Mittuniversitetet, avdelningen för samhällsvetenskap.
What drives high female cabinet representation globally –
The impact of women’s parliamentary representation over time and their status in national cabinets
C-uppsats i statsvetenskap vid Mittuniversitetet, HT-VT 2020-21
Victor Spang Arthursson Abstract
The aim of this study is to contribute to previous research by improving the time perspective when investigating what impact female parliamentary inclusion has on the levels of female ministers in national cabinets globally. In detail, it focuses on the impact a considerable level of women’s parliamentary representation over time and their status in national cabinets. Politicians’ experience and seniority is built up over time, and the hypothesis put forward is that when heads of states select their cabinet members the power exercised by ever larger numbers and more experienced women visible within parliaments will be difficult to ignore when selecting candidates for cabinet appointments. Using data from 2000 to 2019, this article shows that the longer parliaments have had considerable levels of female parliamentarians the more women will have minister positions. It also shows that there are differences between regions globally, with parts of Asia having a particularly weak performance. This article further demonstrates the importance of including a time aspect when investigating parameters that might need time before their impact is visible, and that testing against momentary data values risks producing inconsistent results.
Page i Table of contents
Abstract ...... i
Table of contents ...... ii
List of tables ...... iv
List of figures ...... iv
1. Introduction ...... 1
2. Previous research ...... 3
2.1. Female MPs ...... 3
2.2. Development ...... 4
2.3. Democracy ...... 6
2.4. Religion ...... 7
2.5. Party ideology ...... 7
2.6. Gender equality ...... 8
2.7. Seniority and expertise ...... 9
2.8. Shortcomings and alternative findings ...... 10
3. Hypothesis and expectations ...... 11
3. Method ...... 12
3.1. Methodology ...... 12
3.1.1. Research design ...... 12
3.1.2. Data ...... 13
Main sources for dependent and independent variable ...... 13
Additional sources for control variables ...... 13
Validity and reliability...... 15
Page ii Selection criteria ...... 16
Dependent variable ...... 16
Independent and control variables ...... 16
3.1.3. Model overview ...... 20
Model 1 – Basic (M1) ...... 20
Model 2 – Basic controlled for Women’s suffrage (M2) ...... 21
Model 3 – Full (M3) ...... 21
Model 4 – Basic controlled for Extended periods (M4) ...... 21
Model 5 – Basic controlled for HDI (M5) ...... 21
Model 6 – Full controlled for Functioning state (M6) ...... 22
Model 7 – Full controlled for HDI + Functioning state (M7) ...... 22
Model 8 – Basic controlled Regions (M8) ...... 22
3.2. Results ...... 22
4. Conclusion and discussion ...... 31
5. References ...... 34
6. Datasets ...... 36
7. Appendix ...... 38
Page iii List of tables
Table 1. Women in cabinet dataset example ...... 17
Table 2. Boxplot details ...... 25
Table 3. Overview of results M1-M7 ...... 26
Table 4. Overview of results M8 ...... 29
List of figures
Figure 1. Scatterplot Women in cabinet 2019 ...... 23
Figure 2. Boxplot Women in cabinet 2019 ...... 24
Page iv 1. Introduction
Much has been done since New Zealand became the world’s first self-governing country to introduce universal women’s suffrage in 1894. While the numbers of women participating in politics is on an upwards trajectory, much is still needed to be done before any claims to equality in politics can be made. As of 2020, women’s average worldwide inclusion in parliaments, upper and lower houses combined, stands at 24.9% only, with big variation between countries. The current average of ministerial positions held by women is 22.1%.
Plenty of research has thoroughly investigated what factors might lead to a high level of representation of women within national parliaments (WIP). Certain factors seem consistent, such as a proportional elective system and quotas, leaving us with a good insight into what areas are important to focus on to achieve a greater parliamentary gender balance in the future.
However, while numerous works have studied what leads to a high parliamentary inclusion of women, fewer studies have been focussing on how to achieve a high proportion of female members within national cabinets1 (WIC). And for the few that have, many outcomes have been inconclusive. Several study only limited number of cases or manage to explain only part of the observations2. Others are by now relatively dated3, and it’s questionable how well they still hold, especially since many indicate a shift in how party politics affect female cabinet inclusion since the late 1990s4. And yet others manage to show a connection only in certain political environments5. They tend to underline the importance of early women’s suffrage, the elective system and parliamentary representation as contributing factors to a high WIC, but none of the studies carried out has been able to offer a universal link that will – across nations where cabinets are appointed by
1 Krook and O’Brien, 2012, p. 841. 2 Ibid. Claveria, 2014 3 Reynolds, 1999. 4 Claveria, 2014; Stockemer and Sundström, 2018. 5 Högström, 2015.
Page 1 the head of state freely under normal circumstances – undoubtedly lead to increased female cabinet inclusion.
One aspect that hasn’t been exclusively studied is what impact a considerable female parliamentary inclusion over a longer period of time has on the cabinet composition. This connection could be shown to have a substantial impact as representation alone seems to be not enough. Many studies have hinted towards a visibility aspect6 meaning that the head of the state often picks their ministers from the parliament members7 and when being selected experience and seniority is of crucial importance and can be expected to have a correlation with the chosen candidates. As these traits are built up over time, the mere presence of women in parliaments is likely to be insufficient to guarantee a presence also in cabinets. Over time it will become difficult for the head of state to ignore the pressure exercised by the ever more present and powerful female parliamentarians.
According to the United Nations, “Gender equality is not only a fundamental human right, but a necessary foundation for a peaceful, prosperous and sustainable world.”8 And cabinets, most nations’ executive political branches, is where the most powerful political positions are normally to be9 found. Consequently, as little research is available and the results to various degrees inconclusive or dated, it’s of central interest and importance that more research is conducted into this matter to provide clear insights which could lead to an improved situation. As a time perspective relating to the parliamentary inclusion hasn’t been exclusively studied, it’s important to extend the present research including this.
This study thus aims to contribute to previous research by improving the time perspective. It will do so quantitatively over a majority of the world’s nations. Additionally, it will introduce certain control variables noted in prior research to test for potential additional influences such as development, functioning of state, years of women’s universal suffrage and regionality. The question this study aims to answer is:
6 Högström, 2012; Claveria, 2014; Reynolds, 1999. 7 Stockemer, 2018, p. 666. 8 United Nations Sustainable Development Goals 9 Claveria, 2014, p. 1157.
Page 2 Does a considerable female parliamentary inclusion over a longer time frame lead to a higher female cabinet presence compared to nations where lower numbers of women have been included in parliaments for a shorter period of time?
The reason for including an element of considerable parliamentary inclusion is that the visibility aspect assumes that greater female visibility in politics (parliaments) should lead to a higher probability of women being selected for a cabinet position. But, as seen above, seniority is also of crucial importance and it’s thus necessary to investigate this from a chronological as well as quantitative perspective.
2. Previous research
There are some contradicting results when it comes to what impact various factors have on WIC, and to which extent they can explain any relationship. The sections below will investigate these conditions and whether or not the previous research is in alignment with their impacts
2.1. Female MPs The level of WIP is one of the most recurring factors included in previous research and the one that is most commonly a statistically significant parameter. Krook and Obrien’s 2012 study utilised the percentage of WIP to measure women’s influence within what they referred to as their Elite hypothesis. Their argument was that female MPs impact on the demand side of female ministers in lobbying for inclusion of women in cabinets, as well as on the supply side providing a greater pool of potential cabinet ministers to choose from10. The hypothesis managed to explain 72 cases, or 60%, of their observations, with the percentage of women in parliament being one of the variables positively corresponding with the number of WIC.
In 2014 Silvia Claveria attempted to account for cross-national and over time variation in WIC. One of her hypotheses was that “The higher the percentage of women in parliament, the more women there will be in cabinet”11. She found this to be a statistically significant factor, but only when a threshold of 20% female MPs had been reached12. Unfortunately, her sample size was relatively
10 Krook and O’Brien, 2012, 848. 11 Claveria, 2014, p. 1163. 12 Ibid., p. 1168.
Page 3 small, including only 23 governments of advanced industrial democracies for the period 1980– 2010, which raises questions as to how universally applicable her findings are.
An earlier study from 1999 by Andrew Reynolds investigated the number of WIP and WIC in 180 nation states and had as its aim to identify factors that contribute to, or hinder, female inclusion in these organs. Within his Political/historical context Reynolds utilised the percentage of women in the legislature as one variable to measure the impact on WIC, and one of the conclusions from this model was that the familiarity and acceptance of women in powerful positions was deemed of great importance. The model managed to explain 37% of the variance in cabinet representation with the variable women MPs being the best predicator followed by number of elections since 1945 and number of years of female suffrage.
While Daniel Stockemer and Aksel Sundström focused on party ideology and government turnover in their 2018 article, they did, however, include the variable Women’s representation in the legislature in their alternative explanations13. Within their models, where they conclude that government turnovers mattered more than party dynamics, they found that the control variable Women’s representation in the legislature was the only alternative variable with statistical significance, but only in part of their models14 covering as few as 54 mainly Western countries.
2.2. Development Another area that is frequently assumed to impact on women’s inclusion in cabinets is the level of development within a nation. Different studies define development differently though, with the variables including GDP, Female Socioeconomic Development, Level of Education and the UNDP’s Human Development Index (HDI).
In his 2015 study John Högström investigated whether development and democracy positively affect gender equality in cabinets15. He used GDP per capita to operationalise socioeconomic development and carried out extensive tests across 191 nations. The conclusion was that GDP per
13 Stockemer and Sundström, 2018, p. 664, p. 666. 14 Ibid., p. 669-670. 15 Högström, 2013, p. 352.
Page 4 capita was having a positive contribution, but only in democracies and developed countries, not in autocracies or less developed nations16.
Claveria instead used Level of Education to define socioeconomic development. Her hypothesis was that the higher the level of women’s education, the more women will be in cabinets17. After several tests, she ultimately found the variable to be statistically insignificant and negative and consequently rejects her socioeconomic development hypothesis18.
Reynolds hypothesised that the level of female socioeconomic development will help determine the number of women in the legislature and cabinet19. To test this hypothesis, he used the UNDP’s Gender Related Development Index (GRDI) to define development20. He devised a preliminary model where this, amongst other variables, was included but found the GRDI variable statistically insignificant21.
Krook and O’Brien argued that GDP as a mean to measure development levels is less preferable to HDI and chose the latter to represent development22. However, the variable showed to have statistical significance only in one model managing to explain a mere 9 out of 117 countries23.
Finally, Stockemer and Sundström used GDP per capita to operationalise economic development. Results-wise, they found only limited support for their development variable in one out of seven models, representing the time period before year 2000.
16 Ibid., p. 352. 17 Claveria, 2014, p. 1159-1160. 18 Ibid., p. 1166-1167. 19 Reynolds, 1999, p. 552. 20 Ibid., p. 567. 21 Ibid., p. 570. 22 Krook and O’Brien, 2012, p.848. 23 Ibid., p. 850 & p. 852.
Page 5 2.3. Democracy What impact then does “democracy”, the golden bullet, have on women’s ministerial representation? Several of the studies referred to above do in fact include varying measurements of the variable democracy.
Starting, appropriately, with Högström, and his “democracy” titled study. He notes that several indexes measuring democracy have been constructed over the years, but unfortunately many are incomplete. Therefore, he chose the Freedom House Index with the expectation that higher levels of democracy should be correlated with higher levels of WIC 24. The results were somewhat inconclusive when it comes to the impact of democracy, and the level of democracy only had a positive effect in certain types of political regimes25.
Reynolds also used the Freedom House’s Index to define his variable level of democracy26. His preliminary results, however, found that the index of democracy was not statistically significant, and it was subsequently excluded from his final model27.
When it comes to Krook and O’Brien, they too included a measurement of democracy in their study. They tested coding cases both using the Freedom House’s classification for free and partially free countries as well as using the Polity IV index, which focuses on executive power, and is published by Systemic Peace. They found limited support for the hypothesis, managing to classify only 20% of the nations28.
Stockemer and Sundström included democracy in their study as well, using a dummy variable to code the level of democracy using the Polity IV index. While having a positive expectation on the results they failed to show any significant influence from this control variable.
24 Högström, 2013, p. 338. 25 Ibid., p. 352. 26 Reynolds, 1999, p. 567. 27 Ibid., p. 570. 28 Ibid., p. 850 & p. 847, p. 853.
Page 6 2.4. Religion Turning the focus to religion, or cultural factors as they are often referred to, and starting with Claveria. In her study she defined culture through religion and the variable was operationalised as percentage of protestants29. As for education though, her hypothesis was rejected as political institutions turned out to have a higher explanatory power than sociocultural factors30.
Högström took a different approach in his study. Similarly to Claveria, he used the percentage of Protestants in a country as variable, but in addition, he also added a variable for the percentage of Muslims31. His findings were mixed, with the variables having different impacts and only for certain levels of development32.
Reynolds too included religion in his research from 1999. He notes that very few popular religions can lay claims to being women friendly when it comes to election of women to office, and while underlining that it’s a matter of degrees of discrimination, his hypothesis was that there will be fewer women elected to office where the dominant religion is especially adverse to women33. He found contradictory results compared to Högström and only certain variables were statistically significant34.
2.5. Party ideology Political culture and party ideology is another factor that traditionally has been expected to have an impact on women’s representation in politics, and most of the authors referred to have also looked into this area. Reynolds hypothesised that the number of WIC will be influenced by an increase of women-friendly parties, based on the ideology of the government35. To test this, he checked for
29 Ibid., p. 1165. 30 Ibid., p. 1167. 31 Ibid., p. 339. 32 Ibid., p. 345. 33 Ibid., p. 551-552. 34 Ibid., p. 570. 35 Ibid., p. 554.
Page 7 composition of government as well as for the proportion of left parties within the government, and managed to partially confirm his hypothesis 36.
Krook and O’Brien mentions, without presenting any hypothesis, that more female ministers have been found to be appointed by left-wing governments and categorised the governments in the study as either left- or right-leaning37. They found support for the claim within their mixture model, but in the traditional regression model the results were indistinguishable from zero38.
Claveria, too, included party ideology in her research. She hypothesised that left-wing parties can be expected to have higher levels of WIC39. The results were statistically significant and she noted that they were different from previous findings40.
Finally, Stockemer and Sundström found limited support for the influence of party ideology. They too hypothesised that a head of government from a left-leaning party would be more likely to appoint female ministers41. Their conclusion was that before 2000, leftist parties tended to nominate somewhat more women to cabinet positions, but that since then, liberal parties have taken the lead. In both cases conservative party governments were found to nominate the fewest women42.
2.6. Gender equality Another recurring theme in previous research is the variable gender equality. Högström used the number of years since a country ratified the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW) as an indicator of gender equality43. The results pointed
36 Ibid., p. 571. 37 Krook and O’Brien, 2012., p. 847. 38 Ibid, p. 852. 39 Claveria, 2014, p. 1162. 40 Ibid., p. 1168. 41 Stockemer and Sundström, 2018., p. 663. 42 Ibid., 2018, p. 669. 43 Högström, 2013, p. 339.
Page 8 towards an early ratification having a positive impact in the least-developed countries as well as in democracies44.
Also Krook and O’Brien used number of years since CEDAW ratification as their measurement of gender equality within their Equality hypothesis45. As we have seen above, they found little support for this hypothesis, and in their regression test the variable showed up as having insignificant statistical impact46.
Reynolds used suffrage for his hypothesis that countries with a longer history of women participating in the political sphere would increases the number of WIC. He coded his variable as the number of years of suffrage and found statistical significance on the 10% level for this variable, noting that history was an important factor47.
Suffrage was also the measurement that Krook and O’Brien used to define countries with a long history of equal political rights. They argued that a long period of equality also should benefit women’s inclusion in cabinets but with no conclusive findings48.
2.7. Seniority and expertise Another consideration mentioned briefly in several studies, but not explicitly examined, is the fact that cabinet members often are selected due to their seniority and expertise. Stockemer and Sundström, Högström and Krook and O’Brien explicitly discuss this in their studies49. It is thus likely that in parliaments where female inclusion is a relatively new phenomena, women might not yet have had time to build up necessary expertise and consequentially might not yet be considered as eligible for minister appointment by the head of state. Additionally, which Claveria touches upon when referring to a parliamentary threshold, and Högström alludes to50, the greater the number of female parliamentarians, and the more visible they are, the more likely they might be to be selected for cabinet positions. Furthermore, the more power they hold, the more pressure they could exercise
44 Ibid., p. 351. 45 Krook and O’Brien, 2012, p. 848. 46 Ibid, p. 853. 47 Reynolds, 1999, p. 566, p. 571. 48 Krook and O’Brien, 2012, p. 666, p. 669. 49 Stockemer and Sundström, 2014, p. 666; Krook and O’Brien, 2012, p. 851. 50 Högström, 2013, p. 157.
Page 9 demanding inclusion of women when the head of state makes their cabinet appointments, which would be a very difficult force to ignore.
2.8. Shortcomings and alternative findings Throughout these studies there are many findings that seem to contribute to a higher proportion of female cabinet members, but as we have seen also some that are inconclusive or contradictory. Others have been deduced from limited data sets and can therefore not be expected to be universally applicable.
The single most recurring contributing factor to high cabinet inclusion quoted is the connection between the number of women in parliament positions and the number of women holding cabinet seats. This however only has an explanatory property in 60% of Krook’s and O’Brien’s case, is inconclusive in Claveria’s study, and manages to explain only 37% of the variance in Reynolds’, by now rather dated, research.
So, studies carried out on data from a limited number of nations is one challenge in several of the studies reviewed above. Similarly, frequently recurring variables measuring development, democracy, religion, party ideology and a tradition of gender equality likewise get very mixed results.
For Stockemer and Sundström, the impact of party ideology and turnover of government, moving from one ideology to another, is the focus on their study, rather than WIP. They find most support for their turnover hypothesis in their study covering only 53, mainly Western and industrialised, nations51. This is logical and perhaps an unsurprising finding, as a new government will select new members to the entire cabinet, while an incumbent government tends to in large degrees appoint the same members to the cabinet that were present in their previous period52. But the correlation they show could very likely be caused by the hypothesis that this study is intending to investigate, as for each successive shift in government, one political term will have passed, and during this term, the women in the parliament will have gained additional expertise and seniority compared to at the time
51 Stockemer and Sundström, 2014, p. 670. 52 Ibid., 2018, p. 670.
Page 10 of the previous government formation. And as we do know that globally the number of women in parliament is on an upwards trajectory53, it’s safe to assume that in a majority of cases the number of WIP will be greater than it was during the previous term.
Claveria, not surprisingly, found that women’s presence in previous cabinets didn’t seem to be a contributing factor, and I argue that while plausible, it’s unlikely to be the determining factor - that is rather the supply of suitable women to select cabinet members from and the power women exercise in parliaments.
Finally, the argument that women with seniority and expertise could be more likely to be selected for minister appointments, unfortunately hasn’t been operationalised or investigated in any of the reviewed studies.
3. Hypothesis and expectations
No single conclusive factor has yet been found leading to a higher level of female cabinet inclusion, and tests have mainly been carried out cross-sectionally.
As we can see from the studies referred to above, while we do know that in some cases a link between women in parliaments and inclusion in cabinets exists, this has not been the focus of their research but merely one of many variables included in models exploring other theories and often on limited data.
The hypothesis put forward in this study is that, besides having a functioning and relatively corruption free political system, the simple fact that a certain proportion of women is present in a parliament is not sufficient to guarantee their inclusion in cabinets. Their seniority and visibility need to reach a certain level first, for which time is needed. The system needs to be functioning and relatively corruption free, as otherwise other mechanisms, such as nepotism or bribery, can be expected to impact on appointments to powerful political roles.
If there’s a connection between considerable female representation in parliaments AND the time they have been present and inclusion in cabinets, the opposite should also be valid, i.e., nations where women are included but have just recently gained representation should have lower levels of WIC. This could explain the deviating observations in previous research, as that research has only
53 “Proportion of women parliamentarians worldwide reaches ‘all-time high’”, UN News, 2021.
Page 11 focussed on the levels of female members of parliament at a given point in time. My theory is that this is the case, and this will be investigated longitudinally on a global level.
3. Method
3.1. Methodology
3.1.1. Research design Scientific problems can be investigated either qualitatively or quantitatively, or as a combination of these two methodologies54. When the case number is large, with data in matrix form and lacking in depth, or the intention is to verify55 a hypothesis or to investigate a causal relationship, quantitative methods are usually the most appropriate solution.
As the intention with this research is to investigate the impact of relatively few variables on a large number of cases – in this case all selection-conforming56 nations globally – and the dataset will be in large matrix form, a quantitative methodology has been chosen. By not opting for a case study approach and being able to look at the entire population, the representativeness will undoubtedly be higher and the significance of the test greater.
Predictability is another important aspect in many studies, and through utilising advanced quantitative methods it could be possible to estimate a formula predicting what range a case would be within given certain values for the input variables57. Following this study, it could thus be possible to get an understanding of what level of female parliamentary representation a nation needs to reach and sustain in order to increase its female cabinet representation to a certain level. This should however be taken with a pinch of salt, as social sciences aren’t deterministic to nature. It is more accurate to discuss this in terms of a statistical or probabilistic relationship, where the outcome of the analysis will be a ballpark, around which the outcome given certain input variables is likely to be positioned. As many prior studies have determined what factors increase parliamentary female representation (such as quotas), governments that wish to improve their
54 Gerring, 2017, chapter 7.2 (as my copy of this book is an ebook, there are no fixed page numbers, and I’ll refer to the chapters instead from here on) 55 Ibid., chapter 5.2. 56 See section on data collection 57 Djurfeldt, 2019, p. 157, p. 160, p. 319.
Page 12 cabinet composition would have indicators on what to focus on to reach a certain level within a set timeframe to bring about political change and better gender equality.
Qualitative methods could however play an important role in future follow-up research into this subject; not only could they be employed to verify the theory through in-depth studies – that no other hidden factors were in play – but they could also prove useful in explaining any deviant cases from the general results of this Large-C study58.
3.1.2. Data
Main sources for dependent and independent variable
The main source for the dependent as well as the independent variable WIP and WIC comes from the Women in politics reports, published by the Inter-Parliamentary Union59 (IPU), which contain data for both variables. They have been collected from the World Bank’s DataBank on Gender Statistics60 as the DataBank can combine the various variables into one set. Where data is lacking, when possible, data from the nearest available year will be used as a substitute or alternative sources will be consulted. Due to the large number of countries, nations will be excluded if data cannot be sourced with relative straightforwardness, as the scope otherwise would creep beyond the limits of this study. Due to various issues in the data or missing data, considerable time has been invested in investigating individual nations. If during such research, it has been found that a country has circumstances under which the theory cannot be tested, then that nation has been excluded61.
In total, the data set contains 1700 data points for 170 countries confirming to the selection criteria.
Additional sources for control variables
A number of additional data sources are needed for the socioeconomic, political and geographical tests that are going to be carried out in addition to the main test. While not being of primary focus for this study, the reason for including those is that they have either seen to have various levels of success in previous studies and it is thus interesting to understand their behaviour in this study with
58 Gerring, 2017, chapter 5.1. 59 “Monthly ranking of women in national parliaments”, IPU. 60 Report saved as “Female parliament and cabinet data”. Permalink to download in References section. 61 See appendix for any exclusions.
Page 13 its different approach, or, as for functioning of state or subregions, to understand if these new variables carry any additional explanatory capability.
Many researchers, particularly within the fields of economics, traditionally use GDP per capita to assess a nation’s level of development. While this in certain cases and to some levels can be aligned with a state’s development, for this study it has been deemed too narrow, as it merely focusses on the economic output of a country without taking other factors into account. Many other indexes are available, but none of these indexes is without its flaws, and scientists are still debating their relative advantages and disadvantages, leading to ever refined models being put forward62. It seems unlikely that one model will prevail that can adequately capture all factors that contribute to explain a certain nation’s level of development for all scientific disciplines.
For this study, the Human Development Index has been selected as it’s a widely known and used index, incorporating dimensions for economy, life expectancy and education amongst its indicators making it a suitable choice. It’s widely accessible and the input variables defining nations various levels are transparent and can be easily contested by the research community. It also covers all nations and has a clear four tier division into categories. 2020 data has been used for convenience and accuracy, as potential errors can be assumed to be addressed on a yearly basis, and the difference from 2019 can further be presumed to be minor as development is a slow process.
Relating to functioning of state, in terms of its stability or sustainability, there are fewer obvious options of datasets and indexes to choose from. While the Freedom House publishes its global report on political rights and civil liberties annually, it has a strong focus on the values of the UN’s Human Rights and ranges from 0 to 12 only. Consequently, for this study the decision is instead to use the yearly Fragile States Index by The Fund for Peace. This index has several advantages as its focus is on states’ fragility (or inversely, their stability) and is categorised in cohesion, economic, political, social and cross cutting indicators63. Its reliability can be considered high as the quantitative data is also peer-reviewed on a yearly basis by a team of social science researchers64. It
62 Karpowicz, 2008. 63 “Indicators”, The Fund for Peace, 2021. 64 “Methodology”, The Fund for Peace, 2021.
Page 14 includes political indicators assessing confidence in state institution and processes, the integrity of elections and the nature of political transitions and in non-democratic states the degree to which the government is representative of the population which it governs. 65 The index can thus be considered a good approximation of functioning of state, in which capacity it’s going to be used throughout this study.
The data for Women’s suffrage has been collected from Pamela Paxton’s “Women in Parliament Dataset, 1893-2003”, which contains global data for the year when universal suffrage was first granted to women for nearly every nation66.
The final dataset needed the data to sort nations into their respective continents. There should be no concerns relating to the validity of such a dataset, but for full transparency it has been retrieved from Kaggle and incorporated into the full dataset67.
Validity and reliability
Considering the credible data sources and their very empirical nature, both their validity – how well we are measuring what we set out to represent – and reliability – how reliable the numbers are – can be expected to be high for the main variables, as the parliament and cabinet numbers are easily verifiable and not impacted by external factors68. The human development and functioning state measurements can be less accurate as statistics from poorer nations with less resources devoted to data collection can be expected to contain errors or be imprecise. But as these variables are only used to investigate whether any differences can be seen between various levels of development and functioning of state, and are published by credible sources, any minor fluctuations should not impact the actual hypothesis under investigation. The data for womens’ suffrage and for classifying subregions is is easily verifiable, and as Pamela Paxton, Professor of Sociology and Government at the University of Texas, is well-known and widely cited (over 17,000 citations according to Google Scholar), there are no reasons to suspect any issues in her collection of suffrage data.
65 2017 Fragile States Index, p. 30, 2017. 66 Paxton, Pamela, Jennifer Green, and Melanie M. Hughes, 2008. 67 “Country Mapping - ISO, Continent, Region”, Kaggle Inc., 2020. 68 Ibid., p. 104-105.
Page 15 Selection criteria
A final word is appropriate in relation to the data, before moving on to the model overview. The test would bear little validity if it was carried out on observations which are not confirming to the underlying assumption. The critical assumption from the hypothesis is that the hypothesis can be expected to hold true in functioning and relatively corruption free political systems. For the purpose of this study, the functioning of state categories, defined in the Data section above, will be used and only countries falling into the partly and well-functioning categories considered as selection confirming. This set will be referred to as the basic set from here on, and the full set including also the category non-functioning states used for certain extended tests will be referred to as the full set.
Dependent variable
The dependent variable Women in cabinet is the number of seats in the national cabinet held by women as a percentual proportion of the total number of seats. Certain nations have been excluded from the full dataset for various reasons. For example, as the aim of this study is to investigate if a considerable female parliamentary representation over time contributes to a higher level of cabinet representation, countries where a cabinet gender quota is in place have been excluded as this link is rendered invalid. Additionally, countries where the head of state doesn’t select their cabinet members are also excluded from this study. Needless to say, bypassing the selection process which the hypothesis is based on will render that nation irrelevant, and including it would have risked undermining or skewing the results.
Independent and control variables
Women in parliament
The main independent variable is women in parliament, and as this study aims to establish whether considerable female parliamentary representation over a longer period of time leads to a high proportion of female cabinet ministers, data from a longer time period will be needed. The variable is defined as the percentual proportion of women of the total number of seats.
As a definition of considerable level of female parliamentary representation, a level of 20 percent female parliament members will be used as threshold, slightly below 2020’s average. The reason for this is that it’s the threshold Claveria identified in her research. Additionally, as female representation both within parliaments and cabinets has been a challenge historically, it can be assumed that the parliamentary representation starts low to then increase with time. As 20% of the
Page 16 total number of MPs is the same as 40% of the perfectly equal level of 50% WIC, it’s a reasonable starting point to understand the relationship between a considerable level of parliamentary representation and its over-time impact on WIC.
To be relevant moving forward the chosen time period has been set to the last 20 years, starting from year 2000. 20 years have been chosen because subsequent parliamentary appointments can be expected to be required for any elected parliamentarian to achieve the required seniority to be considered for cabinet selection or to be able to exercise sufficient pressure to have an impact on cabinet appointments. Year 2000 is also a suitable point of departure as previous research has indicated a shift in global political trends from the end of the 1990s. As most countries hold election every four to five years, with some going to the ballots even more infrequently, five years have been chosen as time interval. Every country will call for election at different dates and years, but there is no need for the data to be from the year the election actually took place; more important is to include enough time periods to sufficiently guarantee that multiple elections, and cabinet reshuffles, have taken place. Unfortunately, while parliamentary gender composition data exists up until 2021, I’ve only been able to locate cabinet data up until year 2019. I will hence use 2019 as final year for the data collection. This means that data is needed for the years 2000, 2005, 2010, 2015 and 2019.
The variable will be coded into time periods of consecutive high parliamentary female representation. A nation with no prior considerable (>=20%) female parliamentary representation in 2019 will have this variable set to 0. A nation experiencing this level for the first time in 2019 will be set to 1. A nation which has experienced this level for two consecutive five-year periods will have the variable set to 2, etc:
Table 1. Women in cabinet dataset example
Women in cabinet (%) Women in parliament (%) Country 2019 2000 2005 2010 2015 2019 No. parl. periods Angola 40 15 15 39 37 39 3 Bahrain 4 .. 0 3 8 15 0 Bhutan 10 9 9 9 9 15 0 Bulgaria 37 11 22 21 20 26 4 … … … … … … … … Note: “..” indicates missing data.
Page 17 Development
To investigate what impact potentially different levels of development might have on the theory, data from the HDI will be included. As this data is of ordinal type, it can be directly included in the full multi variate regression tests. A control variable, HDI, will be defined to hold these values.
Additionally, UNDP has defined four subcategories or classifications of development within the HDI. These classifications are very high (>0.800), high (0.700–0.799), medium (0.550–0.699) and low human development (<0.550). To allow us to investigate differences between nations, dummy variables will be created where the base case will be low human development:
Well-functioning state
As already mentioned, the Fragile States Index will be used to construct a control variable defining how well-functioning a given nation is which can then be used in the full multivariate regression analysis. This variable in turn will be used to create dummy variables to allow for comparison between different levels of functioning of states.
The index ranges from 0 to 120, where a higher score means more fragile state. As this study is focusing on stability as opposed to fragility, the FSI score has been inverted to provide a stability score.
To be able to compare between different functioning states categories, the inverted FSI score will be subdivided into three brackets representing non-functioning, partly functioning and well-functioning states. Because no state receives a higher score than 103 for the nations corresponding to the selection criteria, brackets of 35 points each will be used as per the below table. This effectively sets the top scoring country as the limit of the scale, allowing for a more even distribution within the brackets69.
Table 2. Functioning of state categories
Category FSI Non-functioning 0.0-35.0 Partly functioning 35.1–70.0 Well-functioning 70.1–105.0
69 Finland receives the highest score of 103 in 2019’s index. 105 has been chosen as upper limit to avoid leave the well- functioning category with relatively few nations.
Page 18
A handful of countries, twelve to be precise, are not included in the Fragile States Index. According to The Fund for Peace, the reason for this is a combination of them not being UN members and a lack of “a significant sample size of content and data available for that country to allow for meaningful analysis”70. To be able to include these countries, which include such diverse nations as Monaco and Tuvalu, they have been attributed the average score of their subregion as a substitute71.
Finally, dummy variables will be created to allow for comparison between various levels of functioning states, where the base case will be partly functioning state.
Out of the 170 nations, 35 fall within the FSI category “non-functioning states” and are excluded from the basic models.
Regions
The final variables remaining to be defined are the dummy variables indicating regionality. As there are very large dissimilarities between nations on the same continent (West vs. East Europe, Canada vs Brazil in the Americas, etc), subregions has been used keeping Western Europe as baseline.
Women’s suffrage
Using Paxton’s data, the variable Years of universal suffrage has been calculated as the number of years passed since each nation introduced universal suffrage for women and 2019.
Other control variables
As seen in the background section of this article, much research has in great detail and to a large extent investigated how other variables impact the levels of women in cabinets globally. Consequently, there would be little additional value provided in adding further control variables, beyond the ones already identified. Besides being outside the focus of this study, it is questionable whether they would produce any new insights. Due to the limitations for this study, further refining
70 “How Many Countries are Included in the Fragile States Index?”, The Fund for Peace, 2018. 71 All attributions have been indicated in the main dataset.
Page 19 of the theory will have to be left to future research. Ultimately, this study aims to improve on the precision of previous research, as “The constant revision of hypotheses and the formulation of sharper and more precise questions in light of new empirical data increase our knowledge about society”72.
3.1.3. Model overview To analyse the data, linear and multivariate regression methods will be used on the full sample as well as on the subcategories as defined below, resulting in eight models in total. The first step is to test the original hypothesis in its simplest form. Following that, various extensions will be made to investigate any impact depending on data selection as well as testing whether any of the additional control variables have any potential statistical significance. One model, Model 4, will be created with an alternative operationalisation of the main independent variable. The expectation is that a majority of observations will adhere to the proposed hypothesis.
Any outliers or extreme values will be separately investigated in a qualitative manner to understand if they are special cases where hidden, underlying factors contribute to the unexpected results73. It’s of paramount importance to single them out and confirm whether they should be included due to the fact that such outlying or extreme observations could seriously skew the results74.
Model 1 – Basic (M1)
Model 1 is the most simple and straight forward test in this study. It’s also the model most directly corresponding with the proposed hypothesis. It will investigate the impact subsequent, or continuous, periods of considerable female parliamentary representation have on the dependent variable Women in cabinet. As one of the criteria in the theory is that the theory can be expected to hold true in functioning and relatively corruption free political systems, this analysis will be run over a subset of the total number of 170 countries that certify the selection criteria, excluding all non-functioning states as defined in the section above.
72 Djurfeldt, 2019, p. 140. 73 Gerring, 2017, chapter 5.3. 74 Djurfeldt, 2019, p. 359.
Page 20 Model 2 – Basic controlled for Women’s suffrage (M2)
To shine light on the contradicting results relating to women’s suffrage accounted for by Reynolds and Högström, M2 is introduced as an extension of M1 adding the variable Women’s suffrage. As this is significant in Reynolds’ research, it’s interesting to understand if it holds true in a differently designed test 22 years later. This should allow a verification of the previous research and guide whether to include this variable in other models.
Model 3 – Full (M3)
This model has been constructed similarly to M1, but in M3, the criteria functioning and relatively corruption free political system has been removed and the analysis is performed over the full set of states, irrespectively of whether their political system can be considered functioning or not. This test is carried out to be able to assess the model’s precision, and if it decreases then this model can be discarded as the original hypothesis holds true.
Model 4 – Basic controlled for Extended periods (M4)
In the hypothesis put forward, it’s assumed that the time periods of considerable female parliamentary representation are continuous – that they are uninterrupted. However, there might be cases where a short, single, gap emerged, for example between 2010 and 2015, while remaining high for all other periods. In that case, it’s assumed in this study that many of the women that were previously present in the parliament might have returned to the private sector, started businesses, retired, or interrupted their political careers for any other reason. However, this might not necessarily be the case, and there is an argument, however weak, that it might be the least experienced women that are forced out when the number of available minister seats is reduced.
Model 4 will address this argument by testing over the total number of periods of high female parliamentary representation rather than only the continuous, uninterrupted, number of periods. It will with other words be an alternative version of M1 and could provide valuable insight into whether this produces a model that more accurately describes reality, and if the main hypothesis might need amending.
Model 5 – Basic controlled for HDI (M5)
M5 is again an extension of M1. In addition to investigating the connection between the dependent and main independent variable on the subset with non-functioning states excluded, this model will also control for development using the constructed dummy variables. This will examine if there is
Page 21 any inconsistency between the results within the various categories of development as defined within the HDI. Expected insights from this test would be if the hypothesis holds true or false under only certain levels of development.
Model 6 – Full controlled for Functioning state (M6)
M6 aims to explore if the theory can be improved by including the subcategories for functioning state. To be able to study the variance within each group, the dummy variables for functioning of state as defined in the variable section will be used. As it’s of high interest to understand if the hypothesis holds true also in countries defined as non-functioning states, the full (after exclusions) country list will be used for this and the following models.
Model 7 – Full controlled for HDI + Functioning state (M7)
The objective of M7 is to investigate if an extension of the original theory including the absolute level of human development as well as the absolute level of functioning state provides a more precise model with the variables for both human development and functioning of state being statistically significant. If this holds true, with statistical accuracy, then M7 can be considered to be a better explanation than M1 and lead to a revision of the hypothesis.
Model 8 – Basic controlled Regions (M8)
In the 7th and final model, the focus will be turned to regionality to explore whether the theory has different explanatory capacity for different regions in the world. Dummy variables will be used to divide all the theory selection criteria confirming nations into their respective regions. Norway will be attributed “Northern Europe”, India “Southern Asia”, El Salvador “Latin America and the Caribbean”, etc. Western Europe will be held constant as baseline against which all other subregions are tested. The intention of this model is to understand what variation there is between different regions globally, and if it turns out that the theory performs abnormally in any region, this could provide important information for future studies.
3.2. Results The scatterplot (Figure 1), with its fit line added, provides a graphical and easily accessible overview of the relationship between number of continuous 5-year periods (referred to as “periods” from here on) with considerable female parliamentary representation and the corresponding proportion of female minister positions within each government. There is a clear relationship where at the lower end of the spectrum the majority of observations fall below 20% representation, with
Page 22 single instances of observations ranging to 36% (Georgia). At the other end of the spectrum, with at least 5 continuous periods, the relationship is near inverted; most of the observations lie above the 20% level, extending to 67%, with only the Lao People's Democratic Republic (11%) and Namibia (15%) falling below 20%. For the levels in between there is an almost linear relationship between the number of periods and the percentage of female ministers in the cabinets.
Figure 1. Scatterplot Women in cabinet 2019
There are a few nations apparent from the scatterplot that it would be interesting to see some further studies on in future research. For zero continuous periods, those are Georgia, Uruguay, South Korea and Sao Tome and Principe. For two periods, on the upper spectrum, Albania (an especially interesting case due to its mix of traditional values, high unemployment, but also early women’s movements and suffrage, limited from 1921 and full from 194575), France and El Salvador have notably high percentages of female ministers, while the values for Kazakhstan and Turkmenistan could be considered lower than expected. Being both former Soviet republics, and within close geographical proximity, there might exist similar factors that impact on their results. For three time periods, while high, Peru isn’t an outlier, and the most important observation to underline is the
75 “Women in Albania”, Wikipedia, 2021.
Page 23 rather large gap with no nations between Ecuador and Peru. Peru is however scoring remarkably well, with a majority WIC percentage of 55%. For the fourth period, Belarus (one of Europe’s poorest nations) and Tunisia especially stand out. Finally, for the fifth period, Lao PDR and Namibia stands out with a representation of WIC below 20%.
Some interesting observations and further details can be singled out from the boxplot in Figure 2. First, there is an absence of outliers or extremes indicating that no observations differs significantly from the other. There is however an uneven spread between the various periods as well as varying standard deviations, which isn’t unexpected.
Figure 2. Boxplot Women in cabinet 2019
Secondly, the mean values for one and three periods respectively are slightly above and below what could have been expected if the relationship was perfectly linear. The spreads for the same periods are also slightly lower than for surrounding periods. What could be the reasons behind this? Social sciences aren’t exact sciences in the sense technical sciences are, and Djurfeldt mentions that “social sciences casual theory isn’t deterministic”76. As such, it would be unrealistic to expect a perfectly linear relationship.
76 Djurfeldt, 2019, p. 357.
Page 24 Table 2. Boxplot details
Number of continuous par. 5-year periods WIC (mean %) Standard deviation Min Max Observations 0 14.33 9.550 0 36 56 1 21.13 8.476 7 35 16 2 24.17 16.494 4 53 17 3 23.43 12.621 7 55 16 4 34.99 17.705 3 59 12 5 37.96 16.121 11 67 18 Total 22.44 15.138 0 67 135
Additionally, the time periods in between when the female presence and expertise is being established can also be expected to have fewer stable values than would be found at the ends of the spectrum, where women either have very low visibility and participation in national politics or have cemented their roles and gravitas. The shorter/longer time that has passed, the weaker/stronger the relationship can be assumed to be, hence the values could be considered more accurate for zero and four to five periods. There are of course a multitude of individual factors that can be tied to the countries themselves as well as to individual politicians that all will have impacts on the results. The trend, however, is on a clear upwards trajectory and the minimum and maximum values, with a few exceptions for periods two and four, are clearly increasing.
Turning our focus to the (linear) regression analyses, and the results presented in Table 7. M1 receives an adjusted R2 of 0.304, which is relatively good for social sciences, meaning that about 30% in the variation in the WIC variable can be attributed to the independent variable continuous parliamentary periods It’s significant at a level below 0.000 presenting us with a very robust statistical certainty. The constant (14.77) and the variable continuous parliamentary periods (4.56) are both significant at the same level, indicating that the level of female ministers on an average increase with 4.56% for every 5-year period with high levels of WIP, much better than the worlds current average of 0.52 percentage points77.
M2 was introduced to investigate whether Reynolds’ claim that years of women’s suffrage is significant and contributes to the explanatory model, or whether Högströms findings that it’s mainly statistically insignificant, is the case for this study. As can be seen from Table 8, while the model
77 “Facts and figures: Women’s leadership and political participation”, UN Women, 2021.
Page 25 remains significant below the 0.001 level, and with an almost identical R2, the variable itself is highly insignificant and it is consequently not an explanatory factor in this study. The test was additionally carried out for all subsequent models with identical findings. It’s hence safe to conclude that, for both the basic and full range of nations as well as in relation with the independent and control variables tested for within this study, the years of women’s universal suffrage cannot be proven statistically significant and was not added to the results of any of the models M3-M8.
Table 3. Overview of results M1-M7
Variable Basic (M1) Basic with Full Basic Basic + HDI Full + Full+HDI+ suffrage (M2) extended categories functioning functioning (M2) periods (M5) state (M6) state (M7) (M4) Constant 14.473*** 11.599** 15.809*** 14.072*** 12.292** 14.022*** 19.054** (1.499) (3.756) (1.315) (1.568) (4.535) (1.480) (5.962) Cont. par 4.557*** 4.437*** 3.890*** 4.261*** 3.346*** 3.224*** periods (0.590) (0.608) (0.533) (0.606) (0.549) (0.556) Years of 0.043 universal (0.052) suffrage Tot par periods 4.396*** (0.588) HDI medium -0.285 (5.159) HDI high 1.344 (4.831) HDI very high 5.196 (4.752) Non-func state 3.185 (2.450) Well-func state 7.708*** (2.363) HDI index -22.219 (13.126) Func state 0.257** index (0.087) F 59.585 30.073 53.219 55.812 16.114 22.253 22.692 Adjusted R2 0.304 0.303 0.236 0.290 0.311 0.274 0.278 Sig. 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Observations 135 135 170 135 135 170 170 Notes: The unstandardized regression coefficients are listed above, below in brackets the standard errors. *** Significant at the 0.001 level or below, ** significant at the 0.01 level, * significant at the 0.05 level. M5 reference category HDI low. M6 reference category partly functioning state.
Page 26 While M3’s regression analysis in line with M1’s was proving statistically significant at a level below 0.000, as were its constant (15.81) and the value for continuous parliamentary periods (3.89), its R2 value was significantly lower at only 0.236. Consequently, compared to M1 based on the original assumption that the political system needs to be functioning and relatively corruption free, M3 will be rejected.
A scatterplot and boxplot were also generated for M3, using the same independent variable as M1 but extended over the full number of 170 nations, irrespectively of whether they can be considered functioning states or not. Besides Guinea-Bissau being indicated as an outlier for zero periods with a WIC number of over 50%, they don’t provide any further important insights and can be found in the appendix.
M4 is an extension of M1, where the variable continuous parliamentary periods was swapped with total parliamentary periods, again run over the 135 nations that can be considered partly- or well- functioning. The difference between these, as seen previously, is that M4 doesn’t require the periods of high female parliamentary representation to be continuous.
The scatter- and boxplot don’t offer any additional explanatory power compared to M1 and M2 and can be found in the appendix.
The regression analysis was statistically significant below the 0.000 level, with an adjusted R2 of 0.290, proving again that having a partly or well-functioning political framework is essential. Both the constant and the independent variable total parliamentary periods are significant below 0.000, with values of 14.07 and 4.40 respectively. As these values were very similar to those for M1, some further investigation was prompted for. Examining the data more closely, only 15 of the countries conforming to the selection criteria actually had gaps within their time periods, and out of these, the difference compared to counting continuous time periods was larger than one additional period for only five observations. The implication is that the source data the model is run over in M1 and M4 is virtually identical, and the results of the regression analysis consequently show very little difference. The data is thus insufficient to statistically show that there is any difference between continuous and total number of parliamentary periods, but as the logical argument for continuous periods is relatively strong, the originally hypothesis will be retained. In case the situation changes in the future, it would be useful to rerun the test for verification.
For models M5 onwards, as they contain more than one independent variable, the analysis method is multivariate regression analysis. M5 introduces the various levels of development as an extension
Page 27 of M1, and low development was held as a baseline constant. While the model on a whole was statistically significant below a level of 0.000, none of the HDI levels yielded any statistically significant values. To rule out that the choice of baseline variable wasn’t the source behind this, the model was re-run using each of the other HID level categories as baseline, with similar results, all categories in each test being non-significant. The adjusted R2 is slightly higher than that of M1 with 0.5 percentage points, but the model cannot provide any further explanatory details than M1 despite having introduced further control variables. Its contribution to this study is that it validates what many of the prior studies have already shown; development doesn’t seem to contribute to high levels of WIC.
For M6, which was run against the full set of 170 nations, the best results were obtained while using the dummy variable partly functioning state as baseline. Again, the model is statistically significant below the 0.000 level, with an adjusted R2 of 0.274. Interestingly, non-functioning states came out with 3.2 percentage points higher female cabinet representation than partly functioning states, but relatively non-significant (0.195). This could indicate a great variance within the partly functioning states category. Well-functioning states scored better than partly functioning states with 7.71 percentage points, statistically significant below 0.001. The expectation that in well-functioning states, party politics work better and the relation between parliamentary visibility and experience in a higher degree can lead to cabinet positions seems to be true. However, as the adjusted R2 is below that of M1, it fails to explain as much of the variance in WIC and is hence not to be preferred to M1.
As in M6, M7 is run over the full set of nations. But rather than testing for the category of functioning state, M7 uses the absolute level (FSIInverted) of functioning of state for each nation. It also reintroduces development to understand if testing against both generates a better outcome, and as for functioning of state it uses the absolute HDI index. The model is again significant below the 0.000 level, with an adjusted R2 of 0.28, lower than that of M1. As in the separate test for HDI categories, the variable HDI Index is insignificant and strongly negative (-22.22). Should the variable have been significant, it would have unexpectedly indicated that a nation with the maximum level of development (1.000) would have 22% less WIC than a country with the lowest level of development (0.000). As in other studies referred to above, human development isn’t a variable that seem to explain the level of WIC neither when testing against countries alone, nor when testing for how well a state is functioning. Functioning of state however is significant below
Page 28 the 0.01 level, with a value of 0.26. As this variable ranges from 0 to 120, it means that with each increment on its scale of 10, the level of WIC increases with 2.6%.
As the level of development and a nation’s functioning of state can be suspected to be correlated, a separate collinearity test was run on this model following Arndt Regorz’s methodology78. None of the variables had any issues with collinearity.
Table 4. Overview of results M8
Variable Basic with regionality (M8) Constant 26.109*** (5.659) Par periods 3.549*** (0.701) Australia and -15.523 Micronesia -10.732 South-eastern Asia -21.99*** New Zealand (9.558) (8.136) (6.708) Central -22.527** Northern Africa -21.779* Southern -17.309* Asia (7.501) (8.386) Asia (8.136) Eastern -7.412 Northern -1.287 Southern -2.720 Asia (8.136) America (9.633) Europe (6.079) Eastern -12.682* Northern Europe -5.307 Sub-Saharan Africa -10.356 Europe (6.256) (6.066) (5.782) Latin America -4.607 Polynesia -17.776* Western -13.742* and the Caribbean (5.553) (8.809) Asia (6.502) Melanesia -19.09* (8.136) F 6.330 Adjusted R2 0.403 Sig. 0.000 Observations 135 Notes: The unstandardized regression coefficients are listed above, below in brackets the standard errors. *** Significant at the 0.001 level or below, ** significant at the 0.01 level, * significant at the 0.05 level. M8 reference category Western Europe.
The final test run, M8, extends M1 testing also for variation between different global sub-regions. With Western Europe kept as baseline, the only sub-region significant at the 0.001 level is South- eastern Asia, with a value of -21.99. Central Asia is significant at the 0.01 level with -22.31. At the
78 “How to interpret a Collinearity Diagnostics table in SPSS”, Regorz statistics, 2020.
Page 29 0.05 level we have Eastern Europe (-12.62), Melanesia (-19.09), Southern Asia (-17.31) and Western Asia (-13.74). Tests with holding the control variables Central Asia, Latin America and the Caribbean, and South-eastern Asia as baseline, as those were the sub-regions with extreme values or most observations, provided no different results, always non-significant for a majority of sub- regions. M8 was significant below 0.000 and with an adjusted R2 of 0.403, the highest in the test79.
One immediate problem that can be observed with regionality is the great variation within some of the subregions. Northern Europe for example contains both the Nordic countries, traditionally very strong in gender equity and with high levels of WIC, but also Lithuania, Estonia, Latvia, Ireland and the UK, ranging from 8% to 30% of WIC, compared to the Nordic countries ranging from 32% to 61%. It’s thus clear that the subregions themselves contain a high degree of variations which make them an unprecise categorisation of nations. Additionally, it can be questioned if they contain sufficient number of observations to be used in this regression test and confidently draw any conclusions from. North America, the most extreme case, contains only the United States and Canada. Defining the subregions differently or more narrowly is not possible as each sub-region in that case would contain too few observations to make statistical tests meaningful. Conversely, broadening them, to say continents, again increases the variation within each category, and when running a test regression analysis with sub-region replaced with continent, the adjusted R2 decreased to 0.388.
The conclusion drawn from these tests are that while regionality is a factor that can explain a larger part of the variance in certain cases, this is not so across all regions. Nevertheless, M8 shows that the original theory – that considerable female parliamentary inclusion over a longer time frame leads to a higher female cabinet presence compared to nations where lower numbers of women have been included in parliaments for a shorter period of time, represented by M1 – holds true, and that the inclusion of a variable sub-region significantly increases the explanatory capability with ten percentage points across 135 nations globally.
79 A series of tests introducing combinations of the other control variables were run, but they proved insignificant in all tests, lowering the R2 value. These control tests are not presented within this study as they were run only to verify the results.
Page 30 4. Conclusion and discussion
Overall, the goal with this study, to contribute to previous research by improving on the time perspective, ultimately answering the question whether a considerable level of female representation in parliaments and the time they have been present lead to a higher inclusion in national cabinets, can be considered accomplished with validating result. This study shows that there is a strong correlation between time of considerable representation in parliament and inclusion in cabinet. It also displays the opposite; nations where women are included but have just recently gained representation tend to have lower levels of WIC. This is in line with the arguments relating to a visibility aspect put forward by Claveria, Högström and Reynolds, and could be part of the reason behind the deviating observations in previous research which included the relationship between WIP and WIC, as it only focussed on the levels of female members of parliament at a given point in time. It could also be an underlying casual circumstance in Stockemer and Sundström’s study.
The tests also indicate, perhaps with little surprise, that well-functioning states performs better than non- and partly functioning states. As one of the assumptions of the hypothesis is that states need to be functioning and relatively corruption free, this further reinforces its validity.
Similar to other studies by for example Stockemer and Sundström, Claveria and Reynolds, development doesn’t seem to have a direct contribution to high levels of female ministers in a full sample of nations, which also holds true when combined with the measurement for functioning of state. However, the expectation that in well-functioning states, party politics work better and that the relation between increased parliamentary visibility and experience to a higher degree can lead to cabinet positions seems to hold true, as shown by the test carried out through Model 5.
Suffrage too cannot be shown to have statistical significance in any of the tests run in this study. As voting is normally for parliamentary seats, it’s logical that no direct link between universal suffrage for women and number of WIC is shown. There is no reason why women wouldn’t vote for a high number of female MPs during the first election in which they can participate, and once female parliamentary presence has been established, it should follow the hypothesis laid out in this study.
The tests also show that there are significant regional differences, but as we have seen there are also considerable inter-subregional deviations and subregions where the differences are not statistically significant, and it would be interesting for future research to either investigate if recoding the subregions or extending the model with additional variables would provide any further insights in this regard. It’s worth highlighting that South-eastern Asia was the region with most unexpected
Page 31 cases, and an almost reversed relationship between WIP and WIC. Additionally, Reynolds results relating to religion from 1999 included strong correlations for certain religions and including a new way of looking at subregions, where the region could be based on a combination of location and culture/religion, where nearby Muslim nations in the Middle East would be treated separately from Muslim nations in Africa or Oceancia, could potentially uncover new culture geographical insights in future studies.
Causality is always difficult to prove with certainty, especially in cases where the number of impacting circumstances can be near infinite, as is the case with large entities such as nation states. The fact that the explanatory capacity of the models decreases when other potentially explanatory variables are introduced hints in this direction. The additional circumstance, seen in the section Previous research, that no other study has been able to confidently identify alternative variables leading to higher levels of WIC across as big sample as used in this study, further adds to the probability that a causal relationship does indeed exist, and future research should be able to provide further evidence.
Countries that might warrant special attention for future studies fall within two categories: the ones with lower WIC levels than anticipated and the ones with higher levels than expected. Limited time was set aside during this study to verify the data as well as any other obvious reasons for these special cases, but unfortunately no immediate explanations could be individualised. Countries scoring lower than anticipated include Kazakhstan, Turkmenistan, Equatorial Guinea, Philippines, Belarus, Tunisia, Lao PDR and Namibia. Countries with higher levels than expected are amongst others Georgia, Uruguay, Republic of Korea, Albania, France, El Salvador and Peru.
Unfortunately, due to the nature of the data, it hasn’t been possibly to show with statistical certainty that the parliamentary time periods need to be continuous as the number of continuous and total number of periods are almost overlapping. While the argument for continuous periods is somewhat stronger, ultimately it’s of no major concern as both methods of counting fall back to a very similar basic claim; that longer periods of considerable levels of WIP, potentially with some variation between them, does lead to higher numbers of WIC.
To conclude, the hypothesis put forward – the simple fact that a certain proportion of women within functioning and relatively corruption free political systems is present in a parliament is not sufficient to guarantee their inclusion in cabinets as their seniority and visibility need to reach a certain level first – can be considered well established. This has been shown on a global level, tested against additional control variables, and to address the issue with small sample sizes utilised in many of the previous studies it has been completed using one of the most extensive datasets to date including a
Page 32 total number of 170 nations. The positive results are of highest importance since they indicate a path which could lead to higher gender equality within some of the most powerful political institutions ultimately contributing to, as expressed by the UN, a more peaceful, prosperous and sustainable world.
Page 33 5. References
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“2019 Sudanese coup d'état”. Wikipedia, The Free Encyclopedia, Wikimedia Foundation Inc. 19th Jan 2021. https://en.wikipedia.org/wiki/2019_Sudanese_coup_d%27état. Accessed 10th Mar 2021.
“American Samoa Senate”. Wikipedia, The Free Encyclopedia, Wikimedia Foundation Inc. 19th Dec 2020. https://en.wikipedia.org/wiki/American_Samoa_Senate. Accessed 27th Feb 2021.
“Brunei”, Wikipedia, The Free Encyclopedia, Wikimedia Foundation Inc. 24th Feb 2021. https://en.wikipedia.org/wiki/Brunei. Accessed 27th Feb 2021.
“Burundi's Constitution of 2018”. Comparative Constitutions Project, University of Texas at Austin and the University of Chicago, the United States. https://www.constituteproject.org/constitution/Burundi_2018.pdf?lang=en Accessed 3rd May 2021.
“Cameroon: Evolving Gender Quota”. Make Every Woman Count. 10th April 2018. http://www.mewc.org/index.php/gender-issues/political-participation/10890-cameroon-evolving-gender-quota. Accessed 15th Feb 2021.
“Cabinet: Countries Compared”. Nationmaster. 2021. https://www.nationmaster.com/country- info/stats/Government/Executive-branch/Cabinet. Accessed 25th April 2021.
“Cabinet of North Korea”, Wikipedia, The Free Encyclopedia, Wikimedia Foundation Inc. 11th March 2021. https://en.wikipedia.org/wiki/Cabinet_of_North_Korea. Accessed 25th April 2021.
Claveria, Silvia. “Still a ‘Male Business’? Explaining Women’s Presence in Executive Office”. West European Politics. Vol. 37:5, 2014: 1156-1176. DOI: 10.1080/01402382.2014.911479.
“Colombia Case Study”. United Nations Development Programme, Bureau for Development Policy One United Nations Plaza, New York, NY, 10017 the United States. 2012. https://www.weforum.org/agenda/2018/07/colombia-shatters-glass-ceiling-with-gender-equal-cabinet. Accessed 3rd May 2021.
Djurfeldt, Göran; Larsson, Rolf; Stjärnhagen, Ola. “Statistisk verktygslåda. Samhällsvetenskaplig orsaksanalys med kvantitativa metoder”. 3rd edition. Lund: Studentlitteratur AB, 2019.
“Facts and figures: Women’s leadership and political participation”, UN Women, United Nations, New York, the United States. 15th Jan 2021. https://news.un.org/en/story/2021/03/1086582. Accessed 23rd May 2021.
Page 34 Gerring, John. “Case Study Research: Principles and Practices (Strategies for Social Inquiry)”. 2nd edition. Cambridge: Cambridge University Press, 2017.
“Guinea”. Wikipedia, The Free Encyclopedia, Wikimedia Foundation Inc. 28th Feb 2021. https://en.wikipedia.org/wiki/Guinea. Accessed 7th March 2021.
“How Many Countries are Included in the Fragile States Index?”. The Fund for Peace, 1101 14th Street NW, Suite 1020, Washington, D.C. 20009, United States of America. 2018. https://fragilestatesindex.org/indicators. Accessed 2nd May 2021.
Arndt Regorz . “How to interpret a Collinearity Diagnostics table in SPSS”. Regorz statistics. 18th Jan 2020. http://www.regorz-statistik.de/en/collinearity_diagnostics_table_SPSS.html. Accessed 8th May 2021.
“Indicators”. The Fund for Peace, 1101 14th Street NW, Suite 1020, Washington, D.C. 20009, United States of America. 2018. https://fragilestatesindex.org/indicators. Accessed 2nd May 2021.
Högström, John. “Do Development and Democracy Positively Affect Gender Equality in Cabinets?”. Japanese Journal of Political Science. Vol 16 (3), 2015: 332–356. DOI: 10.1017/S1468109915000225
Krook, Mona Lena; O’Brien, Diana Z. “All the President’s Men? The Appointment of Female Cabinet Ministers Worldwide”. The Journal of Politics, Vol. 74, No. 3, Jul., 2012: 840-855.
“Methodology”. The Fund for Peace, 1101 14th Street NW, Suite 1020, Washington, D.C. 20009, United States of America. 2018. https://fragilestatesindex.org/methodology/. Accessed 6th May 2021.
“Proportion of women parliamentarians worldwide reaches ‘all-time high’”, UN News, United Nations, New York, the United States. 5th March 2021. https://news.un.org/en/story/2021/03/1086582. Accessed 23rd May 2021.
“The Minister of State”, Gouvernement Principier (Official Webpage of Monaco). 2021. https://en.gouv.mc/Government-Institutions/The-Government/The-Ministry-of-State/The-Minister-of-State. Accessed 25th April 2021.
“Politics of Liechtenstein”. Wikipedia, The Free Encyclopedia, Wikimedia Foundation Inc. 30th March 2021. https://en.wikipedia.org/wiki/Politics_of_Liechtenstein. Accessed 25th April 2021.
“Politics of Montenegro”. Wikipedia, The Free Encyclopedia, Wikimedia Foundation Inc. 8th April 2021. https://en.wikipedia.org/wiki/Politics_of_Montenegro. Accessed 25th April 2021.
“Politics of Qatar”. Wikipedia, The Free Encyclopedia, Wikimedia Foundation Inc. 13th Feb 2021. https://en.wikipedia.org/wiki/Politics_of_Qatar. Accessed 1st Mar 2021.
“Politics of San Marino”. Wikipedia, The Free Encyclopedia, Wikimedia Foundation Inc. 23rd Aug 2020. https://en.wikipedia.org/wiki/Politics_of_San_Marino. Accessed 25th Feb 2021.
Page 35 “Politics of Switzerland”. Wikipedia, The Free Encyclopedia, Wikimedia Foundation Inc. 17th April 2021. https://en.wikipedia.org/wiki/Politics_of_Switzerland. Accessed 25th April 2021.
“Politics of Vietnam”. Wikipedia, The Free Encyclopedia, Wikimedia Foundation Inc. 10th April 2021. https://en.wikipedia.org/wiki/Politics_of_Vietnam. Switzerland. Accessed 26th April 2021.
Reynolds, Andrew. “Women in the Legislatures and Executives of the World: Knocking at the Highest Glass Ceiling”. World Politics. Vol. 51, No. 4, Jul 1999: 547-572. https://www.jstor.org/stable/25054094.
“Revisiting Rwanda five years after record-breaking parliamentary elections”. UN Women, 220 East 42nd Street, New York, NY 10017 the United States. 23rd Aug 2018. https://www.unwomen.org/en/news/stories/2018/8/feature-rwanda-women-in-parliament. Accessed 3rd May 2021.
“Saudi Arabia”. Wikipedia, The Free Encyclopedia, Wikimedia Foundation Inc. 9th Mar 2021. https://en.wikipedia.org/wiki/Saudi_Arabia. Accessed 10th Mar 2021.
Stockemer, Daniel; Sundström, Aksel. “Women in cabinets: The role of party ideology and government turnover”. Party Politics. Vol. 24(6), 2018: 663–673. DOI: 10.1177/1354068817689954.
“United Nations Sustainable Development Goals. Goal 5: Achieve gender equality and empower all women and girls”. United Nations Development Programme, New York, The United States. https://www.un.org/sustainabledevelopment/gender-equality/. Retrived 14th May 2021.
“Women in Albania”. Wikipedia, The Free Encyclopedia, Wikimedia Foundation Inc. 7th April 2021. https://en.wikipedia.org/wiki/Women_in_Albania. Accessed 8th May 2021.
6. Datasets
“Country Mapping - ISO, Continent, Region”. Kaggle Inc. https://www.kaggle.com/andradaolteanu/country- mapping-iso-continent-region. Accessed 2nd May 2021.
“Gender quotas database”. International Institute for Democracy and Electoral Assistance (International IDEA). Strömsborg, SE-103 34, Stockholm, Sweden. https://www.idea.int/data-tools/data/gender-quotas/country- overview. Accessed 15th Feb 2021.
“Female parliament and cabinet data” dataset. IPU data published by “The World Bank Database, Gender Statistics”. World Bank. 1818 H Street NW, Washington, DC 2043, The United States. https://databank.worldbank.org/Female-parliament-and-cabinet-data/id/7d1a91e3. Accessed 6th Mar 2021.
“Human Development Index” from “Human Development Reports”. United Nations Development Programme, New York, The United States. 2020. http://hdr.undp.org/en/content/download-data. Accessed 2nd May 2021.
“Monthly ranking of women in national parliaments”. Inter-Parliamentary Union. 5, chemin du Pommier, Case postale 330, CH-1218 Le Grand-Saconnex, Geneva, Switzerland.
Page 36 Paxton, Pamela, Jennifer Green, and Melanie M. Hughes. “Women in Parliament Dataset, 1893-2003”. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2008-12-22. doi:10.3886/ICPSR24340.v1.
Page 37 7. Appendix
Exclusions
Quota Country Quota80 % Women in cab81 Burundi “A minimum composition of 30% female is assured.”82 26
Cameron The gender quota is not stated by the country’s Code and 15 it’s the discretion of the political parties to ensure a balanced gender mix.83 Colombia “Women’s participation in Colombia’s upper public 58 administration is regulated by Law 581 of 2000, which establishes that a minimum of 30 percent of appointed positions must be occupied by women in the three branches of public power (executive, legislative and judicial).”84 Eswatini “Ten Senators, at least half of whom shall be female, shall 32 be elected by the members of the House in such manner as may be prescribed by or under any law at their first meeting so as to represent a cross-section of the Swazi society. […] Twenty Senators, at least eight of whom shall be female, shall be appointed by the King acting in his discretion after consultation with such bodies as the King may deem appropriate. (Constitution 2005, Art. 94, par 2, 3)” Nepal “The President shall… form a council of ministers… from 11 among the members of the Federal Parliament on the basis of the principle of inclusion.”8586
Rwanda “…the 2003 Constitution, which set aside a quota of 30 54 per cent women in all decision-making organs.”87
80 All data from Gender Quotas Database unless otherwise stated. 81“Female parliament and cabinet data”, 2021. 82 “Burundi's Constitution of 2018”, 2018. 83 “CAMEROON: Evolving Gender “, 2018. 84 “Colombia Case Study”, p. 4, 2012. 85 “Nepal 2015, 2015. 86 While inclusion, including gender based, is required by the Nepalese constitution when appointing cabinet minsters, this is rarely followed and the female representation at the moment stands at 11% only. 87 “Revisiting Rwanda five years after record-breaking parliamentary elections”, UN Women, 2018.
Page 38 Country Quota80 % Women in cab81 Zimbabwe “The new constitution of Zimbabwe adopted in 2013 21 provides that out of the total 80 senator, 60 senators (6 from each of the 10 provinces) are elected through a proportional representation system ‘in which male and female candidates are listed alternatively, every list being headed by a female candidate’.”
No head of state selection of cabinet ministers
American Samoa Maldives North Macedonia
British Virgin Islands Marshall Islands San Marino
China Monaco Switzerland
Korea, Dem. People's Rep. Montenegro Vietnam
Liechtenstein Nigeria
Lacking data American Samoa French Polynesia Northern Mariana Islands
Aruba Gibraltar Puerto Rico
Bermuda Greenland Sint Maarten (Dutch part)
British Virgin Islands Guam St. Martin (French part)
Brunei Darussalam Isle of Man Turks and Caicos Islands
Cayman Islands Kosovo Virgin Islands (U.S.)
Channel Islands Libya West Bank and Gaza
Curacao Macao SAR, China
Faroe Islands New Caledonia
Non-functioning states This section is included for full transparency when running the below models, ordered ascending based on inverted FSI rating.
Page 2 Yemen, Rep. Guinea-Bissau Mozambique
Somalia Uganda Egypt, Arab Rep.
South Sudan Mali Angola
Syrian Arab Republic Myanmar Bangladesh
Congo, Dem. Rep. Ethiopia Togo
Central African Republic Pakistan Sierra Leone
Chad Kenya Zambia
Afghanistan Congo, Rep. Timor-Leste
Zimbabwe Cote d'Ivoire Eswatini
Iraq Liberia Djibouti
Eritrea Mauritania Lebanon
Niger Venezuela, RB
Other exclusions Examples of this are nations where serious corruption and nepotism is confirmed or strongly suspected, countries ruled by juntas and military leaders, as well as countries with conditions that cannot be expected to adhere to normal selection methods. In severely corrupt countries cabinet minister appointments be expected to be distributed to friends, political and business allies and family members, to mention a few examples, rather than to the most suitable and experienced candidate.
Newly formed nations, which have emerged during the last 15 years, will also be excluded. As this work sets out to examine the impact that a time variable, going back 20 years, has on the dependent variable, it’s clear that countries which don’t have this history are theory non-confirming and again risk having negative impacts on the results, potentially even distorting it. Similarly, countries that don’t adhere to a traditional legislative / executive government structure will consequently also be removed from the data.
Page 38 Country Quote Reason for exclusion American Samoa “…senators are elected according to Samoan Direct election. custom by the county councils and must be holders of a matai title.”88
Brunei “Since 1962, this authority has included No functioning party politics. emergency powers, which are renewed every two years. Brunei has technically been under martial law since the Brunei Revolt of 1962.”89 China “State Council appointed by National People's Parliamentary appointments. While Congress.”90 there is an argument that a higher female representation in the parliament should lead to more women being proposed for the government, this is not what this study is set out to investigate. Guinea “The National Assembly of Guinea, the country's Military coup and not enough recent legislative body, did not meet from 2008 to 2013, data. when it was dissolved after the military coup in December. Elections have been postponed many times since 2007. In April 2012, President Condé postponed the elections indefinitely, citing the need to ensure that they were ‘transparent and democratic’.”91 Korea, Dem. “The cabinet is appointed and accountable to the Parliamentary appointments, see People’s Rep. of Supreme People's Assembly, the North Korean China. Korea unicameral parliament.”92 Liechtenstein “The members of the government are proposed Parliamentary appointments, see by the Parliament and are appointed by the China. Prince.”93 Monaco “The Minister of State is assisted by five Nor appointed by the Minister of Ministers appointed by the Prince.”94 State. Montenegro “The current members of the cabinet were Parliamentary appointments, see elected on December 4th, 2020 by the majority China. vote in the Parliament of Montenegro.”95 Qatar Under the 2003 constitutional referendum it No elections as of 2020. should be a constitutional monarchy with an
88 “American Samoa Senate”, Wikipedia, 2021. 89 “Brunei”, Wikipedia, 2021. 90 “Cabinet: Countries Compared”, Nationmaster, 2021. 91 “Guinea”, Wikipedia, 2021. 92 “Cabinet of North Korea”, Wikipedia, 2021. 93 “Politics of Liechtenstein”, Wikipedia, 2021. 94 “The Minister of State”, Gouvernement Prinipier, 2021. 95 “Politics of Montenegro”, Wikipedia, 2021.
Page 39 Country Quote Reason for exclusion elected legislature, although elections have been consistently pushed back since 2013. --- As of October 2020, no elections have been held.”96 San Marino “The Congress of State is the government of the Parliamentary appointments, see country and wields the executive power. It is China. composed by a variable number of Secretaries of State… that are appointed by the Grand and General Council at the beginning of the legislature.”97 Saudi Arabia “No political parties or national elections are Excluded as oppressive regime where permitted. Critics regard it as a totalitarian normal theories cannot be assumed to dictatorship. The Economist rated the Saudi conform. government as the fifth most authoritarian government out of 167 rated in its 2012 Democracy Index, and Freedom House gave it its lowest ‘Not Free’ rating…”98. Sudan “On 11 April [2019], the Sudanese military No theory confirming parliamentary removed Omar al-Bashir from his position as processes. President of Sudan, dissolved the cabinet and the National Legislature, and announced a three- month state of emergency, to be followed by a two-year transition period. --- Along with the National Legislature and national government, state governments and legislative councils in Sudan were dissolved as well.”99 Switzerland “The Federal Council is elected by the Federal Parliamentary appointments, see Assembly for a four-year term.”100 China. Vietnam “…[The] Government is elected by the deputies Parliamentary appointments, see of the National Assembly for a five-year term.”101 China.
Model 1
Scatter plot
* Chart Builder. GGRAPH /GRAPHDATASET NAME="graphdataset" VARIABLES=ContParPeriods Cab2019 CountryName FSICat MISSING=LISTWISE REPORTMISSING=NO DATAFILTER=FSICat(VALUES=3 2 UNLABELED=INCLUDE) /GRAPHSPEC SOURCE=INLINE /FITLINE TOTAL=NO SUBGROUP=NO. BEGIN GPL SOURCE: s=userSource(id("graphdataset")) DATA: ContParPeriods=col(source(s), name("ContParPeriods")) DATA: Cab2019=col(source(s), name("Cab2019")) DATA: CountryName=col(source(s), name("CountryName"), unit.category()) GUIDE: axis(dim(1), label("Number of continuous parliamentary periods")) GUIDE: axis(dim(2), label("Women in cabinet 2019")) GUIDE: text.title(label("Scatter Plot of Women in cabinet 2019 by Number of continuous ", "parliamentary periods"))
96 “Politics of Qatar”, Wikipedia, 2021. 97 “Politics of San Marino”, Wikipedia, 2021. 98 “Saudi Arabia”, Wikipedia, 2021. 99 “2019 Sudanese coup d'état”, Wikipedia, 2021. 100 “Politics of Switzerland”, Wikipedia, 2021. 101 “Politics of Vietnam”, Wikipedia, 2021.
Page 40 ELEMENT: point(position(ContParPeriods*Cab2019), label(CountryName)) END GPL.
Box plot
* Chart Builder.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=ContParPeriods Cab2019 CountryName FSICat
MISSING=LISTWISE REPORTMISSING=NO DATAFILTER=FSICat(VALUES=2 3 UNLABELED=INCLUDE)
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: ContParPeriods=col(source(s), name("ContParPeriods"), unit.category())
DATA: Cab2019=col(source(s), name("Cab2019"))
DATA: CountryName=col(source(s), name("CountryName"), unit.category())
GUIDE: axis(dim(1), label("Number of continuous parliamentary 5 year periods"))
GUIDE: axis(dim(2), label("Women in cabinet 2019 (%)"))
GUIDE: text.title(label("Simple Boxplot of Women in cabinet 2019 (%) by Number of continuous ",
"parliamentary 5 year periods"))
GUIDE: text.footnote(label("Filtered by Functioning gov cat variable"))
SCALE: linear(dim(2), include(0))
ELEMENT: schema(position(bin.quantile.letter(ContParPeriods*Cab2019)), label(CountryName))
END GPL.
Page 41
Linear regression
REGRESSION
/SELECT=FSICat GE 2
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA
REGRESSION
=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT Cab2019
/METHOD=ENTER ContParPeriods.
Page 42
Model 2
Multivariate regression analysis
REGRESSION
/SE
REGRESSION
LECT=FSICat GE 2
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT Cab2019
/METHOD=ENTER ContParPeriods YearsUniSuff.
Page 43
Model 3
Linear regression analysis
REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT Cab2019 /METHOD=ENTER ContParPeriods.
Page 44
Model 4
Multivariate regression analysis
REGRESSION /SELECT=FSICat GE 2 /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT Cab2019 /METHOD=ENTER TotParPeriods.
Page 45
Model 5
Multivariate regression analysis
REGRESSION
/SELECT=FSICat GE 2
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT Cab2019
/METHOD=ENTER ContParPeriods HDI_Medium HDI_High HDI_VeryHigh.
Page 46
Collinearity test
REGRESSION /SELECT=FSICat GE 2 /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT Cab2019 /METHOD=ENTER ContParPeriods HDI_Medium HDI_High HDI_VeryHigh.
Model 6
Multivariate regression analysis
REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT Cab2019 /METHOD=ENTER ContParPeriods NonFuncDummy WellFuncDummy.
Page 47
Model 7
Multivariate regression analysis
REGRESSION /DESCRIPTIVES MEAN STDDEV CORR SIG N /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT Cab2019 /METHOD=ENTER ContParPeriods HDI FSIInv.
Page 48
Collinearity test
REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT Cab2019 /METHOD=ENTER ContParPeriods HDI FSIInv.
Page 49
Model 8
Multivariate regression analysis
REGRESSION /SELECT=FSICat GE 2 /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT Cab2019 /METHOD=ENTER ContParPeriods Region_AustraliaandNewZealand Region_CentralAsia Region_EasternAsia Region_EasternEurope Region_LatinAmericaCaribbean Region_Melanesia Region_Micronesia Region_NorthernAfrica Region_NorthernAmerica Region_NorthernEurope Region_Polynesia Region_SoutheasternAsia Region_SouthernAsia Region_SouthernEurope Region_SubSaharanAfrica Region_WesternAsia.
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