Cross National Research

Political trust among electoral winners and losers in Europe

Winner-loser trust gap across countries Winner-loser trust gap in Poland Trust differences across parties in Poland Voting for a party that ends up losing the election is known to be associated with lower satisfaction with democracy and trust in the parliament (cf. Martini and Quaranta 2019). How does Poland compare to other European countries? How has the winner-loser trust gap changed in Poland over time, and how have trust levels among supporters of current and former ruling parties changed in periods when they were not in government?

Downloading country-level indicators on participation and economic inequality 2

Data Packages Varieties of Democracy (V-Dem): Dedicated package Polyarchy: Semicolon delimited CSV file -> rio Freedom House: Excel file with by-year sheets Polity IV: SPSS file -> rio Democracy Barometer: Excel file with header in top rows -> rio The Standardized World Income Inequality Database (SWIID): Plain CSV file -> rio World Bank’s World Development Indicators: Dedicated package Merging all datasets Writing to file Shortly after writing this post on importing datasets in different formats (CSV, XLS, XLSX, SAV) to R, I got the following comment:

Downloading country-level indicators on participation and economic inequality

Data Packages Varieties of Democracy (V-Dem): Dedicated package Polyarchy: Semicolon delimited CSV file Freedom House: Excel file with by-year sheets Polity IV: SPSS file Democracy Barometer: Excel file with header in top rows The Standardized World Income Inequality Database (SWIID): Plain CSV file World Bank’s World Development Indicators: Dedicated package Merging all datasets Country graphs Variable graphs Writing to file with Viktoriia Muliavka Social and political scientists often need to put together datasets of country-level political, economic, and demographic variables with data from different sources.

Personal vs. household income in cross-national surveys

Sample correlations Sample correlations by gender Sample correlations by age Sample correlations by education Contrast Conclusion One of the reasons for the harmonization of personal income in addition to household income was to check if the two correlate highly enough to use household income as a substitute for personal income in analyses where economic status is a control variable. This would be great, because household income variables are available in 1177 surveys out of 1721 analyzed in the Survey Data Recycling dataset (SDR) version 1, while personal income only in 453 surveys.

Harmonizing measures of income in cross-national surveys

Data Number of response options Item non-response Distributions Harmonized target variables Next steps with Przemek Powałko Individual economic status is a necessary element of almost all sociological analyses, including studies of political attitudes and behavior. To supplement the already harmonized variables in the Survey Data Recycling dataset (SDR) version 1 and for the purposes of my resesarch of the effects of education on political engagement, Przemek and I harmonized two additional variables: personal income and household income1.

Measuring the level and inequality of political participation with survey data

Political participation in the ESS Country levels of political participation Inequality of political participation Democracy indicators Economic inequality Matrix scatter plots How to measure political inequality? The Variaties of Democracy project (V-Dem) has a set of political equality indicators that capture the extent to which political power is distributed according to wealth and income, membership in a particular social group, gender or sexual orientation (cf. V-Dem Codebook v.

Age distributions in samples from cross-national survey projects

Cross-national survey projects conduct surveys on representative samples of adult populations. How do the distributions of respondents’ age vary across surveys carried out in the same country in different years and different projects? Like in a couple of previous posts (here, here and here) I use data from the Survey Data Recycling dataset (SDR) version 1, which includes selected harmonized variables from 22 cross-national survey projects. SDR only includes surveys that claim to have samples representative for adult populations.

Reliability of survey estimates: Participation in demonstrations

Data Differences within country-years Differences by groups Gender Age Urban/rural residence Education Sampling scheme The growth in cross-national survey projects in the last decades leads to situations when two or more surveys are carried out in the same country and the same year but in different projects, and contain overlapping sets of survey questions. Assuming that the surveys are based on representative samples - a claim that major cross-national survey projects typically make - it could be expected that estimates from surveys carried out in the same country and year are reasonably close.

Shiny app for exploring harmonized cross-national survey data (SDR v.1.0)

Instructions References  In the previous post I wrote about downloading and exploring the Survey Data Recycling (SDR), version 1 dataset, which consists of selected harmonized variables from 22 survey projects, 1966-2013. The SDR project will develop a website for browsing, subsetting, downloading, and visualizing data from the SDR project. This website is currently under construction. Meanwhile, I made a Shiny app with basic functionalities of the future on-line browsing and subsetting tool (also serves as its mock-up): https://mkolczynska.

Exploring the dataset of survey datasets: Survey Data Recycling version 1

Introduction Downloading the SDR data Exploring SDR: availability of variables by project Exploring SDR: availability of variables with different formulations Identifying surveys containing selected variables Subsetting the Master File Country coverage plot Combining data from different survey projects creates new opportunities for research, alas, at the cost of increased volume (obviously) and complexity of the data. The Survey Data Recycling project created a dataset with data from 22 international survey projects.