Faculty of Economics and Administration
Masaryk University
Lost in Transition and Ballot Papers:
Four Papers on Economic Policy
Habilitation Thesis
Lucie Coufalová
Brno 2024
Contents
Introduction 5
1 Lost in Transition: Czech Enterprises on the Road between Central Planning
and Capitalism 9
1.1 Historical background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.2 Communist legacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.3 Data and Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.4 Summary of lessons learned . . . . . . . . . . . . . . . . . . . . . . . . 16
1.4.1 Results of Coufalová and Žídek (2023) . . . . . . . . . . . . . . 16
1.4.2 Results of Coufalová (2024) . . . . . . . . . . . . . . . . . . . . 17
1.5 The relevance of oral history to economic historians . . . . . . . . . . . . 18
1.6 Limitation of the research . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.6.1 Memory issues and subjectivity . . . . . . . . . . . . . . . . . . 19
1.6.2 Snowball sampling and data biases . . . . . . . . . . . . . . . . . 20
1.7 Future research opportunities . . . . . . . . . . . . . . . . . . . . . . . . 21
2 Voting under Information Constraint 23
2.1 Homophily in Voting Behavior: Evidence from Preferential Voting . . . . 25
2.1.1 Data and methodology . . . . . . . . . . . . . . . . . . . . . . . 25
2.1.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.2 The Grass is not Greener on the Other Side: the Role of Attention in Voting
Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.2.1 Data and methodology . . . . . . . . . . . . . . . . . . . . . . . 31
2.2.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.3 Contribution to the literature . . . . . . . . . . . . . . . . . . . . . . . . 35
2.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.5 Future research avenues . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3 Authorship contribution statements 39
Coufalová and Žídek (2023) . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4
Coufalová (2024) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Coufalová, Mikula, and Ševčík (2023) . . . . . . . . . . . . . . . . . . . . . . 39
Coufalová and Mikula (2023) . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
Concluding remarks 41
Bibliography 42
Introduction
This habilitation thesis introduces my most notable contributions to two areas of economic
policy—economic history and voting behavior. Within the first area, I focus mainly on
various aspects of Czech1 economic transition (see, e.g., Coufalová 2019; Coufalová,
Mikula, and Žídek 2020; Coufalová and Žídek 2023; Coufalová, Mikula, and Žídek 2023;
Coufalová, Kolajtová, and Žídek 2023; Coufalová 2024). The thesis summarizes the results
of the two most significant papers—Coufalová and Žídek (2023) and Coufalová (2024)—,
which bring conclusions interesting to economists and historians dealing with centrally
planned economies (CPEs) and economic transition, policymakers, as well as theorists of
oral history.
The second area maps various aspects of voting of Czech populations since the
transition period till 2021 (Coufalová, Mikula, and Ševčík 2023; Coufalová and Mikula
2023; Coufalová, Kolajtová, and Žídek 2023). This thesis presents papers authored by
Coufalová, Mikula, and Ševčík (2023) and Coufalová and Mikula (2023), that have also
relevance for a wider audience, such as literature dealing with presentation order effects.
The first paper by Coufalová and Žídek (2023) is based on a set of more than 100
oral history interviews with entrepreneurs—managers of state-owned enterprises (SOEs)
as well as owners of de novo firms—, policymakers, bankers and brokers, and offers a
more general perspective of actions of economic agents at the microeconomic level. The
previous literature (such as, Sachs 1996; Švejnar and Uvalic 2009; Kučera and Maršál
2015) has primarily focused on changes in the macroeconomic environment, although
the actions of individual economic agents have emerged as the crucial factor explaining
the relative success of Czech economic transition (Brown 1995; Dillon and Wykoff 2002;
Švejnar and Uvalic 2009; Beaulier, Boettke, and Krasnozhon 2012; Kučera and Maršál
2015). This paper presents the personal experiences, frustrations and challenges of the
participants of those events, and shows that the road from socialism to the market economy
1. Although the first 3 years of the transition took place in Czechoslovakia, my research is mostly based
on data from the Czech part of the former federation and therefore this thesis will refer to Czech transition.
6
was a bumpy one. As a result, our approach fills in the blanks and complements the official
written literature and archives.
Coufalová (2024) is based on the same data set of oral history interviews as the
previous paper and aims to identify the determinants of the access to loans in the early
years of the transition period. Giving voice to direct participants of the events of the
time—entrepreneurs, bankers, and policymakers—, I share their personal experience in
such a period of huge turmoil. Three general patterns dominated the interviews that help to
understand who had easiest access to credit in the early years of transition: (1) pressure
from the government; 2) the debts from socialism and other criteria for granting loans; and
finally (3) social capital and corruption. The paper also highlights the relevance of oral
history to economic historians and shows that this approach is complementary to scholarly
written sources and forms an essential part of the historical discourse.
The third paper by Coufalová, Mikula, and Ševčík (2023) deals with the role of
homophily in voting behavior. Homophily is a strong determinant of many types of human
relationships (McPherson, Smith-Lovin, and Cook 2001). It affects, for example, whom
we marry (Skopek, Schulz, and Blossfeld 2011), whom we choose as friends (Adida,
Laitin, and Valfort 2015) and potentially also whom we vote for (Boutyline and Willer
2017; Huber and Malhotra 2017). It can also act as a cue signalling that a candidate with
similar characteristics to the voter’s will share his/her interests. In Coufalová, Mikula,
and Ševčík (2023), we use data on preferential voting from eight Czech parliamentary
elections—held between 1996 and 2021—matched with census and administrative data to
identify the effect of homophily on voting behavior. The system of preferential voting in the
Czech Republic enables us to filter out preferences for political parties and focus solely on
candidates’ individual background characteristics. Consequently, it is suited to an analysis
of homophilic preferences. We focus on almost seven million observations from small
municipalities as they are not likely to be affected by potential electoral list optimization.
We find that a one-percent increase in the share of a municipality’s population whose
education level or age are the same as that of the candidate’s increases the number of
preferential votes the given candidate receives by 0.5% or 0.2% respectively. Our results
also show that there is a strong geographical homophily. Being a resident of a given
municipality substantially increases the number of preferential votes a candidate receives.
The last paper that forms this thesis—Coufalová and Mikula (2023)—addresses a
more general question about how the order in which information is presented affects the
impressions of people who evaluate it. The available literature (for instance Brockington
2003) shows that a lack of information about candidates listed on the electoral list influences
the electoral outcomes producing so-called ballot order effect that gives an advantage to
7
candidates in the top electoral list positions. It is, however, difficult to identify the exact
cause of the ballot order effect as it is confounded by the effect of ranking and the effect of
attention, both working in the same direction. To separate these effects from one another
and identify the effect of attention, we exploit a variation in ballot layout (the quasi-random
location of the break between the front and reverse sides of the ballot) in four consecutive
(2006–2017) Czech parliamentary open list proportional representation (OLPR) elections.
We show that being listed on the reverse side of the ballot paper decreases the number
of preferential votes received by at least 40%. Also in this case, using the system of
preferential voting allows us to filter out the effect of political party preference.
Chapter 1 deals with the difficulties faced by businesses when pivoting from the
CPE to a market-led economy, with special emphasis on legacies of the previous regime,
summarizing the main results of Coufalová and Žídek (2023) and Coufalová (2024). At
the same time, this chapter highlights the relevance of oral history to economic historians.
Chapter 2 discusses papers by Coufalová, Mikula, and Ševčík (2023) and Coufalová and
Mikula (2023), that deal both with mechanisms behind the decision-making of voters in a
low-information election setting. The last chapter defines my contribution to each paper.
The full versions of the four respective papers can be accessed from the following links:
• Coufalová, Lucie, and Libor Žídek. 2023. “Lost in the Transition: Czech Businesses
Pivoting from the Centrally Planned Economy to Capitalism.” Enterprise & Society,
1–32. Available at https://doi.org/10.1017/eso.2023.17.
• Coufalová, Lucie. 2024. “Access to Bank Loans in Economic Transition: An Oral
History Approach.” Business History, 1–26. Available at https://doi.org/10.1080/
00076791.2023.2298348.
• Coufalová, Lucie, Štěpán Mikula, and Michal Ševčík. 2023. “Homophily in Voting
Behavior: Evidence from Preferential Voting.” Kyklos 76(2), 281–300. Available at
https://doi.org/10.1111/kykl.12328.
• Coufalová, Lucie, and Štěpán Mikula. 2023. “The Grass is not Greener on the Other
Side: the Role of Attention in Voting Behavior.” Public Choice 194(1): 205–223.
Available at https://doi.org/10.1007/s11127-022-01030-z.
They are also included in the Appendix to the non-public version of the thesis.
8
Chapter 1
Lost in Transition: Czech Enterprises on
the Road between Central Planning and
Capitalism
Prior to 1989, Czechoslovakia was a CPE ruled by the Communist Party (CP). The country’s
subsequent transformation towards a market-led economy involved the rapid adoption of
several reforms that entailed a total change in the environment in which businesses could
operate, disrupting the order of the understood world. Economic agents were thrown into
the water from one day to the next and had to learn to swim without any prior training
or assistance. This was by no means easy, because post-communism was not a “tabula
rasa” (Pop-Eleches and Tucker 2011) and the communist experience definitely shaped the
post-communist world (LaPorte and Lussier 2011). This persistence of the previous regime
is often referred to as the communist legacy.
During the transition period, companies had to deal with huge burdens—inefficiencies
and distortions—inherited from the previous command system, as well as with the break-up
of Eastern markets (see, e.g., Stark 1992; Sachs 1996; Myant 2003). Moreover, the
available literature also shows that several decades under communist rule affected the
informal institutions, i.e., the way people think—their morals and values—, and behave.
While changing formal institutions like laws can be quick and straightforward, changing
informal institutions generally takes an exceedingly long time (Roland 2012). According
to Clark and Soulsby (1995, p. 216), “[i]t has proven impossible to erase forty years of
state socialism, ideology, institutions and behavioral patterns, and simply inscribe the
new values, structures and appropriate conduct of ‘market capitalism.’” At the same time,
Stark (1992) observes that these legacies of the previous regime impacted the institutional
settings of the transforming economies and their approach towards the reform strategies.
10
Although they followed different paths, the outcome of the reforms was very similar across
all countries.
This chapter begins with a brief summary of the literature underlying the two papers
that form the first part of this thesis. The history of Czechoslovakia under CP rule, as well
as the post-1989 transition period have been extensively studied in the previous literature,
which also pointed out to the legacy of the communist past in the transforming countries.
Even so, there are still gaps in the knowledge of both periods that Coufalová and Žídek
(2023) and Coufalová (2024) seek to fill.
1.1 Historical background
Prior to 1989, Czechoslovakia was a predominantly industrial CPE with very low autonomy
of individual SOEs, fixed prices, virtually no competition, and almost completely isolated
from the global economy. The economy was highly monopolized and dominated by large
enterprises, often the only—or one of the very few—producers of a given product on
the market (Žídek 2019). When they had problems, they received subsidies and couldn’t
go bankrupt because there was no bankruptcy legislation. Their utmost goal was not to
make profit, it was to fulfill the plan, for the purpose of which the SOEs were perversely
motivated to maximize inputs and minimize outputs (Mlčoch 1990). There was very limited
motivation to improve efficiency and production quality, which was further exacerbated
by the CP’s interference in the decision-making processes within and among the SOEs.
Despite the official propaganda, the enterprises and the economy as a whole suffered
uncountable real problems every day (Clark and Soulsby 1995), such as shortages at all
stages of the production chain (Kornai 1980). To assure the plan fulfillment, firms hoarded
production resources, including the labor force, which led to overemployment (Malý and
Herc 1988).
Exporters were not allowed to trade with foreign partners on their own, and foreign
trade was conducted through so-called foreign trade enterprises (FTEs) (Nykryn 1988).
The embargo on imports of advanced Western technology further deepened the problems
of the inefficient command system, leading to a loss of competitiveness of domestically
produced goods (Myant 2003), that were generally not marketable in developed markets
(Půlpán 1993).1 Most trade took place with the countries associated in the Council for
Mutual Economic Assistance—COMECON. Since the 1970s, the economy permanently
lagged behind the developed Western economies in all aspects (Bálek 2007).
After the fall of the Iron Curtain in 1989, the transition from a CPE to a market-led
economy took place. Businesses had to stand on their own two feet overnight, as one of
1. In late 1980s, only 15% of Czechoslovak exports were sold in developed Western markets (FSÚ 1988).
11
the first reform steps was the elimination of subsidies, and competition was allowed. At
the same time, meeting the plan was no longer the objective, having been supplanted by
the objective of making a profit (Žídek 2006). However, the road to a market economy
was a bumpy one, as all of the aforementioned characteristics of the CPE are diametrically
opposed to those of a standard market economy.
The main objective of the reforms at the beginning of the transformation was to create
as quickly as possible a market economy that worked. Among the first changes in the
domestic economy were the liberalization of prices, the removal of subsidies, and the
introduction of a new tax system (Žídek 2006). The reforms also included changes in
relations with foreign countries, such as the liberalization of foreign trade and a major
currency devaluation. Alongside these changes, there was also a very rapid and significant
remodelling of the legal system (Klaus 1999).
One of the first steps was to allow private business, which was followed by the
privatization process. Some of those firms nationalized after 1948 were—after long
discussions within the government—returned to their previous owners (Zeman 2016). The
majority of the almost entirely nationalized economy was transformed to private owners
using various methods. Small business units (mostly shops and services) were included
in so-called small privatization. Large enterprises were privatized via various different
methods, such as direct sales (30.4%), voucher privatization (42.7%), along with other
methods (Loužek 2006). There were also companies that were sold to foreign bidders, for
example, Škoda Auto to the Volkswagen Group (Pavlínek 2008).
In general, the Czech transition was considered successful (see, e.g., Brown 1995;
Dillon and Wykoff 2002; Švejnar and Uvalic 2009; Beaulier, Boettke, and Krasnozhon
2012; Kučera and Maršál 2015).2 Thanks to a restrictive fiscal policy in the early years of
the transition, the government managed to maintain macroeconomic balance with one of the
lowest cumulative drops in GDP (15%) among the former Eastern Bloc, low unemployment
(Švejnar and Uvalic 2009) and relatively modest inflation (Sachs 1996).
On the other hand, the Czech economy also had to deal with a number of problems
inherited from the command system. Sachs (1996) observes that there was a truly painful
structural adjustment. After 1989, the Eastern block countries—the main trading partners of
Czechoslovak companies—, suffered huge transformation recession and defaulted. Given
the technological lagging behind, domestic products were not competitive in Western
markets. Moreover, the political (not economic) decision of the previous regime was to
focus on heavy industry, neglecting other sectors like light industry or services (Teichová
2013). These were all burdens preventing the successful functioning of the market economy.
2. Among the most prominent critics of the Czech transition, see, for example, Stiglitz (1999) and Stiglitz
(2002).
12
The quite extensive literature on Czech(oslovak) transformation (for instance, Sachs
1996; Dyba and Švejnar 1997; Mlčoch 2000; McDermott 2002; Myant 2003; Kučera and
Maršál 2015), mostly deals with macroeconomic adjustments of the Czech economy, even
though successful transformation absolutely required changes at the level of individuals
and firms. Nevertheless, there are some pieces dealing with the micro-economic sphere
worth of mentioning. Clark and Soulsby (see, e.g., Clark and Soulsby 1995; Clark 2000;
Clark and Soulsby 2005) realized a longitudinal case study research (from the 1990s till
2022) dealing with post-1989 changes in management and organization. In Clark and
Soulsby (1995), they focus on how institutions and privatization affected the behavior of key
managers in former SOEs. Clark (2000) highlights the importance of social capital from
the previous regime in founding de novo firms in the transitional period. Krátká (2019)
points to the changes in corporate culture in newly established domestic branches of foreign
companies. Last but not least, there are two studies focusing on selected companies in the
automotive industry. Pavlínek (2008) describes the privatization of Škoda Auto, while
Soulsby (2022) uses a narrative approach to identify legacies of the past in Czech-German
relations.
1.2 Communist legacy
A great body of literature deals with many forms of the communist legacy, which is mostly
associated with a negative connotation. LaPorte and Lussier (2011) show that there is not
just one unique communist legacy, and because of this, there is no exact definition of this
term. Pop-Eleches (2007, p. 910) defines communist legacies as “the structural, cultural,
and institutional starting points of ex-communist countries at the outset of the transition.” It
is the various attributes—political, economic and social—of the past regime that, in some
form, interfere with the functioning of transition economies. Consequently, I understand
communist legacy in a broad sense, as any inheritance of the previous regime that hinders
economic and social development.
Hansen (2012, p. 235) breaks down the term into four categories: “the ideological
legacy, the political legacy, the socioeconomic legacy, and the cultural legacy.”3 The author
argues that the hardest thing to overcome is the socio-economic legacy, i.e., the distortions
from the previous economic system, with its excessive production capacities, oversized
heavy industry, overemployment, lack of skill, environmental issues, infrastructure in a
terrible state, unclear property rights and a dysfunctional banking system.4
3. However, the boundary between the different aspects is not sharp and individual attributes can be
classified into more than one category (Hansen 2012).
4. Nevertheless, each of the transforming economies had its own specific form of central planning, as
well as a different historical experience prior to communism. Therefore, not all communist legacies may be
13
In addition to distortions caused by the command system, a large body of literature also
highlights other important vestiges of the previous regime in the transforming societies—
their values and behavior—that were evident for a long time after the fall of communism
(Roland 2012) and that influenced the economic performance in the transition period,
including doing business.
First, many authors see a strong influence of communism on people’s preferences
regarding the income inequality and the state intervention in the economy (Blanchflower
and Freeman 1997). Although there was general support for the transition process, the
inhabitants of the former socialist countries in general believed in the prominent role of the
state in protecting them (Kluegel, Mason, and Wegener 1999) and assuring their well-being
(Sirovátka, Guzi, and Saxonberg 2019). For example, Alesina and Fuchs-Schündeln (2007)
focus on the reunification of Germany and observing higher support of the inhabitants of
East Germany for the government involvement in the economy and redistribution compared
to West Germans.5 Fuchs-Schündeln and Schündeln (2020) show that individual support
for democracy, redistribution, and the role of government in the economy is conditioned
upon the time the person lived under communism, being stronger in older cohorts. This is in
accordance with Alesina and Fuchs-Schündeln (2007) and Landier, Thesmar, and Thoenig
(2008), who state that younger individuals in general display more pro-capitalist attitudes.
Very little change in values and beliefs of the inhabitants of post-socialist countries even
twenty years following the fall of the regime was observed by Roland (2012). The author
points to very long persistence in informal institutions by comparing the institutional level
in the periods before the onset of communism and observes similarities in the post-1989
preferences for democracy and the role of government in the economy in countries that
once belonged to the Austro-Hungarian Empire.
Libman and Obydenkova (2019) link past membership in the Communist Party of the
Soviet Union (CPSU) with income inequality. They show that Russian regions with higher
share of former CPSU members tend to display lower inequality, which they link to the
prevalence of informal friendship networks constituting another form of communist legacy
identified by the previous literature (Clark 2000; Howard 2003; LaPorte and Lussier 2011).
Informal networks continued to be a principle way of acquiring scarce goods and services
(LaPorte and Lussier 2011), as well as a principal source of social capital needed to found
a new business (Clark 2000). According to Howard (2003) and Pop-Eleches and Tucker
(2013), the reliance on these networks went hand in hand with a widespread lack of trust in
public institutions.
present in all countries, or these legacies of the past may survive in different forms and acquire different
intensities (LaPorte and Lussier 2011).
5. The authors predicted that within one or two generations, these preferences would level off.
14
Many authors (for instance, Fidrmuc 2000; Roberts 2008; Coffey 2013) also observe a
strong legacy of communist values in voting behavior across the post-socialist countries.
During the communist regime, everybody had the right and the obligation to work, and
people had no experience with unemployment. For this reason, unemployment was a
very sensitive topic during the early years of transition and as such, a strong determinant
of electoral support for incumbents. At the same time, Pop-Eleches and Tucker (2013)
point to the influence of communist legacies in civic participation and more recently, they
show that having lived under communist rule increases the left-authoritarian attitudes
(Pop-Eleches and Tucker 2020). Last but not least, Hrbková and Fellegi (2022) observe
reluctance in using some type of legislative or voluntary party quota to increase women’s
political representation in the Czech Republic.
1.3 Data and Methodology
Both papers are initially based on the same data set realized within a project focused on
the transition of Czech businesses from CPE to a market economy (GA20-23131S). The
research team members—mostly from Masaryk University or Institute for Contemporary
History—conducted a set of 102 oral history interviews between 2020 and 2022.6 The
participants were people who—during the early years of transition—had worked in senior
positions in the government (minister, deputy minister, member of the legislative council
of the government), top bureaucratic positions (department head, etc.), the private sector
(owners or managers of SOEs and privatized enterprises, as well as owners of de novo
firms), banking, capital markets and investment privatization funds (IPFs).
When recruiting narrators, we were able to use contact details and commitments from
many narrators from our previous project focused on the functioning on Czechoslovak
socialist economy (GA15-09404S). Most of them worked in the same SOEs during
socialism, often staying with them upon the subsequent transfer into private hands. Some of
them became owners. Since they had worked in the enterprises long before the beginning of
the transformation, they could offer invaluable insight on the impact of the regime change
and the economic paradigm shift on the functioning of the enterprise. In some cases, we
contacted selected privatized SOEs (or its surviving part) and requested contact details of
those who were in charge during the transition period. Personal contacts were the starting
point in the recruitment of narrators from new-born enterprises. We also interviewed
well-known figures with publicly available contact details. This group had the highest
6. Due to restrictions associated with the Covid-19 pandemic, some interviews had to be conducted online
via MS Teams or another platform.
15
Table 1.1: The full set of oral history interviews used in Coufalová and Žídek (2023) and
Coufalová (2024).
Total interviews 102
Gender Male 96
Female 6
Year of birth 1934-1954 69
1955-1969 29
1970- 4
Education Secondary 9
Tertiary 93
Role Policymakers and senior civil servants 29
Banking 11
Broker 6
IPF 2
Business sector 54
from which Engineering 29
from which Others 25
rejection rate because it was a politically sensitive period. In the next step, we proceeded
with the snowball method.
The full set of narrators can be seen in Table 1.1. However, because each paper answers
a different research question, the selection of narrators who are subsequently quoted in
each of them slightly varies.
Following Keulen and Kroeze (2012), Krátká (2019) and Vaněk and Mücke (2022),
we intended to conduct standard oral history interviews. At the beginning, narrators were
always given space to talk freely about their personal and professional lives, which is
essential for understanding the context of the events under investigation (Keulen and Kroeze
2012). This was followed by the second part of the interview, in which we asked the
narrators questions prepared in advance. All interviews were then discussed among the
entire research team and compared with previous interviews, as well as with the available
literature on the topic. In the next stage, the audio recordings were transcribed into text
form and I coded them all using Atlas.ti. While coding, I followed the methodology
described in Gioia, Corley, and Hamilton (2013), Saldaña (2021) or Locke, Feldman, and
Golden-Biddle (2022). In the first step, the whole team created a set of codes that were
based on our previous knowledge on the topic and the specific themes we were interested
in. Then I engaged in manual-open coding, followed by pattern coding, which helped me
to refine the data and find recurring motives—similarities and differences—that became
the basis for the resulting narratives.
16
1.4 Summary of lessons learned
The transition from a CPE to a market economy was a complex process for post-communist
countries. It involved significant structural adjustments, legal reforms, and the development
of new institutions. Both papers show that more than 40 years under communist rule
left vestiges on the functioning of the economy during the transition period via visible
distortions inherited from the command system, or the way people think and behave.
1.4.1 Results of Coufalová and Žídek (2023)
By adopting an approach based on oral history, in Coufalová and Žídek (2023), we point
to several factors that made it difficult for businesses to operate in transition. First, the
distortions created by the command economy implied a particularly heavy burden. The
official economic system changed virtually overnight, but the adjustment of individual
economic actors was not nearly as rapid. The paradigm shift in doing business implied that
managers had to start focusing on meeting criteria other than the plan. Their main goal had
become making a profit. However, this was made difficult at the outset by the oversized
production capacities of the socialist era that resulted from the perverse incentives of the
CPE. Excess capacity also affected the workforce. In order to become profitable, companies
had to start making redundancies, which was one of the central themes of our interviews.
Given the embedded cultural attitude towards unemployment (see 1.2), a common pattern
across the interviews was that layoffs—a decision between business efficiency and the
human and social aspect of redundancies—were felt as a certain trauma by the narrators
even 30 years later.
Second, legacies of communism were also perceived with regards to the informal
institutions. With accordance to the previous literature (see, e.g., Clark and Soulsby 1995),
we observe strong legacies of communism in the behavior of managers. There was, however,
another recurring motif across the interviews, which was the legacy of the previous regime
in the work commitment and ethics of employees. On the other hand, most narrators who
owned the company they had worked for reminisced that working for their own benefit
motivated them to such an extant that their workload was so enormous that in many cases it
led to serious issues, mostly in terms of health. Private ownership thus acted as an antidote
to the vestiges of communism, at least as far as the managers’ workload was concerned.
The third problem lay in the external environment. The transformation to a market
economy brought with it renewed competition on the domestic and foreign markets. In
addition, supply and demand chains were significantly disrupted. This affected most
companies that had previously exported to Eastern markets, which were experiencing an
even greater economic downturn than Czechoslovakia. There is a common pattern in oral
17
testimonies in the heart of Coufalová and Žídek (2023) showing that those companies that
did not lose their customers from socialism had a much smoother start to the transformation.
SOEs exporting to Eastern bloc countries were in a much worse position. Moreover,
according to our narrators, what made it even more difficult was the disintegration of
FTEs, which had been the only ones with experience with trading on foreign markets. In
retrospect, most narrators see this as the biggest mistake of the reforms in this area.
1.4.2 Results of Coufalová (2024)
In Coufalová (2024), I build on previous studies and focus on another problem that
businesses—whether privatized or newly established—had to deal with in the transition
period—the access to credit. The paper is based on testimonies of narrators from among
entrepreneurs, bankers, as well as policymakers, and offers a complex picture of the lending
policy in transition.
The available literature (for instance, Neumann and Egan 1999; Simonson et al. 2000;
Lízal and Švejnar 2002; Myant 2003; Nollen, Kudrna, and Pazderník 2005) in general
emphasizes that large SOEs had easier access to credit. I observe a similar pattern across
the interviews. According to most of our narrators, there were differences stemming from
the size of the company, its owner, the branch in which it operated, as well as its importance
for the functioning of the state. The lack of credit history was reported as another obstacle
to getting credit. Nevertheless, contrary to most of the previous literature, the interviews
show that the boundary between well-known SOEs and new companies without any history
was not sharp, and that some large companies from the socialist era suffered from the
same problems. Moreover, according to our narrators from among bankers, there were still
possibilities for de novo firms.
However, the corporate sector was not the only one that had to deal with major teething
problems. That this was not an easy period was also evident from interviews with bankers
at the time who lacked experience with the standard capitalist way of creating and lending
money. What emerged as the most recurrent motif was political pressure. While the
main figure of the Czech economic transformation—Václav Klaus—repeatedly denied that
there had been any pressure on banks to lend money at that time (see, e.g., Klaus 2006;
Lidovky.cz 2009), our narrators adopted a different narrative strategy. Whereas another
policymaker and close collaborator of Klaus—Dušan Tříska—completely rejected use of
the term ‘pressure’, the narrators from the banking sector felt compelled to lend, and they
associate this coercion with Václav Klaus himself. It is thus at this point where the oral
history emerges as an alternative source of information entering into the conflict between
the dominant discourse of policy makers and the previous written sources (Neumann and
18
Egan 1999; Simonson et al. 2000; Lízal and Švejnar 2002; Myant 2003; Nollen, Kudrna,
and Pazderník 2005), showing some of the benefits of this approach (see below).
Banks also had to deal with certain legacies of communism. While most authors often
criticized them for excessive lending in the early years of transition (see, e.g, Mlčoch 2000;
Dědek et al. 2000; Lízal and Švejnar 2002; Myant 2003; Czesaný et al. 2005; Nollen,
Kudrna, and Pazderník 2005), both bankers as well as policymakers believe that without
borrowing, transformation would not have been possible due to what they called “the
terrible inheritance” of the previous regime—the credit for permanently revolving stocks—,
extricating themselves from any responsibility.
1.5 The relevance of oral history to economic historians
There is often a lack of hard data on historical events, which is especially true for
totalitarian regimes—such was the socialist Czechoslovakia—that usually do not offer
reliable information, as they publish and preserve only data favorable to them. As a
consequence, the extant sources from those periods do not correctly reflect the reality and
other types of data, such as oral testimonies of direct participants of those events, may
constitute a crucial source of information (see, e.g., Coufalová, Mikula, and Žídek 2020,
2023).
However, the available literature shows that oral history has its place even in democratic
regimes. For whenever it is true that he/she who writes history has a strong instrument of
power, the author has control over the process of signification (Lipartito 1995; Hansen 2012),
as he/she can emphasize some pieces of information while silencing others, depending
on his/her political purposes. According to Mordhorst and Schwarzkopf (2017, p. 1168),
“political movements and states can become the ‘winners’ of political struggles by writing
history. To lend themselves as a weapon in political conflict, historical narratives often
throw overboard nuances, inconsistencies and contingencies.” By questioning the official
discourse, oral testimonies help us learn about the silenced part of history. It gives space to
a “bottom-up perspective that shines new light on unexplained sides of the daily life of the
non-hegemonic classes.” (Maclean, Harvey, and Stringfellow 2017, p. 1218)
At the same time, there is no reason to believe that oral history must necessarily refute
written history. It is simply another layer, a minority view. It fills in the gaps in the written
sources and offers the human dimension of historical events (Hammond and Sikka 1996).
The two versions complement each other, giving rise to a more complex picture of a given
historical period (Jones and Comunale 2019).
19
As described in section 1.1, previous literature on Czech economic transition mostly
offers macroeconomic perspective, and for the sake of brevity leaves aside the individual
standpoints of each actor. Narrators’ testimonies presented in Coufalová and Žídek (2023)
and Coufalová (2024) uncover personal experiences of many aspects of economic activity
in the period under review and show the human dimensions of change at that time. They
enrich the economic and business history by bringing to light how difficult it was for
all the participants to navigate in an environment of a widespread lack of knowledge so
characteristic for the transition period, and to overcome the deeply rooted bad habits from
the previous regime. They highlight the frustrations and challenges of economic agents
involved in those changes, answer different questions and offer alternative points of view to
written sources. These personal experiences thus show how we can expand the traditional
perception of economic transformation by means of economic and social science methods.
1.6 Limitation of the research
The previous section showed that oral history has its undeniable benefits. There are,
however, also some pitfalls associated with this method.
1.6.1 Memory issues and subjectivity
First, oral history is based on the narrator’s memory, which can be selective and contaminated
by his/her experiences occurring after the reported event, as well as political opinions and
current situation (Weick 1995; Bernstein, Nourkova, and Loftus 2008; Beard 2017). This
happens especially when the reported events are very distant in time.7
Second, according to Abrams (2016, p. 55), “there is no such thing as an unmediated
narrative – a pure or transparent oral representation of past experience. (...) [O]ral history
is a dialogic process; it is a conversation in real time between the interviewer and the
narrator, and then between the narrator and what we might call external discourses or
culture.” As the interview is an interaction between two subjectivities—the narrator and
the interviewer—who “cooperate to create a shared narrative” (p. 54), its outcome, as well
as its interpretation, is influenced by the impression that both participants form of the other
based on their respective social identities. This may influence the accuracy of the result
(Beard 2017) and the approach to oral testimonies that cannot be taken as an objective truth
(Hansen 2012; Jones and Comunale 2019).
7. However, the time gap can also have some advantages. For example, it can increase the willingness
of narrators to talk about certain events that are politically sensitive (Maclean, Harvey, and Stringfellow
2017; Soulsby 2022). In our case, more than 30 years have passed after the transition and a great part of the
narrators no longer have any political or business ambitions.
20
Notwithstanding, the main goal of oral history is not to find facts; it is especially
appreciated because it helps to understand “how people make sense of the past” (Thiessen
2019, p. 62). It is not a contradiction to written sources or archives; rather, it complements
them answering a different question. There is not only one and correct history, as it
“can be re-framed by remembering some things and forgetting others” (Hansen 2012, p.
701). Consequently, oral testimonies can enrich the previous economic literature, as the
participants’ personal experiences can contextualize the described events and give them
meaning.
On the other hand, the relationship between oral history and written literature is
bidirectional, as the written sources influence the narrators’ opinions as well (Maclean,
Harvey, and Stringfellow 2017). The collective memory and the prevailing ideology also
affect how we make sense of the past (S. Berger 2009; Beard 2017).
1.6.2 Snowball sampling and data biases
Another drawback concerns the snowball sampling, which can be very useful, as personal
recommendations provide trust, especially when it comes to interviewing working elites
(Bailey 2019), but at the same time, it does not provide a representative sample that could
be applied to the whole population. Nevertheless, this was not our goal. We aimed to
complement the written literature and archives, offering the human side of the story with
first hand experiences of direct participants of the given events. At the same time, the last
interviews brought virtually no new information from which we conclude that we have
reached a saturation point.
Our data are also biased in some ways. First of all, the set of oral history interviews is
biased towards highly educated men. The educational bias is a consequence of the fact
that we interviewed very capable people with some power to decide. The bias in gender is
the result of the corporate culture of the time, as there were very few women in leadership
positions prior to 1989, as well as during the transition period. Another bias was due to the
focus of the projects, which dealt mainly with enterprises in transition, so that the data
was dominated by narrators from the corporate sphere (53%), even though there were also
participants from other sectors of the economy. Moreover, there is an observable, so-called
survival bias within the interviewed entrepreneurs, because we interviewed primarily
managers or owners from SOEs, as well as new-born companies that survived the transition
period.
Last but not least, there may be a certain degree of self-selection bias. All participants
could oppose the publication of the results of the interviews. Nevertheless, they knew
that the information would be accessible for the whole research team. In the Czech
environment, the transition period is known as the “wild 90s” (Pehe 2019) and is often
21
linked to corruption (Lízal and Kočenda 2001; Lízal and Švejnar 2002). Consequently, I
follow Giacomin and Jones (2021) and Giacomin, Jones, and Salvaj (2021) and hypothesize
that the data suffers from this bias due to the fact that potential participants who were
involved in some sensitive practices, such as corruption or other illegal activities, might
refuse to participate in our research, in light of the especially high rejection rate from the
group of public figures.
1.7 Future research opportunities
There are still many aspects of economic transition that have not yet been sufficiently
explored. One of them is the role played by the bureaucracy in the whole process. The
existing literature on the subject (see 1.1) deals with the reforms themselves, and the
names of the reformers who headed the various bureaus are well known to the public.
However, one of the recurring topics in the interviews was the extremely important role
played by senior civil servants in the whole process. This is another area where the oral
history approach can make a significant contribution to the understanding of economic
transformation in the Czech Republic, as well as to the general literature on the role of
bureaucrats in times of huge economic and political turmoils (for instance, Van de Walle
1989; Suleiman 1999; Nemec 2001).
Second, the Czech Republic was not the only country to go through the harsh changes
from a CPE to a market-driven economy. However, the individual economies of the former
Eastern bloc differed significantly in terms of the position of enterprises during socialism
and in terms of macroeconomic stability in both periods. Consequently, the impact of
the communist legacy on the economy varies across different countries based on their
specific historical, political, and institutional contexts. Thus research focusing on the
functioning of the business sector—as well as other aspects of the transition process—in
other former CPEs could be a promising avenue for future research. Conducting case
studies based on oral history in specific post-communist countries can provide a more
in-depth understanding of the economic challenges and transformations they faced.
Third, economic transitions certainly do not have to be explored only through oral
testimonies. There are many doors opened to statistical analysis. The text mentions that
the liberalisation of foreign trade was an integral part of the transformation. The fall of
the Iron Curtain led to an exogenous shock to the market access of border regions, which
is supposed to have affected the location decisions of economic actors. The economic
activity and residents used to move towards regions with higher market access—usually
measured by market potential—and leave regions with poor market opportunities (Davis
and Weinstein 2002; Brülhart, Crozet, and Koenig 2004; Brülhart and Koenig 2006;
22
Redding and Sturm 2008; Ahlfeldt et al. 2015; Cosar and Fajgelbaum 2016; Brülhart,
Carrère, and Robert-Nicoud 2018; Eberhard-Ruiz and Moradi 2019). The unexpected fall
of the Iron Curtain and the subsequent liberalization of foreign trade with Austria thus offer
the perfect opportunity to explore the impact of market access on population location and
economic activity.
Fourth, the command system characteristic for socialist economies required certain
simplifications to make it manageable, which resulted in monopolization of the economy
and a high concentration of scarce resources (see 1.1). As a consequence, education and
research, management of state conglomerates, as well as foreign trade, were localized in
large cities. It can be hypothesized that this high concentration formed another communist
legacy, which affected the economic and entrepreneurial activity in the subsequent periods
(Davis and Weinstein 2002, 2008; Bleakley and Lin 2012; Ahlfeldt et al. 2015), forming
yet another promising area for future research.
Last but not least, another opportunity is to link this economic activity to the voting
behaviour of the inhabitants of the regions in question, contributing both to the literature on
voting behavior in transition countries (see 1.2), as well as to the general economic voting
literature (Lewis-Beck and Paldam 2000; Evans and Pickup 2010; Ansolabehere, Meredith,
and Snowberg 2014; Chzhen, Evans, and Pickup 2014; Hansford and Gomez 2015).
Chapter 2
Voting under Information Constraint
Previous literature identifies several key factors behind voting behavior, which can be
subdivided into three broad groups: (1) the social factors, such as social class, gender
and race (Lazarsfeld, Berelson, and Gaudet 1968; Andersen and Heath 2003; Adjei
2013); (2) the psychological or psycho-social factors, which comprises partisanship or
party identification (Campbell et al. 1980; Harder and Krosnick 2008; Strijbis 2014;
Bakker, Rooduijn, and Schumacher 2016); and finally (3) the economic—often called
rational-choice—determinants, which emphasizes the role of rationality, uncertainty and
information, and assumes that voters remunerate or punish the incumbents depending on
the economic performance (see, for example, Downs 1957; Lewis-Beck and Paldam 2000;
Lewis-Beck and Stegmaier 2000; Evans and Pickup 2010; Ansolabehere, Meredith, and
Snowberg 2014; Rogers 2014; Hansford and Gomez 2015; Dippel et al. 2022; Gabriel,
Klein, and Pessoa 2022).
According to Stone (2017), voters choose candidates who they think are best prepared
for holding the office. However, to make the right choice, the voter must have information
about the candidates. Brockington (2003) shows that voters usually do not know most of
the candidates on the ballot at all. This is especially true for ballot papers with long lists of
names or many voting decisions to be made (Augenblick and Nicholson 2016; Seib 2016).
So, how do voters make their decision facing such information constraint?
The electoral system for the Parliament of the Czech Republic creates an environment
akin to a natural experiment enabling us to identify several factors influencing voters’
decisions. Czech Parliament consists of two chambers: the lower house known as the
Chamber of Deputies and the upper house known as the Senate. We use data from the
elections to the former. For the purpose of these elections, the country is, since 2000,
divided into 14 constituencies1, which match the country’s administrative regions (“kraj”).
1. There were only 8 constituencies in the 1996 and 1998 elections.
24
The elections are based on the OLPR system. Voters cast votes for a given party and
at the same time, they can—if they wish—cast up to four preferential votes2 affecting
the order of candidates established by the party. Czech political parties can nominate
up to 343 candidates in the whole country with up to 36 candidates per constituency.3
It is thus unlikely that voters know them all. In such a situation, they may abstain from
voting—producing an effect known as roll off or drop off—or use available simplifying
heuristics and cues (Brockington 2003).
The empirical literature (for instance King and Leigh 2009; Carnes and Lupu 2016;
Portmann and Stojanović 2019) shows that under information constraint, voters often assess
candidates’ abilities on the basis of their characteristics and stereotypes associated with
them. At the same time, they can also give preference votes to candidates who share their
own socio-demographic or attitudinal characteristics (McPherson, Smith-Lovin, and Cook
2001), which is known as homophily. In Coufalová, Mikula, and Ševčík (2023), we use
data on preferential voting from 1996 to 2021 Czech parliamentary elections matched with
2001 and 2011 Census data and identify the effect of homophily on voting behavior.
Another strand of literature shows that candidates and parties listed in the top positions
on the ballot paper are more likely to win voters’ support by virtue of their position; this is
known as the ballot order effect (see, e.g., Miller and Krosnick 1998; Geys and Heyndels
2003; Lutz 2010; Chen et al. 2014; Kim, Krosnick, and Casasanto 2015; Marcinkiewicz
and Stegmaier 2015; Blom-Hansen et al. 2016; Van Erkel and Thijssen 2016; Däubler and
Rudolph 2020; Flis and Kaminski 2022). One of its possible reasons is so-called effect of
attention. In Coufalová and Mikula (2023), we use combined data on preferential voting
in Czech parliamentary elections with data on ballot layouts to show that checking the
other side of the ballot requires additional use of cognitive abilities—attention—by voters,
who already suffer from mental fatigue. This effect of attention per se affects electoral
outcomes.
2. Before 2010, it was only possible to cast 2 preferential votes.
3. In 1996 and 1998 elections, parties could nominate up to 60 candidates in some constituencies. Since
2000, the constituencies (regions) and their maximum numbers of candidates as set by law are: Prague (36),
Moravian-Silesian (36), Central Bohemian (34), South Moravian (34), Ústí nad Labem (26), Olomouc (23),
South Bohemian (22), Zlín (22), Plzeň (20), Hradec Králové (20), Vysočina (20), Pardubice (19), Liberec
(17), and Karlovy Vary (14).
25
2.1 Homophily in Voting Behavior: Evidence from Preferential
Voting
Given the large electoral lists with up to 36 candidates, voters do not know most of
them. However, they can use rich secondary information about candidates’ background
characteristic, which is available on the ballot (see Figure 2.1 for an example of an annotated
ballot) to assess their suitability for the office.
We hypothesize that voters are more likely to give their preferential vote to candidates
who are similar to them in these socio-demographic characteristics, as these candidates may
understand better their problems, while candidates from other social classes and groups
may prioritize other topics.
2.1.1 Data and methodology
The estimation sample contains all free elections to the Chamber of Deputies that took
place in the independent Czech Republic—from 1996 to 2021. The primary data set
used in our empirical analysis is an electoral database4, which contains the secondary
information on candidates also available to voters on the ballot (name, age, political party
affiliation, occupation, municipality of residency), together with the electoral outcomes,
including the number of preferential votes received. The data was aggregated at the level of
municipalities. As a results, we have a rich data set with one observation for each candidate,
municipality and election.
The electoral database contains exact data on candidates’ ages that was then classified
into seven categories. Candidates also list one or more occupations5 on the ballot that
was/were then manually encoded into various categories according to the ISCO (level 1)
classification.
Information on education and gender was inferred from the name and candidate
description. Tertiary education is signaled by the presence of academic titles. The
candidates’ genders were inferred from their names—matching them with a database
published by the Ministry of the Interior of the Czech Republic, which includes the
frequency of each name for each gender6. We assign each candidate the gender that is
more frequent for his/her name.
4. The electoral database for 2006–2021 elections is publicly available at
https://volby.cz/opendata/opendata.htm. Data that cannot be found at https://volby.cz/ was requested from the
Czech Statistical Office (infoservis@czso.cz).
5. When a candidate listed more than one occupation, the first one was used
6. This database is not available any more due to GDPR. We use the version that was released in 2015.
26
(a)Frontside(b)Backside
Figure2.1:Exampleofaballotfromthe2010parliamentaryelections
Note:1–constituency;2–partynumber;3–partyname;4–candidate’spositionontheballot(ranking);5–candidate’sname(signalingalsogenderandeducation);6–
age;7–occupation;8–municipalityofresidence;9–affiliationwithapoliticalparty.
27
Table 2.1: Similarity between candidates and municipalities’ populations
Municipalities
All Small
(1) (2)
Share of population with the same education level 51.046 49.351
as the candidate (%) (43.762) (44.603)
Share of population with the same occupation 9.703 8.985
as the candidate (%) (6.290) (6.533)
Share of population of the same age 13.472 13.432
as the candidate (%) (3.185) (3.616)
Share of population of the same gender 49.941 50.066
as the candidate (%) (2.460) (3.090)
Candidate lives in the municipality (= 1) 0.002 0.0002
(0.044) (0.015)
Note: “Small municipalities” are defined as municipalities with populations below the median in the
respective constituency and election. Municipalities that met this condition at least once are included in
Column 2. The table contains means of similarity measures. Standard deviations are reported in parentheses.
Values are calculated using data from the 1996, 1998, 2002, 2006, 2010, 2013, 2017, and 2021 parliamentary
elections; 2001 and 2011 censuses and annual data on municipal-level demographic structure.
In order to estimate the effect of homophily, we define four similarity measures as the
percentage of the adult population that shares the specific variant of the given characteristic
with the candidate. These are continuous measures for each of these characteristics that
can theoretically range from 0 to 100%. An additional dummy variable was defined that
takes the value one for the municipality in which the candidate lives (Table 2.1 describes
the variables measuring the similarities between the candidates and the municipalities’
population). Two municipality-level data sources were used to construct these variables—
annual data compiled by the Czech Statistical Office from census and administrative records
(age and gender), and 2001 and 2011 population censuses (education and occupation).
Given that the nomination processes are—to a large extent—closed-door procedures, we
do not know whether the political parties are aware of the possible homophilic preferences
and do actually optimize the composition of their electoral list in order to match the
population characteristics in the given constituency. Nevertheless, if this were true, it
would lead to an endogeneity problem, and would present a challenge to identifying the
causal effects of homophily. This is why we restrict the estimation sample to populations
below the median, which are an unattractive target for parties and thus such optimization is
unlikely there. In 2011, the median municipality population was 426 inhabitants, and in the
28
whole period under observation, only 7.1% of the total population lived in municipalities
below the median.
We estimate the effect of homophily using the following empirical specification:
log(𝐸( 𝑝𝑣 𝑐 𝑝𝑚𝑒)) = 𝛾𝛾𝛾H 𝑐𝑚𝑒 + log(𝑣 𝑝𝑚𝑒) + 𝜃 𝑚 + 𝜃 𝑐𝑒 (2.1)
where 𝑝𝑣 is the number of preferential votes received by candidate 𝑐 running for party
𝑝 in municipality 𝑚 and election 𝑒. The vector H contains variables of interest: an
indicator variable for the candidate being a resident in the given municipality and four
continuous variables defined as the percentages of the municipality’s population that
share the candidate’s education level (tertiary, lower than tertiary), occupation (ISCO1 or
unclassified), age group (18–29, 30–39, 40–49, 50–59, 60–69, 70–79, 80–89), and gender.
Variable 𝑣 is an offset variable that controls for the number of votes cast for the respective
party. It effectively expresses the maximum number of preferential votes a given candidate
can receive. 𝜃 𝑚 is the municipality fixed effect that controls for time-invariant municipality
characteristics that may affect residents’ propensity to use preferential votes—such as
education level or other unobserved characteristics. 𝜃 𝑐𝑒 is a full set of candidate fixed
effects (including constant) that controls for the candidate’s individual characteristics, the
popularity of the party he/she is running for, and his/her position on the ballot list. These
candidate fixed effects ensure that the homophily effect is estimated within the candidate.
The data contains a large share of zeros (82.5%) in the dependent variable. Consequently,
we estimate the regression (2.1) using Poisson Pseudo-Maximum Likelihood (PPML)
estimator as suggested by Silva and Tenreyro (2011). We use robust standard errors
clustered by ballot—i.e. one cluster for each party, election, and constituency/region—and
municipality.
2.1.2 Results
The results contained in Table 2.2 indicate the presence of homophily in voting behavior,
as candidates receive more preferential votes in municipalities with a larger share of the
population sharing their personal characteristics. The coefficients of similarity measures
for education and age are statistically significant suggesting the positive effect of homophily.
A one percent increase in the share of the municipality’s population whose education level
or age are the same as the candidate’s increases the number of preferential votes that the
candidate receives by 0.5% and 0.2% respectively. The heterogeneity analysis7 shows that
these effects are especially driven by young and tertiary-educated candidates.
7. The results of the heterogeneity analysis can be seen in the full version of the paper in the appendix ??.
29
Table 2.2: Effect of homophily on voting behavior in small municipalities
Dependent variable:
Number of preferential votes received
(1) (2) (3) (4) (5) (6)
Share of population with the same education level 0.0047∗∗∗ 0.0050∗∗∗
as the candidate (%) (0.0009) (0.0009)
Share of population with the same occupation 0.0013∗∗ 0.0008
as the candidate (%) (0.0006) (0.0006)
Share of population of the same age 0.0021∗∗∗ 0.0017∗∗∗
as the candidate (%) (0.0006) (0.0006)
Share of population of the same gender 0.0005 0.0004
as the candidate (%) (0.0008) (0.0008)
Candidate lives in the municipality (= 1) 3.0339∗∗∗ 3.0248∗∗∗
(0.0559) (0.0568)
Candidate fixed effect Yes Yes Yes Yes Yes Yes
Municipality fixed effect Yes Yes Yes Yes Yes Yes
Observations 6,844,538 6,466,865 6,844,538 6,844,538 6,844,538 6,466,865
Notes: The table contains estimates of coefficients 𝛾 from Equation (2.2). The sample is limited to
municipalities with population below the constituency and election median. Standard errors clustered by
ballot and municipality are reported in parentheses: ∗, ∗∗ and ∗∗∗ denote statistical significance at 10%, 5%,
and 1%. 438,373 observations in Columns 1, 3, 4, and 5, and 368,784 observations in Columns 2 and 6 were
excluded due to having a fixed effect with perfect fit.
Moreover, our results also point to the potential presence of occupational homophily,
although this effect is lower in magnitude—a one percent increase in the share of the
municipality’s population working in the same occupation group as the candidate increases
the number of preferential votes that candidate receives by 0.13%—and is statistically
significant only at 5%.
Last but not least, our results also show that being a resident of the given municipality
significantly increases the number of preferential votes an observed candidate receives,
thus indicating the presence of geographical homophily. In other words, running for seats
in their place of residence makes candidates obtain significantly more preferential votes.
The reason may be that voters believe that if their neighbor is elected, the prestige of their
community will grow and the interests of its residents will be better protected, which is in
accordance with the available literature, such as Önder, Portmann, and Stadelmann (2018)
who observed an increased responsiveness of Swiss deputies to referendum results in their
municipalities of residence than in the rest of municipalities.
These findings have an important implication for the political marketing and campaigns’
optimization. We show that candidates—and the heterogeneity analysis identifies that
this especially concerns those who are young and educated—should focus their individual
campaign on groups that are similar to themselves.
30
2.2 The Grass is not Greener on the Other Side: the Role
of Attention in Voting Behavior
Reading large electoral lists also poses great cognitive demands on voters making the
decision-making process enormously complex (Brockington 2003; Kim, Krosnick, and
Casasanto 2015; Van Erkel and Thijssen 2016; Augenblick and Nicholson 2016; Seib
2016). This may lead voters to base their decisions on another cue available to them—the
candidate’s position on the ballot (Brockington 2003), producing so-called ballot order
effect (see, e.g., Miller and Krosnick 1998; Geys and Heyndels 2003; Lutz 2010; Chen
et al. 2014; Kim, Krosnick, and Casasanto 2015; Marcinkiewicz and Stegmaier 2015;
Blom-Hansen et al. 2016; Van Erkel and Thijssen 2016; Däubler and Rudolph 2020; Flis
and Kaminski 2022). The available literature mostly highlights the strong preference for
candidates in top positions (Miller and Krosnick 1998; Ho and Imai 2008; Lutz 2010;
Marcinkiewicz and Stegmaier 2015; Van Erkel and Thijssen 2016; Flis and Kaminski
2022).
There are two possible explanations for this phenomenon: the effect of ranking and
the effect of attention. The effect of ranking consists of the believe of less informed voters
that parties list their best candidates on the top positions of the ballot. At the same time,
however, reading electoral lists is cognitively demanding. In general, when reading a list of
items, there is a tendency to remember and process the information presented at the start
of the list better than information in the middle or at the end. This cognitive limitation
is applicable also to voting behavior (Kim, Krosnick, and Casasanto 2015). Voters who
lack information about the candidates must expend cognitive and other resources to make
their decision (Van Erkel and Thijssen 2016). Nevertheless, as their mental abilities and
cognitive effort are limited, the lower down the list they read, the less attention they pay to
the listed names. They are, therefore, less likely to support candidates placed lower on
electoral lists (Miller and Krosnick 1998; Lutz 2010). Both these effects jointly lead to the
number of preferential votes being non-increasing in position on the candidate list (see
Figure 2.2).
31
Figure 2.2: Distribution of preferential votes and last row number on the front side of the
ballot
0
5
10
15
20
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Position on the ballot
Share(%)
Preferential votes
Last row
Note: Sample contains only double-sided ballots from the 2006, 2010, 2013, 2017 parliamentary elections
with at least one candidate listed on each side of the ballot.
2.2.1 Data and methodology
As the attention and ranking effects both work in the same direction, it is challenging
to disentangle them. Nevertheless, the setting of Czech Parliamentary elections offers a
unique opportunity to separate one from the other.
We use constituency-level data for elections to the Chamber of Deputies that took place
in 2006–2017. The Czech electoral lists are printed on A5-sized ballots. In numerous
cases—one third of the observations—the electoral list overflows to the reverse side of the
ballot. Since the margins of the ballot, the length of the party name, or the description of
individual candidate characteristics may vary across parties and constituencies, the location
of the break can be considered as quasi-random. Moreover, by showing that candidates
listed in the proximity of the break do not differ in observable characteristics such as age,
gender, education, or occupation (see Table 2.3), we can consider that there is no electoral
list optimization by the parties in the close proximity of the break.
Last but not least, no candidate running from the position in the close proximity of the
page break was elected in any of the 2006–2017 elections, which reinforces the validity of
our assumption. Figure 2.2.1 also shows that the number of preferential votes between
neighbouring positions on the same side of the ballot list is very small, which suggests that
there is no ranking effect in the proximity of the break.
32
Figure 2.2.1: Position of the front side’s last row on the ballot
0.0
0.3
0.6
0.9
b−3 b−2 b−1 b+1 b+2 b+3
Position on the ballot
Preferentialvotes(%ofallvotes)
Side of the ballot
Front
Reverse
Note: Sample contains only double-sided ballots from the 2006, 2010, 2013, 2017 parliamentary elections
with at least one candidate listed on each side of the ballot. The percentage of preferential votes is calculated
as the number of preferential votes received divided by the total number of votes cast for all parties.
As a first step, we drop the ballots with only one side and estimate the effect of attention
using data on candidates listed in positions immediately before and after the page break in
the following empirical specification:
log(𝐸( 𝑝𝑣 𝑐𝐵)) = 𝛾𝑟 𝑠 𝑐𝐵 + 𝛽𝛽𝛽X 𝑐𝐵 + log(𝑣 𝐵) + 𝜃 𝐵 + 𝜅 𝐵 (2.2)
where 𝑝𝑣 is the number of preferential votes received by candidate 𝑐 running on ballot 𝐵.
Coefficient 𝛾 is an indicator variable for the reverse side of the ballot 𝑟 𝑠 that identifies the
effect of attention. X is a vector of individual characteristics of the candidate that may
affect electoral support: age, occupation, education, and gender. 𝑣 is an offset variable
which controls for the number of ballots cast for the respective party and consequently
for the maximum number of preferential votes the candidate can receive. 𝜅 𝐵 is the fixed
effect for the number of candidates on the ballot list, because the lower the number of
candidates, the higher the probability of any single candidate receiving a preferential vote.
𝜃 𝐵 represents the full set of ballot fixed effects including a constant that controls for the
popularity and characteristics of the party in question and of the election in question. It
ensures that the attention effect is estimated within the ballot.
The regression (2.2) was estimated using the PPML estimator, due to potential
overdispersion. We use robust standard errors, which are clustered by ballot.
33
Table 2.3: Descriptive statistics and balance tests at the page break
Candidate Difference in
last on the first on the in means
front side reverse side (2) − (1)
(1) (2) (3)
Age (years) 45.042 45.665 0.623
(13.060) (14.084)
Male (= 1) 0.689 0.718 0.029
(0.464) (0.451)
Education (ISCED 5, = 1) 0.011 0.016 0.005
Short-cycle tertiary education (0.102) (0.125)
Education (ISCED 6, = 1) 0.045 0.050 0.005
Bachelor’s or equivalent (0.207) (0.219)
Education (ISCED 7, = 1) 0.354 0.317 −0.037
Master’s or equivalent (0.479) (0.466)
Education (ISCED 8, = 1) 0.024 0.032 0.008
Doctorate or equivalent (0.152) (0.175)
Education (other tertiary, = 1) 0.018 0.005 −0.013∗
(0.135) (0.073)
Occupation (ISCO 1, = 1) 0.206 0.211 0.005
Managers (0.405) (0.409)
Occupation (ISCO 2, = 1) 0.230 0.206 −0.024
Professionals (0.421) (0.405)
Occupation (ISCO 3, = 1) 0.187 0.203 0.016
Technicians and associate professionals (0.391) (0.403)
Occupation (ISCO 4, = 1) 0.063 0.045 −0.018
Clerical support workers (0.244) (0.207)
Occupation (ISCO 5, = 1) 0.026 0.045 0.018
Service and sales workers (0.160) (0.207)
Occupation (ISCO 6, = 1) 0.008 0.016 0.008
Skilled agricultural, forestry and fishery workers (0.089) (0.125)
Occupation (ISCO 7, = 1) 0.055 0.055 0.000
Craft and related trades workers (0.229) (0.229)
Occupation (ISCO 8, = 1) 0.042 0.037 −0.005
Plant and machine operators, and assemblers (0.201) (0.189)
Occupation (ISCO 9, = 1) 0.005 0.000 −0.005
Elementary occupations (0.073) (0.000)
Occupation (Unclassified in ISCO, = 1) 0.177 0.182 0.005
(0.382) (0.386)
Observations 379 379
Notes: Columns (1) and (2) contain means and standard errors in parentheses. Column (3) reports differences
in means and 𝑝−values from the 𝑡−test: ∗, ∗∗ and ∗∗∗ denote statistical significance at 10%, 5% and 1%.
Sample is limited to the baseline estimation sample.
34
2.2.2 Results
Table 2.4: Effect of attention on electoral support
Dependent variable:
Number of preferential votes (log)
Specification
Baseline (𝑏 − 1 vs 𝑏 + 1) Adjusted (𝑏 − 2 vs 𝑏 + 1)
(1) (2) (3) (4) (5) (6)
Reverse side (= 1) −0.526∗∗∗ −0.514∗∗∗ −0.546∗∗∗ −0.405∗∗∗ −0.439∗∗∗ −0.474∗∗∗
(0.085) (0.090) (0.096) (0.075) (0.063) (0.058)
Ballot FE Yes Yes Yes Yes Yes Yes
Education category Yes Yes Yes Yes
Age category Yes Yes Yes Yes
Gender (male) Yes Yes Yes Yes
Ballot length FE Yes Yes Yes Yes
Occupation Yes Yes
Observations 758 758 758 750 750 750
Notes: The table contains estimates of the coefficient of interest (𝛾) from Equation (2.2). Standard errors
clustered by ballot are in parentheses: ∗, ∗∗ and ∗∗∗ denote statistical significance at 10%, 5%, and 1%. The
sample contains only double-sided ballots with at least one candidate listed on each side of the ballot paper.
Observations for which a fixed effect with perfect fit was identified are excluded from the sample.
Our results (see Table 2.4) indicate that being listed on the reverse side leads to the
drop in the number of received preferential votes by at least 40%. These results are robust
across specifications and are comparable in magnitude to those by Ho and Imai (2006) who
focus on a 2003 California recall election based on a single member district (SMD) system.
They find that minor candidates—those who received no votes in many districts—placed
on the second page of the ballot paper receive 40% less votes just because of being listed
on the reverse side. Consequently, we provide evidence that the effect of attention is robust
across electoral systems.
To confirm the assumption that there is no ranking effect in the close proximity of
the break, we performed placebo tests where we artificially shifted the break position by
one to two positions up and down and re-estimated equation (2.2). The estimates8 were
close to zero and statistically insignificant, thus corroborating the absence of ranking in the
close proximity of the break. The only exception was the placebo shift by one position up.
Indeed, comparing the number of preferential votes received by the last two candidates on
the (true) front side of the ballot, we observed that the last candidate (𝑏 − 1) receives more
votes then the penultimate one (𝑏 − 2), which is in accordance with the literature on ballot
8. For the complete set of results of placebo tests, see Table 3 in the full version of the paper.
35
layout, such as Marcinkiewicz and Stegmaier (2015) and Blom-Hansen et al. (2016) who
have found that people tend to prefer candidates listed on the last row of the ballot paper or
ballot column.9 Thus, although we focus on candidates whose chances of being elected are
very low, our results add to the literature on ballot design (see, e.g., Geys and Heyndels
2003; Ho and Imai 2006; Blom-Hansen et al. 2016; Pierzgalski, Górecki, and Stępień 2020;
Flis and Kaminski 2022), which can have serious consequences for the election results
(Pierzgalski, Górecki, and Stępień 2020; Flis and Kaminski 2022). In the case of the 2014
Polish election, it even led to accusations of electoral fraud (Flis and Kaminski 2022).
2.3 Contribution to the literature
This chapter adds to various strands of literature on voting behavior. Within the sociological
model, Lazarsfeld, Berelson, and Gaudet (1968) show that swing voters are affected by
their social environment. Socio-economic status, religion and area of residence emerge
as strong factors behind voting decisions. Undecided voters are often influenced by the
opinions of those around them who are similar to themselves. We contribute to the debate
by showing that these factors also play a role in the voter-candidate relationship. Although
the literature highlights that candidate personality can only sway voters from one party to
another in less than 10% of cases (Van Holsteyn and Andeweg 2010), in OLPR systems, the
electoral outcome is often very close and the effect of homophily identified by Coufalová,
Mikula, and Ševčík (2023) emerges as a relevant cue for uninformed voters and can thus be
a decisive factor.
While the social model is more concerned with factors of a long-term nature, it does
not explain why some individuals belonging to one social group decide as if they pertained
to another social group. This is because short-term factors that are specific to each election
also come into play. These are addressed by the psycho-social model of voting behavior,
which is based on the idea of partisanship, understood as psychological affinity to some
party. Moreover, the mechanisms behind the social model are often psychological in nature,
so the line between both models of voting behavior is often blurred (Antunes 2010). To
make it even more complicated, authors defending the third—rational choice—model
introduced by Downs (1957) argue that voters do not make decisions based on psychological
factors, but rather on the information available to them and rationality (see, e.g., Antunes
2010).
9. In columns (4)–(6) in Table 2.4, we replace the baseline control group with candidates listed in the
position (𝑏 − 2). The effect is lower in magnitude—33.3–37.7%—, but still substantial and statistically
significant, suggesting that the page break lowers the number of votes a candidate receives.
36
Focusing on preferential votes allows us to filter out the effect of political party
preference and the effect of economic performance. Decoupling these influences makes it
possible to fully compare our results to those of other countries with different historical
experience. There is thus no reason to suspect that the effects of homophily and attention
are an exclusively Czech phenomena. The Czech electoral system and ballot design
offer a unique opportunity to study the impact of both effects on voting behavior, but the
results apply also to other countries because preferential voting is—with its nuances—very
common in Europe as well as in other countries. Moreover, in countries where giving
preferential votes is compulsory, these effects may be even stronger.10
Last but not least, in Coufalová and Mikula (2023), we describe an important psychological
aspect of voting, which is its cognitive complexity. However, the results obtained are
relevant to a much wider audience. They also contribute to the general literature examining
how the presentation order affects choice (see, e.g., Kim, Krosnick, and Casasanto 2015),
which was observed in relation to the vertical position of an item in internet surveys
(Tourangeau, Couper, and Conrad 2013), as well as in preference judgment in horizontal
ordering (Englund and Hellström 2012). The ordering effect was identified also by J. Berger
(2016), who shows that the position of an article in a journal issue can—due to our limited
cognitive abilities—affect the number of citations the article receives. And finally, the
results of product taste tests—such as those of wines—are conditioned by the order of
testing as well (Dean 1980; Mantonakis et al. 2009).
2.4 Limitations
Of course, the use of observational data also has some limitations. The first issue relates to
our research on homophily, as we do not see the characteristics of people who actually go
out to vote. This is even more striking in the case of gender homophily, where we did not
obtain robust results, partly due to the fact that both genders are almost equally represented
across municipalities. In our ongoing research, we will investigate the presence of gender
homophily using a different method—survey experiment.
10. Preferential voting is optional in the Czech Republic or Switzerland (Portmann and Stojanović 2019)
and mandatory, for example, in Poland and Finland. The different systems also differ in the number of votes
that voters can (or must) cast. While in some countries or states (Finland, Poland and Bavaria), voters cast
just one preferential vote (Marcinkiewicz and Stegmaier 2015; Söderlund, Schoultz, and Papageorgiou 2021),
in others (Czech Republic, Luxembourg, Lithuania, Greece, Slovakia and Switzerland), they may select
multiple candidates, with or without a threshold (Marcinkiewicz and Stegmaier 2015). In some countries (for
instance, Switzerland), they can also cast negative votes or even choose candidates from more than one ballot
list, which is known as panachage (Portmann and Stojanović 2019).
37
A certain limitation in the methodology used in Coufalová and Mikula (2023) is that
the order of candidates on the ballot is not randomized. However, we show that there is no
difference in observable characteristics of candidates’ neighbouring with the break and that
the break varies within party as well as within constituency, which makes it difficult for
parties and individual candidates to guess the location of the break in advance. Moreover,
no candidate running on a position neighbouring with the break was elected in any of the
elections we’ve examined. We provide several reasons to believe that political parties do
not optimize the composition of the electoral list with respect to the location of the break
and thus the location of the break can be considered quasi-random.
2.5 Future research avenues
Our findings may have important implications for candidate list optimization, opening
doors for future research in several areas. First, some population groups—such as women,
less educated people, or blue-collar workers—are poorly represented on the electoral lists.
Esteve Del Valle, Broersma, and Ponsioen (2022) employ exponential random graph models
on tweets to describe network parameters and individual characteristics that facilitate
communication among members of Dutch parliament. They show that if voters can feel
that there is no one who defends their interest, it could lead them to adopt extreme political
positions. If homophily drives voters’ decision as we showed in Coufalová, Mikula, and
Ševčík (2023), parties could attract more voters by diminishing the under-representation of
these groups on the electoral list. This could help reduce polarisation in society.
Moreover, Gerber, Henry, and Lubell (2013) show that similarities reduce the transaction
costs of engaging in collective action which increases the probability of collaboration in
regional planning networks. Thus, the existence of homophily can affect the distribution of
political resources and consequently the policy outcome. The extent to which this is true
could be a promising area for future research.
Third, Van Holsteyn and Andeweg (2010) observe that some candidates’ characteristics
can attract voters to another party. With regards to the page placement, Ho and Imai (2006)
focus on the SMD system and find that while the electoral results of high-profile candidates—
those with high chance of being elected and interesting for the media matches—are not
affected by their position on the ballot, the minor candidates listed on the reverse side of
the ballot receive substantially less votes than candidates on the first page. In Coufalová
and Mikula (2023), we show that the same occurs in the OLPR system. Candidates placed
lower on the ballot paper attract substantially fewer votes. Most voters do not pay attention
directly to candidates placed on the reverse side of the ballot. Thus, in order to attract more
votes, parties should place candidates who could attract voters’ attention on top positions
38
of the ballot list. Although this was out of the scope of the second paper presented here,
the efficacy of such a strategy could also be an avenue to pursue in future voting behavior
research.
Furthermore, there are also many opportunities for future research arising from the
rich secondary information available on Czech ballots. One possibility is to focus on
candidates’ first names. The available literature shows that the given first names can
be understood as an informative signal of the candidate’s characteristics, such as his/her
individualism (Ogihara et al. 2015; Ogihara 2023) or nationalist sentiment (Jurajda and
Kovač 2021). Individualism is often seen as a strong determinant of individual preferences
for redistribution (Bazzi, Fiszbein, and Gebresilasse 2020, 2021). Other authors point to
the discrimination of immigrant-origin candidates, being the origin signalled by their last
names. Last names that are common abroad may in turn evoke negative sentiment towards
these candidates (Guzi and Mikula 2021).
Last but not least, the Czech historical context offers many quasi-natural experiments.
One of them was the exposure to foreign media during the socialist period. We expect
the access to information provided by the Western media to have an impact on voting,
migration, FDI or location of foreign companies. Studying the subsequent economic
transition from a centrally planned system to a market economy may be an opportunity to
expand the economic voting literature by exploring support for economic transformation,
as outlined in the previous chapter (see 1.7)
Chapter 3
Authorship contribution statements
The authors are listed in alphabetical order.
Lost in the Transition: Czech Businesses Pivoting from the Centrally
Planned Economy to Capitalism (Coufalová and Žídek 2023)
• Author’s contribution share: 70% (corresponding author)
• Author’s relevant contribution: Data curation and analysis; Literature review;
Conceptualization and methodology; Writing (original draft and review).
Access to Bank Loans in Economic Transition: An Oral History
Approach (Coufalová 2024)
• Author’s contribution share: 100% (corresponding author)
• Author’s relevant contribution: Data curation and analysis; Literature review;
Conceptualization and methodology; Writing (original draft and review).
Homophily in Voting Behavior: Evidence from Preferential Voting
(Coufalová, Mikula, and Ševčík 2023)
• Author’s contribution share: 40%
• Author’s relevant contribution: Literature review; Conceptualization and methodology;
Writing (original draft and review).
40
The Grass is not Greener on the Other Side: the Role of Attention in
Voting Behavior (Coufalová and Mikula 2023)
• Author’s contribution share: 50%
• Author’s relevant contribution: Literature review; Conceptualization and methodology;
Writing (original draft and review).
Concluding remarks
This habilitation thesis presents selected papers that together contribute to the literature on
economic policy. The two papers included in the first chapter uncover the main problems
of Czech enterprises (and to a lesser extent banks) during the Czech transition period.
Their main contribution is showing the relevance of the oral history method for economic
historians. Oral testimonies emerge as complementary to scholarly written sources and
form an essential part of the historical discourse.
The second chapter presents two papers dealing with voting behavior in the Czech
Republic and some of the specific features of the Czech electoral system. Preferential
voting allows us to filter out the influence of political party and economic performance.
The ability to separate these effects creates an important foundation for further research.
The two subsections on future research avenues indicate the paths my research will
largely take in the future. Historical experience in the form of the fall of the Iron Curtain
offers a number of unexplored natural experiments related to both areas of research presented
here. For instance, in our ongoing studies, we hypothesize that the high concentration of
scarce resources prior to 1989 affected economic activity in the transition period or that the
spatial variation in access to Western television stations has influenced the accumulation of
human capital and the subsequent socio-economic development of regions in the post-1989
period.
Another promising area for future research, building on both chapters, is economic
voting, specifically the support for economic transformation.
42
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