\begin{center}
\Large{Appendix} \\
\Large{Kinship and Ideology} 
\end{center}
\part{Appendix} % Start the appendix part
\parttoc % Insert the appendix TOC
\newpage
\section*{Study 1}


\subsection*{Item Wording for Key Variables}

\paragraph*{Anti-LGBT Attitudes} ``Gay men and lesbians should be free to live their own life as they wish''  (Coded: Agree strongly=0; Agree=.25; Neither agree nor disagree=.5; Disagree=.75; Disagree strongly=1)
\paragraph*{Gender attitudes}
 ``When jobs are scarce, men should have more right to a job than women'' (Coded: Agree strongly=1; Agree=.75; Neither agree nor disagree=.5; Disagree=.25; Disagree strongly=0)

\paragraph{Government benefits} All questions began with the following question stem: Social benefits and services in [country]...
        \begin{itemize}
            \item ....place too great a strain on the economy. (Coded: Agree strongly=0; Agree=.25; Neither agree nor disagree=.5; Disagree=.75; Disagree strongly=1)
            \item ... prevent widespread poverty?. (Coded: Agree strongly=0; Agree=.25; Neither agree nor disagree=.5; Disagree=.75; Disagree strongly=1)
            \item ... lead to a more equal society? (Coded: Agree strongly=0; Agree=.25; Neither agree nor disagree=.5; Disagree=.75; Disagree strongly=1)
            \item ... cost businesses too much in taxes and charges?  (Coded: Agree strongly=1; Agree=.75; Neither agree nor disagree=.5; Disagree=.25; Disagree strongly=0)
            \item  ... make people lazy? (Coded: Agree strongly=1; Agree=.75; Neither agree nor disagree=.5; Disagree=.25; Disagree strongly=0)
            \item  ... make people less willing to care for one another? (Coded: Agree strongly=1; Agree=.75; Neither agree nor disagree=.5; Disagree=.25; Disagree strongly=0)
            \end{itemize}
\paragraph{Government Responsibility} Composite of three items asked in two waves. ``People have different views on what the responsibilities of governments should or should not be. For each of the tasks I read out please tell me on a score of 0-10 how much responsibility you think governments should have. 0 means it should not be governments' responsibility at all and 10 means it should be entirely governments' responsibility...
\begin{itemize}
    \item ensure a reasonable standard of living for the old?
    \item ensure a reasonable standard of living for the unemployed?
    \item ensure sufficient child care services for working parents?
\end{itemize}

\paragraph{Income differences}
\begin{itemize}
    \item The government should take measures to reduce differences in income levels. (Coded: Agree strongly=0; Agree=.25; Neither agree nor disagree=.5; Disagree=.75; Disagree strongly=0)
\end{itemize}

\paragraph{Political Engagement}
\begin{itemize}
    \item Did R work for a political party or action group in the last 12 months? (0=No, 1=Yes)
    \item Did R contact a politician or government official in the last 12 months? (0=No, 1=Yes)
    \item Did R wear or display a campaign badge or sticker in the last 12 months? (0=No, 1=Yes)
    \item Did R sign a petition in the last 12 months? (0=No, 1=Yes)
    \item How interested in politics is R? (0=Not at all interested; .33=Hardly interested; .67=Quite interested; 1=Very interested)
\end{itemize}

\subsection*{Sample Size}
\begin{table}[ht]
\caption{N by country}  
\centering
\begin{tabular}{rlrlr}
  \hline
 & countryname & n & countryname.1 & n.1 \\ 
  \hline
1 & Austria & 2767 & Greece & 6145 \\ 
  2 & Belgium & 2898 & Hungary & 2506 \\ 
  3 & Bulgaria & 3628 & Iceland & 623 \\ 
  4 & Croatia & 2305 & Ireland & 7143 \\ 
  5 & Cyprus & 2226 & Israel & 6713 \\ 
  6 & Czechia & 2091 & Italy & 1807 \\ 
  7 & Denmark & 154 & Lithuania & 3052 \\ 
  8 & Estonia & 1505 & Luxembourg & 667 \\ 
  9 & Finland & 3438 & Netherlands & 2768 \\ 
  10 & France & 2894 & Norway & 3247 \\ 
  11 & Germany & 5827 & Poland & 5934 \\ 
   \hline
\end{tabular}
\end{table}
\subsection*{Results in Tabular Form}
\begin{table}
\caption{Kinship Attitudes and LGBT Attitudes}
\begin{subtable}{1\textwidth}
\sisetup{table-format=-1.2}   % 2 decimals, leave space for minus sign
\centering
\subcaption[]{Father's Kinship Scores}
\scalebox{.5}{
\input{tables/ess_lgbt_father}
}
\end{subtable}
\begin{subtable}{1\textwidth}
\sisetup{table-format=4.0} % integer values only, up to 4 digits
\subcaption[]{Mother's Kinship Scores}

\centering
\scalebox{.5}{
\input{tables/ess_lgbt_mother}}
\end{subtable}
\end{table}
\FloatBarrier

\begin{table}
\caption{Kinship Attitudes and Gender Attitudes}
\begin{subtable}{1\textwidth}
\sisetup{table-format=-1.2}   % 2 decimals, leave space for minus sign
\centering
\subcaption[]{Father's Kinship Scores}
\scalebox{.5}{
\input{tables/ess_gender_father}
}
\end{subtable}
\begin{subtable}{1\textwidth}
\sisetup{table-format=4.0} % integer values only, up to 4 digits
\subcaption[]{Mother's Kinship Scores}

\centering
\scalebox{.5}{
\input{tables/ess_gender_mother}}
\end{subtable}
\end{table}

\FloatBarrier

\begin{table}
\caption{Kinship Attitudes and Government Responsibility Attitudes}
\begin{subtable}{1\textwidth}
\sisetup{table-format=-1.2}   % 2 decimals, leave space for minus sign
\centering
\subcaption[]{Father's Kinship Scores}
\scalebox{.5}{
\input{tables/ess_govtresp_father}
}
\end{subtable}
\begin{subtable}{1\textwidth}
\sisetup{table-format=4.0} % integer values only, up to 4 digits
\subcaption[]{Mother's Kinship Scores}

\centering
\scalebox{.5}{
\input{tables/ess_govtresp_mother}}
\end{subtable}
\end{table}
\FloatBarrier

\begin{table}
\caption{Kinship Attitudes and Government Benefits Attitudes}
\begin{subtable}{1\textwidth}
\sisetup{table-format=-1.2}   % 2 decimals, leave space for minus sign
\centering
\subcaption[]{Father's Kinship Scores}
\scalebox{.5}{
\input{tables/ess_benfits_father}
}
\end{subtable}
\begin{subtable}{1\textwidth}
\sisetup{table-format=4.0} % integer values only, up to 4 digits
\subcaption[]{Mother's Kinship Scores}

\centering
\scalebox{.5}{
\input{tables/ess_benfits_mother}}
\end{subtable}
\end{table}
\FloatBarrier

\begin{table}
\caption{Kinship Attitudes and Income Differences Attitudes}
\begin{subtable}{1\textwidth}
\sisetup{table-format=-1.2}   % 2 decimals, leave space for minus sign
\centering
\subcaption[]{Father's Kinship Scores}
\scalebox{.5}{
\input{tables/ess_incomediffs_father}
}
\end{subtable}
\begin{subtable}{1\textwidth}
\sisetup{table-format=4.0} % integer values only, up to 4 digits
\subcaption[]{Mother's Kinship Scores}

\centering
\scalebox{.5}{
\input{tables/ess_incomediffs_mother}}
\end{subtable}
\end{table}
\FloatBarrier

\begin{table}[!ht]
\caption{Impact of Ancestral Kinship Strength on NSC}
\begin{subtable}{1\textwidth}
\sisetup{table-format=-1.2}   % 2 decimals, leave space for minus sign
\centering
\subcaption[]{Father's Kinship Scores}
\scalebox{.5}{
\input{tables/ess_nfs_father}
}
\end{subtable}
\begin{subtable}{1\textwidth}
\sisetup{table-format=4.0} % integer values only, up to 4 digits
\subcaption[]{Mother's Kinship Scores}

\centering
\scalebox{.5}{
\input{tables/ess_nfs_mother}}
\end{subtable}
\end{table}
\FloatBarrier
\input{tables/interactioness}
\FloatBarrier


\subsection*{Sensitivity Analyses}
We copy and paste the written output from the sensitivity analyses below, as created by the sensemakr package in R:
\paragraph{Kinship Strength and Gender Attitudes}
nb: Weuse father's kinship score here; analysis using mother's kinship score is almost identical.

-- Partial R2 of the treatment with the outcome: an extreme confounder (orthogonal to the covariates) that explains 100\% of the residual variance of the outcome, would need to explain at least 0.59\% of the residual variance of the treatment to fully account for the observed estimated effect.

-- Robustness Value, q = 1: unobserved confounders (orthogonal to the covariates) that explain more than 7.39\% of the residual variance of both the treatment and the outcome are strong enough to bring the point estimate to 0 (a bias of 100\% of the original estimate). Conversely, unobserved confounders that do not explain more than 7.39\% of the residual variance of both the treatment and the outcome are not strong enough to bring the point estimate to 0.

-- Robustness Value, q = 1, alpha = 0.05: unobserved confounders (orthogonal to the covariates) that explain more than 6.06\% of the residual variance of both the treatment and the outcome are strong enough to bring the estimate to a range where it is no longer 'statistically different' from 0 (a bias of 100\% of the original estimate), at the significance level of alpha = 0.05. Conversely, unobserved confounders that do not explain more than 6.06\% of the residual variance of both the treatment and the outcome are not strong enough to bring the estimate to a range where it is no longer 'statistically different' from 0, at the significance level of alpha = 0.05.

\paragraph{Kinship Strength and LGBT Attitudes}
-- Partial R2 of the treatment with the outcome: an extreme confounder (orthogonal to the covariates) that explains 100\% of the residual variance of the outcome, would need to explain at least 1.16\% of the residual variance of the treatment to fully account for the observed estimated effect.

-- Robustness Value, q = 1: unobserved confounders (orthogonal to the covariates) that explain more than 10.28\% of the residual variance of both the treatment and the outcome are strong enough to bring the point estimate to 0 (a bias of 100\% of the original estimate). Conversely, unobserved confounders that do not explain more than 10.28\% of the residual variance of both the treatment and the outcome are not strong enough to bring the point estimate to 0.

-- Robustness Value, q = 1, alpha = 0.05: unobserved confounders (orthogonal to the covariates) that explain more than 9.27\% of the residual variance of both the treatment and the outcome are strong enough to bring the estimate to a range where it is no longer 'statistically different' from 0 (a bias of 100\% of the original estimate), at the significance level of alpha = 0.05. Conversely, unobserved confounders that do not explain more than 9.27\% of the residual variance of both the treatment and the outcome are not strong enough to bring the estimate to a range where it is no longer 'statistically different' from 0, at the significance level of alpha = 0.05.
\paragraph{Kinship Strength and Government Benefits}
-- Partial R2 of the treatment with the outcome: an extreme confounder (orthogonal to the covariates) that explains 100\% of the residual variance of the outcome, would need to explain at least 0.11\% of the residual variance of the treatment to fully account for the observed estimated effect.

-- Robustness Value, q = 1: unobserved confounders (orthogonal to the covariates) that explain more than 3.27\% of the residual variance of both the treatment and the outcome are strong enough to bring the point estimate to 0 (a bias of 100\% of the original estimate). Conversely, unobserved confounders that do not explain more than 3.27\% of the residual variance of both the treatment and the outcome are not strong enough to bring the point estimate to 0.

-- Robustness Value, q = 1, alpha = 0.05: unobserved confounders (orthogonal to the covariates) that explain more than 1.54\% of the residual variance of both the treatment and the outcome are strong enough to bring the estimate to a range where it is no longer 'statistically different' from 0 (a bias of 100\% of the original estimate), at the significance level of alpha = 0.05. Conversely, unobserved confounders that do not explain more than 1.54\% of the residual variance of both the treatment and the outcome are not strong enough to bring the estimate to a range where it is no longer 'statistically different' from 0, at the significance level of alpha = 0.05.
\paragraph{Kinship Strength and Income Differences}
-- Partial R2 of the treatment with the outcome: an extreme confounder (orthogonal to the covariates) that explains 100\% of the residual variance of the outcome, would need to explain at least 0.03\% of the residual variance of the treatment to fully account for the observed estimated effect.

-- Robustness Value, q = 1: unobserved confounders (orthogonal to the covariates) that explain more than 1.58\% of the residual variance of both the treatment and the outcome are strong enough to bring the point estimate to 0 (a bias of 100\% of the original estimate). Conversely, unobserved confounders that do not explain more than 1.58\% of the residual variance of both the treatment and the outcome are not strong enough to bring the point estimate to 0.

-- Robustness Value, q = 1, alpha = 0.05: unobserved confounders (orthogonal to the covariates) that explain more than 0.48\% of the residual variance of both the treatment and the outcome are strong enough to bring the estimate to a range where it is no longer 'statistically different' from 0 (a bias of 100\% of the original estimate), at the significance level of alpha = 0.05. Conversely, unobserved confounders that do not explain more than 0.48\% of the residual variance of both the treatment and the outcome are not strong enough to bring the estimate to a range where it is no longer 'statistically different' from 0, at the significance level of alpha = 0.05.


\section*{Study 2}
\subsection*{Description of LGBT measure}
For 193, the ILGA records if homosexuality is legal, if there are constitutional protections for LGBT people, if there are broad legal protections against discrimination, if there are legal protection against employment discrimination, if there is hate crime legislation, if there is a ban of conversion therapy, if the country allows same-sex marriage, if the country allows same-sex unions, if the country allows joint adoption by LGBT couples, if the country allows second parent adoption. 

Wecombined the two adoption laws into one dichotomous variable (i.e., if the country allows either one or both types of adoptions) and the civil union/marriage laws into one dichotomous marriage (i.e., if the country allows either one or both civil unions or marriages). Wethen summed across the 9 dichotomous measures and rescaled to lie between 0 and 1. 

\paragraph{Economic Attitudes}
-- Partial R2 of the treatment with the outcome: an extreme confounder (orthogonal to the covariates) that explains 100\% of the residual variance of the outcome, would need to explain at least 0.21\% of the residual variance of the treatment to fully account for the observed estimated effect.

-- Robustness Value, q = 1: unobserved confounders (orthogonal to the covariates) that explain more than 4.48\% of the residual variance of both the treatment and the outcome are strong enough to bring the point estimate to 0 (a bias of 100\% of the original estimate). Conversely, unobserved confounders that do not explain more than 4.48\% of the residual variance of both the treatment and the outcome are not strong enough to bring the point estimate to 0.

-- Robustness Value, q = 1, alpha = 0.05: unobserved confounders (orthogonal to the covariates) that explain more than 3.14\% of the residual variance of both the treatment and the outcome are strong enough to bring the estimate to a range where it is no longer 'statistically different' from 0 (a bias of 100\% of the original estimate), at the significance level of alpha = 0.05. Conversely, unobserved confounders that do not explain more than 3.14\% of the residual variance of both the treatment and the outcome are not strong enough to bring the estimate to a range where it is no longer 'statistically different' from 0, at the significance level of alpha = 0.05.

\subsection*{Sensitivity Analyses}
We copy and paste the written output from the sensitivity analyses below, as created by the sensemakr package in R.

\paragraph{LGBT Laws}

Unobserved confounders (orthogonal to the covariates) that explain more than 14.87\% of the residual variance of both the treatment and the outcome are enough to reduce the absolute value of the effect size by 100\%. Conversely, unobserved confounders that do not explain more than 14.87\% of the residual variance of both the treatment and the outcome are not strong enough to reduce the absolute value of the effect size by 100\% .

Unobserved confounders (orthogonal to the covariates) that explain more than 0.3\% of the residual variance of both the treatment and the outcome are enough to reduce the absolute value of the effect size by 100\% at the significance level of alpha = 0.05 . Conversely, unobserved confounders that do not explain more than 0.3\% of the residual variance of both the treatment and the outcome are not strong enough to reduce the absolute value of the effect size by 100\% at the significance level of alpha = 0.05.

An extreme confounder (orthogonal to the covariates) that explains 100\% of the residual variance of the outcome, would need to explain at least 2.53\% of the residual variance of the treatment to fully account for the observed estimated effect.

\paragraph{Social Expenditures}
Unobserved confounders (orthogonal to the covariates) that explain more than 16.69\% of the residual variance of both the treatment and the outcome are enough to reduce the absolute value of the effect size by 100\%. Conversely, unobserved confounders that do not explain more than 16.69\% of the residual variance of both the treatment and the outcome are not strong enough to reduce the absolute value of the effect size by 100\% .

Unobserved confounders (orthogonal to the covariates) that explain more than 0\% of the residual variance of both the treatment and the outcome are enough to reduce the absolute value of the effect size by 100\% at the significance level of alpha = 0.05 . Conversely, unobserved confounders that do not explain more than 0\% of the residual variance of both the treatment and the outcome are not strong enough to reduce the absolute value of the effect size by 100\% at the significance level of alpha = 0.05 .

An extreme confounder (orthogonal to the covariates) that explains 100\% of the residual variance of the outcome, would need to explain at least 3.24\% of the residual variance of the treatment to fully account for the observed estimated effect.

\section*{Study 3}

\subsection*{Full Results}

\input{tables/wvs_final_table}
\newpage
\subsection*{Item Wording for Key Variables}

\subsubsection*{Cultural Variables}
\paragraph{Homosexuality} ``Please tell me for each of the following whether you think it can always be justified, never be justified, or something in between, using this card. – Homosexuality''  (Coded: 0 ``always'' to 1 = ``never'')

\paragraph{Abortion} ``Please tell me for each of the following whether you think it can always be justified, never be justified, or something in between, using this card. – Abortion'' (Coded: 0 ``always'' to 1 = ``never'')

\subsubsection*{Economic Variables}
\paragraph{Equalize Incomes-Incentives for Individual Effort} ``On this card you see a number of opposite views on various issues. How would you place your views on this scale?''  (Coded: 0 = ``incomes should be made more equal'' to 1 ``there should be greater incentives for individual effort'')

\paragraph{Individual-State Responsibility for Providing} ``On this card you see a number of opposite views on various issues. How would you place your views on this scale?''  (Coded: 0 ``the state should take more responsibility to ensure that everyone is provided for'' to 1 = ``individuals should take more responsibility for providing for themselves'')
\subsubsection*{Political Engagement Variables}
\paragraph{Important in your life: Politics} ``Please say, for each of the following, how important it is in your life – Politics''  (Coded: 1=very important; $.\overline{6}$=quite important; $.\overline{3}$=not important; 0=not at all important)

\paragraph{Interest in Politics} ``How interested
would you say you are in politics''  (Coded: 1=very interested; $.\overline{6}$=somewhat interested; $.\overline{3}$=not very interested; 0=not at all interested)
\subsubsection*{Authoritarianism Variables}
\paragraph{Good Manners for children} ``Here is a list of qualities which children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five? – Good manners''  (Coded: Not Authoritarian=0; Authoritarian=1)

\paragraph{Independence for children} ``Here is a list of qualities which children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five? – Independence''  (Coded: Authoritarian=0; Not Authoritarian=1)

\paragraph{Obedience for children} ``Here is a list of qualities which children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five? – Obedience''  (Coded: Not Authoritarian=0; Authoritarian=1)

\paragraph{Imagination for children} ``Here is a list of qualities which children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five? – Imagination''  (Coded: Authoritarian=0; Not Authoritarian=1)


\subsection*{Sample Size and Ethnic Groups per Country}

\begin{multicols}{2}
\begin{itemize}
    \item Algeria: 2 ethnicities; n = 285
    \item Azerbaijan: 5 ethnicities; n = 121
    \item Bangladesh: 2 ethnicities; n = 1148
    \item Armenia: 4 ethnicities; n = 2673
    \item Bulgaria: 2 ethnicities; n = 722
    \item Belarus: 3 ethnicities; n = 2887
    \item China: 2 ethnicities; n = 26
    \item Ethiopia: 6 ethnicities; n = 1654
    \item Ghana: 13 ethnicities; n = 2555
    \item Indonesia: 8 ethnicities; n = 3269
    \item Iran: 4 ethnicities; n = 1172
    \item Iraq: 2 ethnicities; n = 371
    \item Kazakhstan: 17 ethnicities; n = 2356
    \item Jordan: 4 ethnicities; n = 688
    \item Kyrgyzstan: 8 ethnicities; n = 139
    \item Latvia: 7 ethnicities; n = 940
    \item Macao: 2 ethnicities; n = 790
    \item Malaysia: 5 ethnicities; n = 1917
    \item Moldova: 5 ethnicities; n = 1737
    \item Nigeria: 25 ethnicities; n = 2386
    \item Pakistan: 3 ethnicities; n = 2456
    \item Philippines: 7 ethnicities; n = 1516
    \item Russia: 8 ethnicities; n = 2989
    \item Saudi Arabia: 4 ethnicities; n = 292
    \item Serbia: 2 ethnicities; n = 916
    \item Vietnam: 3 ethnicities; n = 50
    \item Zimbabwe: 2 ethnicities; n = 1143
    \item Thailand: 3 ethnicities; n = 2484
    \item Ukraine: 11 ethnicities; n = 1473
    \item North Macedonia: 2 ethnicities; n = 72
    \item Uzbekistan: 4 ethnicities; n = 86
\end{itemize}
\end{multicols}

\subsection*{Results by Each Cultural and Economic Attitude Question}

\input{tables/wvs_final_culture}
\input{tables/wvs_final_econ}

\subsection*{Sensitivity Analyses}
We copy and paste the written output from the sensitivity analyses below, as created by the sensemakr package in R.

An extreme confounder (orthogonal to the covariates) that explains 100\% of the residual variance of the outcome, would need to explain at least 2.07\% of the residual variance of the treatment to fully account for the observed estimated effect.

Unobserved confounders (orthogonal to the covariates) that explain more than 13.53\% of the residual variance of both the treatment and the outcome are strong enough to bring the point estimate to 0 (a bias of 100\% of the original estimate). Conversely, unobserved confounders that do not explain more than 13.53\% of the residual variance of both the treatment and the outcome are not strong enough to bring the point estimate to 0.

Unobserved confounders (orthogonal to the covariates) that explain more than 12.7\% of the residual variance of both the treatment and the outcome are strong enough to bring the estimate to a range where it is no longer 'statistically different' from 0 (a bias of 100\% of the original estimate), at the significance level of alpha = 0.05. Conversely, unobserved confounders that do not explain more than 12.7\% of the residual variance of both the treatment and the outcome are not strong enough to bring the estimate to a range where it is no longer 'statistically different' from 0, at the significance level of alpha = 0.05.

\subsection*{Models Predicting Authoritarianism}

\input{tables/wvs_final_aut}

