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)

Limits of Descriptive Representation

Having spent quite a bit on trying to understand political representation, I know how easy it is to forget the wider context. Here I want to highlight just two things.

First, even if the political representation of different groups is a good thing, we mustn’t forget that most political systems do not revolve around ethnic difference or gender, but about economic growth, the availability of jobs, or security and stability more widely.

Second, there’s a paper Robert Goodin that neatly outlines the limits of descriptive representation in representing diversity — whilst maintaining legislatures where deliberation and debate remains possible. While he may not account enough for multiple group membership and the fact that not every legislator needs to take part in every debate, Goodin’s argument is a good reminder to keep in mind the bigger picture: why do we care about political representation? After all, with opinion polls we have a good instrument capturing the preferences of the population…

Goodin, Robert E. 2004. “Representing Diversity.” British Journal of Political Science 34 (3): 453–468. doi:10.1017/S0007123404000134.

Measuring Descriptive Representation

In the last two weeks I had several conversations on how to best measure descriptive representation (i.e. the numerical representation of groups). I treated this in my recent monograph, but also in a conference paper in 2011. In my view, there are three important points: (1) What’s best depends on your research question. (2) It is important to include the population and the representatives. (3) I recommend two measures as follows: Ri / Pi for measuring the representation of a single group (e.g. a specific minority group, or all minorities combined as opposed to the majority population); for the situation at the national level, I prefer the Rose index (1 – 0.5 * |Ri – Pi|) over the Gallagher index (but following recent simulations I have undertaken, less strongly than previously). Ri stands for the proportion of a group among the representatives, Pi for the proportion among the population.

Why Aren’t They There: Additional Figures

There’s always more to say, and many potential figures did not make it into my research monograph on political representation. I have added some additional figures on Figshare. I have added additional plots on the distributions of representation scores in different policy domains, but also included some of the figures in the book, including the theoretical framework of political representation developed in the book.

‘Why Aren’t They There?’ A Study of Political Representation

It’s been in the works for a long time, but I have the pleasure to announce the publication of my monograph.

9780955820397_cvr.inddWhy Aren’t They There? is a comprehensive study of political representation in a cross-national format. It examines the representation of women, ethnic groups, and policy positions in a cross-country comparison.

The book includes an analysis of the representation of women over time, and presents a critical view of the effectiveness of quotas. Using new data on ethnic groups in legislatures, the book is a significant step forward in the analysis of political representation. The representation of issue positions is examined in eight policy domains. The systematic approach of the book allows a ground-breaking examination of how different forms of representation – women, ethnic groups, issue positions – are interlinked.

It examines aspects that are unattainable in studies focusing on only a single form of representation. This results in a comprehensive understanding of political representation, and leads to important and policy-relevant insights for electoral engineering.

Link to ECPR Press | Table of Contents | Sample Chapter