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Comment and Controversy
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Measuring Democratic Backsliding
Andrew T. Little, University of California, Berkeley, USA
Anne Meng, University of Virginia, USA
ABSTRACT Despite the general narrative that the world is in a period of democratic decline,
there have been surprisingly few empirical studies that assess whether this is systematically
true. Most existing studies of global backsliding are based largely if not entirely on
subjective indicators that rely on expert coder judgment. Our study surveys objective
indicators of democracy (e.g., incumbent performance in elections) and finds little evidence
of global democratic decline during the past decade. To explain the discrepancy in trends
between expert-coded and objective indicators, we consider the role of coder bias and
leaders strategically using more subtle undemocratic action. Although we cannot rule out
the possibility that the world is becoming less democratic exclusively in ways that require
subjective judgment to detect, this claim is not justified by existing evidence.
The current conventionalnarrative is that the world is
in a period of global democratic decline. Headlines
such as “How Democracy Is Under Threat Across the
Globe”(August 19, 2022) appear regularly in the
NewYorkTimes, and the Washington Post made
“Democracy Dies in Darkness”its slogan in 2017. Freedom House
opened its 2022 annual report by stating that “Global freedom faces
adirethreat”(Repucci and Slipowitz 2022). The Varieties of
Democracy (V-Dem) 2023 annual report claimed that “The level
of democracy enjoyed by the average global citizen in 2022 is down
to 1986 levels”and “Advances in global levels of democracy made
over the last 35 years have beenwiped out”(Wiebrecht et al. 2023).A
substantial uptick in academic papers on democratic backsliding
has emerged in recent years, which often raise alarm in the title:
“Democratic Regress in the Contemporary World”(Haggard and
Kaufman 2021)necessitates“Facing up to the Democratic
Recession”(Diamond 2015)because“A Third Wave of Autocratiza-
tion Is Here”(Lührmann and Lindberg 2019).
To make accurate claims of this type, we need reliable ways to
measure democracy. This is a notoriously difficult problem (see,
e.g., Boix, Miller, and Rosato 2013; Cheibub, Gandhi, and Vreeland
2010; Przeworski et al. 2000). Scholars do not agree on how we
should define democracy in the first place—and even components
with near consensus such as holding free and fair elections are
difficult to measure. As a result, there are surprisingly few
empirical studies that assess whether the narrative of global
democratic decline is systematically true.
Our primary contribution is to highlight and explore the
consequences of a key distinction between different democracy
indicators. Some are objective and factual, such as whether the
incumbent party loses and accepts defeat in an election. Other
indicators are subjective and rely on the judgment of expert coders
to answer questions such as whether a particular election can be
considered “free and fair.”
Recent studies that find evidence of global backsliding rely
heavily if not entirely on subjective indicators. Whereas expert-
coded data have many advantages—including wide coverage of
various dimensions related to democracy—the more they rely on
human judgment, the more they can be systematically biased. This
raises the possibility that a perceived global decline of democracy
may be driven by changes in coder bias rather than actual changes
in regime type.
WHAT WE DO
To address this challenge,we survey objective indicators of democ-
racy. For theoretical and pragmatic reasons, we adopt a “quasi-
minimalist”conception of democracy that centers on the presence
of free and fair elections in which losers accept the results, and we
focus our empirical analysis on indicators of electoral competitive-
ness. We also believe that full democracy requires other features
such as checks and balances, free media, and rights protections.
These dimensions generally are more difficult to measure objec-
tively; therefore, we include themwhen feasible, and we encourage
future research to continue in this direction.
© The Author(s), 2024. Published by Cambridge University Press on behalf of American
Political Science Association. This is an Open Access article, distributed under the terms
of the Creative Commons Attribution licence (http://creativecommons.org/licenses/
by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the
original article is properly cited.
Andrew T. Little is associate professor of political science at the University of
California, Berkeley. He can be reached at andrew.little@berkeley.edu.
Anne Meng is associate professor of politics at the University of Virginia. She can be
reached at ameng@virginia.edu.
doi:10.1017/S104909652300063X PS •2024 1
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Practically speaking, we describe trends in de facto and de jure
electoral competitiveness, executive constraints, and media free-
dom. In general, we find little evidence of backsliding on these
variables during the past decade. At least on these measures, the
global pattern is one of democratic stability or stagnation rather
than decline.
First, we examine electoral outcomes. If staying in power is the
ultimate endgame for anti-democratic leaders, then incumbent
electoral performance and electoral turnover are the most impor-
tant outcomes to study. In addition to the intrinsic importance of
turnover, preventing electoral loss is a key goal of many undem-
ocratic actions, such as banning opposition parties, controlling the
media, and placing loyalists in control of election-management
bodies. Even the types of subtle actions that many fear are the
dominant mode of contemporary authoritarianism and backslid-
ing (Guriev and Treisman 2020; Luo and Przeworski 2019; Prze-
worski 2019) typically are reflected in election results.
Are incumbent leaders and their parties increasingly dominat-
ing elections? Using various measures and data sources, the
answer is a reasonably clear “no.”The rate of ruling-party and
individual-leader turnover has remained fairly constant since the
late 1990s. If anything, the vote shares of winners in executive
elections and seat shares of winners in legislative elections have
decreased in recent years. The share of elections with real multi-
party competition also does not exhibit any decline.
Second, we examine data outside of the electoral arena, begin-
ning with executive constraints. Constitutional rules that limit
executive power have been fairly constant if not slightly increasing
since the end of the Cold War. Moreover, despite recent promi-
nent cases, there has been no uptick in the rate of leaders evading
term limits. Although media freedom is particularly difficult to
measure, we present suggestive evidence on this front by exam-
ining the number of journalists who have been jailed and killed.
This picture is mixed: beginning in the early 2000s, there has been
an increase in journalists who have been jailed but also a decrease
in the number of those killed for doing their job.
What is driving the differences between expert-coded and
objective measures? We consider two possibilities, which are
formalized in online appendix section C. First, changes in the
media environment or changing coder standards may have led to a
time-varying bias in expert surveys that could make it appear that
the world is becoming less democratic absent any true trend.
Alternatively, leaders may be strategically shifting to more subtle
means of backsliding that are more difficult to detect with objec-
tive measures.
These explanations are not mutually exclusive, but we argue
that coder bias likely explains at least some of the discrepancy. We
also provide evidence that media coverage of backsliding has
increased in recent years. Future research should establish more
definitive evidence on these (or other) mechanisms.
In summary, it may be the case that major backsliding is
occurring precisely in ways that elude objective measurement.
However, this is an extraordinary claim. The onus should be on
those who are making alarmist claims about the decline of democ-
racy to provide stronger evidence than has been presented to date.
WHY IT MATTERS
Democratic backsliding is an important problem from a theoret-
ical and practical perspective, and we do not want to discourage
scholars from studying its causes and consequences. Our focus is
on broad trends, and we are not asserting that backsliding is not
happening anywhere. There are approximately 200 countries in
the world and backsliding most likely is happening in some of
them at any given time. We believe, for instance, that backsliding
has occurred recently in places such as Hungary and Venezuela.
Although a correct accounting of global trends is a key first
step, it is arguably more important to understand where backslid-
ing is happening, how it happens (Chiopris, Nalepa, and Vanberg
2021; Cinar and Nalepa 2022; Grillo and Prato 2020; Helmke,
Kroeger, and Paine 2022; Svolik 2018), and when it leads to
democratic breakdown (Brownlee and Miao 2022; Miller 2021;
Treisman 2022). To diagnose the problem correctly, we need
accurate measures of whether and how much backsliding is
occurring. Focusing on a few prominent cases of backsliding
creates misleading views about the resilience of democratic insti-
tutions. Furthermore, if leaders generally are not undermining
certain rules (e.g., elections and term limits) but instead are
focusing on eroding other types of institutions or civil liberties,
then taking measurement seriously will help researchers, policy
makers, and citizens to better target solutions.
Our core contention is that if the world is experiencing major
backsliding in the aggregate, we should expect to see evidence of
this effect on the objective measures—particularly incumbent
leaders and their parties winning elections—but we do not.
EXISTING DATA AND SUBJECTIVE MEASUREMENT
This sectionsurveys and highlights potential problems with expert-
coded measures. All replication data used in this article are available
at Little and Meng (2023).
Defining Backsliding
Democratic backsliding and democratic erosion—which we use
interchangeably—can be defined as “the state-led debilitation or
elimination of any of the political institutions that sustain an
existing democracy”(Bermeo 2016, 5). Other scholars argue that
backsliding can occur in any country, regardless of regime type, and
they define backsliding as “a deterioration of qualities associated
with democratic governance, within any regime. In democratic
regimes, it is a decline in the quality of democracy; in autocracies,
it is a decline in democratic qualities of governance”(Waldner and
Our core contention is that if the world is experiencing major backsliding in the aggregate,
we should expect to see evidence of this effect on the objective measures—particularly
incumbent leaders and their parties winning elections—but we do not.
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Lust 2018, 95). Similarly, Lührmann and Lindberg (2019)contend
that any country can “autocratize,”regardless of regime type.
There are three general categories of backsliding in the existing
literature: shifts from (1) liberal democracy toward electoral
democracy; (2) democracy to autocracy (i.e., “breakdown”); and
(3) institutionalized autocracy to personalist autocracy. Although
these three types of backsliding are all relatively different, the
literature has not always made clear distinctions among them; as a
result, the discussion often is muddied by conceptual confusion
(Lueders and Lust 2018).
For simplicity and consistency with much existing scholarship,
we include all countries in the sample for the main analysis and
typically focus on average levels of variables. This captures posi-
tive gains toward democratization in addition to backsliding.
Doing so also captures subtle shifts as well as more major regime
changes—although the latter are weighted more heavily. The third
section of this article discusses differential trends by regime type
and analyzes the distribution of democratic change in different
periods, which does not change our main findings.
Existing Data and Aggregate Trends
Scholars generally have used three existing datasets to
examine backsliding: V-Dem, Freedom House, and Polity.
Online appendix table A.1 summarizes these measures and their
criteria. Figure 1 plots the average yearly democracy scores from
1980 onward.
1
We use the 1980–2021 time window for all of our
graphs (subject to availability constraints) to keep the focus on
relatively recent times while also providing a sense of the
“baseline”before the third wave of democratization. Here and
throughout most of the analysis, we use unweighted averages
rather than weighting by population or another “importance”
metric because using population weights is not the norm in
political science. We discuss the implications of using population
weights when summarizing the results.
Following a dramatic increase in the 1990s and a modest
increase in the 2000s, V-Dem and Freedom House democracy
scores declined in the past decade. However, as Skaaning (2020,
1539, emphasis added) noted, “This drop is minuscule and clearly
within the boundaries of the statistical confidence levels offered by
V-Dem in order to account for potential measurement error.”
There is no decline in Polity scores; however, the data stop in
2018 and we therefore focus less on this source moving forward.
2
Other scholars have pointed out that these trends appear weaker
than the general narrative might suggest, either globally
(Anderson, Brownlee, and Clarke 2021; Treisman 2022) or region-
ally (Arriola, Rakner, and van de Walle 2023).
3
In fact, Levitsky
and Way (2015) noted that many studies raised concerns about
democratic decline before there was any evidence in the data.
Nevertheless, even studies that question the narrative of backslid-
ing generally do not consider potential biases in the underlying
data. Instead, disagreements about whether we are in a current
period of global democratic decline have centered on differing
emphasis and interpretations of the trends.
Potential Problems with Subjective Measures
To produce annual democracy scores, Freedom House meets with
expert analysts to reach a consensus of country-level scores.
V-Dem codings are done mostly via expert surveys. In both cases,
considerable subjective judgment is required to produce democ-
racy scores. The issues raised apply to any expert-coded data;
however, this discussion focuses on V-Dem measures because they
are admirably transparent about the coding decisions that produce
the scores, release coder-level data, and use a combination of
subjective and objective variables.
4
An objective variable is based in fact rather than a combination
of opinion and fact. A useful “litmus test”is whether multiple
qualified experts with access to the needed information would
reach the same conclusion (Cheibub, Gandhi, and Vreeland 2010).
A clear example is the vote share of the winner of a presidential
election. An objective indicator from V-Dem is the variable “per-
centage of the population with suffrage,”which asks: “What share
of adult citizens as defined by statute has the legal right to vote in
national elections?”These variables can be constructed on the
basis of clear observational criteria and require little if any judg-
ment from coders.
In contrast, a more subjective question that feeds into V-Dem’s
polyarchy index is the “election free and fair”variable, which asks
experts: “Taking all aspects of the pre-elections period, election
day, and the post-election process into account, would you con-
sider this national election to be free and fair?”Experts are given
five possible responses ranging from “0: No, not at all. The
elections were fundamentally flawed and the official results had
little if anything to do with the ‘will of the people’” to “4: Yes.
There was some amount of human error and logistical restrictions
but these were largely unintentional and without significant conse-
quences [emphasis added].”Even well-informed coders can dis-
agree widely about the definitions of “fundamentally flawed”and
“largely unintentional”(Weidmann 2022). In fact, V-Dem coders
disagree with non-trivial frequency. There typically are between
five and 10 coders for each observation. As described in
online appendix section A.2, disagreement is relatively similar
across variables: the average standard deviation across coders who
are asked the same question usually ranges from 20% to 25% of the
scale. Most variables are on a 0-to-3- or 0-to-4-point scale, which
corresponds to disagreeing with the average by almost 1 point.
5
V-Dem uses a measurement model to correct for some of the
problems associated with coders using different scales (Pemstein
et al. 2018). Intuitively, this model allows for expert coders to be
“harsher”or “more lenient”. This method is valuable for ensuring
that countries do not seem more or less democratic by getting an
unusual draw of coders, and it increases the precision of the
estimates for individual observations. However, the model
…studies that question the narrative of backsliding generally do not consider potential
biases in the underlying data. Instead, disagreements about whether we are in a current
period of global democratic decline have centered on differing emphasis and interpretations
of the trends.
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assumes that the coders’standards and biases are constant over
time.
6
There are valid reasons to question this assumption. Increased
attention on the topic of backsliding raises significant concerns
that coder perceptions of democracy may have shifted recently.
There has been a substantial increase in reporting on backsliding
in recent years (Gottlieb et al. 2022), and this attention can paint a
misleading picture for observers. Current news articles often cite
countries on the extreme end of the backsliding spectrum (e.g.,
Hungary and Russia) in their discussions of global democracy.
Readers may begin to believe that these outlier countries represent
the norm rather than the exception (Brownlee and Miao 2022). We
discuss these ideas in more detail after completing the description
of empirical patterns.
THE SEARCH FOR MORE OBJECTIVE MEASURES
The problems raised in the previous section motivate our search
for more objective measures. We first look at de facto election
outcomes, asking if incumbent leaders and their parties are dom-
inating elections. We next examine indicators of whether the
electoral process allows for competition in practice—for example,
by allowing multiple parties. Moving beyond elections, we exam-
ine trends in executive constraints and attacks on the press.
Our data on elections and freedom of association are from the
National Elections Across Democracy and Autocracy (NELDA)
dataset, which contains several objective variables about all elec-
tions from 1945 to 2020 (Hyde and Marinov 2012). Data on parties,
elections, and legislatures are from The World Bank’s Database of
Political Institutions (Cruz, Keefer, and Scartascini 2021). The
only original data we present are about executive constraints,
which we collected by expanding the data from Meng (2020)on
de jure rules that limit executive power. Data on term-limit eva-
sions are from Versteeg et al. (2020). Finally, for freedom of
expression, we use data from the New York–based nonprofit
organization Committee to Protect Journalists.
For our primary analyses of these individual datasets, we use
the full global sample of countries that they included. We continue
to use the 1980–2021 time window for all of our graphs, subject to
data availability.
7
Additional details on the data used in this
section are included in online appendix section B.
It is important to mention that most of our objective measures
focus on electoral institutions and outcomes and do not include
certain aspects of democracy, such as civil liberties. The implica-
tions of what we can and cannot capture with these objective
measures is discussed in the fourth section.
Are Incumbents Losing?
Democracy is a multidimensional concept, but perhaps the most
important feature is that “[incumbent] parties lose elections”
(Przeworski 1991, 10). Although the rate of incumbents losing
reflects other factors as well (e.g., how successfully they govern),
it provides a simple measure of this key feature of democracy.
Despite a few cases in which it is difficult to code whether the
winner of an election should be considered the candidate of the
ruling party, the construction of this variable requires little
judgment; therefore, we classify turnover as an objective indica-
tor.
In addition to the theoretical importance of turnover, many of
the undemocratic actions taken by leaders—banning opposition
parties, controlling the media, and placing loyalists in control of
election-management bodies—aim to influence the probability of
winning elections. As a result, comparing average rates of turnover
across periods provides crucial information about whether leaders
are insulating themselves successfully in power.
Indeed, rates of incumbent loss have changed dramatically over
the long term, from virtually never in the early-nineteenth century
to about a third of the time in the 2000s (Przeworski 2015). Do
incumbents continue to lose elections at similar rates?
Using data from NELDA, we start by presenting trends in
whether the incumbent’s party won the election in question. To
Figure 1
Average Democracy Scores by Year
1980 1990 2000 2010 2020
0.0 0.2 0.4 0.6 0.8 1.0
Year
Average Polyarchy Score
2021 Average
1980 1990 2000 2010 2020
0.0 0.2 0.4 0.6 0.8 1.0
Year
Average Normalized FH Score
2021 Average
1980 1990 2000 2010 2020
0.0 0.2 0.4 0.6 0.8 1.0
Year
Average Normalized Polity Score
2018 Average
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smooth out volatility driven by the fact that the set of countries
holding elections in a given year changes, we pull the data for each
country-year from the most recent election in NELDA if it hap-
pened within the past six years and then take averages of this
variable.
The left panel of figure 2 plots the yearly averages for “incum-
bent party victory in previous election”from 1980 to 2020. Although
the trend is somewhat noisy, there was a general increase from 1980
to 2000 and then no obvious pattern after that. This graph encom-
passes all legislative andpresidential elections, which include some
in which the incumbent party risked losing control of the executive
and others that did not (e.g., a legislative election in a presidential
system, such as midterm elections in the United States). The same
broad pattern holds when the data are subsetted into elections in
which the office of the incumbent leader was contested (middle
panel) or not contested (right panel).
Next we present data from the Database of Political Institutions
(DPI) to assess when the winning leaders and parties—whether
incumbent or challenger—were dominating elections. The left
panel of figure 3 plots the yearly average winner vote share in
presidential elections (shown in red) and the yearly average seat
share of the winning party in the legislature (shown in blue). Both
of these measures generally declined during the entireperiod, which
indicates that, if anything, winners were less dominant in recent
elections.
The right panel of figure 3 plots how long the current incum-
bent party has been in power. Although the average of this variable
has been increasing, this change is driven by long-lived autocratic
ruling parties.
8
The dotted line plots the average of a truncated
sample for which the length of time in power was capped at
20 years. When excluding outliers (i.e., the dotted line), this
variable remains relatively constant.
Further Measures of Competition
We next present additional variables from the DPI that reflect the
competitiveness of elections. The left panel of figure 4 plots the
share of countries in which the executive controls all houses that
have lawmaking powers. This variable slightly increased to about
55% around 2010 but has not returned to the pre-1990s levels when
executives controlled all branches of lawmaking in more than 70%
of countries. This may suggest backsliding, although alternating
between divided and unified government can be a routine part of
democratic politics in presidential systems.
The right panel of figure 4 plots executive and legislative
competitiveness indices from the DPI, which assigns higher num-
bers for elections in which multiple parties compete and win.
In addition to the theoretical importance of turnover, many of the undemocratic actions
taken by leaders—banning opposition parties, controlling the media, and placing loyalists
in control of election-management bodies—aim to influence the probability of winning
elections. As a result, comparing average rates of turnover across periods provides crucial
information about whether leaders are insulating themselves successfully in power.
Figure 2
Proportion of Elections in Which the Incumbent Party Loses
1980 1990 2000 2010 2020
0.0 0.1 0.2 0.3 0.4 0.5
Year
Incumbent Party Loses
1980 1990 2000 2010 2020
0.0 0.1 0.2 0.3 0.4 0.5
Year
Incumbent Party Loses
1980 1990 2000 2010 2020
0.0 0.1 0.2 0.3 0.4 0.5
Year
All Elections Incumbency at Stake Incumbency not at Stake
Incumbent Party Loses
0.0
0.1
0.2
0.3
0.4
0.5
Incumbent Part
y
Lose
s
0.0
0.1
0.2
0.3
0.4
0.5
Incumbent Part
y
Lose
s
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There is a clear upward trend for both the executive and legislative
indices, which indicates an increase in the competitiveness of
elections.
NELDA also has relatively objective variables that capture
whether opposition parties are prevented from running, whether
voters have a choice on the ballot, and other similar measures. We
use these NELDA variables to construct two indices that reflect
the extent to which elections are free and fair.
The first index reflects levels of de facto and de jure multi-
partyism. It includes the following measures: whether (1) oppo-
sition parties were allowed to compete in the election and no
parties were banned, (2) multiple parties were technically legal,
and (3) voters had a choice on the ballot. We average these
variables to create a “multiparty”index with four levels ranging
from 0 to 1.
The second index captures various other electoral-process
violations and includes the following variables: (1) were previous
elections suspended, (2) had the current incumbent violated a
term limit, and (3) did an opposition party boycott the election. In
each case, we coded 1 for answering “no”and 0 otherwise;
averaging the three variables gives a “(lack of ) process violation”
index with three levels ranging from 0 to 1.
The left panel of figure 5 plots the average of the multiparty
index from 1980 to 2020, again pulling data from the previous
election. The average of this index increased from around 0.7 to
0.95 in the 1990s and was relatively stable after that, which
indicates that most elections meet a bare minimum standard of
competition. The right panel shows that process violations also
were rare, and the index was stable for the past 20 years.
Although neither of these indices captured more subtle viola-
tions of fair elections, they do reveal that major violations are not
becoming more common.
Finally, one way that backsliding may show up in these data
would be a decrease in the number of executive and legislative
elections that are held. This does not seem to be the case; as shown
in online appendix figure 16, the number of elections remained
fairly constant during the past two decades.
In summary, incumbent leaders have been equally if not less
dominant in recent elections—precisely the opposite of what we
expected to see in a period of global democratic decline.
Figure 3
Additional Measures of Winning/Incumbent Party Dominance
Legislature
President
1980 1990 2000 2010 2020
40 50 60 70 80
Year
Winner vote/seat share
1980 1990 2000 2010 2020
051015
Year
Par ty Tenure
Average
Average (truncated)
Figure 4
Are Elections Competitive?
1980 1990 2000 2010 2020
0.50 0.60 0.70
Year
Exec Controls All
1980 1990 2000 2010 2020
34567
Year
Competitiveness Index
Legislature
Executive
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Executive Constraints
Another important dimension of democracy is whether there are
constraints on the chief executive. In a frequently cited article,
Bermeo (2016, 10) pointed to executive aggrandizement as a
“common form of backsliding [that] occurs when elected execu-
tives weaken checks on executive power.”Have leaders increas-
ingly dismantled institutions that check their power? To assess
this question, we use expanded data from Meng’s(2020) study,
which provides the proportion of countries that have constitu-
tional rules designating (1) term limits, (2) succession procedures,
and (3) rules for dismissing the leader.
The left panel of figure 6 plots the trends in the existence of
term limits (shown in black), rules for dismissal (shown in green),
and succession procedures (shown in red), all of which revealed a
marked increase after the end of the Cold War. In recent decades,
term limits have been flat whereas rules for dismissal and succes-
sion have continued in a slightly upward trend.
Even with term limits on record, leaders may attempt to
circumvent these laws to stay in power. To explore this possibility,
we use data from Versteeg et al. (2020), who recorded all instances
in which an incumbent attempted to circumvent existing term
limits, including attempts that did not involve formal changes on
paper (e.g., having the court reinterpret the constitution). The
dataset identifies 234 instances from 2000 to 2018 in which an
incumbent reached the end of a term and was ineligible for
reelection due to existing term limits. A serious attempt to evade
term limits occurred in 60 cases (26% of all observations), and all
attempts originated exclusively from authoritarian or hybrid
regimes. Of the 60 attempts to evade term limits, 34 succeeded.
The data show that rates of term-limit evasion—both successful
and unsuccessful—do not exhibit a clear trend, as illustrated in the
right panel of figure 6.
Attacks on the Press
Freedom of expression is an important aspect of democracy that is
particularly difficult to measure. Efforts to influence the media
often are hidden, and even measuring the bias of a particular outlet
relative to some objective standard proves challenging.
Nevertheless, an important type of outcome that plausibly can
be objectively measured is punitive actions toward journalists. In
particular, the Committee to Protect Journalists maintains a
database of all journalists who were killed or jailed as a result of
doing their job since 1992. Each observation gives the date of being
jailed or killed, the country where the event occurred, and a
categorical classification of the reason for the event.
9
The database
is updated continuously as more information is collected. This
Figure 5
Average of the Multiparty Index (Left) and Process-Violations Index (Right)
1980 1990 2000 2010 2020
0.0 0.2 0.4 0.6 0.8 1.0
0.0 0.2 0.4 0.6 0.8 1.0
Year
Multiparty Index
1980 1990 2000 2010 2020
Year
Process Violation Index
0
.
0
0
.
2
0
.
4
0
.
6
0
.
8
1
.
0
Pro
cess
Viol
a
tion In
dex
Figure 6
Trends in Executive Constraints
1980 1990 2000 2010 2020
0.2 0.4 0.6 0.8 1.0
01234 65
Year
Proportion with Constraint
20052000 2010 2015
Year
Term Limit Evasions
Dismissal
Term Limit
Sucess
All Attempts
Sucession
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could create bias if more recent events are easier to observe; if so,
this would create the impression that the situation for journalists
is becoming worse.
Figure 7 plots the trends in these data from 1992 to 2021. Each
panel contains the raw data shown in gray and a smoothed trend
shown in black. The left panel includes all journalists who were
jailed for doing their job; the patterns are similar when cases are
restricted to those in which the reason was explicitly listed as
“anti-state.”This is the first indicator of a clear “bad”trajectory
during the past decade, although it is the continuation of a trend
that started around 2000. However, the trend is significantly
different for journalists who were murdered (right panel).
10
After
a notable increase in the late 1990s and early 2000s, this number
has decreased steadily since around 2008.
There are several ways to interpret these findings. It may be
that the more authoritarian states with control over the judiciary
can use jailing as a tool to silence dissent, whereas leaders of semi-
democratic countries must resort to more extreme measures. It
also may reflect the softening of tactics used by dictators to
maintain power (Guriev and Treisman 2020), which arguably
could be interpreted as becoming less autocratic. We encourage
future research to explore what is driving this pattern.
Taking Stock
To summarize the objective indicators and compare trends among
different sets of countries, we construct a simple aggregate objec-
tive index by normalizing all individual variables between 0 and
1 when we can do so and taking the average for each country-year.
Specifically, we take the average of percentage of the population
with suffrage from V-Dem; presidential vote shares, winning-party
seat shares, incumbent-party time in office (truncated at 20 years),
legislative competitiveness, and executive competitiveness from
the DPI; whether the incumbent party lost the last election, the
multiparty index, and the process-violations index from NELDA;
and the presence of term limits, succession rules, and dismissal
rules.
11
To be clear, this simple index is meant only to summarize the
aggregate trends described previously; it is not an appropriate
substitute for existing democracy indices on a country-year level.
Because the input variables are measured on different scales, the
magnitude of the index does not have a clear substantive
interpretation. Nevertheless, changes in the average index can
provide a general sense of how indicators related to the quality
of democracy have changed over time. For an approach that
attempts to approximate democracy scores using only objective
indicators, see Weitzel et al. (2023).
The left panel of figure 8 plots the trends of this aggregate
objective index over the past 40 years in a thick line. For
comparison, the thin black line plots the average V-Dem poly-
archyindexscoreandthethingraylineplotstheFreedomHouse
democracy score. For this graph, only country-years that appear
in both V-Dem and Freedom House were included. The objective
index is higher than either, although this simply reflects the fact
that many of the components have a “low bar”(e.g., multiparty
elections) and therefore has no substantive meaning. More
important is the change over time, the main change for all three
indices being a substantial increase at the end of the Cold War.
Since 2000, trends have been somewhat different: whereas the
indices based on expert coding modestly declined, the objective
index generally continued to increase, albeit at a slower pace.
The objective index in 2020 was almost as high as it has ever
been.
So far, all of our analysis has given equal weight to all countries
that have data on the relevant indicator. However, some scholars
have argued that weighting by population is better for capturing
the experience of the average citizen rather than the average
country (see, e.g., Alizada et al. 2022). The middle panel of figure
8presents the same trends weighted by population. As discussed
in the respective publications, this generates a more pessimistic
trend in the V-Dem polyarchy index and Freedom House scores
during the past decade. For our objective index, population
weighting also generates a notable decrease beginning around
2018 but a more volatile trend throughout as well. This is driven by
the fact that individual country trends can move sharply based on
the outcome of a particular election or another shift on one of our
underlying indicators. The right panel of figure 8 illustrates that
the trend is much smoother when the two most populous coun-
tries, India and China (which have had noisy trends in recent
years), are omitted. This somewhat attenuates the decrease for
Freedom House and V-Dem’s polyarchy index scores and leads to
a similar trend as the unweighted graph for the objective index.
Overall, there is evidence that trends in recent years appear worse
Figure 7
Trends in Journalists Jailed (Left) and Murdered (Right)
1992 2002 2012 2021 1992 2002 2012 2021
070140
02550
Year
Journalists Jailed
Year
Journalists Murdered
0
25
5
0
J
ournalists Murdere
d
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when weighting by population, but this conclusion is highly
sensitive to the coding of the largest countries.
Distributions of Change
By looking at all countries in the aggregate, improvements in
autocracies may be “canceling out”decreases in democratic scores
of democracies. We address this concern in two ways. First, we
examine differences in regime type by separating our sample of
countries into democracies and autocracies, as classified by Boix,
Miller, and Rosato (2013).
12
The left panel of figure 9 shows the
trend in incumbent party loss by regime type. The thick black line
represents all countries (see figure 2), the green line represents all
democracies, and the blue line represents all autocracies. The
dotted line corresponds to the proportion of countries coded as
democratic. Unsurprisingly, incumbent party loss was more fre-
quent in countries coded as democratic. After a spike in incumbent
party loss (particularly among democracies) in the 1990s, the rate
has been relatively flat in both subgroups for the past 15 years.
The right panel of figure 9 plots the trend for the objective
index. The overall trends are similar; the increase in the 1990s was
driven by more countries being classified as democracies as well as
the increasing average score of nondemocracies. The average of
the objective index was relatively flat for both subgroups for the
past 20 years. (Online appendix section A.7 includes similar
graphs for expert-coded democracy scores. If anything, their
decline is driven more by autocracies becoming even less demo-
cratic.)
Second, figure 10 shows the distribution of country-level
changes for the objective index (top row), V-Dem polyarchy index
(middle row), and Freedom House (bottom row). We focus on
comparing five-year averages across decades.
13
For example, the
top-left facet plots the distribution of the change in the average
Figure 8
Unweighted and Weighted Average Indices
1980 1990 2000 2010 2020
Year
Unweighted Average of Indices
1980 1990 2000 2010 2020
Year
Pop–Weighted Indices
1980 1990 2000 2010 2020
0.3 0.4 0.5 0.6 0.80.7 0.9
0.3 0.4 0.5 0.6 0.80.7 0.9
0.3 0.4 0.5 0.6 0.80.7 0.9
Year
Pop–Weighted Indices (No India/China)
P
op–Wei
g
hted Indices
0
.
3
0
.
4
0.5
0
.
6
0.8
0
.
7
0
.
9
0.3
0.4
0.5
0.6
0.8
0.7
0.9
Pop–Wei
g
hted Indices (No India/China)
Figure 9
Subsetting by Regime Type
1980 1990 2000 2010 2020
0.10.0 0.2 0.50.3 0.4 0.6
0.2 0.4 0.6 0.8
Year
1980 1990 2000 2010 2020
Year
Incumbent Party Loss
Objective Index
All
All
Aut
Aut
Dem
Dem
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PS •2024 9
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objective index for 1981–1985 to the average for 1986–1990, and the
second-from-left facet plots the change from 1986–1990 to 1991–
1995.
14
If it were the case that more backsliders were being offset by
more democratizers, we might see a bimodal distribution—or at
least more changes farther from zero in the facets corresponding to
the past two decades. In fact, we see the opposite: there generally
are fewer countries experiencing major changes in the recent
periods in either direction across all three indices. (See figure 8
in online appendix section A.8 for a plot of the mean and standard
deviation of these changes over time.) Although it is true that in
recent years some countries have become less democratic and
others have become more democratic, this has always been true.
As shown in figure 10, for all of these periods and indices, there are
countries moving in both directions, and recent years do not
exhibit more volatility.
MAKING SENSE OF THE DATA
So far, our study demonstrates that two different empirical patterns
emerged, depending on what type of democracy indicator we
examined. Subjective indicators raise concerns that aggregate levels
of democracy exhibited a modest decline during the past decade.
Objective indicators werelargely stable. How canwe reconcile these
differing accounts? Online appendix section C presents two theo-
retical models that make sense of the patterns we document.
Time-Varying Coder Bias
First, we develop a non-strategic model of coders. This model
shows that if all coders receive a common shock to the information
that they use to make decisions (e.g., increased media reporting on
backsliding), this can create a time-varying bias that complicates
inferences about true democratic change. In particular, changes in
aggregate democracy scores are equal to the real change plus the
change in this common shock. Intuitively, a change in coder bias
could explain why expert-coded measures of democracy have
shown a global trend of backsliding whereas objective measures
do not. Why might this common bias have changed over time?
The formalization raises several possibilities, two of which are
highlighted here.
First, it may be that improved communication technology (e.g.,
increasing Internet and cell-phone penetration and social media)
has made it easier to detect and broadcast undemocratic behavior.
If so, even for a fixed level of real undemocratic behavior, experts
may see more coverage.
15
Rising standards and heightened atten-
tion in recent years also may lead coders to consider the same
behavior to be a more serious assault on democracy.
If coders properly adjust for these biases, they will not neces-
sarily create problems. Unfortunately, fully adjusting for biases is
a challenge even for extremely well-informed and sophisticated
thinkers. There is strong evidence across a variety of topics and
subject pools that people make substantial errors when faced with
selection problems in the samples that they observe (see Enke
2020 for a prominent recent example and Brundage, Little, and
You 2022 for a review).
Second, if the experts doing the coding have “motivated
beliefs”to think that the country-year they are coding is relatively
democratic or undemocratic, this can skew individual and aggre-
gate coding. If the coders’motives change over time, the bias that
this creates changes over time as well. One concrete way that this
may play out also relates to the rise of a media narrative that the
Figure 10
Distribution of Changes Over Five-Year Periods Across Decades
–0.4
Freedom House Polyarchy Objective Index
0.0 0.4 –0.4 0.0 0.4 –0.4 0.0 0.4 –0.4 0.0 0.4 –0.4 0.0 0.4 –0.4 0.0 0.4 –0.4 0.0 0.4
–0.4 0.0 0.4 –0.4 0.0 0.4 –0.4 0.0 0.4 –0.4 0.0 0.4 –0.4 0.0 0.4 –0.4 0.0 0.4 –0.4 0.0 0.4
–0.4 0.0 0.4 –0.4 0.0 0.4 –0.4 0.0 0.4 –0.4 0.0 0.4 –0.4 0.0 0.4 –0.4 0.0 0.4 –0.4 0.0 0.4
1981 – 1990 1986 – 1995 1991 – 2000 1996 – 2005 2001 – 2010 2006 – 2015 2011 – 2020
0
.4 0.
0
0.4 –0.4 0.
0
0.4 –0.
4
0.
0
0
.4 –0.
4
0
.0 0.4 –0.
4
0
.0 0.4 –0.4 0.0 0.4 –0.
4
0.0
0
.
4
0
.4 0.
0
0.4 –0.4 0.
0
0.4 –0.
4
0.
0
0
.4 –0.
4
0
.0 0.4 –0.
4
0
.0 0.4 –0.4 0.0 0.4 –0.
4
0.0
0
.
4
Comment and Controversy: Special Issue on Democratic Backsliding
.............................................................................................................................................................................................................................................................
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world is becoming less democratic, particularly concerns about
democratic backsliding in the United States. Without joining the
debate about whether the United States became substantially less
democratic during the Trump presidency, there was (and is)
considerable attention to this possibility (Carey et al. 2020). These
concerns close to home may have made experts—many of whom
are based in the United States—“want to see”ways in which the
countries that they are coding fit into a global narrative of
backsliding or what they are seeing close to home. This possibility
is especially troubling if the types of country experts willing to take
the time to work as coders include those most concerned about
backsliding.
Although the objective indicators are much less likely to be
affected by these types of coder bias, they may be biased in other
ways. Nevertheless, for the purposes of our study, this generates
problems only if this bias has changed over time. For instance,
electoral performance can be influenced by factors other than how
free and fair the elections are. Incumbents may perform well in an
election because they did a good job in office and voters recognize
this. It is possible that incumbent leaders have done a worse job in
office in recent years—for example, producing worse electoral
outcomes, being more corrupt, or choosing unpopular policies—
but took more anti-democratic actions to insulate themselves at
the ballot box. We are not aware of any evidence about this effect,
but we encourage more research on this idea.
Strategic Attacks on Democracy
Second, we consider a strategic model of a leader who decides
whether to engage in “subtle”or “blatant”undemocratic actions.
We consider changes that may have caused leaders to substitute
more subtle democratic violations, such as an increased penalty for
being seen as undemocratic or leaders becoming “better”at subtle
violations. Either change could explain why subjective indicators
—which arguably can better detect subtle violations—have
declined in relative terms.
Although increased penalties for observed undemocratic
behavior may cause incumbents to switch from blatant to subtle
actions, they also should result in fewer violations overall—pre-
cisely because such actions become more costly. There also is scant
evidence of increasing penalties; if anything, Hyde (2020) suggests
that international promotion of democracy has waned in recent
years.
Furthermore, if leaders are becoming better at subtle demo-
cratic violations, they should be winning elections with larger
margins and at higher rates, which is inconsistent with previously
presented evidence. Moreover, in many cases, improved commu-
nication technology and media attention to backsliding should
make it more difficult for subtle democratic violations to go
unnoticed.
In summary, it certainly is plausible that the nature of attacks
on democracy has changed in some ways. For example, Svolik
(2019) finds that the share of democratic breakdowns driven by
executive takeovers rather than military coups increased from
approximately 50% in the 1970s to approximately 90% more
recently. However, a shift in backsliding strategies does not mean
that there is more backsliding overall. Theories on the changing
nature of anti-democratic actions should account for the fact that
successful attacks on democracy, as measured by leaders insulat-
ing themselves in power, have not increased.
Evidence on Mechanisms
Finally, we present suggestive evidence that media coverage may
create the types of bias highlighted in the coder model. Of course,
as with the democracy assessments, changes in media coverage
may reflect changes in the actual frequency of democratic back-
sliding or how much individual events are covered. Nevertheless, it
is instructive to observe the trends.
The top panels in figure 11 plot data from Gottlieb et al. (2022),
who coded reports of events of democratic backsliding using
primary and secondary sources. The top-left panel shows a sub-
stantial increase in reports since 2000 that peaked in 2019, fol-
lowed by a rapid decrease in 2020 and 2021. The top-right panel
plots the average number of events per country with at least one
event, which more than doubled from 2000 to a peak in 2018.
Figure 11
Media and Academic Coverage
1980 1990 2000 2010 2020 2000 2005 2010 2015 2020
150100500
Year Year
1980 1990 2000 2010 2020 2000 2005 2010 2015 2020
Year Year
Search Hits
6004002000
Events
500020000
Google Scholar Hits
5.04.03.02.0
Events per Country
Democratic Transition
Democratic Backsliding
+Democratic Erosion
1980
1990
2000
2010
2020
2000
2005
2010
2015
2020
Ye a
r
Ye a r
500
0
200
0
0
G
oogle Scholar Hit
s
5.0
4.0
3.0
2.0
Eve
nts per Count
r
D
emocratic Transitio
n
Democratic Backslidin
g
+D
emocrat
i
c
E
ros
i
o
n
.............................................................................................................................................................................................................................................................
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The bottom-left panel of figure 11 plots the number of articles
in the New York Times that included the term “democratic
backsliding”or “democratic erosion”by year, shown by the thick
black line. The thinner lines are the individual search terms, with
“erosion”above “backsliding.”After generally trending downward
from 1980 until the early 2000s, there was a significant spike
around 2008. There was an even larger spike that began in the
2010s; however, there was a decrease from 2020 to 2021, which
perhaps reflects a decrease in emphasis after the Trump presi-
dency.
The bottom-right panel in figure 11 plots Google Scholar hits
for “democratic transition”—a relatively neutral term, although
one that generally corresponds to countries becoming more dem-
ocratic—and the sum of “democratic backsliding”and “demo-
cratic erosion.”Although “democratic backsliding”and
“democratic erosion”have not yet caught up with “democratic
transition”, the use of “democratic transition”has been declining
in recent years and the more negative terms have been exploding.
There is evidence of increased media and scholarly attention to
democratic erosion. Given that this increase is more dramatic than
even subjective measures of democratic backsliding, it is difficult
not to infer that the trend at least partially is driven by more
intense media coverage of these events. We encourage future
research to explore this and other mechanisms in more detail.
CONCLUSION
Democratic backsliding is an important topic, and we believe that
it is crucial to provide an accurate depiction of the current state of
the world. This article makes two general contributions: substan-
tive and methodological.
First, the common claim that we are in a period of massive
global democratic decline is not clearly supported by empirical
evidence. As other scholars have noted, expert-coded measures
document only a weak decline in recent years. We add to this
observation that on objective indicators, there is minimal evidence
of global backsliding. Of course, we are not claiming that back-
sliding is not occurring in any particular country. However, if the
world is experiencing major backsliding in the aggregate, we
should expect to see evidence of this on objective measures (e.g.,
incumbent leaders and their parties winning elections at higher
rates).
Second, we highlight measurement concerns regarding time-
varying bias in expert-coded data. Subjective indicators have the
advantage of measuring concepts for which it is difficult to collect
objective data, but this broader coverage may come at the cost of
accuracy and replicability. We encourage future research to
improve expert-coding practices, specifically regarding potential
time-varying biases. Perhaps one way forward is to ask experts to
collect data on more objective indicators—for example, the per-
centage of state ownership of media as a measure of media
freedom.
We conclude with a more general point about recognizing the
limits of our knowledge. In recent years, political science
researchers have become more precise about making strong claims
based on data. These standards are a key advancement in our
discipline, and careful scrutiny is one of the best contributions that
academics can give to society. It is imperative that we apply the
same level of rigor and comprehensiveness when we study trends
in global democracy.
In one sense, this is a modest study: rather than stating that
“the conventional wisdom that we are in a period of global
democratic decline is definitively wrong,”we simply are stating
that the evidence is not sufficiently strong to make this claim.
Recognizing this uncertainty is important and should not be
discouraging. Parsing what we do and do not know about a topic
is a first step for making scientific progress (Feynman 2009). We
hope this study helps scholars determine where to focus their
future efforts.
ACKNOWLEDGMENTS
A previous version of this article was circulated with the title
“Subjective and Objective Measurement of Democratic
Backsliding.”Many thanks to Ashley Anderson, Rob Blair, Carles
Boix, Jason Brownlee, Michael Coppedge, Wiola Dziuda, Ryan
Enos, Anthony Fowler, Scott Gehlbach, Jessica Gottlieb, Allison
Grossman, Guy Grossman, Gretchen Helmke, James Hollyer, Will
Howell, Susan Hyde, Ethan Kaplan, Marko Klăsnja, Carl Henrik
Knutsen, Andrej Kokkonen, Jacob Lewis, Ellen Lust, Neil Mal-
hotra, Juraj Medzihorsky, Rob Mickey, Mike Miller, Gerardo
Munck, Monika Nalepa, Pippa Norris, Brendan Nyhan, Jack
Paine, Tom Pepinsky, Barbara Piotrowska, Adam Przeworski,
Darin Self, Tara Slough, Pavi Suryanarayan, Milan Svolik, Jan
Teorell, Dan Treisman, Nic Van de Walle, David Waldner, Nils
Weidmann, three anonymous reviewers, and the editors of PS and
#PolisciTwitter for helpful comments and discussion. We also are
grateful to audiences at the American Political Science Associa-
tion Annual Meeting, Georgetown University, King’s College
London, Peking University, Polarization Research Lab, Political
Economy of Democratic Backsliding Conference, University of
North Carolina Chapel Hill, University of Chicago, Vanderbilt
University, Washington Political Economy Conference, and
Washington State University for their generous feedback. Kamya
Yadav and Melle Scholten provided excellent research assistance.
DATA AVAILABILITY STATEMENT
Research documentation and data that support the findings of this
study are openly available at the PS: Political Science & Politics
Harvard Dataverse at https://doi.org/10.7910/DVN/G2SQ6Y.
SUPPLEMENTARY MATERIAL
To view supplementary material for this article, please visit http://
doi.org/10.1017/S104909652300063X.
CONFLICTS OF INTEREST
The authors declare that there are no ethical issues or conflicts of
interest in this research. ▪
NOTES
1. Freedom House and Polity scores are rescaled to range from 0 and 1.
Online appendix section A.3 examines the median and other quantiles of
V-Dem, Polity, and Freedom House scores, which demonstrate even less evidence
of backsliding.
2. Polit y also has unusual recent cod ing choices. For example, after the election of
Donald Trump in 2016, Polity lowered the US Polity2 score from 10 to 8, which
is a lower score than the United States had during the Jim Crow era or before
women’s suffra ge. The US score was lowered to 7 in 2019 and then to 5 in 2 020—
which, by conventional standards, is no longer a democracy. Other countries
with a score of 5 include Somalia and Haiti. This does not pass a face-validity
check.
3. Online appendix A.9 discusses varying trends across regions.
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4. Online appendix A.4 contrasts the trends in subindices of the polyarchy index
variable, which contain more objective-versus-subjective variables.
5. Lueders and Lust (2018) also showed that there is considerable disagreement
between the various democracy datasets, which further underscores the subjec-
tive nature of these scores.
6. Fariss (2014) developed a measurement model that allows for changing standards
over time, with an application to human rights abuses. He argued that human
rights advocates have become better at monitoring abuses; therefore, expert
coders making decisions based on reports have information about more viola-
tions and effectively code more harshly as a result. Detecting this requires
specifying some variables that are not subject to the problem of changing
standards, analogous to our objective measures.
7. At various points, we also present data for subsets by features including regime
type, region, and a balanced panel of countries that existed for the entire time
range.
8. Examples of these extremely long-lived parties include the Chinese and Viet-
namese Communist Party, People’s Action Party in Singapore, Parti Démocra-
tique Gabonais in Gabon, and Botswana Democratic Party in Botswana.
9. See https://cpj.org/data-methodology for more information about the method-
ology.
10. The database also includes journalists who were killed in combat zones and on
“dangerous assignments,”which we infer are mostly not government attempts to
silence the media.
11. We do not include the journalists who were killed and jailed because (1) the
temporal range is more limited, and (2) the majority of country-years are zero on
this measure.
12. See https://sites.google.com/site/mkmtwo/data for data updated through 2020.
13. We present five-year averages to smooth out the volatility of the objective index,
but the pattern is similar when we look at changes in individual years as well.
14. Each bin corresponds to 0.05 of the scale and the middle bin is centered at 0, so it
contains countries with a change between -0.025 and 0.025.
15. A related possibility isthat standardshave risen overtime. Therefore,experts coding
the same observation in 2023 may be harsher compared to experts coding in 2015.
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