To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

The classical approach to testing for structural change employs retrospective tests using a historical data set of a given length. Here we consider a wide array of fluctuation-type tests in a monitoring situation-given a history period for which a regression relationship is known to be stable, we test whether incoming data are consistent with the previously established relationship. Procedures based on estimates of the regression coefficients are extended in three directions: we introduce (a) procedures based on OLS residuals, (b) rescaled statistics and (c) alternative asymptotic boundaries. Compared to the existing tests our extensions offer ease of computation, improved size in finite samples for dynamic models and better power against certain alternatives, respectively. We apply our methods to three data sets, German M1 money demand, US labour productivity and S&P 500 stock returns.

This paper explores a unique dataset of all the SET ratings provided by students of one university in Poland at the end of the winter semester of the 2020/2021 academic year. The SET questionnaire used by this university is presented in Appendix 1. The dataset is unique for several reasons. It covers all SET surveys filled by students in all fields and levels of study offered by the university. In the period analysed, the university was entirely in the online regime amid the Covid-19 pandemic. While the expected learning outcomes formally have not been changed, the online mode of study could have affected the grading policy and could have implications for some of the studied SET biases. This Covid-19 effect is captured by econometric models and discussed in the paper. The average SET scores were matched with the characteristics of the teacher for degree, seniority, gender, and SET scores in the past six semesters; the course characteristics for time of day, day of the week, course type, course breadth, class duration, and class size; the attributes of the SET survey responses as the percentage of students providing SET feedback; and the grades of the course for the mean, standard deviation, and percentage failed. Data on course grades are also available for the previous six semesters. This rich dataset allows many of the biases reported in the literature to be tested for and new hypotheses to be formulated, as presented in the introduction section. The unit of observation or the single row in the data set is identified by three parameters: teacher unique id (j), course unique id (k) and the question number in the SET questionnaire (n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9} ). It means that for each pair (j,k), we have nine rows, one for each SET survey question, or sometimes less when students did not answer one of the SET questions at all. For example, the dependent variable SET_score_avg(j,k,n) for the triplet (j=Calculus, k=John Smith, n=2) is calculated as the average of all Likert-scale answers to question nr 2 in the SET survey distributed to all students that took the Calculus course taught by John Smith. The data set has 8,015 such observations or rows. The full list of variables or columns in the data set included in the analysis is presented in the attached filesection. Their description refers to the triplet (teacher id = j, course id = k, question number = n). When the last value of the triplet (n) is dropped, it means that the variable takes the same values for all n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9}.Two attachments:- word file with variables description- Rdata file with the data set (for R language).Appendix 1. Appendix 1. The SET questionnaire was used for this paper. Evaluation survey of the teaching staff of [university name] Please, complete the following evaluation form, which aims to assess the lecturer’s performance. Only one answer should be indicated for each question. The answers are coded in the following way: 5- I strongly agree; 4- I agree; 3- Neutral; 2- I don’t agree; 1- I strongly don’t agree. Questions 1 2 3 4 5 I learnt a lot during the course. ○ ○ ○ ○ ○ I think that the knowledge acquired during the course is very useful. ○ ○ ○ ○ ○ The professor used activities to make the class more engaging. ○ ○ ○ ○ ○ If it was possible, I would enroll for the course conducted by this lecturer again. ○ ○ ○ ○ ○ The classes started on time. ○ ○ ○ ○ ○ The lecturer always used time efficiently. ○ ○ ○ ○ ○ The lecturer delivered the class content in an understandable and efficient way. ○ ○ ○ ○ ○ The lecturer was available when we had doubts. ○ ○ ○ ○ ○ The lecturer treated all students equally regardless of their race, background and ethnicity. ○ ○

In 2018, 28 of November, in Latvia the amendments to Section 32 (3) of the Labor Law entered into force, according with it employers are obliged to indicate in the advertisement wage. This database continue wages monitoring started in 2019 and show data observation for 2021. 2019 year was first year in Latvia, when based on job advertisement analysis it is possible to conclude about salary by occupations, salary grow. Advertisement analysis is operational pointer in comparison with official statistic data. This dataset represent job advertisement collection from biggest Latvian job advertisement web cv.lv . Data was collected by week in 2021 in Q1-Q2, near 1700 advertisements per week. After collecting dataset was cleared from advertisements, in which it was not possible to identify occupations. After data cleaning dataset consist of 41 138 advertisements. First salary monitoring year (2020) data is possible see here Skribans, Valerijs (2021), “Job advertisement and salary monitoring dataset for Latvia in 2020”, Mendeley Data, V1, doi: 10.17632/f3s8h6dzzf.1

The classical paradigm of asymptotic theory employed in econometrics presumes that model dimensionality, p, is fixed as sample size, n, tends to inifinity. Is this a plausible meta-model of econometric model building? To investigate this question empirically, several meta-models of cross-sectional wage equation models are estimated and it is concluded that in the wage-equation literature at least that p increases with n roughly like n l/4, while that hypothesis of fixed model dimensionality of the classical asymptotic paradigm is decisively rejected. The recent theoretical literature on large-p asymptotics is then very briefly surveyed, and it is argued that a new paradigm for asymptotic theory has already emerged which explicitly permits p to grow with n. These results offer some guidance to econometric model builders in assessing the validity of standard asymptotic confidence regions and test statistics, and may eventually yield useful correction factors to conventional test procedures when p is non-negligible relative to n.

Dataset accompanying the publication "Introduction to cross-section spatial econometric models with applications in R". This paper introduces the spatial component in cross-section econometric estimations and specifically, the spatial dependence effect inherent in some of the variables involved in the modelling process. First, the spatial structure of the data from thematic maps is observed and Moran's spatial autocorrelation indicators are presented. Subsequently, the spatial weights matrix is built under different specifications. Finally, several modelling specification strategies are shown and the interpretation of the estimated coefficients. The theoretical concepts are illustrated with examples and their corresponding R software codes. This code and databases are available in this repository. Exploratory Spatial Data Analysis (ESDA) and spatial econometrics.

Computers are now involved in many economic transactions and can capture data associated with these transactions, which can then be manipulated and analyzed. Conventional statistical and econometric techniques such as regression often work well, but there are issues unique to big datasets that may require different tools. First, the sheer size of the data involved may require more powerful data manipulation tools. Second, we may have more potential predictors than appropriate for estimation, so we need to do some kind of variable selection. Third, large datasets may allow for more flexible relationships than simple linear models. Machine learning techniques such as decision trees, support vector machines, neural nets, deep learning, and so on may allow for more effective ways to model complex relationships. In this essay, I will describe a few of these tools for manipulating and analyzing big data. I believe that these methods have a lot to offer and should be more widely known and used by economists.

The programs replicate tables and figures from "Human Decisions and Machine Predictions", by Kleinberg, Lakkaraju, Leskovec, Ludwig, and Mullainathan.

Replication files for David Slichter, "The Employment Effects of the Minimum Wage: A Selection Ratio Approach to Measuring Treatment Effects,” Journal of Applied Econometrics, forthcoming

Firstly, I’ve provided a .do file called sr.do which contains general code for implementing the selection ratio approach, with detailed instructions written as comments in the code.

For the minimum wage application, the main data file is mw_final.dta. A .csv version is also provided. Observations are a county in a time period. I have added self-explanatory variable labels for most variables. A few variables warrant a clearer explanation:

adj1-adj14: List of FIPS codes of all counties which are adjacent to the county in question. Each variables holds one adjacent county, and counties with fewer than 14 neighbors will have missing values for some of these variables.

change, logchange: Minimum wage this quarter - minimum wage last quarter, measured either in dollars or in logs.

time, t1-t108: The variable "time" converts years and quarters into a univariate time period, with time=1 in 1990Q1 and time=108 in 2016Q4. t1-t108 are indicators for each of these time periods.

lnemp_1418, lnearnbeg_1418, lnsep_1418, lnhira_1418, lnchurn_1418: Logs of employment, earnings, separations, hires, and churn, respectively, for 14-18 year olds.

gt1-gt6: Dummies for inclusion in each of the six comparisons used for the main (i.e., not spillover-robust) analysis. All treated counties which neighbor a control country take value 1 for each of these variables; all other treated counties take value 0. Among control counties, gt1=1 if the county neighbors a treated county and 0 otherwise, gt2=1 if the county has gt1=0 but neighbors a gt1=1 county, gt3=1 if county has gt1=gt2=0 but neighbors a gt2=1 county, etc.

h2-h6: Dummies for inclusion in each of the first spillover-robust (i.e., excluding border counties only) comparisons. Among control counties, h2-h6 are equal to gt2-gt6. Among treated counties, h2-h6 are equal to 1 if the treated county has gt1=0 but borders a gt1=1 county, and 0 otherwise.

k3-k6: Dummies for inclusion in each of the second spillover-robust (i.e., excluding two layers) comparisons. Among control counties, these variables are equal to gt3-gt6. Among treated counties, all observations take value 1 except those with gt1=1 or h2=1.

The data sources are as follows. The minimum wage law series is taken from David Neumark's website (https://www.economics.uci.edu/~dneumark/datasets.html). The economic variables are taken from the QWI, which I accessed via the Ithaca Virtual RDC. County adjacency files were downloaded from the Census Bureau (https://www.census.gov/geo/reference/county-adjacency.html).

The file main.do then runs the analyses. The resulting output file containing results is results.dta.

For the incumbency application, the main data file is incumb_final.dta. A .csv version is also provided. This file is drawn from Caughey and Sekhon's (2011) data; see their description of most variables here: https://doi.org/10.7910/DVN/8EYYA2

The key added variables are _IDistancea1-_IDistancea50, which are dummies for inclusion in the 50 comparisons used in the paper. Treated observations (i.e., Democratic wins) with margin of victory below 5 points have each of these variables equal to 1. Control observations have these variables equal to 1 if they fall within the margin of victory range, e.g., _IDistancea9=1 for control observations with Republican margin of victory between 8 and 9 points. Note that these variables are redefined by the code for the analyses of treatment effects away from the discontinuity. Lastly, there is a variable called RepWin which is the treatment variable when treatment is defined as a Republican winning.

The file sr_incumb.do then performs the analysis.

Please contact me with any questions at slichter@binghamton.edu.

This research tests three hypotheses: 1) Internet diffusion has a significant effect on within-province income inequality in Indonesia; 2) Internet diffusion has a significant nonlinear effect on within-province income inequality in Indonesia; and 3) Internet diffusion has a significant effect on within-province income inequality in Indonesia differing by per capita income and education level. An important finding in this research is that Internet diffusion has a significant effect on income inequality as indicated by the positive influence of the Internet on income inequality between regions. In addition, the influence of the internet tends to be nonlinear and interacts differently with the heterogeneity of each province. The attached data is in .dta and .do format which can be processed with STATA by running the file. Original data was collected from the Badan Pusat Statistik (BPS) website which was processed into STATA. IneqPaper14.dta is a data set and Paper_2013-2019.do contains commands for processing data set files. IneqPaper15_2013-2019.dta is the final processed data.

Recent software developments are reviewed from the vantage point of reproducible econometric research. We argue that the emergence of new tools, particularly in the open-source community, have greatly eased the burden of documenting and archiving both empirical and simulation work in econometrics. Some of these tools are highlighted in the discussion of two small replication exercises.

Dataser of the Likert Scale in xlsx format

This dataset consists of full database of finantial and operational data of Portuguese firms covering the period 2014-2019. In addition, geographical location data is also shared, in order to construct the spatial weights matrix. The Stata do. file is also shared with the computed routines explained in the manuscript. Any question/inquiry should be addressed to samuelf@utad.pt.

This dataset is about book subjects and is filtered where the books is Dealing with econometrics : real world cases with cross-sectional data, featuring 4 columns: authors, book subject, books, and publication dates. The preview is ordered by number of books (descending).

Phillips and Sul (Journal of Applied Econometrics 2009, 24, 1153-1185) provide an algorithm to identify convergence clubs in a dynamic factor model of economic transition and growth. We provide a narrow replication of their key results, using the open source R software instead of the original GAUSS routines. We are able to exactly replicate their results on convergence clubs, corresponding point estimates and standard errors. We comment on minor differences between their reported results and their clustering algorithm. We propose simple adjustments of the original algorithm to make manual intervention unnecessary. The adjustments allow automated application of the algorithm to other data.

This dataset is about books and is filtered where the book is Dealing with econometrics : real world cases with cross-sectional data, featuring 5 columns: author, BNB id, book, book publisher, and publication date. The preview is ordered by publication date (descending).

Data set used in the paper entitled "Nonlinear Exchange Rate Pass-Through in Timber Products: The Case of Oriented Strand Board in Canada and the United States" by Goodwin, Holt, and Prestemon

This data package contains the material necessary for replicating the article. There are three datasets and three scripts that will replicate the results in the article. See ReadMe file for further information.

We use historical data for the land-based passenger (in passenger-kilometers (km)) across 38 countries and freight transport (in tonne-km) for 43 countries between 1990 and 2018 from the Transport Outlook of the International Transport Forum (ITF) transport database, to investigate the key drivers of transport energy demand source: ITF. (2019). ITF Transport Outlook 2019. ITF Transport Outlook 2019. https://www.oecd-ilibrary.org/transport/itf-transport-outlook-2019_transp_outlook-en-2019-en

We collect the historical socioeconomic variables from the World Bank’s global open data bank source: World Bank. (2020). Data Bank: World Development Indicators. https://databank.worldbank.org/source/world-development-indicators

For this scenario analysis, we rely on the shared socioeconomic pathways (SSPs) from the IIASA database (Riahi et al., 2017). source: Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., … Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153–168. https://doi.org/10.1016/j.gloenvcha.2016.05.009 Available Online: https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about

The lack of data disaggregated by country and end-use sector in countries of interest was a significant drawback in the data collection process. We make a crucial assumption in this modeling exercise that historical demand profiles in developing countries track the global average per capita transport trends. Therefore, the resulting estimates are indicative and must be interpreted within this analysis's scope given the future is unknown and highly uncertain.

This data was collected to investigate how intolerance to distress and anxiety affects some of the behaviors that can be changed in response to the Covid-19 pandemic. This dataset comes from a survey of 2,868 people in Turkey about the effects of the Covid -19 pandemic. The dataset is ideal for studying how the Covid -19 pandemic shaped people's intolerance to distress and anxiety. The survey looked at personal cleaning habits, bank/credit card usage, online shopping, personal security, and stockpiling. The data also included whether an individual or a household member had been officially diagnosed with Covid-19 and socio-demographic data.