Understanding Society (the UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex, and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

The Understanding Society: Calendar Year Dataset, 2020, is designed to enable cross-sectional analysis of individuals and households relating specifically to their annual interviews conducted in the year 2020, and, therefore, combine data collected in three waves (Waves 10, 11 and 12). It has been produced from the same data collected in the main Understanding Society study and released in the longitudinal datasets SN 6614 (End User Licence) and SN 6931 (Special Licence). Such cross-sectional analysis can, however, only involve variables that are collected in every wave in order to have data for the full sample panel. The 2020 dataset is the first of a series of planned Calendar Year Datasets to facilitate cross-sectional analysis of specific years. Full details of the Calendar Year Dataset sample structure (including why some individual interviews from 2021 are included), data structure and additional supporting information can be found in the document '8987_calendar_year_dataset_2020_user_guide'.

As multi-topic studies, the purpose of Understanding Society is to understand short- and long-term effects of social and economic change in the UK at the household and individual levels. The study has a strong emphasis on domains of family and social ties, employment, education, financial resources, and health. Understanding Society is an annual survey of each adult member of a nationally representative sample. The same individuals are re-interviewed in each wave approximately 12 months apart. When individuals move they are followed within the UK and anyone joining their households are also interviewed as long as they are living with them. The fieldwork period for a single wave is 24 months. Data collection uses computer-assisted personal interviewing (CAPI) and web interviews (from wave 7), and includes a telephone mop up. From March 2020 (the end of wave 10 and 2nd year of wave 11), due to the coronavirus pandemic, face-to-face interviews were suspended and the survey has been conducted by web and telephone only, but otherwise has continued as before. One person completes the household questionnaire. Each person aged 16 or older participates in the individual adult interview and self-completed questionnaire. Youths aged 10 to 15 are asked to respond to a paper self-completion questionnaire. In 2020 an additional frequent web survey was separately issued to sample members to capture data on the rapid changes in people’s lives due to the COVID-19 pandemic (see SN 8644). The COVID-19 Survey data are not included in this dataset.

Further information may be found on the "https://www.understandingsociety.ac.uk/documentation/mainstage"> Understanding Society main stage webpage and links to publications based on the study can be found on the Understanding Society Latest Research webpage.

Co-funders
In addition to the Economic and Social Research Council, co-funders for the study included the Department of Work and Pensions, the Department for Education, the Department for Transport, the Department of Culture, Media and Sport, the Department for Community and Local Government, the Department of Health, the Scottish Government, the Welsh Assembly Government, the Northern Ireland Executive, the Department of Environment and Rural Affairs, and the Food Standards Agency.

End User Licence and Special Licence versions:
There are two versions of the Calendar Year 2020 data. One is available under the standard End User Licence (EUL) agreement, and the other is a Special Licence (SL) version. The SL version contains month and year of birth variables instead of just age, more detailed country and occupation coding for a number of variables and various income variables have not been top-coded (see xxxx_eul_vs_sl_variable_differences for more details). Users are advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The SL data have more restrictive access conditions; prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. The main longitudinal versions of the Understanding Society study may be found under SNs 6614 (EUL) and 6931 (SL).

Low- and Medium-level geographical identifiers produced for the mainstage longitudinal dataset can be used with this Calendar Year 2020 dataset, subject to SL access conditions. See the User Guide for further details.

Suitable data analysis software
These data are provided by the depositor in Stata format. Users are strongly advised to analyse them in Stata. Transfer to other formats may result in unforeseen issues. Stata SE or MP software is needed to analyse the larger files, which contain about 1,900 variables.

Stata replication files for "Reading fiction and economic preferences of rural youth in Burkina Faso" to appear in Economic Development and Cultural Change in 2019. The Stata dataset contains observations for the 557 treatment and control in the program, identified by an id variable. Most variables are suffixed by mars13, mai13, aout13, mars14 and mai14 the French abbreviations of month and then year, for the session in which the variable was measured. Many variable names and labels are in French. Consult paper for English equivalents of variable names after running replication do file.

Understanding Society, (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

The Understanding Society: Calendar Year Dataset, 2020, is designed to enable cross-sectional analysis of individuals and households relating specifically to their annual interviews conducted in the year 2020, and, therefore, combine data collected in three waves (Waves 10, 11 and 12). It has been produced from the same data collected in the main Understanding Society study and released in the longitudinal datasets SN 6614 (End User Licence) and SN 6931 (Special Licence). Such cross-sectional analysis can, however, only involve variables that are collected in every wave in order to have data for the full sample panel. The 2020 dataset is the first of a series of planned Calendar Year Datasets to facilitate cross-sectional analysis of specific years. Full details of the Calendar Year Dataset sample structure (including why some individual interviews from 2021 are included), data structure and additional supporting information can be found in the 8988_calendar_year_dataset_2020_user_guide.

As multi-topic studies, the purpose of Understanding Society is to understand the short- and long-term effects of social and economic change in the UK at the household and individual levels. The study has a strong emphasis on domains of family and social ties, employment, education, financial resources, and health. Understanding Society is an annual survey of each adult member of a nationally representative sample. The same individuals are re-interviewed in each wave approximately 12 months apart. When individuals move they are followed within the UK and anyone joining their households are also interviewed as long as they are living with them. The fieldwork period for a single wave is 24 months. Data collection uses computer-assisted personal interviewing (CAPI) and web interviews (from wave 7), and includes a telephone mop-up. From March 2020 (the end of wave 10 and 2nd year of wave 11), due to the coronavirus pandemic, face-to-face interviews were suspended and the survey has been conducted by web and telephone only, but otherwise has continued as before. One person completes the household questionnaire. Each person aged 16 or older participates in the individual adult interview and self-completed questionnaire. Youths aged 10 to 15 are asked to respond to a paper self-completion questionnaire. In 2020 an additional frequent web survey was separately issued to sample members to capture data on the rapid changes in people’s lives due to the COVID-19 pandemic (see SN 8644). The COVID-19 Survey data are not included in this dataset.

Further information may be found on the "https://www.understandingsociety.ac.uk/documentation/mainstage"> Understanding Society main stage webpage and links to publications based on the study can be found on the Understanding Society Latest Research webpage.

Co-funders
In addition to the Economic and Social Research Council, co-funders for the study included the Department of Work and Pensions, the Department for Education, the Department for Transport, the Department of Culture, Media and Sport, the Department for Community and Local Government, the Department of Health, the Scottish Government, the Welsh Assembly Government, the Northern Ireland Executive, the Department of Environment and Rural Affairs, and the Food Standards Agency.

End User Licence and Special Licence versions:
There are two versions of the Calendar Year 2020 data. One is available under the standard End User Licence (EUL) agreement, and the other is a Special Licence (SL) version. The SL version contains month and year of birth variables instead of just age, more detailed country and occupation coding for a number of variables and various income variables have not been top-coded (see xxxx_eul_vs_sl_variable_differences for more details). Users are advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The SL data have more restrictive access conditions; prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. The main longitudinal versions of the Understanding Society study may be found under SNs 6614 (EUL) and 6931 (SL).

Low- and Medium-level geographical identifiers produced for the mainstage longitudinal dataset can be used with this Calendar Year 2020 dataset, subject to SL access conditions. See the User Guide for further details.

Suitable data analysis software
These data are provided by the depositor in Stata format. Users are strongly advised to analyse them in Stata. Transfer to other formats may result in unforeseen issues. Stata SE or MP software is needed to analyse the larger files, which contain about 1,900 variables.

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)

For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 12 release notes:Adds 2019 data.Version 11 release notes:Changes release notes description, does not change data.Version 10 release notes:The data now has the following age categories (which were previously aggregated into larger groups to reduce file size): under 10, 10-12, 13-14, 40-44, 45-49, 50-54, 55-59, 60-64, over 64. These categories are available for female, male, and total (female+male) arrests. The previous aggregated categories (under 15, 40-49, and over 49 have been removed from the data). Version 9 release notes:For each offense, adds a variable indicating the number of months that offense was reported - these variables are labeled as "num_months_[crime]" where [crime] is the offense name. These variables are generated by the number of times one or more arrests were reported per month for that crime. For example, if there was at least one arrest for assault in January, February, March, and August (and no other months), there would be four months reported for assault. Please note that this does not differentiate between an agency not reporting that month and actually having zero arrests. The variable "number_of_months_reported" is still in the data and is the number of months that any offense was reported. So if any agency reports murder arrests every month but no other crimes, the murder number of months variable and the "number_of_months_reported" variable will both be 12 while every other offense number of month variable will be 0. Adds data for 2017 and 2018.Version 8 release notes:Adds annual data in R format.Changes project name to avoid confusing this data for the ones done by NACJD.Fixes bug where bookmaking was excluded as an arrest category. Changed the number of categories to include more offenses per category to have fewer total files. Added a "total_race" file for each category - this file has total arrests by race for each crime and a breakdown of juvenile/adult by race. Version 7 release notes: Adds 1974-1979 dataAdds monthly data (only totals by sex and race, not by age-categories). All data now from FBI, not NACJD. Changes some column names so all columns are <=32 characters to be usable in Stata.Changes how number of months reported is calculated. Now it is the number of unique months with arrest data reported - months of data from the monthly header file (i.e. juvenile disposition data) are not considered in this calculation. Version 6 release notes: Fix bug where juvenile female columns had the same value as juvenile male columns.Version 5 release notes: Removes support for SPSS and Excel data.Changes the crimes that are stored in each file. There are more files now with fewer crimes per file. The files and their included crimes have been updated below.Adds in agencies that report 0 months of the year.Adds a column that indicates the number of months reported. This is generated summing up the number of unique months an agency reports data for. Note that this indicates the number of months an agency reported arrests for ANY crime. They may not necessarily report every crime every month. Agencies that did not report a crime with have a value of NA for every arrest column for that crime.Removes data on runaways.Version 4 release notes: Changes column names from "poss_coke" and "sale_coke" to "poss_heroin_coke" and "sale_heroin_coke" to clearly indicate that these column includes the sale of heroin as well as similar opiates such as morphine, codeine, and opium. Also changes column names for the narcotic columns to indicate that they are only for synthetic narcotics. Version 3 release notes: Add 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 Arrests by Age, Sex, and Race (ASR) data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains highly granular data on the number of people arrested for a variety of crimes (see below for a full list of included crimes). The data sets here combine data from the years 1974-2019 into a single file for each group of crimes. Each monthly file is only a single year as my laptop can't handle combining all the years together. These files are quite large and may take some time to load. Col

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.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study used the National Incident-Based Reporting System (NIBRS) to explore whether changes in the 2000-2010 decade were associated with changes in the prevalence and nature of violence between and among Whites, Blacks, and Hispanics. This study also aimed to construct more accessible NIBRS cross-sectional and longitudinal databases containing race/ethnic-specific measures of violent victimization, offending, and arrest. Researchers used NIBRS extract files to examine the influence of recent social changes on violence for Whites, Blacks, and Hispanics, and used advanced imputation techniques to account for missing values on race/ethnic variables. Data for this study was also drawn from the National Historical Geographic Information System, the Census Gazetteer, and Law Enforcement Officers Killed or Assaulted (LEOKA). The collection includes 1 Stata data file with 614 cases and 159 variables and 2 Stata syntax files.

The Health Survey for England, 2000-2001: Small Area Estimation Teaching Dataset was prepared as a resource for those interested in learning introductory small area estimation techniques. It was first presented as part of a workshop entitled 'Introducing small area estimation techniques and applying them to the Health Survey for England using Stata'. The data are accompanied by a guide that includes a practical case study enabling users to derive estimates of disability for districts in the absence of survey estimates. This is achieved using various models that combine information from ESDS government surveys with other aggregate data that are reliably available for sub-national areas. Analysis is undertaken using Stata statistical software; all relevant syntax is provided in the accompanying '.do' files.

The data files included in this teaching resource contain HSE variables and data from the Census and Mid-year population estimates and projections that were developed originally by the National Statistical agencies, as follows:

• The main data file, 'hse_data.dta', is a reduced version of the HSE for 2000 and 2001. In order to combine data from two years of the HSE in a consistent way some changes have been made to the weights in each year. Additionally, some recoding of the limiting long term illness (LLTI), disability and the age variable has also been undertaken.
• File 'practical_1_task_5_data.dta' contains population counts and model mobility disability rates (estimated during practical 1) distinguishing single year of age and sex for the six case study districts.
• File 'practical_2_data.dta' contains the aggregate data required for Practical 2, including age- and sex-specific rates of LLTI (Census) for six UK case study districts, age- and sex-specific rates of mobility disability for England (HSE), and population counts for the six districts.
• File 'pop_data_practical_3.dta' contains population counts for the six districts (by age, sex and LLTI status) required for practical 3

The original HSEs for 2000 and 2001 are held at the UK Data Archive under SNs 4628 and 4912 respectively. Full details of the recoding of HSE variables and how the aggregate data was produced can be found in the data documentation.

This unrestricted access data collection is freely available to download under an Open Government Licence from the UK Data Service. Note that the files should be unzipped/saved to the C: drive of the computer to be used; all syntax assumes files are saved at this location.

These data represent a meta-dataset of observations on per household economic value - represented by per household willingness-to-pay (WTP) - for improvements in coastal marsh habitat, drawn from stated-preference studies in the research literature. The metadata allow estimation of benefit transfer functions via meta-regression modeling. Within these econometric functions, the dependent variable is a comparable estimate of economic value (e.g., WTP) drawn from extant primary valuation studies. Independent variables represent observable factors hypothesized to explain variation in this value measure across observations. These functions can be used to produce out-of-sample predictions of WTP for coastal marsh habitat improvements at sites for which no primary valuation studies have been conducted. They can also be used to understand the factors associated with systematic variations in marsh habitat values across different sites and studies. These data are described in Vedogbeton, H. and R.J. Johnston. 2020. Commodity Consistent Meta-Analysis of Wetland Values: An Illustration for Coastal Marsh Habitat. Environmental and Resource Economics 75(4), 835-865, and allow replication of the results presented therein. The metadata are extracted from primary studies that estimate total (use and nonuse) per household WTP for changes in the quantity or quality of coastal marsh wildlife habitats or their services, in US and Canada. These studies were identified via a systematic review of the literature. The metadata combine information provided by these primary non-market valuation studies with publicly available external data extracted from sources such as the US Census, US National Historical GIS (https://www.nhgis.org/), and US Fish and Wildlife Service National Wetlands Inventory (https://www.fws.gov/wetlands/Data/Mapper.html). Studies included in the metadata are restricted to those that estimate total per household WTP for coastal wetland habitat changes using generally accepted stated preference methods, report theoretically comparable and quantifiable measures of economic value, and provide sufficient information to enable inclusion in the metadata. The data are further restricted to observations from studies conducted in the US or Canada, and published between 1990 and 2016, inclusive. The resulting metadata include 141 total observations of WTP per household from 23 studies published from 1990 to 2016, with all values adjusted to 2016 USD. These 141 habitat-value observations are identified by the variable changsize = 0 within the data. Because the meta-analysis is restricted to WTP in the positive domain, two negative-WTP observations were subsequently dropped, leading to the 139 habitat-value observations reported in Vedogbeton and Johnston (2020). An additional 18 metadata observations are drawn from similar primary stated preference studies that estimate total WTP for changes in coastal marsh area (or size), where these area increases provide habitat combined with other wetland services such as flood control, water filtration, aesthetics, recreation, and habitat. These additional observations are used for the habitat-and-area value models in the paper, and are identified by the variable changsize = 1 within the data. The combined data include 159 total observations (141 habitat-value and 18 habitat-and-area value observations). The metadata compile variables characterizing (1) the scope [magnitude] of the valued habitat change and the spatial scale of the wetland area affected by the change, (2) the type of habitat, marsh and uses affected, (3) regions sampled by the primary study, and (4) original study methodology used to measure the value(s), sample size from these studies, and year. The categorical variable "code" identifies how each of these observations are used within the data analysis of Vedogbeton and Johnston (2020). The attached pdf file, "Stata Code_Marsh Meta", provides illustrative Stata (v16) code used to generate some of the primary model results in this article, using the data. Note that some variable labels in the Stata code differ slightly from those used in the published paper. This project was supported by National Science Foundation grant 1427105.

This replication folder describes the Stata v17 “do file” (code file) for statistical analysis for "Food inflation and child undernutrition in low and middle income countries " by Derek Headey & Marie Ruel. This do file can be used to replicate the analysis in the study mentioned above, published in Nature Communications. The study uses a combination of Demographic Health Survey (DHS) data for child, maternal, household level variables and national level indicators on real food price changes drawn from FAOSTAT, as well as conflict and climate variables. In summary, this is a large multi-country DHS dataset merged with FAO food and total consumer price indices (CPIs) and various other national level control variables. These are DHS surveys from 2000 onwards only.

The authors cannot publicly share the DHS data but can share it upon request, provided we can obtain approval from the DHS implementers. To make a request to access the data for this paper, please email Derek Headey at d.headey@cgiar.org. Alternatively researchers can access the raw DHS data from: https://dhsprogram.com/data/available-datasets.cfm and the country level indicators from the Food and Agriculture Organisation Consumer Prices data portal (https://www.fao.org/faostat/en/#data/CP) as well as The World Bank World Development Indicators (https://databank.worldbank.org/source/world-development-indicators) for obtaining data on various control variables.

The files enclosed are related to the dataset (STATA format), and variables description, of the article: "Climate Change and Migration: Is Agriculture the Main Channel? ( Falco, C., Galeotti M. and Olper, A) Submitted to Global Environmental Change.

Two Stata .do files (Replication_Main_Dofile.do and Replication_AppendixB_Dofile) are provided that replicate all tables and four figures in the manuscript, and all tables and figures in the supplemental materials with our two final datasets. However, our contract with Core Logic prohibits us from distributing or sharing data with third parties. We provide our dataset for our estimation in main body as a Stata.dta file (Main_estimation.dta), but with restricted variables filled in as a string “Proprietary: Core Logic” which will prevent estimation of our models using the .do file.

Descriptions of the dependent and independent variables considered in this study.

Abstract (en): The National Ambulatory Medical Care Surveys (NAMCS) supply data on ambulatory medical care provided in physicians' offices. The 2006 survey contains information from 29,392 patient visits to 1,455 physicians' offices. Data are available on the patient's smoking habits, reason for the visit, expected source of payment, the physician's diagnosis, and the kinds of diagnostic and therapeutic services rendered. Other variables cover drugs/medications ordered, administered, or provided during office visits, with information on medication code, generic name and code, brand name, entry status, prescription status, federal controlled substance status, composition status, and related ingredient codes. Information is also included on the physician's specialization and geographic location. Demographic information on patients, such as age, sex, race, and ethnicity, was also collected. In addition, the 2006 survey contains two new sampling strata which are from 104 Community Health Centers (CHCs) and 200 oncologists. Microdata file users should be fully aware of the importance of the "patient visit weight" (PATWT) and how it must be used. Information about the patient visit weight is presented in the codebook. If more information is needed, the staff of the Ambulatory Care Statistics Branch can be consulted by calling (301) 458-4600 during regular working hours. Prior to this data release, researchers could not make physician-level estimates with publicly available NAMCS data. For 2006, a new "physician weight" (PHYSWT) variable was added to the first record for each individual physician in the dataset. Office visits made within the United States by patients of nonfederally-employed physicians who were primarily involved in office-based patient care activities, but not engaged in the specialties of radiology, pathology, or anesthesiology. The 2006 NAMCS utilized a multistage probability sample design. Primary sampling units (PSUs) were selected in the first stage, physician practices within PSUs in the second stage, and patient visits to selected physicians in the third stage. 2011-10-12 Changes to the CPSUM variable have been made within the dataset. Funding insitution(s): United States Department of Health and Human Services. National Institutes of Health. National Cancer Institute. Per agreement with the National Center for Health Statistics (NCHS), ICPSR distributes the data file and text of the technical documentation for this collection as prepared by NCHS.A portion of NAMCS 2006, the sampling stratum of 200 oncologists, was made possible through funding from the National Cancer Institute.The Stata dataset (.dta file) made available by ICPSR does not contain all of the value labels found within the .do file supplied by ICPSR. Specifically, the value labels that are composed primarily of ICD-9 codes have been omitted from the .dta file. Those data users interested in applying the value labels to the dataset will be able to edit the Stata setup files, which include the aforementioned labels, provided by ICPSR.

Effect of education expenditure on contraceptive use and non-use using logistic regression.

The data set (saved in Stata *.dta and .txt) contains all observations (Norwegian supreme court cases 2008-2018 decided in five-justice panels) and variables (independent variables measuring complexity of cases and the dependent variable measuring time in hours scheduled for oral arguments) relevant for a complete replication of the the study. ABSTRACT OF STUDY: While high courts with fixed time for oral arguments deprive researchers of the opportunity to extract temporal variance, courts that apply the “accordion model” institutional design and adjust the time for oral arguments according to the perceived complexity of a case are a boon for research that seeks to validate case complexity well ahead of the courts’ opinion writing. We analyse an original data set of all 1,402 merits decisions of the Norwegian Supreme Court from 2008 to 2018 where the justices set time for oral arguments to accommodate the anticipated difficulty of the case. Our validation model empirically tests whether and how attributes of a case associated with ex ante complexity are linked with time allocated for oral arguments. Cases that deal with international law and civil law, have several legal players, are cross-appeals from lower courts are indicative of greater case complexity. We argue that these results speak powerfully to the use of case attributes and/or the time reserved for oral arguments as ex ante measures of case complexity. To enhance the external validity of our findings, future studies should examine whether these results are confirmed in high courts with similar institutional design for oral arguments. Subsequent analyses should also test the degree to which complex cases and/or time for oral arguments have predictive validity on more divergent opinions among the justices and on the time courts and justices need to render a final opinion.

Measuring dynamic capabilities in new ventures: Exploring strategic change in US green goods manufacturing using website data. Firm website and Dun and Bradstreet (DUNS) measures, US Green Goods SMEs, 2008-2012 (212 data variables; 298 total observations; 223 observations without missing variables). Anonymized. Stata dta format and Stata do file. The website data for the target firms is derived from archived website data from the Wayback Machine. We also use business data for these firms from Dun and Bradstreet. See associated paper for added details including definitions of "green goods" manufacturing and small and medium-sized enterprises (SMEs); enterprise sample selection; and methods applied to use website data combined with other available business data to gauge enterprise capabilities for market sensing and responding. In the analytic model, we use data variables for two time periods, 2008-09 and 2010-11, to explain sales growth for green goods enterprises in two later time periods, from 2010 to 2012.

Community-level variables of respondents, data from PMA-Ethiopia, 2019 (n = 6117).

Abstract

Background: Adolescent girls in Kenya are disproportionately affected by early and unintended pregnancies, unsafe abortion and HIV infection. The In Their Hands (ITH) programme in Kenya aims to increase adolescents' use of high-quality sexual and reproductive health (SRH) services through targeted interventions. ITH Programme aims to promote use of contraception and testing for sexually transmitted infections (STIs) including HIV or pregnancy, for sexually active adolescent girls, 2) provide information, products and services on the adolescent girl's terms; and 3) promote communities support for girls and boys to access SRH services.

Objectives: The objectives of the evaluation are to assess: a) to what extent and how the new Adolescent Reproductive Health (ARH) partnership model and integrated system of delivery is working to meet its intended objectives and the needs of adolescents; b) adolescent user experiences across key quality dimensions and outcomes; c) how ITH programme has influenced adolescent voice, decision-making autonomy, power dynamics and provider accountability; d) how community support for adolescent reproductive and sexual health initiatives has changed as a result of this programme.

Methodology ITH programme is being implemented in two phases, a formative planning and experimentation in the first year from April 2017 to March 2018, and a national roll out and implementation from April 2018 to March 2020. This second phase is informed by an Annual Programme Review and thorough benchmarking and assessment which informed critical changes to performance and capacity so that ITH is fit for scale. It is expected that ITH will cover approximately 250,000 adolescent girls aged 15-19 in Kenya by April 2020. The programme is implemented by a consortium of Marie Stopes Kenya (MSK), Well Told Story, and Triggerise. ITH's key implementation strategies seek to increase adolescent motivation for service use, create a user-defined ecosystem and platform to provide girls with a network of accessible subsidized and discreet SRH services; and launch and sustain a national discourse campaign around adolescent sexuality and rights. The 3-year study will employ a mixed-methods approach with multiple data sources including secondary data, and qualitative and quantitative primary data with various stakeholders to explore their perceptions and attitudes towards adolescents SRH services. Quantitative data analysis will be done using STATA to provide descriptive statistics and statistical associations / correlations on key variables. All qualitative data will be analyzed using NVIVO software.

Study Duration: 36 months - between 2018 and 2020.

Geographic coverage

Narok and Homabay counties

Analysis unit

Households

Universe

All adolescent girls aged 15-19 years resident in the household.

Sampling procedure

The sampling of adolescents for the household survey was based on expected changes in adolescent's intention to use contraception in future. According to the Kenya Demographic and Health Survey 2014, 23.8% of adolescents and young women reported not intending to use contraception in future. This was used as a baseline proportion for the intervention as it aimed to increase demand and reduce the proportion of sexually active adolescents who did not intend to use contraception in the future. Assuming that the project was to achieve an impact of at least 2.4 percentage points in the intervention counties (i.e. a reduction by 10%), a design effect of 1.5 and a non- response rate of 10%, a sample size of 1885 was estimated using Cochran's sample size formula for categorical data was adequate to detect this difference between baseline and end line time points. Based on data from the 2009 Kenya census, there were approximately 0.46 adolescents girls per a household, which meant that the study was to include approximately 4876 households from the two counties at both baseline and end line surveys.

We collected data among a representative sample of adolescent girls living in both urban and rural ITH areas to understand adolescents' access to information, use of SRH services and SRH-related decision making autonomy before the implementation of the intervention. Depending on the number of ITH health facilities in the two study counties, Homa Bay and Narok that, we sampled 3 sub-Counties in Homa Bay: West Kasipul, Ndhiwa and Kasipul; and 3 sub-Counties in Narok, Narok Town, Narok South and Narok East purposively. In each of the ITH intervention counties, there were sub-counties that had been prioritized for the project and our data collection focused on these sub-counties selected for intervention. A stratified sampling procedure was used to select wards with in the sub-counties and villages from the wards. Then households were selected from each village after all households in the villages were listed. The purposive selection of sub-counties closer to ITH intervention facilities meant that urban and semi-urban areas were oversampled due to the concentration of health facilities in urban areas.

Qualitative Sampling

Focus Group Discussion participants were recruited from the villages where the ITH adolescent household survey was conducted in both counties. A convenience sample of consenting adults living in the villages were invited to participate in the FGDS. The discussion was conducted in local languages. A facilitator and note-taker trained on how to use the focus group guide, how to facilitate the group to elicit the information sought, and how to take detailed notes. All focus group discussions took place in the local language and were tape-recorded, and the consent process included permission to tape-record the session. Participants were identified only by their first names and participants were asked not to share what was discussed outside of the focus group. Participants were read an informed consent form and asked to give written consent. In-depth interviews were conducted with purposively selected sample of consenting adolescent girls who participated in the adolescent survey. We conducted a total of 45 In-depth interviews with adolescent girls (20 in Homa Bay County and 25 in Narok County respectively). In addition, 8 FGDs (4 each per county) were conducted with mothers of adolescent girls who are usual residents of the villages which had been identified for the interviews and another 4 FGDs (2 each per county) with CHVs.

Sampling deviation

N/A

Mode of data collection

Face-to-face [f2f] for quantitative data collection and Focus Group Discussions and In Depth Interviews for qualitative data collection

Research instrument

The questionnaire covered; socio-demographic and household information, SRH knowledge and sources of information, sexual activity and relationships, family planning knowledge, access, choice and use when needed, exposure to family planning messages and voice and decision making autonomy and quality of care for those who visited health facilities in the 12 months before the survey. The questionnaire was piloted before the data collection and the questions reviewed for appropriateness, comprehension and flow. The questionnaire was piloted among a sample of 42 adolescent girls (two each per field interviewer) 15-19 from a community outside the study counties.

The questionnaire was originally developed in English and later translated into Kiswahili. The questionnaire was programmed using ODK-based Survey CTO platform for data collection and management and was administered through face-to-face interview.

Cleaning operations

The survey tools were programmed using the ODK-based SurveyCTO platform for data collection and management. During programming, consistency checks were in-built into the data capture software which ensured that there were no cases of missing or implausible information/values entered into the database by the field interviewers. For example, the application included controls for variables ranges, skip patterns, duplicated individuals, and intra- and inter-module consistency checks. This reduced or eliminated errors usually introduced at the data capture stage. Once programmed, the survey tools were tested by the programming team who in conjunction with the project team conducted further testing on the application's usability, in-built consistency checks (skips, variable ranges, duplicating individuals etc.), and inter-module consistency checks. Any issues raised were documented and tracked on the Issue Tracker and followed up to full and timely resolution. After internal testing was done, the tools were availed to the project and field teams to perform user acceptance testing (UAT) so as to verify and validate that the electronic platform worked exactly as expected, in terms of usability, questions design, checks and skips etc.

Data cleaning was performed to ensure that data were free of errors and that indicators generated from these data were accurate and consistent. This process begun on the first day of data collection as the first records were uploaded into the database. The data manager used data collected during pilot testing to begin writing scripts in Stata 14 to check the variables in the data in 'real-time'. This ensured the resolutions of any inconsistencies that could be addressed by the data collection teams during the fieldwork activities. The Stata 14 scripts that perform real-time checks and clean data also wrote to a .rtf file that detailed every check performed against each variable, any inconsistencies encountered, and all steps that were taken to address these inconsistencies. The .rtf files also reported when a variable was

Socio-demographic and reproductive (individual-level) characteristics of respondents, data from PMA-Ethiopia, 2019 (n = 6117).