Income advantages of communist party members before and During the transformation process

ABSTRACT

In this study contradictory hypotheses are tested about the changing income advantages of Communist Party (CP) members derived from the Elite Circulation Thesis and the Elite Reproduction Thesis, using cross-sectional datasets from before and during the transformation process. CP members are matched with non-CP members on several important income determinants such as human capital, occupational class, market capital, age, gender, and marital status. Independent-samples t-tests, on differences in mean personal (ln)income reveal that CP members earn more than non-CP members do before and during the transformation process. An ANOVA shows that the income advantages of CP members are most persistent in the Czech Republic and Russia while they get smaller in Slovakia and Hungary. Comparing the four countries suggests that the remaining income advantages of CP members may partly be explained by transformation specific differences between countries.

Keywords:

• income advantages
• CP members
• market transformation
• Eastern Europe
• matching

Notes

²The typology based on market penetration, introduced by Szelényi and Kostello (1996), is of less use for our objective. It does not distinguish between CEE countries during the transformation process, making it difficult to classify the four countries we analyze in this study according to this typology.

³The Czech Republic and Slovakia are treated as having the same origin (Czechoslovakia), and therefore Slovakia is classified the same as the Czech Republic.

⁴In practice, Russia has experienced less regime change than the Czech Republic and Hungary. Regime change in Russia followed an oppositional movement that originated from within the Moscow apparatus, while the Communist regimes in Central Europe (Czechoslovakia, Hungary, Poland) were overthrown by strong oppositional challengers who allied with regime defectors (King 2000; McFaul 2002).

⁵Russia's market reforms were initiated relatively late and were characterized by Yeltsin's massive privatization of public firms. The privatization program consisted of market evaluation of assets favoring corporate actors, and financial resources were utilized. This would place Russia in between Czechoslovakia and Hungary. However, it is known that the massive privatization was too dramatic a change and did not really work. Compared to other CEE countries, Russian entrepreneurs and small firms have been confronted with many obstacles arising from onerous and pervasive bureaucratic interference (Stern 1998). For this reason, we consider Russia as the opposite of Czechoslovakia and locate Hungary in between Czechoslovakia and Russia.

Another important and interesting issue is the distinction between former and current CP members. These are different groups that might have been affected differently during the transformation process. Unfortunately, there is not enough information available in our data sets for a systematic analysis the income advantage of these groups.

⁶In the 1984 dataset on Czechoslovakia and the 1998, 2000, and 2001 datasets on Russia, information is available on what kind of CP membership people had. The categories were paid position, unpaid position, rank-and-file, and never been a member. Whether there is a relationship between this variable and the EGP scheme was tested via non-parametric tests. In all datasets (except the 2001 Russian dataset), a relationship was found between the two variables, meaning that CP members with paid positions are found more in the first EGP classes and the rank-and-file CP members are found more in the lower EGP classes. This provides some justification for the approximation for high- versus low-ranking CP members based on the EGP scheme.

⁷We want to stress that both matching and regression analysis intend to analyze the relationship between two variables while controlling (hold constant) for other variables. Matching aims to do this precisely (using non-members who score the same on control variables as CP members do) while in the case of regression analysis this is done based on average scores on the control variables.

⁸Using propensity scores, matching is often being criticized for not taking systematic differences in unmeasured variables into account. So the observed differences between CP and non-CP members would provide biased estimates of the ‘treatment effect’. This is, of course, true, but this criticism also applies to OLS regressions or, for that matter, most other techniques used in the social sciences.

⁹In the 1984 dataset on Czechoslovakia, no exact matches were found for 50 CP members in Czech territory and 52 in Slovak territory. For the other datasets, the number of CP members for whom no exact matches were found are 115 (the Czech Republic 1993), 99 (Slovakia 1993), 101 (Hungary 1986), 95 (Hungary 1993), 52 (Russia 1998), 45 (Russia 2000), and 64 (Russia 2001).

¹⁰We assumed that the variances were unequal between the two groups and performed an independent-samples t-test with separate variance estimates (t≠test).

¹¹From here on, where we use income, it should be read as ln(income).

¹²We thank an anonymous reviewer of European Societies for pointing this out to us.

Additional information

Notes on contributors

Willem-Jan Verhoeven

Willem-Jan Verhoeven is assistant professor at the department of Criminology and a member of the Research School Safety and Security in Society of the School of Law, Erasmus University Rotterdam. His research interests include comparative research and meta-analysis in Sociology and Criminology

Henk Flap

Henk Flap is professor of sociology at the department of Sociology, Utrecht University, and board member of the Interuniversity Center for Social Science Theory and Methodology (ICS). His research interests are social networks and organizations

Jos Dessens

Jos Dessens is assistant professor at the department of Methodology & Statistics, and a member of the Interuniversity Center for Social Science Theory and Methodology (ICS) of the Social Science Faculty, Utrecht University. His research interests include social stratification, and quantitative methods for the analysis of categorical data

Wim Jansen

Wim Jansen is assistant professor at the department of Methodology & Statistics, and a member of the Interuniversity Center for Social Science Theory and Methodology (ICS) of the Social Science Faculty, Utrecht University. His research interests include social stratification and inequality