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The generation of waste by the paper industry has attracted great attention over the last decades among other reasons because the demand for recycled waste paper has considerably increased. As the paper industry is closely intertwined to the rest of industries in the production system, its activity exerts both a direct and indirect influence on the volume of waste generated by its supplier industries. The purpose of this study is to shed some light on the evolution of the volume of waste generated by the suppliers of the Spanish paper industry over the period 2005–2010 using an Economic Input–Output Life Cycle Assessment Model. In particular we focus on the evolution of the volume of waste generated by firms of the own paper industry. We employ data from different waste surveys conducted by the Spanish National Statistics Institute and input–output tables extracted from the World Input–Output Database. The results obtained show that the waste generated by suppliers amounted to 1250 thousand tonnes in 2010, an important volume if we take into account that the waste generated by the paper industry in 2010 amounted to 1739 thousand tonnes. The analysis of the evolution of the waste generated by suppliers reveals that there is a high degree of concentration, both in terms of industries and in terms of waste categories. In addition, the decrease in the volume of waste generated by supplier firms within the own paper industry reflects not only the growing importance of recycled paper as raw material for paper-making but also the important investments in technology made by this industry in Spain.

To review the highest impact studies published from the UK Biobank and assess their contributions to "precision medicine." We reviewed 140 of 689 studies published between 2008 and May 2019 from the UK Biobank deemed to be high impact by citations, alternative metric data, or publication in a high impact journal. We classified studies according to whether they (1) were largely methods papers, (2) largely replicated prior findings or associations, (3) generated novel findings or associations, (4) developed risk prediction models that did not yield clinically significant improvements in risk estimation over prior models or (5) developed models that produced significant improvements in individualized risk assessment, targeted screening, or targeted treatment. This final category represents "precision medicine." We classified 15 articles as category 1, 33 as category 2, 85 as category 3, six as category 4, and one as category 5. In this assessment of the first 7 years of the UK Biobank and first 4 years of genetic data availability, the majority of high impact UK Biobank studies either replicated known associations or generated novel associations without clinically relevant improvements in risk prediction, screening, or treatment. This information may be useful for designers of other cohort studies in terms of input to design and follow-up to facilitate precision medicine research.

The usage of psychological networks that conceptualize psychological behavior as a complex interplay of psychological and other components has gained increasing popularity in various fields of psychology. While prior publications have tackled the topics of estimating and interpreting such networks, little work has been conducted to check how accurate (i.e., prone to sampling variation) networks are estimated, and how stable (i.e., interpretation remains similar with less observations) inferences from the network structure (such as centrality indices) are. In this tutorial paper, we aim to introduce the reader to this field and tackle the problem of accuracy under sampling variation. We first introduce the current state-of-the-art of network estimation. Second, we provide a rationale why researchers should investigate the accuracy of psychological networks. Third, we describe how bootstrap routines can be used to (A) assess the accuracy of estimated network connections, (B) investigate the stability of centrality indices, and (C) test whether network connections and centrality estimates for different variables differ from each other. We introduce two novel statistical methods: for (B) the correlation stability coefficient, and for (C) the bootstrapped difference test for edge-weights and centrality indices. We conducted and present simulation studies to assess the performance of both methods. Finally, we developed the free R-package bootnet that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods. We showcase bootnet in a tutorial, accompanied by R syntax, in which we analyze a dataset of 359 women with posttraumatic stress disorder available online., Comment: Accepted for publication in Behavior Research Methods

Since the first patient was detected in India in late February 2020, the SARS-CoV-II virus is playing havoc on India. After the first wave, India is now riding the second wave. As was in the case of European countries like Italy and the UK, the second wave is more contagious and at the time of writing this paper, the per day infection is as high as 400,000. The alarming thing is it is not uncommon that people are getting infected multiple times. On the other hand, mass vaccination has started step by step. There is also a growing danger of potential third wave is unavoidable, which can even infect kids and minors. In this situation, an estimation of the dynamics of SARS-CoV-II is necessary to combat the pandemic. We have used a modified SEIRD model that includes vaccination and repeat infection as well. We have studied India and 8 Indian states with varying SARS-CoV-II infections. We have shown that the COVID-19 wave will be repeated from time to time, but the intensity will slow down with time. In the most possible situation, our calculation shows COVID-19 will remain endemic for the foreseeable future unless we can increase our vaccination rate manifold.

Research on heat waves (periods of excessively hot weather, which may be accompanied by high humidity) is a newly emerging research topic within the field of climate change research with high relevance for the whole of society. In this study, we analyzed the rapidly growing scientific literature dealing with heat waves. No summarizing overview has been published on this literature hitherto. We developed a suitable search query to retrieve the relevant literature covered by the Web of Science (WoS) as complete as possible and to exclude irrelevant literature (n = 8,011 papers). The time-evolution of the publications shows that research dealing with heat waves is a highly dynamic research topic, doubling within about 5 years. An analysis of the thematic content reveals the most severe heat wave events within the recent decades (1995 and 2003), the cities and countries/regions affected (United States, Europe, and Australia), and the ecological and medical impacts (drought, urban heat islands, excess hospital admissions, and mortality). Risk estimation and future strategies for adaptation to hot weather are major political issues. We identified 104 citation classics which include fundamental early works of research on heat waves and more recent works (which are characterized by a relatively strong connection to climate change)., 40 pages, 2 tables, and 9 figures

The currently ongoing COVID-19 pandemic confronts governments and their health systems with great challenges for disease management. Epidemiological models play a crucial role, thereby assisting policymakers to predict the future course of infections and hospitalizations. One difficulty with current models is the existence of exogenous and unmeasurable variables and their significant effect on the infection dynamics. In this paper, we show how a method from nonlinear control theory can complement common compartmental epidemiological models. As a result, one can estimate and predict these exogenous variables requiring the reported infection cases as the only data source. The method allows to investigate how the estimates of exogenous variables are influenced by non-pharmaceutical interventions and how imminent epidemic waves could already be predicted at an early stage. In this way, the concept can serve as an “epidemometer” and guide the optimal timing of interventions. Analyses of the COVID-19 epidemic in various countries demonstrate the feasibility and potential of the proposed approach. The generic character of the method allows for straightforward extension to different epidemiological models.

We investigate the problem of mathematical modeling of new corona virus (Covid19) in Poland and tries to predict the upcoming wave. A Gaussian mixture model is proposed to characterize the COVID-19 disease and to predict a new / future wave of COVID-19. This prediction is very much needed to prepare for medical setup and continue with the upcoming program. Specifically, data related to the new confirmed cases of COVID-19 per day are considered, and then we attempt to predict the data and statistical activity. A close match between actual data and analytical data by using the Gaussian mixture model shows that it is a suitable model to present new cases of COVID-19. In addition, it is thought that there are N waves of COVID-19 and that information for each future wave is also present in current and previous waves as well. Using this concept, predictions of a future wave can be made.

Leading indicators are important variables in business cycle forecasting. We use wavelet analysis to investigate the lead-lag stability of German leading indicators in time-frequency space. This method permits a time-varying relation of the leading indicators to the reference cycle allowing simultaneously to focus on lead-lag stability at the specific business cycle frequencies. In this way we analyze an index of new orders, a survey-based index of business expectations, an index of stock market returns and the interest rate term spread. We confirm that most of these indicators are indeed leading the reference cycle most of the time, but the number of months leading varies considerably over time and is associated with a great deal of estimation uncertainty.

We study an alternative approach to determine the final league table in football competitions with a premature ending. For several countries, a premature ending of the 2019/2020 football season has occurred due to the COVID-19 pandemic. We propose a model-based method as a possible alternative to the use of the incomplete standings to determine the final table. This method measures the performance of the teams in the matches of the season that have been played and predicts the remaining non-played matches through a paired-comparison model. The main advantage of the method compared to the incomplete standings is that it takes account of the bias in the performance measure due to the schedule of the matches in a season. Therefore, the resulting ranking of the teams based on our proposed method can be regarded as more fair in this respect. A forecasting study based on historical data of seven of the main European competitions is used to validate the method. The empirical results suggest that the model-based approach produces more accurate predictions of the true final standings than those based on the incomplete standings.

Non-invasive estimation of the pressure gradient in cardiovascular stenosis has much clinical importance in assisting the diagnosis and treatment of stenotic diseases. In this research, a systematic comparison is conducted to investigate the accuracy of a group of stenosis models against the MRI- and catheter-measured patient data under the aortic coarctation condition. Eight analytical stenosis models, including six from the literature and two proposed in this study, are investigated to examine their prediction accuracy against the clinical data. The two improved models proposed in this study consider comprehensively the Poiseuille loss, the Bernoulli loss in its exact form, and the entrance effect, of the blood flow. Comparison of the results shows that one of the proposed models demonstrates a cycle-averaged mean prediction error of −0.15 ± 3.03 mmHg, a peak-to-peak prediction error of −1.8 ± 6.89 mmHg, which is the best among the models studied., This research was funded by the European Community’s Seventh Framework Programme (FP7/2007–2013) under the grant agreement number 224495 (euHeart project).

This paper presents empirical results on coronavirus infection and fatality rates from cross-country regressions for OECD economies and a sample of middle- and high-income countries. We include environmental, economic, medical, and policy variables in our analysis to explain the number of corona cases and deaths per million. We find a significant positive impact of local air pollution on infection rates in the whole sample and on fatality rates for OECD countries. Obesity rates have a positive effect on cases and deaths across the different estimation equations. The strategy of aiming to achieve herd immunity has a significant positive effect on infections as well as on death rates. The first affected countries have significantly higher mortality rates, revealing the lack of experience and medical capacity to deal with the pandemic in an initial phase. Postponing – and fighting - the pandemic could save lives in many countries and generate considerable economic benefits. Other medical and policy variables discussed in the public sphere do not show a significant impact in the regression analysis. Our results suggest that improving air quality and fighting obesity helps reduce the negative effects of a coronavirus pandemic significantly. Policy options for fighting a second epidemic wave should take into account the results from this study in order to optimize global epidemic policy., International Economics and Economic Policy, 17 (4), ISSN:1612-4804, ISSN:1612-4812

Gambling is a popular leisure activity but also a significant public health issue in Australia. The severity of gambling in Australia is characterised by unprecedented gambling expenditures and losses representing the highest gambling rates in the world, which has led to renewed interests in understanding the factors influencing gambling behaviours. We contribute to the debate on the determinants of gambling by providing the first study that examines the impact of ethnic diversity on gambling. Using data from the Household, Income and Labour Dynamics in Australia survey, we find that ethnic diversity is positively associated with gambling. This result is robust to alternative estimation approaches, alternative ways of measuring ethnic diversity and irrespective of whether gambling is measured using the Problem Gambling Severity Index, gambling expenditures or number of gambling activities. Our results also suggest stronger effects of ethnic diversity for problem gamblers compared to gamblers in other risk categories. These results support the need for policies that promote social capital in diverse communities to reduce the risks of social isolation, which is an important motivator of gambling behaviour.

Only limited data are available in France on the incidence and health expenditure of type 2 diabetes. The objective of this study, based on national health insurance administrative database, is to describe the expenditure reimbursed to patients newly treated for type 2 diabetes and the proportion of expenditure attributable to diabetes. The study is conducted over a 6-year period from 2008, the year of incidence of treated diabetes, to 2014. Type 2 diabetic patients aged 45 years and older are identified on the basis of their drug consumption. To estimate expenditure attributable to diabetes, a matched control group is selected among more than 13 million beneficiaries over 44 years old not taking antidiabetic treatment. The expenditure attributable to diabetes is estimated by two methods: simple comparison of reimbursed health expenditure between both groups, and a difference-in-differences method including control variables. The cohort of incident type 2 diabetic patients comprises 170,013 patients in 2008. Mean global reimbursed expenditure is €4700 per patient in 2008 and €5500 in 2015. Expenditure attributable to diabetes, estimated by direct comparison with controls, is €1500 in the first year. We, thus, observe a decrease in the following year due to decreased hospitalisations, and then expenditure increase by an average of 7% per year to reach €1900 in the eighth year after the initiation of treatment.

Introduction The first wave of COVID-19 pandemic period has drastically changed people’s lives all over the world. To cope with the disruption, digital solutions have become more popular. However, the ability to adopt digitalised alternatives is different across socio-economic and socio-demographic groups. Objective This study investigates how individuals have changed their activity-travel patterns and internet usage during the first wave of the COVID-19 pandemic period, and which of these changes may be kept. Methods An empirical data collection was deployed through online forms. 781 responses from different countries (Italy, Sweden, India and others) have been collected, and a series of multivariate analyses was carried out. Two linear regression models are presented, related to the change of travel activities and internet usage, before and during the pandemic period. Furthermore, a binary regression model is used to examine the likelihood of the respondents to adopt and keep their behaviours beyond the pandemic period. Results The results show that the possibility to change the behaviour matter. External restrictions and personal characteristics are the driving factors of the reduction in ones' daily trips. However, the estimation results do not show a strong correlation between the countries' restriction policy and the respondents' likelihood to adopt the new and online-based behaviours for any of the activities after the restriction period. Conclusion The acceptance and long-term adoption of the online alternatives for activities are correlated with the respondents' personality and socio-demographic group, highlighting the importance of promoting alternatives as a part of longer-term behavioural and lifestyle changes.

We demonstrate a Bayesian method for the “real-time” characterization and forecasting of partially observed COVID-19 epidemic. Characterization is the estimation of infection spread parameters using daily counts of symptomatic patients. The method is designed to help guide medical resource allocation in the early epoch of the outbreak. The estimation problem is posed as one of Bayesian inference and solved using a Markov chain Monte Carlo technique. The data used in this study was sourced before the arrival of the second wave of infection in July 2020. The proposed modeling approach, when applied at the country level, generally provides accurate forecasts at the regional, state and country level. The epidemiological model detected the flattening of the curve in California, after public health measures were instituted. The method also detected different disease dynamics when applied to specific regions of New Mexico.

The aim of this study was to evaluate observed and future inbreeding level in Polish Holstein-Friesian cattle population. In total, over 9.8 mln animals were used in the analysis coming from the pedigree of Polish Federation of Cattle Breeders and Dairy Farmers. Inbreeding level, as an average per birth year, was estimated with the method accounting for missing parent information with the assumption of year 1950 as the base year of the population. If an animal had no ancestral records, an average inbreeding level from its birth year was assigned. Twice the average inbreeding level served as relatedness of the animal to the population, which enabled estimation of inbreeding in its offspring. The future inbreeding of potential offspring was estimated as an average of animals (bulls and cows) available for mating in a certain year. It was observed that 30-50% of animals born between 1985 and 2015 had no relevant ancestral information, which is caused by a high number of new animals and/or entire farms entering the national milk recordings. For the year 2015, the observed inbreeding level was 3.30%, which was more than twice the inbreeding with the classical approach (without missing parent information) and higher by 0.4% than the future inbreeding. The average increase of inbreeding in years 2010-2015 was 0.10%, which is similar to other countries monitored by World Holstein-Friesian Federation. However, the values might be underestimated due to low pedigree completeness. The estimates of future inbreeding suggested that observed inbreeding could be even lower and also increase slower, which indicates a constant need to monitor rate of increase in inbreeding over time. The most important aspect of presented results is the necessity to advise individual farmers to keep precise recordings of the matings on their farm in order to improve the pedigree completeness of Polish Holstein-Friesian and to use suitable mating programs to avoid too rapid growth of inbreeding.

A key feature of this project is the estimation of broad industry sectors for each carcinogen, occupational circumstance and cancer site. This information is critical to prioritise interventions that aim to reduce workplace-related cancer. This paper presents results for the current burden of occupational cancer by industry sector on the basis of attributable cancer registrations for 2004, for all industries for which there are at least 10 attributable registrations.

Respondent-driven sampling (RDS) has become increasingly popular for sampling hidden populations, including injecting drug users (IDU). However, RDS data are unique and require specialized analysis techniques, many of which remain underdeveloped. RDS sample size estimation requires knowing design effect (DE), which can only be calculated post hoc. Few studies have analyzed RDS DE using real world empirical data. We analyze estimated DE from 43 samples of IDU collected using a standardized protocol. We find the previous recommendation that sample size be at least doubled, consistent with DE = 2, underestimates true DE and recommend researchers use DE = 4 as an alternate estimate when calculating sample size. A formula for calculating sample size for RDS studies among IDU is presented. Researchers faced with limited resources may wish to accept slightly higher standard errors to keep sample size requirements low. Our results highlight dangers of ignoring sampling design in analysis.

Many health technology assessment committees have an explicit or implicit reference value (often referred to as a ‘threshold’) below which new health technologies or interventions are considered value for money. The basis for these reference values is unclear but one argument is that it should be based on the health opportunity costs of funding decisions. Empirical estimates of the marginal cost per unit of health produced by a healthcare system have been proposed to capture the health opportunity costs of new funding decisions. Based on a systematic search, we identified eight studies that have sought to estimate a reference value through empirical estimation of the marginal cost per unit of health produced by a healthcare system for England, Spain, Australia, The Netherlands, Sweden, South Africa and China. We review these eight studies to provide an overview of the key methodological approaches taken to estimate the marginal cost per unit of health produced by the healthcare system with the aim to help inform future estimates for additional countries. The lead author for each of these papers was invited to contribute to the current paper to ensure all the key methodological issues encountered were appropriately captured. These included consideration of the key variables required and their measurement, accounting for endogeneity of spending to health outcomes, the inclusion of lagged spending, discounting and future costs, the use of analytical weights, level of disease aggregation, expected duration of health gains, and modelling approaches to estimating mortality and morbidity effects of health spending. Subsequent research estimates for additional countries should (1) carefully consider the specific context and data available, (2) clearly and transparently report the assumptions made and include stakeholder perspectives on their appropriateness and acceptability, and (3) assess the sensitivity of the preferred central estimate to these assumptions. Supplementary Information The online version contains supplementary material available at 10.1007/s40273-021-01087-6.

This paper provides evidence that external financial status is an important determinant of firms’ responses to trade liberalization. Based on the difference-in-differences (DID) estimation strategy and data from Chinese firms, we find that input tariff reduction has a significantly positive effect on export quality for firms with high credit constraints but has no significant impact on firms with low credit constraints. This finding suggests that trade liberalization leads to the upgrading of export quality by firms that face binding credit constraints. We also find that the quality upgrading of intermediate inputs and the enhancement of productivity can plausibly explain the upgrading of export quality by firms with high credit constraints. Our paper has some important implications for trade and financial policies.

Automated vehicles (AVs) are expected to operate on public roads, together with non-automated vehicles and other road users such as pedestrians or bicycles. Recent ethical reports and guidelines raise worries that AVs will introduce injustice or reinforce existing social inequalities in road traffic. One major injustice concern in today’s traffic is that different types of road users are exposed differently to risks of corporal harm. In the first part of the paper, we discuss the responsibility of AV developers to address existing injustice concerns regarding risk exposure as well as approaches on how to fulfill the responsibility for a fairer distribution of risk. In contrast to popular approaches on the ethics of risk distribution in unavoidable accident cases, we focus on low and moderate risk situations, referred to as routine driving. For routine driving, the obligation to distribute risks fairly must be discussed in the context of risk-taking and risk-acceptance, balancing safety objectives of occupants and other road users with driving utility. In the second part of the paper, we present a typical architecture for decentralized automated driving which contains a dedicated module for real-time risk estimation and management. We examine how risk estimation modules can be adjusted and parameterized to redress some inequalities.

Analysis of data with nonignorable nonresponse is an important and challenging task. Although some methods have been developed for inference under nonignorable nonresponse, they are only available for independent data. In this paper, we develop a two-stage propensity score adjustment method to estimate longitudinal time series models with nonignorable missingness. In particular, the response probability or propensity score is first estimated via solving the mean score equation based on the observed sample. Then, the inverse propensity scores are employed to conduct weighting adjustment for a composite likelihood based estimation. The propensity scores weighted estimation equations are shown to yield consistent and asymptotic normal estimators. Simulation studies and application to AIDS Clinical Trial data are presented to evaluate the performance of the proposed method.

This paper explores the advantages of international trade and foreign direct investment (FDI), which can be the main factors of transferring technology to economies. More specifically, it seeks the importance of international trade and FDI as the main channels of technology transfer between countries either in a region or at the international level. In this context, relevant spillovers arising from trade and FDI play important roles in achieving economic growth, capital accumulation and economic well-being, providing a path for sustainable development. The main goal of this paper is thus to investigate the possible implementation of regional economic through cooperation in trade and investment in a region such as the Economic Cooperation Organization (ECO). Accordingly, the country members can benefit from spillover effects on their economic growth, as a major factor to appoint sustainable development. The relevant methodology relies on estimation of a panel economic growth model in which we have used the obtained empirical results to assess the effects of trade and FDI spillovers on economic growth over the period 1995–2018. The empirical results have indicated significantly positive effects of the spillovers on economic growth of the ECO country members, as the case study, implying sustainable growth in the long run.

This paper proposes a framework for land-use land cover (LULC) simulation for urban growth estimation. The framework couples Cellular Automata Markov (CAM) and agent-based modeling (ABM) to explore the impact of socioeconomic factors, spatial neighborhoods, stakeholder choices, and development plans on LULC. The approach applies CA model to examine the spatiotemporal change in LULC patterns and ABM to observe the role of different socioeconomic drivers and commercial factors in the simulated environment to predict future urban LULC. For Dehradun city of India, the analysis of spatial patterns shows spatial accuracy of 87.86%. Most of the urban agriculture and vacant areas are converted to commercial, low-, medium-, and high-density residential areas. The coupled CAM-ABM model was found more efficient in prediction compared to the traditional CAM model. The methodologies presented in the paper can help decision makers foresight the requirements, thus leading to better resource management and informed decision making.

An equity warrant allows warrant holders to buy stocks of listed companies at a certain price on a promissory day. Classified by the time of exercise right, there are two types of equity warrant, namely European equity warrants and American equity warrants. The European equity warrants allow the buyers to exercise their rights only on the expiring date. In contrast, American warrants enable the buyers to exercise their rights before or on the expiring date. Based on the assumption that the firm’s value follows an uncertain exponential Ornstein–Uhlenbeck process instead of a stochastic process, this paper mainly solves the pricing problems of European and American equity warrants. The most significant advantage of this paper is offering the corresponding formulas to calculate equity warrants, which are not complicated compared with the classical stochastic financial theory. Besides, the minimum cover estimation method is applied to estimate the parameters in an uncertain exponential Ornstein–Uhlenbeck equation, and a simulation example is provided to show the effectiveness of the proposed pricing formulae.

The distance estimation of objects from a single camera can play a crucial role in many real-life applications, such as mobile robot navigation, autonomous vehicles and camera-based assistive devices for visually impaired people. This paper addresses the problem of distance estimation of people from a monocular camera. The paper proposes using face features, in addition to body features, with various regression algorithms to estimate the distance from a camera. These features are extracted from the bounding boxes of the people, detected in camera images. Two datasets, an ideal manual one and a practical automatic one, have been built for training and validation purposes. Various metrics, including a novel one proposed in this paper, have been utilized to evaluate the different regression models. The results reveal that the distance estimation errors have been significantly reduced using the body and face features together for all used regression algorithms, and a distance precision of about 98% has been achieved.

This paper is concerned with a nonparametric estimator of the regression function based on the local linear estimation method in a twice censoring setting. The proposed method avoid the problem of boundary effect and reduces the bias term. Under suitable assumptions, the strong uniform almost sure consistency with rate is established and the finite sample properties of the local linear regression smoother is investigated by means of a simulation study.

This paper updates and extends the systematic review and meta-analysis of Wardman and Batley (Transportation 41:1041–1069, 2014), which hitherto was the most comprehensive study of the impacts of punctuality on passenger rail demand in the literature. Whereas the 2014 paper covered 51 elasticities from 6 studies in Great Britain published between 2003 and 2011, this updated paper adds 11 subsequent British studies yielding a further 201 observations. The meta-model recovers a range of significant effects, relating to whether the elasticity was short versus long run, flow type and distance, season versus nonseason tickets, the relevant measure of lateness, and whether the purpose of the study was specifically the estimation of late time elasticities. Allowance was also made for study quality-related issues. The data indicated that, despite dynamic models being commonplace, there is some uncertainty as to how long the long run is. Alongside the meta-model, the paper also reports new econometric evidence that addresses some gaps in existing evidence and knowledge, especially in relation to functional form and non-linearity of effects. Findings from both strands of analysis would seem to suggest that rail industry guidance has tended to overstate the demand impacts of punctuality.

This paper examines the learning-by-exporting effect in Chinese manufacturing firms from 2005 to 2007. The traditional view is that exporting can lead to increased productivity by facilitating access to the global market and, thus, information and cutting-edge technologies. This process has been explained by the learning-by-exporting theory, which is supported by mixed empirical evidence. A semiparametric estimation method was used to measure firm-level productivity and examine the contingent impact of exports on productivity. On the one hand, the exporting firms exhibited significantly higher productivity and faster growth than the non-exporting firms. On the other hand, the effect of exporting on productivity was dependent upon firms’ innovation behavior. Therefore, the learning-by-exporting effect is contingent on a firm’s innovation capabilities. Only sufficiently innovative firms could actually experience faster growth through exports and innovation. For non-innovative firms, exporting could even result in decreased productivity. This paper successfully reconciles the mixed findings from the existing literature and explains why both positive and negative evidence can simultaneously and reasonably exist under learning-by-exporting theory.

This paper aims to study the ability of the corporate governance of banks to reduce non-performing loans. The dynamic panel GMM estimation is applied to 184 US commercial banks over 2000–2013 period. Given that bank-size groups have different risk profiles, the full sample of US banks is divided into three asset-size classes. For every asset-size group of banks, we successively integrate five corporate governance variables in addition to the bank-specific and macroeconomic variables in separate regressions. The central finding of this research is that small banks are characterized by a weak and fragile corporate governance system which leads to a bad loan quality. This result can be explained by small banks’ reliance on personal connections. Concerning medium banks, we notice a sound corporate governance system. As far as large banks are concerned, it is to mention that the corporate governance system of these banks is neutralized. On account of the high level of liquidity, large banks are engaged in excessive lending practices without taking notice of the undue losses. This paper notably contributes to the financial literature via empirically proving that the effect of banks’ corporate governance on their loan quality depends on bank size.

The main scope of the paper is to review the construction method of (multi-)regional input–output tables (RIOTs) in the Czech Republic at the NUTS 3 regional level. We compare the estimation of regional IO tables based on a modified cross-hauling adjusted regionalisation method (CHARM) applied by the authors with application of the hybrid approach of (Sixta and Vltavska in Stat Stat Econ J 97:4–14, 2017) from the Department of Statistics at the University of Economics in Prague. The aim of the presented paper is to contribute to the discussion about the suitability of alternative methodological approaches from the perspective of quality and time. We assume that despite the lack of official, published RIOTs by state statistical offices, the existing methodology of the relatively simpler non-survey CHARM method represents an adequate option for the generation of regional input–output tables suitable for further analytical research.

Labour regulation varies across different states in India. Using the state-level labour amendments of the Industrial Dispute Act (IDA), Besley and Burgess (Q J Econ 119:91–134, 2004) classified states as pro-worker and pro-employer states. Bhattacharjea (Indian J Labor Econ 49(2):211–232, 2006; Working paper, Center on Democracy, Development, and The Rule of Law Freeman Spogli Institute for International Studies, Stanford University, 2009) criticized Besley and Burgess’s (2004) paper both in terms of its methodology and in terms of its overall coding of the labour regulation index. The current paper uses the Besley and Burgess’s (2004) methodology, classifies states as flexible and inflexible based on the state-level amendments of the IDA and looks into the issue of manufacturing employment for the three-digit manufacturing industries across Indian states. Data on industrial outputs and employment were extracted from ASI three-digit manufacturing industries for the period from 1980–1981 to 2006–2007. The employment function was estimated for both total workers (measured as ‘total persons engaged’) and production workers (measured as ‘workers’) as dependent variables regressed on the labour market regulation index. The variables are found to be stationary under panel unit root tests. Controlling for industry-fixed and year-fixed effects, we found stringent labour regulation to adversely affect manufacturing employment. We did not find enough evidence to support the hypothesis that labour-intensive industries are worst affected from rigid labour laws. The results are found to be robust under dynamic panel estimation.

Quantifying the change in field expected ultimate recovery across time is an old and complex topic fraught with ambiguity and misinterpretation. In this paper, changes in recovery estimates performed by the Bureau of Ocean Energy Management in the US Gulf of Mexico are evaluated based on 782 offshore fields that began producing after 1975 and ceased production before 2017. At the time of first production, field expected ultimate recovery estimates totaled 6.3 billion barrels oil equivalent (38.6 exajoules), but by the end of their life, the fields produced 8.5 billion barrels oil equivalent (52 exajoules), a 35% growth. On an individual field basis, however, median recovery estimates grew less than 2%. The purpose of this paper is to explain the differences in these two statistics and quantify how expected ultimate recovery is influenced by field age, discovery year, reserve category, peak ratio, product type, size, and water depth to understand better the contribution of individual factors in reserves evaluation. Consistent interpretation of reserves category is probably the most important factor in application. Field age and size are positively correlated with changes in expected ultimate recovery in both the shallow water and deepwater regions of the US Gulf of Mexico, pre- and post-1988 evaluation periods are a significant factor, and a development variable referred to as peak ratio exhibits a meaningful correspondence. The results of this analysis show that Bureau of Ocean Energy Management procedures for expected ultimate recovery estimation in the US Gulf of Mexico post-1988 are consistent with the application of probable reserves classifications; these highlight the need for a precise understanding of factor inputs in reserves growth studies.

This paper deals with the estimation of spatial econometric models of employment rate across 259 NUTS 2 (Nomenclature of Units for Territorial Statistics) regions of the European Union in 2018 regarding different region-specific factors. Since, spatial autocorrelation and spatial heterogeneity often occur jointly, the paper is oriented at verification of two hypotheses. Hypothesis 1 related to the existence of the spatial autocorrelation, i.e., that the regional employment process is not a spatially isolated process, was confirmed. Based on the estimation of Spatial Durbin Model, direct, indirect and total spatial impacts were quantified and verified. The results proved the significant impact of neighbouring regions for GDP and compensation of employees variables in explaining regional employment rate. Significant influence of factors like educational attainment level and population density seems to be limited only to the particular region. Hypothesis 2 reflected the existence of the spatial heterogeneity. Based on the geographically weighted regression the assumption of spatial variability of the model parameters was also verified. The regional employment in the EU seems to be affected by both spatial effects and the presented approaches thus represent two different insights into the complex spatial character of the modelled process.

Rapid development in the Internet of Things every day more and more devices are touching the human world from CCTV cameras to light sensors to washing machines to smoke sensors etc. When IoT device communicates with each other there are many issues and challenges regarding security and privacy. One of the important issue is trust management and how to calculate the trust score. This issue and challenge are proposed in this paper. The proposed method makes trust score calculation in IoT very much promising. The fuzzy method is proposed in this paper that is more reliable than other fuzzy approaches used in the past. This fuzzy approach uses three linguistic values for six parameters which are calculated from data collected from IoT devices. The output of the fuzzy method is a fuzzy set which has three linguistic value. In this paper, we have also generated real-time datasets using a fuzzy approach and calculating all six parameters whose values are extracted from devices. We generate two datasets and compare them to check our approach of calculating the trust score is correct or not.

This paper seeks to test the intra-city convergence of house prices using the comprehensive suburban house price and census data for a key Australian city. It applies the regression-based β-convergence test combined with spatial econometric models to examine the conditional convergence of house prices across 300+ suburbs in the Melbourne metropolitan area. This paper finds evidence of house price convergence when the econometric model controls for spatial effects and a few other suburban characteristics, such as population size, economic profile, education and employment profile, and crime rate. However, the convergence rate is found to be decelerated in the post-GFC periods. The estimation results suggest a faster convergence rate for units/apartments than for freestanding houses. There is also evidence of spatial effects, implying that house price growth in Melbourne suburbs is spilling over into the neighboring suburbs. These findings are expected to shed new light on understanding the residential real estate market and price dynamics within a city in the context of a developed nation and help design relevant housing policies.

In recent years, data-driven research has attracted significant attention from both industry and academia due to its success in medical, transportation, finance and other fields. How could data tightly integrate with control is a hot issue being explored. This paper provides a brief survey on the relationship of data and control. Four types of data-based control studies are considered, namely post-identification control, direct data control, learning control, and observer-based control. The observer based control can provide real-time observation and estimation of the unknown dynamic systems. It has a great potential in solving complex online control problems. This paper classifies and analyzes the observer integrated control, covering the latest research progress and forecasting the development trend in this direction. This paper concludes with a summary advantages and disadvantages of data-based controls.

With the background of hyperurbanization and a jobs–housing imbalance in Beijing and other megacities in China, this study aims at developing a systematic toolkit of demand estimation and route planning for long-distance commuter bus lines. Taking the express bus services (EBS) in the Changping Corridor in Beijing as an example, this paper presents the use of a route-planning algorithm. Mobile phone location (MPL) data serves as a valid instrument for the origin–destination (OD) estimation, which provides a new perspective to identify the locations of homes and jobs. The OD distribution matrices are specified via geocoded MPL data. To minimize the aggregated travel time and attract potential passengers, this study subsamples long-distance commuting trips among the OD matrices by determining the operation distance threshold. The threshold can be solved by comparing the aggregated travel time of EBS to the travel time of local buses and private cars. Using the distance threshold and other operational parameters, we process an iterative computation to determine the length of routes, number of lines, and stop spacing and location. Compared to local buses and private cars, the planned EBS lines could reduce aggregated travel time by at least 20%. The results demonstrate that the method presented in this study is applicable and flexible. This paper may lead to new research directions for route planning of long-distance commuter buses. The limitations of this study and the future research agenda are also discussed.

In 2006 there was quite some discussion on how to estimate the conditional estimation error in the chain-ladder (CL) model of Mack. Buchwalder, Buhlmann, Merz and Wuthrich (BBMW) (ASTIN Bull 36(2):521–542, 2006) proposed another estimator than the one derived by Mack (ASTIN Bull 23(2):213–225, 1993). These two estimators are also found in a broader context by new authors in recent papers. In the present paper we examine the theoretical properties of the two estimators and come to the conclusion that the BBMW estimator has some major deficiencies compared with the Mack estimator. It takes much less information of the observed triangle into account, the averaging is done over inappropriate sets and it does not properly fit to the Mack CL-model.

This paper proposes a stochastic frontier model with three composed errors, and therefore six error components. As in the metafrontier literature, firms belong to groups with a group-specific frontier. A firm has a level of short-run and long-run inefficiency relative to its group-specific frontier, as in existing models with two composed errors and four error components. But now there is also a group-specific inefficiency, that is, a shortfall of the group-specific frontier from the best practice metafrontier. The paper shows how to estimate this model and how to extract predictions of the various inefficiencies.

Existing research on funding disparities between historically black colleges and universities (HBCUs) and non-HBCUs primarily focuses on government funding and pays little attention to foundation giving. This paper helps to fill this gap by examining the effects of funding by the Charles Koch Foundation—a major funder of economics research in the USA—on the economics research productivity at HBCUs. Using data from the foundation’s tax forms, the Integrated Postsecondary Education Data System (IPEDS), and Scopus, this paper examines factors that explain the foundation’s grant results and how the foundation’s money affects economics research productivity at HBCUs. The paper estimates the effects of Koch funding on three different measures of HBCU productivity: number of publications, citations, and publications in top economics journals. We report ordinary least squares, two-step generalized linear model, and Poisson results that show little or no impact from (a) recipiency of a Koch grant, (b) the total amount of grant dollars, or (c) the average amount of grant dollars on research productivity. We also obtain event study and difference-in-differences estimates of the effects of Koch grants to HBCUs on publications and publications per faculty member and again find no statistically significant impacts. The evidence obtained—consistent across different model specifications and estimation techniques—does not support the hypothesis that Koch’s funding to HBCUs is primarily geared towards enhancing the research productivity of these colleges and universities. The paper discusses alternative explanations for the Charles Koch Foundation’s targeting strategy and its involvement with particular HBCUs.

This paper provides the humanitarian community with an automated tool that can detect a disaster using tweets posted on Twitter, alongside a portal to identify local and regional Non-Governmental Organisations (NGOs) that are best-positioned to provide support to people adversely affected by a disaster. The proposed disaster detection tool uses a linear Support Vector Classifier (SVC) to detect man-made and natural disasters, and a density-based spatial clustering of applications with noise (DBSCAN) algorithm to accurately estimate a disaster’s geographic location. This paper provides two original contributions. The first is combining the automated disaster detection tool with the prototype portal for NGO identification. This unique combination could help reduce the time taken to raise awareness of the disaster detected, improve the coordination of aid, increase the amount of aid delivered as a percentage of initial donations and improve aid effectiveness. The second contribution is a general framework that categorises the different approaches that can be adopted for disaster detection. Furthermore, this paper uses responses obtained from an on-the-ground survey with NGOs in the disaster-hit region of Uttar Pradesh, India, to provide actionable insights into how the portal can be developed further.

Crime forecasting has been one of the most complex challenges in law enforcement today, especially when an analysis tends to evaluate inferable and expanded crime rates, although a few methodologies for subsequent equivalents have been embraced before. In this work, we use a strategy for a time series model and machine testing systems for crime estimation. The paper centers on determining the quantity of crimes. Considering various experimental analyses, this investigation additionally features results obtained from a neural system that could be a significant alternative to machine learning and ordinary stochastic techniques. In this paper, we applied various techniques to forecast the number of possible crimes in the next 5 years. First, we used the existing machine learning techniques to predict the number of crimes. Second, we proposed two approaches, a modified autoregressive integrated moving average model and a modified artificial neural network model. The prime objective of this work is to compare the applicability of a univariate time series model against that of a variate time series model for crime forecasting. More than two million datasets are trained and tested. After rigorous experimental results and analysis are generated, the paper concludes that using a variate time series model yields better forecasting results than the predicted values from existing techniques. These results show that the proposed method outperforms existing methods.

This paper concerns squared Msplit(q) estimation and its robustness against outliers. Previous studies in this field have been based on theoretical approaches. It has been proven that a conventional analysis of robustness is insufficient for Msplit(q) estimation. This is due to the split of the functional model into q competitive ones and, hence, the estimation of q competitive versions of the parameters of such models. Thus, we should consider robustness from the global point of view (traditional approach) and from the local point of view (robustness in relation between two “neighboring” estimates of the parameters). Theoretical considerations have generally produced many interesting findings about the robustness of Msplit(q) estimation and the robustness of the squared Msplit(q) estimation, although some of features are asymptotic. Therefore, this paper is focused on empirical analysis of the robustness of the squared Msplit(q) estimation for finite samples and, hence, it produces information on robustness from a more practical point of view. Mostly, the analyses are based on Monte Carlo simulations. A different number of observation aggregations are considered to determine how the assumption of different values of q influence the estimation results. The analysis shows that local robustness (empirical local breakdown points) is fully compatible with the theoretical derivations. Global robustness is highly dependent on the correct assumption regarding q. If it suits reality, i.e. if we predict the number of observation aggregations and the number of outliers correctly, then the squared Msplit(q) estimation can be an alternative to classical robust estimations. This is confirmed by empirical comparisons between the method in question and the robust M-estimation (the Huber method). On the other hand, if the assumed value of q is incorrect, then the squared Msplit(q) estimation usually breaks down.

In this paper, we highlight some recent developments of a new route to evaluate macroeconomic policy effects, which are investigated under the framework with potential outcomes. First, this paper begins with a brief introduction of the basic model setup in modern econometric analysis of program evaluation. Secondly, primary attention goes to the focus on causal effect estimation of macroeconomic policy with single time series data together with some extensions to multiple time series data. Furthermore, we examine the connection of this new approach to traditional macroeconomic models for policy analysis and evaluation. Finally, we conclude by addressing some possible future research directions in statistics and econometrics.

The paper estimates banks’ total factor efficiency (TFE) as well as TFE of each production factor by incorporating banks’ overall risk endogenously into bank’s production process as undesirable by-product in a Global-SMB Model. Our results show that, compared with a model incorporated with banks’ overall risk, a model considering only on-balance-sheet risk may over-estimate the integrated TFE (TFIE) and under-estimate TFE volatility. Significant heterogeneities of bank TFIE and TFE of each production factor exist among banks of different types and regions, as a result of still prominent unbalanced development of Chinese commercial banks. Based on the estimated TFIE, the paper further investigates the determinants of bank efficiency, and finds that shadow banking, bank size, NPL ratio, loan to deposit ratio, fiscal surplus to GDP ratio and banking sector concentration are significant determinants of bank efficiency. Besides, a model with risk-weighted assets as undesirable outputs can better capture the impact of shadow banking involvement.