This project points to an article in The Stata Journal describing a set of routines to preprocess nominal data (firm names and addresses), perform probabilistic linking of two datasets, and display candidate matches for clerical review.The ado files and supporting pattern files are downloadable within Stata.

Explore the historical Whois records related to stata-journal.com (Domain). Get insights into ownership history and changes over time.

Revised STATA do-file and dataset prepared for journal article resubmission.

This is a dataset underlying article "Relational Work and Its Pitfalls: Nonprofits’ Participation in Government-Sponsored Voluntary Accreditation" published by Journal of Public Administration Research and Theory

Stata dta and do file of the paper "Stoop, J., Van Soest, D., and Vyrastekova, J. (2018). Rewards and cooperation in social dilemma games. Journal of Environmental Economics and Management, 88, 300-310".The do file shows the statistical analyses of this paper, showed in the order in which they appear in the paper.

Univariable meta-regression analysis results for the prevalence of depression among hypertensive patients in Ethiopia.

This record contains the Stata do files for "Provider practice style and patient health outcomes: The case of heart attacks", Journal of Health Economics 47 (2016) 64–80 by J. Currie, W. B. MacLeod and J. van Parys. The data is not publicly available.

Stata code for Diagnosing Expertise: Decision Making and Performance Among Physicians', forthcoming in Journal of Labor Economics, January 2017.

These files contain Stata (v11/12) do and dta files necessary to produce the results from the 1992 CNES, 1996 ISL, and 2008-09 ANES presented in the article: Djupe, Paul A., Scott D. McClurg, and Anand E. Sokhey. Forthcoming. “The Political Consequences of Gender in Social Networks.” British Journal of Political Science.

This data was used in generating the results for the paper Justifying Abortion Choices: the Role of Cultural Traits and Education, Journal of Social Economics.

Original dataset, replication data and do-file for Citi and Justesen (2014) "Measuring and explaining regulatory reform in the EU: A time-series analysis of eight sectors, 1984-2012", European Journal of Political Research 53(4), 709-726. Three files are available * Stata .dta file with replication data * Stata do-file to replicate results * Stata .dta and Excel files with regulatory data across eight different sectors The codebook is available in the online appendix at http://onlinelibrary.wiley.com/doi/10.1111/1475-6765.12061/abstract

Characteristics of the included studies and prevalence of depression.

September 1., 2016 REPLICATION FILES FOR «THE IMPACT OF STATE TELEVISION ON VOTER TURNOUT», TO BE PUBLISHED BY THE BRITISH JOURNAL OF POLITICAL SCIENCE The replication files consist of two datasets and corresponding STATA do-files. Please note the following: 1. The data used in the current microanalysis are based on the National Election Surveys of 1965, 1969, and 1973. The Institute of Social Research (ISF) was responsible for the original studies, and data was made available by the NSD (Norwegian Center for Research Data). Neither ISF nor NSD are responsible for the analyses/interpretations of the data presented here. 2. Some of the data used in the municipality-level analyses are taken from NSD’s local government database (“Kommunedatabasen”). The NSD is not responsible for the analysis presented here or the interpretation offered in the BJPS-paper. 3. Note the municipality identification has been anonymized to avoid identification of individual respondents. 4. Most of the analyses generate Word-files that are produced by the outreg2 facility in STATA. These tables can be compared with those presented in the paper. The graphs are directly comparable to those in the paper. In a few cases, the results are only generated in the STATA output window. The paper employs two sets of data: I. Municipal level data in entered in STATA-format (AggregateReplicationTVData.dta), and with a corresponding data with map coordinates (muncoord.dta). The STATA code is in a do-file (ReplicationOfAggregateAnalysis.do). II. The survey data is in a STATA-file (ReplicationofIndividualLevelPanel.dta) and a with a corresponding do-file (ReplicationOfIndividualLevelAnalysis 25.08.2016.do). Please remember to change the file reference (i.e. use-statement) to execute the do-files.

Replication files include the dataset (in the xsls file) and the Stata .do file (in the .txt file). Analyses were performed using Stata 17.

STATA_Reporting Quality of Randomized Controlled Trial Abstracts Among High-Impact General Medical Journals: A Review and AnalysisThe following file contains STATA data for the 2015 review and analysis of reporting quality of randomized controlled trial abstracts in five high-impact general medical journals based on adherence to the CONSORT for abstracts guidelines.STATA_Reporting Quality of Randomized Controlled Trial Abstracts Among High-Impact General Medical Journals.pdf

These data were used to generate the results in the article “Household Food Waste Trending Upwards in the United States: Insights from a National Tracking Survey,” by Ran Li, Yiheng Shu, Kathryn E. Bender & Brian E. Roe, which has been accepted for publication in the Journal of the Agricultural and Applied Economics Association (doi – pending). The Stata code used to generate results is available from the authors upon request. U.S. residents who participate in consumer panels managed by a commercial vendor were invited by email or text message to participate in a two-part online survey during four waves of data collection: February and March of 2021 (Feb 21 wave, 425 initiated, 361 completed), July and August of 2021 (Jul 21 wave, 606 initiated, 419 completed), December of 2021 and January of 2022 (Dec 21 wave, 760 initiated, 610 completed), and February, March and April of 2022 (Feb 22 wave, 607 initiated, 587 completed). We are not able to determine if any respondents participated in multiple waves, i.e., if any of the observations are repeat participants. All participants provided informed consent and received compensation. Inclusion criteria included age 18 years or older and performance of at least half of the household food preparation. No data was collected during major holidays, i.e., the weeks of the Fourth of July (Independence Day), Christmas, or New Years. Recruitment quotas were implemented to ensure sufficient representation by geographical region, race, and age group. Post-hoc sample weights were constructed to reflect population characteristics on age, income and household size. The protocol was approved by the local Internal Review Board. The approach begins with participants completing an initial survey that ends with an announcement that a follow-up survey will arrive in about one week, and that for the next 7 days, participants should pay close attention to the amounts of different foods their household throws away, feeds to animals or composts because the food is past date, spoiled or no longer wanted for other reasons. They are told to exclude items they would normally not eat, such as bones, pits, and shells. Approximately 7 days later they received the follow-up survey, which elicited the amount of waste in up to 24 categories of food and included other questions (see supplemental materials for core survey questions). Waste amounts in each category are reported by selecting from one of several ranges of possible amounts. The gram weight for categories with volumetric ranges (e.g., listed in cups) were derived by assigning an appropriate mass to the midpoint of the selected range consistent with the food category. For the categories with highly variable weight per volume (e.g., a cup of raw asparagus weighs about 7 times more than a cup of raw chopped arugula), we use the profile of items most consumed in the United States to determine the appropriate gram weight. For display purposes, the 24 categories are consolidated into 8 more general categories. Total weekly household food waste is calculated by summing up reported gram amounts across all categories. We divide this total by the number of household members to generate the per person weekly food waste amount.

Binary logistic regression analysis on female adolescents’ reproductive health decision-making capacity and modern contraceptive use.

Replication package for "Business disruptions from social distancing"

Please cite as

Koren, Miklós and Rita Pető. 2020. "Replication package for «Business disruptions from social distancing»" [dataset] Zenodo. http://doi.org/10.5281/zenodo.4012191

License and copyright

All text (*.md, *.txt, *.tex, *.pdf) are CC-BY-4.0. All code (*.do, Makefile) are subject to the 3-clause BSD license. All derived data (data/derived/*) are subject to Open Database License. Please respect to copyright and license terms of original data vendors (data/raw/*).

Data Availability Statements

The mobility data used in this paper (SafeGraph 2020) is proprietary, but may be obtained free of charge for COVID-19-related research from the COVID-19 Consortium. The authors are not affiliated with this consortium. Researchers interested in access to the data can apply at https://www.safegraph.com/covid-19-data-consortium (data manager: Ross Epstein, ross@safegraph.com). After signing a Data Agreement, access is granted within a few days. The Consortium does not require coauthorship and does not review or approve research results before publication. Datafiles used: /monthly-patterns/patterns_backfill/2020/05/07/12/2020/02/patterns-part[1-4].csv.gz (Monthly Places Patterns for February 2020, released May 7, 2020), /monthly-patterns/patterns/2020/06/05/06/patterns-part[1-4].csv.gz (Monthly Places Patterns for February 2020, released June 5, 2020) and /core/2020/06/Core-USA-June2020-Release-CORE_POI-2020_05-2020-06-06.zip (Core Places for June 2020, released June 6, 2020). The COVID-19 Consortium will keep these datafiles accessible for researchers. The authors will assist with any reasonable replication attempts for two years following publication.

All other data used in the analysis, including raw data, are available for reuse with permissive licenses. Raw data are saved in the folder data/raw/. The Makefile in each folder shows the URLs used to download the data.

SafeGraph

Citation

SafeGraph. "Patterns [dataset]"; 2020. Downloaded 2020-06-20.

License

Proprietary, see https://shop.safegraph.com/ or https://www.safegraph.com/covid-19-data-consortium (data manager: Ross Epstein, ross@safegraph.com)

O*NET

Citation

U.S. Department of Labor/Employment and Training Administration, 2020. "O*NET Online." Downloaded 2020-03-12.

License

CC-BY-4.0 https://www.onetonline.org/help/license

Current Employment Statistics

Citation

U.S. Bureau of Labor Statistics. 2020. "Current Employment Statistics." https://www.bls.gov/ces/ Downloaded 2020-03-15.

License

Public domain: https://www.bls.gov/bls/linksite.htm

National Employment Matrix

Citation

U.S. Bureau of Labor Statistics. 2018. "National Employment Matrix." https://www.bls.gov/emp/data/occupational-data.htm Downloaded 2020-03-15.

License

Public domain: https://www.bls.gov/bls/linksite.htm

Crosswalk

Citation

U.S. Bureau of Labor Statistics. 2019. "O* NET-SOC to Occupational Outlook Handbook Crosswalk." https://www.bls.gov/emp/classifications-crosswalks/nem-onet-to-soc-crosswalk.xlsx Downloaded 2020-03-15.

License

Public domain: https://www.bls.gov/bls/linksite.htm

American Time Use Survey

Citation

U.S. Bureau of Labor Statistics. 2018. “American Time Use Survey.” https://www.bls.gov/tus/.

We are using the following files:

• Respondent File
• Activity File
• Who File
• Replicate Weights
• Leave Module 2017-18

License

Data is in public domain.

County Business Patterns

Citation

U.S. Bureau of the Census. 2017. "County Business Patterns." Available at https://www.census.gov/programs-surveys/cbp.html

License

https://www.census.gov/data/developers/about/terms-of-service.html

Dataset list

Raw data

| Data file | Source | Notes | Provided |

|-----------|--------|----------|----------|

| data/raw/bls/industry-employment/ces.txt | BLS Current Employment Statistics | Public domain | Yes |

| data/raw/bls/atus/*.dat | BLS Time Use Survey | Public domain | Yes |

| data/raw/bls/employment-matrix/matrix.xlsx | BLS National Employment Matrix | Public domain | Yes |

| data/raw/bls/crosswalk/matrix.xlsx | ONET-SOC to Occupational Outlook Handbook Crosswalk | Public domain | Yes |

| data/raw/onet/*.csv | ONET Online | Creative Commons 4.0 | Yes |

| data/raw/census/cbp/*.txt | County Business Patterns | Public domain | Yes |

| not-included/safegraph/02/*.csv| SafeGraph | Available with Data Agreement with SafeGraph | No |

| not-included/safegraph/05/*.csv| SafeGraph | Available with Data Agreement with SafeGraph | No |

Clean data

| Data file | Source | Notes | Provided |

|-----------|--------|----------|----------|

| data/clean/industry-employment/industry-employment.dta | BLS Current Employment Statistics | Public domain | Yes |

| data/clean/time-use/atus.dta | BLS Time Use Survey | Public domain | Yes |

| data/clean/employment-matrix/matrix.dta | BLS National Employment Matrix | Public domain | Yes |

| data/clean/onet/risks.csv | ONET Online | Creative Commons 4.0 | Yes |

| data/clean/cbp/zip_code_business_patterns.dta | County Business Patterns | Public domain | Yes |

Derived data

| Data file | Source | Notes | Provided |

|-----------|--------|----------|----------|

| data/derived/occupation/* | Various sources | Public domain | Yes |

| data/derived/time-use/atus_working_at_home_occupationlevel.dta | BLS Time Use Survey | Public domain | Yes |

| data/derived/crosswalk/* | Various sources | Public domain | Yes |

| not-included/safegraph/naics-zip-??.csv| SafeGraph | Available with Data Agreement with SafeGraph | Yes, with permission of SafeGraph |

| data/derived/visit/visit-change.dta| SafeGraph | Aggregated to 3-digit NAICS industries | Yes, with permission of SafeGraph |

Computational requirements

Software Requirements

• Stata (code was last run with version 16.1)
  • estout (from http://www.stata-journal.com/software/sj14-2/)
  • running make install from the root of the folder will install estout locally, and should be run once.

Portions of the code use bash scripting (make, wget, head, tail), which may require Linux or Mac OS X.

The entry point for analysis is analysis/Makefile, which can be run by GNU Make on any Unix-like system by

cd analysis
make

The dependence of outputs on code and input data is captured in the respective Makefiles.

We have used Mac OS X, but all the code should run on Linux and Windows platforms, too.

Hardware

The analysis takes a few minutes on a standard laptop.

Description of programs

1. Raw data are in data/raw/. This data is saved as it has been received from the data publisher, downloaded by the respective Makefiles. Each folder has a README.md with data citation and license terms.
2. Clean data are in data/clean/. Each folder has a Makefile that specifies the steps of data cleaning.
3. Analysis data are in data/derived/. Each folder has a Makefile that

Summary of studies involved in the meta-analysis.

This study consists of data files that code the data availability policies of top-20 academic journals in the fields of Business & Finance, Economics, International Relations, Political Science, and Sociology. Journals that were ranked as top-20 titles based on 2003-vintage ISI Impact Factor scores were coded on their data policies in 2003 and on their data policies in 2015. In addition, journals that were ranked as top-20 titles based on most recent ISI Impact Factor scores were likewise coded on their data polices in 2015. The included Stata .do file imports the contents of each of the Excel files, cleans and labels the data, and produces two tables: one comparing the data policies of 2003-vintage top-20 journals in 2003 those journals' policies in 2015, and one comparing the data policies of 2003-vintage top-20 journals in 2003 to the data policies of current top-20 journals in 2015.