Firm-level data from 2009 to 2018 of 34 large gold mines in Developing countries. The data is used to compute the deterministic, dynamic environmental and technical efficiencies of large gold mines in developing countries. Steps to reproduce1. Run the R command to generate dynamic technical and dynamic inefficiencies per every two subsequent period (i.e period t and t+1)2. combine the results files of inefficiencies per period generated in R into a panel (see the Excel files in the results folder)3. Import the excel folder into Stata and generate the final results indicated in the paper.

This repository contains the data for indicators which were reanalysed in the paper:

Ho L, Mercer SW, Henderson D, Donaghy E, Guthrie B. Did the UK Quality and Outcomes Framework pay-for-performance programme improve quality of primary care? Systematic review with quantitative synthesis.

For any queries, please contact Bruce Guthrie, Professor of General Practice, University of Edinburgh bruce.guthrie@ed.ac.uk

Data is contained in a set of Excel files. Also provided is the STATA code used in analysis which uses the itsa command to fit interrupted time series analysis models, and lincom to estimate absolute impact at 1 and 3 years after intervention. Users will have to import from wherever they save these files (our own import and graph save commands are commented out).

Please refer to these documents for details of how to use itsa and how to use lincom for this purpose.

Linden A. Conducting interrupted time-series analysis for single- and multiple-group comparisons. The Stata Journal. 2015;15(2):480-500 https://journals.sagepub.com/doi/10.1177/1536867X1501500208

Linden A. A comprehensive set of postestimation measures to enrich interrupted time-series analysis. The Stata Journal. 2017;17(1):73-88 https://journals.sagepub.com/doi/epdf/10.1177/1536867X1701700105

Description

This dataset contains data from an online experiment designed to test whether economically equivalent penalties—fees (paid before taking) and fines (paid after taking)—influence prosocial behaviour differently. Participants played a modified dictator game in which they could take points from another participant.

The dataset is provided in Excel format (Full-data.xlsx), along with a Stata do-file (submit.do) that reshapes, cleans, and analyses the data.

Data Collection

• Platform: oTree

• Recruitment: Prolific

• Sample size: 201 participants

• Design: Each participant played 20 rounds: 10 in the control condition and 10 in one treatment condition (fee or fine). Order of blocks was randomised.

• Payment: 200 points = £1. One round was randomly selected for payment.

Variables

Identification

• session – Session number

• id – Participant ID

• treatment – Assigned treatment (1 = Fee, 2 = Fine)

• order – Order of blocks (0 = Control first, 1 = Treatment first)

Decision Rounds

For each round, participants made decisions in both control (c) and treatment (t) conditions.

• c1, t1, c2, t2, … – Tokens available and/or allocated across control and treatment rounds.

• takeX – Amount taken from the other participant in case X.

Norm Elicitation

Social norms were elicited after the taking task. Variables include empirical, normative, and responsibility measures at both extensive and intensive margins:

• eyX, etX – Empirical expectations (beliefs about what others do)

• nyX, ntX – Normative expectations (beliefs about what others think is appropriate)

• ryX, rtX – Responsibility measures

• casenormX – Case identifier for norm elicitation

Demographics

From survey responses:

• Sex – Gender

• Ethnicitysimplified – Simplified ethnicity category

• Countryofresidence – Participant’s country of residence

Other

• order, session – Experimental setup metadata

Stata Do-File (analysis.do)

The .do file performs the following steps:

1. Data Preparation

   • Import raw Excel file

   • Reshape from wide to long format (cases per participant)

   • Declare panel data (xtset id)

2. Variable Generation

   • Rename variables for clarity (e.g., take for amount taken)

   • Generate treatment dummies (treat)

   • Construct demographic dummies (gender, race, nationality)

3. Analysis Preparation

   • Create extensive and intensive margin variables

   • Generate expectation and norm measures

4. Output

   • Ready-to-analyse panel dataset for regression and statistical analysis

This dataset includes a complete record of the 36,066 public comments submitted to the Commodity Futures Trading Commission (CFTC) in response to notices of proposed rule-making (NPRMs) implementing the Dodd-Frank Act over a 42-month period (January 14, 2010 to July 16, 2014). The data was exported from the agency’s internal database by the CFTC and provided to the authors by email correspondence following a cold call to the CFTC public relations department. The source internal database is maintained by the CFTC as part of its internal compliance with the Administrative Procedures Act (APA) and includes all rule-making notices that appear in the Federal Register. Owing to the salience and publicity of the Dodd-Frank Act, the CFTC made a special tag in its database for all comments submitted in response to rules proposed under the authority of the Dodd-Frank Act. This database thus includes all comments which the CFTC considers relevant to the Dodd-Frank reform. In short, the CFTC gave t..., This dataset was exported by the CFTC from their internal database of public comments in response to NPRMs. The uploaded file is the exact raw data generated by the CTFC and provided to the authors. An updated version of the data file including the author's classifications based on the organization value will be uploaded when the related work is accepted for publication., , # Dodd Frank Financial Reform at the CFTC - Public Comments, January 14th, 2010 to July 16th, 2014

Description of the data and file structure

NOTE: The Comment Text ( and variables) are longer than the maximum character count of Microsoft Excel cells (32,767 characters). All analysis should take this into account and import the .txt file directly into your analysis program (R, Stata, etc.) rather than attempt to edit or modify the data in Excel before using computational analysis.

There are two files provided:

1. DoddFrankCommentsAll(uncompressed).txt - the full raw data file from the CFTC internal database of all 36,066 comments on NPRMs
2. (2014-07-30) CFTC Original Codebook.xlsx - the codebook provided by the CTFC with the raw data. Originally provided as email text, formatted in Excel by authors.

Codebook:Â

| Variable | Explanation ...