The data file contains the publication set of the author which can be used for testing the bibrep.ado command in Stata.

PROCare-2023 Project

All collected data, data analysis coding, and crude output are made available. These files can be complemented by those registred prior to data collection (https://zenodo.org/records/8322740).

Content

PROCare-questions.pdf Copy of online survey with question codes (name) and values

PROCare – 2023_codes.xlsx Conversion of survey question names to STATA names

PROCare-dataset.xlsx Full datset without MetaData. For metadata see files PROCare-questions.pdf and PROCare – 2023_codes.xlsx

PROCare-2023.do Executable command STATA file for running full analysis

PROCare-2023.txt Crude STATA export files with all results

Using the dataset

The full dataset is made available for secondary analysis. The coded data is found on PROCare-dataset.xlsx. Metadata for understanding codes require using the files PROCare-questions.pdf & PROCare – 2023_codes.xlsx.

This file is the crude file with all the data entry including partially completed questionnaires and duplicates.

Running the analysis

Full analysis can be run using STATA (version 5.0) by downloading all files and running the PROCare-2023.do file with crude data in the Source_files folder.

SI Dataset S1 (Country Level Dataset): To obtain results on the energy market, energy sovereignty and electricity prices, follow the below Stata command line to reproduce the results and replace the variable DEP_VAR_1 by Perc_Renew_Elec, Perc_Gas_Elec, Perc_Coal_Elec, Perc_Oil_Elec, Gas_Import, Elec_Capacity_Factor, Elec_Hous_Price, Elec_Ind_Price. To obtain results on carbon emission from different energy sources for electricity production, follow the below Stata command line to reproduce the results, and replace the variable DEP_VAR_2 by Change_Co2_Gas, Change_T otal_Elec_Pollution, Change_Co2_Coal, or Change_Co2_Oil.eventstudyinteract DEP_VAR_1 g_m4 g_m3 g_m2 g_0 g_1 g_2 g_3, cohort(Year_Event) control_cohort(never_treat) covariates(Share_Green_Seats Gdp_LN Gdp_Growth_Rate Energy_Consump_LN Elec_Consump_LN Perc_Nuclear_Elec_L1 Perc_Gas_Elec_L1 Perc_Oil_Elec_L1 Perc_Coal_Elec_L1 Perc_Hydro_Elec_L1 Perc_Solar_Elec_L1 Perc_Wind_Elec_L1 Elec_Hous_Price_L1 Elec_Ind_Price_L1 Household_Gas_Price_L1 Industrial_Gas_Price_L1 Gas_Import_L1 Elec_Capacity_Factor_L1) absorb(Country_ID_encode Year) vce(cluster Country_ID_encode)eventstudyinteract DEP_VAR_2 g_m4 g_m3 g_m2 g_0 g_1 g_2 g_3, cohort(Year_Event) control_cohort(never_treat) covariates(Share_Green_Seats Gdp_LN Gdp_Growth_Rate Energy_Consump_LN Elec_Consump_LN46Perc_Nuclear_Elec_L1 Perc_Gas_Elec_L1 Perc_Oil_Elec_L1 Perc_Coal_Elec_L1 Perc_Hydro_Elec_L1 Perc_Solar_Elec_L1 Perc_Wind_Elec_L1 Change_Co2_Coal_L1 Change_Co2_Gas_L1 Change_Co2_Oil_L1 Change_Total_Elec_Pollution_L1 Household_Gas_Price_L1 Industrial_Gas_Price_L1 Gas_Import_L1 Elec_Capacity_Factor_L1) absorb(Country_ID_encode Year) vce(cluster Country_ID_encode)reghdfe DEP_VAR_1 InvNS Share_Green_Seats Gdp_LN Gdp_Growth_Rate Energy_Consump_LN Elec_Consump_LN Perc_Nuclear_Elec_L1 Perc_Gas_Elec_L1 Perc_Oil_Elec_L1 Perc_Coal_Elec_L1 Perc_Hydro_Elec_L1 Perc_Solar_Elec_L1 Perc_Wind_Elec_L1 Elec_Hous_Price_L1 Elec_Ind_Price_L1 Household_Gas_Price_L1 Industrial_Gas_Price_L1 Gas_Import_L1 Elec_Capacity_Factor_L1, absorb(Country_ID_encode Year) vce(cluster Country_ID_encode)reghdfe DEP_VAR_2 InvNS Share_Green_Seats Gdp_LN Gdp_Growth_Rate Energy_Consump_LN Elec_Consump_LN Perc_Nuclear_Elec_L1 Perc_Gas_Elec_L1 Perc_Oil_Elec_L1 Perc_Coal_Elec_L1 Perc_Hydro_Elec_L1 Perc_Solar_Elec_L1 Perc_Wind_Elec_L1 Change_Co2_Coal_L1 Change_Co2_Gas_L1 Change_Co2_Oil_L1 Change_Total_Elec_Pollution_L1 Household_Gas_Price_L1 Industrial_Gas_Price_L1 Gas_Import_L1 Elec_Capacity_Factor_L1, absorb(Country_ID_encode Year) vce(cluster Country_ID_encode)ivreghdfe DEP_VAR_1 (InvNS=Num_Reactors_Closed) Share_Green_Seats Gdp_LNGdp_Growth_Rate Energy_Consump_LN Elec_Consump_LN Perc_Nuclear_Elec_L1 Perc_Gas_Elec_L1 Perc_Oil_Elec_L1 Perc_Coal_Elec_L1 Perc_Hydro_Elec_L1 Perc_Solar_Elec_L1 Perc_Wind_Elec_L1 Elec_Hous_Price_L1 Elec_Ind_Price_L1 Household_Gas_Price_L1 Industrial_Gas_Price_L1 Gas_Import_L1 Elec_Capacity_Factor_L1, absorb(Country_ID_encode Year) vce(cluster Country_ID_encode)ivreghdfe DEP_VAR_2 (InvNS=Num_Reactors_Closed) Share_Green_Seats Gdp_LN Gdp_Growth_RateEnergy_Consump_LN Elec_Consump_LN Perc_Nuclear_Elec_L1 Perc_Gas_Elec_L1 Perc_Oil_Elec_L1 Perc_Coal_Elec_L1 Perc_Hydro_Elec_L1 Perc_Solar_Elec_L1 Perc_Wind_Elec_L1 Change_Co2_Coal_L1 Change_Co2_Gas_L1 Change_Co2_Oil_L1 Change_Total_Elec_Pollution_L1 Household_Gas_Price_L1 Industrial_Gas_Price_L1 Gas_Import_L1 Elec_Capacity_Factor_L1, absorb(Country_ID_encode Year) vce(cluster Country_ID_encode)SI Dataset S2 (Firm Carbon Intensity Dataset): Follow the below Stata command line to reproduce the results, and replace the variable DEP_VAR with Direct_GHG_Emission or Indirect_GHG_Emission.eventstudyinteract DEP_VAR g_m4 g_m3 g_m2 g_0 g_1 g_2 g_3, cohort(Year_Event) control_cohort(never_treat) covariates(Share_Green_Seats Gdp_LN Gdp_Growth_Rate Energy_Consump_LN Elec_Consump_LN Perc_Nuclear_Elec_L1 Perc_Gas_Elec_L1 Perc_Oil_Elec_L1 Perc_Coal_Elec_L1 Perc_Hydro_Elec_L1 Perc_Solar_Elec_L1 Perc_Wind_Elec_L1 Household_Gas_Price_L1 Industrial_Gas_Price_L1 Gas_Import_L1 Elec_Capacity_Factor_L1 Size_L1 Ptbi_L1 Leverage_L1 Firm_Age_LN_L1) absorb(Firm_ID Industry_Year_encode) vce(cluster Firm_ID)reghdfe DEP_VAR InvNS Share_Green_Seats Gdp_LN Gdp_Growth_Rate Energy_Consump_LN Elec_Consump_LN Perc_Nuclear_Elec_L1 Perc_Gas_Elec_L1 Perc_Oil_Elec_L1 Perc_Coal_Elec_L1 Perc_Hydro_Elec_L1 Perc_Solar_Elec_L1 Perc_Wind_Elec_L1 Household_Gas_Price_L1 Industrial_Gas_Price_L1 Gas_Import_L1 Elec_Capacity_Factor_L1 Size_L1 Ptbi_L1 Ptbi_Vol_L1 Leverage_L1 Firm_Age_LN_L1, absorb(Firm_ID Industry_Year_encode) vce(cluster Firm_ID)SI Dataset S3 (Firm Environmental Dataset): Follow the below Stata command line to reproduce the results, and replace the variable DEP_VAR with one among EWE, EWE_Emission, and EWE_Ressource.eventstudyinteract DEP_VAR g_m4 g_m3 g_m2 g_0 g_1 g_2 g_3, cohort(Year_Event) control_cohort(never_treat) covariates(Share_Green_Seats Gdp_LN Gdp_Growth_Rate Energy_Consump_LN Elec_Consump_LN Perc_Nuclear_Elec_L1 Perc_Gas_Elec_L1 Perc_Oil_Elec_L1 Perc_Coal_Elec_L1 Perc_Hydro_Elec_L1 Perc_Solar_Elec_L1 Perc_Wind_Elec_L1 Household_Gas_Price_L1 Industrial_Gas_Price_L1 Gas_Import_L1 Elec_Capacity_Factor_L1 Size_L1 Ptbi_L1 Ptbi_Vol_L1 Leverage_L1 Firm_Age_LN_L1) absorb(Firm_ID_encode Industry_Year_encode) vce(cluster Firm_ID_encode)reghdfe DEP_VAR InvNS Share_Green_Seats Gdp_LN Gdp_Growth_Rate Energy_Consump_LN Elec_Consump_LN Perc_Nuclear_Elec_L1 Perc_Gas_Elec_L1 Perc_Oil_Elec_L1 Perc_Coal_Elec_L1 Perc_Hydro_Elec_L1 Perc_Solar_Elec_L1 Perc_Wind_Elec_L1 Household_Gas_Price_L1 Industrial_Gas_Price_L1 Gas_Import_L1 Elec_Capacity_Factor_L1 Size_L1 Ptbi_L1 Ptbi_Vol_L1 Leverage_L1 Firm_Age_LN_L1, absorb(Firm_ID_encode Industry_Year_encode) vce(cluster Firm_ID_encode)

Code and data to reproduce all results and graphs reported in Tannenbaum et al. (2022). This folder contains data files (.dta files) and a Stata do-file (code.do) that stitches together the different data files and executes all analyses and produces all figures reported in the paper. The do-file uses a number of user-written packages, which are listed below. Most of these can be installed using the ssc install command in Stata. Also, users will need to change the current directory path (at the start of the do-file) before executing the code. List of user written packages (descriptions): revrs (reverse-codes variable) ereplace (extends the egen command to permit replacing) grstyle (changes the settings for the overall look of graphs) spmap (used for graphing spatial data) qqvalue (used for obtaining Benjamini-Hochberg corrected p-values) parmby (creates a dataset by calling an estimation command for each by-group) domin (used to perform dominance analyses) coefplot (used for creating coefficient plots) grc1leg (combine graphs with a single common legend) xframeappend (append data frames to the end of the current data frame)

The dataset covers the period from July 01, 2015 to December 02, 2022. It includes daily frequency time series for a set of 27 variables. Description of the variables and sources of data are given in the paper. The command code file includes commands for carrying out the empirical analysis using STATA 17. Some parts of the analysis have been performed using drop-down menus.

Contains the simulated data and Stata code used to produce the results for the manuscript titled "Evaluating methods of outlier detection when benchmarking clinical registry data – a simulation study", accepted for publication in the Health Services and Outcomes Research Methodology Journal.data_files.zip (code to generate all files in "do_files\simstudy1_preparation.do"):raw_data - the .dta files produced from running the user-written hiersim command (https://doi.org/10.26180/24480889.v1)summary_data - the .dta files produced from summarising of the results across each unique simulated scenario and method combination (performance measure average and 95% Monte Carlo confidence intervals)parameter_check - the .dta files produced from summarising the simulated data parameters across each unique simulated scenario (performance measure average and 95% Monte Carlo confidence intervals)do_files.zip:simstudy1_preparation.do - the code to run the simulations (using the hiersim command, available at https://doi.org/10.26180/24480889.v1) and create summary datasets (performance measures and parameter checks)simstudy1_manuscript.do - the code to produce the figures included in the main manuscriptsimstudy1_supplementary.do - the code to produce the table and figures included in the manuscript supplementary material

Replication files include: A Readme explaining how to run the replication commands, two datasets from the Arkansas Poll (2014 and 2016), and a Stata DO file that replicates all results.

Method.txt is the stata command code that verifies the minimum dataset used to draw conclusions and results from this article.

Note that constant terms output from the Royston-Parmar have not been displayed.P values are from Wald test of variables in regression outputs.TVC – indicates time varying covariates which are graphically displayed in Figure 3.o – implies omitted from model by Stata due to colinearity. This occurred as the stpm2 command does not recognize factor variables and dummy variables were used.In Cox models, underlined terms imply failure of Cox PH assumption using Stata command estat phtest, detail. No variable failed Cox PH assumption i.

The programme file contains the commands for calculation of population weighted mean TB indicators and commands related to multilevel model development.

CGD working paper 26, New Data, New Doubts: Revisiting "Aid, Policies, and Growth, by CGD non-resident fellow William Easterly, research fellow David Roodman, and Ross Levine (also published as "Aid, Policies, and Growth: Comment" in the American Economic Review, June 2004), concludes that the Burnside and Dollar (2000) finding that aid raises growth in a good policy environment is not statistically robust. This dataset is a four-year panel covering 1966–97. It includes all the Burnside and Dollar data, and Easterly, Levine and Roodman's expanded data set. The data used in this Working Paper is available in Excel format and Stata 7 format. A .do command file generates all the results in the paper in Stata 7 format. All are included in the zip file.

Disruptive Behavior Disorders (DBDs) is one of the most common mental health problems among children in Uganda and SSA. Yet, to our knowledge no research has studied parenting stress (PS) among caregivers of children with DBDs, or investigated which risk factors originate from the child, parent, and contextual environment. Using a rigorous analytical approach, we aimed to: 1) identify different types and; 2) examine factors associated with PS and how correlates differ according to the type of stress experienced among caregivers of children with DBDs in low-resourced Ugandan communities. We used data from 633 caregivers of children with DBDs from SMART-Africa Uganda study. PS, was measured using the 36-item Parenting Stress Index-Short Form (PSI-SF). To identify focal correlates related to child/parent/contextual environment, we performed variable importance screening using the Stata command -gvselect- and specified mixed/melogit multilevel modeling with random effects. Secondly, focal correlates were included in the cross-fit partialing out lasso linear/logistic regression (double machine-learning) model. Caregivers mostly experienced stress from parental distress and caring for a child with difficult behavior. As scores increased by one unit on: caregiver mental health distress, PSI-SF increased by 0.23 (95% CI = 0.15, 0.32) (reflecting higher stress levels); Child difficulties, PSI-SF increased by 0.77 (95% CI = 0.52, 1.02). Contrastingly, for every one unit increase in family cohesion scores, PSI-SF decreased by 0.54 (95% CI = -0.84, -0.23). Caregivers with college/diploma/undergraduate/graduate education had less stress than those completing primary only or never attended school [Coefficient = -8.06 (95% CI = -12.56, -3.56)]. Family financial supporters had significantly higher Parental distress than caregivers who were not [Coefficient = 2.68 (95% CI = 1.20, 4.16)]. In low-resource settings like Uganda where mental health support is limited, community-based family-focused and economic empowerment interventions that improve community support systems and address financial barriers can reduce stress levels of caregivers of children with DBDs.

Contains the simulated data and Stata code used to produce the results for the manuscript titled "Accuracy of site benchmarking in clinical quality registries of varying size".data_files.zip (code to generate all files in "do_files\simstudy2_preparation.do"):raw_data - the .dta files produced from running the user-written hiersim command (https://doi.org/10.26180/24480889.v1)summary_data - the .dta files produced from summarising of the results across each unique simulated scenario and method combination (performance measure average and 95% Monte Carlo confidence intervals)parameter_check - the .dta files produced from summarising the simulated data parameters across each unique simulated scenario (performance measure average and 95% Monte Carlo confidence intervals)do_files.zip:simstudy1_preparation.do - the code to run the simulations (using the hiersim command, available at https://doi.org/10.26180/24480889.v1) and create summary datasets (performance measures and parameter checks)simstudy1_manuscript.do - the code to produce the figures included in the main manuscriptsimstudy1_supplementary.do - the code to produce the figures included in the manuscript supplementary material

This study analyzed the factors influencing adoption of fall armyworm management practices (FAW MPs) and the impacts of the adoption of these management practices on maize yield and household maize income. The findings show that relative to non-adoption, the joint adoption of a combination of early planting and pesticide application had greater impacts on both maize yield and household maize income. Our findings indicate that efforts should be geared towards training farmers to practice different combination of the FAW MPs in controlling fall armyworm on their farms while improving their access to support services such as extension and input supply. The dataset is a primary data with most of the variables coded in the binary form and some as dummy variables. The data can be analyzed and interpreted using the multinomial endogenous switching regression model in STATA through the usage of the selmlog command.

SPSS raw Studies 1-3 datasets as well as a Stata .do file (including instructions and codebook) for: Sheldon, K. M., Sommet, N., Corcoran, M., & Elliot, A. J. (2018). Feeling Interpersonally Controlled While Pursuing Materialistic Goals: A Problematic Combination for Moral Behavior. Personality and Social Psychology Bulletin. https://doi.org/10.1177/0146167218766863

Stata do-file containing the commands used to run the statistical analyses. (DO)

Subgroup analysis for different parameters with sufficient data compared with a composite reference standard.

Subgroup analysis for different parameters with sufficient data compared with culture.

Regression coefficients and 95% confidence intervals for focal covariates estimated using cross-fit partialing out lasso inference estimator for total parenting stress scale, and PD domain.