Do female mayors make a difference? Evidence from -South Tyrol

MASTER THESIS

Submitted in Partial Fulfilment of the Requirements for the Degree of MASTER OF SCIENCE in Strategic Management

A.Univ.-Prof. Mag. Dr. Kurt PROMBERGER Department of Strategic Management, Marketing and Tourism The University of Innsbruck School of Management

Submitted by Tobias GATTERER

Innsbruck, May 2020

Abstract

Gender role differences always existed and different attributes were assigned to both, male and female role models. Every day we are surrounded by stereotypes. Especially in politics, these stereotypes are still considered very strong. While for example male mayors are often viewed as a ruling individual, with a strong mindset, female mayors are viewed in a more social way, caring more about social topics and the ideals of responsibility. However, we still lack knowledge whether these stereotypes also occur once women receive a political position such as mayor. Thus, it was the aim of this study to investigate whether there is a difference between men and women regarding the usage of the yearly budget at municipality level. 34 municipalities in Trentino-South Tyrol with female mayors were investigated and compared with male leading municipalities by looking at the yearly budget from 2016-2018. Significant differences were found for the public savings rate, the Missions “education and right to study”, “youth policies, sports and leisure”, as well as all Missions in 2018. These results lead to the conclusion that even though there might be some minor differences, each mayor abides by certain rules and does not have a lot of leeway regarding the distribution of the budget.

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Table of contents Table of contents ...... 3 Introduction ...... 6 Literature review ...... 7 Factors influencing why women are not voted ...... 8 Differences of women and men in politics ...... 8 Women in politics ...... 11 Differences in financial performance ...... 12 Calculating the financial performance...... 14 The municipality missions ...... 15 Aim of the thesis ...... 19 Method ...... 19 Research approach ...... 19 Sample ...... 20 Data collection ...... 22 Measures ...... 23 Analytical approach ...... 24 Test 1 - key figures...... 24 Test 2 – difference in social areas...... 26 Results ...... 27 Test 1 – key figures ...... 27 Test 1 – key figures by groups ...... 28 EFQ...... 30 Group 2 (500-1000)...... 30 Group 3 (1001-2500)...... 30 Group 4 (2501-5000)...... 30 Group 5 (5001-10000)...... 30 FSQ...... 30 Group 2 (500-1000)...... 30 Group 3 (1001-2500)...... 31 Group 4 (2501-5000)...... 31 Group 5 (5001-10000)...... 31 ÖSQ...... 31 Group 2 (500-1000)...... 31 Group 3 (1001-2500)...... 31

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Group 4 (2501-5000)...... 32 Group 5 (5001-10000)...... 32 SDSQ...... 32 Group 2 (500-1000)...... 32 Group 3 (1001-2500)...... 32 Group 4 (2501-5000)...... 32 Group 5 (5001-10000)...... 32 Test 2 – missions ...... 33 Missions (4 / 5 / 6 / 12)...... 33 Ʃ Missions (2016 / 2017 / 2018)...... 33 Test 2 – missions by group ...... 35 Group 2 (500-1000)...... 35 Group 3 (1001-2500)...... 35 Group 4 (2501-5000)...... 36 Group 5 (5001-10000) ...... 37 Discussion ...... 39 Differences regarding EFQ, FSQ, ÖSQ and SDSQ ...... 39 Differences by groups...... 41 Differences by missions...... 43 Differences between missions by group...... 44 Theoretical and managerial implications ...... 45 Limitations ...... 47 Conclusion ...... 47

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List of tables

Table 1: Areas of responsibility according to gender from 2017 (data taken from Atz et al., 2019) ...... 9 Table 2: Gender distribution of political areas in Tulln, Austria (Kletzer & Neumayr, 2006) 13 Table 3: Investigated municipalities and their population divided by gender of mayor ...... 21 Table 4: Reference values for EFQ, FSQ, ÖSQ and SDSQ ...... 25 Table 5: Mean, median and t-test results for all municipalities and differentiated between female and male leading municipalities ...... 27 Table 6: Mean, median and t-test results for group 2, 3, 4 and 5, differentiated between female and male leading municipalities ...... 29 Table 7: Mean, median and t-test results according to Missions and differentiated between female and male leading municipalities ...... 34 Table 8: Mean, median and t-test results according to groups and Missions, and differentiated between female and male leading municipalities ...... 38

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Introduction “Ladies and gentlemen, I stand before you tonight in my green chiffon evening gown, my face softly made up, my fair hair gently waved…the Iron Lady of the Western World” (Margaret Thatcher, quoted in Charteris-Black, 2005, p. 87).

Gender role differences always existed and different attributes were assigned to both, male and female role models. Margaret Thatcher, one of the most formative figures in the 1980s in politics, was often described as strong, dominant and competitive, especially for her way of doing politics (Steinberg, 2008), even though such attributes would normally be associated with men (Heilman, 2012). The choice of Thatcher’s words in her speech to the Finchley Conservatives seem deliberate to make her point as a woman in a political world mainly governed by men (Charteris-Black, 2005).

Every day we are surrounded by stereotypes, also in politics, and they seem to get more accepted. Especially gender stereotypes are very common: Montgomery and Norton (1981) found that women have a different communication style than men. They value interpersonal political relationships more than men and prefer a protective communication strategy (Montgomery & Norton, 1981). Men, on the other hand, are perceived as more ambitious, anti- supportive and aggressive within their communication style (Warfel, 1984).

The same goes for media content where woman play a big role. In 1977, the U.S Commission on Civil Rights reported that there is a lot of media content where women were only seen in traditional family roles and social contexts (Tuchman, G. 1979). Over long periods, this media content could also have led to capture women only in this kind of “feminine” context, where they are inhibited by their gender role expectations.

Such stereotypes lead to several questions: Do women behave differently than men in political positions? If so, does it influence the governments financial performance? Why are there in general more men than women in the position of mayor? Do we really need more women in decision making positions, which influence the whole world with their economy? Does it make a difference? In order to get an answer to these questions, we need to take a look at the gender distribution in positions as mayor from the past and today.

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Literature review «La Femme a le droit de monter sur l’échafaud; elle doit avoir également celui de monter à la Tribune.» [Women have the right to climb on the scaffold; they must also have the right of going up to the tribune] (Gouges, 1791, Article 10). During the French Revolution, freedom and equality for everyone was claimed. This led to a new way of equal thinking and is often referred to as the trigger point of establishing politics also for women (Mesmer, 1988).

At the end of the 1800s, a new way of understanding gender culture transpired in Switzerland. Differences between men and woman were seen in a new perspective and those differences were used to establish a new understanding of physical and cultural abilities (Hausen, 1976). This new “male domination” split gender stereotype abilities apart. Every gender had their own abilities, their own way of working and their own reserved tasks. Even though every gender had their own tasks and abilities, teamwork and fusion between men and women was the only way of this understanding and concept working (Bourdieu, 1997).

However, in the beginning of the 20th century, step by step, woman got more accepted to take part in political actions. In 1906, Finland was the first European country to create a law that enabled women to vote (Jad, 2014). This was the first time in European history that women were allowed to vote. Following this first step, it did not take long for the first women to get elected as mayor. In Austria, history about the first female mayor is controversial. Zenzi Hölzl from the SPÖ is considered the first female mayor in Austria (Gloggnitz) from 1948-1958 (Rathkolb, 2010). However, Steininger (2000) considers Maria Krenn in Groß-Siegharts (Niederösterreich) in 1953 as Austria’s’ very first female mayor. From then on, more and more focus of social and political interest is given to women in the political arena. Specific attention is given to women’s involvement in organizations, especially in political actions. The number of women seeking and gaining political involvement is increasing, which suggests there is a breakdown in the barriers to political offices encountered by the female part of the population (Dubeck, 1976).

Steps to include more women have also been made in the European Union. In 2012, about 32% of the associates of regional assemblies and municipalities in Europe were ruled by women (European Commission, 2013). As a pioneer example, Spain is one of the countries ranging above average, with about 35% of women’s participation – a big sign for equality (European Commission, 2013).

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Factors influencing why women are not voted

Even though nowadays women take part in elections and can be equally voted for like men, there are lots of factors and theories, which can influence the election process and make it difficult for women to get voted (Atz, Bernhart, & Promberger, 2019). Deber (1982) explains the under-representations of women in political offices by four different categories:

1. Self-selection (less women than men want to be part of political offices)

2. Targeting (there is a stronger competition for women and less chances for them to be voted)

3. Political resources (Women have less historical background in politics and less support from parties than men)

4. Sexism

Even though there is only little evidence for the last category (Sexism), the other categories influence the election process for women, and evidence was found in qualitative and quantitative studies on why women don’t get elected in Austria (Hofer & Wolfgruber, 1999). During the municipality elections in March 1999 in Salzburg, about 32 interviews were held in 12 municipalities with the top candidates for the position as mayor. Evidence for reasons of the underrepresentation of women at local and political level was found; 401 telephone interviews with the population additionally proved the previously mentioned categories (Hofer & Wolfgruber, 1999).

Despite the above-mentioned challenges, the numbers of women in political parties and positions are constantly rising. Simultaneously, there are theories claiming that women act differently in the same political positions compared to men (Atz et al., 2019). Women tend to bring the “typical female” qualities into their work. They have a different style of managing things than men and tend to be more communicative (Araujo & Tejedo-Romero, 2016).

Differences of women and men in politics

The participation of both, women and men, in politics (communities) and the participation on decisions is essential for the quality of the commune policy. Through different point of views and perspectives from both genders, the different interests of the commune’s people can be better considered. A higher participation of women is therefore not only desirable due to democratic considerations, but also due to social, political and economic considerations.

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Women might bring other topics and questions to the political discussion, whereby the spectrum of these discussion is extended (Reinwald, Damyanovic, & Weber, 2011).

In addition, an increase of women in political and social parties as council members or mayors influenced the structure and power of organizations (Batista-Medina, 2015). This power access can also have a positive influence on economic and social outcomes (OECD 2014). Studies in Sweden showed that a bigger proportion of women in political parties influence the economy, income and employment of the country in a positive way (Wängnerud & Sundell, 2012). Details on the financial aspects will be further discussed in the parts “Differences in financial performance” (page 12-13) and “Calculating the financial performance” (page 14-15). Research in Northern (South-Tyrol) was conducted regarding the different responsibilities in municipalities between men and women. The social resorts “Social rights, social & family politics”, “Education & right to education”, “Protection and enhancement of cultural goods and activities”, “Youth, sports and leisure”, defined in the study, were researched including participants of both genders. The results were clear, as 45 to 76 % of these resorts were ruled by women only (see Table 1). This study showed impressively how women tend to get and choose political responsibilities in the social sector (Atz et al., 2019).

Table 1: Areas of responsibility according to gender from 2017 (data taken from Atz et al., 2019)

Male Female Area n % n % Social rights, social and family policy 35 24.3 109 75.0 Education and the right to education 42 31.3 92 68.7 Protection and enhancement of cultural goods and activities 47 36.2 83 63.8 Youth, sports and leisure 115 55.0 94 45.0 Spatial planning and housing 86 76.1 27 23.9 Concern a faction 78 76.5 24 23.5 Economic development and competitiveness 98 79.0 26 21.0 Tourism 76 79.2 20 20.8 Sustainable development and protection of the territory and the 209 82.0 46 18.0 environment Transport and right to promote mobility 106 82.8 22 17.2 Institutional services, administration and management 192 83.8 37 16.2 Public order and security 56 88.9 7 11.1 Ambulance and civil protection 69 90.8 7 9.2 All areas not explicitly assigned 8 100.0 0 0.0

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According to Geser (2009), women and men have different preferences in terms of political areas, topics and resorts. Even though women are getting more and more into traditionally male- intended areas and topics, there is still an unconscious gap between interpersonal communication of both genders. While areas ruled by men are more technical and organizational including judiciary, construction and finance, women tend to choose areas including education, healthcare or social aspects (Hoecker, 1998), also referred to as soft resorts. Details on soft resorts will be further discussed in “The municipality Missions” (page 15-18).

Similar results were found by Shapiro (1981-1982), who showed that female candidates for a political position were more likely to be perceived as competent in three areas: "improving our educational system," "maintaining honesty & integrity in government," and "dealing with health problems" (p.69). Men, on the other hand, were more likely to be rated as competent in "dealing with military issues" and "making decisions on farm issues” (Shapiro, 1981-1982, p. 71).

This was also underlined in a comparative analysis from 2500 Swiss parties, showing that sections with a high percentage of woman seem very different than sections with a high percentage of men in different aspects. Parties with more woman tend to be more communicative than parties mostly dominated by men (Geser, 2009).

When splitting key items of political work into female and male, Rosenwasser and colleagues (1987) assigned dealing with terrorism or crime, military issues and ensuring an adequate military defence system to the male part. This role of Commander-in-Chief of the Armed Forces had to be fulfilled by the president itself and was pretended to be masculine. In contrast, women were assigned to solving issues in the educational system, guaranteeing the rights of racial minorities, solving problems of people with disabilities and handicaps, and dealing with issues of the older generations (Rosenwasser, Rogers, Fling, Silvers-Pickens, & Butemeyer, 1987).

It seems that if a woman achieves a higher position in a political office, new areas are created to keep her out of the “men’s business”. This was the case with Hertha Firnberg, who had to overtake the new founded ministry of science & research, or Susanne Riess-Passer, who was vice chancellor and had to lead the public sport services (Steininger, 2000).

Women’s political priorities can also be seen also in their electoral behaviour. The Centre for the American Woman in Politics states that women, in comparison to men, stand for politics

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without military commitment, which controls the possession of weapons, looks more on the protection of the environment and helps political disadvantaged people from different origin or skin colour (Steininger, 2000).

Women in politics

Woman in politics were described by Costantini and Craik (1972, p. 223) with three main attributes:

Self-Confidence: Assertive, affiliative, outgoing, persistent – willing to take action. Things need to get done, impatience with people standing in the way. Good impressions matter a lot.

Dominance: Forceful, strong-willed and persevering individual. Very confident in her abilities and direct in her behaviour.

Achievement: Seen by others as intelligent and hardworking. Determined to do well and usually succeeds. Goal-centred motives rather than competitive ones. Dealings with others in a trustful and optimistic way.

According to this classification, female leaders tend to take a more forceful, effective and socially ascendant way with an ambivalent manner, while male leaders do it quite differently. They tend to express a similar personal style in a more easy-going, direct and uncomplicated way (Costantini & Craik, 1972).

Additionally, female leaders tend to adopt a different style of leadership than male leaders (Kim & Shim, 2003). While male leaders tend to adapt to rules and individualism, women tend to take care of the ideals of responsibility. They seem to be more communicative, cooperative, democratic and open for different ideas and are more concerned in the well-being of others (Eagly & Karau, 2002). Generally, it seems that women focus more upon local issues. They are more responsive to actions where moral plays a role. They seem to be more sensitive in personal matters and less comfortable with political conflict and contention (Almond & Verba, 1965).

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Differences in financial performance

There exists a lot of evidence that women tend to use different qualities and behaviours than men. A lot of research has been done about why women do not get elected, about the differences of men and women in political parties, what factors influence political behaviour and if women do act differently than men in the same political position.

However, less research was conducted on how women influence the structures and processes of political activities, financially and in regard to the distribution of the yearly available budget. This leads to the question, whether women do really make an economical difference when they occupy political positions.

Studies by Atz and colleagues (2019) showed that women tend to work more in and for social areas. At the community level, there are many decisions, that are not only socially, but also politically and economically relevant, on how and where to spend the major part of the budget. This could for example include the decision on how much to spend on maintenance of sport grounds, building a new bus station, better health care for children, a new kinder garden etc. No matter where the budget is spent, priorities have to be set, which will not always satisfy all the community’s members requests or desires (Atz et al., 2019).

A gender budget initiative by Klatzer, Neumayr, (2006) tried to show differences on how men and women in different political positions in municipalities influence the budget distribution. Table 2 shows an example of the distribution of men and women in the political areas of the Austrian municipality Tulln. In this example, evidence for cross connections to traditional role models can be found: Women rather work in social areas, which could be traced back to traditional gender role allocations. In these “soft or social” areas, the budget often seems to be lower than in the “non-social” areas.

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Table 2: Gender distribution of political areas in Tulln, Austria (Kletzer & Neumayr, 2006)

Area Men Women Construction & Planning 9 1

Finance and Human Resources 9 1 Canal and Water pipes 9 1 Sports 9 1 Public facilities 9 1 Order & Security 8 2 Environment & Healthcare 8 2 Tourism 7 3 Family 5 5 Accommodation & Social 5 5 School, Youth & Culture 5 5

It is at municipality level, where most of the important decisions and actions happen between the government and the citizens (Sandoval-Almazan & Ramon Gil-Garcia, 2012). Municipalities take a greater role and responsibility in the well-being of their citizens as they are much closer to them as the government is. Through being closer to their people, the relationship between the government and the population is improved and trust into the government increased.

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Calculating the financial performance.

To get a financial overview of the performance of each municipality, different measures can be used. In a study in Austria, Spiss, Promberger, Bodemann and Mayr (2010) used a comparative key figure analysis to get an overview of the financial situation of the municipalities of Ötztal. This comparative key figure analysis consists of five different measurement figures, which I will use to compare the financial performance of each municipality (lead by women compared to lead by men) in Trentino-South Tyrol:

− Self-financing rate (EFQ) − Free financial peak (FSQ) − Public savings rate (ÖSQ) − Debt service tax rate (SDSQ) − Indebtedness (VSD) − Asset development (SEQ)

Except for Indebtedness (VSD) and Asset development (SEQ), I will use the other four measurement figures (EFQ, FSQ, ÖSQ and SDSQ) as a basis to compare the financial performance of each municipality lead by women compared to each municipality lead by men in Trentino-South Tyrol. The Indebtedness cannot be calculated due to a missing amount of different numbers/values of the municipalities, such as loans, present value of the leasing obligations and liabilities from the past. Also, the asset development encounters great problems as a calculation is currently not possible due to the lack of comprehensive property and depreciation lists. The debt service tax rate will only be calculated approximately due to restrictions of numbers.

The self-financing rate (EFQ) shows the extent to which the current expenses and the capital expenditure (expenditure on asset management without financial transactions) can be covered by the current earnings and income on capital (earnings on asset management without financial transactions). If there is a steady decline, this means that the scope for self-financing will be reduced in the long term and the difference to 100 can only be financed through net new debt or the reversal of reserves (for details, refer to Biwald, 2005).

The free financial peak (FSQ) shows the future financial scope of a municipality within which new projects or measures can be implemented (please refer to Enzinger & Papst, 2009). To calculate the free financial peak, the economic result is calculated, which is the difference

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between the current earnings, the current expenses and the loan repayments (ongoing ordinary repayments).

The public savings rate (ÖSQ), also called profitability, represents the ratio of the cross-section current income to the cross-section current expenditure and thus shows the extent to which funds are used to repay and finance expenditures on asset management, and are available for new investments and the associated follow-up costs. The higher the public savings rate, the more funds will be available to the community in the future. A negative savings rate shows that the communities are financially difficult and serious remedial measures must be considered. A value of zero shows that the current income only covers current expenses. Also, here, measures on how to improve the financial situation should be considered (Biwald, 2005).

The debt service tax rate (SDSQ) shows the share of own taxes and income shares, which is to be spent on debt service, while the debt duration (VSD) is an indicator that can be used for the indebtedness of a community (Biwald, 2005). To calculate the SDSQ, one needs to know the debt service, which includes the sum of loan repayments and liability interests (subsidies for redemption of loans & interest deducted). Subsidies for redemption of loans & interest are at the individual municipality level in Italy not available. Therefore, I will use the liability interests without the deduction of the subsidies for redemption of loans & interest.

The municipality missions

The theoretical background needed for this thesis is not yet complete, as the Missions are not completely defined yet. Due to the legislative decree of the 23rd of June 2011, Nr. 118, different categories (called Missions) have been defined, which are equal for every municipality in Trentino-South Tyrol and build the basis of the yearly revenues and expenditures of those:

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1. Institutional, general and management services

2. Justice

3. Public order and security

4. Education and right to study

5. Protection and enhancement of cultural assets and activities

6. Youth policies, sports and leisure

7. Tourism

8. Land use and housing construction

9. Sustainable development and protection of the territory and the environment

10. Transport and the right to mobility

11. Civil aid

12. Social rights, social policies and family

13. Health protection

14. Economic development and competitiveness

15. Policies for work and professional training

16. Agriculture, agri-food policies and fisheries

17. Energy and diversification of energy sources

18. Relations with other territorial and local autonomies

19. International relations

20. Funds and provisions

50. Public debt

60. Financial advances

99. Services for third parties

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In their book on gender differences in municipality politics, Atz et al. (2019) tested which competencies women “own” in municipality politics and which gender is mostly responsible for the “social resorts/competencies”. In order to do so, they subdivided the Missions into four so called “social resorts”, which will be used as the basis of this empirical, quantitative study.

These four Missions are:

- MISSION 4 Education and right to study

- MISSION 5 Protection and enhancement of cultural assets and activities

- MISSION 6 Youth policies, sports and leisure

- MISSION 12 Social rights, social policies and family

However, the four Missions have still to be defined in detail. It is still unknown, if every municipality has the same understanding of “Education and right to study”, “Protection and enhancement of cultural assets and activities“, “Youth policies, sports and leisure” or “Social rights, social policies and family”. As this topic has not yet been researched, no clear definition of it exists. The distribution of the budget within each individual Mission is made by the mayor and the accounting team. Every municipality in Trentino-South Tyrol has an equal internal accounting software, which gives a structural overview of each Mission and its components and sub-components. Based on these internal accounting information of the municipality “Mühlbach”, a broad definition of the four above mentioned “social” Missions will be created and then further serves as the basis of this thesis. In the following, the Missions will be defined:

Mission 4 consists of six subcomponents. The first one is pre-school education, including all services and expenditures used for the extension of the kindergarten, the availability of enough places for all ages and their services. It can include monthly contributions for different areas of the children needs and family friendlier reductions for families in need.

The second subcomponent are other non-university education orders, including primary and secondary schools. This could be Canteen services on all days of the week, maintenance of the different buildings and services such as WIFI in all schools.

University education, including all relevant orders and services for the university are the third subcomponent of this Mission. This includes availability of places, offers of degrees and

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educational opportunities. As this mostly refers to big municipalities, it is not relevant in the case of smaller municipalities.

The fourth subcomponent is higher technical education, including all orders and services for schools with the focus of practical education.

The fifth subcomponent includes ancillary education services. These are additional education services that might be needed in special cases such as language courses to facilitate integration of immigrants.

Lastly, the sixth subcomponent refers to all services and orders relevant for the right to study.

Mission 5 consists of two subcomponents. Firstly, there is the enhancement of historic assets. This includes all the support of different non-profit associations within the municipality with historic background and contributions for renewals of special non-profit buildings, such as a church and others. Secondly, cultural activities and various interventions in the cultural sector, which consist of yearly contributions to societies that are culturally active, such as choirs, bands, educational committees, summer camp for young musicians etc. are part of this Mission.

Mission 6 is divided into “sport and free time” and “young people”. Sport and free time include all services for renovation and maintenance of sports grounds, playgrounds, outdoor swimming pools and other sports-related buildings. Ongoing funding for such are also included. The subcomponent young people include financial support of youth service in collaboration with the neighbour municipalities such as staff expenses, youth rooms, summer and winter offers for the youth, financing of a night liner, yearly celebrations of the legal age etc.

Mission 12 includes interventions for children and minors and for kindergarten, such as summer care for the children of the kindergarten and the primary school students. Additionally, the Mission refers to disability interventions, (e.g., access for disabled people to day-care centres or housing for the elderly) interventions for subjects at risk of social exclusion like people with immigrational background, interventions for families (e.g. special support or bonuses for underaged children) and interventions for the right to housing (e.g. in form of subsidized housing). Additionally, Mission 12 includes planning and governance of the network of social and health services, cooperation and associations as well as necropsy and cemetery services.

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Aim of the thesis

Being able to overcome the barriers created by gender typing in our society, it might be necessary for women to create higher income and educational attainment (Campbell, et al., 1960). This leads to the hypothesis that women as mayor spend the municipalities income in different way than men do. The influence of women’s’ political representation on municipalities or as council members or even mayors is still under-explored. I want to close this gap and investigate whether women in the position as mayor tend to use the yearly budget (on municipality base) in more social areas compared to men. Thus, the following research question will be addressed:

Do female mayors make a difference?

The following hypothesis will be tested:

H0: There is no difference in the financial performance of municipalities lead by women compared to those lead by men.

H1: There is a difference in the financial performance of municipalities lead by women compared to those lead by men.

H0: There is no difference regarding the spending behaviour in social areas in municipalities lead by women compared to those lead by men.

H2: There is a difference regarding the spending behaviour in social areas in municipalities lead by women compared to those lead by men.

Method Research approach

This study used an observational research design. To extend current research on the topic of behaviour of female leaders in political positions such as a mayor, a quantitative research approach using secondary data on municipalities in Trentino-Alto Adige was applied. It closes the gap of understanding if there are some essential differences in the distribution- or spending behaviour of mayors based on their gender.

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Sample

The sample used in this thesis is a non-probability sample. Due to the knowledge given about the population in South Tyrol, a judgmental sample was taken by using all municipalities with female leading mayors not only in South Tyrol, but also including Trentino. At the time of the study, Trentino-South Tyrol had 34 different municipalities with a leading female mayor (please see Table 3).

To compare each variable (municipality) individually, one homogeneous variable for every female leading municipality was needed. As the basis of the calculation of the yearly budget available for every municipality depends on the number of the people living in it, the selection of the 34 male leading municipalities in Trentino-South Tyrol was based on the population numbers. For an overview of the chosen municipalities, please refer to Table 4. Using this pre-selection, it was possible to compare the individual municipalities (in relation to the number of people living in a municipality) and the overall numbers (female vs. male in total).

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Table 3: Investigated municipalities and their population divided by gender of mayor

Municipalities with Municipalities with Population Population female mayors male mayors 253 261 579 583 649 637 675 678 680 692 Senale-San Felice 762 Avelengo 778 Rumo 806 Ospedaletto 795 808 Livo 804 Tubre 969 Verano 959 Ronzo-Chienis 996 Tenna 989 1160 1162 1174 Imer 1183 Magrè sulla SdV 1274 Rodengo 1240 1475 1475 1481 1481 Meltina 1692 Aldino 1656 Montagna 1701 Gargazzone 1726 1833 1851 Campo di Trens 2656 Funes 2629 2775 Nago-Torbole 2815 Bronzolo 2808 Falzes 2823 Ultimo 2896 Chienes 2887 2947 2945 2965 2963 3035 3048 3168 3158 San Martino in Passiria 3255 Naz-Sciaves 3230 San Candido 3367 Dobbiaco 3351 Cornedo all'Isarco 3430 Termone sulla SdV 3431 3843 Tione di 3660 San Michele all'Adige 3863 3778 4514 4580 Chiusa 5215 Laces 5214 Caldaro sulla SdV 8104 Renon 7955

To compare the municipalities not only on an individual level, but also on a group level, I predefined eight different groups, depending on the numbers of the population. These groups were already defined for Austria (for details, see https://www.statistik.at/wcm/idc/idcplg?IdcService=GET_PDF_FILE&RevisionSelectionMet hod=LatestReleased&dDocName=049720). However, I found no similar classification that was done so far in Trentino-South Tyrol.

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Keeping the most important variable (female and male mayors) under consideration, such a comparison might show if there are differences between the spending/distribution behaviour of smaller and bigger municipalities. The eight groups were defined as follows:

- Group 1 (Population of <500) - Group 2 (Population of 500-1000) - Group 3 (Population of 1001-2500) - Group 4 (Population of 2501-5000) - Group 5 (Population of 5001-10000) - Group 6 (Population of 10001-20000) - Group 7 (Population of 20001-50000) - Group 8 (Population of >50001)

Considering that the smallest municipality in my sample had a population of 253 and the biggest one 8104, only five of seven different groups seemed appropriate.

Data collection

Atz et al., (2019) predefined which of the resorts in the municipalities of South Tyrol are the “social resorts”. To test hypothesis 2, this definition was used.

Since the legislative decree of the 23rd of June 2011, Nr. 118, every municipality in Italy must give transparent access to their financial/budget estimate and the final revision sheet. This means that every municipality in Italy has to go through the same procedure in defining the yearly budget. There are several rules, structures and programs or software to follow and use, which are the same for every municipality. This ensures a transparent and equal municipality budget. This data (the financial/budget estimate, the final revision sheet…), available for every municipality in Italy, can be found at the official website of the capital of Italy for public finance data:

BANCA DATI AMMINISTRAZIONI PUBBLICHE I DATI DELLA FINANZA PUBBLICA ACCESSIBILI A TUTTI https://openbdap.mef.gov.it/

All relevant information about the individual municipalities was collected from this website.

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Measures

For the purpose of this thesis, data on the yearly budget for the four key figures (EFQ, FSQ, ÖSQ and SDSQ) were collected from the above-mentioned website, to calculate the financial performance of each municipality. To receive information about the differences in spending behaviour in the social resorts Mission 4, Mission 5, Mission 6 and Mission 12, data of each municipality was collected, too. Generally, each Mission contains planned expenses and actual expenses of one year and is split into different parts:

1. Current expenses (these can be different for every municipality) For example, current expenses for library services or others, depending on the different services each municipality offers. 2. Investments (all investments that increase the municipalities capital, for example construction work on buildings or new projects. Bigger investments need to be considered in the quantitative approach, as they could lead to adulterated results.) 3. Expenditure to increase financial assets

For the purpose of this thesis, current expenses as well as investments were also considered as overall expenses for each municipality. Even though some municipalities might have higher investments compared to others, the budget is based on the population. Thus, also the pre- selection of municipalities is based on a similar population number, which in turn would refer to a similar budget.

Expenditure to increase financial assets must be seen from another perspective. They are not considered as ongoing and therefore not relevant. Special cases, for example bigger amounts or outliers will still be considered.

These different parts are considered and included in the official data sheet as “total payments”, which is available online. The “total payments” of each Mission and each municipality from the years 2016 to 2018 were used, as only data from these years was available. Even though, these years did not cover the full period of a mayor’s govern ship (May 2015 to May 2020), analysing only three from the five years might still lead to significant results.

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Analytical approach

Test 1 - key figures.

To test hypothesis 1, public finance data was used to calculate the key figures EFQ, FSQ, ÖSQ and SDSQ and to give an overview of the financial performance at municipality level. All the data used was taken from: http://www.bdap.tesoro.it/sites/openbdap/cittadini/bilancideglienti/bilancientipubbammentvig /bilanciregionientiorganismi/Pagine/SchedaContenutoBilanciArmonizzati.aspx

I used the “Rendiconto – Schemi di Bilancio”, which are the final numbers each municipality obtained and spent, and not only the pre-assumed numbers from the estimated budget. In the following, the data regarding all earnings of each municipality were taken from “1. Rendiconto Entrate”. For the expenses a different approach had to be used. As the complete data set of all expenses (3. Rendiconto Spese) of every municipality was too big to be exported by a regular computer software, an export of the data was taken from “5. Rendiconto Spese Riepilogo Titoli”. Additionally, all missing data in this export was checked and completed by filtering and checking each municipality online from “1. Rendiconto Spese”. Using this approach, it was possible to collect all data available. Numbers from the municipality of Laces, and Volano could be completed by online filtering.

Key figures were calculated using the following equations:

(퐶푢푟푟푒푛푡 푒푎푟푛푖푛푔푠 + 푖푛푐표푚푒 표푛 푐푎푝푖푡푎푙) 퐄퐅퐐 = 100 (1) (퐶푢푟푟푒푛푡 푒푥푝푒푛푠푒푠 + 푐푎푝푖푡푎푙 푒푥푝푒푛푑푖푡푢푟푒)

(퐸푐표푛표푚푖푐 푟푒푠푢푙푡) 퐅퐒퐐 = 100 (2) (퐼푛푐표푚푒 푓푟표푚 푝푟표푓푖푡 & 푠푢푟푝푙푢푠 푑푖푠푡푟푖푏푢푡푖표푛)

(퐶푢푟푟푒푛푡 푒푎푟푛푖푛푔푠 − 푐푢푟푟푒푛푡 푒푥푝푒푛푠푒푠) Ö퐒퐐 = 100 (3) (퐶푢푟푟푒푛푡 푒푥푝푒푛푠푒푠)

(퐷푒푏푡 푠푒푟푣푖푐푒) 퐒퐃퐒퐐 = 100 (4) (퐶푢푟푟푒푛푡 푒푎푟푛푖푛푔푠 푓푟표푚 푡푎푥푒푠, 푐표푛푡푟푖푏푢푡푖표푛푠 & 푠푒푡푡푙푒푚푒푛푡푠)

For interpretation and analysis, reference values based on Biwald (2005) were used. For details, please refer to Table 4 below. 24

Table 4: Reference values for EFQ, FSQ, ÖSQ and SDSQ

Key figure Reference values EFQ • Very good > 110% • Good > 100% • Average > 90% • Sufficient > 80% • Inadequate < 80%

FSQ • Very good > 15% • Good > 12% • Average > 8% • Sufficient > 3% • Inadequate < 3%

ÖSQ • Very good > 25% • Good > 20% • Average > 15% • Sufficient > 5% • Inadequate < 5%

SDSQ • Very good < 10% • Good < 15% • Average < 20% • Sufficient < 25% • Inadequate > 25%

Some municipalities had missing numbers in different years (2016, 2017, 2018) in their earnings and/or expenses. It was therefore not always possible to calculate all key figures for the 68 municipalities. To be able to compare the female leading municipalities numbers with the respective homogenous male leading municipality, some municipalities had to be excluded. To do so, a list was created with two columns – one for the female leading municipalities and one for the male leading ones. These columns were sorted by the number of population (small to big) to compare each municipality with its respective homogeneous “partner”. If the key figure of one municipality was not calculable due to missing numbers, the other homogeneous municipality was also excluded, to keep the sample number the same. Within this approach, the average value of the key figure of all female and all male municipalities was calculated.

Differences between female and male municipalities’ numbers were investigated using an independent-sample t test using “IBM SPSS Statistics 22”. As the values of the municipalities could have a very wide range (high standard deviation), also the median will be considered as

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a meaningful value. Depending on the results of the independent-sample t test, the municipalities will be compared individually; each female municipality by its homogeneous male municipality.

To test whether there is a significant difference of the means of the municipality’s population size (and still differentiating between female and male mayors), the data was split into the five mentioned groups according to their size. Subsequently, an independent-sample t test was done.

Test 2 – difference in social areas.

To test hypothesis 2, data on the budget of municipalities lead by women with those lead by men was compared, to see whether there is a difference in the amount of money female and male mayors spend regarding social areas.

Same as in Test 1, the “Rendiconto – Schemi di Bilancio” was used. However, this hypothesis is focusing only on the social areas. Therefore, only the four social missions were used. The export was taken from “4. Bilancio di Previsione Spese Riepilogo Titoli”. This data includes all the expenses of every municipality structured by all Missions. By filtering and summarizing all required data was gathered.

Some municipalities had missing numbers in different years (2016, 2017, 2018) within some Missions. Therefore, the same approach of excluding some municipalities had to be used as in Test 1. An independent-sample t test was done to investigate whether there is a significant difference between the female and male municipalities’ expenses in each social area (Mission 4, 5, 6 and 12 each individually). Based on the results of the independent-sample t test, the municipalities are compared individually.

To investigate whether there is an overall difference in the social areas between female and male municipalities, another independent-sample t test was performed, using the sum of all social Mission of each year, still considering the difference of female and male leaders.

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Results Test 1 – key figures

Using an 95% confidence interval, the independent t test revealed no significant difference between female and male municipalities. Therefore, H0 cannot be rejected: There is no difference in the financial performance of municipalities lead by women compared to those lead by men. However, there was a tendency regarding FSQ2018 (p = 0.07). For detailed numbers, please refer to Table 5.

Table 5: Mean, median and t-test results for all municipalities and differentiated between female and male leading municipalities

Female Male N M ± SD Z M ± SD Z p t df EFQ2016 64 1.17 ± 0.24 1.12 1.18 ± 0.37 1.12 0.88 -0.15 62 EFQ2017 66 1.11 ± 0.33 1.05 1.08 ± 0.21 1.08 0.61 0.51 64 EFQ2018 48 1.09 ± 0.25 1.05 1.02 ± 0.15 1.04 0.19 1.33 46 FSQ2016 40 0.07 ± 0.09 0.07 0.07 ± 0.17 0.11 0.94 -0.07 38 FSQ2017 38 0.04 ± 0.09 0.06 0.07 ± 0.14 0.10 0.45 -0.76 36 FSQ2018 48 0.05 ± 0.14 0.03 0.11 ± 0.11 0.14 0.07 -1.83 46 ÖSQ2016 64 0.26 ± 0.28 0.23 0.20 ± 0.17 0.17 0.33 0.98 62 ÖSQ2017 68 0.18 ± 0.13 0.18 0.21 ± 0.17 0.19 0.51 -0.66 66 ÖSQ2018 50 0.16 ± 0.15 0.15 0.21 ± 0.14 0.19 0.18 -1.37 48 SDSQ2016 38 0.66 ± 0.56 0.61 0.49 ± 0.56 0.32 0.35 0.94 36 SDSQ2017 34 0.69 ± 0.58 0.66 0.49 ± 0.55 0.35 0.30 1.60 32 SDSQ2018 20 0.72 ± 0.70 0.44 0.57 ± 0.62 0.37 0.63 0.5 18

The EFQ values of municipalities lead by women and lead by men are on average both very good (>110%). However, considering these 3 years, the numbers are continuously declining. Even though in 2018 the values still score >100% (good).

On average, the FSQ lead by women are only sufficient (>3%), while those lead by men are on average (>8%). However, the numbers of the female leading municipalities are declining to almost inadequate (<3%) over the years, while the male municipalities are inclining to good (>12%).

The public savings rate (ÖSQ) for municipalities lead by women is good (>20%). However also here, I can see a steady decline to a value of average (>15%) over these 3 years. The ÖSQ for male municipalities in comparison is average (>15%) and inclining over the years to almost good. 27

Looking at the debt service tax rate (SDSQ) of both, female and male leading municipalities, on average, the values are inadequate (>25%). While the numbers for female leading municipalities are constantly declining, the municipalities lead by men are inclining. To mention here is that the numbers of the female leading municipalities are twice as high as the male ones. Also, the decline of the female leading municipalities is higher than the incline of the male municipalities.

Test 1 – key figures by groups

To receive a more detailed picture, municipalities were compared by groups regarding their annual budget. Results are presented in Table 6.

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Table 6: Mean, median and t-test results for group 2, 3, 4 and 5, differentiated between female and male leading municipalities

Group 500-1000 Group 1001-2500 Female Male Female Male N M ± SD Z M ± SD Z p t df N M ± SD Z M ± SD Z p t df EFQ2016 16 1.17 ± 0.17 1.13 1.39 ± 0.66 1.16 0.37 -0.93 14 EFQ2016 14 1.20 ± 0.18 1.21 1.19 ± 0.24 1.18 0.95 0.06 12 EFQ2017 18 1.10 ± 0.47 0.97 1.09 ± 0.30 1.09 0.98 0.02 16 EFQ2017 16 1.09 ± 0.48 0.97 1.11 ± 0.25 1.09 0.89 -0.15 14 EFQ2018 16 1.08 ± 0.31 1.03 0.90 ± 0.14 0.90 0.17 1.46 14 EFQ2018 10 0.94 ± 0.17 0.94 1.12 ± 0.21 1.14 0.19 -1.43 8 FSQ2016 10 0.09 ± 0.11 0.05 0.16 ± 0.10 0.15 0.30 -1.11 8 FSQ2016 6 0.09 ± 0.03 0.08 0.13 ± 0.09 0.18 0.52 -0.70 4 FSQ2017 10 0.06 ± 0.12 0.02 0.17 ± 0.10 0.15 0.15 -1.62 8 FSQ2017 6 0.09 ± 0.05 0.07 0.05 ± 0.12 0.08 0.67 0.46 4 FSQ2018 14 0.07 ± 0.10 0.07 0.14 ± 0.07 0.15 0.16 -1.50 12 FSQ2018 10 0.19 ± 0.10 0.22 0.18 ± 0.11 0.16 0.90 0.13 8 ÖSQ2016 16 0.19 ± 0.11 0.21 0.20 ± 0.19 0.17 0.89 -0.14 14 ÖSQ2016 14 0.30 ± 0.14 0.28 0.24 ± 0.16 0.23 0.45 0.79 12 ÖSQ2017 18 0.16 ± 0.13 0.15 0.23 ± 0.20 0.20 0.44 -0.79 16 ÖSQ2017 16 0.28 ± 0.10 0.26 0.23 ± 0.14 0.19 0.44 0.81 14 ÖSQ2018 16 0.18 ± 0.14 0.15 0.21 ± 0.12 0.23 0.66 -0.44 14 ÖSQ2018 10 0.28 ± 0.13 0.32 0.27 ± 0.17 0.24 0.91 0.12 8 SDSQ2016 10 0.65 ± 0.93 0.08 0.51 ± 0.77 0.17 0.79 0.27 8 SDSQ2016 6 0.82 ± 0.19 0.87 0.69 ± 0.36 0.88 0.62 0.53 4 SDSQ2017 10 0.68 ± 0.97 0.08 0.54 ± 0.82 0.21 0.82 0.24 8 SDSQ2017 6 0.76 ± 0.17 0.80 0.52 ± 0.25 0.53 0.24 1.39 4 SDSQ2018 8 0.95 ± 1.00 0.64 0.72 ± 0.82 0.42 0.74 0.35 6 SDSQ2018 Group 2501-5000 Group 5001-10000 Female Male Female Male N M ± SD Z M ± SD Z p t df N M ± SD Z M ± SD Z p t df EFQ2016 28 1.16 ± 0.31 1.07 1.08 ± 0.17 1.06 0.37 0.91 26 EFQ2016 4 1.25 ± 0.11 1.25 1.11 ± 0.05 1.11 0.24 1.66 2 EFQ2017 26 1.12 ± 0.09 1.08 1.03 ± 0.13 1.00 0.07 1.92 24 EFQ2017 4 1.25 ± 0.08 1.26 1.13 ± 0.07 1.13 0.23 1.70 2 EFQ2018 18 1.18 ± 0.22 1.09 1.04 ± 0.05 1.05 0.08 1.86 16 EFQ2018 4 1.13 ± 0.14 1.13 1.09 ± 0.04 1.09 0.75 0.37 2 FSQ2016 18 0.06 ± 0.11 0.08 -0.00 ± 0.21 0.07 0.45 0.77 16 FSQ2016 4 0.01 ± 0.04 0.01 0.08 ± 0.00 0.08 0.15 -2.25 2 FSQ2017 16 0.00 ± 0.09 0.03 -0.01 ± 0.14 0.03 0.87 0.17 14 FSQ2017 4 0.06 ± 0.10 0.06 0.16 ± 0.07 0.16 0.39 -1.09 2 FSQ2018 20 -0.00 ± 0.11 0.01 0.05 ± 0.11 0.04 0.31 -1.04 18 FSQ2018 4 -0.13 ± 0.14 -0.13 0.18 ± 0.00 0.18 0.09 -3.03 2 ÖSQ2016 28 0.27 ± 0.41 0.15 0.17 ± 0.18 0.15 0.39 0.87 26 ÖSQ2016 4 0.32 ± 0.08 0.32 0.34 ± 0.02 0.34 0.83 -0.24 2 ÖSQ2017 28 0.12 ± 0.11 0.16 0.15 ± 0.14 0.16 0.56 -0.60 26 ÖSQ2017 4 0.31 ± 0.16 0.31 0.47 ± 0.09 0.47 0.35 -1.22 2 ÖSQ2018 20 0.08 ± 0.14 0.08 0.14 ± 0.07 0.15 0.23 -1.23 18 ÖSQ2018 4 0.17 ± 0.02 0.17 0.47 ± 0.08 0.47 0.03 -5.33 2 SDSQ2016 18 0.57 ± 0.41 0.61 0.46 ± 0.58 0.36 0.64 0.48 16 SDSQ2016 SDSQ2017 16 0.66 ± 0.46 0.62 0.45 ± 0.53 0.36 0.40 0.87 14 SDSQ2017

SDSQ2018 8 0.53 ± 0.34 0.54 0.58 ± 0.61 0.41 0.89 -0.14 6 SDSQ2018

Note: Group 1 could not be considered due to missing numbers. Blank spaces indicate that no calculation was possible due to missing numbers.

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EFQ.

Group 2 (500-1000).

The EFQ values of municipalities lead by women and lead by men are on average very good (>110%). No significant difference was found between female and male leading municipalities. Both female and male municipalities were declining over the years to a good (>100%) and average (>90%) score.

Group 3 (1001-2500).

No significant difference was found. The EFQ values are on average very good (>110%) with decreasing values over the years in female leading municipalities to an average (>90%). Male leading municipalities are also decreasing, but still staying on a very good score (>110%).

Group 4 (2501-5000).

The scores for both female and male municipalities were good (>100%) with a small incline in the female and a small decline in the male leading municipalities. No significant difference between female and male municipalities was found, however, there was a trend for EFQ2017 (p = 0.07) and EFQ2018 (p = 0.08).

Group 5 (5001-10000).

Both female and male municipalities scored a value of >110% (very good). Similar to group 2- 4, no significant difference was found. Both municipalities were somewhat decreasing over the years.

FSQ.

Group 2 (500-1000).

While the municipalities lead by female mayors scored on average a score of >3% (sufficient), the male leading municipalities received a scored of >15% (very good). No significant difference between female and male municipalities was found. The scores obtained from municipalities lead by women increased over the years, while the male leading municipalities remained constant.

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Group 3 (1001-2500).

Different results were found for group 3 with a lower score for the female municipalities of >8% (average) and a higher score for the male municipalities (>15%, very good). Also, no significant difference was found. While the female leading municipalities increased over the years to very good (>15%), the male leading municipalities remained stable.

Group 4 (2501-5000).

Female leading municipalities in group 4 obtained an average score (>8%), which is declining over the years to an inadequate score (<3%). In comparison, male leading municipalities started with a sufficient score (>3%) with a small decline over the years. No significant difference was found.

Group 5 (5001-10000).

In group 5, the female municipalities’ score is inadequate (<3%) with a fluctuating decline. An incline was found for the male municipalities from a score of >8% (average) to >15% (very good). No significant difference between female and male municipalities was found. This group only consisted of 4 municipalities and was only considered as a matter of form.

ÖSQ.

Group 2 (500-1000).

While female leading municipalities received an average score of >20% (good), male leading municipalities only scored >15% (average). Over the years, female leading municipalities declined to >15% (average), while male leading municipalities inclined to >20% (good). No significant difference between female and male municipalities was found.

Group 3 (1001-2500).

Group 3 obtained a similar score in female and male municipalities. Female municipalities obtained a score of >25% (very good), while men municipalities >20% (good), with both groups somewhat inclining over the years. No significant difference was found.

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Group 4 (2501-5000).

In group 4, both groups of municipalities received a score of >15% (average). While the female leading municipalities declined over the years to >5% (sufficient), the male municipalities were stable. Again, no significant difference was found.

Group 5 (5001-10000).

In group 5, both municipalities obtained a score of >25% (very good). While the female municipalities were declining over the years to >15% (average), the male municipalities were inclining. Evidence for a significant difference was found for 2018 (p = 0.03).

Comparing all groups, it is evident that while the female municipalities are mostly declining over the years, the male municipalities are stable or inclining.

SDSQ.

Group 2 (500-1000).

While the female leading municipalities in this group started with a score of <10% (very good), the male leading municipalities started with a score of <20% (average). Both leading municipalities inclined over the years to >25% (inadequate). No evidence for significant difference was found.

Group 3 (1001-2500).

As data for 2018 was missing, only the years 2016 and 2017 were considered. Also, the scores for both groups of municipalities were inadequate (>25%), declining from 2016 to 2017. No significant difference was found. However, this group contained only six municipalities.

Group 4 (2501-5000).

Also, group 4 obtained an inadequate score (>25%) for both, female and male municipalities. While the numbers for the female municipalities were declining, the numbers for the male municipalities were inclining. Again, no significant difference was found.

Group 5 (5001-10000).

Group 5 could not be considered in the debt service tax rate, as the numbers in this group were not complete.

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Test 2 – missions

Using a 95% confidence interval, no significant difference between female and male municipalities’ spending behaviour could be found. Therefore, I cannot reject H0: There is no difference regarding the spending behaviour in social areas in municipalities lead by women compared to those lead by men. Mission 6 for 2017, a tendency towards a significant difference was found (p = 0.07). Detailed numbers are presented in Table 7.

Missions (4 / 5 / 6 / 12).

Comparing the values of Mission 4, both female and male leading municipalities are increasing their expenses on average about ~23% over the last years. Expenses of the male leading municipalities are higher compared to the female leading municipalities.

In Mission 5, female and male leading municipalities are spending about the same amount of money. However, while the female leading municipalities expenses do only show small fluctuations over the years, the male leading municipalities increase their expenses about 36%.

Both, female and male leading municipalities in Mission 6 spent about the same amount of money in these areas with a slight increase over the years.

In the last social sector, Mission 12, there is a difference in the spending behaviour of female and male leading municipalities. While both are increasing their expenses over the years, the female leading municipalities started with expenses about 200.000€ in this sector, keeping it mostly stable. The male leading municipalities in comparison, started with expenses of about 100.000€, increasing it to almost 300.000€ over the years.

Ʃ Missions (2016 / 2017 / 2018).

The overall expenses show a very different picture. While in 2016, the expenses of both female and male leading municipalities are similar, over the years, the female leading municipalities decreased their expenses by 8%. On the other side, the male leading municipalities increased their expenses by 39%.

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Table 7: Mean, median and t-test results according to Missions and differentiated between female and male leading municipalities

Female Male N M ± SD Z M ± SD Z p t df Mission 4 2016 64 391965 ± 448557 198034 394932 ± 453302 224551 0.98 -0.03 62 Mission 4 2017 68 374726 ± 478830 189167 430950 ± 612080 221353 0.67 -0.42 66 Mission 4 2018 64 524298 ± 704471 234909 584710 ± 807224 285282 0.75 -0.32 62 Mission 5 2016 64 268553 ± 443123 134896 206688 ± 251426 145503 0.50 0.69 62 Mission 5 2017 66 272005 ± 457349 128927 276086 ± 288691 154735 0.97 -0.04 64 Mission 5 2018 64 250789 ± 347344 134736 236631 ± 238316 197198 0.85 0.19 62 Mission 6 2016 64 208307 ± 264888 112829 317344 ± 614653 99147 0.36 -0.92 62 Mission 6 2017 64 181930 ± 192383 122998 381871 ± 580486 134213 0.07 -1.85 37.73 Mission 6 2018 64 279957 ± 469743 121714 329602 ± 578012 111164 0.71 -0.38 62 Mission 12 2016 64 295205 ± 302197 195713 255902 ± 395609 111547 0.66 0.45 62 Mission 12 2017 66 256245 ± 286239 179384 332510 ± 379858 184814 0.36 -0.92 64 Mission 12 2018 64 260215 ± 227569 204776 313729 ± 227569 294881 0.43 -0.80 62 Ʃ Missions 2016 64 1164032 ± 1074013 828864 1174864 ± 1193500 799564 0.97 -0.04 62 Ʃ Missions 2017 62 1148925 ± 1128941 766368 1465721 ± 1463384 1007869 0.34 -0.95 60 Ʃ Missions 2018 64 1315260 ± 1184065 759282 1464673 ± 1357211 1111249 0.64 -0.47 62

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Test 2 – missions by group

Group 2 (500-1000).

While the female leading municipalities spent a little less money in Mission 4 than the male leading municipalities, the numbers increased over the years for the female leading municipalities, while the male leading municipalities expenses remained mostly constant.

In Mission 5, the female leading municipalities spent about 40.000€ on average, while the male leading municipalities only spent about 20.000€. While the female leading municipalities expenses increased by about 20% over the years, the male leading municipalities decreased by about 25%.

Both, female and male leading municipalities in Mission 6 spent similar amounts of money. Also, both are increasing their expenses in this area over the years by about 100%.

A similar result was found in Mission 12. Both leading municipalities, female and male, started with an alike value in 2016. However, the female leading municipalities increased their expenses over the years by about 200%, while the male leading municipalities by about 15%.

Overall, the female leading municipalities started with a spending value of about 300.000€ in 2016. The male leading municipalities started with a value of about 250.000€. While the female leading municipalities increased their expenses to about 500.000€ over the years, the male leading municipalities decreased slightly.

However, within group 2, no significant differences between female and male leading municipalities were found. Detailed numbers are presented in Table 8.

Group 3 (1001-2500).

The expenses of female and male leading municipalities in Mission 4 started in 2016 with almost the same values. While the female leading municipalities decreased over the years only a little bit, the male leading municipalities increased by about 30%.

In Mission 5, both leading municipalities are very similar and increase their expenses over the years a little.

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Within Mission 6, the female leading municipalities increased their expenses by around 100%, while the male leading municipalities remained almost constant. However, the female leading municipalities started with a value about 100% lower than the male leading municipalities.

Mission 12 shows a very different picture. The female leading municipalities’ expenses fluctuated a lot, with values of about 155.000€ in 2016, 75.000€ in 2017 and 205.000€ in 2018. In comparison to that, the male leading municipalities started with a value of about 90.000€ in 2016, decreasing over the years to 75.000€.

In total, the female leading municipalities’ expenses were fluctuating over the years, while the male leading municipalities’ expenses increased a little. Both values are very similar. However, also group 3 showed no significant differences between female and male leading municipalities’ expenses in Mission 4 for 2017 (p = 0.02). For detailed numbers please refer to Table 8.

Group 4 (2501-5000).

Group 4 consisted of the highest number of municipalities in comparison to the other groups.

Mission 4 shows very similar results for female and male leading municipalities’ expenses. Both are increasing their expenses over the years a little.

Within Mission 5, some slight differences between both leading municipalities’ expenses were found. While the female leading municipalities’ expenses decreased a little bit, starting from about 185.000€ in 2016 to 160.000€ in 2018, the male leading municipalities’ expenses fluctuated a lot. They started with expenses at about 210.000€ in 2016, increased to 345.000€ in 2017 and decreased again to 290.000€ in 2018.

Mission 6 shows very similar expenses for both, female and male leading municipalities. However, the female leading municipalities’ expenses are decreasing over the years by around 10%, while the male leading municipalities’ expenses are increasing by 20%.

A similar result was found in Mission 12. While the female leading municipalities’ expenses were decreasing by ~20% over the years, the male leading municipalities’ expenses are increasing by ~80%.

In summary, the female leading municipalities’ expenses are decreasing a little bit, while the male leading municipalities’ expenses are increasing by about 50%. Nevertheless, the values

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are very similar. However, only within Mission 12 in 2018 a tendency for a significant difference between female and male leading municipalities’ expenses was found (p = 0.08). For detailed numbers please see Table 8.

Group 5 (5001-10000)

This group consists only of 4 municipalities due to missing values and was considered only as a matter of form.

While the female leading municipalities’ expenses in Mission 4 over the year are steadily increasing from about 1.400.000€ in 2016 to 1.900.000€ in 2018, the male leading municipalities’ expenses are fluctuating a lot. From 1.800.000€ in 2016, to 2.400.000€ in 2017, to 1.500.000 in 2018.

The same increasing in female leading municipalities’ spending and fluctuation in male leading municipalities’ spending was found in Mission 5. Only the values per se were lower.

Mission 6 showed a different result. Female leading municipalities’ expenses decreased from about 1.000.000€ in 2016 to 370.000€ in 2018. Male leading municipalities’ expenses increased from around 1.000.000€ in 2016 to 2.000.000€ in 2018.

Similar values were obtained within Mission 12 for both, female and male leading municipalities’ expenses. While the female leading municipalities’ expenses decreased by around 15%, the male leading municipalities’ expenses decreased by 30%.

Summarizing, the female leading municipalities’ expenses increased a little, while the male leading municipalities’ expenses fluctuated over the years. Significant differences between female and male leading municipalities’ expenses were found in Mission 6 2017 (p = 0.02), Mission 6 2018 (p = 0.00) and Ʃ Mission 2018 (p = 0.02). Detailed numbers are shown below.

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Table 8: Mean, median and t-test results according to groups and Missions, and differentiated between female and male leading municipalities

Group 2 (500-1000) GroupGroup 3 3 (1001 (1001--2500)-2500)

Female Male FemaleFemale MaleMale

N M ± SD Z M ± SD Z p t df NN MM ± ± SD SD ZZ MM ± ± SD SD ZZ pp tt t dfdf Mission 4 2016 8 93380 ± 54088 85938 234386 ± 388466 139544 0.33 -1.02 14 MissionMission 4 4 2016 2016 77 166146166146 ± ± 110295 110295 193701193701 210617210617 ± ± 78468 78468 180708180708 0.400.40 --0.87-0.87 1212 Mission 4 2017 9 108874 ± 87476 73723 184753 ± 207711 144002 0.33 -1.01 16 MissionMission 4 4 2017 2017 88 124105124105 ± ± 86694 86694 101870101870 224698224698 ± ± 72266 72266 201380201380 0.020.02 --2.52-2.52 1414 Mission 4 2018 8 175570 ± 192243 107539 276828 ± 339806 134018 0.48 -0.73 14 MissionMission 4 4 2018 2018 88 170038170038 ± ± 96463 96463 179952179952 869186869186 ± ± 1426957 1426957 241005241005 0.210.21 --1.38-1.38 7.067.06 Mission 5 2016 8 74099 ± 74348 38788 48368 ± 57238 20937 0.45 0.78 14 MissionMission 5 5 2016 2016 77 9795897958 ± ± 84856 84856 7804678046 101035101035 ± ± 62100 62100 8489984899 0.940.94 --0.08-0.08 1212 Mission 5 2017 8 79342 ± 78969 62476 32232 ± 29740 19037 0.14 1.58 14 MissionMission 5 5 2017 2017 88 116516116516 ± ± 132687 132687 3998739987 167135167135 ± ± 159329 159329 8806688066 0.500.50 --0.69-0.69 1414 Mission 5 2018 8 74386 ± 84033 50189 31157 ± 33638 15622 0.20 1.35 14 MissionMission 5 5 2018 2018 88 118988118988 ± ± 110829 110829 8801888018 132168132168 ± ± 83078 83078 106989106989 0.790.79 --0.27-0.27 1414 Mission 6 2016 8 26618 ± 18016 23980 40050 ± 48158 17209 0.48 -0.74 8.92 MissionMission 6 6 2016 2016 77 8757287572 ± ± 87676 87676 4067340673 203105203105 ± ± 219212 219212 105083105083 0.220.22 --1.30-1.30 1212 Mission 6 2017 8 33983 ± 29558 34537 61186 ± 64730 30504 0.30 -1.08 14 MissionMission 6 6 2017 2017 88 9647796477 ± ± 87738 87738 6842968429 150244150244 ± ± 120666 120666 117855117855 0.330.33 --1.02-1.02 1414 Mission 6 2018 8 80806 ± 91245 47313 29315 ± 18061 33132 0.16 1.57 7.55 MissionMission 6 6 2018 2018 88 9498994989 ± ± 52734 52734 9633396333 145184145184 ± ± 79622 79622 115169115169 0.160.16 --1.49-1.49 1414 Mission 12 2016 8 56205 ± 63472 34057 37225 ± 25889 30764 0.45 0.78 14 MissionMission 12 12 2016 2016 77 162124162124 ± ± 123150 123150 155851155851 9223692236 ± ± 54782 54782 8778687786 0.200.20 1.371.37 1212 Mission 12 2017 9 73859 ± 87277 21085 81297 ± 94430 29831 0.86 -0.17 16 MissionMission 12 12 2017 2017 88 116306116306 ± ± 111469 111469 7439674396 268562268562 ± ± 508463 508463 8358483584 0.420.42 --0.83-0.83 1414 Mission 12 2018 8 99821 ± 77522 91416 50045 ± 48470 35034 0.15 1.54 11.75 MissionMission 12 12 2018 2018 88 238813238813 ± ± 188245 188245 204776204776 211649211649 ± ± 224635 224635 7525875258 0.800.80 0.260.26 1414 Ʃ Missions 2016 8 250303 ± 114673 300413 360032 ± 366598 251027 0.43 -0.81 14 ƩƩ Missions Missions 2016 2016 77 513801513801 ± ± 205807 205807 507658507658 606994606994 ± ± 225315 225315 664162664162 0.440.44 --0.81-0.81 1212 Ʃ Missions 2017 7 312670 ± 210535 321258 276244 ± 138501 265213 0.71 0.38 12 ƩƩ Missions Missions 2017 2017 88 453406453406 ± ± 198547 198547 445607445607 810640810640 ± ± 575881 575881 588850588850 0.120.12 --1.66-1.66 1414 Ʃ Missions 2018 8 430584 ± 171701 498479 387348 ± 388931 237391 0.78 0.29 14 ƩƩ Missions Missions 2018 2018 88 622829622829 ± ± 266080 266080 623159623159 13581891358189 ± ± 1495888 1495888 674248674248 0.210.21 --1.37-1.37 7.447.44 Group 4 (2501-5000) GroupGroup 5 5 (5001 (5001--10000)-10000)

Female Male FemaleFemale MaleMale

N M ± SD Z M ± SD Z p t df NN MM ± ± SD SD ZZ MM ± ± SD SD ZZ pp tt t dfdf Mission 4 2016 14 560552 ± 438027 450361 406524 ± 212945 390640 0.25 1.18 18.82 MissionMission 4 4 2016 2016 22 13901701390170 ± ± 197715 197715 13901071390107 17780531778053 ± ± 538507 538507 17780531778053 0.440.44 --0.96-0.96 22 Mission 4 2017 14 541457 ± 451251 435538 459619 ± 288445 416679 0.57 0.57 26 MissionMission 4 4 2017 2017 22 15932141593214 ± ± 410409 410409 15932141593214 23539582353958 ± ± 1568766 1568766 23539582353958 0.580.58 --0.66-0.66 22 Mission 4 2018 14 732836 ± 793267 477270 461417 ± 353226 417255 0.25 1.17 26 MissionMission 4 4 2018 2018 22 18764931876493 ± ± 798553 798553 18764931876493 15413871541387 ± ± 259213 259213 15413871541387 0.630.63 0.560.56 22 Mission 5 2016 14 447980 ± 610996 185624 307585 ± 321569 212476 0.45 0.76 26 MissionMission 5 5 2016 2016 22 517088517088 ± ± 66316 66316 517088517088 473761473761 ± ± 41365 41365 473761473761 0.520.52 0.780.78 22 Mission 5 2017 14 410826 ± 613253 177195 431390 ± 307099 344720 0.91 -0.11 26 MissionMission 5 5 2017 2017 22 821651821651 ± ± 454677 454677 821651821651 607684607684 ± ± 358536 358536 607684607684 0.650.65 0.520.52 22 Mission 5 2018 14 320255 ± 357825 163506 369796 ± 253688 288973 0.68 -0.42 26 MissionMission 5 5 2018 2018 22 997339997339 ± ± 608300 608300 997339997339 544232544232 ± ± 72064 72064 544232544232 0.410.41 1.051.05 22 Mission 6 2016 14 270179 ± 156150 225664 439820 ± 830536 169620 0.60 -0.75 26 MissionMission 6 6 2016 2016 22 10277021027702 ± ± 68267 68267 10277021027702 11265111126511 ± ± 338714 338714 11265111126511 0.730.73 --0.40-0.40 22 Mission 6 2017 14 268070 ± 201113 199653 518368 ± 656452 152709 0.19 -1.36 15.42 MissionMission 6 6 2017 2017 22 512554512554 ± ± 74276 74276 512554512554 16356321635632 ± ± 236580 236580 16356321635632 0.020.02 --6.41-6.41 22 Mission 6 2018 14 486567 ± 653144 200199 333784 ± 393039 212719 0.46 0.75 26 MissionMission 6 6 2018 2018 22 370159370159 ± ± 109584 109584 370159370159 22391472239147 ± ± 125448 125448 22391472239147 0.000.00 --15.87-15.87 22 Mission 12 2016 14 463433 ± 322644 313426 404395 ± 511083 240577 0.72 0.37 26 MissionMission 12 12 2016 2016 22 679416679416 ± ± 87860 87860 679416679416 760007760007 ± ± 97178 97178 760007760007 0.480.48 --0.87-0.87 22 Mission 12 2017 14 356162 ± 227701 272282 471173 ± 333780 390689 0.30 -1.07 26 MissionMission 12 12 2017 2017 22 937318937318 ± ± 525722 525722 937318937318 748122748122 ± ± 269474 269474 748122748122 0.700.70 0.450.45 22 Mission 12 2018 14 316070 ± 246679 248474 508220 ± 303865 433130 0.08 -1.84 26 MissionMission 12 12 2018 2018 22 596413596413 ± ± 212204 212204 596413596413 415339415339 ± ± 178711 178711 415339415339 0.450.45 0.920.92 22 Ʃ Missions 2016 14 1742145 ± 770784 1581486 1558325 ± 1057297 1061326 0.60 0.53 26 ƩƩ Missions Missions 2016 2016 22 36143153614315 ± ± 283625 283625 36143153614315 41383344138334 ± ± 738677 738677 41383344138334 0.450.45 --0.94-0.94 22 Ʃ Missions 2017 14 1576518 ± 853530 1209396 1880551 ± 965397 1632591 0.39 -0.88 26 ƩƩ Missions Missions 2017 2017 22 38647383864738 ± ± 1465086 1465086 38647383864738 53453975345397 ± ± 1960197 1960197 53453975345397 0.480.48 --0.86-0.86 22 Ʃ Missions 2018 14 1855729 ± 1102582 1513848 1673218 ± 828370 1592417 0.63 0.50 26 ƩƩ Missions Missions 2018 2018 22 38404053840405 ± ± 131536 131536 38404053840405 47401604740160 ± ± 133886 133886 47401064740106 0.020.02 --6.78-6.78 22

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Discussion The main aim of this thesis was to answer the question if female mayors make a difference by investigating whether female mayors tend to use the yearly budget (on municipality base) differently than men in the same position do. A second aim was to investigate the social areas in more detail and compare the spending behaviour between female and male leading municipalities.

Generally, there is not much difference between the female and male municipalities’ expenses. Significant results were only obtained for Group 5 ÖSQ 2018 (p = 0.03) when comparing the municipalities individually (Test 1) and for Group 3 Mission 4 2017 (p = 0.02); Group 5 Mission 6 2017 (p = 0.02); Group 5 Mission 6 2018 (p = 0.00) and Group 5 Ʃ Mission 2018 (p = 0.02) when comparing the groups and Missions (Test 2). Thus, one can draw the conclusion that the gender of the mayor does not necessarily influence the distribution of the budget. This can have several reasons, which will be explained in more detail in the following.

Differences regarding EFQ, FSQ, ÖSQ and SDSQ

Looking at the median of each key figure, the numbers as such do not differ much. In general, female leading municipalities score a slightly lower value within all key figures than male leading municipalities, however, the differences were not significant. A possible explanation for this similarity might be the external circumstances: According to Haenle (2012), the structure, size and tasks of every municipality can be the main motives for the distribution of the budget. Thus, as the yearly budget is mostly fixed for each municipality and the mayor has to follow certain rules, he or she might have only little autonomy in deciding its distribution. Another explanation might be found by looking at the numbers of the municipalities from the years before. This could indicate if the values in general are decreasing over the years or if this is due to the different mayor. However, these key figures need to be considered with care, as they are often overlapping and can influence each other. For example, having a low EFQ can be cause of an indebtedness (VSD), which in turn can cause a low SDSQ and can eventually also lead to a lower ÖSQ.

The steady decrease in the EFQ for both, female and male leading municipalities, might be a sign that the current expenses and the capital expenditure cannot be covered by the current earnings and income on capital over the long-term. This leads to a reduced scope for self- financing, which can only be financed through net new debt or reversal of reserves (Biwald, 2005). Compared to Spiss et al. (2010), who investigated the EFQ in the Ötz Valley in Austria, 39

the scores of the municipalities in Trentino-South Tyrol obtained in this thesis were higher. This very good score in Trentino-South Tyrol might indicate that these municipalities are able to finance themselves without making many debts. South Tyrol on its own has a very good economy. The unemployment rate is at about 3%, which indicates economically full employment. In comparison, Trentino has an unemployment rate about 4,5%. South Tyrol’s cultural diversity as well as bilingualism opens new opportunities for its economy (Lechner & Moroder, 2008). Another important factor is tourism: The earnings from tourism and public administration lead to a high tax revenue. This could be the main factor responsible for the high EFQ. About 90% of these taxes are kept by South Tyrol itself, due to its unique autonomy status. Italy as its state gets only about 10% of these taxes. This could explain why Trentino- South Tyrol has such a good EFQ. However, it would be interesting to know if the high EFQ is only as high because of South Tyrol’s autonomy status, or if Trentino itself plays also such a big role.

The FSQ can be seen as an indicator for future financial scope, within which new projects can be implemented. When looking at the numbers, the male leading municipalities increased, while the score for the female leading municipalities decreased. Even though not significant for 2016 and 2017, a trend towards significance in 2018 was evident (p = 0.07), with a difference of 0.11 between female and male leading municipalities. This might indicate that the male leading municipalities might rather be able to implement new projects in the future compared to their female counterparts. This could in turn have an impact on the budget of the successor. In addition, the difference might be explained by the structure, size and tasks of every municipality, as these can be main motives influencing the budgetary distribution (Haenle, 2012). The motives can have a long background history. For example, public facilities can be spun off as in-house operations for some municipalities, while other municipalities do not spin them off and have to consider them in the calculation of the FSQ as an investment. Therefore, it could be interesting to check if the female leading municipalities in general had more larger investments over the last years as the male leading municipalities, and if that was the case, if these investments were considered as investments or in-house operations.

Even though the numbers regarding ÖSQ show that on average, all municipalities do not have real financial problems, the declining trend should be observed over the long-term. The municipalities’ welfare could suffer from a steady decline, as contributions to organizations or staff might need to be cut to save money. Italy’s new accounting system also underwent some significant changes regarding investments and current expenses. In 2016, a uniform accounting-

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system for municipalities, called “Harmonization of public accounting” was introduced. Due to the state law of December 12, 2016, No. 25, investment expenditures are accounted differently than before. The whole investment expenditures for one year have to be covered by the municipalities’ earnings in beforehand. This leads to a balance in the municipalities’ household of earnings and expenses (Buchhaltungs- und Finanzordnung, Einheitstext über die örtlichen Körperschaften, Nr. 267, 2000; Buchhaltungs- und Finanzordnung der Gemeinden und Bezirksgemeinschaften der Autonomen Provinz Bozen, Nr.25, Art. 2, 2016). This could have led to a lower ÖSQ in the long-term. However, older data should be investigated to prove this assumption.

The debt service tax rate (SDSQ) for both leading municipalities is more than inadequate. The score for the female leading municipalities is even lower than the male leading one, which could be due to the already low numbers in general. We might also consider outsourcing of municipal facilities as a motive (see FSQ). This might reduce the municipality’s debt level in the budget, even though the overall debt has not decreased at all (Haenle, 2012). The low scores match the decrease of the EFQ over the years. More investments lead to a higher debt, which could also be a reason for this.

Differences by groups.

At the group level, only one significant difference between female and male leading municipalities was found in Group 5 (ÖSQ 2018, p = 0.03). Reasons for these similarities could be the same as at an individual level: mayors mostly have to work with a fixed budget, and the numbers might be influenced by external motives such as a municipality’s structure, size and tasks (Haenle, 2012).

When looking at the EFQ of all groups, no big differences are obvious. All the scores are quite similar and the decline over the years corresponds to the decline already found on an individual level. In Group 5, the numbers obtained were a bit different, as both, female and male leading municipalities received a score of almost >110% (very good). However, this group only consisted of four municipalities and was only considered as a matter of form.

It seems that the bigger the population, the lower is the score for FSQ. One reason could be that public buildings used to be financed by the state, nowadays, however, the municipalities have to self-finance more projects. This might lead to a lower EFQ and can also influence the FSQ, as less money is available for new investments and projects. However, the constant decline

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found when comparing the municipalities on an individual level is not found when comparing the groups. Group 4 and 5 are the only groups with declining numbers. This decline on an individual level (Test 1) might be due to a higher deviation of the numbers within Group 4 and 5. Additionally, the male leading municipalities in group 2 scored far better than the female leading municipalities. This matches the general picture that male leading municipalities generally received better scores. It would be interesting to see, if this would change, if we compare South-Tyrol and Trentino.

For the ÖSQ, only one significant difference was found, namely in 2018 in Group 5 (p = 0.03). Group 5 is the group with the highest budget and thus, it seems that there is only a difference between female and male leading municipalities with more than 5000 inhabitants. However, it was also the group with the fewest municipalities (n = 4), and thus, results have to be looked at with care. Looking at the ÖSQ within all groups, there is not always a steady decline in the numbers. The declining trend from before can only be found within female leading municipalities. Male leading municipalities do not decrease at all; they stay constant or even increase over the years. Various reasons are given in literature as a possible explanation for this development. In Germany, the introduction of the Riester pension to promote the private pension provision (see e.g. Corneo et al. 2009 and Börsch-Supan et al. 2006, 2008). Also, the increasing inequality of household income (Klär & Slacalek, 2006) or the increase in caution saving due to the development of the labour market (Bartzsch, 2006) can be possible causes.

Regarding the debt service tax rate (SDSQ), all groups received a very bad score of inadequate (>25%). Over the years, female leading municipalities generally decrease, while the male leading municipalities increase. Interestingly, it seems that the bigger the population, the better is the obtained score. This inadequate score shows that over 75% of the share of own taxes and income shares are used to pay back the debt service. This could be explained by borrowed loans, eventually also from the past from another mayors’ period, which influence the debt service tax rate. The mayor also has a leeway in the tax-payback (e.g. municipal property tax). Hypothetically, woman could tend to desire less tax as payback than man do. Infante (1983) compares the cultural essence of the dominant, competitive and aggressive men with the social, submissive, cooperative, warm and sensitive woman. Traditionally, women were expected to be cooperative and supportive (Wadsworth et al., 1987). This cooperative and supportive essence might eventually be found in the payback behaviour.

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Differences by missions.

Comparing the medians of both, female and male leading municipalities expenses, a trend towards an increasing spending behaviour can be seen. In general, male leading municipalities tend to spend more in social sectors than female do. This does not only refute this thesis’ hypothesis but is also in contrast to findings from literature. Eagly et al. (1995) showed that women are reported far better in feminine-dominated areas such as education and government or other social related services. In addition, Geser (2009) showed that woman tend to act more for social, youth- and educational or healthcare sections than men do. However, this does not seem to be the case regarding municipalities in Trentino-South Tyrol.

Only in Mission 12 (social rights, social policies and family) there was a very big difference in the amounts of expenses of female and male leading municipalities. While both are increasing their expenses over the years, the female leading municipalities started with expenses around 200.000€ and kept them constant, while the male leading municipalities started with expenses of 100.000€ and increased them by almost 200%. However, these differences were not significant.

The overall expenses show a very different picture. In 2016, they are similar between female and male leading municipalities. Over the years, the female leading municipalities decreased their expenses, while the male leading municipalities increased them. This is again opposing research that claims that female leaders tend to give more attention to social areas and therefore spend more money in these areas (Wadsworth et el., 1987; Steininger, 2000). Wadsworth et al. (1987) argues that traditionally, women were expected to be cooperative and supportive to the status quo – which has been and still is the participation in mainly social areas. Also, Steininger (2000) argues that there are four main areas in which women tend to put special focus on: Education, social affairs, youth and family, healthcare and sports. However, this is in contrast to the results of this thesis, as no significant differences were found and purely descriptive, male mayors spent more money in these areas.

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Differences between missions by group.

When comparing the Missions in the different groups, some significant differences were found: In Group 3, there was a significant difference for Mission 4 2017 (p = 0.02). Additionally, Group 5 showed significant differences for Mission 6 2017 (p = 0.02) and 2018 (p > 0.001), as well as for the sum of all Missions in 2018 (p = 0.02).

In Group 3, the female municipalities expenses generally fluctuated a lot with very high values, while the male leading municipalities kept decreases or increases more stable. Interestingly, a significant difference was found for Mission 4 in year 2017, where the female leading municipalities had the lowest expenses of the three years. As Mission 4 refers to “Education and right to study”, one would expect that female mayors put more focus into this Mission, as according to a review, the focus of their education was always put on “feminine” social skills (Schröder, 1995). This thesis’ results are opposing this view. However, no significant differences in Mission 4 were found in the other three groups, which could indicate that this was only an outlier.

Group 5 showed significant differences for Mission 6. This Mission refers to “Youth policies, sports and leisure”. Female leading municipalities decreased their expenses, while male leading municipalities increased them. This means that in the female leading municipalities, less money was spent for sport facilities and activities for young people. A notable increase of female participation in sports at all levels happened due to changes in social definitions of what was considered appropriate female behaviour. However, considering this significant change in the participation, there is still a non-uniformly distribution of women in all different types of sport activities (Firebaugh, 1989). The reality is that this regular participation is limited by different factors, including gender stereotyping. Disparities exist in terms of access, authorization and freedom of choice (Deem, 1986; Henderson & Bialeschki, 1991; Wearing & Wearing, 1988). Breuer, Hallmann, Wicker & Feiler (2010) also found that regarding gender, sport is considered more masculine and ruled by the male part of the population. Thus, men generally are likely to spend more money on sports related areas than women do. The higher percentage of men participating in sports might be a reason why men tend to be more active in this section also in the position as mayors This therefore might influence the investments into sport-related buildings such as soccer arenas or in different sport associations and memberships. These investments can be relatively high in comparison to other investments within Mission 6. However, the ecological surroundings of these municipalities can also play a role for higher

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investments within this sector. An US study from Huang & Humphreys (2012) showed, that countries with greater access to sports facilities tend to participate more in physical activity, and therefore tend to invest more into sport-related topics. The differences in this thesis could also be due to the small sample size and thus need to be proved with more municipalities. Debt paybacks could also have played a role here, because they increase the overall expenses. It would be interesting to know, if the male leading municipalities of Group 5 had big investments in sport arenas or something similar.

Interestingly, in Group 2 of Mission 5, the female leading municipalities expenses increased by about 20%, while the male leading municipalities expenses decreased by around 25%. This Mission includes the protection and enhancement of cultural assets and activities. This difference could be explained in possible restructures of the overall expenses: Potentially, this could also be due to higher unique investments needed in other Missions. Male leading municipalities could have restructured the overall expenses which led to the decrease in those municipalities.

Group 4 consisted of the highest number of municipalities in comparison to the other groups. Only within Mission 12 in 2018 a tendency for a significant difference between female and male leading municipalities’ expenses was found (p = 0.08). In this year, the male leading municipalities spend about 40% more in social rights, social policies and family. This tendency, however, does refute this thesis’ hypothesis and the current available literature described in the introduction. As a significant difference was only found in 2018, it would be interesting to follow these numbers over the next years to see if a steady significance can be found, or if this result is only an outlier.

Theoretical and managerial implications

Generally, there seem to be only minor differences between female or male mayors and their handling of the budget in the social resorts, as only few significant differences were detected. A mayor’s main aim is to represent the welfare of the community at its best. However, the scope in doing so is very small in this political position. As a female mayor, it can be even harder. According to Atkinson (1984), it is somewhat of a dilemma being for a woman in such a position, which in general has been anticipated for men. That is, not being allowed to behave like men, but still be damned, if not. Being efficient, tough and decisive, but still at the same time making no concessions in maintaining the woman’s femininity seems to be adequate. Even

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though these assumptions are out-dated, and women’s’ participation in the parliament and communes are seen differently, stereotyping is still there (Gonzenbach 1981).

There are many factors that influence the decisions a mayor can and has to make, such as the local council, the community committee and the tourist association of each municipality. They all can have a big impact on the decisions of a mayor. A mayor has to respect the desires and opinions of every part of the municipality and therefore decisions are mostly not made by him or herself, but rather a joint decision. Of course, generally the mayor has the last word, but he or she is under pressure of other association, parties or councils and committees. A mayor is not only restricted by that, but also by bureaucracy, legal issues, rules, and guidelines from the past. Thus, these are all possible motives which might influence and impact the decisions and the spending behaviour.

Worth mentioning is also that the overall welfare of a municipality can not only be measured by the key figures and numbers calculated in the chapters above. It should not be the main goal for a mayor to keep these numbers good and well-balanced. Every municipality has a different population density with different economical and natural surroundings. Some might have more tourism as others, others might focus more on industry. These and other factors can change the outcome. To calculate a municipalities welfare only by numbers in my opinion is not useful and almost impossible. A lot of different numbers like unemployment rate, birth rate, economic circumstances and many other factors would have to be part of it. Every municipality has its own desires and needs, which have to be tried to be fulfilled at its best by the mayor.

Unfortunately, too few analytical studies have been made about the impacts of female’s holding important office roles (Gehlen, 1977). Only limited research has addressed this question of what difference it makes if women are elected to office. The lack of attention to this particular question is at least partly a function of the limited period of time, during which there have been sufficient numbers of female elected officials, to allow for systematic observation (Saltzstein, 1986).

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Limitations The presented study had some limitations, which have to be mentioned. First, the sample was limited by the numbers of female leading municipalities in Trentino-South Tyrol. This led to a smaller overall sample size, as only the same amount of male leading municipalities could be considered. Secondly, the data taken from the official website of the capital of Italy for public finance data was not complete. The database was only recently established by all Italian municipalities and different accounting teams, which might have led to errors. No data prior to 2016 was also available. Limitations were also given in the calculation of the key figures. The initial calculation model was taken from Austria, Biwald (2005). Some small changes within the key figures had to be made (e.g. SDSQ) to fit it to the Italian accounting system, as the numbers differ in terms of levels (i.e. the budget data in Italy has more subdivisions than in Austria). This was another reason that some key figures (e.g., VSD and SEQ) could not be considered in this study.

Conclusion In conclusion, it seems that female and male mayors spent the budget equally regarding the social resorts. These results can have several reasons: The distribution comes hand in hand with communication with the accountants of each municipality, and the accountant’s relationship to the prior mayor can influence their distribution decisions. Every mayor has also to consider and finish the projects of his or her predecessor, which influences the distribution of the budget. In this context, it would be interesting to know whether the mayor from the previous period was female or male, and how her/his spending behaviour was. Additionally, bigger projects with one-time investments can completely change the overall picture of the distribution. Furthermore, every mayor is bound to follow certain rules regarding the budgetary allocation. Thus, a smaller leeway in this context also leads to less gender differences.

As the significances found in this study were very small and not always 100% clear (i.e. no clear trend of a significance between the key figures and the missions), further studies should consider using a different approach in the future. Factors and motives such as the municipality’s sociodemographic and economic situation can influence the mayor’s decision of the distribution of the yearly budget. Thus, doing a thorough qualitative study would be highly recommended. By conducting interviews with female and male mayors one could further investigate this thesis’ hypothesis and find further variables and factors that influence these decisions.

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Note of thanks

Lastly, I want to say thank you, to all the people involved in this process of writing my Master Thesis. I want to thank my professor Kurt Promberger and all members of the Department of Strategic Management, Marketing and Tourism from the University of Innsbruck. Special thanks to the EURAC research team, particularly Davide Maffei, which helped me gathering all the necessary data for my statistical research approach.

It was not always easy for me to find motivation to work on my thesis. I want to thank my mother, which was continuously here for me to listen to my daily phone calls. Even if I was complaining and criticising about the same stuff every time, she always had an open ear for me. Thanks to my father, who always had a calm mind and asked, if it is not finally time to start working instead of studying. He never worried about me not finishing my studies. That shows how much he believes in me.

My brother, who just called me when he was bored and wanted to talk about unnecessary stuff, so I do not waste too much time working on my thesis. However, at the end he helped me a lot with his unique opinions – even though mostly we are not the same one.

Special thanks to my sister Katharina, which was here for me, nevertheless the situation. While working and finishing her PHD, she found always time to help me with my questions and suggestions. Without her, I would not have put so much enthusiasm and motivation in this work.

Finally, I want to say thank you to all my friends who were here for me, especially in this difficult time, in which human contact is not as it was before. My friends from my hometown, which are still a big part of my life, even though I am mostly far away from them. My friends Florian, Stefano and Gaia for all the messages and phone calls – I really appreciate you as one of my closest friends. And, of course my roommates Jonas and Fabrizio. Quarantine with you guys couldn’t have been better.

I hope we will always be a big family, nevertheless the situations.

THANK YOU!

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