ERN: Other Econometrics: Data Collection & Data Estimation Methodology (Topic)最新文献

{"title":"On Population-Weighted Density","authors":"J. Ottensmann","doi":"10.2139/ssrn.3119965","DOIUrl":"https://doi.org/10.2139/ssrn.3119965","url":null,"abstract":"Population-weighted density is the mean of the densities of subareas of a larger area weighted by the populations of those subareas. It is an alternative to the conventional density measure, total population divided by total area. This paper shows that population-weighted density is equal to conventional density plus the variance in density across the subareas divided by the conventional density. This density alternative is very dependent on the size and configuration of the subareas, an issue that has not been adequately addressed by most users. Urban sprawl is associated with lower densities, and the choice of the appropriate density measure is dependent upon the negative consequences of sprawl being considered. Comparison of conventional and population-weighted densities show the latter to be more highly skewed and to sometimes rank urban area densities very differently. Population-weighted density is more strongly related to the size of the urban area, especially size in earlier years, demonstrating the effect of the timing of urban growth on density.","PeriodicalId":384078,"journal":{"name":"ERN: Other Econometrics: Data Collection & Data Estimation Methodology (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131159507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heterodox Theories of Economic Growth and Income Distribution: A Partial Survey","authors":"A. Dutt","doi":"10.1111/joes.12243","DOIUrl":"https://doi.org/10.1111/joes.12243","url":null,"abstract":"Heterodox theories of economic growth and income distribution are surveyed, focusing on major theories and recent contributions. First, a general framework for examining growth and distribution is discussed, in terms of which classical†Marxian and post†Keynesian–Kaleckian and other theories are presented. Since this framework examines how variables are determined in equilibrium, second, dynamics behind equilibria are examined, focusing on goods market and labor market changes. Third, the framework is extended in a variety of ways to examine additional models addressing productivity growth and technical change, money and inflation, finance and debt, additional distributional considerations, multisector issues, open economy questions, and the environment. It is concluded that the literature on heterodox theories of growth and distribution is vibrant, large, and growing, and addresses many issues that are ignored or neglected in orthodox theories, including power, unemployment, and aggregate demand.","PeriodicalId":384078,"journal":{"name":"ERN: Other Econometrics: Data Collection & Data Estimation Methodology (Topic)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124834104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supply Chain Integration: A Review and Bibliometric Analysis","authors":"A. Asgari, Abubakar Hamid, Nader Ale Ebrahim","doi":"10.4172/2162-6359.1000447","DOIUrl":"https://doi.org/10.4172/2162-6359.1000447","url":null,"abstract":"Supply chain integration has been widely identified as a key research topic by both practitioners and academicians. In such environment, it is essential to vividly illustrate the publications contribution during the period of time and recognize research area and interests as well as the direction of research trend for future studies. With the availability of bibliometric data and variety of analytical tools for evaluation purposes, disregarding bibliometric analysis would be a missed opportunity for this area. Therefore, the current research attempts to deliver a comprehensive comparison thorough using rigorous bibliometric tools that provides a better understanding not previously fully grasped or evaluated by prior studies in the area of supply chain integration. The objective of this research is to recognize the global scientific production; the most productive authors, journals, articles and countries as well as to extract the most influential articles. The analysis begins by identifying over 500 published studies during the period of 1980 to 24th February 2016, which are then purified to works of proven influence and those authored by influential investigators. Web of Science Core Collection (formerly known as ISI), category of management was utilized to identify the relevant articles. Gaps are also discussed in knowledge about literature and bibliometrics analysis. The findings provide wisdom and a vigorous roadmap for further investigation in this field.","PeriodicalId":384078,"journal":{"name":"ERN: Other Econometrics: Data Collection & Data Estimation Methodology (Topic)","volume":"379 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124732258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incentives for Replication in Economics","authors":"S. Galiani, P. Gertler, Mauricio Romero","doi":"10.2139/ssrn.2999062","DOIUrl":"https://doi.org/10.2139/ssrn.2999062","url":null,"abstract":"Replication is a critical component of scientific credibility as it increases our confidence in the reliability of the knowledge generated by original research. Yet, replication is the exception rather than the rule in economics. In this paper, we examine why replication is so rare and propose changes to the incentives to replicate. Our study focuses on software code replication, which seeks to replicate the results in the original paper using the same data as the original study and verifying that the analysis code is correct. We analyse the effectiveness of the current model for code replication in the context of three desirable characteristics: unbiasedness, fairness and efficiency. We find substantial evidence of “overturn bias” that likely leads to many false positives in terms of “finding” or claiming mistakes in the original analysis. Overturn bias comes from the fact that replications that overturn original results are much easier to publish than those that confirm original results. In a survey of editors, almost all responded they would in principle publish a replication study that overturned the results of the original study, but only 29% responded that they would consider publishing a replication study that confirmed the original study results. We also find that most replication effort is devoted to so called important papers and that the cost of replication is high in that posited data and software are very hard to use. We outline a new model for the journals to take over replication post acceptance and prepublication that would solve the incentive problems raised in this paper.","PeriodicalId":384078,"journal":{"name":"ERN: Other Econometrics: Data Collection & Data Estimation Methodology (Topic)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134179284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the Choice of Combined Statistical Areas","authors":"J. Ottensmann","doi":"10.2139/SSRN.2955456","DOIUrl":"https://doi.org/10.2139/SSRN.2955456","url":null,"abstract":"Some Metropolitan Statistical Areas (MSAs) fail to encompass the full extent of metropolitan areas. Combined Statistical Areas (CSAs), combinations of Core-Based Statistical Areas, are larger and may be more a more appropriate choice for certain analyses. Differences between MSAs and CSAs (and some MSAs are not even included in CSAs) range from minor to the combination of large MSAs, with population increases ranging from a few percent more than doubling. The sharing of transportation infrastructure in the form of commuter rail service and shared airports demonstrates the integration of areas combined into CSAs. In addition, the extent of the MSAs defined for the 2000 census are comparable to current CSAs, which arise from a subsequent change in how metropolitan areas are defined.","PeriodicalId":384078,"journal":{"name":"ERN: Other Econometrics: Data Collection & Data Estimation Methodology (Topic)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128083982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Idea of ‘Emergent Properties’ in Data Privacy: Towards a Holistic Approach","authors":"S. Esayas","doi":"10.1093/ijlit/eaw015","DOIUrl":"https://doi.org/10.1093/ijlit/eaw015","url":null,"abstract":"‘The whole is more than the sum of its parts.’ \u0000This article applies lessons from the concept of ‘emergent properties’ in systems thinking to data privacy law. This concept, rooted in the Aristotelian dictum ‘the whole is more than the sum of its parts’, where the ‘whole’ represents the ‘emergent property’, allows systems engineers to look beyond the properties of individual components of a system and understand the system as a single complex. Applying this concept, the article argues that the current EU data privacy rules focus on individual processing activity based on a specific and legitimate purpose, with little or no attention to the totality of the processing activities – i.e. the whole – based on separate purposes. This implies that when an entity processes personal data for multiple purposes, each processing must comply with the data privacy principles separately, in light of the specific purpose and the relevant legal basis. \u0000This (atomized) approach is premised on two underlying assumptions: \u0000(i) distinguishing among different processing activities and relating every piece of personal data to a particular processing is possible, and \u0000(ii) if each processing is compliant, the data privacy rights of individuals are not endangered. \u0000However, these assumptions are untenable in an era where companies process personal data for a panoply of purposes, where almost all processing generates personal data and where data are combined across several processing activities. These practices blur the lines between different processing activities and complicate attributing every piece of data to a particular processing. Moreover, when entities engage in these practices, there are privacy interests independent of and/or in combination with the individual processing activities. Informed by the discussion about emergent property, the article calls for a holistic approach with enhanced responsibility for certain actors based on the totality of the processing activities and data aggregation practices.","PeriodicalId":384078,"journal":{"name":"ERN: Other Econometrics: Data Collection & Data Estimation Methodology (Topic)","volume":"18 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130787925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transparency, Reproducibility, and the Credibility of Economics Research","authors":"Garret Chistensen, E. Miguel","doi":"10.1257/JEL.20171350","DOIUrl":"https://doi.org/10.1257/JEL.20171350","url":null,"abstract":"There is growing interest in enhancing research transparency and reproducibility in economics and other scientific fields. We survey existing work on these topics within economics and discuss the evidence suggesting that publication bias, inability to replicate, and specification searching remain widespread in the discipline. We next discuss recent progress in this area, including through improved research design, study registration and pre-analysis plans, disclosure standards, and open sharing of data and materials, drawing on experiences in both economics and other social sciences. We discuss areas where consensus is emerging on new practices, as well as approaches that remain controversial, and speculate about the most effective ways to make economics research more credible in the future. ( JEL A11, C18, I23)","PeriodicalId":384078,"journal":{"name":"ERN: Other Econometrics: Data Collection & Data Estimation Methodology (Topic)","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133312512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Case Study: How Misinterpreting Probabilities Can Cost You the Game","authors":"K. Rotthoff","doi":"10.2139/ssrn.2867694","DOIUrl":"https://doi.org/10.2139/ssrn.2867694","url":null,"abstract":"Using data to make future decisions can increase the odds of success in many aspects of life, however, using the data incorrectly can be worse than not using any data at all. In this study, I present a case where a collegiate football coach attempted to use data to enhance the chances of success. In fact, because of his misinterpretation the dependence (or independence) of odds across his play-calling, his play-calling was not only sub-optimal but was detrimental to his team. This case study is designed as a way to clarify this common mistake our students make when interpreting data.","PeriodicalId":384078,"journal":{"name":"ERN: Other Econometrics: Data Collection & Data Estimation Methodology (Topic)","volume":"31 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132725562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linear Probability Models (LPM) and Big Data: The Good, the Bad, and the Ugly","authors":"S. Chatla, Galit Shmueli","doi":"10.2139/ssrn.2353841","DOIUrl":"https://doi.org/10.2139/ssrn.2353841","url":null,"abstract":"Linear regression is among the most popular statistical models in social sciences research. Linear probability models (LPMs) - linear regression models applied to a binary outcome - are used in various disciplines. Surprisingly, LPMs are rare in the IS literature, where logit and probit models are typically used for binary outcomes. LPMs have been examined with respect to specific aspects, but a thorough evaluation of their practical pros and cons for different research goals under different scenarios is missing. We perform an extensive simulation study to evaluate the advantages and dangers of LPMs, especially in the realm of Big Data that now affects IS research. We evaluate LPM for the three common uses of binary outcome models: inference and estimation, prediction and classification, and selection bias. We compare its performance to logit and probit, under different sample sizes, error distributions, and more. We find that coefficient directions, statistical significance, and marginal effects yield results similar to logit and probit. Although LPM coefficients are biased, they are consistent for the true parameters up to a multiplicative scalar. Coefficient bias can be corrected by assuming an error distribution. For classification and selection bias, LPM is on par with logit and probit in terms of class separation and ranking, and is a viable alternative in selection models. It is lacking when the predicted probabilities are directly of interest, because predicted probabilities can exceed the unit interval. We illustrate some of these results through by modeling price in online auctions, using data from eBay.","PeriodicalId":384078,"journal":{"name":"ERN: Other Econometrics: Data Collection & Data Estimation Methodology (Topic)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124484873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What Statistics Canada Survey Data Sources Are Available to Study Neurodevelopmental Conditions and Disabilities in Children and Youth?","authors":"Rubab G. Arim, L. Findlay, D. Kohen","doi":"10.11575/SPPP.V9I0.42601","DOIUrl":"https://doi.org/10.11575/SPPP.V9I0.42601","url":null,"abstract":"Researchers with an interest in examining and better understanding the social context of children suffering from neurodevelopmental disabilities can benefit by using data from a wide variety of Statistics Canada surveys as well as the information contained in administrative health databases. Selective use of a particular survey and database can be informative particularly when demographics, samples, and content align with the goals and outcomes of the researcher’s questions of interest. Disabilities are not merely conditions in isolation. They are a key part of a social context involving impairment, function, and social facilitators or barriers, such as work, school and extracurricular activities. Socioeconomic factors, single parenthood, income, and education also play a role in how families cope with children’s disabilities. Statistics indicate that five per cent of Canadian children aged five to 14 years have a disability, and 74 per cent of these are identified as having a neurodevelopmental condition and disability. A number of factors must be taken into account when choosing a source of survey data, including definitions of neurodevelopmental conditions, the target group covered by the survey, which special populations are included or excluded, along with a comparison group, and the survey’s design. Surveys fall into categories such as general health, disability-specific, and children and youth. They provide an excellent opportunity to look at the socioeconomic factors associated with the health of individuals, as well as how these conditions and disabilities affect families. However rich the information gleaned from survey data, it is not enough, especially given the data gaps that exist around the health and well-being of children and older youths. This is where administrative and other data can be used to complement existing data sources. Administrative data offer specific information about neurological conditions that won’t be collected in general population surveys, given the nature of such surveys. While researchers can glean information from survey data such as functional health and disability, social inclusion or exclusion, and the role of social determinants in the lives of these children and their families, administrative data can identify rare neurodevelopmental conditions and disabilities not captured in general surveys. Analyzing information from all these sources can lead to a more nuanced understanding of the economic and social impacts, and functional limitations in daily living, that patients and their families experience with certain neurodevelopmental conditions and disabilities. Statistics Canada surveys offer a plethora of information for researchers interested in neurodevelopmental disabilities and social determinants of health. As these surveys are national in their scope, they provide a wealth of information for statistical analysis from people across Canada. This information can be used to inform researchers, policy","PeriodicalId":384078,"journal":{"name":"ERN: Other Econometrics: Data Collection & Data Estimation Methodology (Topic)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123221333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}