The sample SAS and Stata code provided here is intended for use with certain datasets in the National Neighborhood Data Archive (NaNDA). NaNDA (https://www.openicpsr.org/openicpsr/nanda) contains some datasets that measure neighborhood context at the ZIP Code Tabulation Area (ZCTA) level. They are intended for use with survey or other individual-level data containing ZIP codes. Because ZIP codes do not exactly match ZIP code tabulation areas, a crosswalk is required to use ZIP-code-level geocoded datasets with ZCTA-level datasets from NaNDA. A ZIP-code-to-ZCTA crosswalk was previously available on the UDS Mapper website, which is no longer active. An archived copy of the ZIP-code-to-ZCTA crosswalk file has been included here. Sample SAS and Stata code are provided for merging the UDS mapper crosswalk with NaNDA datasets.

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.

This repository contains all replication materials for " Reexamining the Effect of Mass Shootings on Public Support for Gun Control" by David J. Barney and Brian F. Schaffner. The data included are as follows: 3 CCES panel datasets, supplementary data for merging, 2 fully-merged CCES panel datasets. In addition, we include two .do files of Stata code, one of which prepares the data for analysis, and one of which replicates the analyses presented in our paper.

This code merges multiple years of Crime Survey of England and Wales (CSEW) and/or the British Crime Survey (BCS). The current version merges the BCS and CSEW up to the CSEW 2023/2024. The purpose of these code is to help researchers to quickly and easily combine multiple survey sweeps of the CSEW and BCS.

By combining multiple survey sweeps, people are able to look at, for instance, trends in violence. Furthermore, using such a combined file enables you to look at specific offences, population groups, or consequences, that do not have a high enough frequency if you would use only a single year.

This is a Stata do file, access to Stata is therefore required, as is access to all the BCS and CSEW that you want to merge. In specifying the code, you can decide which files you want to merge. Namely, which years of the Crime Surveys you want to merge and if you want the bolt-on datasets that provide uncapped codes, the adolescent and young adult panels, and/or if you want to use the ‘non-white’ panel. This code does not harmonize variables that are different between years.

All original data resources are available via Related Resources.

This code merges multiple years of Crime Survey of England and Wales (CSEW) and/or the British Crime Survey (BCS). The purpose of these code is to help researchers to quickly and easily combine multiple survey sweeps of the CSEW and BCS.

By combining multiple survey sweeps, people are able to look at, for instance, trends in violence. Furthermore, using such a combined file enables you to look at specific offences, population groups, or consequences, that do not have a high enough frequency if you would use only a single year.

This is a Stata do-file, access to Stata is therefore required, as is access to all the BCS and CSEW that you want to merge. In specifying the code, you can decide which files you want to merge. Namely, which years of the Crime Surveys you want to merge and if you want the bolt-on datasets that provide uncapped codes, the adolescent and young adult panels, and/or if you want to use the ‘non-white’ panel. This code does not harmonize variables that are different between years.

What is the relationship between environment and democracy? The framework of cultural evolution suggests that societal development is an adaptation to ecological threats. Pertinent theories assume that democracy emerges as societies adapt to ecological factors such as higher economic wealth, lower pathogen threats, less demanding climates, and fewer natural disasters. However, previous research confused within-country processes with between-country processes and erroneously interpreted between-country findings as if they generalize to within-country mechanisms. In this article, we analyze a time-series cross-sectional dataset to study the dynamic relationship between environment and democracy (1949-2016), accounting for previous misconceptions in levels of analysis. By separating within-country processes from between-country processes, we find that the relationship between environment and democracy not only differs by countries but also depends on the level of analysis. Economic wealth predicts increasing levels of democracy in between-country comparisons, but within-country comparisons show that democracy declines as countries become wealthier over time. This relationship is only prevalent among historically wealthy countries but not among historically poor countries, whose wealth also increased over time. By contrast, pathogen prevalence predicts lower levels of democracy in both between-country and within-country comparisons. Our longitudinal analyses identifying temporal precedence reveal that not only reductions in pathogen prevalence drive future democracy, but also democracy reduces future pathogen prevalence and increases future wealth. These nuanced results contrast with previous analyses using narrow, cross-sectional data. As a whole, our findings illuminate the dynamic process by which environment and democracy shape each other.

Methods Our Time-Series Cross-Sectional data combine various online databases. Country names were first identified and matched using R-package “countrycode” (Arel-Bundock, Enevoldsen, & Yetman, 2018) before all datasets were merged. Occasionally, we modified unidentified country names to be consistent across datasets. We then transformed “wide” data into “long” data and merged them using R’s Tidyverse framework (Wickham, 2014). Our analysis begins with the year 1949, which was occasioned by the fact that one of the key time-variant level-1 variables, pathogen prevalence was only available from 1949 on. See our Supplemental Material for all data, Stata syntax, R-markdown for visualization, supplemental analyses and detailed results (available at https://osf.io/drt8j/).

This tool--a simple csv or Stata file for merging--gives you a fast way to assign Census county FIPS codes to variously presented county names. This is useful for dealing with county names collected from official sources, such as election returns, which inconsistently present county names and often have misspellings. It will likely take less than ten minutes the first time, and about one minute thereafter--assuming all versions of your county names are in this file. There are about 3,142 counties in the U.S., and there are 77,613 different permutations of county names in this file (ave=25 per county, max=382). Counties with more likely permutations have more versions. Misspellings were added as I came across them over time. I DON'T expect people to cite the use of this tool. DO feel free to suggest the addition of other county name permutations.

The analysis considers the role of non-performing loans (NPLs) for bank lending rates on newly granted loans. It is based on euro area data. The focus is on an effect caused by the stock of NPLs that extends beyond losses that banks have already incorporated into their reported capital positions. The paper assesses the channels through which such an effect occurs most importantly whether it runs through banks' idiosyncratic funding costs.

File 0 contains a description of the data used for the analysis. It does not contain actual data as most data used for the analysis is confidential. The file contains the names of the Stata-dta-Files in which the datasets are stored. These Stata-dta-Files are the starting point for the data processing which is activated by the code in the subsequent Stata-do-Files.

Files 1-3 contain the code for processing SNL and Bankscope / Orbis data. This data includes the banking group level data for the analysis (most importantly NPL / regulatory capital data). File 1 contains the code for the processing of SNL data. File 2 contains the code for the of the Bankscope / Orbis data. File 3 contains the code for merging SNL and Bankscope / Orbis data.

Files 4-6 contain the code for processing the CSDB data which includes data on the cost of bond funding on the banking group level, iBSI / iMIR data which includes data on lending rates and lending volumes on the single bank level and the macroeconomic data. File 4 contains the code for the processing of the CSDB data. Note that this data is initially on the single security level and is processed such, that information on costs of bond funding on the banking group level is retrieved. File 5 contains the code for the processing of the iBSI / iMIR data. File 6 contains the code for the processing of the macroeconomic variables.

File 7 contains the code for merging all datasets. File 8 contains the code for producing the descriptive statistics in Section 3 of the paper. File 9 contains the code for the estimation of Equations 1 and 3 of the paper. File 10 contains the code for the estimation of Equations 1 and 3 with random samples (Appendix D of the paper). File 11 contains the code for estimations with loan growth as dependent variable (Section 5.2 of the paper).

Files 12 and 13 contain code for the data processing and estimation of Equation 2 on the banking group level.

This data depository contains all experimental materials, data, and code for Spamann, Lawyers' Role-Induced Bias ... All experimental materials (i.e., exercise and survey instrument) are in the pdf file Spamann_experimentalmaterials_all.pdf. The dataset Newman.dta (Stata 14.2) contains the data collected. The Stata do-file Spamann_role_bias_code.do generates the three figures and other reported statistical information reported in the version of the paper originally posted to SSRN in May 2019. Spamann_role_bias_code_revised.do generates the four figures and other reported statistical information reported in the revision submitted to JLS in March 2020 and ultimately accepted by the journal. Both do-files use Newman.dta. Newman.dta is the result of merging 6 csv files generated by Qualtrics in each of the six semesters from students' survey responses. These 6 csv files, and the do-file rawdata_merge_clean.do to merge them, are also included.

The Infant Feeding Survey (IFS) has been carried out every five years since 1975, in order to establish information about infant feeding practices. Government policy in the United Kingdom has consistently supported breastfeeding as the best way of ensuring a healthy start for infants and of promoting women's health. Current guidance on infant feeding is as follows:

• breastmilk is the best form of nutrition for infants;
• exclusive breastfeeding is recommended for around the first six months (26 weeks) of an infant's life;
• infant formula is the only recommended alternative to breastfeeding for babies who are under 12 months old;
• around six months is the recommended age for the introduction of solid foods for infants, whether breastfed or fed on breastmilk substitutes;
• breastfeeding (and/or breastmilk substitutes) should continue beyond the first six months, along with appropriate types and amounts of solid foods;
• mothers who are unable to, or choose not to, follow these recommendations should be supported to optimise their infants' nutrition.

Since the IFS began, the content of the survey has evolved to reflect the prevailing government policy agenda, while recognising the importance of maintaining consistency over time to allow comparison and trend analysis. The first IFS in 1975 took place in England and Wales only. From 1980 the survey covered Scotland, while from 1990 Northern Ireland was also included. The 2005 survey was the first to provide separate estimates for England, Wales, Scotland and Northern Ireland, as well as for the UK as a whole, and to provide estimates of exclusive breast-feeding (where the baby is given only breast milk, no other liquids or solids).

Further information about the IFS series may be found on the Health and Social Care Information Centre website (search for 'Infant Feeding Survey').

The UK Data Archive holds IFS data from 1985 onwards. A separate survey, Infant Feeding in Asian Families, 1994-1996, covering England only, is held under SN 3759.

The 2010 IFS was based on an initial representative sample of mothers who were selected from all UK births registered during August and October 2010. Three stages of data collection were conducted, with Stage 1 being carried out when babies were around 4-10 weeks old, Stage 2 when they were 4-6 months old, and Stage 3 when they were 8-10 months old. A total of 10,768 mothers completed and returned all three questionnaires. For the first time in 2010, additional questions were included alongside the main Stage 2 questionnaire for mothers of multiple births.

Users should note that the UK Data Archive study currently includes questionnaire data from Stages 1, 2 and 3 and the multiple births data, with Excel data tables relating to survey methodology and sampling error.

The main aims of the 2010 survey were broadly similar to previous IFS, and were as follows:

• to establish how infants born in 2010 were being fed and to provide national figures on the incidence, prevalence and duration of breastfeeding and exclusive breastfeeding;
• to examine trends in infant feeding practices over recent years, in particular to compare changes between 2005 and 2010;
• to investigate variations in feeding practices among different socio-demographic groups and the factors associated with mothers' feeding intentions and with the feeding practices adopted in the early weeks;
• to establish the age at which solid foods are introduced and to examine practices associated with introducing solid foods up to 9 months;
• to measure the proportion of mothers who smoke and drink during pregnancy, and to look at the patterns of smoking and drinking behaviour before, during and after the birth; and
• to measure levels of awareness of and registration on the Healthy Start scheme and understand how Healthy Start vouchers are being used. (The Healthy Start scheme provides support for mothers in receipt of certain benefits and tax credits. Vouchers are provided that can be spent on milk, infant formula, fresh fruit or vegetables for pregnant women and children under 4 years old and coupons are also available for free vitamins for pregnant women, mothers and babies.)

For the second edition (July 2013), data and documentation from Stage 3 of the survey were added to the study.

Linking files in Stata - a warning
Stata users should note that the case identifier variable (ID) number structure may differ across datasets for all three stages. The letter prefixing the ID number may be upper case in one dataset and lower case in another. This is related to whether an online, face-to-face, CATI or postal route was used to complete the questionnaire- for example one respondent has the ID number 'E00157' in Stage 1 and Stage 2, but 'e00157' in Stage 3. Apart from the upper/lower case prefix letter, the ID number is exactly the same. However, the Stata command used to link the datasets (the 'merge' function) requires an exact match on the matching variable (ID), so if the prefix letter is lower case in one stage and upper case in another stage, Stata will reject the link and assume those cases are different respondents. At present, 441 cases are affected by this. The original datasets were compiled in SPSS, which does not distinguish between the upper and lower case prefix letters while merging datasets.

Note from the depositor, September 2016:
The depositor has sent the following note to data users: "An error in the Stage 1 dataset has been identified. Ninety-nine mothers stated that it was their first birth (Q3), that they had a total of 1 child (Q4) but then also selected the option to say that they had a multiple birth (Q5). The Stage 2 and Stage 3 data are unaffected and no figures in the published report or tables are affected. Users analysing the Stage 1 dataset should take this anomaly into account when including multiple births data in Stage 1 in their analysis."

These are datasets produced by geographically combining Living-Standards Measurement Study - Integrated Studies on Agriculture (LSMS-ISA) data spanning 3 waves, from 2009 to 2012, and refugee data provided by the UNHCR at the settlement level. The Stata code for running the analyses is also provided.

This folder contains replication files for the paper 'Including Scalable Nutrition Interventions in a Graduation Model Program: Experimental Evidence from Ethiopia'. Specifically, it includes: a) edcc_all tables.do: A do-file with replication code for Tables 1-7, and Annex Tables A1, A2, B1, C1, C2 and D1 in the paper. Note that some manual formatting still needs to be applied to the tables once the .doc file is outputted by Stata (e.g. merging table cells where row labels overflow a line); b) spir_household data.dta: Household-level dataset from all survey rounds collected as part of the Strengthen the PSNP4 Institutions and Resilience trial (2018-2021) and limited to variables relevant to the analysis. This dataset is used for all the tables except from Table 2, C1, C2 and D1; c) spir_child level data.dta: Child-level (1 row per child) anthropometrics dataset from all survey rounds. This dataset is used for the anthropometry analysis in Table 2 and Annex Tables C1 and C2; d) spir_midline data.dta Child level dataset from the midline survey round used for the first panel of Table D1, replication of results from the Alderman et al 2022 Food Policy paper. If you have any questions, reach out to Heleene Tambet at the International Food Policy Research Institute, h.tambet@cgiar.org.

What is IPGOD? The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has derived information about the applicants who filed these IP rights, to allow for research and analysis at the regional, business and individual level. This is the 2019 release of IPGOD. # How do I use IPGOD? IPGOD is large, with millions of data points across up to 40 tables, making them too large to open with Microsoft Excel. Furthermore, analysis often requires information from separate tables which would need specialised software for merging. We recommend that advanced users interact with the IPGOD data using the right tools with enough memory and compute power. This includes a wide range of programming and statistical software such as Tableau, Power BI, Stata, SAS, R, Python, and Scalar. # IP Data Platform IP Australia is also providing free trials to a cloud-based analytics platform with the capabilities to enable working with large intellectual property datasets, such as the IPGOD, through the web browser, without any installation of software. IP Data Platform # References The following pages can help you gain the understanding of the intellectual property administration and processes in Australia to help your analysis on the dataset. * Patents * Trade Marks * Designs * Plant Breeder’s Rights # Updates ### Tables and columns Due to the changes in our systems, some tables have been affected. * We have added IPGOD 225 and IPGOD 325 to the dataset! * The IPGOD 206 table is not available this year. * Many tables have been re-built, and as a result may have different columns or different possible values. Please check the data dictionary for each table before use. ### Data quality improvements Data quality has been improved across all tables. * Null values are simply empty rather than '31/12/9999'. * All date columns are now in ISO format 'yyyy-mm-dd'. * All indicator columns have been converted to Boolean data type (True/False) rather than Yes/No, Y/N, or 1/0. * All tables are encoded in UTF-8. * All tables use the backslash \ as the escape character. * The applicant name cleaning and matching algorithms have been updated. We believe that this year's method improves the accuracy of the matches. Please note that the "ipa_id" generated in IPGOD 2019 will not match with those in previous releases of IPGOD.