which allows us to model many preferences' structures and to overcome the limitations embedded in other approaches. Second, we elicit the parameters of the aggregation operator from an expert panel of Italian policymakers in Social Policy, and Economics scholars. Our results highlight that Mediterranean countries exhibit lower Inclusion levels than Northern/Central countries, and that this disparity has grown in the last decade. Our results complement and partially challenge existing evidence from data-driven aggregation methods. [ABSTRACT FROM AUTHOR]

This paper studies commodity taxation in a model featuring heterogeneous consumers, imperfect competition, and tax salience. We derive new formulas for the incidence and marginal excess burden of commodity taxation highlighting interactions between tax salience and market structure. We estimate the necessary inputs to the formulas by using Nielsen Retail Scanner and Consumer Panel data covering grocery stores and households in the U.S. and detailed sales tax data. We estimate a large amount of pass-through of taxes onto consumer prices and find that households respond more to changes in prices than taxes. We also estimate significant heterogeneity in tax salience across households. We calibrate our new formulas using these results and conclude that essentially all of the incidence of sales taxes falls on consumers, and the marginal excess burden of taxation is larger than estimates based on standard formulas that ignore imperfect competition and tax salience. [ABSTRACT FROM AUTHOR]

Sequential monitoring of dynamic processes is an active research area because of its broad applications in different industries and scientific research projects, including disease screening in medical research. In the literature, it has been shown that dynamic screening system (DySS) is a powerful tool for sequential monitoring of dynamic processes. To detect a disease (e.g. stroke) for a patient, existing DySS methods first estimate the regular longitudinal pattern of certain disease predictors (e.g. blood pressure, cholesterol level) from an in‐control (IC) dataset that contains observations of a group of non‐diseased people, and then compare the longitudinal pattern of the observed disease predictors of the given patient with the estimated regular longitudinal pattern. A signal of disease occurrence is triggered if their cumulative difference exceeds a certain level, facilitated by a built‐in control chart. In practice, a dataset containing longitudinal observations of the disease predictors of both non‐diseased and diseased people is often available in advance, from which it is possible to explore the relationship between the disease occurrence and the longitudinal pattern of the disease predictors. This relationship should be helpful for disease screening. In this paper, a new DySS method is suggested based on this idea. Numerical studies confirm that it can improve the existing DySS methods for disease screening. [ABSTRACT FROM AUTHOR]

The association between the compositions of microbial communities and various host phenotypes is an important research topic. Microbiome association research addresses multiple domains, such as human disease and diet. Statistical methods for testing microbiome–phenotype associations have been studied recently to determine their ability to assess longitudinal microbiome data. However, existing methods fail to detect sparse association signals in longitudinal microbiome data. In this paper, we developed a novel method, namely aGEEMIHC, which is a data-driven adaptive microbiome higher criticism analysis based on generalized estimating equations to detect sparse microbial association signals from longitudinal microbiome data. aGEEMiHC adopts generalized estimating equations framework that fully considers the correlation among different observations from the same subject in longitudinal data. To be robust to diverse correlation structures for longitudinal data, aGEEMiHC integrates multiple microbiome higher criticism analyses based on generalized estimating equations with different working correlation structures. Extensive simulation experiments demonstrate that aGEEMiHC can control the type I error correctly and achieve superior performance according to a statistical power comparison. We also applied it to longitudinal microbiome data with various types of host phenotypes to demonstrate the stability of our method. aGEEMiHC is also utilized for real longitudinal microbiome data, and we found a significant association between the gut microbiome and Crohn's disease. In addition, our method ranks the significant factors associated with the host phenotype to provide potential biomarkers. [ABSTRACT FROM AUTHOR]

As an influential economic entity, China's economic growth pattern is changing. The traditional way brought high-speed growth as well as caused environmental damage. The concept of environmental innovation, which combines the advantage of technology innovation and environmental consideration, has become a vital means to achieve sustainable development. Due to the double externality characteristic of environmental innovation, the government plays an important role in its development. Based on provincial panel data from 2008 to 2017, this paper first examines the distinct influences of various economic incentive instruments on environmental innovation. Then, the moderation role of marketization is explored. The results show that governments' direct financial expenditure on environmental protection has a positive impact on environmental innovation, while the effect of the environmental tax is not significant. In addition, the impact is negatively moderated by marketization. [ABSTRACT FROM AUTHOR]

This paper investigates the quasi-maximum likelihood estimation of short dynamic panel data models. We consider their estimation on both fixed effects and random effects specifications and propose a Hausman test when exogenous variables are present. For a dynamic panel model, initial conditions play important roles in model structure and estimation, and they give rise to a between equation under the random effects framework. With the between equation properly defined, we show that the random effects model can be decomposed into a within equation and a between equation; hence, the random effects estimate is a pooling of the within and between estimates. Thus, our paper extends the pooling in the static panel data model (Maddala, 1971a) to the setting of dynamic panel data. This decomposition of a dynamic panel data model is revealing and valuable for estimation and the formulation of a Hausman test to test the possible correlation of individual effects with included regressors. Monte Carlo experiments are conducted to investigate the finite sample performance of estimators and the Hausman test. An empirical application of growth convergence in OECD countries is provided. [ABSTRACT FROM AUTHOR]

This paper develops new tests against a structural break in panel data models with common factors when T is fixed, where T denotes the number of observations over time. For this class of models, the available tests against a structural break are valid only under the assumption that T is 'large'. However, this may be a stringent requirement—more commonly so in datasets with annual time frequency, in which case the sample may cover a relatively long period even if T is not large. The proposed approach builds upon existing generalized method of moments methodology and develops Distance-type and Lagrange Multiplier-type tests for detecting a structural break, both when the break point is known and when it is unknown. The proposed methodology permits weak exogeneity and/or endogeneity of the regressors. In a simulation study, the method performed well, in terms of size and power, as well as in terms of successfully locating the time of the structural break. The method is illustrated by testing the so-called 'Gibrat's Law', using a dataset from 4,128 financial institutions, each one observed for the period 2002–2014. [ABSTRACT FROM AUTHOR]

This paper proposes nonparametric kernel-smoothing estimation for panel data to examine the degree of heterogeneity across cross-sectional units. We first estimate the sample mean, autocovariances, and autocorrelations for each unit and then apply kernel smoothing to compute their density functions. The dependence of the kernel estimator on bandwidth makes asymptotic bias of very high order affect the required condition on the relative magnitudes of the cross-sectional sample size (⁠|$N$|⁠) and the time-series length (⁠|$T$|⁠). In particular, it makes the condition on |$N$| and |$T$| stronger and more complicated than those typically observed in the long-panel literature without kernel smoothing. We also consider a split-panel jackknife method to correct bias and construction of confidence intervals. An empirical application illustrates our procedure. [ABSTRACT FROM AUTHOR]

Chinese governments at all levels have been using various policies to encourage innovation by firms. However, few studies considered the superimposed effects of multilevel innovation policies. Based on the panel data of 443 Chinese innovation–oriented firms from 2008 to 2011, this study investigates the complementarities between national and local science and technology programmes. The heterogeneous effects across stages of the innovation process are also considered. Results show that the national science and technology (S&T) programmes significantly increase firms' external research and development (R&D) expenditure but inhibit the commercialisation. In addition, the national and local S&T programmes complement each other in promoting external R&D expenditure while presenting substitutive in increasing patent outputs. This paper further finds that national and local programmes are more likely to be complementary at high subsidy intensity. This study contributes to the development of policy mix theory and has manifold implications for designing pertinent innovation policy systems [ABSTRACT FROM AUTHOR]

Adaptive survey designs are increasingly used by survey practitioners to counteract ongoing declines in household survey response rates and manage rising fieldwork costs. This paper reports findings from an evaluation of an early-bird incentive (EBI) experiment targeting high-effort respondents who participate in the 2019 wave of the US Panel Study of Income Dynamics. We identified a subgroup of high-effort respondents at risk of nonresponse based on their prior wave fieldwork effort and randomized them to a treatment offering an extra time-delimited monetary incentive for completing their interview within the first month of data collection (treatment group; N  = 800) or the standard study incentive (control group; N  = 400). In recent waves, we have found that the costs of the protracted fieldwork needed to complete interviews with high-effort cases in the form of interviewer contact attempts plus an increased incentive near the close of data collection are extremely high. By incentivizing early participation and reducing the number of interviewer contact attempts and fieldwork days to complete the interview, our goal was to manage both nonresponse and survey costs. We found that the EBI treatment increased response rates and reduced fieldwork effort and costs compared to a control group. We review several key findings and limitations, discuss their implications, and identify the next steps for future research. [ABSTRACT FROM AUTHOR]

This paper analyses the instrumental variables (IV) approach put forward by Norkute et al. (2021), in the context of static linear panel data models with interactive effects present in the error term and the regressors. Instruments are obtained from transformed regressors, thereby it is not necessary to search for external instruments. We consider a two-stage IV (2SIV) and a mean-group IV (MGIV) estimator for homogeneous and heterogeneous slope models, respectively. The asymptotic analysis reveals that: (i) the |$\sqrt{\textit {NT}}$| -consistent 2SIV estimator is free from asymptotic bias that may arise due to the estimation error of the interactive effects, while (ii) existing estimators can suffer from asymptotic bias; (iii) the proposed 2SIV estimator is asymptotically as efficient as existing estimators that eliminate interactive effects jointly in the regressors and the error, while (iv) the relative efficiency of the estimators that eliminate interactive effects only in the error term is indeterminate. A Monte Carlo study confirms good approximation quality of our asymptotic results. [ABSTRACT FROM AUTHOR]

Background Few studies into chronic fatigue syndrome (CFS) have emphasized work-related consequences, including return to work after illness. Aims This paper explores socio-demographic, work and clinical characteristics that are associated with occupational status among patients who were assessed at baseline and a follow-up point. Methods Longitudinal data were assessed from patients affected by CFS who attended an outpatient CFS treatment service between 2007 and 2014. Employment status at baseline and follow-up was available for 316 patients. Data were also included on gender, age, duration of CFS, fatigue severity, type and number of treatment sessions, coping strategies, functional impairment, common mental disorders and physical functioning. Results Most patients were female (73%) and had been affected by CFS for longer than 2 years (66%). Patients were followed up for an average of 285 days and over this period 53% of patients who were working remained in employment. Of the patients who were not working at baseline, 9% had returned to work at follow-up. However, of those working at baseline, 6% were unable to continue to work at follow-up. Age, fatigue severity, functional impairment, cognitive and behavioural responses, and depressive symptoms impacted on a patients' work status at follow-up. Conclusions The findings indicated that it is possible for people with CFS to remain in work or return to work, despite having had a disabling illness. Work-related outcomes should be targeted in all people of working age. [ABSTRACT FROM AUTHOR]

We argue that inefficiency can be part of a strategic self-repositioning adjustment process beyond the general interpretation of poor performance. Based on the rational inefficiency hypothesis, we examine the simultaneous dynamics of efficiency and animal welfare improving investments over time in dairy farms. Using rotating panel data from Swedish dairy farms and implementing multidirectional efficiency analysis and Markov transitional dynamic analysis, the paper provides evidence that for some farms inefficiency is temporary and part of rational decision along their trajectory towards multi-efficiency (high efficiency-high animal welfare). The findings show the importance of time dynamics in efficiency achievements with implications to cross-sectional view. [ABSTRACT FROM AUTHOR]

The identification of factors associated with mental and behavioural disorders in early childhood is critical both for psychopathology research and the support of primary health care practices. Motivated by the Millennium Cohort Study, in this paper we study the effect of a comprehensive set of covariates on children's emotional and behavioural trajectories in England. To this end, we develop a quantile mixed hidden Markov model for joint estimation of multiple quantiles in a linear regression setting for multivariate longitudinal data. The novelty of the proposed approach is based on the multivariate asymmetric Laplace distribution which allows to jointly estimate the quantiles of the univariate conditional distributions of a multivariate response, accounting for possible correlation between the outcomes. Sources of unobserved heterogeneity and serial dependency due to repeated measures are modelled through the introduction of individual‐specific, time‐constant random coefficients and time‐varying parameters evolving over time with a Markovian structure respectively. The inferential approach is carried out through the construction of a suitable expectation–maximization algorithm without parametric assumptions on the random effects distribution. [ABSTRACT FROM AUTHOR]

In this paper we investigate how co-ethnic networks affect the economic success of immigrants. Using longitudinal data of immigrants in Germany and including a large set of fixed effects and pre-migration controls to address the possible endogeneity of initial location, we find that immigrants in districts with larger co-ethnic networks are more likely to be employed soon after arrival. This advantage fades after four years, as migrants located in places with smaller co-ethnic networks catch up due to greater human capital investments. These effects appear stronger for lower-skilled immigrants, as well as for refugees and ethnic Germans. [ABSTRACT FROM AUTHOR]

This paper studies the effects of large-scale sport events with live spectators on COVID-19 infection trends at the local population level. Specifically, we compare the development of incidence rates in 41 German Nomenclature of Territorial Units for Statistics level 3 (NUTS-3) districts hosting a professional football match with at least 1,000 spectators vis-à-vis similar districts without hosting a match. Our empirical analysis builds on difference-in-difference and dynamic event study estimation for panel data. Synthetic control method is applied as a robustness check. While our findings generally do not point to significant treatment effects for the full sample of match locations, we find some noteworthy exceptions. Districts hosting first league matches with spectator attendance above the median (> 6,300 persons) and, particularly, matches without strict face mask requirements experienced a significant relative rise in incidence rates 14 days after the match. We also find that intra-district mobility increases on match days in treated districts, highlighting the significance of professional football matches as mobility-based infection transmission channel. [ABSTRACT FROM AUTHOR]

In this paper, we consider efficiency improvement in a nonparametric panel data model with cross-sectional dependence. A generalised least squares (GLS)-type estimator is proposed by taking into account this dependence structure. Parameterising the cross-sectional dependence, a local linear estimator is shown to be dominated by this type of GLS estimator. Also, possible gains in terms of rate of convergence are studied. Asymptotically optimal bandwidth choice is justified. To assess the finite sample performance of the proposed estimators, a Monte Carlo study is carried out. Further, some empirical applications are conducted with the aim of analysing the implications of the European Monetary Union for its member countries. [ABSTRACT FROM AUTHOR]