The Political Consequences of Economic Shocks

Abstract

This paper evaluates long-term political consequences of severe economic shocks by combining a nationally-representative survey of Russians’ political behaviors with long-term subnational economic data tracing Russia’s post-Soviet economic transition. We show that the shock of transition has durably activated a limited but important sub-population of Russians while deactivating others. Surprisingly, much of the variation in contemporary political participation across Russia’s population can be explained by local economic conditions experienced by Russians in the early 1990s: Durable patterns of participation seem to have been “locked in” by economic trauma early in the transition period and are not influenced by the subsequent post-Soviet economic recovery or contemporary economic conditions.

FUNDING

We are grateful to the Yale MacMillan Center for International and Area Studies for their support of this research.

The views expressed in this article are those of the authors and do not reflect the official policy or position of the Department of the Army, Department of Defense, or the U.S. government.

Notes

1. William M. Reisinger, Arthur H. Miller, and Vicki L. Hesli, “Public Behavior and Political-Change in Post-Soviet States,” Journal of Politics 57, no. 4 (1995): 941–70; Samuel H. Barnes, “The Changing Political Participation of Postcommunist Citizens,” International Journal of Sociology 36, no. 2 (2006): 76–98.

2. Patrick Bernhagen and Michael Marsh, “Voting and Protesting: Explaining Citizen Participation in Old and New European Democracies,” Democratization 14, no. 1 (2007): 44–72.

3. G. Bingham Powell and Guy D. Whitten, “A Cross-National Analysis of Economic Voting: Taking Account of the Political Context,” American Journal of Political Science 37, no. 2 (1993): 391–414; Sam Wilkin, Brandon Haller, and Helmut Norpoth, “From Argentina to Zambia: A World-wide Test of Economic Voting,” Electoral Studies 16, no. 3 (1997): 301–16; Jan Fidrmuc, “Economics of Voting in Post-Communist Countries,” Electoral Studies 19, nos. 2-3 (2000): 199–217; Raymond Duch, “A Developmental Model of Heterogeneous Economic Voting in New Democracies,” American Political Science Review 95, no. 4 (2001): 895–910; Kurt Weyland, “Economic Voting Reconsidered: Crisis and Charisma in the Election of Hugo Chavez,” Comparative Political Studies 36, no. 7 (2003): 822–48; William Mishler and John P. Willerton, “The Dynamics of Presidential Popularity in Post-Communist Russia: Cultural Imperative versus Neo-institutional Choice?” Journal of Politics 65, no. 1 (2003): 111–41; Joshua A Tucker, Regional Economic Voting: Russia, Poland, Hungary, Slovakia and the Czech Republic, 1990–1999 (Cambridge: Cambridge University Press, 2006); Raymond Duch and Randolph Stevenson, The Economic Vote: How Political and Economic Institutions Condition Election Results (New York: Cambridge University Press, 2008).

4. Henry examines citizen complaints as a more common and less politicized means of airing grievances in Russia, exploring the degree to which this participatory mechanism contributes to authoritarian resilience as opposed to political liberalization. Laura A Henry, “Complaint-making as Political Participation in Contemporary Russia,” Communist and Post-Communist Studies 45, no. 3 (2012): 243–54.

5. Karrie Koesel and Valerie Bunce, “Putin, Popular Protests, and Political Trajectories in Russia: A Comparative Perspective,” Post-Soviet Affairs 28, no. 4 (2012): 403–23.

6. Graeme Robertson, “Protesting Putinism: The Election Protests of 2011–2012 in Broader Perspective,” Problems of Post-Communism 60, no. 2 (2013): 11–23; Stephen Crowley, “Monotowns and the Political Economy of Industrial Restructuring in Russia,” Post-Soviet Affairs (2015): 1–26.

7. Samuel A Greene, “Beyond Bolotnaia: Bridging Old and New in Russia’s Election Protest Movement,” Problems of Post-Communism 60, no. 2 (2013): 40–52.

8. Alfred B Evans, “Protests and Civil Society in Russia: The Struggle for the Khimki Forest,” Communist and Post-Communist Studies 45, no. 3 (2012): 233–42.

9. Greene, “Beyond Bolotnaia.”

10. Denis Volkov, “The Protesters and the Public,” Journal of Democracy 23, no. 3 (2012): 55–62.

11. Debra Javeline and Vanessa A Baird, “The Surprisingly Nonviolent Aftermath of the Beslan School Hostage Taking,” Problems of Post-Communism 58, nos. 4–5 (2011): 3–22.

12. It is important to recognize that shock therapy imposes different cost schedules on various groups. Given their short(er) shadow of the future, workers approaching retirement and pensioners stand to lose the most. The deeper the bottom of the J curve and longer the time needed to reach a full recovery, the less likely they are to support shock therapy. Trading short-term costs for long-term gains is more appealing to the youth, since they can expect to reap the full rewards of faster growth in the longer run.

13. Adam Przeworski, Democracy and the Market (Cambridge: Cambridge University Press, 1991).

14. In particular, Hellman argues that the short-term winners stand to gain from stalled or partial reforms, thus becoming an obstacle to further reforms. Joel S. Hellman, “Winners Take All: The Politics of Partial Reform in Postcommunist Transitions,” World Politics 50, no. 2 (1998): 203–34.

15. WDI, World Development Indicators, 2010, accessed November 7, 2010, http://data.worldbank. org/data-catalog/world-development-indicators.

16. “Table 7.1: Selected Per Capita Product and Income Series in Current and Chained Dollars,” Bureau of Economic Analysis, U.S. Department of Commerce. Bureau of Economic Analysis, 2011, accessed February 16, 2011, http://www.bea.gov.

17. Howard Schuman and Jacqueline Scott, “Generations and Collective Memories,” American Sociological Review 54, no. 3 (1989): 359–381; Howard Schuman and Amy D. Corning, “Collective Knowledge of Public Events: The Soviet Era from the Great Purge to Glasnost,” American Journal of Sociology 105, no. 4 (2000): 913–56.

18. Robert Person, Nothing to Gain But Your Chains: Popular Support for Democracy and Authoritarianism in the Former Soviet Union (Doctoral dissertation. Yale University, 2010).

19. Robert S. Erikson, Michael MacKuen, and James A Stimson, The Macro Polity (New York: Cambridge University Press, 2002), 179.

20. For a concise review of the grievance-based literature on political protest, see Russell Dalton, Alix Van Sickle, and Steven Weldon, “The Individual–Institutional Nexus of Protest Behaviour,” British Journal of Political Science 40, no. 01 (2010): 51–73.

21. Ibid., 4–5.

22. Ibid., 5.

23. Stephen White, Political Culture and Soviet Politics (New York: St. Martin’s Press, 1979); Donna Bahry, “Politics, Generations, and Change in the USSR,” in Politics, Work, and Daily Life in the USSR: A Survey of Former Soviet Citizens, ed. James R. Millar (New York: Cambridge University Press, 1987); Schuman and Scott, “Generations and Collective Memories”; James L. Gibson, Raymond M. Duch, and Kent L. Tedin, “Democratic Values and the Transformation of the Soviet Union,” Journal of Politics (1992): 329–71; Schuman and Corning, “Collective Knowledge of Public Events”; M. Kent Jennings and Laura Stoker, “Generational Change, Life Cycle Processes, and Social Capital” (Paper prepared for a workshop on “Citizenship on Trial: Interdisciplinary Perspectives on the Political Socialization of Adolescents,” McGill University, Montreal, 2002); Richard Rose, William Mishler, and Neil Munro, Russia Transformed: Developing Popular Support for a New Regime (Cambridge/New York: Cambridge University Press, 2006); Person, Nothing to Gain But Your Chains.

24. M. Kent Jennings and Richard G. Niemi, Generations and Politics: A Panel Study of Young Adults and Their Parents (Princeton, N.J.: Princeton University Press, 1981); Schuman and Scott, “Generations and Collective Memories”; Duane F. Alwin and Jon A. Krosnick, “Aging, Cohorts, and the Stability of Sociopolitical Orientations over the Life Span,” American Journal of Sociology (1991): 169–95; Schuman and Corning, “Collective Knowledge of Public Events”; M. Kent Jennings, Laura Stoker, and Jake Bowers, “Politics Across Generations: Family Transmission Reexamined” (Institute of Governmental Studies, University of California, Berkeley, 2001).

25. Richard G. Niemi and Mary A. Hepburn, “The Rebirth of Political Socialization,” Perspectives on Political Science 24, no. 1 (1995): 7–16.

26. Jennings and Niemi, Generations and Politics; Anders Westholm, “The Perceptual Pathway: Tracing the Mechanisms of Political Value Transfer Across Generations,” Political Psychology 20, no. 3 (1999): 525–51; Michael McDevitt and Steven Chaffee, “From Top-Down to Trickle-Up Influence: Revisiting Assumptions About the Family in Political Socialization,” Political Communication 19, no. 3 (2002): 281–301; Christopher H. Achen, “Parental Socialization and Rational Party Identification,” Political Behavior 24, no. 2 (2002): 151–70.

27. Stephen White and Ian McAllister, “Political Participation in Postcommunist Russia: Voting, Activism, and the Potential for Mass Protest,” Political Studies 42 (1994): 593–615; Barnes, “Changing Political Participation of Postcommunist Citizens.”

28. Bernhagen and Marsh, “Voting and Protesting.”

29. Russia, like the Soviet Union, marks the end of World War II on May 9. November 7 marks the anniversary of the Bolshevik Revolution of 1917.

30. The fact that such permits are usually granted to the Communists (but often denied to liberal opposition parties) is indicative of the fact that despite their opposition status and ability to mobilize individuals, the government does not regard the Communists as a threat to political stability.

31. Herbert Kitschelt, “The Formation of Party Systems in East Central Europe,” Politics and Society 20, no. 1 (1992): 26.

32. Anna Grzymala-Busse, Redeeming the Communist Past: The Regeneration of Communist Parties in East Central Europe (Cambridge, Cambridge University Press, 2002).

33. For a more detailed discussion of why failure to account for design effects and sampling probabilities in the analysis of complex survey data will result in biased coefficients and standard errors, see the following: Leslie Kish, Survey Sampling, Wiley Classics Library (New York: Wiley, 1965), Ralph E. Folsom and Rick L. Williams, Design Effects and the Analysis of Survey Data, Education Commission of the States; National Assessment of Educational Progress (Research Triangle Park, NC: Research Triangle Institute, 1982); Eun Sul Lee and Ronald N. Forthofer, Analyzing Complex Survey Data (Thousand Oaks, CA: Sage Publications, 2006); James Woods and Carl McCurley, “Design Effects in Complex Sampling Designs” (Paper presented at the annual meeting of the the Midwest Political Science Association, Chicago, Illinois, 1994); and David A. Lacher, Lester R. Curtin, and Jeffrey P. Hughes, “Why Large Design Effects Can Occur in Complex Sample Designs: Examples from the NHANES 1999–2000 Survey” (Proceedings of the Survey Research Methods Section, American Statistical Association, 2004).

34. In the Russian political hierarchy, raions correspond roughly to counties in the American context. They are nested within Russia’s 83 “federal subjects” (oblasts, republics, krais, autonomous oblasts, autonomous okrugs, and federal cities), which are roughly comparable to American states. These federal subjects are nested within Russia’s federal districts. There were seven federal districts in Russia at the time the survey was carried out in 2007. Since then the addition of the North Caucasus Federal District (2010) and the Crimean Federal District (2014), the total number has increased to nine. The federal district is the highest level of political aggregation below the national level.

35. Due to the subjective nature of such an endeavor in the absence of objective data, we refrain from theorizing or coding relative costs of the various activities. The “cost” of participating in a particular activity might be a direct financial one, or (more likely) might consist of the opportunity cost of engaging in activities that vary in the degree to which they are time consuming. Joining an organization and participating in its activities may entail a significant time commitment if the member is active, while signing a petition requires minimal time. Contacting an official may be a brief matter, or it may require the time-consuming navigation of a byzantine bureaucracy. Additionally, we cannot talk about costs of political action without considering the risk of legal sanctions for participating in more contentious political activities such as demonstrating. For the sake of the present study, though, it is worth recalling the political context at the time the survey was executed. Throughout the Yeltsin era and Putin’s first two terms as president, political demonstrations of most stripes were tolerated and entailed a relatively low risk of arrest or violence. As a point of anecdotal support, one of this article’s authors observed several political demonstrations in 2007–2008 by opposition groups and parties like the CPRF, Vladimir Zhirinovsky’s Liberal Democratic Party of Russia, Garry Kasparov’s liberal “Other Russia” movement, nationalist Eduard Limonov’s National Bolshevik Party, and others. None of these events were characterized by mass harassment and arrests of citizen participants. The same could not be said today, where protests in Russia since Putin’s return to the Kremlin in 2012 carry much higher risk and cost than they did in the earlier eras. While future research will seek to place these participatory activities within robust theoretical cost framework that is empirically supported, the present study refrains from doing so for the reasons noted above.

36. The Russian word for “meeting” used in the survey (sobranie) usually denotes a meeting of a political party or political organization that is attended by members to discuss platforms and other party business.

37. Phrasing of political actions in Russian is as follows: attending a political meeting (uchastvoval v politicheskom sobranii); contacting the media to express one’s views (sviazyvalsia s predstaviteliami sredstvami massovoi informatsii chtoby vyrazit’ svoyu tochku zreniia); signing a petition (podpisanie petitsii); joining an organization or group in support of something or someone (vstupil v organizatsiiu ili gruppu, vystupaiushuiu v podderzhku chego-libo ili kogo-libo); and taking part in a demonstration (uchastvoval v demonstratsiiakh). Summary statistics for these variables appear in the online appendix, available at http://www.robert-person.com.

38. Because the survey question does not explicitly define “the distant past,” it is possible that this answer choice is capturing participatory acts that took place prior to the collapse of the Soviet Union, when political participation took place in a very different political context. However, when our analysis is restricted to the subpopulation that was under 18 years old in 1991 (and were most likely too young to participate in many of these political activities in the Soviet era), we nonetheless find support for the main results that are central to our argument based on analysis of the full data set. Thus, it is unlikely that our findings are driven by Soviet-era participatory acts but more likely capture political activities in the years since the collapse of the Soviet regime.

39. Missing responses in the original unimputed dataset, for which the respondent either answered “don’t know” or refused to answer, are as follows: political meeting (4.66 percent), contact media (5.26 percent), petition (5.80 percent), join organization (5.19 percent), demonstrate (4.86 percent). Outright refusals to answer were minimal, ranging from 1.13 percent (join organization) to 1.73 percent (demonstrate).

40. One might question whether these answer choices presented in the order given map onto an underlying propensity to participate, as we argue they do. For example, one could argue that answer choice 2 (participated in past but not in the last year) demonstrates a lower propensity to participate than answer choice 1 (has never participated but might). Such an interpretation may be plausible but diverges from the approach prevalent in the literature on political participation that utilizes data from the World Values Survey and similar surveys of participation from which our questions and answer set were derived. Most studies divide respondents into those who have never participated in an act vs. those who have participated in the act at some point, underscoring the assumption we share that those who have actually taken the action demonstrate a higher propensity to act than those who have never done so even though they say they might. Recoding our data into this binary form and analyzing with a logit model, as is done in the online statistical appendix (http://www.robert-person.com), produces results similar to those of the ordered logit analysis that we utilize in the main analysis. For similar treatment of participation survey data, see the following: Ronald Inglehart and Gabriela Catterberg, “Trends in Political Action: The Developmental Trend and the Post-Honeymoon Decline,” International Journal of Comparative Sociology 43, nos. 3-5 (2002): 300–316; Dalton, Van Sickle, and Weldon, “Individual–Institutional Nexus of Protest Behavior”; Neal Caren, Raj Andrew Ghoshal, and Vanesa Ribas, “A Social Movement Generation Cohort and Period Trends in Protest Attendance and Petition Signing,” American Sociological Review 76, no. 1 (2011): 125–51; Cindy D Kam, “Risk Attitudes and Political Participation,” American Journal of Political Science 56, no. 4 (2012): 817–36.

41. Predicted values of factor 1 were calculated in Stata following an orthogonal rotation in order to generate the participation index variable. Summary statistics appear in the online appendix (http://www.robert-person.com).

42. It is worth noting that the word demonstratsiia covers a wide range of events, to include the pro-Regime “demonstrations” common during the Soviet Union (such as May Day, Victory Day, etc.), and thus could account for the large number of individuals reporting such activities “in the distant past.” In order to tease out whether this increased reporting of “demonstrating” may in fact be capturing Soviet-era pro-regime demonstrations, we conducted an ordered logit regression of the DEMONSTRATE variable on dummy variables for age cohorts (see Figure 4 for cohorts). The excluded category was the cohort that was under 10 years of age in 1990. Not surprisingly, cohorts older than these children of the Gorbachev era were more likely to have demonstrated. However, when a continuous variable for age is included, the significance on the cohort variables disappears, while age remains statistically significant. Thus, older individuals are more likely to have participated in a demonstration at some point in their life by virtue of their greater number of years alive to do so. However, when age is controlled for, it appears that older (Soviet) generations are not significantly more likely to report demonstrating than the younger (post-Soviet) generation.

43. Some may question the reliability of official Russian economic statistics. A 2013 OECD assessment of Russia’s statistical system found that “[i]n principle, Rosstat compiles its national accounts estimates in accordance with the concepts, definitions, classifications, guidelines and recommendation of the 1993 SNA.” The latter refers to the 1993 System of National Accounts developed by Eurostat, the UN, the IMF, the OECD, and the World Bank. The 1993 SNA established a “coherent, consistent and integrated set of macroeconomic accounts, balance sheets and tables based on a set of internationally agreed concepts, definitions, classifications and accounting rules.” While the 2013 OECD report details some departures from the SNA standard, on whole it finds Russia’s statistical practices to generally conform to the OECD’s standards of compliance, coverage and timeliness, quality of price and volume measures, and exhaustiveness. See the following: OECD, OECD Assessment of the Statistical System and Key Statistics of the Russian Federation (Paris: Organization for Economic Co-operation and Development Secretariat, 2013), 30, http://www.oecd.org/std/Assessment-of-the-Statistical-System-and-Key-Statistics-of-the-Russian-Federation.pdf; System of National Accounts (Inter-Secretariat Working Group on National Accounts, 1993), 1, http://unstats.un.org/unsd/nationalaccount/docs/1993sna.pdf. Russian economic data used in this article is taken from Regiony Rossii: Statisticheskii sbornik (Regions of Russia: Statistical Handbook) (Mocow: Gosudarstvennyi komitet Rossiiskoi Federatsii po statistike, 1998); Regiony Rossii: Statisticheskii sbornik (Regions of Russia: Statistical Handbook) (Moscow: Gosudarstvennyi komitet Rossiiskoi Federatsii po statistike, 2000) and Regiony Rossii: Sotsial’no-ekonomicheskie pokazateli. Statisticheskii sbornik (Regions of Russia: Socioeconomic Indicators. Statistical Handbook) (Moscow: Federal’naia sluzhba gosudarstvennoi statistik, 2004); Regiony Rossii: Sotsial’no-ekonomicheskie pokazateli. Statisticheskii sbornik (Regions of Russia: Socioeconomic Indicators. Statistical Handbook) (Moscow: Federal’naia sluzhba gosudarstvennoi statistik., 2006); Regiony Rossii: Sotsial’no-Ekonomicheskie Pokazateli. Statisticheskii Sbornik (Regions of Russia: Socioeconomic Indicators. Statistical Handbook) (Moscow: Federal’naia sluzhba gosudarstvennoi statistik, 2008).

44. All economic indicators have been adjusted for inflation and converted to constant 2000 USD.

45. The variable is calculated as (income1992 – income1990) /(1992 – 1990).

46. It is worth mentioning that regional economic conditions may not be the only society-wide forces shaping an individual’s propensity to participate in political activities. To be sure, the 1990s were a period of significant turbulence across the economic, political, and social realms. We don’t discount that events such as the October 1993 dissolution of the Duma, constitutional and other institutional changes, the disillusionment with Yeltsin, the ascendance of Vladimir Putin, and his increasingly authoritarian rule may influence how Russians engage in politics. However, it is important to remember that these were nationwide events that would have been experienced by all Russians. Our research design leverages significant cross-sectional subnational variation in economic indicators that can be measured on a regional level in order to better understand the lasting impact of economic performance on participation. Gauging the impact of national-level “treatments” such as those described above would require a panel data set, which we unfortunately lack.

47. Recall that the survey was conducted in November 2007, making this a reliable measure of a voter’s intent for that election.

48. This included the parties Yabloko under Grigory Yavlinsky, the Union of Rightist Forces (often known by its Russian initials as SPS) under Nikita Belykh, and the Democratic Party of Russia under Andrei Bogdanov. None of these parties met the 7 percent threshold required to win seats in the 2007 Duma election.

49. Survey respondents who reported occupations of worker, skilled worker, foreman, or military/police were coded as blue-collar.

50. Kish, Survey Sampling; Folsom and Williams, Design Effects and the Analysis of Survey Data; Lee and Forthofer, Analyzing Complex Survey Data; Woods and McCurley, “Design Effects in Complex Sampling Designs”; Lacher, Curtin, and Hughes, “Why Large Design Effects Can Occur in Complex Sample Designs.”

51. Missing individual survey-level data were multiply imputed with five imputations using Amelia II software. The full battery of demographic, behavioral, and attitudinal measures recorded in the broader Russia survey project were used in the imputation process. The complete questionnaire for this survey is available from the authors upon request. The imputed data is then analyzed using the “mim” module in Stata. See the following: Gary King et al., “Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation,” American Political Science Review 95, no. 1 (2001): 49–69; James Honaker and Gary King, “What to Do About Missing Values in Time-Series Cross-Section Data,” American Journal of Political Science 54, no. 2 (2010): 561–81; Patrick Royston, John C. Galati, and John B. Carlin, “A New Framework for Managing and Analyzing Multiply Imputed Data in Stata,” The Stata Journal 8, no. 1 (2008): 49–67.

52. This raises the question of whether the oblast in which the respondent was surveyed was in fact the oblast in which he or she experienced the economic collapse of the 1990s. Unfortunately the survey did not include data on an individual’s residence history, though research on internal migration in Russia in the post-Soviet period suggests that this is not a major stumbling block to our analysis. While there has certainly been migration within Russia since the collapse of the Soviet Union, particularly from the far northern and eastern parts of the country to the central/western regions, data suggest that inter-regional migration that might cloud our analysis shouldn’t be overstated. Stephen Wegren and A. Cooper Drury, “Patterns of Internal Migration During the Russian Transition,” Journal of Communist Studies and Transition Politics 17, no. 4 (2001): 15–42. White cites Russian official statistics showing a decline in internal migration during the post-communist period, falling from 4.7 million in 1989 to 2.9 million in 1993, and just under 2 million in 2004. Importantly, White also notes that “[the] Russian norm, however, is within-region migration…. Generally, it seems that migrants head for local cities.” Anne White, “Internal Migration Trends in Soviet and Post-Soviet European Russia,” Europe-Asia Studies 59, no. 6 (2007): 892–93. This suggests that while there may be some statistical noise in our survey that might attenuate our results for oblast-level economic data, it remains much more likely that survey respondents have remained within the same oblast throughout the transition period. Nonetheless, it is also worth noting that such statistical noise in the independent variables generated by respondents migrating from the region in which they experienced the economic collapse would generally have the effect of attenuating regression coefficients toward zero. Thus, it is likely that any results finding a relationship between regional economic conditions and participatory patterns are in fact stronger than those suggested by the regressions.

53. White and McAllister, “Political Participation in Postcommunist Russia”; Tianjian Shi, Political Participation in Beijing (Cambridge, MA: Harvard University Press, 1997); Yang Zhong and Jie Chen, “To Vote or Not to Vote,” Comparative Political Studies 35, no. 6 (2002): 686; Stephen White and Ian McAllister, “Dimensions of Disengagement in Post-Communist Russia,” Journal of Communist Studies and Transition Politics 21, no. 1 (2004): 81–97; Baogang He, “A Survey Study of Voting Behavior and Political Participation in Zhejiang,” Japanese Journal of Political Science 7, no. 03 (2006): 225–50; Bernhagen and Marsh, “Voting and Protesting.”

54. One might reasonably question whether party identification might be a similarly problematic variable as subjective orientation variables. In addressing this challenge we must resort to a plausibility assessment of counterfactuals: For example, are people with a preexisting inclination to participate in politics more likely to subsequently identify as communists because the CPRF holds frequent rallies? Or, does identification with the communist cause come first, followed by participation that is motivated by the party’s grievances? We believe that the latter scenario is more plausible and that party identification—itself the product of earlier political socialization and other socioeconomic influences—precedes and motivates political participation.

55. Table 3 has been condensed to only show significance levels on the post-transition economic variables to save space. All coefficients on the TRANS_RATE and TRANS_SQ variables in Table 3 have negative signs. Complete regression tables that include all coefficients for all age groups appear in the online statistical appendix (http://www.robert-person.com).

56. In two instances (joining an organization and demonstrating), the squared term is statistically significant while the linear term fails to reach significance. Such a result is nonetheless consistent with our argument and other findings, as the significance of the squared term confirms the curvilinear nature of the relationship between x and y. This can be visualized by comparing plots of the two different functions: y = –1x – x² vs. y = –x². The latter has a more gradual parabolic curve than the former and reaches its global extremum when x = 0 but nonetheless conforms with the statistically significant curvilinear relationship that we find in our analysis.

57. RETRO and REG INC have a correlation of 0.89, raising possible concerns that this collinearity is washing out any results. However, neither achieves significance when it is included individually in the regressions without the other variable.

58. For examples of scholarship that uses this binary coding of participation survey data in comparing those who have ever participated against those who have not participated, see Inglehart and Catterberg, “Trends in Political Action”; Dalton, Van Sickle, and Weldon, “Individual–Institutional Nexus of Protest Behavior”; Caren, Ghoshal, and Ribas, “A Social Movement Generation Cohort and Period Trends in Protest Attendance and Petition Signing”; and Kam, “Risk Attitudes and Political Participation.”

59. See http://www.robert-person.com.

60. The figures present predicted probabilities that a person participated in the past year or participated in the distant past. Calculations are based on holding continuous variables at their mean and selecting reasonable values for binary variables. The key variable of interest, TRANS_RATE, is varied while accounting for simultaneous variation in its squared term, TRANS_SQ.

61. White-collar occupations included owner of company, head of company, head of department/section, highly qualified specialist, specialist, office worker.

62. As would be expected, AGE and RETIRED are correlated (0.75). However, the coefficients and significance of each variable does not change substantially when the other is dropped front the model.

63. It is tempting but somewhat challenging to link the results found in this study with the wave of political protests that swept through Russia during the 2011–2012 elections cycle, touched off by the awkwardly orchestrated resumption of Vladimir Putin’s rule as president following four years under the latter’s handpicked “placeholder,” Dmitri Medvedev. For a more complete discussion of key causes and implications of the protests in comparative perspective, see Koesel and Bunce, “Putin, Popular Protests, and Political Trajectories in Russia.”In considering the implications for this research, several caveats are in order. First, it is essential to remember that major demonstrations surrounding the elections were largely confined to Moscow, and therefore not representative of all of Russia (or Russians). As Volkov notes based on surveys conducted by the well-respected independent public opinion polling firm, the Levada Center, “[Protest participants in the 2011–2012 mobilizations] were atypical of Russians in general, and even of Moscow residents” in their demographic characteristics (Volkov, “The Protesters and the Public,” 57). Thus, comparing a geographically confined protest event to the nationally representative survey results employed in our study is akin to comparing apples to oranges. Furthermore, what we do know about the Moscow protests and their participants gives us little insight to the deep effects of economic transition on political participation that are the key insight of this article. This is partly a function of limited data: we know little about the socioeconomic life experiences of the participants, nor do we have the benefit of observing subnational variation in economic trajectories and demonstrations since the protests took place almost entirely in the capital. Indeed, it is this subnational regional variation that gives our 2007 survey results leverage over the question of long-term impacts of economic collapse on political participation.Nonetheless, Volkov’s data do resonate with our findings in other ways. First, he finds that protest participants were highly educated, with 80 percent having some post-secondary education, consistent with our finding that higher levels of education are correlated with all forms of political participation. Furthermore, Volkov notes that while the protests began with a preponderance of youth in attendance, the demographics of the protests quickly widened: “By February 2012, participants in the 18-to-24 age bracket were accounting for just a fifth of all participants, roughly the same number as those aged 55 and older. Most protesters were middle aged” (57). This is consistent with our finding that those (middle-aged) generations who were coming of age around the post-Soviet transition are, at times, active participants in political acts, conditional on their economic experiences.

64. Robert Putnam, Making Democracy Work: Civic Traditions in Modern Italy (Princeton, NJ: Princeton University Press, 1993).