This replication package contains the raw data and code to replicate the findings reported in the paper. The data are licensed under a Creative Commons Attribution 4.0 International Public License. The code is licensed under a Modified BSD License. See LICENSE.txt for details.

Software requirements

All analysis were done in Stata version 16:

• Add-on packages are included in scripts/libraries/stata and do not need to be installed by user. The names, installation sources, and installation dates of these packages are available in scripts/libraries/stata/stata.trk.

Instructions

1. Save the folder ‘replication_PLOS’ to your local drive.
2. Open the master script ‘run.do’ and change the global pointing to the working direction (line 20) to the location where you save the folder on your local drive
3. Run the master script ‘run.do’ to replicate the analysis and generate all tables and figures reported in the paper and supplementary online materials

Datasets

• Wave 1 – Survey experiment: ‘wave1_survey_experiment_raw.dta’
• Wave 2 – Follow-up Survey: ‘wave2_follow_up_raw.dta'
• Map: shape-files ‘plz2stellig.shp’ ‘OSM_PLZ.shp’, area codes ‘Postleitzahlengebiete-_OSM.csv’_, (all links to the sources can be found in the script ‘04_figure2_germany_map.do’)
• Pretest: ‘pre-test_corona_raw.dta’
• For Appendix S7: ‘alter_geschlecht_zensus_det.xlsx’, ‘vaccination_landkreis_raw.dta’, ‘census2020_age_gender.csv’ (all links to the sources can be found in the script ‘06_AppendixS7.do’)
• For Appendix S10: ‘vaccination_landkreis_raw.dta’ (all links to the sources can be found in the script ‘07_AppendixS10.do’)

Descriptions of scripts

1_1_clean_wave1.do
This script processes the raw data from wave 1, the survey experiment
1_2_clean_wave2.do
This script processes the raw data from wave 2, the follow-up survey
1_3_merge_generate.do
This script creates the datasets used in the main analysis and for robustness checks by merging the cleaned data from wave 1 and 2, tests the exclusion criteria and creates additional variables
02_analysis.do
This script estimates regression models in Stata, creates figures and tables, saving them to results/figures and results/tables
03_robustness_checks_no_exclusion.do
This script runs the main analysis using the dataset without applying the exclusion criteria. Results are saved in results/tables
04_figure2_germany_map.do
This script creates Figure 2 in the main manuscript using publicly available data on vaccination numbers in Germany.
05_figureS1_dogmatism_scale.do
This script creates Figure S1 using data from a pretest to adjust the dogmatism scale.
06_AppendixS7.do
This script creates the figures and tables provided in Appendix S7 on the representativity of our sample compared to the German average using publicly available data about the age distribution in Germany.
07_AppendixS10.do
This script creates the figures and tables provided in Appendix S10 on the external validity of vaccination rates in our sample using publicly available data on vaccination numbers in Germany.

Stata Corp Llc Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Data and code needed to reproduce the results of the paper "Effects of community management on user activity in online communities", available in draft here.

Instructions:

1. Unzip the files.
2. Start with JSON files obtained from calling platform APIs: each dataset consists of one file for posts, one for comments, one for users. In the paper we use two datasets, one referring Edgeryders, the other to Matera 2019.
3. Run them through edgesense (https://github.com/edgeryders/edgesense). Edgesense allows to set the length of the observation period. We set it to 1 week and 1 day for Edgeryders data, and to 1 day for Matera 2019 data. Edgesense stores its results in a file called JSON network.min.json, which we then rename to keep track of the data source and observation length.
4. Launch Jupyter Notebook and run the notebook provided to convert the network.min.json files into CSV flat files, one for each netwrk file
5. Launch Stata and open each flat csv files with it, then save it in Stata format.
6. Use the provided Stata .do scripts to replicate results.

Please note: I use both Stata and Jupyter Notebook interactively, running a block with a few lines of code at a time. Expect to have to change directories, file names etc.

Terra Stata Construction Pty Limited Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Strengthening Governance for Export Competitiveness: A Study of Institutional Quality in Selected Asian Developing EconomiesBy Achinthya Koswattahmkos@ou.ac.lkData file and the Do file (STATA)

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

Addressing sample selection bias for machine learning methods (replication data)

Dylan Brewer and Alyssa Carlson

Accepted at Journal of Applied Econometrics, 2023

Overview

This replication package contains files required to reproduce results, tables, and figures using Matlab and Stata. We divide the project into instructions to replicate the simulation, the result from Huang et al (2006), and the application.

Simulation

For reproducing the simulation results

Included files in *\Simulation with short descriptions:

• SSML_simfunc: function that produces individual simulations runs
• SSML_simulation: script that loops over the SSML_simfunc for different DGP and multiple simulation runs
• SSML_figures: script that generates all figures for the paper
• SSML_compilefunc: function that compiles the results from SSML_simulation for the SSML_figures script

Steps for replicating simulation:

1. Save SSML_simfunc, SSML_simulation, SSML_figures, SSML_compilefunc to the same folder. This location will be referred to as the FILEPATH.
2. Create OUTPUT folder inside the FILEPATH location.
3. Change the FILEPATH location inside SSML_simulation and SSML_figures.
4. Run SSML_simulation to produce simulation data and results.
5. Run SSML_figures to produce figures.

Huang et al replication

For reproducing the Huang et. al. (2006) replication results.

Included files in *\HuangetalReplication with short descriptions:

• SSML_huangrep: script that replicates the results from Huang et. al. (2006)

Obtaining the dataset:

Go to https://archive.ics.uci.edu/dataset/14/breast+cancer and save file as "breast-cancer-wisconsin.data"

Steps for replicating results:

1. Save SSML_huangrep and the breast cancer data to the same folder. This location will be referred to as the FILEPATH.
2. Change the FILEPATH location inside SSML_huangrep
3. Run SSML_huangrep to produce results and figures.

Application

For reproducing the application section results.

Included program files in *\Application with short descriptions:

• G0_main_202308.do: Stata wrapper code that will run all application replication files
• G1_cqclean_202308.do: Cleans election outcomes data
• G2_cqopen_202308.do: Cleans open elections data
• G3_demographics_cainc30_202308.do: Cleans demographics data
• G4_fips_202308.do: Cleans FIPS code data
• G5_klarnerclean_202308.do: Cleans Klarner gubernatorial data
• G6_merge_202308.do: Merges cleaned datasets together
• G7_summary_202308.do: Generates summary statistics tables and figures
• G8_firststage_202308.do: Runs L1 penalized probit for the first stage
• G9_prediction_202308.m: Trains learners and makes predictions
• G10_figures_202308.m: Generates figures of prediction patterns
• G11_final_202308.do: Generates final figures and tables of results
• r1_lasso_alwayskeepCF_202308.do: Examines the effect of requiring the control function is not dropped from LASSO
• latexTable.m: Code by Eli Duenisch to write LaTeX tables from Matlab (https://www.mathworks.com/matlabcentral/fileexchange/44274-latextable)

Included non-confidential data in subdirectory `*\Application\Data`:

• \CAINC30: County level income and demographics data from the BEA
• \CPI: CPI data from the BLS
• \KlarnerGovernors: Carl Klarner's Governors Dataset available at https://dataverse.harvard.edu/dataset.xhtml?persistentId=hdl:1902.1/20408

Confidential data suppressed in subdirectory `*\Application\CD`:

These data cannot be transferred as part of the data use agreement with the CQ Press. Thus, the files are not included.

• \CQ_county: County level election outcomes available from http://library.cqpress.com/elections/login.php?requested=%2Felections%2Fdownload-data.php
• \CQ_open: Open elections available from http://library.cqpress.com/elections/advsearch/elections-with-open-seats-results.php?open_year1=1968&open_year2=2019&open_office=4

There is no batch download--downloads for each year must be done by hand. For each year, download as many state outcomes as possible and name the files YYYYa.csv, YYYYb.csv, etc. (Example: 1970a.csv, 1970b.csv, 1970c.csv, 1970d.csv). See line 18 of G1_cqclean_202308.do for file structure information.

Steps for replicating application:

1. Download confidential data from the CQ Press.
2. Change the working directory in G0_main_202308.do on line 18 to the application folder.
3. Change local matlabpath in G0_main_202308.do on line 18 to the appropriate location.
4. Set directory and file path in G9_prediction_202308.m and G10_figures_202308.m as necessary.
5. Run G0_main_202308.do in Stata to run all programs.
6. All output (figures and tables) will be saved to subdirectory *\Application\Output.

Contact

Contact Dylan Brewer (brewer@gatech.edu) or Alyssa Carlson (carlsonah@missouri.edu) for help with replication.

This dataset contains the full data and Stata do-files used to reproduce all empirical results from the paper “Export slowdown and increasing land supply: Local government’s responses to export shocks in China” by Qiuyi Wang, Shuping Wu, Jing Wu

This STATA program calculates CFI for each patient from analytic data files containing information on patient identifiers, ICD-9-CM diagnosis codes (version 32), ICD-10-CM Diagnosis Codes (version 2020), CPT codes, and HCPCS codes. NOTE: When downloading, store "CFI_ICD9CM_V32.tab" and "CFI_ICD10CM_V2020.tab" as csv files (these files are originally stored as csv files, but Dataverse automatically converts them to tab files). Please read "Frailty-Index-STATA-code-Guide" before proceeding. Interpretation, validation data, and annotated references are provided in "Research Background - Claims-Based Frailty Index".

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

Version 3 release notes: Adds data for 2016.Order rows by year (descending) and ORI.Version 2 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The Hate Crime data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about hate crimes reported in the United States. The data sets here combine all data from the years 1992-2015 into a single file. Please note that the files are quite large and may take some time to open.Each row indicates a hate crime incident for an agency in a given year. I have made a unique ID column ("unique_id") by combining the year, agency ORI9 (the 9 character Originating Identifier code), and incident number columns together. Each column is a variable related to that incident or to the reporting agency. Some of the important columns are the incident date, what crime occurred (up to 10 crimes), the number of victims for each of these crimes, the bias motivation for each of these crimes, and the location of each crime. It also includes the total number of victims, total number of offenders, and race of offenders (as a group). Finally, it has a number of columns indicating if the victim for each offense was a certain type of victim or not (e.g. individual victim, business victim religious victim, etc.). All the data was downloaded from NACJD as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. The only changes I made to the data are the following. Minor changes to column names to make all column names 32 characters or fewer (so it can be saved in a Stata format), changed the name of some UCR offense codes (e.g. from "agg asslt" to "aggravated assault"), made all character values lower case, reordered columns. I also added state, county, and place FIPS code from the LEAIC (crosswalk) and generated incident month, weekday, and month-day variables from the incident date variable included in the original data. The zip file contains the data in the following formats and a codebook: .csv - Microsoft Excel.dta - Stata.sav - SPSS.rda - RIf you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

Description

This dataset contains the Panel Export Tax (PET) dataset introduced by Solleder (2013) and its extension, which is used in Solleder (2020). Export taxes were collected from official sources.

The dta file can be opened with STATA 14 or above. The csv file is a comma-separated value file. The separator is ',', and the first row is variable names. The content is the same in both files. Variables are:

• exporter: ISO 3166 3-character country codes, string;
• commoditycode: product 6-digit HS codes in HS revision 1992 (H0), numeric;
• Value: export tax rate in %, numeric;
• year: year, numeric;
• partial: dummy variable, set to 1 if tax is partial (i.e. not all national tariff lines under the 6-digit HS code are affected), numeric;
• specific: dummy variable, set to 1 if tax is specific (Value contains then the ad valorem equivalent of the tax, see Solleder (2013) for details, numeric;
• origin: contains "PET" if the row comes from the original PET dataset (Solleder, 2013) or "Extention" if it comes from Solleder (2020), string.

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.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The Palestinian Central Bureau of Statistics (PCBS) carried out four rounds of the Labor Force Survey 2017 (LFS). The survey rounds covered a total sample of about 23,120 households (5,780 households per quarter).

The main objective of collecting data on the labour force and its components, including employment, unemployment and underemployment, is to provide basic information on the size and structure of the Palestinian labour force. Data collected at different points in time provide a basis for monitoring current trends and changes in the labour market and in the employment situation. These data, supported with information on other aspects of the economy, provide a basis for the evaluation and analysis of macro-economic policies.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a representative sample on the region level (West Bank, Gaza Strip), the locality type (urban, rural, camp) and the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered all Palestinian households who are a usual residence of the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The methodology was designed according to the context of the survey, international standards, data processing requirements and comparability of outputs with other related surveys.

---> Target Population: It consists of all individuals aged 10 years and Above and there are staying normally with their households in the state of Palestine during 2017.

---> Sampling Frame: The sampling frame consists of the master sample, which was updated in 2011: each enumeration area consists of buildings and housing units with an average of about 124 households. The master sample consists of 596 enumeration areas; we used 494 enumeration areas as a framework for the labor force survey sample in 2017 and these units were used as primary sampling units (PSUs).

---> Sampling Size: The estimated sample size is 5,780 households in each quarter of 2017.

---> Sample Design The sample is two stage stratified cluster sample with two stages : First stage: we select a systematic random sample of 494 enumeration areas for the whole round ,and we excluded the enumeration areas which its sizes less than 40 households. Second stage: we select a systematic random sample of 16 households from each enumeration area selected in the first stage, se we select a systematic random of 16 households of the enumeration areas which its size is 80 household and over and the enumeration areas which its size is less than 80 households we select systematic random of 8 households.

---> Sample strata: The population was divided by: 1- Governorate (16 governorate) 2- Type of Locality (urban, rural, refugee camps).

---> Sample Rotation: Each round of the Labor Force Survey covers all of the 494 master sample enumeration areas. Basically, the areas remain fixed over time, but households in 50% of the EAs were replaced in each round. The same households remain in the sample for two consecutive rounds, left for the next two rounds, then selected for the sample for another two consecutive rounds before being dropped from the sample. An overlap of 50% is then achieved between both consecutive rounds and between consecutive years (making the sample efficient for monitoring purposes).

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire was designed according to the International Labour Organization (ILO) recommendations. The questionnaire includes four main parts:

---> 1. Identification Data: The main objective for this part is to record the necessary information to identify the household, such as, cluster code, sector, type of locality, cell, housing number and the cell code.

---> 2. Quality Control: This part involves groups of controlling standards to monitor the field and office operation, to keep in order the sequence of questionnaire stages (data collection, field and office coding, data entry, editing after entry and store the data.

---> 3. Household Roster: This part involves demographic characteristics about the household, like number of persons in the household, date of birth, sex, educational level…etc.

---> 4. Employment Part: This part involves the major research indicators, where one questionnaire had been answered by every 15 years and over household member, to be able to explore their labour force status and recognize their major characteristics toward employment status, economic activity, occupation, place of work, and other employment indicators.

Cleaning operations

---> Raw Data PCBS started collecting data since 1st quarter 2017 using the hand held devices in Palestine excluding Jerusalem in side boarders (J1) and Gaza Strip, the program used in HHD called Sql Server and Microsoft. Net which was developed by General Directorate of Information Systems. Using HHD reduced the data processing stages, the fieldworkers collect data and sending data directly to server then the project manager can withdrawal the data at any time he needs. In order to work in parallel with Gaza Strip and Jerusalem in side boarders (J1), an office program was developed using the same techniques by using the same database for the HHD.

---> Harmonized Data - The SPSS package is used to clean and harmonize the datasets. - The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency. - All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables. - A post-harmonization cleaning process is then conducted on the data. - Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

The survey sample consists of about 30,230 households of which 23,120 households completed the interview; whereas 14,682 households from the West Bank and 8,438 households in Gaza Strip. Weights were modified to account for non-response rate. The response rate in the West Bank reached 82.4% while in the Gaza Strip it reached 92.7%.

Sampling error estimates

---> Sampling Errors Data of this survey may be affected by sampling errors due to use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators: the variance table is attached with the final report. There is no problem in disseminating results at national or governorate level for the West Bank and Gaza Strip.

---> Non-Sampling Errors Non-statistical errors are probable in all stages of the project, during data collection or processing. This is referred to as non-response errors, response errors, interviewing errors, and data entry errors. To avoid errors and reduce their effects, great efforts were made to train the fieldworkers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, carrying out a pilot survey, as well as practical and theoretical training during the training course. Also data entry staff were trained on the data entry program that was examined before starting the data entry process. To stay in contact with progress of fieldwork activities and to limit obstacles, there was continuous contact with the fieldwork team through regular visits to the field and regular meetings with them during the different field visits. Problems faced by fieldworkers were discussed to clarify any issues. Non-sampling errors can occur at the various stages of survey implementation whether in data collection or in data processing. They are generally difficult to be evaluated statistically.

They cover a wide range of errors, including errors resulting from non-response, sampling frame coverage, coding and classification, data processing, and survey response (both respondent and interviewer-related). The use of effective training and supervision and the careful design of questions have direct bearing on limiting the magnitude of non-sampling errors, and hence enhancing the quality of the resulting data. The implementation of the survey encountered non-response where the case ( household was not present at home ) during the fieldwork visit and the case ( housing unit is vacant) become the high percentage of the non response cases. The total non-response rate reached14.2% which is very low once compared to the household surveys conducted by PCBS , The refusal rate reached 3.0% which is very low percentage compared to the

The data and programs replicate tables and figures from “Evaluating the Impact of Export Finance Support on Firm-level Export Performance: Evidence from Pakistan”, by Fabrice Defever, Alejandro Riaño, Gonzalo Varela using Stata. Please see the ReadMe file for additional details.

We provide the stata files that allow to reproduce the results presented in the paper
"Crop Prices and Deforestation in the Tropics" by Nicolas Berman, Mathieu Couttenier, Antoine Leblois and Raphaël Soubeyran.

The replication folder contains different files:
1- *.dta file: database
2- *.do file: do-file containing the codes to replicate the results (figures and tables)
3- * Ancillary data:
.csv file: data needed to produce a map of the initial forest cover (in 2000).
.dta additional files to run sensitivity analysis

Simply change the path to files (on line 25 of the replication_code.do file) to re-run the analysis:
** Change pathway to load and save the data
global dir ".../Replication_files_BCLS_2022"

Stata 17 was used for this work.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

In any society, the human element represents the basis of the work force which exercises all the service and production activities. Therefore, it is a mandate to produce labor force statistics and studies, that is related to the growth and distribution of manpower and labor force distribution by different types and characteristics.

In this context, the Central Agency for Public Mobilization and Statistics conducts "Quarterly Labor Force Survey" which includes data on the size of manpower and labor force (employed and unemployed) and their geographical distribution by their characteristics.

By the end of each year, CAPMAS issues the annual aggregated labor force bulletin publication that includes the results of the quarterly survey rounds that represent the manpower and labor force characteristics during the year.

----> Historical Review of the Labor Force Survey:

1- The First Labor Force survey was undertaken in 1957. The first round was conducted in November of that year, the survey continued to be conducted in successive rounds (quarterly, bi-annually, or annually) till now.

2- Starting the October 2006 round, the fieldwork of the labor force survey was developed to focus on the following two points: a. The importance of using the panel sample that is part of the survey sample, to monitor the dynamic changes of the labor market. b. Improving the used questionnaire to include more questions, that help in better defining of relationship to labor force of each household member (employed, unemployed, out of labor force ...etc.). In addition to re-order of some of the already existing questions in much logical way.

3- Starting the January 2008 round, the used methodology was developed to collect more representative sample during the survey year. this is done through distributing the sample of each governorate into five groups, the questionnaires are collected from each of them separately every 15 days for 3 months (in the middle and the end of the month)

----> The survey aims at covering the following topics:

1- Measuring the size of the Egyptian labor force among civilians (for all governorates of the republic) by their different characteristics. 2- Measuring the employment rate at national level and different geographical areas. 3- Measuring the distribution of employed people by the following characteristics: gender, age, educational status, occupation, economic activity, and sector. 4- Measuring unemployment rate at different geographic areas. 5- Measuring the distribution of unemployed people by the following characteristics: gender, age, educational status, unemployment type "ever employed/never employed", occupation, economic activity, and sector for people who have ever worked.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a sample of urban and rural areas in all the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

----> Sample Design and Selection

The sample of the LFS 2006 survey is a simple systematic random sample.

----> Sample Size

The sample size varied in each quarter (it is Q1=19429, Q2=19419, Q3=19119 and Q4=18835) households with a total number of 76802 households annually. These households are distributed on the governorate level (urban/rural).

A more detailed description of the different sampling stages and allocation of sample across governorates is provided in the Methodology document available among external resources in Arabic.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire design follows the latest International Labor Organization (ILO) concepts and definitions of labor force, employment, and unemployment.

The questionnaire comprises 3 tables in addition to the identification and geographic data of household on the cover page.

----> Table 1- Demographic and employment characteristics and basic data for all household individuals

Including: gender, age, educational status, marital status, residence mobility and current work status

----> Table 2- Employment characteristics table

This table is filled by employed individuals at the time of the survey or those who were engaged to work during the reference week, and provided information on: - Relationship to employer: employer, self-employed, waged worker, and unpaid family worker - Economic activity - Sector - Occupation - Effective working hours - Work place - Average monthly wage

----> Table 3- Unemployment characteristics table

This table is filled by all unemployed individuals who satisfied the unemployment criteria, and provided information on: - Type of unemployment (unemployed, unemployed ever worked) - Economic activity and occupation in the last held job before being unemployed - Last unemployment duration in months - Main reason for unemployment

Cleaning operations

----> Raw Data

Office editing is one of the main stages of the survey. It started once the questionnaires were received from the field and accomplished by the selected work groups. It includes: a-Editing of coverage and completeness b-Editing of consistency

----> Harmonized Data

• The STATA is used to clean and SPSS is used harmonize the datasets.
• The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency.
• All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

analyze the area resource file (arf) with r the arf is fun to say out loud. it's also a single county-level data table with about 6,000 variables, produced by the united states health services and resources administration (hrsa). the file contains health information and statistics for over 3,000 us counties. like many government agencies, hrsa provides only a sas importation script and an as cii file. this new github repository contains two scripts: 2011-2012 arf - download.R download the zipped area resource file directly onto your local computer load the entire table into a temporary sql database save the condensed file as an R data file (.rda), comma-separated value file (.csv), and/or stata-readable file (.dta). 2011-2012 arf - analysis examples.R limit the arf to the variables necessary for your analysis sum up a few county-level statistics merge the arf onto other data sets, using both fips and ssa county codes create a sweet county-level map click here to view these two scripts for mo re detail about the area resource file (arf), visit: the arf home page the hrsa data warehouse notes: the arf may not be a survey data set itself, but it's particularly useful to merge onto other survey data. confidential to sas, spss, stata, and sudaan users: time to put down the abacus. time to transition to r. :D

analyze the survey of consumer finances (scf) with r the survey of consumer finances (scf) tracks the wealth of american families. every three years, more than five thousand households answer a battery of questions about income, net worth, credit card debt, pensions, mortgages, even the lease on their cars. plenty of surveys collect annual income, only the survey of consumer finances captures such detailed asset data. responses are at the primary economic unit-level (peu) - the economically dominant, financially interdependent family members within a sampled household. norc at the university of chicago administers the data collection, but the board of governors of the federal reserve pay the bills and therefore call the shots. if you were so brazen as to open up the microdata and run a simple weighted median, you'd get the wrong answer. the five to six thousand respondents actually gobble up twenty-five to thirty thousand records in the final pub lic use files. why oh why? well, those tables contain not one, not two, but five records for each peu. wherever missing, these data are multiply-imputed, meaning answers to the same question for the same household might vary across implicates. each analysis must account for all that, lest your confidence intervals be too tight. to calculate the correct statistics, you'll need to break the single file into five, necessarily complicating your life. this can be accomplished with the meanit sas macro buried in the 2004 scf codebook (search for meanit - you'll need the sas iml add-on). or you might blow the dust off this website referred to in the 2010 codebook as the home of an alternative multiple imputation technique, but all i found were broken links. perhaps it's time for plan c, and by c, i mean free. read the imputation section of the latest codebook (search for imputation), then give these scripts a whirl. they've got that new r smell. the lion's share of the respondents in the survey of consumer finances get drawn from a pretty standard sample of american dwellings - no nursing homes, no active-duty military. then there's this secondary sample of richer households to even out the statistical noise at the higher end of the i ncome and assets spectrum. you can read more if you like, but at the end of the day the weights just generalize to civilian, non-institutional american households. one last thing before you start your engine: read everything you always wanted to know about the scf. my favorite part of that title is the word always. this new github repository contains t hree scripts: 1989-2010 download all microdata.R initiate a function to download and import any survey of consumer finances zipped stata file (.dta) loop through each year specified by the user (starting at the 1989 re-vamp) to download the main, extract, and replicate weight files, then import each into r break the main file into five implicates (each containing one record per peu) and merge the appropriate extract data onto each implicate save the five implicates and replicate weights to an r data file (.rda) for rapid future loading 2010 analysis examples.R prepare two survey of consumer finances-flavored multiply-imputed survey analysis functions load the r data files (.rda) necessary to create a multiply-imputed, replicate-weighted survey design demonstrate how to access the properties of a multiply-imput ed survey design object cook up some descriptive statistics and export examples, calculated with scf-centric variance quirks run a quick t-test and regression, but only because you asked nicely replicate FRB SAS output.R reproduce each and every statistic pr ovided by the friendly folks at the federal reserve create a multiply-imputed, replicate-weighted survey design object re-reproduce (and yes, i said/meant what i meant/said) each of those statistics, now using the multiply-imputed survey design object to highlight the statistically-theoretically-irrelevant differences click here to view these three scripts for more detail about the survey of consumer finances (scf), visit: the federal reserve board of governors' survey of consumer finances homepage the latest scf chartbook, to browse what's possible. (spoiler alert: everything.) the survey of consumer finances wikipedia entry the official frequently asked questions notes: nationally-representative statistics on the financial health, wealth, and assets of american hous eholds might not be monopolized by the survey of consumer finances, but there isn't much competition aside from the assets topical module of the survey of income and program participation (sipp). on one hand, the scf interview questions contain more detail than sipp. on the other hand, scf's smaller sample precludes analyses of acute subpopulations. and for any three-handed martians in the audience, ther e's also a few biases between these two data sources that you ought to consider. the survey methodologists at the federal reserve take their job...

Stata do-files and data to support tutorial "Sensitivity Analysis for Not-at-Random Missing Data in Trial-Based Cost-Effectiveness Analysis" (Leurent, B. et al. PharmacoEconomics (2018) 36: 889).Do-files should be similar to the code provided in the article's supplementary material.Dataset based on 10 Top Tips trial, but modified to preserve confidentiality. Results will differ from those published.