Does Consensus DemocracyReduce Social Inequality?

Tak Huen Chau∗ †

29 April 2017

1 Introduction and Preliminaries

In Patterns of Democracy (2012), Lijphart vehemently argued in favor ofconsensus democracy in producing ’kinder, gentler’ government. In partic-ular, his regression appeared to support the claim that democracies whichare more ’consensus’ in nature, which attempt to include as many people aspossible in the decision making process reduce social inequality.

Due to space constraints, I would focus on the empirical effects of con-sensus democracies on reducing economic inequality and will not entertaindetailed discussions on gender equality or voter turnout. I will use economicinequality as a proxy of social inequality. As Dahl (1996) argued ”many re-sources that flow directly or indirectly from one’s position in the economicorder can be converted into political resources”. Moreover, recent papershave also demonstrated that economic advantages translate into political ad-vantages by contributions, resulting in drowning out opposing views (Wintersand Page, 2009), (Winters, 2012).

Gender inequality is closely linked to economic inequality (Dollar andGetti, 1999). In Lijphart’s data, the Gini coefficient has a significant rela-tionship with women’s representation in parliament at 0.05 level1. Also, I

∗Merton College — takhuen.chau@merton.ox.ac.uk†Tutor: Dr. Sergi Pardos-Prado — sergi.pardos@merton.ox.ac.uk1Details of the regression can be found in Appendix A. The regression has controlled

for logged population sizes, but not the HDI, because the women’s participation is alreadyincluded in the HDI, so it is not controlled for in order to prevent double counting.

argue that measuring political equality by income distribution is more validthan voter turnout. This is because many confounding variables, such asthe electoral system (e.g. open lists, proportional representation) and voterID laws, may affect voter turnout (Mycoff, Wagner and WIlson, 2009), in-stead of social, economic or political equality. Thus, internal validity is low,compared to economic inequality indicators.

In this essay, I first develop a theoretical argument based on Tsebelis’s(2011) veto players, arguing that consensus democracies are not more effec-tive in reducing economic inequality, because of a greater number of vetopoints present. Then, I replicate Lijphart’s original regression of the Ginicoefficient (circa 2000) against the executive-parties dimension. I show thatthe results are weakened once an additional outlier is removed. Afterwards,I argue that the Gini coefficient is not a good indicator of the institutionalimpacts on social inequality; instead, the difference between the Gini coeffi-cients pre-intervention and post-intervention is more valid. Finally, I wouldalso demonstrate that the executive-parties dimension does not demonstratestatistically significant effect with the post-intervention poverty gap, anotherimportant measure of inequality.

2 Theoretical Argument

In this section, I will first outline Lijphart’s argument that consensus democ-racies reduce social inequality; then, I develop an argument against Lijphartbased on the concept of veto players.

Lijphart (2012) wrote in favor of the notion that consensus democraciesreduce the level of income inequality, as measured by the Gini coefficientcirca 2000, and increase the level of voter turnout and women’s participationin parliament, serving a proxy of gender equality. Particularly, Lijphart ar-gued that the executive-parties dimension of consensus democracies, whichis characterized by features such as being multi-party, proportionally repre-sentative, corporatist and with a balanced executive-legislature relationship,causes a more politically equal outcome compared to majoritarian systems.His explanation lies in the assumption that consensus democracies incorpo-rate as many citizens as possible in the decision process; as such, the outcomecan better reflect the interests of a supermajority and will lead to a more evendistribution of resources.

However, I would argue that the nature of consensus democracies hampers

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democracies’ ability to redistribute resources and alleviate social inequality.When a broader coalition is required for policy change, more veto players -political actors whose agreement is necessary for political change to occur(Tsebelis, 2011)2 - will result. As such, consensus democracies incorporatemore veto players, making deviations from the status quo more difficult,and weakening the degree of redistribution. In fact, cross-national analysisdemonstrated that the more veto points a country has, the greater policyinertia there will be (Tsebelis, 2000). In the case of income inequality andveto players, a study conducted with 24 long-standing democracies foundthat democracies with more electoral veto players3 shows a statistically sig-nificant relationship with increased income inequality measured by the Ginicoefficient (Stepan and Linz, 2011).

My argument provides a plausible alternative to Lijphart’s overarchingclaim that consensus democracies reduce social inequality. In the followingsection, I would apply empirical analysis to investigate whether Lijphart’sclaim hold.

3 Empirical Analysis

3.1 Lijphart’s Original Regression

Lijphart used the Gini coefficient as an indicator of economic inequality,which measures the percentage of the area between the 45-degree curve,which signifies perfect equality, and the actual cumulative distribution of in-come, over the total area below the 45-degree curve. As such, Gini coefficientof ranges from 0, perfect equality (everybody receives the same income), to1 (or 100%), perfect inequality (one person receives all the income).

Then, Lijphart ran a multivariate regression of Gini Coefficient (circa2000) against the executive-parties dimension in 29 countries, excluding Botswanaas an outlier. After controlling for the logged population size in 2009 andthe Human Development Index (HDI) in 2010, there is still a statisticallysignificant relationship between the two variables at 0.01 level. In Table 1, I

2Veto players may not have veto power on their own when their winset is containedin another player’s. However, for simplicity, I would use ’veto players’ and ’veto points’interchangeably in this essay.

3This include lower house veto, upper hose veto, presidential veto and constitutionalveto.

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have re-run Lijphart’s multivariate regression as Model 2, while Model 1 is asimple bivariate regression.

Lijphart’s results can be summarized by the following:

Gini = 76.552−3.040(ExecParties)−0.929(LoggedPop)−38.332(HDI)+ε(1)

ε ∼ N (0, 5.3182) (2)

Lijphart’s result shows that for every one point increase along the executive-parties dimension, there would be a corresponding decrease by 3.040 percent-age points in the Gini coefficient.

Table 1: Lijphart’s Original Model

Dependent variable:

Gini Coefficient (2000)

(1) (2)

Executive-Parties (1981-2010) −4.540∗∗∗ −3.040∗∗∗

(1.267) (1.039)Logged Population (2009) −0.929

(0.666)HDI (2010) −38.332∗∗∗

(11.844)Constant 36.024∗∗∗ 76.552∗∗∗

(1.279) (12.518)

Observations 30 29R2 0.314 0.481Adjusted R2 0.290 0.419Residual Std. Error 6.993 (df = 28) 5.318 (df = 25)F Statistic 12.832∗∗∗ (df = 1; 28) 7.725∗∗∗ (df = 3; 25)

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

3.2 Problems with Lijphart’s Analysis

In this sub-section, I would discuss two aspects of Lijphart’s analysis - theuse of control variables and the exclusion of outliers; I would return to discuss

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whether the Gini coefficient pre-taxes and transfers is a valid indicator in thenext sub-section.

On the use of control variables, I would argue that Lijphart has cor-rectly identified two key aspects to control for oand that controlling for so-cial spending and inequality-adjusted HDI may render statistically significantrelationships null. Firstly, as shown in Table 2, the relationship betweenthe HDI and Gini coefficient is significant at the 0.01 level. As the HDIincludes education quality, per capita income and life expectancy (UNDP,2017) and has a strong relationship with the Gini coefficient, not control-ling for it results in over-estimating the effects of a consensus institution onincome inequality. Secondly, literature has shown a moderately strong nega-tive relationship between a country’s population size and income inequality(Deaton and Paxson, 1997); therefore, controlling for the logged populationsize prevents confounding effects caused by it. Thirdly, the inclusion of so-cial spending and inequality-adjusted HDI as control variables is incorrect.For social spending, it is a part of government action on inequality, whichcan be better viewed as an intermediacy between institution and the impacton inequality. Controlling for it would change the question from whetherconsensus democracy reduces inequality to whether consensus democracy ismore effective in reducing inequality, given the same spending level. Forinequality-adjusted HDI, it includes two components: the HDI and an ad-juster subtracting points depending on the level of inequality (UNDP, 2017).Controlling for the inequality-adjusted HDI includes the independent vari-able in the control variable, which will underestimate the actual impact ofthe degree of consensus democracies on inequality.

While I agree with Lijphart’s treatment of control variables, his treatmentof outliers is problematic. He removed Botswana as an ’extreme outlier’in his analysis, he failed to provide any justification beyond his personaljudgement. An alternative to Lijphart’s judgement would be to use thequartile method, which prevents charges of cherry-picking. Applying themethod, I find Argentina to be an outlier (Figure 1 ).

After excluding Argentina, Lijphart’s result becomes weaker - the resultsare now confident at 0.05 level (Model 2 ) instead of at 0.01 level (Model 1 ),and the regression coefficient drops substantially from -3.0 to -2.6.

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Table 2: Lijphart’s Results and with Outliers Removed

Dependent variable:

Gini Coefficient (2000)

(1) (2)

Executive-Parties (1981-2010) −3.040∗∗∗ −2.593∗∗

(1.039) (0.967)Logged Population (2009) −0.929 −1.027

(0.666) (0.610)HDI (2010) −38.332∗∗∗ −35.627∗∗∗

(11.844) (10.885)Constant 76.552∗∗∗ 74.754∗∗∗

(12.518) (11.469)

Observations 29 28R2 0.481 0.476Adjusted R2 0.419 0.411Residual Std. Error 5.318 (df = 25) 4.862 (df = 24)F Statistic 7.725∗∗∗ (df = 3; 25) 7.276∗∗∗ (df = 3; 24)

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

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Figure 1: Box Plot of Gini Coefficients (2000)

3.3 Measuring Redistribution, Not Distribution

I argue the fundamental problem with Lijphart’s analysis is that it identifiesthe wrong independent variable. In this section, I would suggest measur-ing redistribution instead of inequality can significantly reduce confoundingvariables. Using the absolute difference between the Gini coefficient pre-intervention and post-intervention to measure the effects in reducing inequal-ity by redistribution, one can prevent the many confounding variables thatmay affect the validity of Lijphart’s analysis. For example, it may be thecase that an exogenous cultural factor makes a democracy more consensusand equal in nature, highlighted by Scandinavian countries; alternatively, itis possible that UK former colonies are more majoritarian and more unequalbecause of colonial influence on institutional design and economic reliance ontrade and finance, which generate inequality. While measuring redistributioncannot completely avert the problem of cultural bias, it can eliminate poten-tial confounders in the initial distribution and instead focus on the changein distribution as a result of other variables.

Firstly, would justify my use of the Gini coefficient in lieu of other indica-tors, such as the 20/20 ratio or the Palma index. The Gini coefficient has astrength of reflecting inequality across the entire distribution (Varian, 2003)

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and each economic agent carries equal weighting. A common criticism to theGini coefficient is that it is oversensitive to the changes at the two extremeends of the distribution; instead, a ratio scale comparing the ratio of incomeof the top to the bottom (e.g. Palma index, 20/20 ratio) should be used,which ’averages out’ the extreme ends. However, this critique assumes thatthe skewing at the two ends should carry a smaller weight than the changesin the middle because the latter is more relevant to a greater proportion ofagents. In the context of political equality, however, I believe this assump-tion does not hold, because extreme inequality at the top end does have astrong if not disproportionate effect on political inequality in the form of po-litical contributions and lobbying (Winters and Page, 2009). Moreover, dueto the lack of data, the Gini coefficient is one of the few choices available forcross-national analysis beyond OECD countries.

I now introduce a set of post-intervention Gini coefficient in the year of20014. By applying the quartile test (Figure 2), I have removed outliers5

in the post-intervention Gini (2001). In Table 3 6, a similar pattern as the

Figure 2: Box Plot of Gini Coefficients after intervention (2001)

4Due to the lack of data, I have taken the point of reference at 2001 instead of 2000.The sources of the new data can be found in Appendix B.

5Barbados and Jamaica are the outliers in this set of data.6Model 1 shows the regression without the removal of outliers; model 2 shows the

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previous regression has shown: the executive-parties dimension is significantwith the post-intervention Gini at 0.01 level without the outliers removedand at 0.05 level with the outliers removed.

Table 3: Regression of the post-intervention Gini Coefficient againstExecutive-parties Dimension

Dependent variable:

Gini coefficient after intervention (2001)

(1) (2)

Executive-Parties (1981-2010) −3.603∗∗∗ −2.291∗∗

(1.023) (0.989)Logged Population (2009) −0.478 0.301

(0.539) (0.528)HDI (2010) −31.038∗∗ −21.936∗

(11.791) (10.944)Constant 63.055∗∗∗ 47.112∗∗∗

(11.272) (11.212)

Observations 32 30R2 0.455 0.302Adjusted R2 0.396 0.222Residual Std. Error 5.501 (df = 28) 4.803 (df = 26)F Statistic 7.784∗∗∗ (df = 3; 28) 3.751∗∗ (df = 3; 26)

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

Now, the redistribution effect on the Gini coefficient can be obtained,which is the difference in absolute terms between the Gini coefficient beforeintervention and the Gini coefficient after intervention. For consistency, thelatter two are from the same set of data circa 2001. Before running theregression, I first check whether there are outliers. As shown in Figure 3,there are no outliers.

Then, I conduct a regression of the redistribution effect on the Gini co-efficient against the executive-parties dimension with the logged populationand HDI controlled, and the results are displayed as model 2 in Table 4, while

regression with the outliers removed.

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Figure 3: Box Plot of the reduction of Gini in absolute terms (2001)

model 1 is Lijphart’s original regression result for comparison7.As shown in Table 4, the executive-parties dimension fails to even reach

significance at 0.1 level with the redistribution effect on the Gini coefficient.This demonstrates that there fails to be a significant relationship between thetwo variables. The above corroborates my hypothesis that consensus democ-racies and a higher degree of relative equality is exogenously determined byother variable(s), such as culture, because the regression result does not sup-port the notion that consensus democracy leads to stronger redistribution.

3.4 Poverty Gap

Another indicator which highlight social inequality is the poverty gap index,which is the average of the sum of the gap between an individuals’ incomeand the poverty line, which is taken to be half of the median income in thiscase8. The poverty gap can be calculated by the following equation9:

PovertyGapIndex =1

N

q∑j=1

(z − yjz

)(3)

Compared to the poverty rate, the poverty gap highlights the intensity ofpoverty, and complements the Gini coefficient. This is because the poverty

7Note that the dependent variables are different.8Individuals with an income above the poverty line has a poverty gap of zero.9N is the total population, q is the total population of poor who are living at or below

the poverty line, z is the poverty line, and yj is the income of the poor individual j.

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Table 4: Regression of the reduction in Gini due to intervention againstExecutive-parties Dimension

Dependent variable:

Gini (2000) Reduction in Gini (2001)

(1) (2)

Executive-Parties (1981-2010) −3.040∗∗∗ 1.893(1.039) (1.322)

Logged Population (2009) −0.929 −0.503(0.666) (0.825)

HDI (2010) −38.332∗∗∗ 39.943∗

(11.844) (22.337)Constant 76.552∗∗∗ −15.180

(12.518) (17.402)

Observations 29 28R2 0.481 0.235Adjusted R2 0.419 0.140Residual Std. Error 5.318 (df = 25) 5.951 (df = 24)F Statistic 7.725∗∗∗ (df = 3; 25) 2.462∗ (df = 3; 24)

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

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gap highlights the intensity of relative deprivation of residents at the bottomof the income distribution and abstracts away the distributive properties atthe top / middle end. Relative to other parts of the income distribution,the marginal impact per dollar has stronger impact on education, healthand political rights, such as the exercise of right to vote below the povertyline due to diminishing returns on capital (Abadie, 2004). Thus, given theimportance of measuring the intensity of deprivation, the poverty index oughtto be considered10.

I now introduce cross-national data on the poverty gap. After testing foroutliers (Figure 4 ), which there is none, I conduct a multivariate regressionwith the poverty gap against the executive-parties dimension, while control-ling for logged population and the HDI. The results are presented in Table

Figure 4: Box Plot of the Poverty Gap (2006)

5. Results indicate that there are no statistically significant relationship be-tween the two variables; and in fact, the p-value is over 43%, which castsstrong doubts over consensus democracies’ relationship with a less deprived,more equal society.

10Due to the lack of data, the effects of policy intervention regrettably cannot be included

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Table 5: Regression of the Poverty Gap against Executive-parties Dimension

Dependent variable:

Poverty Gap (2006)

Executive-Parties (1981-2010) −1.088(1.360)

Logged Population (2009) 2.154∗∗

(0.798)HDI (2010) 26.554

(27.301)Constant −16.357

(23.349)

Observations 24R2 0.411Adjusted R2 0.322Residual Std. Error 5.420 (df = 20)F Statistic 4.648∗∗ (df = 3; 20)

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

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4 Conclusion

In this essay, I have presented an alternative argument based on veto playersagainst Lijphart’s overarching claim that consensus democracies reduce socialinequality. After removing outliers, introducing more relevant indicators,such as the redistribution effects on Gini and the poverty gap in lieu of theGini coefficient, my empirical analysis demonstrated that there does not exista statistically significant relationship between consensus democracies and agreater degree of social equality. Therefore, those who accept Lijphart’sconclusion without strong doubts may have one thought too few. 2

Word count: 2197 (excluding headings, footnotes, tables, fig-ures and appendices, as instructed.)

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References

Abadie, Alberto (2004). “Poverty, Political Freedom, and the Roots of Ter-rorism”. In: SSRN Electronic Journal. doi: 10.2139/ssrn.617542.

Deaton, Angus S. and Christina H. Paxson (1997). “The Effects of Economicand Population Growth on National Saving and Inequality”. In: Demog-raphy 34.1, p. 97. doi: 10.2307/2061662.

Dollar, David and Roberta Gatti (1999). Gender inequality, income, andgrowtharegoodtimesgoodforwomen. Vol. 1. Development Research Group,The World Bank Washington, DC.

Hlavac, Marek (2015). stargazer: Well-Formatted Regression and SummaryStatistics Tables. R package version 5.2. Cambridge, USA: Harvard Uni-versity. url: http://CRAN.R-project.org/package=stargazer.

Jaime-Castillo, Antonio M. (2012). “Economic Inequality and Electoral Par-ticipation: A Cross-Country Evaluation”. In: SSRN Electronic Journal.doi: 10.2139/ssrn.1515905.

Lijphart, Arend (2012). Patterns of Democracy. 2nd ed. Yale UniversityPress.

Mycoff, Jason D., Michael W. Wagner, and David C. Wilson (2009). “TheEmpirical Effects of Voter-ID Laws: Present or Absent?” In: PS: PoliticalScience & Politics 42.01, pp. 121–126. doi: 10.1017/s1049096509090301.

Programme, United Nations Development (2017). Human Development Index(HDI) ‖HumanDevelopmentReports. url: http://hdr.undp.org/en/content/human-development-index-hdi.

Stepan, Alfred and Juan J. Linz (2011). “Comparative Perspectives on In-equality and the Quality of Democracy in the United States”. In: Perspec-tives on Politics 9.04, pp. 841–856. doi: 10.1017/s1537592711003756.

Tsebelis, George (2000). “Veto Players and Institutional Analysis”. In: Gov-ernance 13.4, pp. 441–474. doi: 10.1111/0952-1895.00141.

— (2011). Veto Players. 1st ed. Princeton University Press.Varian, Hal R (2003). Intermediate microeconomics. 1st ed. W. W. Norton

& Company.Winters, Jeffrey A (2012). Oligarchy. 1st ed. Cambridge University Press.Winters, Jeffrey A. and Benjamin I. Page (2009). “Oligarchy in the United

States?” In: Perspectives on Politics 7.04, p. 731. doi: 10.1017/s1537592709991770.

Note: The background references are indicated in Appendix B - AdditionalData

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A Additional Empirical Analysis

Apart from the regressions presented in the main body, I have also conductedadditional modeling. While these tables are not cleaned up, I hope they maybe useful for readers who are interested11.

Table 6:

Dependent variable:

gini ca 2000

eff num parl parties 1981 2010 1.418(1.126)

pct minimal winning one party cabinet 1981 2010 0.120∗∗

(0.050)index of exec dominance 1981 2010 −1.015∗

(0.498)index of disproportionality 1981 2010 0.176

(0.226)index of interest group pluralism 1981 2010 2.308

(1.394)pop in thousands 2009) −0.870

(0.577)hdi 2010 −25.008∗∗

(9.852)Constant 51.138∗∗∗

(13.221)

Observations 28R2 0.687Adjusted R2 0.578Residual Std. Error 4.115 (df = 20)F Statistic 6.282∗∗∗ (df = 7; 20)

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

11The variable, pop in thousands 2009, is logged.

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Table 7:

Dependent variable:

gini ca 2000

eff num parl parties 1981 2010 −1.389∗

(0.770)pop in thousands 2009) −0.806

(0.650)hdi 2010 −39.976∗∗∗

(11.522)Constant 80.818∗∗∗

(12.511)

Observations 28R2 0.401Adjusted R2 0.326Residual Std. Error 5.201 (df = 24)F Statistic 5.349∗∗∗ (df = 3; 24)

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

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Table 8:

Dependent variable:

gini ca 2000

pct minimal winning one party cabinet 1981 2010 0.087∗∗∗

(0.028)pop in thousands 2009) −0.729

(0.588)hdi 2010 −33.938∗∗∗

(10.581)Constant 65.364∗∗∗

(11.621)

Observations 28R2 0.511Adjusted R2 0.450Residual Std. Error 4.696 (df = 24)F Statistic 8.369∗∗∗ (df = 3; 24)

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

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Table 9:

Dependent variable:

gini ca 2000

index of exec dominance 1981 2010 0.257(0.441)

pop in thousands 2009) −0.924(0.696)

hdi 2010 −39.554∗∗∗

(12.203)Constant 75.456∗∗∗

(13.040)

Observations 28R2 0.329Adjusted R2 0.245Residual Std. Error 5.503 (df = 24)F Statistic 3.921∗∗ (df = 3; 24)

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

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Table 10:

Dependent variable:

gini ca 2000

index of disproportionality 1981 2010 0.421∗∗

(0.174)pop in thousands 2009) −1.248∗

(0.640)hdi 2010 −35.358∗∗∗

(11.161)Constant 72.890∗∗∗

(11.787)

Observations 28R2 0.453Adjusted R2 0.385Residual Std. Error 4.968 (df = 24)F Statistic 6.630∗∗∗ (df = 3; 24)

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

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Table 11:

Dependent variable:

gini ca 2000

index of interest group pluralism 1981 2010 3.374∗∗∗

(0.917)pop in thousands 2009) −1.189∗∗

(0.560)hdi 2010 −31.314∗∗∗

(10.088)Constant 65.717∗∗∗

(10.828)

Observations 28R2 0.565Adjusted R2 0.510Residual Std. Error 4.432 (df = 24)F Statistic 10.384∗∗∗ (df = 3; 24)

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

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Table 12:

Dependent variable:

gini abs diff

eff num parl parties 1981 2010 −0.811(1.510)

pct minimal winning one party cabinet 1981 2010 −0.028(0.070)

index of exec dominance 1981 2010 0.560(0.746)

index of disproportionality 1981 2010 −0.435(0.284)

index of interest group pluralism 1981 2010 −1.795(1.890)

pop in thousands 2009) 0.316(0.955)

hdi 2010 18.400(25.619)

Constant 4.342(21.086)

Observations 28R2 0.355Adjusted R2 0.129Residual Std. Error 5.988 (df = 20)F Statistic 1.571 (df = 7; 20)

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

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Table 13:

Dependent variable:

poverty 2006

eff num parl parties 1981 2010 0.152(1.831)

pct minimal winning one party cabinet 1981 2010 −0.004(0.082)

index of exec dominance 1981 2010 0.064(0.746)

index of disproportionality 1981 2010 −0.039(0.349)

index of interest group pluralism 1981 2010 1.780(1.990)

pop in thousands 2009) 2.066∗

(0.994)hdi 2010 29.721

(31.354)Constant −22.239

(29.546)

Observations 24R2 0.434Adjusted R2 0.186Residual Std. Error 5.940 (df = 16)F Statistic 1.752 (df = 7; 16)

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

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B Additional Data

In the data frame, I have added four columns of new data - Gini post-intervention, Gini pre-intervention (2001), differences in Gini in absoluteterms, and the poverty gap.

Sources:

Gini Coefficients (2001)

Solt, Frederick. 2016. ”The Standardized World Income Inequality Database.”Social Science Quarterly 97. SWIID Version 5.1, July 2016.

Lustig, Nora, Pessino, Carola and Scott, John, 2013 ”The impact of taxesand social spending on inequality and poverty in Argentina, Bolivia, Brazil,Mexico, Peru and Uruguay: An Overview” Public Finance Review

Poverty Gap (2006)

Organisation for Economic Co-Operation and Development, 2017, OECDStatistics (Available at: https://data.oecd.org/inequality/poverty-gap.htm)[Accessed: 28 April 2017]

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Table 14: Additional datacountry gini r 2001 gini 2001 gini abs diff poverty 2006ARG 45.602 51.519 5.917 NAAUL 31.653 43.860 12.206 26.283AUT 25.697 41.725 16.029 20.368BAH 52.257 58.326 6.070 NABAR NA NA NA NABEL 27.944 49.055 21.111 19.050BOT NA NA NA NACAN 31.439 43.171 11.732 30.199CR 25.889 36.685 10.796 19.720DEN 22.503 46.333 23.830 24.850FIN 24.593 46.227 21.634 18.939FRA 27.798 35.562 7.764 23.900GER 27.491 51.065 23.574 NAGRE 33.300 NA NA 23.614ICE NA NA NA 24.579IND 34.233 NA NA NAIRE 31.301 38.981 7.680 21.050ISR 34.549 44.663 10.114 32.752ITA 33.497 46.115 12.618 32.247JAM 48.037 57.896 9.859 NAJPN 30.416 49.190 18.774 33.111KOR 30.027 46.963 16.935 36.600LUX 26.000 41.734 15.734 18.897MAL 29.740 51.058 21.318 NAMAU 37.123 42.590 5.467 NANET 24.792 41.450 16.658 18.772NOR 24.973 46.526 21.552 33.600NZ 33.245 46.893 13.648 22.600POR 35.453 60.338 24.885 27.434SPA 33.648 40.643 6.995 40.045SWE 25.196 47.779 22.583 26.568SWI 28.019 42.660 14.642 NATRI NA NA NA NAUK 34.481 48.520 14.039 33.096URU 43.257 44.520 1.263 NAUS 36.874 47.280 10.406 37.027

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C Code

data <− read . csv ( ’ extradata . csv ’ )#Test f o r r e l a t i o n s h i p between gender e q u a l i t y and

income i n e q u a l i t yco r r <− lm(data$ g i n i ca 2000 ˜ data$womens pa r l

r e p r e s e n t a t i o n 2010 + log (data$pop in thousands2009) )

#Rat iona le f o r e x c l u d i n g HDI as a c o n t r o l : women ’ spar l iamentary r e p r e s e n t a t i o n i s ∗a l r e a d y i n c l u d e d∗in HDI

summary( co r r )#S t a t i s t i c a l l y s i g n i f i c a n t r e l a t i o n s h i p at 5% between

women ’ s r e p r e s e n t a t i o n in par l iament and Ginic o e f f i c i e n t at 5%

#Therefore , the essay w i l l f o c u s on economic i n e q u a l i t yas a g e n e r a l measure o f s o c i a l i n e q u a l i t y

#Now, I w i l l be t e s t i n g L i jphar t ’ s o r i g i n a l r e g r e s s i o n#Without c o n t r o l l i n g f o r p o p u l a t i o n and HDImodel0 <− lm(data$ g i n i ca 2000 ˜ data$exec p a r t i e s

1981)summary( model0 )

#Li jphar t ’ s r e s u l t sdata$ g i n i ca 2 0 0 0 [ 7 ] <− NA #L i j p h a r t exc luded Botswanamodel1 <− lm(data$ g i n i ca 2000 ˜ data$exec p a r t i e s 1981

+ log (data$pop in thousands 2009) + data$hdi 2010)summary( model1 )

#Now, I w i l l be t e s t i n g f o r o u t l i e r s in Gini (2000)data$ g i n i ca 2 0 0 0 [ 7 ] <− 60 .5 #To t e s t f o r o u t l i e r s , we

need to add Botswana back in the data frameg i n i c2000 o u t l i e r s <− boxplot (data$ g i n i ca 2000)$outboxplot (data$ g i n i ca 2000 , main=” Gini C o e f f i c i e n t

(2000) ” , xlab=” Gini c o e f f i c i e n t ” )

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#O u t l i e r : Argentina#Removing o u t l i e r s :data$ g i n i ca 2 0 0 0 [ 1 ] <− NAdata$ g i n i ca 2 0 0 0 [ 7 ] <− NA #L i j p h a r t exc luded Botswana

#Now, I w i l l be t e s t i n g L i jphar t ’ s o r i g i n a l r e g r e s s i o nwi th o u t l i e r s removed

model1new <− lm(data$ g i n i ca 2000 ˜ data$exec p a r t i e s1981 + log (data$pop in thousands 2009) + data$hdi2010)

summary( model1new )

#Now, I w i l l decompose the e f f e c t s o f ∗each∗ o f theexec−p a r t i e s components on the Gini (2000)c o e f f i c i e n t

model2 <− lm(data$ g i n i ca 2000 ˜ data$e f f num par lp a r t i e s 1981 2010 + data$pct minimal winning oneparty cab ine t 1981 2010 + data$index o f execdominance 1981 2010 + data$index o fd i s p r o p o r t i o n a l i t y 1981 2010 + data$index o fi n t e r e s t group p lu ra l i sm 1981 2010 + log (data$pop inthousands 2009) + data$hdi 2010)

summary( model2 )

#Now, I w i l l t e s t the e f f e c t o f ∗each∗ o f the dimensionon Gini (2000)

dim1 <− lm(data$ g i n i ca 2000 ˜ data$e f f num par lp a r t i e s 1981 2010 + log (data$pop in thousands 2009)+ data$hdi 2010)

summary( dim1 )dim2 <− lm(data$ g i n i ca 2000 ˜ data$pct minimal winning

one party cab ine t 1981 2010 + log (data$pop inthousands 2009) + data$hdi 2010)

summary( dim2 )dim3 <− lm(data$ g i n i ca 2000 ˜ data$index o f exec

dominance 1981 2010 + log (data$pop in thousands2009) + data$hdi 2010)

summary( dim3 )

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dim4 <− lm(data$ g i n i ca 2000 ˜ data$index o fd i s p r o p o r t i o n a l i t y 1981 2010 + log (data$pop inthousands 2009) + data$hdi 2010)

summary( dim4 )dim5 <− lm(data$ g i n i ca 2000 ˜ data$index o f i n t e r e s t

group p lu ra l i sm 1981 2010 + log (data$pop inthousands 2009) + data$hdi 2010)

summary( dim5 )

#Now, I t e s t the e f f e c t o f the Execut ive−P a r t i e sdimension on the Gini c o e f f i c i e n t a f t e r i n t e r v e n t i o n

model3 <− lm(data$ g i n i r 2001 ˜ data$exec p a r t i e s1981 + log (data$pop in thousands 2009) + data$hdi2010)

summary( model3 ) #Caution : o u t l i e r s are not removed y e t#Removign o u t l i e r s :p lo t2 <− boxplot (data$ g i n i r 2001 , main=” Gini a f t e r

i n t e r v e n t i o n (2001) ” , xlab=” Gini c o e f f i c i e n t ” )p lo t2$outp lo t2data$ g i n i r 2 0 0 1 [ 4 ] <− NAdata$ g i n i r 2001 [ 20 ] <− NAdata$ g i n i abs d i f f [ 4 ] <− NAdata$ g i n i abs d i f f [ 2 0 ] <− NA#Now, I t e s t the e f f e c t o f the Execut ive−P a r t i e s

dimension on the i n e q u a l i t y−a d j u s t e d wi th o u t l i e r sremoved

model3b <− lm(data$ g i n i r 2001 ˜ data$exec p a r t i e s1981 + log (data$pop in thousands 2009) + data$hdi2010)

summary( model3b )

#Gist o f a n a l y s i n g Gini : to see whether the exec−p a r t i e s dimension have an impact on ∗ reduc ing∗ Gini

#F i r s t l y , I w i l l t e s t whether t h e r e are o u t l i e r s in thei n d i c a t o r :

boxplot (data$ g i n i abs di f f , main=” D i f f e r e n c e s inreduc ing Gini in abso lu t e terms (2001) ” , xlab=”

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Percentage Points in Gini ” )

#Now, I t e s t the e f f e c t o f the Execut ive−P a r t i e sdimension on the a b s o l u t e d i f f e r e n c e between Ginipost−t r a n s f e r s and Gini pre−t r a n s f e r s

model4 <− lm(data$ g i n i abs d i f f ˜ data$exec p a r t i e s1981 + log (data$pop in thousands 2009) + data$hdi2010)

summary( model4 )

#Now, I t e s t the e f f e c t o f the Execut ive−P a r t i e sdimension on the pover ty gap post−i n t e r v e n t i o n

boxplot (data$poverty 2006 , main=” Poverty Gap” , xlab=’Percentage Points ’ ) #No o u t l i e r s

model5 <− lm(data$poverty 2006 ˜ data$exec p a r t i e s1981 + log (data$pop in thousands 2009) + data$hdi2010)

summary( model5 )

#Test ing e f f e c t s o f each o f the dimensions on ther e d u c t i o n in Gini and the pover ty gap

model6 <− lm(data$ g i n i abs d i f f ˜ data$e f f num par lp a r t i e s 1981 2010 + data$pct minimal winning oneparty cab ine t 1981 2010 + data$index o f execdominance 1981 2010 + data$index o fd i s p r o p o r t i o n a l i t y 1981 2010 + data$index o fi n t e r e s t group p lu ra l i sm 1981 2010 + log (data$pop inthousands 2009) + data$hdi 2010)

summary( model6 )model7 <− lm(data$poverty 2006 ˜ data$e f f num par l

p a r t i e s 1981 2010 + data$pct minimal winning oneparty cab ine t 1981 2010 + data$index o f execdominance 1981 2010 + data$index o fd i s p r o p o r t i o n a l i t y 1981 2010 + data$index o fi n t e r e s t group p lu ra l i sm 1981 2010 + log (data$pop inthousands 2009) + data$hdi 2010)

summary( model7 )

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#L i m i t a t i o n s : d id not account f o r the e f f e c t o fi n t e r v e n t i o n due to a l a c k in cross−n a t i o n a l data

#Package f o r g e n e r a t i n g t a b l e sin s ta l l . packages ( ” s t a r g a z e r ” )

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