The dataset for analysis consists of all MPs elected30 to the last two terms of the national parliaments in Belarus, Estonia, Georgia, Latvia, and Lithuania. Total number of the parliamentarians in the sample is 1237. The numbers of MPs by gender, term, and country are presented in Table SM3.1.1 in the Appendix.
There are two main reasons why these countries are chosen for the analysis. First, these countries vary in their political regimes. According to Freedom House 2019 data, Baltic countries are “free” countries with consolidated democracies: Estonia is assigned 94 out of 100 points31, Latvia – 87 points, and Lithuania – 91 points. Georgia is described as “partly free” country with its 63 out of 100 points. Finally, Belarus is a “not free” country and is assigned 19 out of 100 points (Freedom House, Freedom in the World 2019). Moreover, there are between-countries variations in the electoral characteristics which are considered important for women’s legislative representation. For instance, Belarus has a majoritarian electoral system (First-Past-The-Post), Georgia and Lithuania have mixed parallel electoral systems, while Estonia and Latvia employ open-list proportional representation.
Therefore, by analyzing women and men’s career pathways in these countries, we can see whether MPs who are elected in a democratic country differ from MPs who are allowed to be elected in an authoritarian country. Do the common historical background and a long history of Communist rule diminish the differences between, on the one hand, female and male MPs and, on the other hand, between MPs in different countries? Do the diverse trajectories of countries’ development after the collapse of the Soviet Union, for instance EU membership, facilitate the between-countries differences? Although focusing primarily on the gender gaps in various characteristics of MPs, this study can shed light on these questions as well.
30 Unfortunately, the data on the candidates participating in the national parliamentary elections is not available or
is difficult to collect. Therefore, I cannot assess what factors contribute to the election of candidates and which factors prevent them from being elected.
31 Aggregate Score: 0 = Least Free, 100 = Most Free (Freedom House, Freedom in the World 2019. Retrieved from
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Second reason for analyzing these five countries is data availability. Unfortunately, not many post-communist countries provide the basic biographical information about their parliamentarians on the national parliaments’ websites. The data is either very scarce or in the national languages only which makes it difficult and time-consuming to collect the necessary information. Countries included in this analysis have most of the data available in English32, which made it possible to collect it by applying web-scraping techniques. This information was then complemented with the data from Every Politician database, Wikipedia, and Wikidata.
The main indicator for the analysis is gender of MPs. I assign the value “1” if parliamentarian is a woman and the value “0” if parliamentarian is a man. Afterwards, I am able to analyze gender gaps in several MPs’ characteristics of interest. All of the variables are measured at the beginning of the national parliament’s term:
1. Personal characteristics:
a. Age of a parliamentarian – continuous variable. b. Number of children – continuous variable.
c. Family status – categorical variable with five categories: single, married, in a domestic partnership, divorced, widowed.
d. Occupational background – five dummy variables: law, business and economics, education, social work, health care and medicine. As a proxy for occupational background, I use the information on their education, namely which subject they studied at the university / institute / vocational school. Although not every MP works according to the formal education received, this operationalization makes the data collection easier.
e. Academic degree – categorical variable with three categories: secondary education, higher (BA or MA) education, PhD. BA and MA degrees are merged into one category because for many MPs there is no information on which particular degree they obtained. Also, for MPs who received their education in the Soviet Union, there is one “specialist” degree which is the only five-year higher education diploma that was available.
2. Professional characteristics:
f. Party of a parliamentarian. In accordance with Hypotheses 6 and 7, I analyze parties in regard to two variables. First variable is categorical and shows which party family MP’s party belongs to: agrarian, conservative, ethnic-regional, liberal, nationalist, social-democratic, socialist, or whether MP ran as an independent candidate. Second variable is continuous and measures
32 The exceptions are the 2012 – 2016 and the 2015 – 2019 terms of the national parliaments in Belarus and in
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ideology of the party, ranging from left to right. The data for both variables is provided by the Manifesto Project Database (Volkens et al., 2019).
g. Incumbency of a parliamentarian – a dummy variable measuring whether she / he was elected in the previous term of the national parliament. The value “1” is assigned for being an incumbent and the value “0” otherwise.
h. Political experience of a parliamentarian. In accordance with Hypotheses 9 and 10, I create two variables showing whether parliamentarians had some executive / appointive or legislative / elective experience before entering the national parliament. First variable is a dummy where the value “1” is assigned if MP was elected to a legislature or appointed as a mayor or minister at any level of government and the value “0” otherwise. Second variable is ordinal and takes into account the “number” of experiences and their levels, meaning that I differentiate if a parliamentarian had a prior political experience at the municipal, regional33, national level, or at several of them.
Descriptive statistical methods are used to analyze the data. With the dummy variables, I apply a proportion testing which shows whether a proportion of female MPs in some indicator is higher / lower than the proportion of male MPs. I analyze categorical variables using Pearson’s chi-squared test and continuous variables using Welch two-samples t-test. The results of the tests are presented for the whole sample and by country. Although descriptive statistics does not show the causal relationship between the variables, it is an appropriate tool for the first exploratory analysis of the biographical data of MPs. Obtained results can help to see the general patterns in the data and to raise research questions for either more advanced statistical analysis or a case study of the country with the most interesting or unusual results.