Article

# Disinformation and Regime Survival


Yuko Sato [1] [](https://orcid.org/0000-0002-7458-6134) and Felix Wiebrecht [2] [](https://orcid.org/0000-0002-9159-5024)


Political Research Quarterly
2024, Vol. 77(3) 1010–1025
© The Author(s) 2024


[DOI: 10.1177/10659129241252811](https://doi.org/10.1177/10659129241252811)


Abstract

Disinformation has transformed into a global issue and while it is seen as a growing concern to democracy today,
autocrats have long used it as a part of their propaganda repertoire. Yet, no study has tested the effect of disinformation
on regime stability and breakdown beyond country-specific studies. Drawing on novel measures from the Digital Society

–
Project (DSP) estimating the levels of disinformation disseminated by governments across 148 countries between 2000
2022 and from the Episodes of Regime Transformation (ERT) dataset, we provide the first global comparative study of
disinformation and survival of democratic and authoritarian regimes, respectively. The results show that in authoritarian
regimes, disinformation helps rulers to stay in power as regimes with higher levels of disinformation are less likely to
experience democratization episodes. In democracies, on the other hand, disinformation increases the probability of
autocratization onsets. As such, this study is the first to provide comparative evidence on the negative effects of
disinformation on democracy as well as on the prospects of democratization.


Keywords
disinformation, propaganda, democratization, autocratization, regime survival


Introduction


Disinformation by political actors is a growing concern
worldwide, and its deleterious effects were becoming
palpable even before the COVID-19 pandemic and
Russia’s invasion of Ukraine (e.g., Bennett and
Livingston 2018). Here, we concur with prior conceptualizations of disinformation and define it as purposefully
created information that “has the function of misleading”
(Fallis 2015, 422) and is “intentionally and verifiably
false” (Allcott and Gentzkow 2017, 213). Although
disinformation has long been part of dictatorships’ propaganda machines, autocrats appear to have become more
blatant in “spinning” false narratives in attempts to secure
their hold on power (e.g., Guriev and Treisman 2022;
Tenove 2020). Anti-pluralists and aspiring autocrats in
democracies such as the United States, Brazil, Germany,
and Sweden are also increasingly spreading “fake news”
(e.g., Larsson 2020; Zimmermann and Kohring 2020).
Targeted campaigns abroad by regimes such as Russia,
China, and Iran are adding further stress to democracies
(e.g., Hjorth and Adler-Nissen 2019; Pomerantsev 2015).
As information is a fundamental resource for voters to

hold governments accountable, disinformation is characterized as one of the key challenges to democracy (e.g.,
Benkler, Faris, and Roberts 2018).


Despite the abundance of disinformation and the
growing concern around it, comparative research on the
issue and its political consequences is rare (for exceptions
see Piazza 2022, Humprecht, Esser, and Van Aelst 2020;
Hunter 2023). In autocracies, disinformation is an inherent part of propaganda, but to date, empirical research
has almost exclusively studied the cases of Russia and
China (e.g., Huang 2015; Huang 2018; Rozenas and
Stukal 2019). Here, the primary focus of these studies
is on governments’ strategies for information control and
dissemination. At the same time, the macro-level political
consequences, such as its effect on regime stability, have
been understudied. In democracies, most prior research
has focused on questions at the individual level, such as
voters’ exposure and susceptibility to disinformation (e.g.,
Enders et al. 2021; Erlich and Garner 2023; Hjorth and
Adler-Nissen 2019), but also less on its consequences on
the political system. Thus, it remains debated whether


1 Waseda University, Shinjuku-ku, Japan
2 University of Liverpool, Liverpool, UK


Corresponding Author:
Felix Wiebrecht, University of Liverpool, 8 Abercromby Square,
Liverpool L69 3BX, UK.
[Email: felix.wiebrecht@liverpool.ac.uk](mailto:felix.wiebrecht@liverpool.ac.uk)


disinformation poses an immediate threat to democracy or
remains a marginal phenomenon without far-reaching
consequences (e.g., Allcott and Gentzkow 2017;
Jungherr and Schroeder 2021; Lanoszka 2019).
In this paper, we seek to analyze the consequences of
disinformation used by governments on political systems more specifically and combine insights from both
regime types into a common framework of the effect of
disinformation on regime survival. We argue that disinformation proves to be an effective tool for dictators
to retain their hold on power and show that democratization is less likely in authoritarian regimes that
disseminate more disinformation. On the other hand,
we also echo concerns surrounding growing disinformation in democracies and suggest that higher levels of
disinformation are associated with onsets of autocrat
ization. Taken together, we suggest that disinformation
is detrimental to democracy across regime types. In
order to test this argument, we go beyond the existing
China/Russia- (for autocracies) and US-centrism (for
democracies) of prior studies and employ a comparative
study. We draw on measures of disinformation from the
Digital Society Project (DSP) (Mechkova et al. 2022)
and combine it with the Episodes of Regime Transformation (ERT) dataset (Edgell et al. 2023) that
identifies episodes of autocratization as well as liberalization. These datasets allow us to conduct a cross
national time-series study to systematically examine the
effects of disinformation on regime survival across
148 countries between 2000 and 2022.
Empirically, we find that once we disaggregate the
sample by regime types, our results show support for the
regime-stabilizing function of disinformation in authoritarian regimes, making democratization episodes overall
less likely. In democracies, higher levels of disinformation
increase the probability of autocratization onsets and
democratic breakdowns. To explain this finding, we point
to the fact that disinformation in democracies promotes
polarization in society, which inflates the risk of onsets of
autocratization episodes. These results are robust to the
inclusion of a number of control variables, alternative
empirical modelings, and an instrumental variable (IV)
analysis that addresses endogeneity concerns.
The contributions of this study are twofold. First, we
overcome the scope limitations of previous studies and
add further generalizability to prior studies on disinformation. Research on authoritarian regimes’ use of disinformation is mostly based on findings from China and
Russia (e.g., Huang 2015; Rozenas and Stukal 2019)
while studies on disinformation’s threat to democracy
primarily emanate from the United States (e.g., Tucker
et al. 2018). As such, this study is the first to identify the
link between disinformation and regime survival globally
as well as across autocracies and democracies.


Second, the findings from this study contribute to
debates about the impact of disinformation. Although
research on authoritarian regimes has identified propaganda as a pillar of regime stability (e.g., Carter and Carter
2021; Huang 2015), others noted that propaganda and
especially disinformation can also backfire (e.g., Huang
2018; Wang and Huang 2021). Adding to this debate, we
find that across autocracies, disinformation can be an
effective tool for dictators and is more likely to stabilize
autocracies. Likewise, while many identify disinformation as a major challenge for democracies (e.g., Bennett
and Livingston 2018), others see the reach of disinformation, and consequently, its influence as limited (e.g.,
Allcott and Gentzkow 2017; Grinberg et al. 2019; Guess,
Nagler, and Tucker 2019; Lanoszka 2019). Despite such
mixed results, we highlight that disinformation is associated with severe consequences, namely, the onset of
autocratization episodes and democratic breakdown.
In the following, this paper will introduce its theoretical framework and hypotheses of how disinformation
affects regime stability both in autocracies and democracies. Next, we introduce the empirical strategy to test
these arguments. Then, we present the statistical results of
the effect of disinformation on regime survival. Finally,
we discuss the possible implications of our findings in the
conclusion.


Defining Disinformation


Especially since the presidency of Donald Trump, disinformation and “fake news” have received heightened
attention in popular as well as academic discourses. While
notions such as “misinformation,” “disinformation,”
“conspiracy theory,” and “propaganda” are often used
interchangeably, it is important to retain conceptual
clarity. Starting from the broadest category, we follow
prior research in this line of work and define misinformation as claims “that contradict or distort common
understandings of verifiable facts” (Persily and Tucker
2020, 10). Disinformation, then, is a subcategory of
misinformation with the difference being that it is purposely disseminated (e.g., Persily and Tucker 2020;
Tucker et al. 2018). In other words, both concepts describe
false claims but while misinformation may be shared
accidentally and without malicious intent, disinformation
“has the function of misleading” (Fallis 2015, 422) and
deceiving. Although the question of intent may be difficult
to prove, “organized attempts” of disseminating false
information can be seen as a good indicator of such
(Persily and Tucker 2020). Typical examples of disinformation may include Russia’s strategic disinformation
campaigns domestically and abroad (e.g., Pomerantsev
2015) but also unsubstantiated claims of electoral fraud in
the US (e.g., Berlinski et al. 2021).


As such, disinformation is different from other concepts such as conspiracy theories, even though they may
often go hand in hand in practice. While truthfulness, or
the lack thereof, is a defining element of disinformation,
conspiracy theories, however, can rarely be classified as
verifiably false and instead often share the belief into
secretive elites that exercise control over society (e.g.,
Pirro and Taggart 2022; Sunstein and Vermeule 2009).
Yet, disinformation shares some overlap with the concept
of propaganda. The latter is defined as a “deliberate,
systematic attempt to shape perceptions, manipulate
cognitions, and direct behavior to achieve a response that
furthers the desired intent of the propagandist” (Jowett
and O’Donnell 2018, 6). As a broader strategy, this may
and often does, contain disinformation (e.g., Lanoszka
2019) but also entails presenting and framing true pieces
of information in a way that “disparages opposing
viewpoints” (Tucker et al. 2018, 3). Elements of disinformation have therefore long been part of autocrats’
broader propaganda and information control strategies
including in Rwanda and Nazi Germany (e.g., Adena et al.
2015; Yanagizawa-Drott 2014).


Disinformation in Autocracies: Pillar of

Regime Stability


Naturally, disinformation is more prevalent in dictatorships than in democracies (Boese et al. 2022). Not only do
authoritarian regimes frequently disseminate disinformation but they also do so in an environment in which
alternative channels of information are hardly available.
In other words, disinformation from official channels is
often the only ‘truth’ as it cannot be verified or triangulated with different sources (e.g., Gleditsch, Mac´ıasMedell´ın, and Rivera 2023; Guriev and Treisman 2022).
We hold that this strategy is an effective tool for dictators
to remain in power as it hampers prospects for democratization in autocracies. In particular, it insulates dictators
from mass protests, and we identify two primary ways in
which disinformation affects citizens’ willingness to
protest.
First, disinformation can directly deflect responsibility
and blame from dictators, making it more difficult for
people to rally against them. In the absence of alternative
sources of information and tight control of the internet,
disinformation is significantly harder to detect, creating an
opportunity for autocratic governments to present their
performance better than it actually is (Boese et al. 2022).
For example, authoritarian regimes regularly and frequently manipulate statistics on indicators such as economic growth (Martinez 2022) and deaths from COVID19 (e.g., Annaka 2021). Instead, both Russia and China,
for instance, repeatedly blame the West for bad economic


situations and escalating tensions with them (e.g.,
Rozenas and Stukal 2019). Accordingly, disinformation
often increases citizens’ support for the regime while
decreasing their motivation to protest against the government (e.g., Guriev and Treisman 2022). Even if citizens do not change their attitude about the government’s
performance themselves as a result of propaganda, they
may still believe that others have been persuaded, making
coordination more difficult (e.g., Buckley et al. 2022;
Huang and Cruz 2021). Consequently, in a regime that is
dominated by propaganda and disinformation, consensus
over the government’s performance is difficult to establish, and thus, collective action problems are unlikely to
be solved. Ultimately, people may be less inclined to
mobilize against the regime.
In addition, disinformation, similar to propaganda, may
not only deter citizens from protesting because of its persuasiveness but may also signal the government’s strength to
regime critics (e.g., Huang 2015). They may be in a better
position to identify disinformation as such because of access
to international sources and networks. Yet, blatant disinformation can showcase a regime’s grip on society and its
intolerance for open debates. A regime that is willing to
severely manipulate the information environment and disseminate false narratives, is likely to also resort to traditional
forms of repression. For potential dissidents, extensive
disinformation campaigns may signal the government’s
extensive reach and make them less willing to protest in fear
of likely repression. Indeed, prior work shows that digital
repression including disinformation campaigns often goes
hand in hand with traditional repression tools (e.g., KendallTaylor, Frantz, and Wright 2020). In consequence, as a
government resorts more explicitly to disinformation, potential regime critics may abstain from protesting due to the
fear of repression.
On the other hand, disinformation may not only disincentivize regime opponents from protesting against the
regime, but it may also mobilize regime supporters to rally
in favor of the government. Pieces of disinformation often
claim to identify out-groups as perpetrators or causes of
societal issues, whether they are internal (e.g., opposition
actors, ethnic and religious minorities) or external (e.g.,
the US, the EU, or migrants generally). Simultaneously,
this may enable autocrats to present themselves and their
supporters as victims—a narrative that is often used to
mobilize supporters (e.g., Ekiert, Perry, and Yan 2020;
Pirro and Taggart 2022). Once pro-regime supporters are
mobilized, these rallies also secure autocrats’ hold on
power since they signal strength and restrain mobilization
against the regime (Hellmeier and Weidmann 2020).
In line with these suggested mechanisms, we hypothesize that high levels of disinformation are detrimental to the chances of democratization in authoritarian
regimes. Our first hypothesis is thus, as follows:


H1: Autocracies are less likely to experience democratization when the government is more actively
disseminating disinformation.


Disinformation in Democracies: Threat

of Autocratization


In addition to dictators’ long-established propaganda
strategies, political leaders in some democracies also
increasingly use disinformation (Boese et al. 2022). In
Poland, for example, public broadcasters had until
2023 increasingly become amplifiers of the previous
right-wing government’s disinformation campaigns attacking migrants and discrediting civil society. [1] Instances
like this distort people’s preferences and pose a challenge
to democratic systems’ capacity for inclusion and reasoned deliberation (McKay and Tenove 2021). Therefore,
we expect disinformation to be a significant factor in
undermining democracies and making autocratization
more likely. [2]

Disinformation is primarily disseminated by antipluralist parties and actors in government attempting to
remain in power (Boese et al. 2022), including Republicans in the US (e.g., Allcott and Gentzkow 2017), radical
right parties in Europe (e.g., Bennett and Livingston 2018;
Hameleers and Minihold 2022), or pro-Russian parties in
Ukraine (e.g., Erlich and Garner 2023; Peisakhin and
Rozenas 2018). Through false stories targeting competitors (e.g., Pirro and Taggart 2022; Tenove 2020;
Zimmermann and Kohring 2020), anti-pluralists attempt
to boost their own popularity or avoid blame in government (e.g., Pirro and Taggart 2022). Unfortunately for
democracy, being exposed to disinformation also has an
impact on voting choices at least in some cases
(Cantarella, Fraccaroli, and Volpe 2023; Zimmermann
and Kohring 2020).
More generally, however, disinformation also threatens
democracy due to its inherent potential to polarize society.
Disinformation can affect citizens’ trust in democratic

institutions and, thus, their preference for a democratic
regime. Trump’s false allegations of electoral fraud in
2020, for instance, reduced trust in electoral integrity (e.g.,
Berlinski et al. 2021). Other examples include unjustified
campaigns against expert commissions and institutions
that undermine their credibility and trust in them (e.g.,
McKay and Tenove 2021). Often, an inherent part of
disinformation is the use of “false claims, conspiracy
theories, chauvinistic language, and visual imagery to
stoke moral revulsion toward particular individuals, political parties, and social groups” (McKay and Tenove
2021, p. 709). In turn, disinformation and the branding of
political opponents also inflate negative feelings and
distrust, reinforce partisan identities, increase polarization


of public opinion, and even instigate violence (Berlinski
et al. 2021; Hjorth and Adler-Nissen 2019; Peisakhin and
Rozenas 2018).
In such highly polarized contexts, voters may be more
likely to sacrifice democratic principles to elect a candidate who champions their party or interests (Graham
and Svolik 2020; Svolik 2019). Over time, these differences may transform into separate political camps with
distinct understandings of what constitutes factual information. Such “pernicious polarization” (McCoy and
Somer 2019) may be unsustainable for democracies.
Based on these proposed effects, we suggest that
disinformation increases democracies’ chances of auto
cratization and propose the following hypothesis:


H2: Democracies are more likely to experience autocratization when the government is more actively
disseminating disinformation.


In sum, following these proposed theoretical mechanisms, we argue that disinformation stabilizes authoritarian regimes and destabilizes democracies. In the
following, we will illustrate our research design and
empirical strategy.


Research Design


Despite burgeoning bodies of literature on disinformation
and propaganda, comparative analyses are rare. Most
existing work has focused on individual-level effects,
primarily in the United States (for disinformation) as well
as China and Russia (for propaganda). While these have
led to tremendous progress in the study of these phenomena, we complement them with a comparative
country-level study to understand their systemic effects on
political regimes based on a global sample.
In order to test the relationship between disinformation
and regime survival, we combine data from two primary
sources. First, we utilize data from the Digital Society
Project (DSP) (Mechkova et al. 2022). Among others, this
data contains variables on online censorship, polarization,
and politicization of social media from 2000 to
2022 around the globe. [3] For the purposes of this paper, we
are primarily interested in the variable that captures domestic disinformation efforts by the respective government (v2smgovdom). [4] This is an expert-coded variable
describing how prevalent the dissemination of false information by the government to influence its own population is and ranges from “extremely often” to “never or
almost never.” [5] The experts’ ratings are aggregated using
a Bayesian item response theory measurement model that
can account for coder differences (Pemstein et al. 2018).
This variable enables us to capture the core definition of


disinformation—purposefully created information that
“has the function of misleading” (Fallis 2015).
As a dependent variable, we consider two sets of
variables. The first set of dependent variables is onsets of
democratization and autocratization episodes. Democratization or autocratization episodes are defined as periods of substantial and sustained improvements or
declines of democratic attributes measured with V-Dem’s

Electoral Democracy Index (EDI) (Maerz et al. 2023). We
call the first year of such episodes as democratization/
autocratization “onset.” We utilize the Episodes of Regime Transformation (ERT) dataset (Edgell et al. 2023),
which contains a complete sample of democratization and
autocratization episodes between 1900 and 2022 that is
comparable to existing datasets (Maerz et al. 2023). [6] Here,
we code the country-years in which a democratization or
autocratization episode starts as one and zero otherwise.
The country-years in ongoing episodes are excluded. We
primarily estimate how the government’s use of disinformation increases or decreases the probability of a
country experiencing the onset of a democratization or
autocratization episode. However, we also consider how
disinformation affects the likelihood of democratic

transition and democratic breakdown as a result of de
mocratization or autocratization episode onsets. Using the
ERT dataset, we identify whether democratization or
autocratization episodes resulted in a regime transition. [7]

Figure 1 illustrates the general relationship between the
level of disinformation and the EDI. The figure shows a
clear negative relationship between the government’s use
of disinformation and the quality of democracy. The figure
shows that stable democracies (such as Belgium and
Latvia) and autocracies (such as North Korea, Venezuela,
and Cuba) are clustered at the lowest and highest end of
the range of disinformation. Figure 1 further demonstrates
the relationship between disinformation and whether
countries democratized or autocratized in the observed

year. Generally, countries undergoing autocratization tend
to have higher levels of disinformation (above the fitted
line), while states experiencing democratization tend to
observe lower levels of disinformation (below the fitted
line). In the cases of Fiji in 2002 and Afghanistan in 2001,
the governments seem unable to effectively use disinformation despite their autocratic characteristics. On the
other hand, while levels of democracy in Brazil in
2022 and the United States in 2020 are relatively high, the
autocratizing government extensively uses disinformation. In sum, disinformation tends to correlate with both
the levels of democracy and transition episodes. We
further test these relationships using statistical models.
Second, to illustrate the mechanisms behind the relationship between disinformation and regime stability, we
draw attention to disinformation’s potential for mobilization and polarization of society. First, as the theory


indicates, the government’s use of disinformation, especially under autocracies, may decrease the citizens’
willingness to protest against the government, while it is
often used to mobilize the regime supporters to show their
legitimacy. Second, disinformation under democracy
tends to be used to polarize the voters intentionally.
Therefore, we estimate the effect of disinformation on the
levels of (i) mass mobilization and (ii) polarization of
public opinion. Mass mobilization is measured by
V-Dem’s mobilization for democracy (v2cademmob) and
autocracy (v2caautmob) variables which measure the
scale and frequency of pro-democratic and pro-autocratic
mobilization in society. To measure polarization, we use
V-Dem’s political polarization variable (v2cacamps),
which captures the extent to which political differences
affect social relationships beyond political discussions. [8]


Empirical Approach


To estimate the relationship between the government’s use
of disinformation and regime stability, we run two sets of
statistical analyses: estimating the effects of disinformation on regime transformation and its mechanism. To
demonstrate the heterogeneous effect of disinformation in
different regime types, we further divide the sample into
democratic and autocratic regimes based on V-Dem’s
Regimes of the World (RoW) classification (Lührmann,
Tannenberg, and Lindberg 2018). As our primary interest
is the effect of disinformation on regime transitions, we
exclude the analysis of autocratization in autocracies and
democratization in democracies.

To estimate episode onset, we use a probit model in
which those experiencing the beginning of an episode are
treated as ones. Since we use panel data, we have taken
several measures to account for different forms of esti
mation error. First, we use clustered standard errors by
countries to account for heteroskedasticity as well as
autocorrelation. Second, we employ fixed-effects models
to account for unobservable confounding effects derived
from country- or year-specific characteristics. Here,
however, we encounter a common issue in fixed-effects
analyses of dichotomous dependent variables that capture
relatively rare events: the separation problem, in which
dropping non-event-experiencing units from the analysis
results in bias. To address this issue, we first relax some
assumptions and include regional dummies and a linear
time trend to account for global trends instead of countryand year-fixed effects. In addition, we present both
conditional probit and linear versions of our fixed-effects
specifications, with the latter making use of all observations. We follow the recommendations of Beck (2020)
in reporting marginal effects for our key independent
variables from fixed-effects probit analyses and linear
fixed-effects analyses using the same limited sample to


Figure 1. Correlation between Disinformation and the Level of Electoral Democracy.
Note: Lines show the predicted levels of electoral democracy with 95% confidence intervals. Names of countries are labeled if the countries are in
episodes of democratization or autocratization with the highest residuals or observed values of disinformation are less than the 1st percentile and higher
than the 99th percentile of entire observations.


allow for a meaningful comparison between the
specifications. [9]

Second, to estimate the relationship between disinformation and polarization and mobilization, we use OLS
models with country- and year-fixed effects taking account of all unobservable factors.

To estimate the relationships, we include a number of
control variables to account for potential confounding
factors between disinformation and regime survival. The
first set of variables relates to countries’ economic situation. Since Lipset’s (1959) seminal work, existing studies
indicate a strong correlation between levels of economic
development and democratic regimes’ development and
stability (e.g., Przeworski and Limongi 1997). In addition,
studies indicate that negative economic growth is a predictor of regime breakdown either through democratization (e.g., Teorell 2010) or democratic breakdown (e.g.,
Bernhard, Reenock, and Nordstrom 2004). Such economic conditions may also affect the government’s capacity to use disinformation (Rozenas and Stukal 2019).
Thus, we control for GDP per capita and GDP growth
rate, extracted from the World Bank (WDI 2023). As
autocratic leaders often use education as a tool of in
doctrination for their ideology, such levels of indoctrination affect both the voters’ susceptibility to the


government’s disinformation efforts and their support for
the regime. Accordingly, we control for indoctrination
potential in education (v2xed_ed_inpt) from the V-Dem
dataset (Coppedge et al. 2023; Northmore-Ball et al.
2023).
The second set of variables relates to the government’s
motivation to use a disinformation strategy. First, we
control for the population size as it might affect a polity’s
susceptibility to disinformation and regime change.
Second, we control for internet penetration rates. As
contemporary disinformation primarily spreads through
the internet and social media (e.g., Trauthig, Martin, and
Woolley 2023), the effect of disinformation on democratization or autocratization may depend on the share of
internet users among the population. Third, we control for
the government’s internet filtering capacity to account for
the extent to which a government can produce and disseminate disinformation in the first place. This variable is
also taken from the DSP.

The last set of variables relates to countries’ democratic

embeddedness. First, we include the regional levels of
democracy across six world regions in the empirical
models. This variable controls for the diffusion effects of

democratization (e.g., Brinks and Coppedge 2006) and
autocratization (e.g., Lührmann and Lindberg 2019) from


neighboring countries and the government’s motivation of
controlling information during such episodes. Second, we
control for countries’ previous experience under democracy (democratic stock). Studies indicate that the
institutionalization of democratic institutions increases the

probability of democratization and the stability of democracy (Boese et al. 2021; Svolik 2015). We draw on a
recently developed measure of democratic stock based on
V-Dem’s EDI (Edgell et al. 2020).
Descriptive statistics of all variables used in our em[pirical models are presented in Table A1 in the Appendix.](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)


Potential Endogeneity and Instrument


Regime transformation is associated with technological
change, that is, the liberalization of information and
communications technology increases the probability of
democratization, or dictators limit the diffusion of information to lengthen their time in office (e.g., Knutsen
2015). At the same time, leaders only choose disinformation strategies to avert democratization (autocracies) or
promote autocratization (democracies) if the internet is
highly influential in citizens’ lives. Accordingly, our
model estimates are likely biased if we include internet
penetration due to the potential confounding effects. The
exclusion of internet penetration from the model will also
cause the estimate to be biased since, in that case, disinformation may capture the impact of the internet on
regime transformation through other ways than
disinformation.

In addition, recent studies increasingly reveal the
shortcomings of fixed effects regressions to show the
causal effect with the longitudinal data (e.g., Imai and Kim
2019; Imai and Kim 2021). The models are fundamentally
based on the within-unit comparison, and they presume
the absence of dependency between the past outcome and
the treatment assignment, which is potentially violated in
this study.
To alleviate such endogeneity concerns, we perform an
instrumental variable (IV) analysis. Following Jha and
Kodila-Tedika (2020), we use technological adoption in
communication in 1500 CE as an instrument for internet

penetration. As Comin, Easterly, and Gong (2010) show,
cross-country differences in technological adoption in the
communication industry in 1500 CE can explain current
cross-country variations in technological states. This is
because the technological advantage, such as lower cost of
adopting new technology and innovation, economies of
scale, and cross-sectoral technological spillovers, persists
over a long time. The data for the technological adoption
index in communication in 1500 CE is extracted from

the Cross-country Historical Adoption of Technology
(CHAT) dataset (Comin and Hobijn 2009). In addition,
to incorporate the global levels of technological


advancement over time, we use the exogenous variation in
average worldwide internet penetration following Hunter
(2023).
Thus, we consider the instrument as the interaction of a
state-specific variable (technological adoption in communication in 1500 CE) and the time-invariant variable
(average worldwide internet penetration).
Since there is little reason to believe that technological
adoption in communication in 1500 CE and the average
worldwide internet penetration rate will affect the likelihood of regime transformation today other than via its
effects on internet penetration, they are valid instruments.
Moreover, we find that the technological adoption in
communication in 1500 CE, as well as the average
worldwide internet penetration, are strong predictors of
internet penetration today, making them robust
instruments.


Results


First, we disaggregate the sample into democratic and
autocratic regimes (Table 1) and show if the level of
disinformation affects episode onsets. Models 1–3 indicate the effects of variables on the probability of democratization onset in autocracies. The results

demonstrate that the government’s use of disinformation
negatively affects the probability of democratization
onset, and the effect is statistically significant at high
confidence levels. The results hold when including
country- and year-fixed effects and the marginal effect of
the probit model is comparable to that of the linear
[probability model (Table B1 in Appendix). In addition, we](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
present a baseline treatment effect without any covariates
to alleviate the potential overfitting problem of including
[too many controls in a two-way fixed-effects model (Table](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
[B2 in Appendix). By excluding all covariates, we found a](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
consistent result with the main estimate.

Next, Models 4–6 indicate the effect of variables on the
likelihood of an autocratization onset in democracies. The

effect of government disinformation is positive and statistically significant indicating that disinformation increases the probability of a country to start autocratizing.
This result is consistent when including country- and year[fixed effects (Models 5 and 6, and Table B1 in Appendix)](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
[and excluding all covariates (Table B2 in Appendix). The](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
results indicate that disinformation decreases the stability
of democracies.


Figure 2 further shows the predicted probabilities of
democratization onsets in autocracies (left panel) and
autocratization onsets in democracies (right panel). The
left panel indicates that the predicted probabilities of
democratization onset decrease on average from 10% to
0% when the disinformation scores move from the lowest

to the highest in autocracies. On the other hand, the right


Table 1. The Effect of Disinformation on Democratization and Autocratization Onsets.


Dependent variables:


(1) (2) (3) (4) (5) (6)
Democratization onset Autocratization onset


Disinformation �0.322*** �0.519*** �0.039** 0.285*** 0.731*** 0.070**

(0.065) (0.085) (0.018) (0.077) (0.116) (0.029)
Internet penetration �0.580 �1.005 *** �0.063 0.399 2.088*** �0.032
(0.469) (0.375) (0.062) (0.546) (0.487) (0.080)
Indoctrination potential �0.446* �0.322 �0.020 0.244 0.852 0.155
(0.251) (0.370) (0.087) (0.406) (0.747) (0.215)
GDP growth �0.028*** �0.032*** �0.003* �0.018* �0.011 �0.002
(0.010) (0.006) (0.002) (0.010) (0.007) (0.001)
GDP per capita (log) �0.051 0.109 �0.004 �0.224** �0.486*** �0.005
(0.073) (0.094) (0.015) (0.103) (0.161) (0.026)
Population (log) �0.064* �1.687*** �0.114* 0.073 3.685*** 0.191*
(0.037) (0.384) (0.066) (0.049) (0.733) (0.100)
Internet filtering capacity 0.029 �0.137 �0.013 0.301*** 0.309*** 0.030*
(0.055) (0.119) (0.020) (0.064) (0.089) (0.018)
Democratic stock �0.058 �19.550*** �1.955** 1.530 68.822*** 1.643
(0.982) (4.615) (0.787) (0.931) (5.833) (0.698)
Regional democracy levels 2.834 0.220 20.451*** 0.617
(2.382) (0.481) (2.565) (0.410)
t 0.060 0.121***
(0.040) (0.039)
t [2] �0.002 �0.005***
(0.002) (0.002)
Constant �0.099 26.309*** 2.010* �1.434 �99.710*** �4.203**

(0.858) (6.488) (1.135) (0.988) (13.161) (1.844)


Sample Autocracies Democracies


Observations 1314 1311 1311 1454 1452 1452

Log likelihood �150.368 �127.990 �143.230 �107.654
Adjusted R [2] 0.110 0.077
Region FE YES NO NO YES NO NO
Country FE NO YES YES NO YES YES

Year FE NO YES YES NO YES YES

Model Probit Probit OLS Probit Probit OLS


Notes: Standard errors clustered by country. ***, **, * significant at 0.01, 0.05, 0.10, respectively. Countries that do not have multiple-year observations
are excluded from the analysis for two-way fixed-effects model (Models 2, 3, 5, and 6).


Figure 2. Predicted probabilities (democratization and autocratization onsets).
Note: Lines show the predicted probabilities with the 95% confidence intervals. The left figure is generated from Model 1 and the right figure is
generated from Model 4 of Table 1.


panel indicates that the predicted probability of an autocratization onset increases as governments’ use of
disinformation increases. This increase amounts to, on
average, from 0% to 7% when disinformation levels move
from the lowest to the highest in democracies.
Next, we test how disinformation strategies used by
governments affect regime stability using the two-step
model developed by Boese et al. (2021). In this analysis,
we estimate both the probability of (i) the onset of episodes and (ii) the breakdown of the regime (democratic
transition or breakdown) separately. Models 11–12 in
[Table B3 in Appendix test the effects of variables on](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
selection into democratization episodes and democratic
transition. The results indicate that the government’s
disinformation strategy significantly decreases the probability of democratization onsets. Model 12 assessing the
effects of factors on democratic transition also indicates

                                   that disinformation has negative and statistically signifi
cant effects. Thus, the result demonstrates that disinformation is effective in preventing democratization onsets
and democratic transition altogether.
[Models 13–14 in Table B3 indicate the effects of](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)

variables on selection into autocratization episodes and
democratic breakdown, respectively. Model 13 indicates
that the government’s use of disinformation significantly
increases the probability that the country will experience
the onset of autocratization. The coefficient of the second
stage is also positive and statistically significant, indicating that it affects the probability of democratic
breakdown.
Thus, we find empirical support for the two hypotheses: (i) autocracies are less likely to experience democratization when high levels of disinformation are
prevalent (H1) and (ii) democracies are more likely to
experience autocratization when high levels of disinformation are prevalent (H2). In addition, we find that in both
autocratic and democratic regimes, the government’s
disinformation strategy is significantly associated with
both episodes’ onsets and (the absence of) regime
transitions.


Sensitivity Test


Next, we present the results from the models using an
instrumental variable approach (Table 2). We instrument
internet penetration with the technological adoption in
communication in 1500 CE and average global internet
penetration. The technological adoption in communication in 1500 and global internet penetration rates jointly
are significant predictors of internet penetration in both
models in autocracies and democracies. Specifically, the
F-statistics is greater than the rule-of-thumb value of 10,
suggesting that the instruments are strong. With this
specification, the effects of disinformation are compatible


with the main models in Table 1. Moreover, the coefficient
for internet penetration for both models is not statistically
significant, suggesting that disinformation is the mechanism through which the internet promotes or averts regime
transformation. The results add confidence to our main
results. [10]


Mechanisms


In order to illustrate the mechanisms behind the findings
above, we draw attention to disinformation’s potential for
mobilization and polarization of society. We test how
disinformation affects regime stability through increasing/
declining polarization and mobilization of society
(Table 3). The models indicate the effects of disinformation on Mass Mobilization for Democracy (Models 9–

–
10), Mass Mobilization for Autocracy (Models 11 12),
and Political Polarization (Models 13–14).
In authoritarian regimes, while disinformation positively affects the levels of polarization (Model 13), the
government’s use of disinformation does not promote prodemocratic mobilization (Model 9). On the other hand,
disinformation increases the level of autocratic mobili
zation (Model 11). However, using the instrumental
[variable approach (Table C1 in Appendix), we find that](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
disinformation does not increase either form of mobili
zation or polarization in autocracies. These results may
indicate that the observed effects of disinformation are due

to the confounding effects of internet penetration, e.g.,
internet availability increases both the probability of
democratization and the government’s use of disinformation. Nevertheless, this result is in line with the findings
that disinformation helps dictators avoid democratization
and keep the status quo. Regimes with the highest levels
of disinformation generally avoid liberalization episodes
altogether.
Second, in democracies, the government’s use of
disinformation has a strongly positive and significant
impact on political polarization (Model 14), meaning
higher levels of disinformation are linked to a more polarized society. Such political polarization may destabilize
democracy and thus create an opportunity for autocratization onsets. In line with this, the results from Models
10 and 12 further indicate that the government’s disinformation increases both pro-democratic and proautocratic mobilization in democracies. These results

are consistent when using the instrumental variable ap[proach (Table C1), which adds confidence to our findings.](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
Thus, disinformation in democracies can destabilize society and may lead to an onset of autocratization.
In sum, we find strong evidence that disinformation
helps dictators to remain in power and makes democratization less likely. In democracies, since democracies
retain alternative sources of accurate information,


Table 2. Instrument Variable Approach.


Dependent variable:


Democratization Autocratization

(7) (8)


First-stage regression. DV: Internet penetration
Comm.tech. in 1500 CE �0.491*** 0.675***

(0.149) (0.086)
Ave. Internet penetration 0.922*** 0.714***
(0.071) (0.057)
Comm.tech. in 1500 CE*Ave. Internet penetration 0.307*** �0.242***
(0.070) (0.032)
F statistics 18.980*** 57.334***

Second-stage regression. DV: Democratization/Autocratization
Disinformation �0.055** 0.054*

(0.027) (0.032)
Internet penetration (predicted) 0.173 �0.010
(0.438) (0.362)
Indoctrination potential 0.002 0.244
(0.143) (0.285)
GDP growth �0.002 �0.002
(0.002) (0.002)
GDP per capita (log) �0.019 0.029
(0.020) (0.027)
Population (log) �0.055 0.177
(0.200) (0.135)
Internet filtering capacity �0.005 0.035
(0.028) (0.024)

Democratic stock �1.113 1.210

(1.959) (1.138)
Regional democracy levels 0.500 0.814
(0.903) (1.205)

Constant 0.916 �4.252
(3.676) (2.735)


Sample Autocracies Democracies


Observations 820 1114
Adjusted R [2] 0.008 0.088
Country FE YES YES

Year FE YES YES


Notes: Standard errors clustered by country. ***, **, * significant at 0.01, 0.05, 0.10, respectively.


disinformation will rather polarize society. Such polarization may create an opportunity for democratic
breakdown.


Robustness Check


We ran several robustness tests to check the sensitivity of
our results. We conduct robustness checks on the effect of

disinformation by considering alternative datasets and
model specifications.
First, our estimate may be sensitive to the identification of
regime transformation episodes, which are determined by
the ERT dataset (Edgell et al. 2023) (i.e., +/� 0.10 overall


[change of the EDI). Table B4 shows the effect of disin-](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
formation on the probability of regime transformation onsets
is consistent with the main result (Table 1) using either a

� �
lower (+/ 0.075) or higher (+/ 0.125) threshold.
Second, testing the sensitivity of the findings is especially important for estimating the probabilities of a
[rare event. The models in Table B5 of the Appendix](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
show the results of the duration models (Beck, Katz, and
Tucker 1998). The effect of disinformation on democratization onsets is consistent with the main result,
namely that disinformation decreases the probability of
democratization in autocracies. The effect of disinfor
mation on autocratization onsets in democracies,


Table 3. Mechanisms.


Dependent variable


Dem. Mobilization Aut. Mobilization Polarization


Aut. Dem. Aut. Dem. Aut. Dem.
Sample


(9) (10) (11) (12) (13) (14)


Disinformation 0.086 0.375*** 0.384*** 0.308*** 0.460*** 0.424***

(0.132) (0.102) (0.121) (0.117) (0.105) (0.065)
Internet penetration �0.176 �0.528** 0.257 �0.193 0.235 �0.384
(0.308) (0.256) (0.322) (0.258) (0.324) (0.236)
Indoctrination potential �1.269 1.787* 0.127 0.850 0.029 1.637**
(0.827) (0.974) (0.434) (0.733) (0.366) (0.752)
GDP growth �0.016** 0.006 �0.007* 0.005 �0.005* 0.001
(0.007) (0.005) (0.004) (0.005) (0.003) (0.004)
GDP per capita (log) �0.390** 0.009 0.094 �0.008 �0.221*** �0.147
(0.192) (0.116) (0.073) (0.074) (0.069) (0.097)
Population (log) �0.549 �1.357*** �0.090 �0.758* �0.105 �0.722*
(0.353) (0.366) (0.289) (0.392) (0.345) (0.385)
Internet filtering capacity 0.003 �0.075 0.029 �0.123 0.076 �0.144*
(0.100) (0.081) (0.098) (0.083) (0.104) (0.077)
Democratic stock 0.473 2.907 10.728** 2.087 2.773 1.805

(6.474) (3.561) (4.579) (3.437) (5.635) (3.263)
Regional democracy levels �2.195 0.905 �0.673 0.604 �0.914 �2.131
(1.863) (1.294) (1.703) (1.546) (1.753) (1.728)
Constant 12.266* 22.957*** 0.233 11.469 2.899 15.576**

(6.619) (6.616) (4.932) (7.068) (5.674) (7.057)


Country FE YES YES YES YES YES YES

Year FE YES YES YES YES YES YES

Observations 1462 1636 1462 1645 1462 1655
Adjusted R [2] 0.771 0.843 0.850 0.880 0.872 0.928


Notes: Standard errors clustered by country. ***, **, * significant at 0.01, 0.05, 0.10, respectively.


however, disappears once we control for autocorrelation
effects, while the direction of the effect is consistent with the
main result. This null finding may be because of the limited
time period (21 years) combined with the rare events [11] that
make it difficult to estimate the relationship with the duration
model. Using the residual as a dependent variable to alleviate
problems in estimation using a rare event as a dependent
[variable (McGrath 2015) (Table B6); however, we found all](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
consistent results with the main models in Table 1.

Third, we present analyses with different lags of the
[independent variables (Table B7 in Appendix). The result](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
indicates that, in autocracies, disinformation has a consistent and negative effect on democratization onsets
between t to t� 4. However, when we use the change in
the level of disinformation between the observation year
and the year before to account for the possible autocor[relation effect (Table B8), the result is still consistent with](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
the main models in Table 1. On the other hand, in democracies, only disinformation at t and t � 5 have significant effects promoting autocratization onset. The result


may indicate that disinformation has relatively short-term
[effects on autocratization onsets. Table B8 also shows that](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
the change in disinformation strongly and significantly
affects autocratization onset.

Next, we include additional control variables that may
also affect the level of democracy and probabilities of democratization or autocratization onsets: state capacity taken
[from Hanson and Sigman (2021) (Table B9), state repression](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
capacity extracted from Fariss and Schnakenberg (2014)
[(Table B10), government’s control over social media (Table](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
[B11), judicial and legislative constraints on the executive](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
[(Table B12) both taken from the V-Dem’s dataset (Coppedge](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
et al. 2023), and the occurrence of coups in observation years
[taken from Albrecht, Koehler, and Schutz (2021) (Table](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
[B13). The results are largely consistent with the main results,](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
adding confidence to our findings.
In addition, we test if the general relationship holds by
excluding the commonly studied cases where disinfor[mation is used, Russia, China, and the United States (Table](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
[B14). By excluding these cases, disinformation still affects](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)


the probability of democratization and autocratization onsets. Thus, the statistical result indicates that the observed
relationship is not only driven by the commonly studied
cases but equally applies to a broad sample.
Finally, we show a robustness test for the mechanisms
(Table 3). Here, we use alternative sources of data to estimate both pro-democratic mobilization and polarization. [12]

[Models 7–8 in Table C2 show the effects of variables on the](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)

number of pro-democratic mobilizations observed in the
specific country-year. The data is extracted from the Mass
Mobilization Data (Clark and Regan 2018). In addition, we
replicate the analysis using the mass affective polarization
score proposed by Wagner (2021) using the Comparative
Study of Electoral Systems (CSES) dataset (Model 9 in
[Table C2).](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811) [13] Using these alternative datasets, we found
consistent results with the main analysis.
In sum, we find the most consistent and robust empirical
support for Hypothesis 1 – autocracies are less likely to
experience democratization when high levels of disinformation are prevalent. The positive effect of disinformation
on autocratization onset in democracies (Hypothesis 2) also
holds across different model specifications.


Conclusion


Disinformation is a growing concern worldwide especially given that it seems to coincide with the current wave
of autocratizaton (e.g., Wiebrecht et al. 2023). While
disinformation campaigns have been a prominent tool for
autocracies as part of their propaganda and information
control schemes, such strategies are also increasingly used
by anti-pluralist leaders in democracies and are widely
seen as a threat to democracy. Yet, despite the importance
of the issue, cross-national evidence systematically
demonstrating the effect of disinformation on regime
stability has been largely absent. This study is the first to
fill this gap by conducting cross-national time-series
analyses with a global sample, testing the effect of disinformation on both democratic and autocratic stability.
Our findings provide robust evidence for the fact that
disinformation negatively affects the quality of democracy in any regime type and highlight that disinformation
helps dictators to remain in power as it reduces the
likelihood of democratization in autocracies. We also find
that disinformation is linked to the onset of autocratization
episodes as well as democratic breakdown. These findings
are robust to most model specifications, including an
instrumental variable approach tackling the endogeneity
problem. We trace these results back to the mechanism
that disinformation in autocracies helps the status quo
preventing pro-democratic mobilization from emerging.
On the other hand, in democracies, disinformation may
not necessarily be the leading cause of autocratizaton but
we show that it leads to higher levels of societal


polarization into separate camps of those who believe in
false information (supporting the government) and those
who do not (opposing the government). This polarization,
in which both pro- and anti-democratic forces mobilize
more intensely, can create an opportunity for autocratization as we see from recent cases such as the United

States, Brazil, Poland, and Hungary.
These findings have important implications. First, our
study reveals the effectiveness of the disinformation strategy
used in autocracies to keep their regimes stable. Future
research may test the different mechanisms more explicitly,
especially in contexts other than Russia and China to expand
our knowledge of disinformation in authoritarian regimes.
Second, we confirm the warnings of disinformation as a
threat to democracy empirically and therefore echo calls that
countering disinformation becomes increasingly important
(Winger et al. 2023). On the other hand, our results also
suggest a backlash against disinformation from civil society
and pro-democracy actors, which can be critical for resisting
autocratization (e.g., Bowler, Carreras, and Merolla 2023;
Tomini, Gibril, and Bochev 2023).


Acknowledgments


An earlier version of the paper has been published as Yuko Sato,
Felix Wiebrecht, and Staffan I. Lindberg. 2023. “Disinformation
and Episodes of Regime Transformation.” V-Dem Working
Paper No. 144, University of Gothenburg, V-Dem Institute. We
thank Staffan I. Lindberg for his significant contributions to an

earlier version of the draft. For useful feedback and comments on

previous versions of this paper, we also thank Jennifer McCoy,
Sebastian Hellmeier, Yilin Su, Kevin Aslett, Natalia Bueno,

Anam Kuraishi, Marina Nord, Martin Lundstedt, Fabio An
giolillo, and participants at PSA 2023, MPSA 2023, EPSA 2023,
and APSA 2023 as well as two anonymous reviewers.


Declaration of Conflicting Interests


The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.


Funding


The author(s) disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article: We
recognize support by the Knut and Alice Wallenberg Foundation
to Wallenberg Academy Fellow Staffan I. Lindberg, Grant
2018.0144; by European Research Council, Grant 724191, PI:
Staffan I. Lindberg; as well as by internal grants from the ViceChancellor’s office, the Dean of the College of Social Sciences,
and the Department of Political Science at University of
Gothenburg. The computations of expert data were enabled by
the Swedish National Infrastructure for Computing (SNIC) at the
National Supercomputer Centre, Link¨oping University, partially
funded by the Swedish Research Council through grant
agreement no. 2019/3-516.


ORCID iDs


[Yuko Sato ](https://orcid.org/0000-0002-7458-6134) [https://orcid.org/0000-0002-7458-6134](https://orcid.org/0000-0002-7458-6134)
[Felix Wiebrecht ](https://orcid.org/0000-0002-9159-5024) [https://orcid.org/0000-0002-9159-5024](https://orcid.org/0000-0002-9159-5024)


Supplemental Material


Supplemental material for this article is available online.


Notes


[1. https://ipi.media/polands-division-hinders-fight-against-](https://ipi.media/polands-division-hinders-fight-against-fake-news-2/)

[fake-news-2/.](https://ipi.media/polands-division-hinders-fight-against-fake-news-2/)

2. However, we also acknowledge that isolating the effect of
disinformation is nearly impossible since it is often used by
actors that advance autocratization simultaneously in several areas. Therefore, disinformation may not necessarily be
the leading cause of autocratization, but as we show later in
the paper, it appears to have a causal effect on increased
polarization in society which is also a significant factor for
autocratization (McCoy, Rahman, and Somer 2018).
3. Despite a limited time scope due to data availability, our
analysis covers the most relevant period for evaluating our
hypotheses as internet and social media accessibility significantly increased during this period.
4. It is important to note here that we study disinformation
disseminated by the government in their respective countries. We do not study disinformation campaigns targeting
other countries, such as Russia’s efforts in Central and

Eastern Europe or China’s meddling in Taiwan. These cases
go beyond the scope of this paper.
5. For presentation purposes, we reverse the order of the responses so that “never, or almost never” becomes the lowest
score and “extremely often” the highest score. In addition,
we primarily look at the level of government use of disinformation, but we also test the effect of change in the level

of disinformation as a robustness check to take into account

the temporal dependency in the variable.
[6. See Appendix and Maerz et al. (2023) for more details.](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)

[Table A2 of the Appendix summarizes democratization and](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
autocratization episodes included in the models. We also
present robustness tests using alternative thresholds to
[identify these regime transformation episodes (Table B4 in](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
[Appendix).](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
7. It may be worth mentioning that although our dependent and
independent variables can both be found in the V-Dem
dataset, they are part of different expert surveys and are
therefore, not subject to a bias in which the same experts
code both of our variables. A skeptical reader may suggest
that coders may be more familiar with cases of regime
changes and might have used that as a heuristic to assess the
levels of disinformation in the respective country. However,
as Weidmann (2023) recently shows, there is very little
evidence of an availability bias, that is, recent events in the
country in question driving coder decisions of rating.


8. Again, it may be worth making explicit that these variables are generated through different expert surveys than
our key independent variable. In addition, we run a ro[bustness check with alternative data sources (Table C2 in](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
[Appendix).](https://journals.sagepub.com/doi/suppl/10.1177/10659129241252811)
9. To illustrate the main results, we primarily rely on the first
model with regional dummies. Estimation with a two-way
fixed-effects model in this study can be critically vulnerable
by dropping off 56 countries or 869 observations in au
tocracies and 58 countries or 1033 observations in de
mocracies. Differences in samples may alter the estimates,
especially if the size of the omitted group is large (Beck
2020).
10. We note that this result may need to be cautiously understood. By applying the instrumental variable approach, the
models drop some observations due to the missing data on
technological adoption in communication in 1500 CE. For
instance, 491 observations (34 countries) in autocracies and
338 observations (26 countries) in democracies are dropped
off. The difference in the samples may potentially create an

estimation bias.

11. 38 out of 1454 observations are 1 for the DV (autocratization onset).
12. To our knowledge, relevant data for pro-autocratic mobi
lization in both democratic and autocratic states is

unavailable.

13. Due to the limited number of autocracies in the data, we

perform the analysis only in democracies.


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