Does Female Suffrage Increase Public Support for Government Spending

Does Female Suﬀrage Increase Public Support for Government Spending? Evidence from Swiss Ballots

Katharina E. Hofer ∗ University of St.Gallen

November 30, 2016

Abstract

Do men and women have distinct preferences over the size of governments, as previous literature seems to suggest? To answer this question, I exploit Swiss referendums concerning the federal government’s authorization to levy taxes. A comparison of two similar referendums before and after the female suffrage extension in 1971 reveals that women did not vote more liberally on fiscal issues than men. A mediation analysis allows to evaluate the influence of economic mediators with large gender gaps like employment and income on gender preferences, using post-ballot surveys after comparable votes taken between 1981 and 2004. Women vote more conservatively than men, and mediators have little explanatory power for the gender preference gap. Intrinsic or unobserved gender differences prove to be the driving force for preferences. The results help explain why female suffrage has led to lower government spending in many countries, despite a poorer median voter who could have been expected to prefer more redistribution and more public spending.

Keywords: Female suﬀrage; Gender preference gaps; Government spending; Direct democracy; Mediation JEL Classiﬁcation Numbers: J16, H10, D72

∗Katharina E. Hofer, SEW-HSG University of St.Gallen, Switzerland, [email protected]. For valuable comments, I thank Monika Bütler, Patricia Funk, Martin Huber, and Alois Stutzer, as well as participants at the Meeting of the European Public Choice Society (2013, Zurich, Switzerland), Spring Meeting of Young Economists (2013, Aarhus, Denmark), European Political Science Association Annual Meeting (2013, Barcelona, Spain), CESifo Venice Summer Institute (2013, Venice, Italy), European Economic Association Congress (2013, Gothenburg, Swe- den), Jahrestagung Verein fürSocialpolitik (2013, Düsseldorf,Germany), Swiss Society for Economics and Statistics (2015, Basel, Switzerland), Workshop on Direct Democracy (2016, Berlin, Germany), Workshop on Uncovering Causal Mechanisms (2016, Munich, Germany) and seminar participants at the University of St.Gallen (2016). I appreciate helpful input by the following discussants at conferences and workshops: Luna Bellani, Andreas Bernecker, Krisztina Kis-Katos, and Silke Ubelmesser.¨ 1 Introduction

The relation between female suffrage and fiscal outcomes has been investigated extensively. Several studies document an expansion of government activities once women were given the right to vote. Analyzing historical data from the U.S., Lott and Kenny (1999) find that the introduction of female suffrage increased government spending and revenues as gradually more women made use of their voting rights. Similarly, Abrams and Settle (1999) find that Swiss federal government spending grew by about 12% after the introduction of female suffrage in 1971. Bertocchi (2011) provides empirical support that allowing women to vote increases government spending, however, only in non-Catholic countries in which the cost of disenfranchisement is relatively high. While this research supports the view of larger governments following the extension of suffrage to women, the general account in the literature is mixed at best. A negative connection between female voting and fiscal outcomes has been shown by a stream of the literature. Stutzer and Kienast (2005) who use the variation in the timing of female suffrage in Swiss cantons find that government expenditures at cantonal level decreased after enfranchising women. In a similar vein, Krogstrup and Wälti(2011) observe reduced cantonal budget deficits after female suffrage. Aidt and Jensen (2009) find lower government spending and tax revenues as shares of GDP for western European countries after women got voting rights. Aidt, Dutta, and Loukoianova (2006) provide only weak evidence for any impact of female suffrage on government spending. A common explanation for the expansion of governments following female franchise comes from Meltzer and Richard (1981). They famously hypothesized that enfranchising constituents such that the new decisive voter is poorer than before, increases the demand for redistribution and thus the size of government. Since women have on average lower wages than men, this theory predicts stronger female than male preferences for government spending.1 An alternative explanation for a positive effect concentrates on women’s role as mothers. In marriage, husbands tend to earn more and transfer income to their wives who specialize in household production and child care (Becker 1974). While income differences and specialization are internalized in marriage, the possibility of divorce, however, makes women economically vulnerable if rendered to solely care for the children. Historically increasing divorce rates then lead to higher demand for insurance from the state in form of government spending (Edlund and Pande 2002). Explaining the result of decreasing government spending turns out to be more challenging. Potentially, women have stronger preferences for only some of the budgetary items but feel less strongly about other government spending categories. For example, Aidt, Dutta, and Loukoianova (2006) find that female voting increased spending on health, welfare and education. Aidt and Dallal (2008) report rising social spending out of GDP for six western European countries. Miller (2008) documents rising levels of public health expenditure to enhance child welfare that can be

1E.g., in Switzerland women earned 51% of the male hourly wage in 1930, 66% in 1971 and 67% in 1995, which is a seizable gender wage gap despite its tendency to decrease over time (Swiss Economic and Social History Online Database).

1 attributed to the enfranchisement of women. When women started to vote, many government budgets were still dominated by military or infrastructure expenditure, while items women care for were still underrepresented. Aidt and Jensen (2009) argue that the immediate effect of female franchise might differ from the long-run effect if it took time for women to exercise their franchise.2 Or women take fiscally more conservative decisions due to their preferences which might differ from what men want.3 The wealth of alternative – and sometimes opposing – interpretations demonstrates the need for a more in-depth analysis of the causal relation between female suffrage and government size. The goal of this paper is to determine whether differences in gender preferences for government spending exist and what their determinants are. This could help explain why fiscal variables changed when women received voting rights. In particular, it is interesting to investigate whether such a gender preference gap is due to “being female” or can be explained by observable economic differences between men and women. For example, Husted and Kenny (1997) find that enfranchising poorer men by the repeal of literacy tests and poll taxes in the U.S. has led to higher welfare spending, indicating that potentially not only the gender of the new franchise matters. I exploit the voting outcomes of referendums laying down the constitutional basis for the Swiss government to levy income, capital and goods turnover taxes. They are a measure of preferences for the federal government’s spending: without popular approval at the ballot, the Swiss government would be deprived of its authorization to levy federal taxes which are crucial for financing government expenditures. While taxation of income and consumption is commonly found all over the world, it is a Swiss particularity that voters even nowadays need to accept it’s legislative basis in a referendum every few years. Therefore, over time a large number of comparable votes on the federal financial system exists. Economic gender gaps were pronounced in terms of employment and income at the time women received voting rights. If they were a driving force behind the gender preference gap, this gap should be expected to decrease over time simultaneously with closing socioeconomic gender gaps. If in contrast time-invariant differences between men and women are relevant for spending preferences, the preference gap should more likely persist over time. Surveys did not exist when women received voting rights, calling for a different approach. The average gender preference gap at the time of female suffrage extension is estimated by using the voting outcomes of two such very similar referendums. The first one took place in November 1970 shortly before the extension of suffrage to women in 1971, and the other one directly thereafter. Since the two ballots took place under two distinct suffrage regimes, changes in voting outcomes can be attributed to changes in the electorate. Using voting data at municipal level, I estimate female support by relating changes in the number of voters to changes in the number of yes votes. I refine the estimation by taking into account the share of women in municipal populations.

2Aidt and Dallal (2008) and Lott and Kenny (1999) provide some support for this argument. 3Stutzer and Kienast (2005) provide an institutional interpretation for lower spending growth in Swiss cantons: the eﬀect might stem from the existence of direct democracy instruments in Swiss cantons for which research shows that they are likely to lead to smaller government size (e.g., Feld and Matsusaka (2003) provide some evidence).

2 Though very similar, the two ballot propositions are not identical but differ in so far that the second proposition included a time limit. Even in case of acceptance the second proposition would have required a new referendum after 10 years whereas the first one did not. Traditionally, permanent federal financial orders have been rejected in Switzerland, suggesting that the inclusion of a time limit is an important factor influencing voter decisions. I use voting results from a similar ballot in 1963 under the men-only suffrage to proxy for the difference in the content of the two ballots which might have induced some men to change their vote choice between the two ballots. I provide extensive evidence for the validation of this approximation. Results show that women were not more likely to support the referendums measure than men. In some specifications women even vote fiscally more conservatively. Notwithstanding seizable economic gender gaps which would have predicted a positive gender preference gap, women were as (or even less) supportive of the fiscal referendum than men. Potentially, individual economic factors are less important determinants for fiscal preferences. In theory, it is also possible that economic and intrinsic gender gaps offset each other, leading to a zero or insignificant gender gap. A mediation analysis allows to disentangle the total gender gap into its mediated and residual components using individual-level data from randomized post-ballot surveys after four comparable referendums about the federal fiscal order in 1981, 1991, 1993 and 2004. As argued above, being female is generally related to lower employment and income, which in turn affects preferences for government spending. These variables are called mediators. At the same time, other potentially intrinsically female factors might influence gender support. To decompose both potential channels, I semi-parametrically estimate the direct and indirect effects of being female in a mediation framework. Estimates are based on inverse propensity score weighting, which allows to control for potentially confounding factors affecting both mediators and the outcome variable (Huber 2014, 2015; Imai, Keele, and Yamamoto 2010). The mediation results substantiate that women are on average less likely to support the taxation propositions even well after the original extension of voting rights to women. Surprisingly, there is no evidence for a mediated effect through employment or income at any point in time. Economic gender gaps cannot explain different gender preference neither at times when these gaps were still considerable nor later on. The direct or residual effect (i.e., unexplained by the mediator) in contrast is important for explaining the gender preference gap. This result fits the persistently non-positive difference in gender support for the referendums. Candidate direct effects are gender differences in time preferences or altruism. An extensive sensitivity analysis validates that relatively large departures from the identifying assumptions could be accepted without changing the conclusions. This paper adds to the literature on the effects of franchise extension on government spending. The main contribution is to provide a direct way of eliciting the gender preference gap for government spending from results of referendum votes instead of relating suffrage to government spending. Direct democratic votes are better suited to make statements about preferences than macroeco- nomic outcomes since they involve decision-making by the franchise. Moreover, preferences can be measured at the time of a vote, whereas it takes time until female preferences start materializing and lead to changes in spending. My approach is related to Funk and Gathmann (2015) who

3 utilize post-ballot polls in Switzerland for the analysis of gender preference gaps regarding specific spending items. This paper extends research on consequences of female representation for policy making (e.g., Chattopadhyay and Duflo 2004). The mechanism behind the relation between female voting and aggregate fiscal outcomes potentially runs through changed politicians’ behavior. They either adjust their policies, or women elect new politicians (possibly female ones).4 E.g., Svaleryd (2009) finds that higher shares of women in Swedish local councils lead to more expenditures on child-related items compared to spending for the old. The advantage of using referendum outcomes is that voters are the ultimate decision-makers in the direct democratic process. Their preferences lead to immediate policy consequences. It allows me to go a step further and investigate the causal mechanism behind gender preferences, in particular, the role of economic discrepancies for fiscal preferences which was traditionally expected in the literature. The remainder of the paper is organized as follows. Section 2 provides information on the institutional setting. Section 3 contains the empirical framework, data and results for the estimation of the average gender effect around the enfranchisement of women in Switzerland. In a similar manner, Section 4 deals with estimating the direct and indirect gender effect. The concluding remarks are in Section 5.

2 Institutional Setup

Beginning with the foundation of the Swiss state in 1848, duties were the main revenue source at federal level.5 It took until the First World War, collapsing international trade and growing state expenditure before an income tax was introduced. But income was only taxed in times of need such as during the war, or when budgetary problems got out of hand in the 1930ies. In 1941 a defense tax, the Wehrsteuer (an income and capital tax; referred to as direct federal tax in what follows), was introduced to ﬁnance growing military expenditure. In the same year, a goods turnover tax (Warenumsatzsteuer) was introduced on goods but not on services, resembling a value-added tax (Stockar 2007). Both taxes were a product of an increased need of state revenue during war and emergency times. After the Second World War both taxes remained in place. Besides revenues from duties, the goods turnover tax and the direct federal tax were the most important revenue sources for the Swiss government. In the 1960ies, roughly 10 to 15% of revenues came from the direct federal tax, and around 25% from the goods turnover tax. Revenues from duties then dropped by 10 percentage points (Swiss Statistical Oﬃce 1973). The main reason for the decline was the increasing international integration and the general trend to reducing duties in connection with the World

4Lott and Kenny (1999) also look at the politicians’ voting behavior in the U.S. senate and find that after the introduction of female suffrage politicians voted more liberally. However, they do not show that women were more likely to vote for liberal politicians and did so because they desired higher government spending. 5Information about the history of the Swiss Federal Tax are from Grütter(1968). Oechslin (1967) gives an overview of the overall development of the Swiss tax system.

4 Trade Organization’s rounds (Federal Announcement 1969 II, 754).6 The main items of expenditure at federal level were defense and the social security system which together accounted for nearly 50% of total expenses. Other growing and new expenditure categories were infrastructure and energy, as well as culture and sports. Agricultural expenditure remained relatively stable at around 10% of total expenditure (Swiss Statistical Oﬃce 1974). Due to the initially provisional and time-restricted character of taxation it lacked a permanent constitutional basis, without which the federal government would not have had the right to continue levying federal taxes. In Switzerland, all changes to the constitution are subject to a mandatory referendum (Linder 2007). For a referendum to be successful, the majority of voters and a majority of cantons is required. Table 1 provides an overview of the wealth of ballots concerning the federal government’s admission to ﬁle taxes. Next to the date and outcome of the vote, it reports the time frame of the ballot proposition. Either the constitutional article was time limited, triggering the next referendum within several years. Alternatively the proposition was for a permanent constitutional article. All time-limited propositions were accepted by voters, whereas the permanent orders always failed at ballot. Even nowadays, it thus remains a Swiss particularity that citizens

Table 1 Chronology of Swiss Federal Financial Order Referendums

Ballot date Time limit Decision % yes votes Accepting cantons 06.12.1953 permanent rejected 42.0 3 24.10.1954 1955 - 1958 approved 70.0 21 11.05.1958 1959 - 1964 approved 54.6 17 1/2 08.12.1963 1964 - 1974 approved 77.6 22 15.11.1970 permanent rejected 55.4 10 06.06.1971 1972-1982 approved 72.7 22 12.06.1977 permanent rejected 40.5 1 20.05.1979 permanent rejected 34.6 0 29.11.1981 1982-1994 approved 69.0 23 02.06.1991 permanent rejected 45.6 2 1/2 28.11.1993 1994-2006 approved 66.7 22 28.11.2004 2006-2020 approved 73.8 22

Data about acceptance are available on the homepage of the Swiss Federal Chan- cellery, http://www.bk.admin.ch. The time limits are from Federal Announce- ments published by the Swiss Federal Archive (cf. Appendix A). For approval, the referendum needs more than half of total votes and at least 13 accepting cantons. In 1971, 19 cantons are “full” cantons while six cantons count only as “half” cantons. Since 1978, there are 20 “full” cantons, and still six “half” cantons. Votes from 1970, 1971, 1981, 1991, 1993, and 2004 are used in the empirical part.

6All Federal Announcements (Bundesblatt) are collected by the Swiss Federal Archive (Schweizerisches Bundesarchiv) and published by the Federal Chancellery (Bundeskanzlei). A detailed list and possibility of online access is described in the Appendix A.

5 have to approve the federal financial order. In 1953 the parliament issued the very first proposition for permanently including the direct federal tax and the goods turnover tax in a constitutional article. It was rejected. Only one year later, a similar proposition but this time with a limit of four years was put to the vote, and eventually approved by a majority of voters. It was followed by another temporary financial order from 1959 to 1964,7 and extended by further ten years with some minor changes in 1963 (Federal Announcement 1962 I, 997). The first of the two ballots at the core of this paper’s analysis took place on 15 November, 1970, and the second referendum with a new franchise on 6 June, 1971: Switzerland was the last European country to grant women voting rights at federal level on 7 February, 1971. It came into force on 16 March, 1971 as a result of a century-long fight for women’s rights. Particularly in the aftermath of both world wars when democratization was spreading all across Europe, Swiss women were demanding suffrage more intensively. They received support from male politicians who recognized that the women’s position in society had changed to a more active role in public live and private employment (Ruckstuhl 1986). However, in Switzerland female suffrage could only be brought about by a constitutional amendment, which required the male population to hold a vote on extending the franchise. While at a first ballot in 1959 female suffrage was rejected with 66.9% of the male votes,8 a second run in 1971 saw the majority of voters and majority of cantons accepting the constitutional amendment. The next paragraphs describe the propositions on government spending from 1970 / 1971 and 1981-2004 in more detail.

Ballot proposition 1: 15 November 1970 The government and parliament proposed to discard the time limit from the constitution in the “Federal Enactment about the Amendment of the Federation’s Financial Order”9 (Federal An- nouncement 1969 II, 749). Facing a big budget deﬁcit, income, capital and goods turnover taxes would be increased and old rebates reduced in the 1970 proposition. In more detail, the tax burden would be shifted from the direct income tax to the indirect goods turnover tax such that revenue from the goods turnover tax would increase considerably and revenue from income taxes would stay roughly constant. The enactment encompassed higher goods turnover taxes for retailers (from 3.6 to 4 percent), and for wholesalers (from 5.4 to 6 percent). The income tax set in progressively at an annual income of 8,500 Swiss Francs after deductions (7,700 Swiss Francs before). It allowed for deductions for married couples (2,500 Swiss Francs), children under 18 years and dependents (1,200 Swiss Francs) (Federal Announcement 1970 II, 3). Regarding the income tax, high income households would be worse oﬀ with the new regulation than low income households because of

7The comparably low acceptance rate of this financial order with time limit compared to other time-limited proposition is most likely due to a heated debate of the large interest groups (Bolliger 2010). 8Only three francophone cantons, Geneva (60.0%), Neuenburg (52.2%), and Waadt (51.3%) had a majority favor- ing universal suffrage. They were also the first three cantons to introduce universal cantonal suffrage which is independent of federal voting rights in Switzerland. 9The original German title is “Bundesbeschluss über die Anderung¨ der Finanzordnung des Bundes”.

6 a more progressive system. Married couples or families with many children would be better off compared with the old regulation. The government argued that an increase in goods turnover taxes to generate state revenue was the preferable revenue source: it was not a typical consumption tax because of various exemptions for goods of daily use like food. It mainly taxed investment goods purchased by firms and the government, in addition to goods like alcohol, tobacco, and clothing purchased by households (Federal Announcement 1969 II, 778). Though presumably the biggest load would be paid by enterprises, there seemed to be a general uncertainty about who would carry the burden of the higher goods turnover tax. Critics of the proposition mostly pointed to an unsatisfactory regulation concerning the Swiss cantons (AnnéePolitique Suisse 2012). In particular it lacked a clear division of revenue and expenditures between the federal government and the cantons because direct income taxes were an important revenue source for cantons and municipalities (Federal Announcement 1969 II, 773). All major parties, associations and unions recommended their voters to accept the proposition. Exceptions were the small Liberal Party of Switzerland (LPS), and the Labor Party (PdA) who opposed the proposition for not being progressive enough (AnnéePolitique Suisse 2012). These almost unanimously positive voting recommendations indicate the importance of the issue at stake. On 15 November 1970 the Swiss voters – which was the male eligible population at that point – rejected the proposition in a mandatory referendum. Though 55.4% of the voters were in favor the proposition, it failed to accomplish a majority of cantons: in 13 of 22 cantons the approval rate was below 50 percent. Figure 1 shows the approving (white) and rejecting (grey) cantons. The rejecting cantons were mainly concentrated in rural, German-speaking areas.

Figure 1: Cantonal Approval for Ballot 1 (15 November, 1970)

Note: Cantonal Approval Rates for Fiscal Financial Referendum for Ballot 1 (15 November 1970). Accepting (white) and rejecting (grey) cantons. Based on swissvotes.ch, a project of the Institute of Political Science at the University of Bern, Switzerland, and the Ann´eePolitique Suisse.

7 Ballot proposition 2: 6 June, 1971 The Swiss government immediately prepared a new proposition10 to secure the flow of government revenues (Federal Announcement 1970 II, 1581). In the major parts, the new proposition was identical to the one from 1970.11 The most significant change was the inclusion of a time limit of 10 years (Federal Announcement 1971 I, 487). Even in case of approval at the polls, the federal financial order had to be voted upon again in 1980 at the latest. As a further change, income tax ceilings of 9.5 percent for natural persons and 8 percent for legal persons were included. The income tax schedule became slightly more progressive and started to tax individuals at incomes after deductions of 9,000 Swiss Francs. These measures were taken to account for price inflation. As in the first proposition, the parties and associations almost unanimously asked the voters to accept the proposition in their voting recommendations. Only the Labor Party (PdA), the Swiss Evangelic Party (EVP), and the Alliance of Independents (LdU) were opposed to the proposition because it disregarded deductions for working wives and was not progressive enough (Année Politique Suisse 2012). The ballot proposition was accepted by a majority of voters (72.7%) and all cantons – with universal suffrage.

Ballot propositions in 1981, 1991, 1993, and 2004 The votes in 1981, 1991, 1993, and 2004 are used to decompose the total gender preference gap into its mediated and residual components. The fiscal order approved by voters in 1971 was about to phase out in 1982. Both referendums in 1977 and 1979 which tried to change the tax system from the goods turnover tax as explained above to a permanent fiscal order with value added tax (VAT) similar to other European countries were rejected at ballot. The financial order voted in 1981 and limited to the years 1982 to 1994 was therefore essentially a continuation of the old financial order from 1971. Minor changes included reliefs in the direct income tax which had to be compensated by increases in the goods turnover tax (Federal Announcement 1981 I, 20). In 1991 government and parliament tried again to switch from goods turnover taxes to the VAT. Again, the proposition was rejected at ballot. It was argued that the proposition might have been a too complex package, which led to the rejection. Two years later a new financial order in a less complex proposition finally brought about the change to the VAT system, and secured the fiscal fundament for the federal state until 2006 (AnnéePolitique Suisse 2012). The 2004 vote secured the government’s tax power until 2020.

10“Federal Enactment about the Continuation of the Federation’s Financial Order”. Original title in German is “Bundesbeschluss ¨uber die Weiterf¨uhrung der Finanzordnung des Bundes” 11Comparing the precise wording of both original legislative texts shows that they are almost identical in all paragraphs.

8 3 Average Gender Preference Gap for Government Spending

3.1 Baseline Empirical Framework

The aim is to estimate approval for government spending by gender at the time of franchise extension. I first introduce the notation. A finite population is divided into two groups G, with the realizations g = 1 (female) and g = 0 (male). For each individual, outcome Y (g) is observed and takes on value 1 for supporters of government spending, and 0 else. It is a function of gender. Relating the outcomes to referendum votes, supporters vote “yes” whereas opponents vote “no”. The total effect τ of being female on the probability of supporting government spending is the average treatment effect (ATE). It is the difference in expected support for government spending between women and men. When averaging over the entire population, it can be written in the following way using expected values:

τ = E[Y (1)] − E[Y (0)] (1)

Positive values of (1) point to stronger female than male preferences for government spending, and the opposite for negative values. If the eﬀect were zero, no gender preference gap existed. The empirical counterpart of E[Y (1)] is the female acceptance rate and E[Y (0)] the male acceptance rate. Since the observed total acceptance rate can be written as the gender turnout-weighted sum of female and male acceptance rates, it is enough to estimate the acceptance rate of women from which the ATE follows immediately.

3.2 Estimation Strategy: Identifying the Total Gender Eﬀect

To estimate the female acceptance rate, I follow the idea of Lott and Kenny (1999) who recognize that the effect of female suffrage on voting outcomes depends on how intensely women make use of their voting rights. The intuition is that changes in voting outcomes can be explained by changes in the electorate’s composition. I use the voting results of the two Swiss referendum ballots in 1970 and 1971. As explained above they are very similar content-wise, but women were only allowed to vote on the second date. What makes the analysis more complicated is the fact that the ballots are not entirely identical. They mainly differ with regard to a time limit of ten years in the second proposition. As explained above, propositions regarding the federal financial order including time limits have also been approved by the male voting population, like in 1954, 1958, and 1963. Some men can consequently be expected to change from voting no to yes instead. To account for the change in content, I use voting results of the related vote in 1963 which also included a time limit and was accepted by a large margin (cf. Table 1). Denote the true number of men who have switched from voting no to voting yes in municipality m by ∆menm. It is approximated by a variable ∆mend m and the error term m. ∆menm is the difference between the number of yes votes in the years 1963 and 1970 normalized by the growth rate of the number of voters during the seven year difference. Then the

9 true number of men changing their voting behavior can be written in the following way.

∆menm = ∆mend m + m (2) votersm1970 = yesm1963 ∗ − yesm1970 + m (3) votersm1963

Let acceptancef be the female acceptance rate for the second ballot proposition. For notational ease, denote the year 1970 by t = 1, and 1971 by t = 2. Deﬁne the change in the number of yes votes between the ballots in municipality m as ∆yesm ≡ yesm2 − yesm1, and the change in the number of valid votes as ∆votersm ≡ votersm2 − votersm1. The female acceptance rate can be written in the following form:

∆yesm − ∆menm acceptancef = (4) ∆votersm Female acceptance is the change in the number of yes votes relative to the change in the electorate, net of the change in male voting behavior where ∆menm is deﬁned in (3). Equation (4) can be easily transformed to

∆yesm − ∆menm = acceptancef ∗ ∆votersm (5)

In the last step, I use Equation (3) to account for the approximation of men changing their voting behavior. This leads directly to the baseline estimation equation.

∆yesm − ∆mend m = β1∆votersm + m (6)

Equation (6) is a first difference equation at municipal level. The dependent variable is the change in the number of yes votes net of the number of men switching from voting no to yes. The independent variable is the change in the number of voters. It measures the number of women voting as I will argue below. β1 then identifies the female acceptance rate acceptancef under 0 the exogeneity assumption E(∆votersmm) = 0. I will discuss the identifying assumptions and potential bias in the next section.

3.3 Identifying Assumptions

The validity of the above estimation equation is based on two identifying assumptions. The first one concerns the independent variable ∆votersm measuring the change in the number of voters between the ballots in 1970 and 1971. The main above argument was that the change in the number of voters is connected to the female franchise and thus reflects the number of female voters. I will now discuss the direction of bias if men changed their participation rate between the ballots and potential sources of bias. The direction of the bias depends on whether men decrease or increase participation. If men decreased their participation, the observed ∆votersm would be smaller than the true number of women voting. Estimating (4) would consequently yield an upward-biased coefficient and suggest

10 a too high female acceptance rate. The gender gap defined as the difference between female and male preferences would also be upward-biased. The opposite would be true, if men increased their participation: fewer women than suggested by the measure ∆votersm would vote leading to downward-biased estimates of the average female acceptance rate and the gender gap. With constant male participation no bias would occur, since ∆votersm would precisely measure the number of women voting. Anticipating the main result of a non-negative gender preference gap, the relevant case to be ruled out is a positive turnout response of the male population (because more male turnout leads to a downward-biased gender gap). The first assumption is thus:

Assumption 1 Men are not more likely to participate in ballot 2 than in ballot 1.

Changes of the male participation rate could be expected for two reasons: first, the inclusion of a time limit, and second, as a reaction to female voting. To get evidence for the male turnout reaction due to the time limit, I compare turnout for the two ballots in 1953 and 1954. Both were held under male suffrage. The 1953 vote without time limit was highly contested. 60.27% of the male eligible population turned out. In contrast, the less disputed proposition including a time limit in 1954, drew only 46.77% of eligible men to the polls.12 Hence, the time limit itself is unlikely to induce higher male participation but could potentially even decrease it. There is no unambiguous expectation regarding the participation reaction of men to female voting. On the one hand, male participation might decrease since the marginal benefit to vote is reduced when the electorate roughly doubles. On the other hand, if men expect female preferences to differ from their preferred outcome, they might increase turnout as a strategic response. To explain away a negative gender gap by a male participation response, in addition, a low female participation would be required. Ballot 2 is the first federal voting date after the introduction of female suffrage on which voters decided on two bills.13 Women may not have made use of their new rights immediately but have gradually grown accustomed to exercising the franchise. Figure 2 provides evidence on the female usage of voting rights in Swiss parliamentary elections. The x-axis shows the election year t before and after the introduction of female suffrage in 1971.

The number of voters as share of the Swiss adult population participationt = 100×voterst/adultst is depicted on the y-axis.14 The fraction of voters as compared to the total adult population was steadily decreasing before the introduction of female suﬀrage. The participation rate almost doubles in the 1971 election with universal suﬀrage. Afterwards the participation rate has a decreasing trend

12Turnout on a particular day is influenced by all votes on the ballot list. On the ballot day in 1954 there were no other federal votes, so turnout was truly for the vote under investigation. On the ballot day in 1953 there was one additional federal vote about the protection of waters. It received a smaller turnout than the other vote and was thus unlikely the main reason to turn out on that day. 13The other proposition was about the protection of humans and their environment. 14It is preferable to depict participation in election than in referendums since the latter varies a lot which might be due to the importance of an issue or campaigning effects. I take the total number of Swiss people above 20 years old from Swiss censuses in 1950, 1960, and 1970, and interpolate the numbers for the inter-census years. The data are from the Swiss Statistical Office. Since 1971 eligiblet can be directly used from official election data.

11 Figure 2: Participation Rate in % 60 50 40 Participation in % 30

1955 1963 1971 1979 1987 Election

Note: Participation rate in % (100 × voterst / Swiss adult populationt) for Swiss federal parliamentary elections over time. Without female suffrage (1951-1967) and with female suffrage (since 1971). Data are from the Swiss Statistical Office. again. This contrasts with the observation of Lott and Kenny (1999) who show that the turnout rate in the U.S. continued increasing many years after the introduction of female suffrage. Thus on average women in Switzerland made use of their voting rights relatively quickly. This is not surprising since female suffrage was introduced relatively late in history. The timing coincides with higher education levels among women than in countries that enfranchised women around the first world war. Also, in some cantons women have received female voting rights for cantonal votes independently of federal voting rights such that they have gathered some voting experience even before 1971. The institutional setup allows for a robustness check regarding the number of women voting for a subset of observations. On the day of the second vote, June 6, 1971, 11 cantons had no female suffrage for cantonal votes. Women in these cantons were thus allowed to vote on federal measures but not on cantonal ones. Of these eleven cantons, five held concurrent cantonal votes on 6 June, 1971, and for three of them data from 269 municipalities are available.15 The voters for the cantonal referendums correspond to the number of men voting, whereas federal voters are the sum of men and women voting. Assuming no male roll-off between cantonal and federal votes, the number of women voting follows from the difference between voters on the federal and the cantonal vote. I call this the true number of women. Figure 3 plots the true number of women on the x-axis and the estimated one (∆votersm ≡ voters2m − voters1m) on the y-axis. Most of the observations are close to the 45-degree line, indicating that both numbers are similar. On average, the true number of women voting exceeds the estimated one by -2.5 voters. This check thus suggests that the independent variable is a good measure of female voting. If there was a bias, then female voting

15These were the cantons Graub¨unden,Schwyz, and Uri (data available) and Bern and Thurgau (no municipal data). Data were collected from the Swiss National Library in Bern.

12 Figure 3: Female Voters 4000 3000 2000 Delta voters Delta 1000 0 0 1000 2000 3000 4000 Federal-cantonal voters

Note: Robustness check for independent variable. Subsample of 269 municipalities from cantons without cantonal female suffrage and cantonal referendums taking place on 6 June, 1971. The y- axis shows the independent variable. The x-axis shows the difference in voters between the federal (women voting) and cantonal (women not voting) ballot. would be underestimated and male voting consequently overestimated, which is no threat to the identification strategy. In sum, the above evidence supports Assumption 1. The second identifying assumption relates to the change in content regarding the time limit restriction. The number of men voting yes instead of no because of the time limit, ∆menm, is proxied with voting results from 1963 as stated in equation (3). The validity of the proxy requires relatively time-constant fiscal preferences between 1963 and 1970. Results could be biased if more or less men changed their voting behavior than captured by the proxy. If more men switched to voting yes, my dependent variable ∆yesm − ∆mend m would be larger than it should. An upward- biased female acceptance rate and gender gap would be the consequence. The reverse holds in the eventuality of smaller male support. Again, in light of a estimated non-negative gender preferences gap the case to be argued against is fewer switchers from voting no to voting yes than captured by the proxy.

Assumption 2 Men who have approved of the ﬁrst proposition should also be in favor of the second one which includes a time limit and is thus less radical. The inclusion of the time limit in the second ballot proposition makes some men switch from rejecting to approving.

One argument presumably speaking against constant male preferences between 1963 and 1971 is the rejection of female suffrage in a 1959 referendum followed by its acceptance in 1971. The (male) public opinion regarding female voting turned from decided rejection (33.1% yes share) to decided acceptance (65.7% yes share) suggesting a liberalization of preferences. However, this seems to go against evidence regarding the federal financial order. Even after female suffrage, all three votes trying to implement a permanent constitutional article (1977, 1979, 1991) were rejected. The Swiss

13 seem to have a – virtually time-invariant – preference for not granting the government permanent tax powers. To substantiate this claim, I again provide evidence from the two comparable ballots on the federal financial order in 1953 and 1954. Recall, the first one had no time limit and was rejected, while the second one had a time limit and was approved. The population-weighted average difference in approval rates for the two propositions was 27.7 percentage points, which is substantial, and similar to the difference between 1963 and 1970 amounting to 29.8 percentage points. A t-test of both differences is highly significant. Because preferences between 1953 and 1954 can be assumed time constant, the comparison shows that the inclusion of a time limit is indeed responsible for higher acceptance rates among the male population. Theoretically, some men might have radical preferences and vote against the second proposition even though they supported the first one to protest and signal dissatisfaction. However, based on the supporting evidence from past ballots that including a time limit on average increases voter support, this should rarely be the case.

3.4 Data

I collected a dataset of 2,109 Swiss municipalities with voting information for the relevant ballots on November 15, 1970, June 6, 1971, and December 8, 1963. Voting results include the number of yes and no votes, valid votes and eligible citizens. Data from the cantons of Aargau, Freiburg, and Ticino are not available at municipal level. Instead I include the data from voting districts which comprise several municipalities each for these three cantons adding 26 voting districts to the dataset.16 For the canton Geneva, data are missing for the vote in 1963. Therefore, it is excluded from the analysis. All voting data come from the Political Atlas of Switzerland provided by the Swiss Statistical Oﬃce. Since voting data come from ballots taken at diﬀerent points in time, municipal mutations need to be taken into account, because several municipalities merged during this time. Therefore,

Table 2: Descriptives of Voting Results from 1970 and 1971

Variable Obs Mean Std. Dev. Min Max

% yes1970 2135 46.95 17.43 0 100 % yes1971 2135 73.15 10.67 2.74 100 ∆yesm − ∆mend m 2135 188 862 -115 23678 ∆votersm 2135 277 1251 -87 33728 NOTE: Based on voting data from referendums on 28 November, 1970 and 6 June, 1971. Municipal data, district data in cantons of Aargau, Freiburg and Ticino from the Political Atlas of Switzer- land provided by the Swiss Statistical Oﬃce.

16I have contacted the cantonal archives of the three cantons in question. For only 20 municipalities in the canton of Freiburg complete voting data required for the estimation exist. Using district data is thus the only way to include data from these cantons in the regressions.

14 I adjust the voting data such that all municipalities are comparable. Information on municipal mergers comes from the online register of municipal mutations provided by the Swiss Statistical Oﬃce. Table 2 summarizes descriptive statistics of the main voting variables. It shows the increase in acceptance rates from 46.95% in 1970 to 73.15% in 1971. The dependent variable indicates an increase in yes votes by 188 after proxying for changed male preferences, and the change in voters was 277 on average. I drop all observations for which the dependent and independent variable take on negative values since they go against the intuition that suﬀrage extension led to more voters.17

3.5 Results: Average Treatment Eﬀect

The female acceptance is estimated with a linear estimator and standard errors are clustered at cantonal level to account for potential serial correlation of the error terms among municipalities.18 Table 3 shows the regression results based on the votes in 1970 and 1971. The first row reports the gender preference gap, while the second one refers to the estimate of the female acceptance rate. For the gender gap, I first calculate the male acceptance rate from the official voting result which has to be the gender turnout-weighted sum of the male and female acceptance rates. The gender gap is the difference of the gender acceptance rates. Standard errors are bootstrapped with 1,999 repetitions. Significance is evaluated with a one-sided test, which tests against a positive gender preference gap that might have been expected from literature. I run different specifications excluding municipalities with many inhabitants to account for potential outliers. On average, 1,630 Swiss adults lived in a municipality, and 75 percent of observations have less than 1,200 eligible citizens. In specifications (2)-(5) municipalities with more than 50,000, 10,000, 2,000 and 1,000 inhabitants are dropped respectively. Excluding large municipalities technically means that data from three cantons with district data are excluded, as well as cities. Estimates of the female acceptance rate are highly significant. In the baseline specification (1) with full sample the female acceptance rate amounts to 68.2%. It varies between 64.4% and 69.8% in columns (1)-(5). Compared with the sample average acceptance rate of 73.2%, female support is below the average. Estimates of the gender preference gap confirm negative values throughout all specifications. In columns (2), (4) and (5) a positive gender gap can be rejected: women are not more likely than men to support the federal fiscal order. In economic terms, the gender gap is significant as well. It varies between -14.2 and -18.7 percentage points in the significant specifications, which constitutes a seizable difference.

I refine the independent variable ∆votersm by multiplying it with the share of women in a municipality. Intuitively, if there were no women in a municipality, female suffrage should not have any effect on the change in voting. Conversely, if a municipality was populated by women

17I run the regressions with all observations and the results are robust to the inclusion of all municipalities. 1825 cantons is a relatively small number of clusters. However, Cameron, Gelbach, and Miller (2008) show that after correcting for few clusters, hypotheses are accepted even more often. In their empirical analysis, Hodler, L¨uchinger, and Stutzer (2015) ﬁnd the same for clustering with Swiss cantons.

15 exclusively, the change in voters would reﬂect the change in acceptance with certainty. I get the share of Swiss adult women from the change in the number of eligible voters, %womenm = 19 (eligiblem2 − eligiblem1)/eligiblem2. The population-weighted average share of women in the Swiss adult population amounts to 53.8%, but it varies between 0 and 72.2%. In total, 22 (11) municipalities have shares below (above) the band of 40 to 60%. Municipalities with low shares of Swiss adult women are located in 4 cantons Bern, Graub¨unden, Waadt, and Wallis, and have only 89 inhabitants on average.20 Municipalities with high shares of Swiss women are located in six cantons and have 812 Swiss adults on average. Most of the shares are, however, close to 60% and thus not such big outliers. The regression equation is stated below. The female acceptance rate can then be recovered by calculating acceptancec f = βb3 ∗ %womenm.

∆yesm − ∆mend m = β3%womenm∆votersm + m (7)

Columns (6)-(10) show the results. In comparison to results in (1)-(5) the female acceptance rate drops in all specifications (e.g., from 68.2% to 62.0% in column (1)). Consequently, the estimated gender gap widens and a positive value is rejected in specifications (6), (7) and (9). In column (7) the gender gap takes on the largest value of -28 percentage points which is significantly different from zero. Overall, the results provide evidence for a non-positive gender preference gap, and some weak support for even a negative gender difference.21 The results show that the considerable economic gender gaps in terms of labor force participation or income at the time when women started voting cannot be the (only) driving forces behind differences in preferences. Otherwise, a positive value would have been expected. At the same time, this evidence conforms with some of the findings in the literature suggesting a drop in aggregate public spending with female voting. The non-positive gender gap could result from counter-veiling factors offsetting each other, e.g., economic variables and fiscal conservatism. It is thus interesting to study how the gender preference gap evolves over time when economic gender gaps close to find out whether work-related outcomes can explain differences in preferences.

19 The variable %womenm is calculated assuming away population growth between November 1970 and June 1971. 20Bern, Graubünden,Vaud, and Vallais are the four largest cantons in terms of area, and encompass some of the least densely inhabited regions in Switzerland. 21Switzerland has a strong federal structure, so a substitution effect from federal to cantonal spending due to female suffrage is of concern. Also, some “female” spending items like education mainly fall under the cantons’ responsibil- ity. However, the results of Stutzer and Kienast (2005) suggest decreasing cantonal spending with female suffrage, which speaks against a substitution effect.

16 Table 3: Results: Average Gender Preference Gap

Average Interacted with %womenm (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Sample All <50,000 <10,000 <2,000 <1,000 All <50,000 <10,000 <2,000 <1,000 Gender -0.109 -0.187* -0.077 -0.142* -0.159* -0.237* -0.280** -0.149 -0.176* -0.176 gap (0.113) (0.128) (0.106) (0.105) (0.102) (0.149) (0.158) (0.134) (0.134) (0.151)

Female 0.682*** 0.644*** 0.698*** 0.664*** 0.658*** 0.620*** 0.598*** 0.663*** 0.648*** 0.650*** 17 acceptance (0.026) (0.045) (0.023) (0.021) (0.020) (0.039) (0.075) (0.036) (0.033) (0.036)

Adj. R2 0.983 0.968 0.973 0.961 0.950 0.985 0.970 0.977 0.960 0.941 Obs. 1,926 1,920 1,878 1,651 1,369 1,926 1,920 1,878 1,651 1,369

One-sided test: * p<0.1, ** p<0.05, *** p<0.01. Based on voting data from referendums on 28 November, 1970 and 6 June, 1971. First difference (FD) estimation for Female acceptance. The dependent variable is change in the number of yes votes net of men changing from voting no to yes. The independent variable is the change in the number of voters. Canton-clustered standard errors at cantonal level in brackets. Gender preference gap is calculated from the estimated female acceptance and the official voting result. Standard errors for the gender preference gap are bootstrapped with 1,999 repetitions. Specifications vary by municipality size. In (6)-(10) change in voters is multiplied with the municipal share of Swiss women. 4 Direct and Indirect Gender Effect on Government Spending

4.1 Mediation Framework

For the subsequent analysis, I sketch the framework that allows to identify the causes of gender preference gaps for government spending. My focus is on evaluating whether (part of) the differences in gender preferences can be explained by labor-related outcomes that are typically argued to affect fiscal preferences and tend to vary by gender. Gender affects the outcome support of spending Y via two different channels. First, the gender effect on preferences is mediated through an economic variable (mediator) which is commonly termed the indirect effect. In my study, employed individuals potentially prefer lower taxation and less government spending than individuals not holding a paid occupation. However, employment itself is a function of gender since women are on average less likely to work and more likely to stay at home to care for children than men. Part of the gender effect on support for government spending is thus mediated by employment status. The mediator is an observable M (g) and depends on gender g. In the jargon of the mediation literature M (g) “lies on the causal path” from gender to preferences, where gender marks the start of the causal chain (Baron and Kenny 1986; Imai and Yamamoto 2013). The second channel is henceforth referred to as the direct effect which encompasses all residual effects of gender on preferences with the exception of channels controlled for by the mediator. It reflects intrinsic gender differences or unobserved mediators. The outcome variable is a function of both gender and the mediating variable, Y (g, M (g)) such that the total effect τ is:

τ = E[Y (1)] − E[Y (0)] = E[Y (1,M(1))] − E[Y (0,M(0))] (8)

E[Y (1 , M (1 ))] and E[Y (0 , M (0 ))] are observable outcomes for women and men depending on female and male employment respectively. Decomposition of the total effect is based on the potential outcome framework (e.g., Rubin 2004). Note that potential outcome Y (g, M (1 − g)) is never observed because for a particular individual gender is fixed at g and cannot be varied to 1 − g. The indirect η(g) and direct δ(g) effects are defined as:

η(g) = E[Y (g, M(1))] − E[Y (g, M(0))] (9) δ(g) = E[Y (1,M(g))] − E[Y (0,M(g))] (10)

The indirect effect in (9) captures the difference in expected outcomes when evaluating mediators for both groups while keeping gender constant at g. More specifically, the indirect effect for women η(1 ) = E[Y (1 , M (1 ))] − E[Y (1 , M (0 ))] compares preferences of women with expected female employment M (1 ) to potential female preferences under counterfactual male employment M (0 ). The direct effect in (10) shows how expected outcomes change when comparing a woman to a man, keeping the mediator constant at its value for g. In this study, being female is the treatment. I discuss potential downsides of using an unchange- able characteristic as treatment variable later on. For identification, it is required that assignment

18 into treatment G is random, and mediator M is exogenous when conditioning on G (Huber 2015). While it can be argued that assignment into gender is random or non-manipulable by other factors (at least in the context of this paper), independence of the mediator is a strong assumption. Conditional on G, the error term is not allowed to impact mediators M and the outcome Y at the same time. This assumption is easily violated: for example, conservative attitudes might influence government spending preferences but at the same time also life choices like employment which is a mediator as argued above. The solution is to replace the traditional independence assumption by a set of conditional independence assumptions. Let C be a vector of observables. It confounds G, M , and Y which means that it influences some or all of the three variables. Conservatism would be such a confounding factor by the above argumentation. η(g) and δ(g) are then correctly identified under a Sequential Ignorability Assumption (e.g., Huber 2014; Imai, Keele, and Yamamoto 2010).

Assumption 3 (Sequential Ignorability) 3.1 {Y (g0, m),M(g)} ⊥ G|C ∀ g0, g ∈ {0, 1} 3.2 {Y (g0, m)} ⊥ M|G = g, C = c ∀ g0, g ∈ {0, 1} 3.3 P (G = g|M = m, C = c) > 0 ∀ g ∈ {0, 1}

Assumption 3.1 implies that assignment into treatment (gender) conditional on confounders C is independent of outcome and mediator. Ignorability thus means that besides confounders C all other variables can be ignored, or that all confounders must be observed. Assumption 3.2 goes a step further: assignment into the binary mediator is independent of the outcome after conditioning on gender G and confounders C . For a given set of observed confounders and the same gender, employment can be treated as a random variable. Assumption 3.3 is a common support assumption requiring enough comparable observations for both groups g = 1 and g = 0 in order to have comparable units across both groups. There should be enough individuals in the sample that are similar regarding the mediator and confounders but differ by gender. If employment status and conservatism were the only mediators and confounders, all feasible combinations of the two variables should be observed in the data for men as well as women (e.g., employed and conservative, unemployed and conservative, etc.). None of the variables can be a perfect predictor of gender for mediation analysis to work. A graphical representation of the mediation framework is depicted in Figure 4 and summarizes the causal chains. The solid lines represent direct effects while the dashed lines visualize the indirect gender effect. It makes clear that confounders are pre-treatment variables, whereas the mediator is determined after the treatment.

4.2 Estimation Strategy: Identifying the Direct and Indirect Gender Eﬀect

The standard way of estimating η(g) and δ(g) is through a set of linear equations (e.g., Baron and Kenny 1986; Judd and Kenny 1981, or Blinder (1973) or Oaxaca (1973) for linear wage decom- positions, but without confounding factors). However, linearity imposes a strong functional form

19 Figure 4: Mediaton Framework

G Y

Note: G are groups, M are mediators, C are confounders, and Y the outcome. Solid lines represent direct eﬀects, and dashed ones indirect or mediated eﬀects. Direction of arrows indicates the causal chain. assumption which might be overly restrictive for a bivariate outcome variable (Hicks and Tingley 2011). Huber (2014, 2015) proposes to use a semi-parametric model instead that can accommodate a binary outcome and mediator. The mediator is a function of treatment and confounders due to the order of the causal chain. The outcome is also a function of group and confounders as well as the mediator:

M = χ(G, C, υ) (11) Y = φ(G, M, C, ) (12)

This approach is more flexible and appropriate for my analysis since χ and φ are functions that do not need to be specified more precisely. Denote the correlation of error terms υ and by ρ. Under the Sequential Ignorability Assumptions 3.1 to 3.3 errors are shown to be uncorrelated such that ρ = 0 (Imai, Keele, and Yamamoto 2010). If this holds, Huber (2014) shows that the direct and indirect effects are non-parametrically identified. But Sequential Ignorability poses a strong assumption which cannot be tested for. I will conduct a sensitivity analysis showing how large departures from the perfect world characterized by ρ = 0 can be such that results do not change qualitatively. Identification relies on a reweighing mechanism according to propensity scores P(G = 1 |M , C ) and P(G = 1 |C ) which are the conditional probabilities of assignment into treatment. From this the direct and indirect effects for women can be calculated by inserting g = 1 , replacing propensity scores with its estimates (e.g., from a probit regression), and using sample moments. The effects for men are derived analogously by substituting g = 0 .

Y · 1{G = g} P (G = 1|M,C) 1 − P (G = 1|M,C) η(g) = E − (13) P (G = g|M,C) P (G = 1|C) 1 − P (G = 1|C) Y · G Y · (1 − G) P (G = g|M,C) δ(g) = E − · (14) P (G = 1|M,C) 1 − P (G = 1|M,C) P (G = g|C)

For estimation, I use the normalized variants of (13) and (14) as in Huber (2014) and suggested by

20 Imbens (2004). Their exact forms can be found in Appendix B. The connection between the total eﬀect and the indirect and direct eﬀects is that they can we written as a sum evaluated under the opposite gender respectively:

τ = E[Y (1,M(1))] − E[Y (0,M(0))] = E[Y (1,M(1))] − E[Y (1,M(0))] + E[Y (1,M(0))] − E[Y (0,M(0))] = η(1) + δ(0) (15) = E[Y (1,M(1))] − E[Y (0,M(1))] + E[Y (0,M(1))] − E[Y (0,M(0))] = η(0) + δ(1) (16)

For the estimated counterparts, it is straightforward that τb = ηb(1 ) + δb(0 ) = ηb(0 ) + δb(1 ).

4.3 Data

Post-ballot surveys are conducted shortly after all referendum and initiative ballots at national level in Switzerland since 1981. The project is called VOXit, and the data are published by the Swiss foundation for research in social sciences.22 Randomly chosen respondents answer a questionnaire by telephone. Among the information included are the voting behavior and various socioeconomic controls as well as contextual information. Voters as well as eligible citizens who did not go to the polls answer the questions. Until the end of 1999 they also include the hypothetical answer of the nonparticipating respondents to the question of how they would have decided if they had voted. This allows me to distinguish between preferences of voters and nonvoters. The mediation analysis is based on the four votes regarding the federal financial order held between 1981 and 2004. These are the ballots voted on 29 November, 1981, 2 June, 1991, 28 November, 1993 and 28 November, 2004. Though the details of the constitutional article have changed since the ballot propositions in 1971, the topic is the same as in the propositions analyzed above. The propositions from 1981, 1993 and 2004 include time limits until 1994, 2006, and 2020 respectively. The 1991 proposition does not have a time limit. While it might be of concern that women have not yet grown accustomed to their voting rights in 1971 and potentially hesitated to participate, at the time of the surveys female voting rights were already well established. Moreover, any potentially strategic male voting behavior stemming from the introduction of female suffrage should have ceased to exist by then. I use observations for which the participation and voting decisions as well as the mediator and confounders (which I detail below) are available. Observations from respondent submitting an empty vote are dropped in order to follow the official rule of calculating voting results. I drop observations when the respondent claims to have turned out for the vote but there is an answer for the voting behavior of non-participants in the data set (and vice versa for non-participants with information on voting for that individual). These are only 11 and 17 cases respectively and most

22Data are available online on the following homepage: http://nesstar.sidos.ch/webview/index.jsp

21 likely the result of data mistakes. In the pooled sample of all ballots, there are 2,375 respondents of which 46% are women. 1,634 respondents voted on the measures. A typical concern about using surveys to elicit voter preferences is survey bias: either respondents misrepresent their voting behavior, or they choose not to participate in the survey conditional on their characteristics. To evaluate potential survey bias, Funk (2016) proposes to compare the official voting results with the share of survey respondents claiming they have voted “yes”. Sub- tracting the official results from the survey results based on the voting population only, yields a difference of 10.37 (1981), -1.84 (1991), 1.64 (1993) and 4.1 (2004) percentage points between the two. The last three values confirm Funk’s (2016) result that on average no significant survey bias occurs in votes concerning federal finances. However, the first value points to a seizable survey bias and problems with the accuracy of the survey results from 1981.

4.4 Empirical Speciﬁcation

The outcome Y takes on value 1 if a respondent voted yes or would have voted yes, and 0 else. The main variable of interest is the gender indicator G which becomes 1 for women, and 0 for men. I use employment and income as mediators since factors relating to the labor market are traditionally thought to influence preferences for government spending (Meltzer & Richard 1981), and gender gaps are traditionally large with regard to these variables. It is interesting to consider both variables since they might capture alternative channels. Especially, married female respondents might not be employed but live in a high-income household. Work is 1 if the respondent indicated to be employed, and 0 for individuals who do not work, i.e., the non-employed, the unemployed, students, and retirees. Income was only asked for in the 1993 and 2004 surveys. It is measured as household income with a five-step categorical variable.23 I define Income as 1 if a respondent reports income above the second level which corresponds to above-average household income. Household income reflects the sum of incomes earned in a household, which may be larger for married couples than singles. I will therefore use civil status as a confounder. Table 4 provides a comparison of both mediator means in the subgroups of men and women for all respondents and voters, in the pooled sample and separately for each ballot. There is a negative and persistent gender gap in employment. Women are on average 19.4 percentage points less likely to hold a job than men. The value is similar (18.2) in the subsample of voters. While there is variation in the size of the gender gap across the four ballots, it tends to get smaller over time. As expected, there exists a negative income gender gap. Among all 1993 respondents, women are 9.9 percentage points less likely to have above-average income than men. This gap varies little over time in the subsamples of voters by ballot. The choice of confounders is restricted by survey design. Conceptually, confounders are thought of as pre-treatment variables. The following variables come close to this requirement. To account for cultural differences between the geographical and linguistic areas in Switzerland, I control for

23The ﬁve categories of monthly household income in Swiss Francs are: (1) Income ≤ 3 , 000 ; (2) 3 , 000 < Income ≤ 5 , 000 ; (3) 5 , 000 < Income ≤ 7 , 000 ; (4) 7 , 000 < Income ≤ 9 , 000 ; (5) Income > 9 , 000 .

22 Table 4: T-test of mediators Work and Income by Gender

Sample Women Men Diﬀerence t-statistic p-value Work All All 0.532 0.726 -0.194*** -10.003 0 1981 0.452 0.763 -0.311*** -7.656 0 1991 0.598 0.753 -0.155*** -4.330 0 1993 0.545 0.712 -0.167*** -4.874 0

Voters All 0.523 0.705 -0.182*** -7.697 0 1981 0.447 0.762 -0.315*** -5.750 0 1991 0.602 0.690 -0.088* -1.825 0.069 1993 0.517 0.720 -0.203*** -4.977 0 2004 0.510 0.653 -0.144*** -2.967 0.003

Income All 1993 0.421 0.521 -0.099*** -2.628 0.009

Voters All 0.493 0.610 -0.117*** -3.432 0.001 1993 0.438 0.553 -0.116** 2.555 0.011 2004 0.561 0.695 -0.134*** -2.630 0.009

Based on data from VOX-surveys no. 161, 421, 511, and 862. Data are available online on http://nesstar.sidos.ch/webview/index.jsp. T-test of mediators Work and Income by gender. Evaluated among all survey respondents, voters, in pooled samples of all ballots and separately for all ballots. * p<0.1, ** p<0.05, *** p<0.01. region dummies West, Center, Center-West, and Center-East. The Southern region is left out as reference group.24 Age denotes the respondent’s age, and may inﬂuence employment status as well as preferences in general. If the respondent is Catholic this variable takes on value 1.25 The variable is typically related to work-ethic or conservatism. Other variables decided at the time of birth are not available. However, further socioeconomic factors may play a role for explaining gender preferences. I use several post-treatment outcomes as confounders and subsequently conduct robustness tests showing that results are insensitive to the inclusion of these variables. As explained above, civil status is an important control. Partner

24The region of residence might already constitute a choice if an individual moved away from his home region. Alternatively, I specify German (as opposed to French or Italian) as a language indicator which should mainly be determined by the language spoken in the parental home. It is closely correlated with the regions and results are not sensitive to this speciﬁcation. 25The majority of the Swiss population is either Roman Catholic (46.2% in 1980), or Protestant (45.3% in 1980) (data are from the website of the Swiss Statistical Oﬃce www.bfs.admin.ch).

23 Table 5: T-Tests of Confounders by Gender

Mean Mean (women) (men) Diﬀerence t-statistic p-value Confounders West 0.212 0.216 -0.004 -0.218 0.827 Center 0.246 0.246 0.000 0.002 0.998 Center-West 0.260 0.252 0.008 0.461 0.645 Center-East 0.262 0.249 0.013 0.738 0.461 Age 46.6 48.0 -1.360 -1.941 0.052 Catholic 0.417 0.436 -0.019 -0.956 0.339 Partner 0.633 0.650 -0.017 -0.864 0.388 Education 0.268 0.355 -0.087 -4.592 0 Urban 0.636 0.588 0.049 2.420 0.016 Homeowner 0.418 0.450 -0.032 -1.558 0.119 House 0.378 0.389 -0.010 -0.522 0.602

Data from VOX-surveys no. 161, 421, 511, and 862. Pooled sample with 2,375 respondents. Data are available online on http://nesstar.sidos.ch/webview/index.jsp.

indicates whether the respondent is either married or has a partner, in contrast to being single, divorced or widowed. Education is a well-known driver of income and affects the way individuals make decisions. Education takes on value 1 for individuals with high-school or university degrees. Urban is one if the respondent lives in a city in contrast to a rural area. Urbanity relates to labor market characteristics and exposure to public goods, thus potentially confounding employment and preferences. Homeowner indicates that the respondent lives in his owned property. If the respondent lives in a detached house in contrast to an apartment, House takes on value 1. Both variables relate to the standard of living but potentially also to circumstances an individual originates from. To summarize, the vector of confounders is C = {West, Center, Center-West, Center-East, Age, Catholic, Partner, Education, Urban, Homeowner, House}. Summary statistics of the variables by gender, the mean difference, t-statistics and p-values are reported in Table 5. Though there is little selection into treatment, age, urbanity and most importantly education display significant gender gaps. Propensity scores P (G = 1|M,C) and P (G = 1|C) required for identification are estimated with probit regressions and reported in Appendix D (Tables 12 – 14). For validation of the common support assumption 3.3, histograms of propensity scores are provided. Figure 5 shows exemplary histograms for the mediator Work in the pooled sample of all four ballots. They visualize a sufficient overlap of propensity scores for men and women. There is moreover no mass concentrated at the values zero and one such that the mediator and confounders do not perfectly predict gender and propensity score trimming is unnecessary (Huber, Lechner, and Wunsch 2013). Propensity score histograms by gender for all ballot subsamples are attached in Appendix E.

24 Figure 5: Common Support Histograms

(a) P (G = 1|C) for Women (b) P (G = 1|C) for Men 12 10 10 8 8 6 6 Density Density 4 4 2 2 0 0 .2 .4 .6 .8 .2 .4 .6 .8 P(C) P(C)

(c) P (G = 1|M,C) for Women (d) P (G = 1|M,C) for Men 6 6 5 5 4 4 3 3 Density Density 2 2 1 1 0 0 .2 .4 .6 .8 .2 .4 .6 .8 P(M,C) P(M,C)

Note: Histograms of propensity scores from probit estimates for full sample with mediator Work. P (G = 1|M,C) propensity scores control for the mediator and confounders, P (G = 1|C) only for the latter. Corresponds to column (1) in Table 6. a: Propensity scores P (G = 1|C) based on confounders for women; b: propensity scores P (G = 1|C) based on confounders for men; c: propensity scores P (G = 1|M,C) based on mediator and confounders for women; d: propensity scores P (G = 1|M,C) based on mediator and confounders for men. Based on data from VOX- surveys no. 161, 421, 511, and 862. Pooled sample with 2,375 respondents. Data are available online on http://nesstar.sidos.ch/webview/index.jsp.

25 4.5 Results: Direct and Indirect Eﬀects

Results from the mediation analysis based on the mediator Work are reported in Table 6. Column (1) shows the results for the pooled sample, while in specifications (2)-(4) the results are reported separately for the individual ballots. In columns (5)-(9) the sample is reduced to respondents who reported to have voted. The total gender effect τ in the first row is the marginal effect from a probit regression of acceptance on gender while controlling for all confounders. Significance is determined with a two-sided test. While coefficients are negative throughout almost all specifications, they only turn significant in the total sample (column (1)), in the 1993 ballot (column (4)), and voters in 1991 (column (7)). On average, being female decreases the probability of voting yes by 4.4 percentage points in the total sample. Moreover, a positive gender gap can be rejected at conventional significance levels in six out of nine specifications (columns (1), (3)-(5),(7) and (9)). The direct and indirect effects evaluated for women, δ(1) and η(1), are in the second and third row of the results table. The respective values for men are reported in the lower part of the table for completeness. Standard errors are bootstrapped with 1,999 iterations. The direct effect for women is negative in all specifications. Among all respondents and in the pooled sample of voters the difference is significant. Conditional on work outcomes for women, being female reduces the probability of accepting the proposals by 7.5 percentage points on average (column (1)). The effect is slightly smaller in the sample of voters (6.8 percentage points in column (5)). The mediated effect through employment for women has small economic significance since most coefficients are close to zero. Moreover, it is never significant. Neither a negative nor a positive gender gap caused by gender differences in employment can be rejected. This means than comparing women with average female employment and counterfactual employment of men cannot explain differences in gender preferences. Results for the mediation through income are reported in Table 7. The findings are similar to the ones using employment as a mediator. The total gender gap is always significantly negative and varies between 7.5 and 9.9 percentage points. The direct effect, though always negative, is only significant in the pooled sample of voters from 1993 and 2004. The mediated effect for women always takes on insignificant values. The direct and indirect effects were obtained using a semi-parametric approach. While such estimations generally have the advantage of good robustness since they rely on few functional form assumptions, they produce larger standard errors than parametric estimations. I rerun the mediation analysis using a parametric approach and specify the mediation and outcome regressions (11) and (12) as probit models since both mediator and outcome are binary. But also specifying the mediator equation as a linear function yields very similar results. The derivation of the direct and indirect effects follows Imai, Keele, and Yamamoto (2010). Results are in Appendix C in Tables 8 and 9. Conclusions regarding the total and direct effects are virtually the same and effect size is very similar as in the semi-parametric specification. As expected, standard errors are smaller such that the total and direct effects are negative and significant in almost all specifications. Interestingly,

26 the mediated effect through employment becomes significant in several specifications. Women with average female employment are between 1.2 to 2.1 percentage points more likely to vote yes than women with counterfactual average (and thus higher) male employment. The effect goes into the expected direction that employed individuals should prefer lower public spending levels than not working ones. Quite surprisingly, the indirect effect becomes negative for the mediator income: women with female average income were 1.3 to 1.6 percentage points less likely to vote yes than women with counterfactual male income. This means that women with lower household incomes supported the ballot measures more than women in relatively richer households, though by a pure redistributive motive the opposite would have been expected. This robustness check shows that functional form assumptions play a role for the estimation of mediated effects. In the parametric approach, mediated effects become significant. However, if the model was misspecified, parametric estimation is more prone to error, and semi-parametric estimation should be preferred. The direct effect, in contrast, is robust to different specifications. I will use this parametric specification to check for sensitivity of the results to model misspeci- fication in the next section.

27 Table 6: Results: Direct and Indirect Eﬀects - Mediator Work

(1) (2) (3) (4) (5) (6) (7) (8) (9) Sample All All All All Voters Voters Voters Voters Voters Total τ -0.044** -0.027 -0.095*** -0.053 -0.035 0.024 -0.073* -0.049 -0.055 (0.020) (0.038) (0.037) (0.033) (0.023) (0.048) (0.049) (0.039) (0.041)

Women Direct effect δ(1) -0.075*** -0.057 -0.123** -0.082*** -0.068** -0.038 -0.111 -0.090 -0.062 (0.024) (0.053) (0.058) (0.025) (0.029) (0.086) (0.125) (0.086) (0.079) Indirect effect η(1) 0.004 -0.018 0.000 -0.005 0.004 -0.035 0.005 -0.008 0.008 (0.019) (0.042) (0.044) (0.019) (0.026) (0.066) (0.064) (0.058) (0.169) 28 Men Direct effect δ(0) -0.048* -0.009 -0.095 -0.049* -0.039 0.059 -0.078 -0.041 -0.063 (0.027) (0.055) (0.058) (0.027) (0.035) (0.079) (0.082) (0.066) (0.171) Indirect effect η(0) 0.031** 0.030 0.028 0.029* 0.033* 0.062 0.038 0.041 0.008 (0.016) (0.040) (0.047) (0.016) (0.019) (0.077) (0.119) (0.078) (0.073)

Ballots all 1981 1991 1993 all 1981 1991 1993 2004 Observations 2,375 525 671 771 1,634 287 397 542 408

* p<0.1, ** p<0.05, *** p<0.01. Standard errors in brackets. The binary dependent variable is 1 if the respondent voted yes, 0 if no. Standard errors of total effect τ are from marginal effects after probit estimates. Inverse propensity score weighted results for direct (δ) and indirect (η) effects. Standard errors for direct and indirect effects are based on 1,999 bootstrap iterations. The mediator Work takes on value 1 if the respondent was employed. The confounding variables are West, Center, Center- West, Center-East, Age, Catholic, Partner, Education, Urban, Homeowner, and House. Based on data from VOX-surveys no. 161, 421, 511, and 862. Data are available online on http://nesstar.sidos.ch/webview/index.jsp. Table 7: Results: Direct and Indirect Effects - Mediator Income

(1) (2) (3) (4) Sample Voters Voters Voters All Total τ -0.081*** -0.075* -0.099** -0.076** (0.030) (0.041) (0.043) (0.034)

Women Direct eﬀect δ(1) -0.073* -0.069 -0.097 -0.073 (0.040) (0.105) (0.059) (0.064) Indirect eﬀect η(1) -0.010 -0.009 0.001 -0.011 (0.057) (0.082) (0.165) (0.051)

Men Direct eﬀect δ(0) -0.072 -0.065 -0.101 -0.065 (0.062) (0.091) (0.167) (0.062) Indirect eﬀect η(0) -0.009 -0.006 -0.002 -0.003 (0.032) (0.099) (0.056) (0.055)

Ballots 1993, 2004 1993 2004 1993 Observations 842 488 354 695

* p<0.1, ** p<0.05, *** p<0.01. Standard errors in brackets. The binary dependent variable is 1 if the respondent voted yes, 0 if no. Stan- dard errors of total effect τ are from marginal effects after probit estimates. Inverse propensity score weighted results for direct (δ) and indirect (η) effects. Standard errors for direct and indirect effects are based on 1,999 bootstrap iterations. The mediator Income takes on value 1 if the respondent has above average income. The confounding variables are West, Center, Center-West, Center-East, Age, Catholic, Part- ner, Education, Urban, Homeowner, and House. Based on data from VOX-surveys no. 161, 421, 511, and 862. Data are available online on http://nesstar.sidos.ch/webview/index.jsp.

4.6 Sensitivity

Validity of the Sequential Ignorability Assumptions is crucial for the identification of causal effects. There are two potential sources for violation of these assumptions in my study. First, the treatment in my case is “being female”. Gender, however, is not a typical treatment as e.g. in a medical trial. It is a personal attribute comparable to ethnicity or nationality and thus precedes confounders which are not determined at birth (Greiner and Rubin 2011). In the empirical application above, variables like education are used as confounders. However, they are determined after treatment and thus likely affected by the treatment. This is certainly true for education since men and women make different educational choices. I encounter this problem by checking how sensitive the results are to control for post-treatment variables. Second, a general threat to identification in mediation studies relates to the issue that not all relevant confounders may be observed and controlled for. I

29 conduct a sensitivity analysis which determines how badly the Sequential Ignorability Assumptions need to be violated in order to arrive at different conclusions than in the above analysis. Regarding post-treatment confounders, I rerun the mediation analysis discarding all confounders that are determined after treatment and only keep the regional indicators, catholic faith and age as confounders. The idea is to check whether results are sensitive to controlling for post-treatment confounders. Result Tables 10 and 11 are in the Appendix. Leaving out post-treatment variables leads to more negative total gender gaps and direct effects for women. The effects become significantly different from zero in even more specifications. The mediated effects remain small and insignificant. Controlling for post-treatment variables produces the more conservative results in terms of absolute value and significance. Consequently, conclusions regarding the sign of all effects are robust to this manipulation, and it is uncritical to have post-treatment mediators in the model. A sensitivity analysis as described by Imai, Keele, and Yamamoto (2010) is a way of testing the result’s susceptibility to violation of the Sequential Ignorability Assumptions. In an ideal world, mediated and direct effects are identified under the assumption than no unobserved pre-treatment variables jointly affect the mediator and the outcome. It can be expressed as a zero correlation of the error terms from equations determining the mediator and outcome which is denoted by ρ ≡ corr(υ, ) = 0 .26 The sensitivity analysis allows to determine the direct and indirect effects as functions of the sensitivity parameter ρ. Departures from ρ = 0 point to violation of Sequential Ignorability. ρ > 0 (ρ < 0 ) refers to the case when unobserved confounders affect the mediator and outcome in the same (opposite) direction(s). I conduct the sensitivity analysis for female direct and indirect effects. For the mediator Work the pooled sample of all ballots is used, and for Income the sample of voters from the 1993 and 2004 surveys. The point of departure are the parametrically estimated effects. The reason is that the indirect effect was significantly negative such that a sensitivity analysis is meaningful in this case. The four panels in Figure 6 show plots of ρ against the average (in)direct effect. The gray area refers to the 95% confidence interval of the effects. For the direct effects (panels 6.a and 6.c) the relation between ρ and δ(1 ) is negative. The critical sensitivity parameter equals ρ = −0 .4 (panel 6.a, mediator Work) and ρ = −0 .65 (panel 6.c, mediator Income) respectively. The conclusion regarding a negative direct effect is robust to the omission of all (unobserved) pre-treatment confounders that affect the mediator and outcome in the same direction because δ(1 ) < 0 whenever ρ > 0 . The direct effect would turn zero or positive only if the correlation of error terms gets smaller (i.e., more negative) than −0.4 or −0.65 respectively. The numbers indicate that relatively large departures from Sequential Ignorability are permissible without drawing the conclusion that the direct effect is positive. For the indirect effect, panels 6.b and 6.d indicate that the finding is extremely sensitive to violations of the Sequential Ignorability Assumptions. Recall that the mediated effect through employment was positive. A correlation of error terms as small as ρ = −0 .1 , would result in a zero effect. The negative mediated effect through income would turn zero for ρ = 0 .15 , and even

26Recall from Section 4.2 that these function are M = χ(G, C , υ) and Y = φ(G, M , C , ).

30 Figure 6: Sensitivity Analysis

(a) Mediator Work: Direct Eﬀect (b) Mediator Work: Indirect Eﬀect

Employed Employed 0.2 0.2 0.1 0.1 1 0 0.0 0.0 Average Direct Effect Average Average Mediation Effect Average −0.1 −0.1 −0.2 −0.2

−0.5 0.0 0.5 −1.0 −0.5 0.0 0.5 1.0 Sensitivity Parameter: ρ Sensitivity Parameter: ρ

Income Income 0.2 0.2 0.1 0.1 1 1 0.0 0.0 Average Direct Effect Average Average Mediation Effect Average −0.1 −0.1 −0.2 −0.2

−1.0 −0.5 0.0 0.5 1.0 −1.0 −0.5 0.0 0.5 1.0 Sensitivity Parameter: ρ Sensitivity Parameter: ρ

Note: Sensitivity analysis for direct and indirect eﬀects for women (g = 1 ). Sensitivity parameter ρ = 0 if Sequential Ignorability holds. Based on data from VOX-surveys no. 161, 421, 511, and 862. Pooled sample of all votes in a and b. Voters from surveys 511 and 862 in c and d. a: Mediator Work: δ(1 ) = 0 if ρ = −0 .4 . b: Mediator Work: η(1 ) = 0 if ρ = −0 .1 . c: Mediator Income: δ(1 ) = 0 if ρ = −0 .65 . d: Mediator Income: η(1 ) = 0 if ρ = 0 .15 . positive for sensitivity parameters above this critical value. A complementary way to make statements about sensitivity is through the product of R2 from the mediator and outcome equation (Imai, Keele, and Yamamoto 2010). This measure can express

31 how much of the residual variance an unobserved confounder would have to explain such that the (in)direct effects would turn zero. For the direct effects the respective values are 0.16 (mediator Work) and 0.43 (mediator Income). The high values again underline the robustness of the direct effects to omitted pre-treatment confounders. In contrast, the values for the indirect effects are 0.01 (mediator Work) and 0.02 (mediator Income) respectively, suggesting extreme sensitivity of the model specification. The sensitivity analysis demonstrates that relatively large departures from the ideals of Sequen- tial Ignorability can be accepted without triggering different results regarding the direct effect of being female on government preferences. In contrast, it shows that the indirect effect, which was only significant once making parametric form assumptions, can easily be violated if pre-treatment confounders were not controlled for.

4.7 Discussion

Comparing the total effect to the results from the 1970/1971 referendums in the previous section, suggests qualitatively similar results. Throughout the history of referendums on the federal fiscal order, women are not more inclined to support the ballot measures. However, the size of the gender preference gap is considerably smaller in the post-ballot surveys. There are several explanations. First, though similar, the ballot propositions are not identical, which also might explain part of the variation over time. Further, there is a survey bias in the 1981 survey suggesting problems with the representativeness of the data from that year. It might explain why most of the results for this subsample yield insignificant results. Second, the post-ballot surveys are based on data from votes that took place at later points in time. Time effects not captured by the mediator and confounders – the political context and unobserved factors like ideology – might have changed over time, causing different results. Last, the results from 1970/1971 rely on aggregate voting data and approximations which are sources of measurement error. In contrast, individual-level data allow for precise control over treatment, mediators and outcome which might be an alternative explanation for the different results. The size and significance of the direct effect are considerable and robust, whereas there is little evidence for mediated effects through employment or education. In sum, evidence points to the importance of direct gender effects when explaining the gender gap in support for the fiscal order. It is particularly pronounced in the total population, but less prominent among voters. On the one hand, this may be a relict of the fact that voters constitute a selective subsample of the total population. Though descriptives showed significantly different mediators by gender both among all respondents and voters, voting is a non-random decision and typically depends on observables. This might explain why the results based on voters only tend to be less significant. In general, sample size in the subsamples of individual ballots are relatively small, working against precise estimates. The direct effect may capture several diverse aspects regarding the gender preference gap. Most broadly, the direct effect for women subsumes all differences between men and women that are un- confounded and not captured by the mediator. I.e., it is a residual effect. Therefore, it may

32 not only refer to observable gender differences. Potential alternative explanations are unobserved mediators. Research based on experimental techniques examines gender gaps other than socioeconomic differences which might explain why women could have different preferences for government spending than men (cf. Croson and Gneezy (2009) as well as Shapiro and Mahajan (1986) for literature reviews). Literature documents that women are more risk averse (e.g., Holt and Laury 2002, 2005; Schubert et al. 1999) and dislike competition (Gneezy, Niederle, and Rustichini 2003; Niederle and Vesterlund 2007). Experimental evidence suggests that women are more altruistic, and display less variance in altruism (Andreoni and Vesterlund 2001; Goree, Holt, and Laury 2002, Selten and Ockenfels 1998). Women are found to be more patient than men, suggesting that they care more strongly about the future (Krogstrup and Wälti2011). These variables are unobserved in the survey data and thus candidate explanations for a direct gender effect. While for part of them it is unclear in which direction they should affect preferences for government spending, care for the future or patience would be compatible with preferring smaller governments and reducing the fiscal burden for the next generations.

5 Concluding Remarks

The aim of this paper is to provide evidence for gender preferences for government expenditure from ballot analysis. This method is preferable to analyzing indirect links between the electorate, politicians, and their subsequent choice of budgets or policies since the relation between preferences and subsequent voting behavior is much clearer. The analysis of the gender preference gap around the introduction of female voting rights is based on aggregate voting data such that individual voting behavior remains unobserved. However, I argue extensively that my preference measures are likely to identify gender preference gaps. I find that approval for government spending is not higher among women than men, but potentially the reverse. This is true at the point in time when women got the right to vote, and the finding persists over time. The results are compatible with the literature identifying smaller governments after the franchise was extended to women. Though employment and wage gender gaps were large in the past, time-invariant preferences like fiscal conservatism are more suitable candidate explanations for the observed changes in public spending. Interestingly, around 1971 government budgets were still made of items like military spending which women tend to dislike. More “female” budgetary items like social security or education have become more important government responsibilities over time. If these factors were the sole determinants of the negative gender preference gap, a diminishing gap should have been observed over time. The findings of this paper suggest that part of the fiscal gender preference gap cannot be explained away by employment status or education. Intrinsic or unobserved gender differences play a role in explaining diverse gender preferences. Therefore, it is unlikely that these gender gaps will disappear in future even when socioeconomic gender differences continue changing. This has implications for policy-making and political representation depending on the gender of legislators.

33 Appendix

A Date Sources and Federal Announcements

• Information about mutations of the municipalities are taken from the historical municipality register of the Swiss Statistical Oﬃce available online http : //www.bfs.admin.ch/bfs/portal/de/index/infothek/nomenklaturen/blank/blank/ gem liste/02.html

• The Ann´eePolitique Suisse (2012) is accessible online and provides additional background information on ballots. http : //www.anneepolitique.ch/de/aps − online.php

• Number of voters for cantonal votes is available online from the Centre for research on direct democracy. www.c2d.ch.

• Information about municipalities counting votes together in the canton Bern, and political municipalities in the canton Thurgau were received by email from the Swiss Statistical Oﬃce.

• Data used from Swiss census (1970): total population

• Voting data are from the Political Atlas of Switzerland of the Swiss Statistical Oﬃce. They were retrieved for the following ballots:

– Bundesbeschluss vom 27.09.1963 über die Weiterführung der Finanzordnung des Bundes (Verlängerungder Geltungsdauer von Art.41ter BV und Ermässigungder Wehrsteuer). Ballot on 8 December 1963. – Bundesbeschluss vom 24.06.1970 über die Anderung¨ der Finanzordnung des Bundes. Ballot on 15 November 1970. – Bundesbeschluss vom 09.10.1970 über die Einführung des Frauen- stimm- und Wahlrechts in eidgenössischen Angelegenheiten. Ballot on 7 February 1971. – Bundesbeschluss vom 11.03.1971 über die Weiterführungder Finanzordnung des Bundes. Ballot on 6 June 1971.

• VOX-survey no. 161, 421, 511, and 862 are accessible online. The source is: Longchamp, Claude, Ulrich Klöti,Sibylle Hardmeier, Wolf Linder, Hanspeter Kriesi, Do- minique Wisler, Hans Hirter, Lukas Golder, Marina Delgrande, Lionel Marquis, and Urs Bieri. VOX 105-109 [Datasets]. gfs.bern, Markt- und Sozialforschung; Univer- sitätZürich; UniversitätBern; Universit¨ de Gen¨‘ve. Distributed by FORS, Lausanne. http://nesstar.sidos.ch/webview/index.jsp

34 Federal Announcements are accessible online http : //www.amtsdruckschriften.bar.admin.ch.

• Federal Announcement 1962 I, 997-1014. Botschaft des Bundesrates an die Bundesversamm- lung ¨uber die Weiterf¨uhrungder Finanzordnung des Bundes.

• Federal Announcement 1969 II, 749-807. Botschaft des Bundesrates and die Bundesversamm- lung ¨uber die Anderung¨ der Finanzordnung des Bundes.

• Federal Announcement 1970 II, 1-5. Bundesbeschluss ¨uber die Anderung¨ der Finanzordnung des Bundes.

• Federal Announcement 1970 II, 1581-1608. Botschaft des Bundesrates an die Bundesver- sammlung ¨uber die Weiterf¨uhrungder Finanzordnung des Bundes.

• Federal Announcement 1971 I, 486-491. Bundesbeschluss ¨uber die Weiterf¨uhrungder Finan- zordnung des Bundes.

• Federal Announcement 2003 I, 1531-1565. Botschaft ¨uber die neue Finanzordnung.

35 B Estimators of Direct and Indirect Eﬀects

To estimate the direct and indirect effects, I estimate normalized versions of (13) and (14) (Huber, 2014). For the normalization weights are adjusted such that they add up to one for both men and women. For simplification, write p(M,C) ≡ P (G = 1|M,C) and p(C) ≡ P (G = 1|C), with their their estimated counterparts pb(M,C) and pb(C). Let i denote the index for each of the N observations. Then the direct effect evaluated at g = 1 and g = 0 respectively is identified by the following equations:

" N #" N #−1 " N #" N #−1 X 1 X 1 X 2 X 2 δb(1) = YiWi YiWi − YiWi YiWi (17) i=1 i=1 i=1 i=1

" N #" N #−1 " N #" N #−1 X 3 X 3 X 4 X 4 δb(0) = YiWi YiWi − YiWi YiWi (18) i=1 i=1 i=1 i=1 The four weights are deﬁned as:

1 Gi Wi ≡ Pb(Ci)

2 (1 − Gi)Pb(Mi,Ci) Wi ≡ (1 − Pb(Mi,Ci))Pb(Ci)

3 Gi(1 − Pb(Mi,Ci)) Wi ≡ Pb(Mi,Ci)(1 − Pb(Ci)) 4 1 − Gi Wi ≡ 1 − Pb(Ci)

Pb(Mi,Ci) and Pb(Ci) are estimated with probit regressions, and the rest of the estimator is based on sample moments from which it is straightforward to calculate both δb(1) and δb(0).

36 C Tables Robustness Checks , Center-East , takes on value 1 Work Work ) effects. Standard errors Center-West η , Center , . Based on data from VOX-surveys no. West are from marginal effects after probit esti- ) and indirect ( δ τ House , and Homeowner , Urban , (1) (2) (3) (4) (5) (6) (7) 0.01. Standard errors in brackets. The binary dependent variable is 1 if the (0.00) (0.00)(0.00) (0.05) (0.06) (0.00) (0.04) (0.02) (0.15) (0.01) (0.09) (0.03) (0.06) (0.06) (0.00) (0.02)(0.00) (0.09) (0.01)(0.00) (0.00) (0.01) (0.02) (0.02) (0.05) (0.01) (0.09) (0.17) (0.03) (0.01) (0.06) (0.01) (0.06) (0.05) (0.17) < -0.058*** -0.114*** -0.063* -0.046* -0.096** -0.055 -0.064* Education , τ (1) 0.017*** 0.016** 0.014* 0.017**(0) 0.012** 0.016*** 0.021* 0.017** 0.008 0.012* 0.015** 0.013** 0.019* 0.007 (1) -0.074*** -0.131*** -0.075** -0.061** -0.109** -0.073* 0.071* (0) -0.075*** -0.130*** -0.077** -0.063** 0.108** -0.076* -0.073* δ δ η η 0.05, *** p Partner < , Table 8: Parametric Estimation: Direct and Indirect Effects - Mediator 0.1, ** p Catholic < , * p if the respondent was employed. The confounding variables are Total Women Direct effect Indirect effect for direct and indirect effects are based on 1,999 bootstrap iterations. The mediator Sample All AllMen Direct effect AllIndirect effect VotersBallots VotersObservations Voters Voters respondent voted yes, 0 ifmates. no. Parametric Standard estimation errors with 2,375 of probit total models effect for direct ( allAge 671 1991 771 1993 1,634 all 397 1991 542 1993 408 2004 161, 421, 511, and 862. Data are available online on http://nesstar.sidos.ch/webview/index.jsp.

37 Table 9: Parametric Estimation: Direct and Indirect Eﬀects - Media- tor Income

(1) (2) (3) (4) Sample Voters Voters Voters All Total τ -0.092** -0.083** -0.108** -0.080** (0.01) (0.03) (0.01) (0.02)

Women Direct eﬀect δ(1) -0.078** -0.069* -0.100** -0.069* (0.02) (0.07) (0.01) (0.05) Indirect eﬀect η(1) -0.016*** -0.016** -0.010 -0.013** (0.00) (0.01) (0.12) (0.01)

Men Men Direct eﬀect δ(0) 0.076** -0.068* -0.098** -0.068* (0.02) (0.07) (0.01) (0.05) Indirect eﬀect η(0) -0.014*** -0.014** -0.008 -0.011** (0.00) (0.01) (0.12) (0.01)

Ballots 1993, 2004 1993 2004 1993 Observations 842 488 354 695

* p<0.1, ** p<0.05, *** p<0.01. Standard errors in brackets. The binary dependent variable is 1 if the respondent voted yes, 0 if no. Stan- dard errors of total effect τ are from marginal effects after probit estimates. Parametric estimation with probit models for direct (δ) and indirect (η) effects. Standard errors for direct and indirect effects are based on 1,999 bootstrap iterations. The mediator Income takes on value 1 if the respondent has above average income. The confounding variables are West, Center, Center-West, Center-East, Age, Catholic, Part- ner, Education, Urban, Homeowner, and House. Based on data from VOX-surveys no. 161, 421, 511, and 862. Data are available online on http://nesstar.sidos.ch/webview/index.jsp.

38 Table 10: Sensitivity Analysis: Direct and Indirect Eﬀects - Mediator Work

(1) (2) (3) (4) (5) (6) (7) (8) (9) Sample All All All All Voters Voters Voters Voters Voters Total τ -0.057*** -0.031 -0.116*** -0.061* -0.045* 0.028 -0.098** -0.053 -0.065 (0.020) (0.038) (0.037) (0.033) (0.023) (0.047) (0.049) (0.039) (0.041)

Women Direct effect δ(1) -0.090*** -0.061 -0.151*** -0.089*** -0.079*** -0.031 -0.131 -0.100 -0.071 (0.024) (0.045) (0.048) (0.024) (0.028) (0.060) (0.107) (0.060) (0.107) Indirect effect η(1) 0.008 -0.013 0.005 0.000 0.007 -0.019 -0.001 -0.003 0.007 (0.016) (0.038) (0.034) (0.016) (0.018) (0.050) (0.046) (0.031) (0.107) 39 Men Direct effect δ(0) -0.066*** -0.018 -0.120** -0.061 -0.052* 0.047 -0.097 -0.050 -0.073 (0.025) (0.053) (0.052) (0.025) (0.029) (0.068) (0.067) (0.049) (0.099) Indirect effect η(0) 0.033** 0.030 0.036 0.029 0.034** 0.059 0.033 0.047 0.006 (0.014) (0.029) (0.034) (0.014) (0.017) (0.044) (0.102) (0.045) (0.099)

Ballots all 1981 1991 1993 all 1981 1991 1993 2004 Observations 2,375 525 671 771 1,634 287 397 542 408

* p<0.1, ** p<0.05, *** p<0.01. Standard errors in brackets. The binary dependent variable is 1 if the respondent voted yes, 0 if no. Standard errors of total effect τ are from marginal effects after probit estimates. Inverse propensity score weighted results for direct (δ) and indirect (η) effects. Standard errors for direct and indirect effects are based on 1,999 bootstrap iterations. The mediator Work takes on value 1 if the respondent was employed. Only with pre-treatment confounding variables West, Center, Center-West, Center-East, Age, Catholic. Based on data from VOX-surveys no. 161, 421, 511, and 862. Data are available online on http://nesstar.sidos.ch/webview/index.jsp. Table 11: Sensitivity Analysis: Direct and Indirect Effects - Mediator Income

(1) (2) (3) (4) Sample Voters Voters Voters All Total τ -0.092*** -0.083** -0.109** -0.084** (0.030) (0.041) (0.043) (0.034)

Women Direct eﬀect δ(1) -0.080** -0.075 -0.102** -0.078** (0.033) (0.048) (0.051) (0.038) Indirect eﬀect η(1) -0.015 -0.013 -0.010 -0.018 (0.016) (0.029) (0.044) (0.021)

Men Direct eﬀect δ(0) -0.077** -0.070 -0.099 -0.067 (0.035) (0.050) (0.061) (0.041) Indirect eﬀect η(0) -0.012 -0.008 -0.007 -0.007 (0.013) (0.028) (0.037) (0.018)

Ballots 1993, 2004 1993 2004 1993 Observations 842 488 354 695

* p<0.1, ** p<0.05, *** p<0.01. Standard errors in brackets. The binary dependent variable is 1 if the respondent voted yes, 0 if no. Standard errors of total effect τ are from marginal effects after probit estimates. In- verse propensity score weighted results for direct (δ) and indirect (η) effects. Standard errors for direct and indirect effects are based on 1,999 bootstrap iterations. The mediator Income takes on value 1 if the respondent has above average income. Only with pre-treatment confounding variables West, Center, Center-West, Center-East, Age, Catholic. Based on data from VOX-surveys no. 161, 421, 511, and 862. Data are available online on http://nesstar.sidos.ch/webview/index.jsp.

40 D Tables Propensity Scores

Table 12: Propensity Score Estimates with Mediator Work: Sample of all Respondents

(1) (2) (3) (4) (5) (6) (7) (8) Sample All All All All All All All All

Work -0.729*** -1.292*** -0.707*** -0.628*** (0.062) (0.148) (0.123) (0.106) West 0.395** 0.439** 0.063 0.092 0.397 0.370 0.357 0.432 (0.173) (0.172) (0.171) (0.161) (0.300) (0.294) (0.267) (0.267) Center 0.418** 0.468*** 0.109 0.119 0.434 0.425 0.235 0.316 (0.173) (0.172) (0.176) (0.167) (0.297) (0.291) (0.266) (0.265) Center-West 0.423** 0.457*** 0.038 -0.093 0.366 0.376 0.429 0.526** (0.173) (0.172) (0.163) (0.155) (0.296) (0.290) (0.266) (0.267) Center-East 0.407** 0.447*** 0.320 0.327 0.369 0.467* (0.172) (0.171) (0.295) (0.289) (0.263) (0.263) Age -0.013*** -0.003** -0.026*** -0.007** -0.014*** -0.005 -0.013*** -0.007** (0.002) (0.002) (0.004) (0.003) (0.003) (0.003) (0.003) (0.003) Catholic -0.022 -0.024 -0.049 -0.066 -0.109 -0.091 0.006 -0.002 (0.057) (0.056) (0.127) (0.120) (0.107) (0.105) (0.100) (0.099) Partner 0.020 -0.032 -0.122 -0.076 0.100 0.065 0.154 0.076 (0.057) (0.056) (0.125) (0.121) (0.108) (0.106) (0.102) (0.100) Education -0.314*** -0.291*** -0.267* -0.239 -0.434*** -0.340*** -0.380*** -0.393*** (0.059) (0.057) (0.160) (0.151) (0.123) (0.119) (0.101) (0.099) Urban 0.170*** 0.177*** 0.227* 0.263** 0.018 0.004 0.167 0.167 (0.059) (0.058) (0.126) (0.120) (0.115) (0.113) (0.107) (0.106) Houseowner -0.048 -0.041 0.427** 0.404** -0.212 -0.205 -0.302** -0.272** (0.073) (0.071) (0.173) (0.162) (0.142) (0.140) (0.131) (0.129) House 0.069 0.066 -0.192 -0.184 0.161 0.158 0.151 0.122 (0.073) (0.072) (0.172) (0.161) (0.147) (0.144) (0.133) (0.131) Constant 0.563*** -0.361* 1.877*** 0.151 0.708* -0.204 0.576* -0.150 (0.211) (0.194) (0.322) (0.235) (0.388) (0.347) (0.334) (0.310)

Ballots All All 1981 1981 1991 1991 1993 1993 Observations 2,375 2,375 525 525 671 671 771 771 * p<0.1, ** p<0.05, *** p<0.01. Standard errors in brackets. Propensity score estimates (probit model) with mediator Work. Dependent variable is dummy being 1 for female, 0 for men. Odd columns are P (G = 1|M,C). Even columns are P (G = 1|C). Based on data from VOX-surveys no. 161, 421, 511, and 862.

41 Table 13: Propensity Score Estimates with Mediator Work: Sample of Voters

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) VARIABLES Voters Voters Voters Voters Voters Voters Voters Voters Voters Voters

Work -0.687*** -1.401*** -0.526*** -0.751*** -0.353** (0.075) (0.212) (0.153) (0.130) (0.154) West 0.517** 0.573*** 0.545** 0.546** 0.784 0.769 0.382 0.502 0.361 0.380 (0.221) (0.220) (0.241) (0.224) (0.480) (0.468) (0.341) (0.337) (0.407) (0.410) Center 0.497** 0.538** 0.559** 0.518** 0.582 0.536 0.250 0.360 0.661 0.699* (0.220) (0.219) (0.247) (0.233) (0.470) (0.457) (0.339) (0.336) (0.409) (0.412) Center-West 0.427* 0.472** 0.219 0.033 0.300 0.288 0.535 0.726** 0.556 0.556 (0.221) (0.219) (0.219) (0.205) (0.472) (0.460) (0.342) (0.338) (0.408) (0.412) Center-East 0.448** 0.489** 0.426 0.418 0.506 0.650** 0.604 0.588 (0.219) (0.218) (0.473) (0.461) (0.334) (0.331) (0.408) (0.411) Age -0.014*** -0.005** -0.031*** -0.009* -0.018*** -0.012*** -0.015*** -0.006* -0.000 0.005 (0.002) (0.002) (0.006) (0.005) (0.005) (0.004) (0.004) (0.003) (0.005) (0.004) Catholic 0.036 0.037 -0.167 -0.229 0.138 0.157 0.046 0.068 0.123 0.112 (0.069) (0.068) (0.180) (0.171) (0.141) (0.139) (0.122) (0.119) (0.142) (0.141) Partner -0.040 -0.120* -0.109 -0.055 0.106 0.063 0.093 -0.045 -0.163 -0.202

42 (0.070) (0.069) (0.183) (0.176) (0.146) (0.145) (0.128) (0.124) (0.141) (0.139) Education -0.276*** -0.273*** -0.109 -0.158 -0.351** -0.294* -0.355*** -0.388*** -0.325** -0.323** (0.068) (0.067) (0.208) (0.197) (0.153) (0.150) (0.120) (0.117) (0.130) (0.130) Urban 0.245*** 0.240*** 0.471*** 0.438*** 0.193 0.146 0.173 0.165 0.240* 0.251* (0.071) (0.070) (0.179) (0.168) (0.156) (0.153) (0.129) (0.127) (0.142) (0.141) Houseowner -0.032 -0.018 0.470** 0.356* -0.321* -0.259 -0.218 -0.186 0.001 -0.001 (0.085) (0.084) (0.228) (0.210) (0.192) (0.188) (0.154) (0.150) (0.164) (0.163) House 0.124 0.118 -0.007 -0.033 0.456** 0.414** 0.070 0.047 0.002 0.008 (0.085) (0.084) (0.222) (0.204) (0.196) (0.193) (0.156) (0.152) (0.157) (0.156) Constant 0.455* -0.409* 1.737*** -0.131 0.331 -0.270 0.665 -0.271 -0.218 -0.718 (0.264) (0.245) (0.466) (0.343) (0.559) (0.518) (0.426) (0.389) (0.517) (0.474)

Ballots All All 1981 1981 1991 1991 1993 1993 2004 2004 Observations 1,634 1,634 287 287 397 397 542 542 408 408 * p<0.1, ** p<0.05, *** p<0.01. Standard errors in brackets. Propensity score estimates (probit model) with mediator Work. Dependent variable is dummy being 1 for female, 0 for men. Odd columns are P (G = 1|M,C). Even columns are P (G = 1|C). Based on data from VOX-surveys no. 161, 421, 511, and 862. Table 14: Propensity Score Estimates with Mediator Income

(1) (2) (3) (4) (5) (6) (7) (8) VARIABLES Voters Voters Voters Voters Voters Voters All All

Income -0.255** -0.290** -0.209 -0.218** (0.101) (0.133) (0.167) (0.108) West 0.480* 0.483* 0.458 0.471 0.384 0.359 0.264 0.287 (0.270) (0.269) (0.355) (0.351) (0.430) (0.430) (0.286) (0.285) Center 0.521* 0.504* 0.368 0.344 0.734* 0.715* 0.206 0.202 (0.269) (0.268) (0.352) (0.348) (0.429) (0.429) (0.284) (0.283) Center-West 0.725*** 0.697*** 0.763** 0.708** 0.575 0.561 0.418 0.400 (0.272) (0.270) (0.356) (0.352) (0.431) (0.430) (0.285) (0.283) Center-East 0.697*** 0.660** 0.690** 0.656* 0.665 0.620 0.352 0.334 (0.270) (0.268) (0.350) (0.346) (0.432) (0.431) (0.282) (0.281) Age -0.003 -0.002 -0.009** -0.008** 0.004 0.006 -0.009*** -0.008*** (0.003) (0.003) (0.004) (0.004) (0.005) (0.005) (0.003) (0.003) Catholic 0.181* 0.169* 0.183 0.165 0.222 0.215 0.059 0.051 (0.097) (0.097) (0.127) (0.127) (0.156) (0.155) (0.104) (0.104) Partner -0.140 -0.218** -0.027 -0.096 -0.239 -0.320** 0.062 0.012 (0.102) (0.097) (0.135) (0.130) (0.165) (0.151) (0.108) (0.105) Education -0.321*** -0.384*** -0.360*** -0.436*** -0.326** -0.360*** -0.373*** -0.426*** (0.094) (0.091) (0.130) (0.125) (0.142) (0.139) (0.109) (0.105) Urban 0.265*** 0.226** 0.188 0.127 0.302** 0.292* 0.145 0.116 (0.100) (0.099) (0.136) (0.133) (0.153) (0.153) (0.113) (0.112) Houseowner -0.039 -0.070 -0.192 -0.238 0.157 0.143 -0.228 -0.270* (0.118) (0.117) (0.163) (0.162) (0.179) (0.178) (0.141) (0.139) House 0.047 0.048 0.090 0.098 -0.068 -0.066 0.125 0.134 (0.115) (0.115) (0.164) (0.164) (0.169) (0.169) (0.141) (0.141) Constant -0.345 -0.407 -0.076 -0.106 -0.643 -0.749 0.162 0.108 (0.312) (0.310) (0.407) (0.404) (0.514) (0.507) (0.332) (0.329)

Ballots 1993, 2004 1993, 2004 1993 1993 2004 2004 1993 1993 Observations 842 842 488 488 354 354 695 695 * p<0.1, ** p<0.05, *** p<0.01. Standard errors in brackets. Propensity score estimates (probit model) with mediator Income. Dependent variable is dummy being 1 for female, 0 for men. Odd columns are P (G = 1|M,C). Even columns are P (G = 1|C). Based on data from VOX-surveys no. 511, and 862.

43 E Propensity Score Histograms

a P (G = 1|C) for Women b P (G = 1|C) for Men 6 6 5 5 4 4 3 3 Density Density 2 2 1 1 0 0 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 P(C) P(C) c P (G = 1|M,C) for Women d P (G = 1|M,C) for Men 6 6 5 5 4 4 3 3 Density Density 2 2 1 1 0 0 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 P(M,C) P(M,C)

Figure 7: Histograms of propensity scores from probit estimates for the 1981 survey (all respondents) with mediator Work. P (M,C) propensity scores control for the mediator and confounders, P (C) only for the latter. Corresponds to column (2) in Table 6. a: Propensity scores P (C) based on confounders for women; b: propensity scores P (C) based on confounders for men; c: propensity scores P (M,C) based on mediator and confounders for women; d: propensity scores P (M,C) based on mediator and confounders for men. Based on data from VOX-surveys no. 161.

44 a P (G = 1|C) for Women b P (G = 1|C) for Men 6 8 5 6 4 3 4 Density Density 2 2 1 0 0 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 P(C) P(C) c P (G = 1|M,C) for Women d P (G = 1|M,C) for Men 6 6 5 5 4 4 3 3 Density Density 2 2 1 1 0 0 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 P(M,C) P(M,C)

Figure 8: Histograms of propensity scores from probit estimates for the 1991 survey (all respondents) with mediator Work. P (M,C) propensity scores control for the mediator and confounders, P (C) only for the latter. Corresponds to column (3) in Table 6. a: Propensity scores P (C) based on confounders for women; b: propensity scores P (C) based on confounders for men; c: propensity scores P (M,C) based on mediator and confounders for women; d: propensity scores P (M,C) based on mediator and confounders for men. Based on data from VOX-surveys no. 421.

Figure 9: Histograms of propensity scores from probit estimates for the 1993 survey (all respondents) with mediator Work. P (M,C) propensity scores control for the mediator and confounders, P (C) only for the latter. Corresponds to column (4) in Table 6. a: Propensity scores P (C) based on confounders for women; b: propensity scores P (C) based on confounders for men; c: propensity scores P (M,C) based on mediator and confounders for women; d: propensity scores P (M,C) based on mediator and confounders for men. Based on data from VOX-surveys no. 511.

45 a P (G = 1|C) for Women b P (G = 1|C) for Men 6 6 5 5 4 4 3 3 Density Density 2 2 1 1 0 0 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 P(C) P(C) c P (G = 1|M,C) for Women d P (G = 1|M,C) for Men 6 6 5 5 4 4 3 3 Density Density 2 2 1 1 0 0 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 P(M,C) P(M,C)

Figure 10: Histograms of propensity scores from probit estimates for full sample (only voters) with mediator Work. P (M,C) propensity scores control for the mediator and confounders, P (C) only for the latter. Corresponds to column (5) in Table 6. a: Propensity scores P (C) based on confounders for women; b: propensity scores P (C) based on confounders for men; c: propensity scores P (M,C) based on mediator and confounders for women; d: propensity scores P (M,C) based on mediator and confounders for men. Based on data from VOX-surveys no. 161, 421, 511, and 862.

Figure 11: Histograms of propensity scores from probit estimates for the 1981 survey (only voters) with mediator Work. P (M,C) propensity scores control for the mediator and confounders, P (C) only for the latter. Corresponds to column (6) in Table 6. a: Propensity scores P (C) based on confounders for women; b: propensity scores P (C) based on confounders for men; c: propensity scores P (M,C) based on mediator and confounders for women; d: propensity scores P (M,C) based on mediator and confounders for men. Based on data from VOX-surveys no. 161.

46 a P (G = 1|C) for Women b P (G = 1|C) for Men 6 6 5 5 4 4 3 3 Density Density 2 2 1 1 0 0 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 P(C) P(C) c P (G = 1|M,C) for Women d P (G = 1|M,C) for Men 6 6 5 5 4 4 3 3 Density Density 2 2 1 1 0 0 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 P(M,C) P(M,C)

Figure 12: Histograms of propensity scores from probit estimates for the 1991 survey (only voters) with mediator Work. P (M,C) propensity scores control for the mediator and confounders, P (C) only for the latter. Corresponds to column (7) in Table 6. a: Propensity scores P (C) based on confounders for women; b: propensity scores P (C) based on confounders for men; c: propensity scores P (M,C) based on mediator and confounders for women; d: propensity scores P (M,C) based on mediator and confounders for men. Based on data from VOX-surveys no. 421.

Figure 13: Histograms of propensity scores from probit estimates for the 1993 survey (only voters) with mediator Work. P (M,C) propensity scores control for the mediator and confounders, P (C) only for the latter. Corresponds to column (8) in Table 6. a: Propensity scores P (C) based on confounders for women; b: propensity scores P (C) based on confounders for men; c: propensity scores P (M,C) based on mediator and confounders for women; d: propensity scores P (M,C) based on mediator and confounders for men. Based on data from VOX-surveys no. 511.

47 a P (G = 1|C) for Women b P (G = 1|C) for Men 6 6 5 5 4 4 3 3 Density Density 2 2 1 1 0 0 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 P(C) P(C) c P (G = 1|M,C) for Women d P (G = 1|M,C) for Men 6 6 5 5 4 4 3 3 Density Density 2 2 1 1 0 0 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 P(M,C) P(M,C)

Figure 14: Histograms of propensity scores from probit estimates for the 2004 survey (only voters) with mediator Work. P (M,C) propensity scores control for the mediator and confounders, P (C) only for the latter. Corresponds to column (9) in Table 6. a: Propensity scores P (C) based on confounders for women; b: propensity scores P (C) based on confounders for men; c: propensity scores P (M,C) based on mediator and confounders for women; d: propensity scores P (M,C) based on mediator and confounders for men. Based on data from VOX-surveys no. 862.

Figure 15: Histograms of propensity scores from probit estimates for the surveys from 1993 and 2004 with mediator Income. P (M,C) propensity scores control for the mediator and confounders, P (C) only for the latter. Corresponds to column (1) in Table 7. a: Propensity scores P (C) based on confounders for women; b: propensity scores P (C) based on confounders for men; c: propensity scores P (M,C) based on mediator and confounders for women; d: propensity scores P (M,C) based on mediator and confounders for men. Based on data from VOX-surveys no. 511, and 862.

48 a P (G = 1|C) for Women b P (G = 1|C) for Men 6 6 5 5 4 4 3 3 Density Density 2 2 1 1 0 0 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 P(C) P(C) c P (G = 1|M,C) for Women d P (G = 1|M,C) for Men 6 6 5 5 4 4 3 3 Density Density 2 2 1 1 0 0 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 P(M,C) P(M,C)

Figure 16: Histograms of propensity scores from probit estimates for the 1993 survey (only voters) with mediator Income. P (M,C) propensity scores control for the mediator and confounders, P (C) only for the latter. Corresponds to column (2) in Table 7. a: Propensity scores P (C) based on confounders for women; b: propensity scores P (C) based on confounders for men; c: propensity scores P (M,C) based on mediator and confounders for women; d: propensity scores P (M,C) based on mediator and confounders for men. Based on data from VOX-surveys no. 511.

Figure 17: Histograms of propensity scores from probit estimates for the 2004 survey (only voters) with mediator Income. P (M,C) propensity scores control for the mediator and confounders, P (C) only for the latter. Corresponds to column (3) in Table 7. a: Propensity scores P (C) based on confounders for women; b: propensity scores P (C) based on confounders for men; c: propensity scores P (M,C) based on mediator and confounders for women; d: propensity scores P (M,C) based on mediator and confounders for men. Based on data from VOX-surveys no. 862.

49 a P (G = 1|C) for Women b P (G = 1|C) for Men 6 6 5 5 4 4 3 3 Density Density 2 2 1 1 0 0 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 P(C) P(C) c P (G = 1|M,C) for Women d P (G = 1|M,C) for Men 6 6 5 5 4 4 3 3 Density Density 2 2 1 1 0 0 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 P(M,C) P(M,C)

Figure 18: Histograms of propensity scores from probit estimates for the 1993 survey (all respondents) with mediator Income. P (M,C) propensity scores control for the mediator and confounders, P (C) only for the latter. Corresponds to column (4) in Table 7. a: Propensity scores P (C) based on confounders for women; b: propensity scores P (C) based on confounders for men; c: propensity scores P (M,C) based on mediator and confounders for women; d: propensity scores P (M,C) based on mediator and confounders for men. Based on data from VOX-surveys no. 511.

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