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\title{
Revisiting the Effect of Growing Up in a Recession on Attitudes towards Redistribution
}

\begin{document}
	
\author{
	Jan Bietenbeck\footnote{Lund University and CESifo, DIW Berlin, IZA.  E-mail: jan.bietenbeck@nek.lu.se. Address: Department of Economics, Lund University, P.O. Box 7080, 220 07 Lund, Sweden.}
	~and~Petra Thiemann\footnote{Lund University and IZA. Corresponding author. E-mail: petra.thiemann@nek.lu.se. Address: Department of Economics, Lund University, P.O. Box 7080, 220 07 Lund, Sweden. Phone: +46-46-222 9074.}
}


\setcounter{page}{1}
{\begin{center}
	{\LARGE Tables and Figures for }
	
	\bigskip
	
	{\LARGE \bf Revisiting the Effect of Growing Up in a Recession on Attitudes towards Redistribution}
	
	
	\bigskip
	
	Jan Bietenbeck and Petra Thiemann   
	
	\bigskip
	
	Document part of the replication package, February 2023
\end{center}}


\vspace{1cm}




%%%%%%%


\begin{figure}[!h]
	\caption{United States results}
	\includegraphics[width = 0.95\textwidth]{figure1}
	\label{fig:fig1}
	\caption*{\footnotesize \textit{Note:} The figure presents the original estimates (GS 2014) and the estimates from the narrow and long replications of the baseline specification (Panel A), of the specification with cohort effects (Panel B), and of the specification with additional controls (Panel C, the additional controls are: cohort effects, family income at age 16, father's years of education, religion at age 16, religion at the time of the interview, and interaction effects between current region of residence and survey year). The estimates of the replications come from OLS regressions, the whiskers represent 95\% confidence bands and are computed using analytic standard errors (coefficient $\pm 1.96\widehat{SE}$). The original estimates come from Tables 1--3 in GS and are based on OLS regressions, the whiskers represent 95\% confidence bands and are computed using the wild bootstrap standard errors  reported by GS (coefficient $\pm 1.96\widehat{SE}$). All standard errors are clustered by region of residence at age 16. Sample sizes, coefficient estimates, and significance levels of the replications are reported in Tables~A.3 and A.4 in the online appendix.
	}
\end{figure}


\begin{figure}[ht!]
	\centering
	\caption{Results of the international analysis}
		\includegraphics[width = 0.95\textwidth]{figure2}
	\label{fig:fig2}
	\caption*{\footnotesize \textit{Note:} The figure presents the original estimates (GS 2014) and the estimates from the narrow, long, and wide replications of the baseline specification (Panel A), of the specification with cohort effects (Panel B), and of the specification with additional controls (Panel C, the additional controls are country-by-survey year dummies). The narrow and long replications were conducted in data from the WVS. The wide replication was conducted in data from the ISSP (variables ``reduce differences high-low incomes'' and ``reduce differences rich-poor''). The variables ``society: egalitarian-competitive'' and ``society: welfare-low taxes'' were not collected in the two additional waves included in the long replication sample.  The estimates of the replications come from OLS regressions, the whiskers represent 95\% confidence bands and are computed using analytic standard errors (coefficient $\pm 1.96\widehat{SE}$). The original estimates (GS 2014) come from Tables 5, A.18, and A.19 in GS and are based on OLS regressions, the whiskers represent 95\% confidence bands and are computed using the wild bootstrap standard errors reported by GS (coefficient $\pm 1.96\widehat{SE}$). All standard errors are clustered by country. Sample sizes, coefficient estimates, and significance levels of the replications are reported in Tables~A.7--A.9 in the online appendix.}
\end{figure}

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%\renewcommand{\appendix}{A}
\section*{Appendix A: Supplementary figures and tables}

\begin{figure}[h!tb]
	\centering
	\caption{Economic shock exposure by United States macro region and year of birth}
	%\caption*{Panel A. Figure from the original paper (GS 2014)}
	%	\includegraphics[width=0.7\textwidth]{../../output/GSS_recessions_orig.pdf}
	\caption*{Panel B. Replication}	
		\includegraphics[width=0.7\textwidth]{figureA1.pdf}
	\label{fig:compare_cross-region}
	\caption*{\footnotesize \textit{Notes: }{The figure shows plots of the economic shock variable separately by U.S. macro region and year of birth. The shock variable is defined as a dummy that takes value one if a cohort experienced at least one year in which real regional per capita GDP growth was lower than --3.4 percent during ages 18--25 and zero otherwise (see Appendix~B for details). Panel A reproduces Figure A.1 in the online appendix of the original paper (page 43). Panel B shows the corresponding plots from our replication. }}
\end{figure}

\newpage

\begin{figure}[h!tb]
	\centering
	\caption{General Social Survey: sample extension}
	\includegraphics{figureA2.pdf}
	\label{fig:sample_extension}
	\caption*{\footnotesize \textit{Note: }{The figure displays a histogram of the sample that is used for the long replication based on the General Social Survey data. The gray areas of the bars display the portion of the data that are used in the narrow replication (General Social Survey waves collected in 1972--2010); the white areas of the bars display the portion of the data that is added in the long replication (General Social Survey waves 2012, 2014, 2016, 2018). The sample consists of all observations in the baseline regression with ``help poor'' as the dependent variable (26,961 observations; see Table~\ref{tab:gss_descriptives}, column 7, for more details).}}
\end{figure}

\begin{figure}[h!tb]
	\centering
	\caption{Economic shock exposure by country and year of birth}
	%\caption*{Panel A. Figure from the original paper (GS 2014)}
	%\includegraphics[width=0.9\textwidth]{../../output/figureA2_orig.pdf}
	\caption*{Panel B. Replication}	
	\includegraphics[width=0.9\textwidth]{figureA3.pdf}
	\label{fig:figureA2}
	\caption*{\footnotesize \textit{Note: }{The figure shows plots of the economic shock variable separately by country and year of birth. Recession data come from~Barro and Ursua (2008), Table C.2. Panel A reproduces Figure A.2 in the online appendix of the original paper (page 44). Panel B shows the corresponding plots from our replication. }}
\end{figure}


%
%
\begin{landscape}
	\begin{table}[h!tbp]
		\centering
		\caption{Summary statistics for the General Social Survey}
		\begin{footnotesize}
			\input{tableA1}	
		\end{footnotesize}
		\label{tab:gss_descriptives}%
		\caption*{\footnotesize \textit{Notes:} The table shows the number of observations, means, and standard deviations of key variables in the General Social Survey, separately for the original GS sample (numbers are reproduced from Table A.1 on page 7 of the GS online appendix), the narrow replication sample, and the long replication sample. The statistics for the different variables are based on different estimation subsamples. Specifically, statistics for outcomes are based on the subsamples in Table~1, columns 1--6 in GS; statistics for the shock variable are based on the subsample in Table 1, column 1 in GS; statistics for the first set of control variables (age--income) are based on the subsample in Table 1, column 6  in GS; statistics for the second set of control variables (father's years of education--other at 16) are based on the subsample in Table 3, column 1 in GS. To compute the statistics for the narrow and long replication samples in columns 4--9 in this table, we use the corresponding subsamples.} %The extended sample includes the GSS waves 2012, 2014, 2016, and 2018 in addition to the original waves (1972--2010).
	\end{table}%
\end{landscape}
%
\begin{landscape}
	\begin{table}[h!tbp]
		\centering
		\caption{General Social Survey: baseline specification and replication}
		\begin{tiny}
			\input{tableA2}	
		\end{tiny}
		\label{tab:table1}%
		\caption*{\footnotesize \textit{Note:} The table presents OLS estimates from GS' original paper (odd columns, see Table 1 on page~795 of~GS) and OLS estimates of our narrow replication (even colums). The standard errors in GS are computed using the wild bootstrap (Stata command~\texttt{cgmwildboot}). The standard errors of the replication are analytic standard errors, since the computation of wild bootstrap standard errors using~\texttt{cgmwildboot} is no longer recommended and supported. The table also reports the wild bootstrap p-values, which are computed using~\texttt{cgmwildboot} (``p-value (WBS)'') and, for comparison, the analytic p-values. All regressions include dummies for current region of residence, survey year, region of residence at age 16 and their interaction with age (continuous variable, measured in years). All standard errors are clustered by region of residence at age 16. \\ * significant at the 10\%-level, ** significant at the 5\%-level, *** significant at the 1\%-level.}
	\end{table}%
\end{landscape}
%% Table generated by Excel2LaTeX from sheet 'Tabelle1'
%
%
%
\begin{table}[h!tbp]
		\centering
		\caption{General Social Survey: comparison of the narrow replication with the original results (GS 2014)}
		\begin{scriptsize}
			\input{tableA3}	
		\end{scriptsize}
		\label{tab:gss_extension}%
		\caption*{\footnotesize \textit{Note:} The table displays OLS regression results of the narrow replication for three different specifications: the baseline specification (Panel A, corresponding to Table 1 in GS), the specification with cohort effects (Panel B, corresponding to Table 2 in GS), and the specification with cohort effects and additional controls (Panel C, corresponding to Table 3 in GS). Analytic standard errors, clustered by region of residence at age 16, are in parentheses. The table also reports the difference between the replication results and the point estimates reported by GS (replication -- original) and the p-value for the null hypothesis that the replication estimate is equal to the corresponding point estimate reported by GS (p-value of difference). \\ * significant at the 10\%-level, ** significant at the 5\%-level, *** significant at the 1\%-level.}
\end{table}%
%
%
\begin{table}[h!tbp]
	\centering
	\caption{General Social Survey: comparison of the long replication with the original results (GS 2014)}
	\begin{scriptsize}
		\input{tableA4}	
	\end{scriptsize}
	\label{tab:gss_extension_wide}%
	\caption*{\footnotesize \textit{Note:} The table displays OLS regression results of the long replication for three different specifications: the baseline specification (Panel A, corresponding to Table 1 in GS), the specification with cohort effects (Panel B, corresponding to Table 2 in GS), and the specification with cohort effects and additional controls (Panel C, corresponding to Table 3 in GS). Analytic standard errors, clustered by region of residence at age 16, are in parentheses. The table also reports the difference between the replication results and the point estimates reported by GS (replication -- original) and the p-value for the null hypothesis that the replication estimate is equal to the corresponding point estimate reported by GS (p-value off difference). \\ * significant at the 10\%-level, ** significant at the 5\%-level, *** significant at the 1\%-level.}
\end{table}%

%
\begin{landscape}
	\begin{table}[h!tbp]
		\centering
		\caption{World Values Survey: descriptive statistics}
		\begin{small}
			\input{WVS_descriptives}	
		\end{small}
		\label{tab:WVS_descriptives}%
		\caption*{\footnotesize \textit{Note:} The table presents descriptive statistics, based on the World Values Survey, for the original sample (numbers are reproduced from Table A.1 on page 7 of the online appendix in GS), the narrow and long replication samples. The descriptive statistics are presented for different regression estimation samples. Outcomes: samples based on Table~5, columns 1--8 in GS; shock and control variables: sample corresponding to Table 5, column 1 in GS. We define the samples for the descriptive statistics of the replication samples (columns 4--9 in this table) accordingly, that is, based on the same regression specifications as in GS. The variables ``society: egalitarian-competitive'' and ``society: welfare-low taxes'' were not collected in the two additional waves included in the long replication sample.}
	\end{table}%
\end{landscape}
%
\begin{landscape}
	\begin{table}[h!tbp]
		\centering
		\caption{World Values Survey: baseline specification and replication}
		\begin{tiny}
			\input{tableA6}	
		\end{tiny}
		\label{tab:Table5_replication}%
		\caption*{\footnotesize \textit{Note:} The table presents OLS estimates from the original paper (odd columns, see Table 1 on page~807 of~GS) and OLS estimates of the narrow replication (even colums). The standard errors reported by GS are analytic standard errors, except for the standard errors in columns 7 and 9, which are computed using the wild bootstrap (Stata command~\texttt{cgmwildboot}). The standard errors of the replication are all analytic standard errors, since the computation of wild bootstrap standard errors using~\texttt{cgmwildboot} is no longer recommended and supported. The wild bootstrap p-values for the economic shock variable in columns 8 and 10 are 0.286 and 0.904. respectively. All standard errors are clustered by country. All regressions include dummies for country, survey year, age, income (in brackets), and religion, and country-specific age trends. \\ * significant at the 10\%-level, ** significant at the 5\%-level, *** significant at the 1\%-level.}
	\end{table}%
\end{landscape}
%%
%
%
\begin{landscape}
\begin{table}[h!tbp]
	\centering
	\caption{World Values Survey: comparison of the narrow replication with the original results (GS 2014)}
	\begin{scriptsize}
		\input{tableA7}	
	\end{scriptsize}
	\label{tab:WVS_compare_narrow_orig}%
	\caption*{\footnotesize \textit{Note:}  The table displays OLS regression results of the narrow replication for three different specifications: the baseline specification (Panel A, corresponding to Table 5 in GS), the specification with cohort effects (Panel B, corresponding to Table A.18 in GS), and the specification with cohort effects and country-by-survey year dummies (Panel C, corresponding to Table A.19 in GS). Analytic standard errors, clustered by country, are in parentheses. The table also reports the difference between the replication results and the point estimates reported by GS (replication -- original) and the p-value for the null hypothesis that the replication estimate is equal to the corresponding point estimate reported by GS (p-value of difference). \\ * significant at the 10\%-level, ** significant at the 5\%-level, *** significant at the 1\%-level.}
\end{table}%
\end{landscape}
%
\begin{table}[h!tbp]
		\centering
		\caption{World Values Survey: comparison of the long replication with the original results (GS 2014)}
		\begin{scriptsize}
			\input{tableA8}	
		\end{scriptsize}
		\label{tab:WVS_compare_wide_orig}%
		\caption*{\footnotesize \textit{Note:}  The table displays OLS regression results of the long replication for three different specifications: the baseline specification (Panel A, corresponding to Table 5 in GS), the specification with cohort effects (Panel B, corresponding to Table A.18 in GS), and the specification with cohort effects and country-by-survey year dummies (Panel C, corresponding to Table A.19 in GS). Analytic standard errors, clustered by country, are in parentheses. The table also reports the difference between the replication results and the point estimates reported by GS (replication -- original) and the p-value for the null hypothesis that the replication estimate is equal to the corresponding point estimate reported by GS (p-value of difference). The variables ``society: egalitarian-competitive'' and ``society: welfare-low taxes'' were not collected in the two additional waves included in the long replication sample. \\ * significant at the 10\%-level, ** significant at the 5\%-level, *** significant at the 1\%-level.}
\end{table}%


\begin{table}[ht!]
	\setlength{\tabcolsep}{6pt}
	\centering
	\footnotesize
	\caption{Replication in data from the International Social Survey Programme}
	\input{tableA9.tex}
	\label{tab:ISSP_wide}
	\vspace{4pt}
	\caption*{\footnotesize \textit{Notes:} The table presents OLS regression results. Panel A: the outcome variables were included as reported in the survey (5-point scale for the variable ``reduce differences high-low incomes'' and 4-point scale for the variable ``reduce differences rich-poor''). Panel B: the outcomes were re-coded as binary variables (1 if agree or strongly agree, 0 otherwise).  All specifications control for years of education, dummies for age, survey year, country, married, male, and unemployed, four dummies for religious affiliation, country dummies interacted with (continuous) age, and country dummies interacted with (continuous) years of education. The specifications in colums 2, 3, 5, and 6 additionally control for cohort dummies. The specifications in columns 3 and 6 additionally control for country dummies interacted with dummies for the survey year. Analytic standard errors, clustered by country, are in parentheses. \\ * significant at the 10\%-level, ** significant at the 5\%-level, *** significant at the 1\%-level. }
\end{table}







%
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%%\renewcommand{\appendix}{B}
%\section*{Appendix B: Data appendix}\label{app_B}
%
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%\setcounter{table}{0} \renewcommand{\thetable}{B.\arabic{table}}
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% 
%\section{General Social Survey}
%
%\subsection{Data sources and coverage}
%
%\paragraph{General Social Survey}
%
%\citet{GiulianoSpilimbergo2014} state that they use the General Social Survey waves collected during 1972--2010. As explained by the authors, the General Social Survey is a nationally representative survey and has been conducted each year during 1972--1993 (except for 1979, 1981, and 1992) and every other year since 1994. We use the General Social Survey 1972--2018 Release 3, May 2020, in Stata format (accessed on February 26, 2021, from \url{https://gss.norc.org/}).  
%
%\paragraph{Data used to construct economic shocks}
%
%\citet{GiulianoSpilimbergo2014} construct regional recessions based on state personal income from the Bureau of Economic Analysis (BEA) and adjust the per capita personal income for inflation. They then aggregate deflated state per capita income to the level of the census division to be able to merge it to the General Social Survey data. GS define an economic shock as 1 if capita income growth in a census division was smaller than --3.4 percent on average in a given year, and as 0 otherwise.
%
%%\begin{quote}
%%We construct a measure of regional recessions using data on regional personal income from the Bureau of Economic Analysis (BEA). The BEA has
%%been providing annual estimates of per capita personal income at the state level since 1929. We construct a measure of real per capita personal income using data on state personal income and population data, adjusted for inflation.
%%\end{quote}
%
%Following this description, we first download the data series ``Personal annual income by state'' (SAINC1) from the website of the BEA \url{https://apps.bea.gov/regional/docs/DataAvailability.cfm} (accessed on February 26, 2021). This data source contains aggregated personal income at the state level for the years 1929--2019~\citep{BEA}. 
%
%To adjust for inflation we use annual U.S. CPI data from the the U.S. Bureau of Labor Statistics (downloaded from  \url{https://www.minneapolisfed.org/about-us/monetary-policy/inflation-calculator/consumer-price-index-1913-}, on March 2, 2021). The data is based on the series CPI-U (CPI for all urban consumers), which is the only CPI source available for the years before 1978. The data spans the years 1913--2019.
%
%To compute state-wide growth in per capita income, we first compute the inflation adjusted personal per capita income in each state. We then compute the year-to-year growth of inflation adjusted per capita income by state. Next we aggregate the yearly growth rates within the nine U.S. census divisions (see~\url{https://www.census.gov/programs-surveys/economic-census/guidance-geographies/levels.html} for a mapping of states to divisions), weighting each state by its population (population data is included in the SAINC1 series). Following \citet{GiulianoSpilimbergo2014} we construct a variable ``economic shock,'' which takes the value 1 if the yearly growth rate is on average smaller than --3.4 percent in a census division, and the value 0 otherwise.
%
%
%\subsection{Construction of the variables and samples}
%
%\paragraph{Economic shocks} The General Social Survey data~\citep{GSS} can be combined with data on recessions using respondents' year of birth (equivalent to the survey year minus the age of the repondent) and the census division in which an individual resided at the age 16. Let $t$ be the year of birth. We compute exposure to a recession when young as 1 if the census division in which the individual resided at age 16 was exposed at least once to an economic shock (as defined above) during the years $t + 18, \dots, t + 25$, and as 0 otherwise.    
%
%\paragraph{Outcomes} 
%
%We construct the following outcomes from the General Social Survey:
%
%\begin{itemize}
%	\item ``Help poor.'' Question: ``Washington should do everything possible to improve the standard of living of all poor Americans; they are at Point 1 on this card. Other people
%	think it is not the government's responsibility, and that each person should take care of himself; they are at Point 5. Where would you place yourself on this scale, or haven't you made up your mind on this?'' The answer option were on a scale of 1--5 with 1 = the government should help, 3 = agree with both, 5 = people should care for themselves, with 2 and 4 being the intermediate options that individuals could also choose.  The question was not asked in 1972--1974, 1976--1982, and 1985. %We recoded this variable as six minus the value given by the repondent, such that a larger number indicates a higher desired responsibility of the government.
%	\item ``Assistance poor.'' Question: ``We are faced with many problems in this country, none of which can be solved easily or inexpensively. I'm going to name some of these problems, and
%	for each one I'd like you to tell me whether you think we're spending too much money on it, too little money, or about the right amount. Are we spending too much, too little, or about the right amount on assistance to the poor?'' Answer options: 1 = too little, 2 = about right, 3 = too much. The question was not asked in 1972--1983. %We recoded this variable as 4 minus the value given by the repondent, such that a larger number indicates preference for higher spending.
%	\item ``Work-luck.'' Question: ``Some people say that people get ahead by their own hard work; others say that lucky breaks or help from other people are more important.
%	Which do you think is most important?'' Answer options: 1 = hard work is most important, 2 = hard work and luck are equally important, 3 = luck is most important. The question was not asked in 1972, 1975, 1978, 1983, 1986.
%	\item ``Party affiliation.'' Question:  ``Generally speaking, do you usually think of yourself as a Republican, Democrat, Independent, or what?'' Answer options: 0 = strong democrat, 1 = not very strong democrat, 2 = independent, close to democrat, 3 = independent (neither, no response), 4 = independent, close to republican, 5 = not very strong republican, 6 = strong republican, 7 = other party.  We recoded ``other party'' as missing values. The question was asked in all years. %We recoded the variable as 6 minus the value given.
%	\item ``Political views (political ideology).'' Question: ``We hear a lot of talk these days about liberals and conservatives. I'm going to show you a seven-point scale on which the political views that people might
%	hold are arranged from extremely liberal--point 1--to extremely conservative--point 7. Where would you place yourself on this scale?''  Answer options: 1 = extremely liberal, 2 = liberal, 3 = slightly liberal, 4 =  moderate, middle of the road, 5 = slightly conservative, 6 = conservative, 7 = extremely conservative.  The question was not asked in 1972 and 1973. %We recoded the variable as 8 minus the value given.
%	\item ``Voting democrat.'' The question asked, in each year, what the respondents voted in the last presidential election. Example for 1972: ``In 1972, you remember that McGovern ran for President on the Democratic ticket against Nixon for the Republicans.
%	Do you remember for sure whether or not you voted in that election?'' If the respondent voted: ``Did you vote for McGovern or Nixon?'' Answer options: 1 = McGovern, 2 = Nixon. The question was always coded such that 1 = democratic candidate, 2 = republican candidate. We recoded the variable such that it was 1 if the respondent voted for the democratic candidate, 0 if the respondent voted for the republican candidate, and missing otherwise. The question was asked in all survey years.
%\end{itemize}
%
%Following GS, we code all outcomes such that higher values reflect preferences more in favor of redistribution and more left-leaning political attitudes, reverting the scales if necessary. Individuals who answered ``I don't know,''``other'' or ``does not apply'' were assigned a missing value for the respective question.
%
%
%
%\paragraph{Control variables} 
%
%From the General Social Survey, we construct the variables that mirror the description in GS. 
%
%\begin{itemize}
%	\item Age in years.
%	\item Years of education. 
%	\item Married (binary).
%	\item Black (binary).
%	\item Unemployed (binary).
%	\item Income: total family income, measured in 12 income brackets. 
%	\item Father's years of education. 
%	\item Income at 16: respondent's family income at age 16, measured on a five-point scale (1 = far below average, 2 = below average, 3 = average, 4 = above average, 5 = far above average).
%	\item Religion (dummies for protestant, catholic, christian, jewish, and other).
%	\item Religion at age 16 (dummies for protestant, catholic, christian, jewish, and other). 
%\end{itemize}
% 
%
%\paragraph{Sample restrictions for the narrow replication}  
%
%The sample for the narrow replication includes 28 waves of the General Social Survey  (1972--2010). To construct the estimation sample we use information on all individuals born in 1904 or later. Information on recessions is not available for earlier cohorts. The youngest individuals in this sample are born in 1992, that is, they are at least 18 years old at the time of the latest survey wave. 
%
%\paragraph{Sample restrictions for the long replication}
%
%To extend the sample for the long replication, we include four waves that were collected after the original article was written (2012, 2014, 2016, 2018). In 2020, the survey method was changed (from in-person visits to online interviews). We therefore do not include the most recent waves into the analysis. We include all individuals born in the years 1904--2000. 
%
%
%\section{World Values Survey}
%
%\subsection{Data sources and coverage}
%
%\paragraph{World Values Survey} \citet{GiulianoSpilimbergo2014} state that they use five waves of the WVS (1981--1984, 1990--1993, 1995--1997, 1999--2004, and 2005--2007). We downloaded version 1.6 of the WVS time-series data for Stata, which includes data for these waves as well as for two additional waves conducted in 2010--2014 and 2017--2020~\citep{WVS}. The WVS data do not include data for some European countries, which participated in the sister study of the WVS, the European Values Survey (EVS), but which are included in GS' analysis. We therefore downloaded data for the EVS waves 1990--1993 and 1999--2001 (version 3.1.0, \citealp{EVS}) and merged them with the WVS data.
%
%\paragraph{Data on economic shocks} \citet{GiulianoSpilimbergo2014} base their definition of economic shocks on data on economic disasters compiled by~\citet{BarroUrsua2008}. We extract the relevant information from Table C.2 in the appendix of \citet{BarroUrsua2008}, which for 39 countries denotes the trough and peak years of ``GDP disasters.''
%
%\paragraph{Countries included in the analysis}
%The following 37 countries are included in the analysis: Argentina, Australia, Austria$^{*}$, Belgium$^{*}$, Brazil, Canada, Chile, Colombia, Denmark$^{*}$, Finland, France, Germany, Greece$^{*}$, Iceland$^{*}$, India, Indonesia, Italy, Japan, Mexico, Netherlands, New Zealand, Norway, Peru, Philippines, Portugal$^{*}$, Singapore, South Africa, South Korea, Spain, Sweden, Switzerland, Taiwan, Turkey, United Kingdom, United States, Uruguay and Venezuela (data for countries marked with $^{*}$ come from the EVS, rather than the WVS; the data from \citet{BarroUrsua2008} contain GDP disaster dates for two additional countries, which are however not covered by the WVS or EVS). 
%
%\subsection{Construction of the variables and samples} 
%
%\paragraph{Economic shocks} We combine the WVS and EVS data with the data on economic shocks using information on year of birth. In particular, we construct a dummy variable that takes value 1 if the country  of the respondent experienced a GDP disaster when she was aged 18--25, and 0 otherwise. 
%
%\paragraph{Outcomes} We construct the following outcomes from the WVS and EVS data:
%\begin{itemize}
%	\item ``Government responsibility.'' Question: ``Now I'd like you to tell me your views on various issues. How would you place your views on this scale? 1 means you agree completely with the statement on the left; 10 means you agree completely with the statement on the right; and if your views fall somewhere in between, you can choose any number in between.'' The respondent is then asked about ``Government responsibility''; answers are recorded on a scale from 1--10, with the leftmost answer labeled ``1 People should take more responsibility'' and the rightmost answer labeled ``10  The government should take more responsibility.''
%	\item ``Income equality.'' Question: ``Now I'd like you to tell me your views on various issues. How would you place your views on this scale? 1 means you agree completely with the statement on the left; 10 means you agree completely with the statement on the right; and if your views fall somewhere in between, you can choose any number in between.'' The respondent is then asked about ``Income equality''; answers are recorded on a scale from 1--10, with the leftmost answer labeled ``1 Incomes should be made more equal'' and the rightmost answer labeled ``10 We need larger income differences as incentives.''
%	\item ``Private-state ownership.'' Question: ``Now I'd like you to tell me your views on various issues. How would you place your views on this scale? 1 means you agree completely with the statement on the left; 10 means you agree completely with the statement on the right; and if your views fall somewhere in between, you can choose any number in between.'' The responded is then asked about ``Private vs state ownership of business''; answers are recorded on a scale from 1--10, with the leftmost answer labeled ``1 Private ownership of business should be increased'' and the rightmost answer labeled ``10 Government ownership of business should be increased.''
%	\item ``Society: egalitarian-competitive.'' Question: ``And now, could you please tell me which type of society this country you think this country SHOULD aim to be in the future. For each pair of statements, would you prefer being closer to the first or to the second alternative? First statement: An egalitarian society where the gap between rich and poor is small, regardless of achievement. Second statement: A competitive society, where wealth is distributed according to ones' achievement.'' The answer takes values from 1 to 5: first (1), somewhat closer to first (2), can't say (3), somewhat closer to second (4), second (5).
%	\item ``Society: welfare-low taxes.'' Question: ``And now, could you please tell me which type of society this country you think this country SHOULD aim to be in the future. For each pair of statements, would you prefer being closer to the first or to the second alternative? First statement: A society with extensive social welfare, but high taxes. Second statement: A society where taxes	are low and individuals take responsibility for themselves. The answer takes values from 1 to 5: first (1), somewhat closer to first (2), can't say (3), somewhat closer to second (4), second (5).
%	\item ``Work-luck.'' Question ``Now I'd like you to tell me your views on various issues. How would you place your views on this scale? 1 means you agree completely with the statement on the left; 10 means you agree completely with the statement on the right; and if your views fall somewhere in between, you can chose any number in between.'' The respondent is then asked about ``Hard work brings success''; answers are recorded on a scale from 1-10, with the leftmost answer labeled ``1 In the long run, hard work usually brings a better life'' and the rightmost answer labeled ``10 Hard work does not generally bring success—it is more a matter of luck and
%	connections''.
%	\item ``Political ideology.'' Question: ``In political matters, people talk of `the left' and `the right.' How would you place your views on this scale, generally speaking?'' Answers are recorded on a scale from 1-10, with 1 labeled ``left'' and 10 labeled ``right''.
%	\item ``Party affiliation.'' Question: ``If there were a national election tomorrow, for which party on this list would you vote? If `Don't know': Which party appeals to you most?'' For each country, all major parties are presented on a list. GS use data from Huber and Inglehart (``Expert Interpretations of Party Space and Party Locations in 42 Societies'', \textit{Party Politics}, 1, 73–111, 1995), which places major parties in several countries onto a 1-10 left-right scale based on expert opinions. We matched the parties listed in Huber and Inglehart (1995) to the names given in the WVS and EVS data.	
%\end{itemize}
%
%\noindent It is important to note that not all of the above questions were asked in all countries and waves of the surveys, which leads to very different coverage of the outcomes (which in turn is reflected in the different number of observations in Table \ref{tab:Table5_replication}). Following GS, we code all outcomes such that higher values reflect preferences more in favor of redistribution and more left-leaning political attitudes, reverting the scales if necessary. Individuals who answered ``I don't know'' or similar were assigned a missing value for the respective question.
%
%\paragraph{Control variables} We follow GS and construct a number of control variables from the WVS and EVS data. In particular, we construct dummies for gender, unemployed, married, three dummies for education (low, middle, high), 10 dummies for income deciles, and dummies for belonging to various religious denominations.
%
%\paragraph{Sample restrictions for the narrow replication} We focus on the five waves of the WVS, and the complementary EVS waves, conducted up to 2010. GS mention as the only sample restriction that they exclude immigrants. However, immigration status is measured only in some countries and waves of the WVS and EVS data. We construct the best possible proxy for immigration status by using the answers to the following question, which was only asked in the 1989-1993 and 1994-1998 waves: ``Were you born in this country? -- 1 Yes; If no, where were you born: 2 Latin America, 3 = ...''. We construct a dummy for being an immigrant from this sample, and our estimations exclude those respondents identifying as immigrants (since the data do not allow us to identify immigrants outside the two waves mentioned above, immigrants in those waves continue to be included in the sample).
%
%\paragraph{Sample restrictions for the long replication} The only difference to the narrow replication sample is that we additionally include the two most recent WVS and EVS waves.
%
%\section{International Social Survey Programme}
%\label{sec:issp}
%
%\subsection{Data sources and coverage}
%\paragraph{International Social Survey Programme} We use the following survey years, each of which asked at least one of the two questions used to construct our outcomes: 1985, 1987, 1990, 1991, 1992, 1996, 1998, 1999, 2000, 2006, 2009, 2010, 2016, 2017. We accessed the data on February 14, 2022, through \url{https://issp.org/data-download/archive/}.
%
%\paragraph{Data on economic shocks} We use the data on economic shocks by \cite{BarroUrsua2008}. For a detailed description, see the corresponding paragraph in the description of the WVS data.
%
%\paragraph{Countries included in the analysis} We have data on outcomes and on economic shocks for the following 31 countries, which are included in the sample: Argentina, Australia, Austria, Belgium, Canada, Chile, Denmark, Finland, France, Germany, Iceland, India, Italy, Japan, Mexico, Netherlands, New Zealand, Norway, Philippines, Portugal, South Africa, South Korea, Spain, Sweden, Switzerland, Taiwan, Turkey, United Kingdom, United States, Uruguay, Venezuela.
%
%\subsection{Construction of the variables and samples}
%
%\paragraph{Economic shocks} The construction of this variable follows the construction in the WVS data.
%
%\paragraph{Outcomes} We construct the following two outcomes:
%\begin{itemize}
%	\item ``Reduce differences high-low incomes.'' Question: ``What is your opinion of the following statement: It is the responsibility of the government to reduce the differences in income between people with high incomes and those with low incomes.'' Respondents answer on a 5-point scale. We code answers as follows: 1 ``strongly disagree'', 2 ``disagree'' 3 ``neither agree nor disagree'' 4 ``agree'' 5 ``strongly agree''. We note that the question text in the first part of the question changes slightly between waves, for example to ``Please show how much you agree or disagree with each statement: \dots''
%	\item ``Reduce differences rich-poor.'' Question: ``On the whole, do you think it should or should not be the government's responsibility to reduce income differences between the rich and poor?'' Respondents answer on a 4-point scale. We code answers as follows: 1 ``definitely should not be'' 2 ``probably should not be'' 3 ``probably should be'' 4 ``definitely should be''.
%\end{itemize}
%
%\noindent As with the WVS, both questions were not asked in each wave and all countries (see Table \ref{tab:ISSP_wide} for the sample sizes). 
%
%\paragraph{Control variables} Similar to the WVS data, we construct the following control variables: age, dummies for gender, married, religious denomination, unemployed. We also include years of education; since the measurement of this variable differs between countries and waves, we always control for country, survey year, years of education, and country dummies interacted with years of education in our regressions. ISSP also collects information on income, but the measurement differs strongly between countries and waves and we therefore do not include income as one of our control variables.
%
%
%\paragraph{Sample restrictions} We do not restrict our sample in any way.
%
%\label{Literature}
%
%\bibliographystyle{ecta}
%\bibliography{recessions}

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