Tag: descriptive representation

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Limits of Descriptive Representation

There are many benefits of including minorities of power in decision-making — so-called descriptive representation. This is the case for women who remain numerically under-represented in legislatures around the world, but also ethnic minority groups, and other minorities.

There are, however, practical limitations to including different groups and subgroups in legislatures. Goodin (2004) highlights that there is a tension between including members from different groups and subgroups on the one hand, and the practical ability to debate in the legislature – thus rob them of the possibility of substantively represent their particular subgroup. Goodin highlights that legislatures and governments need not include all groups and subgroups in society to represent the fact of diversity. While this observation highlights why it is impossible to include every subgroup all the time, it should not distract from the need of including ethnic and regional minorities in processes of decision-making, and certainly not be seen as a licence to exclude large groups of society.

It might be helpful to take a longer-term perspective here: rather than focusing on the absence of particular (small) groups in society in any given legislature, focus on persistent exclusion over several legislatures. And bear in mind that representatives act on behalf of groups not directly included in decision-making — so we should also focus on the representation of interests (though this is much harder to do than simply counting, of course).

Goodin, R. 2004. «Representing Diversity». British Journal of Political Science 34 (3): 453–68. https://doi.org/10.1017/S0007123404000134.
Ruedin, Didier. 2013. Why Aren’t They There? The Political Representation of Women, Ethnic Groups and Issue Positions in Legislatures. Colchester: ECPR Press.

Image: CC-by-nc by Guilherme Sales

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Level of Descriptive Representation Unrelated to Population Size

Here’s a null result I’ve long wanted to share: The level of descriptive representation seems unrelated to population size. More specifically, the share of women in national legislatures and the share of ethnic minority groups in national legislatures (relative to their size in the population) does not correlate substantially with the overall population size. I’ve looked into this a few years back after receiving comments that the nature of political representation could be expected to be different in small countries (microstates). I’m still unsure why this would be the case — except that in really small chambers including an additional woman adds much to the share of women.

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Mapping Ethnic Representation Scores in R

Here’s a demonstration of how easy it is to map with the R package rworldmap by Andy South. I map the ethnic representation scores in my 2009 JLS article, available from my Dataverse. I used the tab-delimited file, which contains the country name, Q-scores, R-scores, and a binary indicator of minority presence. I searched&replaced the tabs with commas, and added a new column for the ISO3 country codes.

After that, it’s just a few lines in R:

library(rworldmap)
dta <- read.csv("Representation.csv") # these are the data described above.

> head(dta) gives:
Country ISO3 QScore RScore Present
1 Afghanistan AFG 0.803 0.697 1
2 Albania ALB 0.980 0.510 1
3 Algeria DZA NA NA 1
4 Andorra AND 0.980 0.000 0
5 Angola AGO NA NA 1
6 Antigua & Barbuda ATG 0.910 0.000 0

Next we have to identify the countries. joinCode specifies that I used ISO3, nameJoinColumn specifies the variable with the country abbreviations:

jcd <- joinCountryData2Map(dta, joinCode="ISO3", nameJoinColumn="ISO3")

Next a line from the package vignette that makes the plot use the space available.
par(mai=c(0,0,0.2,0),xaxs="i",yaxs="i")

Now, while mapCountryData(jcd, nameColumnToPlot="QScore") would suffice to draw a map, I used some of the options available (e.g. a blue ocean, light grey for missing data), and drew the legend separately for a little extra control:

mapParams <- mapCountryData(jcd, nameColumnToPlot="QScore", addLegend=FALSE, mapTitle="Ethnic Representation Scores", oceanCol="light blue", missingCountryCol="light grey")
do.call(addMapLegend, c(mapParams, legendWidth=0.5, legendMar = 4))

The title is a bit off, but other than that, I’m pretty happy for a first cut with so little coding.

ers-map
Ruedin, Didier. 2009. ‘Ethnic Group Representation in a Cross-National Comparison.’ The Journal of Legislative Studies 15 (4): 335–54. doi:10.1080/13572330903302448.

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Women in Regional Legislatures

There’s much research on the representation of women in national legislatures. Why is it that in some countries there are many women in parliament, and in others women are almost absent? Research points to aspects of the electoral system, and particularly to (cultural) attitudes about the role of women in society more generally.

If attitudes are such a strong predictor, we would expect them to play a role on a smaller scale, too: in regional legislatures. I have considered the proportion of women in the cantonal parliaments in Switzerland, and found no association with any of the variables I have tested. With cantons dominated by Protestants and others by Catholics, there appears to be enough variation, but alas it doesn’t fit…

Do I simply need better variables to capture relevant attitudes, are second-order elections exempt from the mechanisms suggested for national elections (why?), or is our theory lacking? With regard to the first, I have tried many variables, including the proportion of Catholics, the traditionally dominant religion of the canton, distance to traditional trade routes, or the years when women gained the right to vote. With regard to second-order elections, there is increasing evidence that this might be the case, like that the European Parliament is somewhat different. This leaves us with the last one: the suggested mechanisms are not specified well enough.

Ruedin, Didier. 2012. “The Representation of Women in National Parliaments: A Cross-National Comparison.” European Sociological Review 28 (1): 96–109. doi:10.1093/esr/jcq050.
———. 2013. Why Aren’t They There? The Political Representation of Women, Ethnic Groups and Issue Positions in Legislatures. Colchester: ECPR Press.
Stockemer, Daniel. 2008. “Women’s Representation in Europe — A Comparison Between the National Parliaments and the European Parliament.” Comparative European Politics 6 (4): 463–85. doi:10.1057/cep.2008.2.

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Women in National Legislatures

A simple query to get data by Fabrizio Gilardi meant I was digging out old analyses from my article on the political representation of women in national legislatures. I put of running beta regressions on these data for too long, and now there was no reason not to.

Embarrassingly, in this context I realized that the coefficients in table 3 are incorrect — although marginally. It appears that in the process of changing the dependent variable from one that caters for the percentage of women in the population to ignoring it (makes it much easier to explain), I forgot to replace the entire table with new values (this was in the days before Sweave and odfWeave). Given that the results are essentially the same, I never noticed — but it still feels quite silly. Anyway, here are the corrected numbers, first as coefficient plot comparing the two models:

coef

So the blue dots and lines use gender representation scores as dependent variable; the red dots and lines use the proportion of women in national legislatures as dependent variables. Below the corresponding table:

Representation scores Proportion
(Intercept) 0.653*** 0.154**
(0.058) (0.058)
PR/MMP 0.043* 0.052**
(0.019) (0.019)
Party Quotas 0.003 0.004
(0.005) (0.005)
Statutory Quotas 0.045* 0.041
(0.021) (0.021)
Political Rights -0.001 -0.004
(0.008) (0.008)
Age Democracy 0.000 0.000
(0.000) (0.000)
Professional Jobs 0.000 0.000
(0.001) (0.001)
Nordic 0.147** 0.139**
(0.044) (0.043)
Eastern Europe -0.074 -0.062
(0.040) (0.040)
Asia -0.105** -0.107**
(0.036) (0.036)
Middle East -0.097* -0.124**
(0.044) (0.043)
Sub-Saharan -0.031 -0.019
(0.036) (0.035)
Latin -0.051 -0.048
(0.032) (0.032)
R-squared 0.536 0.577
N 94 94

Beta regression, proportion of women as dependent variable:

Proportion
(Intercept) 0.689**
(0.259)
PR/MMP 0.204*
(0.083)
Party Quotas 0.015
(0.025)
Statutory Quotas 0.219*
(0.096)
Political Rights -0.011
(0.035)
Age Democracy 0.001
(0.001)
Professional Jobs 0.001
(0.004)
Nordic 0.988***
(0.255)
Eastern Europe -0.380*
(0.182)
Asia -0.488**
(0.163)
Middle East -0.469*
(0.193)
Sub-Saharan -0.170
(0.162)
Latin -0.253
(0.146)
Pseudo R-squared 0.562
N 94

(Sorry, the arm package does not seem to support beta regressions at the moment, so no coefficient plot)