Carl Henrik Knutsen, University of Oslo and Peace Research Institute Oslo
Objectives. This article investigates whether economic growth and income level
affect revolution attempts and successful revolutions. Methods. The article conducts
a statistical analysis, mainly using panel data logit models, on a data set including
150 countries with time series from 1919 to 2003. Results. Low short-term growth
increases probabilities of both attempted and successful revolutions. There is some
evidence that higher income levels mitigate revolution attempts, but this is not robust
and further analysis indicates that any association may stem from oil income more
specifically. There is no net effect of income level on successful revolution, but high
income seemingly reduces probability of successful revolution more in democracies
than in dictatorships. Although revolutions occur more frequently after “J curves”
and “decremental deprivation patterns,” this is largely due to economic crises and
not the more complex growth patterns as hypothesized by, respectively, Davies and
Gurr. Conclusion. Low short-term economic growth induces revolutions, whereas
the impact of income level is less clear and seemingly contingent on factors such as
regime type and source of income.
Introduction
Revolutions are inherently difficult to predict (Kuran, 1989), but they
are also difficult to explain (Tilly, 1993). It is, for example, unclear
exactly how income growth and related processes of economic development impact on the probability of revolution. This is exemplified
by the recent literature dealing with the Arab Spring revolts of 2010–
2011. Although some authors propose that the “Arab revolutions were
fueled by poverty, unemployment and lack of economic opportunity”
(Malik and Awadallah, 2013:296), others contend that long-term economic growth and expansion of the middle classes contributed strongly
to the Egyptian and Tunisian uprisings (e.g., Fukuyama, 2012:56). Analogously, poor harvest and crisis years are put forth as preconditions for
∗
Direct correspondence to Carl Henrik Knutsen, Department of Political Science, University
of Oslo, Postboks 1097 Blindern, 0317 Oslo, Norway c.h.knutsen@stv.uio.no. Carl Henrik
Knutsen will share all data and coding for replication purposes. Thanks to participants at the
PAD Seminar at the Centre for Development and the Environment, University of Oslo, at
the Tuesday Seminar at the Department of Political Science, University of Oslo, and at the
Brownbag Lunch Seminar at the Centre for the Study of Civil War, PRIO for very helpful
comments and suggestions.
SOCIAL SCIENCE QUARTERLY, Volume 95, Number 4, December 2014
C 2014 by the Southwestern Social Science Association
DOI: 10.1111/ssqu.12081
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Income Growth and Revolutions∗
921
the European revolutionary uprisings of 1848–1849, but so is economic
transformation and growth from early industrialization (Palmer, Colton,
and Kramer, 2002). Yet, these explanations need not contradict. More than
50 years ago, Davies (1962) noted that Marx and Tocqueville proposed differently signed effects of income growth on revolution. But, while Marx
focused on how long-term structural changes and growth drive revolutions,
Tocqueville focused on short-term crises. Davies synthesized these insights in
his widely regarded J-curve theory: “Revolutions are most likely to occur when
a prolonged period of objective economic and social development is followed
by a short period of sharp reversal” (1962:5).
Still, the above, and other, propositions on income growth and revolutions
arguably remain conjectures. While the literature counts several elaborate theoretical arguments and thorough case studies, there are few statistical analyses
on the economic determinants of revolutions. This article contributes to the
literature by testing whether income level and growth systematically impact
on revolution attempts and successful revolutions, using an extensive data set
with 150 countries and time series from 1919 to 2003. Below, I first briefly
review and discuss potential links. Thereafter, I test the related hypotheses:
the main result is that short-term growth has a robust negative effect on both
attempted and successful revolutions. In contrast, income level does not affect
the probability of successful revolution; but higher oil and gas income may
mitigate revolution attempts. Furthermore, I find little support for Davies’s
above-noted J-curve hypothesis or Gurr’s (1970) decremental deprivation hypothesis. Correlations between these more complex income-growth patterns
and revolutions are largely due to economic crises as such.
Arguments and Hypotheses
One widely held notion is that economic crises spawn political changes, for
instance, policy reforms (see Drazen and Easterly, 2001). Crises, associated
with low short-term growth, may also spur revolutions. First, economic crises
may increase anger and frustration in the population, inducing grievances
toward the regime that may escalate into revolutionary action. Gurr hypothesizes that “men are not likely to be mobilized by new, revolutionary hopes
unless they feel sharply deprived” (1970:121), and economic hardship is a key
factor to deprivation. But, this mechanism may be weaker in democracies;
elections enable voters to throw out incumbents without starting revolutions,
and there is evidence that economic crises induce government change through
elections (e.g., Powell and Whitten, 1993).
Second, economic crises may, rightly or wrongly, induce different actors
to consider the regime less capable of dealing with economic policy, thereby
lowering expectations also of long-term growth under the regime. This may
lead them to calculate that their expected incomes would be higher under
a different regime, in turn inducing them to work for regime change (see
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Income Growth and Revolutions
Social Science Quarterly
Boix, 2003; Acemoglu and Robinson, 2006). Furthermore, economic crises
may trigger policy responses, such as cuts in social spending, which negatively
affect the incomes of large population groups. Ponticelli and Voth (2011),
using data on European countries from 1919 to 2009, find that cuts in
social spending and fiscal austerity are strong predictors of general strikes,
riots, anti-government demonstrations, political assassinations, and attempted
revolutions, and that such policy responses are a main channel through which
crises affect unrest.
Third, actors already hostile to a regime may interpret economic crises as
signals of opportunity to challenge the regime. Regimes may persist through
(threats of ) repression, even if immensely unpopular in the population, and
recessions may affect the probability of revolution mainly through improving
opportunities for challenging the regime, rather than affecting expectations of
future income. An individual may harbor deep resentment toward the existing regime without engaging in revolutionary activities or even expressing
resentment publicly (Kuran, 1989): the expected costs—like loss of education opportunities or even life—substantially exceed the expected benefits,
which is the (change in) probability of the (collective) action being successful
multiplied with expected gains from regime change (Tullock, 2005; Weede
and Muller, 1998). This calculus may be substantially altered if the individual expects many others to coordinate their actions (Kuran, 1989); a large
crowd enhances the probability of success and reduces the probability of being detected and punished if the revolt is unsuccessful. Although the exact
determinants of collective action leading to revolutionary uprisings are hard
to predict, economic crises may constitute one important type of signal that
alleviates collective action problems (Acemoglu and Robinson, 2006).
Several statistical studies have been conducted on short-term growth and
regime stability: Kennedy (2010), for instance, finds that higher growth stabilizes regimes in general, and poor democracies are particularly vulnerable
to economic crises (Przeworski and Limongi, 1997). Bueno de Mesquita and
Smith (2010), for example, find that higher growth also bolsters leader survival, at least in autocracies. Yet, most dictators and dictatorships fall because
of processes other than revolutions, notably coup d’états (Svolik, 2009), and
democracies also break down because of different processes. Hence, it is problematic to infer directly from these estimates; the determinants of coups (see
Powell, 2012), for instance, may be different from those of revolutions. Empirical studies have, however, also been conducted on other relationships with
relevance for the expected effect of short-term growth on revolution. For example, high growth seems to reduce the risk of civil war (Hegre and Sambanis,
2006), and many revolutions, such as the French and Chinese, have taken
place in contexts of civil wars (Skocpol, 1979; Weede and Muller, 1998). Yet,
Goldstone et al. (2010) fail to identify any clear relationship between growth
and the onset of ethnic and revolutionary civil wars. Furthermore, revolutions
in some countries, such as communist Czechoslovakia, have been notable for
their absence of violence, and there is a lack of large-N studies focusing more
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922
923
specifically on growth and revolutions (but see Ponticelli and Voth, 2011).
Whether the mechanisms discussed above generate a negative net effect of
growth thus remains an open empirical question, although the arguments
above produce a clear expectation: high short-term growth expectedly reduces
the probability of revolution (H1). Still, recession-induced grievances may be
directed toward the particular government in democracies through elections.
Hence, short-term growth may have a stronger negative effect on revolution
probability in dictatorships than in democracies (H1a).
Regarding income level, recent evidence indicates that higher income stabilizes all regimes, even dictatorships (Kennedy, 2010), and rich democracies are
far more resilient to breakdown than poor (Przeworski and Limongi, 1997).
Income level also has a robust negative effect on civil war onset (Hegre and
Sambanis, 2006), and Goldstone et al. (2010) find a clear relationship between
infant mortality—another proxy for level of economic development—and revolutionary and ethnic civil wars. Still, as for growth, there is a lack of statistical
studies on income level and revolutions as such. Although the expected net
effect is unclear, income level may influence the probability of revolution
through at least three channels.
First, increased prosperity may change values and attitudes in the population, which may in turn affect the probability of revolution (e.g., Davies, 1962;
Gurr, 1970). Inglehart and Welzel (2006) report that income enhances liberal, freedom-oriented values, thus making citizens demand democracy more
strongly. However, MacCulloch (2004) finds that higher GDP per capita reduces revolutionary sentiment in the population. Hence, income may both
increase the taste for democracy and the distaste for revolution, reducing
the probability of revolution in democracies while leaving the net effect in
autocracies indeterminable.
Second, higher income may alter the expected material benefits from different regime types, both for the current regime’s winning coalition and for
others (e.g., Boix, 2003). Higher income likely increases the absolute value
of resources controlled by government, thus increasing expected gains from
instigating a revolution and instituting a more favorable regime. On the other
hand, a richer economy also increases the opportunity costs of engaging in
revolutionary activities (see Collier and Hoeffler, 2004).
Third, higher income enhances the capacity of state institutions, providing
regimes with better tools to suppress or co-opt threats (Fearon and Laitin,
2003). Some sources of growth, such as natural resources, are more likely
to yield larger windfalls to the regime than others (Bueno de Mesquita and
Smith, 2010); income from, for example, oil production is relatively easy
to monopolize and control. Hence, increased oil income enables regimes to
weaken internal opposition through investing in repressive capacity or coopting potential threats (e.g., Bueno de Mesquita and Smith, 2010). Higher
income, particularly when natural resource based, could thus reduce the probability of revolution. But, sustained growth—and thus higher income—may
also empower groups with preferences for alternative regimes. Economic
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Income Growth and Revolutions
Social Science Quarterly
modernization may increase the relative strength of groups supporting democratization (e.g., Ansell and Samuels, 2010), such as the urban middle
class or organized labor, thereby increasing the probability of revolution in
autocracies.
The arguments above point in different directions regarding how income
level affects the probability of revolution: higher income enhances freedomoriented values, but also reduces the taste for revolutionary actions; higher
income increases the opportunity costs of fighting for the elites, but also for
the populace; higher income yields more revenue for co-optation and repression, but also generates more resources for nonregime actors. Hence, there
may be no systematic effect of income level on the probability of revolution
(H2). Nevertheless, some of the arguments imply that income level should
reduce revolution probability more in democracies than in autocracies (H2a).
Furthermore, income stemming from natural resource extraction may have a
stronger negative effect on revolution probability relative to income stemming
from other sources (H2b).
Further, there may be interaction effects between income level, or alternatively long-term growth, and short-term growth on the probability of revolution (H3). Davies’s J-curve theory, for example, predicts an interactive effect:
economic improvement over time generates rising expectations of increased
welfare also in the future. When combined with a recession, this produces expectation gaps, “a mental state of anxiety and frustration when manifest reality
breaks away from anticipated reality” (Davies, 1962:6). One implication—
Davies notes that growth patterns are not the only causes of expectation gaps
although they are the most important—is that the impact of economic crisis on
revolution probability increases when prior long-term growth increases (H3a).
Nevertheless, there may be different interaction effects between long-term and
short-term growth than that predicted by Davies (1962). Gurr (1970:46–57)
argues that Davies’s J-curve is only one pattern that may increase the risk of
revolution through generating expectation gaps. Gurr particularly focuses on
decremental deprivation patterns (DDPs): low long-term growth followed by
an even worse economic crisis spurs revolution (H3b).
Data and Operationalizations
In the analysis below, I utilize purchasing power parity (PPP) adjusted real
GDP per capita in 1990 USD from Maddison (2006). I enter log of income
level, whereas short-term growth is measured by one-year percentage change
in GDP per capita. I also test different operationalizations below.
“Revolution” has proven difficult to conceptualize precisely. I follow Goodwin (2001:9), who defines revolutions as instances “in which a state or a
political regime is overthrown and thereby transformed by a popular movement in an irregular, extraconstitutional and/or violent fashion,” but multiple
alternative definitions exist (e.g., Gurr, 1970:4; Goldstone, 2001:142). It has
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924
925
proven even more difficult for scholars to agree on a precise operationalization,
and thus an unequivocal list of, revolutions (Weede and Muller, 1998). This
is likely one reason for the few large-N studies on revolutions. Nevertheless,
I apply two different measures that, although clearly imperfect, allow me
to provide estimates on how income level and growth affect attempted and
successful revolutions. Despite validity and reliability problems with the measures, the below analysis constitutes an important contribution to a field where
elaborate theories and thorough case studies exist, but the general relevance of
the proposed mechanisms remain quite uncertain. Some models below cover
almost 7,000 country-years from 150 countries and with time series from
1919 to 2003.
The first operationalization draws on the “Revolutions” measure from Banks
(2008), which actually measures revolutionary attempts, and includes, for
example, armed rebellions motivated by independence from the central government. Hence, the measure does not differentiate between successful and
unsuccessful attempts and may include some irrelevant cases; the exact coding
rules are unclear. Furthermore, the data are mainly coded based on daily files of
the New York Times. This could generate biases due to geographically selective
reporting. Nevertheless, I score country-years experiencing at least one revolution, according to Banks (2008), 1 on an attempted-revolution dummy.
The second operationalization draws on the Archigos data set (Goemans,
Gleditsch, and Chiozza, 2009), and comes closer to measuring successful revolutions. I use data on processes leading to leaders exiting office, presumably
a major objective of revolutionary movements. Two Archigos categories are
relevant for capturing revolution conceptualized as a process at least partly
based on large-scale popular participation. The first is “Leader lost power as
a result of domestic popular protest.” The second, which may include some
irrelevant cases but that definitely includes many relevant cases such as the
Cuban and Chinese revolutions, is “Leader removed by domestic rebel forces.”
The successful-revolution dummy is scored 1 for country-years that included
at least one leader exit in one of these two categories. Leader exits due to
removal by domestic military or government actors, power struggle within the
military, or threat or use of foreign force are not coded as revolutionary exits.
This may lead to relevant cases not being coded as revolutions, since power
struggles within the government or military coups may come at the coattails
of large popular uprisings.
Successful revolutions occur infrequently. From 1919 to 2003, only 55
of 9,068 (0.6 percent) country-years with data also on GDP per capita observed successful revolutions as operationalized above. Attempted revolutions,
as classified in Banks (2008), are more frequent: 1,249 of 7,563 (16.5 percent) country-years experienced at least one. As Table 1 shows, the relative
frequencies of revolutionary attempt and successful revolution are contingent
on income level and growth. Indeed, no country with GDP per capita over
10,000 USD (1990 prices) experienced successful revolution, and few revolutionary attempts occur in very rich countries. Moreover, short-term growth
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Income Growth and Revolutions
Positive growth
Negative growth
Positive growth
Negative growth
0.209 (1,526)
0.323 (973)
0.004 (2,221)
0.014 (1,211)
<1,500
0.168 (1,205)
0.289 (447)
0.007 (1,411)
0.022 (540)
1,500–3,000
0.119 (935)
0.186 (355)
0.002 (1,063)
0.012 (421)
3,000–5,000
0.060 (956)
0.113 (301)
0 (1,055)
0.006 (335)
5,000–10,000
0.029 (765)
0.046 (151)
0 (808)
0 (168)
>10,000
Social Science Quarterly
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NOTES: Data on revolutionary attempts are from Banks (2008), successful revolutions are based on data from Goemans et al. (2009), and GDP data are
from Maddison (2006). GDP per capita is measured in 1990 USD. The number of observations is in parentheses.
Successful revolutions
Revolutionary attempts
GDP per capita
Relative Frequency of Revolution Attempts and Successful Revolutions by Income Level and Short-Term Growth
TABLE 1
926
927
is negatively related with attempted and successful revolutions. The relative
frequency of successful revolution is reduced by more than two-thirds when a
country’s growth changes from negative to positive. Regimes in rich countries
with positive short-term growth are particularly shielded against revolutions.
Indeed, the richest country to experience a successful revolution under positive
growth was Iran in 1979 (4,798 USD).
Yet, other factors may affect both income and revolution, and I run regressions with the following controls. First, regime type may affect both citizens’
desires to partake in revolutionary activities (MacCulloch and Pezzini, 2010)
and income growth. Hence, I control for degree of democracy, measured by
the Polity Index (PI) ranging from –10 (least democratic) to 10 (Marshall
and Jaggers, 2002). Second, regimes tend to become more resilient to threats
over time (e.g., Svolik, 2009), and regime duration may also enhance income
growth. I therefore control for log (regime duration +1), using Polity data.
Third, losses in international wars may increase chances of subsequent domestic rebellion (Weede and Mueller, 1998), and I control for whether a country
experienced loss in war in the current or two previous years, using Correlates of
War data (Reed Sarkees and Wayman, 2010). Fourth, more populous countries
expectedly have more revolutionary attempts, and I control for log population, using data from Banks (2008). Fifth, I control for share of population
living in cities with over 100,000 inhabitants, also from Banks (2008), since
revolutions often originate in major cities. Sixth, several observers highlight
the importance of revolutionary contagion; revolutions are geographically and
temporally clustered, illustrated by the revolutions and revolutionary attempts
in Europe in 1848–1849 and 1989, and in the Middle East and North Africa
in 2010–2011. Therefore, I construct a variable measuring average number
of other countries experiencing successful revolutions in the region in a given
year—using the regional categorization in Knutsen (2011).
For revolutionary attempts, I utilize fixed effects logit models, which control for country-specific effects; particular historical, social, cultural, and geographic factors may impact both economic performance and revolutions.
For successful revolutions, the fixed effects model is less tractable because of
efficiency concerns. The majority of countries never experienced a revolution
leading to leadership change between 1919 and 2003; running a fixed effects
model is equivalent to discarding all information from these countries when
estimating effects. Hence, I employ random effects logit models on successful
revolutions, controlling for additional country-fixed factors, namely, the Ethnic Fractionalization Index from Alesina et al. (2003) and plurality religion
dummies from Knutsen (2011).
Empirical Analysis
Models I (attempt) and II (success) in Table 2 are the main specifications. In brief, H1—on short-term growth reducing revolution probability—is
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Income Growth and Revolutions
0.001
(0.21)
−0.920∗∗∗
(−4.78)
0.030
(0.02)
0.191
(1.09)
−0.016
(−0.74)
49.201∗∗∗
(7.53)
−0.067
(−0.17)
−0.057∗∗
(−2.31)
−0.373∗∗∗
(−2.93)
−0.037∗∗∗
(−5.74)
−0.030∗∗∗
(−3.58)
−0.460∗∗∗
(−11.69)
0.338
(1.08)
0.191
(1.44)
0.011∗∗
(2.46)
8.785∗∗∗
(4.11)
II
Succ. revol.
RE logit
b/(t)
I
Revol. attempt
FE logit
b/(t)
−0.144∗∗
(−2.06)
−0.467∗∗∗
(−11.79)
0.33
(1.05)
0.225∗
(1.68)
0.011∗∗
(2.36)
8.843∗∗∗
(4.14)
0.015
(1.64)
−0.412∗∗∗
(−3.18)
−0.036∗∗∗
(−5.61)
III
Revol. attempt
FE logit
b/(t)
0.671∗∗
(2.05)
−0.900∗∗∗
(−4.60)
−0.181
(−0.14)
0.234
(1.28)
−0.022
(−0.98)
51.121∗∗∗
(7.42)
−0.087∗∗
(−2.03)
−0.001
(−0.00)
−0.062∗∗
(−2.42)
IV
Succ. revol.
RE logit
b/(t)
−0.149∗∗∗
(−2.88)
−0.057∗∗∗
(−5.71)
−0.532∗∗∗
(−11.56)
0.434
(1.30)
0.609∗∗∗
(3.61)
0.012∗∗
(2.49)
8.290∗∗∗
(3.37)
0.108
(0.60)
−0.031∗∗∗
(−4.23)
V
Revol. attempt
FE logit
b/(t)
0.104
(0.91)
−0.031
(−0.78)
−1.339∗∗∗
(−4.94)
0.400
(0.33)
0.087
(0.49)
−0.005
(−0.26)
42.565∗∗∗
(6.09)
−0.224
(−0.52)
−0.052∗∗
(−2.07)
VI
Succ. revol.
RE logit
b/(t)
Social Science Quarterly
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Region revol. avg.
Percent urban
Ln population
Loss war
Ln regime duration
Polity
Ln oil income p.c.
LnGDPpc × Polity
GDP p.c. growth
Ln GDP p.c.
Model
Dependent variable
Estimation
Panel Data Logit Models: At Least One Revolutionary Attempt or at Least One Successful Revolution as Dependent Variables
TABLE 2
928
5,826
126
−2039.51
251.57
<0.0001
I
Revol. attempt
FE logit
b/(t)
III
Revol. attempt
FE logit
b/(t)
5,826
126
−2038.16
254.28
<0.0001
II
Succ. revol.
RE logit
b/(t)
−0.259
(−0.26)
−1.150
(−1.26)
0.157
(0.10)
−0.674
(−0.74)
−21.728
(−0.00)
−3.102∗∗
(−2.26)
−22.106
(−0.00)
−0.656
(−0.59)
−6.385
(−1.62)
6,901
150
−144.09
94.51
<0.0001
−0.298
(−0.29)
−1.390
(−1.42)
−0.192
(−0.12)
−0.693
(−0.71)
−22.472
(−0.00)
−3.186∗∗
(−2.20)
−23.583
(−0.00)
−0.695
(−0.60)
−7.439∗
(−1.78)
6,901
150
−141.87
87.82
<0.0001
IV
Succ. revol.
RE logit
b/(t)
4,695
120
−1612.36
216.66
<0.0001
V
Revol. attempt
FE logit
b/(t)
−0.204
(−0.21)
−0.964
(−1.15)
−0.227
(−0.16)
−0.550
(−0.65)
−18.940
(−0.00)
−2.269∗
(−1.75)
−19.025
(−0.00)
−0.461
(−0.45)
−3.473
(−0.80)
5,771
150
−109.95
78.68
<0.0001
VI
Succ. revol.
RE logit
b/(t)
929
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NOTES: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.10. t-values are in parentheses. Data on revolutionary attempts are from Banks (2008); successful revolutions
are based on data from Goemans et al. (2009). Data are from 1919 to 2003.
N
Countries
Log likelihood
χ2
Prob.> χ 2
Constant
Buddhist+
Hinduist
Orthodox
Protestant
Catholic
Shia
Sunni
Ethnic Fract. Index
Model
Dependent variable
Estimation
TABLE 2—continued
Income Growth and Revolutions
Social Science Quarterly
corroborated, whereas the evidence is mixed on H2 regarding the (lacking)
net relationship between income level and revolutions. More specifically, both
higher growth and higher income level significantly reduce the probability
of revolution attempts (1 percent level) in Model I. The estimated effects are
also quite substantial: consider a small (5 million inhabitants), middle-income
(10,000 USD) dictatorship (PI = –10) that has not recently lost in war, with
one-third of the population in large cities, and where the regime has been in
power for 10 years. Even if no country in the region experienced successful
revolution that year, the estimated probability of attempted revolution is 0.32
if the economy records –4 percent GDP per-capita growth. The probability
is reduced to 0.26 if GDP per capita grows at +4 percent. If the country
had been democratic (polity = 10), the corresponding numbers are 0.20 and
0.16. Income level also has a noticeable effect. Consider the hypothetical dictatorship above and assume income is halved to 5,000 USD; when growing at
+4 percent, this increases the probability of revolution attempt from 0.26 to
0.31. As seen from Model II, short-term growth also reduces the probability
of successful revolution (significant 5 percent). In contrast, income level does
not have an effect (t = –0.17).
Some control variables are also highly significant in Models I or II. For
instance, older regimes are less at risk of experiencing attempted and successful revolutions. Moreover, the average number of revolutions in the region
enhances the probability of both revolution attempts and successes. Democracy and urbanization reduce the risk of revolution attempts. Loss in war and
ethnic fractionalization are, however, statistically insignificant.
I conducted several robustness tests (see http://folk.uio.no/carlhk/for tables). First, I conducted sensitivity checks, leaving out one control variable
at a time, but the results are fairly robust. Second, I changed the regional
revolutionary climate measure from being based on successful to attempted
revolutions, but the results are quite similar. Third, I substituted continuous
growth with a dummy recording whether growth was positive or negative, and
the results are again fairly similar. Fourth, I used one-year lagged growth instead
of current. This does not change the significant effects on attempted revolutions, but growth and income level are now both insignificant for successful
revolutions. Still, although lagged growth mitigates endogeneity concerns, using current growth may be preferable since important arguments above (e.g.,
the signaling and collective action argument) indicate that the impact of economic crisis on revolution operates with short effect-lags. Fifth, I tested models
including youth bulges—share of adults aged 15–24—from Urdal (2006), or
share of manufacturing income going to wages from UNIDO (2011) to proxy
for income inequality. The growth coefficients are robust to including these
controls, whereas income level remains insignificant for successful revolutions,
and also loses significance for revolution attempts. Sixth, growth remains significant when controlling for region (only relevant in Model I) and decade
dummies, whereas income level is insignificant both for attempted and successful revolutions. Seventh, a potentially important control for revolutionary
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930
931
attempts is whether there was at least one attempt the year before. I included
the lagged dependent variable, and once again short-term growth retains significance whereas income level does not. Eighth, I tested linear fixed effects
models on the number of revolutionary attempts; growth is significant (1
percent level), whereas income level is not.
Generally, the result for growth is quite robust, and holds also when substituting the panel logit models with probit models or standard logit models
with errors clustered on country, or when measuring income level a year before
growth. In sum, growth negatively affects attempted and successful revolutions. This result is in line with, for example, Ponticelli and Voth (2011)
on episodes of instability in Europe. In contrast, Goldstone et al. (2010) do
not find that growth is a robust predictor of revolutionary and ethnic civil
wars or “instability onsets” more generally (also including democratic reversals, state collapse, genocide, and politicide)—suggesting political variables
are the key determinants. Yet, Goldstone et al. do not analyze “revolutionary wars” separately—their time series run from 1955 and they identify only
12 such wars—and they do not explicitly investigate how growth relates
to attempted or successful revolutions (also those not associated with mass
violence). Still, their forecasting model includes a more refined regime measure, using dummies drawing on Polity data, and the very unstable character
of partial democracies (see also Hegre et al., 2001) suggests the results in
Table 2 could be due to omitted variable bias. Yet, this is not the case: I added
squared Polity terms to Models I and II, and these are indeed negative and
significant (1 percent); semi-democracies have the highest risks of attempted
and successful revolutions. However, the results on growth (and income level)
were unaltered; the t-value for growth changed from −5.7 to −5.9 in Model
I and from −2.3 to −2.2 in Model II.
In sum, the above analysis constitutes solid evidence that short-term growth
affects revolutions. There is also evidence that high income levels may reduce
the probability of attempted revolution, but this is not robust. There is no
evidence that income level affects successful revolution.
The discussion above indicated that the effect of growth may depend on
regime type (H1a), whereas the effect of income level may depend both on
regime type (H2a) and source of income (H2b). First, I tested H1a by adding
Polity × GDPpcgrowth to the baseline models. The interaction terms are always insignificant (10 percent), whereas the linear growth terms remain highly
significant. Thus, there is no evidence that growth has a stronger effect on
attempted or successful revolution in autocracies; the effect is strong and significant independent of regime type. Second, I tested H2a by including Polity ×
LnGDPpc in the baseline models. These models (III and IV) are reported
in Table 2. There is no evidence of interaction when revolutionary attempt
is the dependent variable. However, for successful revolution, I identify the
expected interaction (significant 5 percent level); higher income may reduce
the probability of leaders being ousted through revolutions more in democracies than in dictatorships. Third, H2b indicated that income from petroleum
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Income Growth and Revolutions
Social Science Quarterly
production may reduce the probability of revolution more than income from
other sources. Models V and VI in Table 2 include (log) oil and gas income per
capita (2,000 USD), from Ross (2011). As anticipated, oil income significantly
reduces the probability of a revolution attempt. Furthermore, log GDP per
capita now turns insignificant; a richer economy, holding oil and gas income
constant, does not reduce the probability of a revolutionary attempt. Hence,
the (nonrobust) result above that higher income reduces the probability of
attempted revolution may be due to the stabilizing effect of natural resource
income. Oil income is, however, insignificant in the successful-revolution
model.
As discussed, there could also be interaction patterns in terms of how longterm and short-term growth combine to affect the probability of revolution.
I investigated H3 by entering GDPpcgrowth × GDPpclevel in the main
model. There was no evidence of an interaction effect, neither on attempted
nor successful revolutions. However, more specific combinations of short-term
and long-term growth patterns may be particularly conducive to revolutions.
H3a stated the J-curve hypothesis from Davies (1962), and H3b followed
Gurr (1970) in proposing that DDPs are particularly conducive to revolution. J curves—operationalized as country-years with negative growth following 15 years with average annual GDP per-capita growth rate higher than
3 percent—constitute 4.6 percent of country-year observations in the sample.
DDP—measured as country-years with negative growth succeeding 15 years
with average growth rate below 1 percent and subject to the condition that
short-term growth is lower than the 15-year average—make up 14.1 percent
of observations.
As shown in Table 3 , the J-curve dummy is significant (5 percent) in
models without controls for successful revolution (Model II), but insignificant
for attempted revolution (Model I). Furthermore, the significant result for
successful revolution vanishes when controlling for short-term growth (Model
IV) or for both short-term growth and 15-year lagged log GDP per capita.
Short-term growth, however, is highly significant (1 percent). Hence, although
J curves are correlated with successful revolutions, closer inspection indicates
the particular J-curve pattern may not increase the risk of revolution. It is
rather economic crises, as such, that have an effect. Model VI in Table 3—
controlling for the full set of variables—does yield a significant J-curve term,
but only at 10 percent. Moreover, this weakly significant result is not robust to
minor changes in specification, like changing the long-term growth period in
the J curve from 15 to 20 years. Hence, there is no stark evidence corroborating
Davies’s hypothesis on periods of increasing income followed by a crisis being
particularly conducive to (successful) revolutions. The evidence for J-curve
patterns generating attempted revolutions is even weaker.
Gurr’s (1970) assertion that DDPs induce revolutions finds somewhat more,
but far from robust, support. Table 3 shows that DDP is significantly correlated
with both attempted and successful revolutions (Models VII and VIII), and the
correlation with successful revolutions remains significant when controlling for
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932
0.226
(1.20)
III
Rev. att.
FE logit
b/(t)
0.497
(1.00)
IV
Succ. rev.
RE logit
b/(t)
−0.043∗∗∗ −0.080∗∗∗
(−6.25)
(−5.69)
0.995∗∗ −0.071
(2.05)
(−0.36)
I
II
Rev. att. Succ. rev.
FE logit RE logit
b/(t)
b/(t)
−0.035∗∗∗
(−4.59)
0.376∗∗
(2.23)
−0.038∗∗∗
(−3.97)
−0.532∗∗∗
(−12.22)
0.564
(1.63)
−0.200
(−1.20)
0.007
(1.42)
−0.067
(−0.31)
V
Rev. att.
FE logit
b/(t)
−0.049
(−1.55)
−0.233
(−0.46)
0.008
(0.21)
−0.884∗∗∗
(−4.50)
−0.067
(−0.05)
0.196
(1.11)
−0.006
(−0.25)
1.266∗
(1.87)
0.451∗∗∗ 1.371∗∗∗
(4.22)
(4.28)
VI
VII
VIII
Succ. rev. Rev. att. Succ. rev.
RE logit FE logit RE logit
b/(t)
b/(t)
b/(t)
X
Succ. rev.
RE logit
b/(t)
0.175
0.751∗∗
(1.47)
(2.08)
−0.037∗∗∗ −0.069∗∗∗
(−5.11)
(−4.32)
IX
Rev. att.
FE logit
b/(t)
0.144
(1.07)
−0.031∗∗∗
(−3.84)
0.361∗∗
−2.13
−0.038∗∗∗
(−3.96)
−0.528∗∗∗
(−12.11)
0.565
(1.63)
−0.197
(−1.18)
0.007
(1.45)
XI
Rev. att.
FE logit
b/(t)
0.370
(0.64)
−0.054∗
(−1.68)
−0.253
(−0.50)
0.011
(0.28)
−0.864∗∗∗
(−4.38)
−0.094
(−0.07)
0.159
(0.91)
−0.005
(−0.21)
XII
Succ. rev.
RE logit
b/(t)
933
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Percent urban
Ln population
Loss war
Ln regime dur.
Polity
GDP p.c. (15-year lag)
GDP p.c. growth
DD pattern
J curve
Model
Dependent
variable
Estimation
J Curves and Decrimental Deprivation Patterns: At Least One Revolutionary Attempt or at Least One Successful Revolution as
Dependent Variables
TABLE 3
Income Growth and Revolutions
N
N
N
IV
Succ. rev.
RE logit
b/(t)
N
8.282∗∗∗
(3.47)
V
Rev. att.
FE logit
b/(t)
52.608∗∗∗
(7.27)
−0.539
(−0.54)
Y
VI
Succ. rev.
RE logit
b/(t)
N
VII
Rev. att.
FE logit
b/(t)
N
VIII
Succ. rev.
RE logit
b/(t)
N
IX
Rev. att.
FE logit
b/(t)
N
X
Succ. rev.
RE logit
b/(t)
N
8.243∗∗∗
(3.45)
XI
Rev. att.
FE logit
b/(t)
52.206∗∗∗
(7.23)
−0.429
(−0.43)
Y
XII
Succ. rev.
RE logit
b/(t)
5,345
7,184
5,345
7,184
4,840
5,872
5,345
7,184
5,345
7,184
4,840
5,872
118
138
118
138
114
131
118
138
118
138
114
131
−2030.44 −270.36 −2009.77 −259.09 −1682.94
−110.67
−2022.41 −264.36 −2008.77 −257.51 −1682.42
−111.95
1.41
4.20
42.74
36.09
215.12
83.24
17.45
18.31
44.74
38.56
216.16
83.33
0.236
0.040
<0.0001 <0.0001
<0.0001
<0.0001
<0.0001 <0.0001
<0.0001 <0.0001
<0.0001
<0.0001
N
I
II
III
Rev. att. Succ. rev. Rev. att.
FE logit RE logit
FE logit
b/(t)
b/(t)
b/(t)
Social Science Quarterly
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NOTES: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.10. t-values are in parentheses. Data on revolutionary attempts are from Banks (2008); successful revolutions are
based on data from Goemans et al. (2009). J curve is operationalized as country-year with negative growth following 15 years with average annual GDP
per-capita growth >3 percent. Decremental deprivation (DD) pattern is operationalized as country-year with negative growth succeeding 15 years with
average growth <1 percent and subject to short-term growth being lower than the 15-year average. Constant, country-dummies, and plurality religion
dummies are omitted from the table. Data are from 1919 to 2003.
N
Countries
Log likelihood
χ2
Prob.> χ2
Religion dummies
Ethnic fract.
Region rev. avg.
Model
Dependent
variable
Estimation
TABLE 3—continued
934
935
growth (5 percent; Model X) and for growth and lagged income (10 percent).
However, when controlling for the full set of variables (Models XI and XII),
DDP turns insignificant, whereas short-term growth retains significance. The
effect of DDP is stronger when substituting 15 with 20 or 25 years as basis for
long-term growth, although neither the effect on attempted nor on successful
revolutions is robust. Thus, Davies (1962) and Gurr (1970) may have been
only partially correct; the evidence presented here suggests it is mainly the
economic crisis component of the more complex growth patterns that has
a robust, independent effect on revolutions. Whether a crisis is preceded by
a time period of sustained growth or stagnation, however, does not seem to
matter that much.
Conclusion
Above, I empirically investigated whether and how income level and growth
are associated with revolutionary attempts and successful revolutions. There
is no evidence indicating that higher income level systematically affects the
probability of successful revolution. There is some, albeit not robust, evidence indicating that higher income reduces the probability of revolutionary
attempts, but further analyses indicate this stems from income from oil and
gas production. In contrast, the analysis finds that short-term growth systematically affects the probabilities of both attempted and successful revolutions.
The results above are based on measures with substantial reliability and validity problems, and better measures and more data collection on revolutionary
episodes would allow future research to test the robustness of the result. Still,
the results presented here give a fairly strong indication that regimes in countries experiencing economic crises are at increased risk of facing revolutionary
threats and of eventually being thrown out of office because of them.
The theories presented in Davies (1962) and Gurr (1970) indicate that the
effect of a crisis is highly contingent on the long-term economic development
pattern preceding it. Davies’s J-curve theory indicates that a high-growth
country such as China is particularly ripe for revolution if a deep recession
were to set in. The DDP described in Gurr (1970) rather indicates that a
low-growth country such as North Korea would be more at risk. The results
in this article indicate that both the leaders of the Chinese Communist Party
and Kim Jong-un have reason to worry if their economies are hit by a deep
recession.
REFERENCES
Acemoglu, Daron, and James A. Robinson. 2006. Economic Origins of Dictatorship and Democracy. New York: Cambridge University Press.
15406237, 2014, 4, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ssqu.12081 by Higher School Of Economics, Wiley Online Library on [25/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
Income Growth and Revolutions
Social Science Quarterly
Alesina, Alberto, Arnaud Devleeschauwer, William Easterly, Sergio Kurlat, and Romain
Wacziarg. 2003. “Fractionalization.” Journal of Economic Growth 8(2):155–94.
Ansell, Ben, and David Samuels. 2010. “Inequality and Democratization: A Contractarian
Approach.” Comparative Political Studies 43(12):1543–74.
Banks, Arthur S. 2008. Cross-National Time-Series Data Archive. User’s Manual.
Binghamton, NY: State University of New York, Binghamton.
Boix, Carles. 2003. Democracy and Redistribution. Cambridge: Cambridge University Press.
Bueno de Mesquita, Bruce, and Alastair Smith. 2010. “Leader Survival, Revolutions, and the
Nature of Government Finance.” American Journal of Political Science 54(4):936–50.
Collier, Paul, and Anke Hoeffler. 2004. “Greed and Grievance in Civil War.” Oxford Economic
Papers 56(4):563–95.
Davies, James C. 1962. “Towards a Theory of Revolution.” American Sociological Review
27(1):5–19.
Drazen, Allan, and William Easterly. 2001. “Do Crises Induce Reform? Simple Empirical Tests
of Conventional Wisdom.” Economics & Politics 13(2):129–57.
Fearon, James D., and David D. Laitin. 2003. “Ethnicity, Insurgency, and Civil War.” American
Political Science Review 97(1):75–90.
Fukuyama, Francis. 2012. “The Future of History: Can Liberal Democracy Survive the Decline
of the Middle Class?” Foreign Affairs 91(1): 53–61.
Goemans, Hein, Kristian Skrede Gleditsch, and Giacomo Chiozza. 2009. “Introducing Archigos: A Dataset of Political Leaders.” Journal of Peace Research 46(2):269–83.
Goldstone, Jack. 2001. “Towards a Fourth Generation of Revolutionary Theory.” Annual
Review of Political Science 4:139–87.
Goldstone, Jack A., Robert H. Bates, David L. Epstein, Ted Robert Gurr, Michael B. Lustik,
Monty G. Marshall, Jay Ulfelder, and Mark Woodward. 2010. “A Global Model for Forecasting Political Instability.” American Journal of Political Science 54(1):190–208.
Goodwin, Jeff. 2001. No Other Way Out: States and Revolutionary Movements, 1945–1991.
Cambridge: Cambridge University Press.
Gurr, Ted R. 1970. Why Men Rebel. Princeton, NJ: Princeton University Press.
Hegre, Håvard, Tanja Ellingsen, Scott Gates, and Nils Petter Gleditsch. 2001. “Toward a
Democratic Civil Peace? Democracy, Political Change, and Civil War, 1816–1992.” American
Political Science Review 95(1):33–48.
Hegre, Håvard, and Nicholas Sambanis. 2006. “Sensitivity Analysis of Empirical Results on
Civil War Onset.” Journal of Conflict Resolution 50(4):508–35.
Inglehart, Ronald, and Christian Welzel. 2006. Modernization, Cultural Change and
Democracy—The Human Development Sequence. Cambridge: Cambridge University Press.
Kennedy, Ryan. 2010. “The Contradiction of Modernization: A Conditional Model of Endogenous Democratization.” Journal of Politics 72(3):785–98.
Knutsen, Carl Henrik. 2011. The Economic Effects of Democracy and Dictatorship. Ph.D. Thesis.
Oslo: University of Oslo.
Kuran, Timur. 1989. “Sparks and Prairie Fires: A Theory of Unanticipated Political Revolution.” Public Choice 61(1):41–74.
MacCulloch, Robert. 2004. “The Impact of Income on the Taste for Revolt.” American Journal
of Political Science 48(4):830–48.
15406237, 2014, 4, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ssqu.12081 by Higher School Of Economics, Wiley Online Library on [25/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
936
937
MacCulloch, Robert, and Silvia Pezzini. 2010. “The Roles of Freedom, Growth, and Religion
in the Taste for Revolution.” Journal of Law and Economics 53(2):329–58.
Maddison, Angus. 2006. The World Economy. Paris: OECD Publishing.
Malik, Adeel, and Bassem Awadallah. 2013. “The Economics of the Arab Spring.” World
Development 45:296–313.
Marshall, Monthy G., and Keith Jaggers. 2002. Polity IV Project—Dataset Users’ Manual.
College Park, MD: Program Center for International Development and Conflict Management,
University of Maryland.
Palmer, R.R., Joel Colton, and Lloyd Kramer. 2002. A History of the Modern World. New York:
McGraw-Hill.
Ponticelli, Jacopo, and Hans-Joachim Voth. 2011. “Austerity and Anarchy: Budget Cuts and
Social Unrest in Europe, 1919–2009.” CEPR Discussion Paper 8513, Centre for Economic
Policy Research.
Powell, Guy Bingham, and Guy D. Whitten. 1993. “A Cross-National Analysis of Economic Voting: Taking Account of the Political Context.” American Journal of Political Science
37(2):391–414.
Powell, Jonathan. 2012. “Determinants of the Attempting and Outcome of Coups d’état.”
Journal of Conflict Resolution 56(6):1017–40.
Przeworski, Adam, and Fernando Limongi. 1997. “Modernization: Theory and Facts.” World
Politics 49(2):155–83.
Ross, Michael L. 2011. Replication Data for: Oil and Gas Production and Value, 1932–2009,
Version 4. Department of Political Science, UCLA.
Sarkees, Meredith Reid, and Frank Wayman. 2010. Resort to War: 1816–2007. Washington,
DC: CQ Press.
Skocpol, Theda. 1979. States and Social Revolutions: A Comparative Analysis of France, Russia
and China. Cambridge: Cambridge University Press.
Svolik, Milan. 2009. “Power-Sharing and Leadership Dynamics in Authoritarian Regimes.”
American Journal of Political Science 53(2):477–94.
Tilly, Charles. 1993. European Revolutions: 1492–1992. Oxford: Blackwell.
Tullock, Gordon. 2005. The Social Dilemma of Autocracy, Revolution, Coup D’Etat, and War.
Indianapolis, IN: Liberty Fund.
UNIDO. (2011). INDSTAT2 2011 ISIC Rev. 3. Data set.UNIDO.
Urdal, Henrik. 2006. “A Clash of Generations? Youth Bulges and Political Violence.” International Studies Quarterly 50(3):607–30.
Weede, Erich, and Edward N. Muller. 1998. “Rebellion, Violence and Revolution: A Rational
Choice Perspective.” Journal of Peace Research 35(1):43–59.
15406237, 2014, 4, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ssqu.12081 by Higher School Of Economics, Wiley Online Library on [25/03/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
Income Growth and Revolutions