An exploration of the association between fuel subsidies and fuel riots

Abstract

Fuel riots are common around the world. Between 2005 and 2018, 41 countries had at least one riot directly associated with popular demand for fuel. We make use of a new international dataset on fuel riots to explore the effects of fuel prices and price regimes on fuel riots. In line with prior expectations, we find that large domestic fuel price shocks are a key driver of riots - as these are often linked to international price shocks. In addition, we report a novel result: fuel riots are closely associated with domestic price regimes. Countries that maintain fixed price regimes - notably net energy exporters - tend to have large fuel subsidies. When such subsidies become unsustainable, domestic price adjustments are large, often leading to riots.

Full text

An exploration of the association between fuel subsidies and
fuel riots
January 14, 2021
Abstract
Fuel riots are common around the world. Between 2005 and 2018, 41 countries
had at least one riot directly associated with popular demand for fuel. We make
use of a new international dataset on fuel riots to explore the effects of fuel prices
and price regimes on fuel riots. In line with prior expectations, we find that large
domestic fuel price shocks are a key driver of riots – as these are often linked to
international price shocks. In addition, we report a novel result: fuel riots are
closely associated with domestic price regimes. Countries that maintain fixed price
regimes – notably net energy exporters – tend to have large fuel subsidies. When
such subsidies become unsustainable, domestic price adjustments are large, often
leading to riots.
1

1 Introduction
In 2019, there were major protests related to energy in Sudan, France, Zimbabwe,
Haiti, Lebanon, Ecuador, Iraq, Chile, and Iran – many of which turned into riots.
Energy-related riots seem also to be relatively common: in most years between
2005 and 2018, there have been riots in at least one or two countries. It is therefore
surprising that there is almost no academic literature that explores the determinants
of fuel riots.1. Although there is a significant literature on energy-related conflict,
it tends to focus on conflict over abundance of fossil fuels (e.g. Van der Ploeg, 2011;
Ross, 2004; Carbonnier & Wagner, 2011). There is, however, evidence that conflict
and unrest may be related to increases in oil prices. In a seminal paper, Dube and
Vargas (2013) show that increases in the international price of oil are associated
with increases in violence in Colombia in municipalities in oil-producing regions.
This result is confirmed in a recent review of 350 studies by Blair, Christensen, and
Rudkin (2020), who conclude that the probability of conflict is positively associated
with increases in oil prices.
However, most of the existing literature focuses on armed conflict and knowledge
about less violent forms of political violence (like riots) is more limited. 2Evidence
on the mechanisms that may explain the association between shocks in international
oil prices and conflict is also limited. This paper addresses these gaps by drawing
on a unique database on fuel riots (Natalini et al., 2020) to examine in detail the
association between fuel riots and price shocks. We find, as expected, a positive
association between international oil prices and fuel riots. We show that this effect is
associated with the domestic price regime and fuel subsidies. We find that countries
that are net energy exporters are much more likely to fix domestic fuel prices to
protect local populations against price rises. However, countries that fix prices tend
to have much larger fuel subsidies and, when these can no longer be sustained, much
bigger domestic price adjustments are needed, often leading to riots.
This is an important contribution because the existing literature on riots and
civil unrest rarely takes into consideration how fluctuations in international prices
of oil may be transmitted to local markets in ways that may drive citizens to riot.
This transmission is not a given because several countries adopt subsidy policies to
cushion local markets against fluctuations in the international price of oil. As long
as these price regimes are sustainable, it is unlikely that changes in the international
price of oil will affect local markets and, therefore, the probability of riots occurring.
While some countries allow international prices to pass through fully to domestic
prices, others fix domestic prices – at least temporarily – in an attempt to protect
domestic consumers from such shocks. However, fixing prices below international
prices generates fuel subsidies whose size depends on the regulated domestic price
and the international price of fuel.
The propensity to use fuel subsidies to protect domestic consumers is often linked
to the structure of the economy. Fuel exporters are particularly likely to have the
kind of consumer price subsidies that are the object of protests (Cheon, Urpelainen,
& Lackner, 2013; Victor, 2009). In energy-rich countries where state capacity to
distribute resources is weak, consumer fuel subsidies tend to be common and resilient
1Henceforth we use the phrase ’fuel riots’ since the vast majority of such riots are, at least superficially,
about the price of fuel. For a detailed definition of fuel riots see (Natalini, Bravo, & Newman, 2020)
2One exception is Natalini (2016) who examine the role of scarcity, prices and political fragility in
driving food and fuel riots using a quantitative and agent-based modelling approach.
2

to reform efforts (Inchauste & Victor, 2017a). Authoritarian regimes are particularly
likely to rely on such subsidy regimes as a source of popular legitimacy (Andresen,
2008; Rosser, 2006). Where other forms of social protection are limited, or natural
resource wealth is highly concentrated, or where economic performance is poor,
subsidies may be seen as part of the social contract (Lockwood, 2015). However,
when such subsidies become unsustainable, governments often attempt to reduce
them by raising fuel prices sharply (Rentschler & Bazilian, 2017; Lockwood, 2015).
When these adjustments result in large increases in the domestic price of oil, social
discontent may rise, potentially increasing the likelihood of protests and riots.
Our paper also contributes to a smaller literature on the social and political
effects of price subsidies. This literature is largely concerned with detailing the
size of subsidies (Coady, Parry, Sears, & Shang, 2017), the distributional impact
of subsidies (Granado, Coady, & Gillingham, 2010), the impact of subsidies on
economic and environmental performance (Rentschler, Kornejew, & Bazilian, 2017;
Erickson et al., 2020), and the impact of subsidy reforms on the poor, among other
groups. There is also a growing literature on the political economy of fossil fuel
subsidy reform (Inchauste & Victor, 2017b; Skovgaard & van Asselt, 2018), which
provides a nuanced understanding of the complexities of policy reform and why so
little progress has been made on reform (Ross, Hazlett, & Mahdavi, 2017). However,
this literature rarely mentions an association between price subsidies and fuel riots,
other than as an explanation of why reforms stop or stall, or as a reason why reforms
are not attempted in the first place.
The paper proceeds as follows. Section 2 describes the data we use in the paper
and brief descriptive statistics. Section 3 discusses the main results including an
the analysis of price regimes as potential mediators of the relationship between fuel
prices and riots; we also present some robustness tests. Section 4 examines why
countries fix prices and create subsidies. Section 5 concludes.
2 Data and descriptive statistics
The data we use in this paper to measure the occurrence of fuel riots comes from the
database published in Natalini et al. (2020), which spans the time-frame 2005-2016.
We have updated the database until the year 2018 using the same methodology
implemented by the authors.
The definition and data we use in this paper to measure the occurrence of fuel
riots comes from Natalini et al. (2020). Fuel riots have been defined by the authors
as ‘incidents of significant unrest – riots, demonstrations, major protests – where
grievances over fuel prices, the prospective removal of subsidies, or fuel availability
were specifically identified as a factor which motivated people involved in the vio-
lent event’ (Natalini et al., 2020). The original database spanned the time-frame
2005-2016 and was here updated until the year 2018 using the same methodology
implemented by the authors. In particular, we performed a manual google search
with a set of keywords to identify events that matched our definition of fuel riots. Al-
though more sophisticated event collection methodologies exist (e.g. machine learn-
ing), these tend to be extremely time-consuming and often result in a large number
of duplicates (e.g. via news feed repositories such as Lexis Nexis), and therefore a
manual approach was preferred (see Newman (2020) for a comprehensive discussion
on challenges with automatised event data collection with the example of food ri-
3

ots). The keywords included different combinations and declinations of the words
fuel/energy, violence, riot/protest and for every combination we reviewed the first
ten pages of results. The search was global in scope and only included English online
newspapers (or translated). Our data could therefore suffer from different types of
biases (e.g. towards larger, more important events) that are very common when
performing global-level research such as in our case and difficult to avoid (Dowd,
Justino, Kishi, & Marchais, 2020). The full database is included as an Appendix
with references to the articles. These data were recorded on a monthly basis and
transformed into a binary variable for whether the country had a fuel riot during
the year/month or not.3
Figure 1 shows the geographical distribution of fuel riots over the period.
Figure 1: Map of incidence of fuel riots 2005-2018
Between 2005 and 2018, we observe 59 country-years in which fuel riots occurred.
These are rare events since there are 3011 country-years in our data. However, fuel
riots happen in quite a few countries: 41 of the 217 countries or jurisdictions in our
dataset experienced a fuel riot over the period. Some countries experienced several
fuel riots in that period: India had seven; Indonesia had five; and China and Yemen
both had three.
Our main independent variables are the international price of oil, the level of fuel
subsidies , and the domestic price regime implemented in each country. Average
international prices for crude oil were sourced from the World Bank’s Commodity
Price Database.4The data on fossil fuel subsidies comes from the IMF’s calculation
of subsidies for the period 2010-2017. We use estimates for ‘total consumer pre-tax
subsidies’, which include four energy sources (oil, natural gas, coal and electricity)
3Doing so loses very little information in the annual data because there were only two countries that
had more than one fuel riot in the same year - India in 2010 and Indonesia in 2013.
4The Pink Sheet – see https://www.worldbank.org/en/research/commodity-markets for details.
4

as these capture the difference between retail prices and international price of the
resource when this is internationally traded (i.e. fuel), and the difference between
the retail price and the user cost (cost of production) for those not usually traded
(i.e. electricity) (Coady et al., 2017). Our data on the domestic price regime is
drawn from an analysis of the linkage between fuel riots and monthly price changes.
This draws on the dataset of international and domestic gasoline prices compiled by
Ross et al. (2017) which contains information about local retail gasoline prices for
157 countries from 2003 to 2015.
Globally, fuel riots are clearly related to the price of oil. Figure 2 shows the
number of fuel riots that took place globally for each year from 2005 to 2018 alongside
the international oil price: as expected, fuel riots spike when international oil prices
spike, since this generally has a direct impact on the domestic price of fuel.
40 60 80 100
Oil price (USD constant 2015)
0 5 10 15
Number of fuel riots
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Figure 2: Fuel riots and international oil prices
The empirical analysis in the next section uses, in addition, a number of con-
trol variables since there are several other observable variables that may affect the
propensity of riots. Since Gurr (1970), a large literature has shown how relative
deprivation and drops in economic standing may give rise to social discontent and
grievances.5Therefore, it is reasonable to expect the number of fuel riots to be
associated with the general development of the country. Mass protests may also
be more likely when there is space for civic engagement, independent media, and
5See the review of this literature in Justino and Martorano (2019).
5

organizations capable of mobilizing support are present (Tilly & Tarrow, 2015). In
addition, our fuel riots variable measures whether there has been a violent riot re-
lated to fuel during that year, which makes it more likely that we will observe fuel
riots in countries with larger populations, simply because there are more people
that might feel sufficiently unhappy to participate in a riot. We include in the main
regressions country-level GDP and population as controls using data compiled from
the World Bank’s World Development Indicators (WDI) database.
3 Results
3.1 Domestic price changes and fuel riots
Our empirical strategy exploits the panel nature of our datasets to eliminate the
possibility that the associations we observe are due to unobserved fixed characteris-
tics of the country, or common time effects. Specifically, we estimate a fixed effects
panel regression of the propensity for a fuel riot against the growth in domestic
and international fuel prices. We control for the possibility of common time effects
(notably changes in the international oil prices) using month dummies for the entire
period. By including country fixed effects, we eliminate the possibility that any
observed association is due to fixed country characteristics that might affect the
likelihood of a fuel riot. We also include the log of per capita GDP and the log of
population as controls. The model that we estimate is:
Riotim = ∆pdom
im + ∆pint
im +lnGDP pciy +lnP opiy +γ m +λi +it (1)
where Riotim indicates that country ihad a riot in month m; ∆pdom
im is the
proportionate change in the domestic fuel price in the preceding month; ∆pint
im is
the proportionate change in the international fuel price in the preceding month;
lnGDP pciy is log GDP per capita; lnP opiy is log of the population; γm is a month
dummy; λi is the country fixed effect; and it represents a random error term.
Table 1 shows that there is a strong and statistically significant positive rela-
tionship between domestic price growth from month to month and fuel riots. The
estimated coefficient suggests that an increase in the growth rate of local prices of,
say 10 percentage points, would roughly quadruple the (initially low) probability of
a fuel riot. However, the relationship with international fuel prices is much weaker
and not statistically significant, even if domestic price changes are omitted, suggest-
ing that riots are driven more by the way in which domestic prices are determined
than by international price fluctuations.
3.2 The role of price regimes
We hypothesized in the introduction to the paper that the effect of fuel prices on fuel
riots discussed above may be affected by price regimes. To analyze this mechanism,
we proceed in three steps. First, we estimate the effect of changes in international
prices on domestic prices to check how international price shocks may be transmitted
to local markets. Second, we test whether fixing domestic prices – which effectively
results in price subsidies on fuel – may cushion domestic prices against international
price changes. Finally, we estimate the effect of such price subsidies on fuel riots.
6

Table 1: Fuel riots and price changes
(1) (2) (3)
Fuel riot Fuel riot Fuel riot
Growth of domestic gasoline price 0.0459∗∗∗ 0.0441∗∗∗
(7.11) (6.98)
Growth of world gasoline price 0.00855 0.0124
(0.47) (0.68)
Log GDP per capita -0.00196 -0.00303 -0.00285
(-0.38) (-0.61) (-0.55)
Log population -0.00129 -0.00112 -0.00134
(-0.16) (-0.14) (-0.17)
Observations 18307 18728 18307
tstatistics in parentheses
Fixed effects regression with month dummies
∗p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.001
As we have discussed, domestic fuel prices are likely to be largely driven by
international price changes, but this relationship can be dampened somewhat by
fixing prices domestically, at least for a while. To assess the extent as to which this
is true, we need a measure of the extent to which prices are fixed. Unfortunately,
we are unaware of any database that indicates the policy regime followed by all
countries over time. However, it is possible to infer the policy regime by looking
at the extent to which prices change.6We therefore use the database of monthly
domestic prices described above to construct a measure of price ‘fixedness’ which is
simply the percentage of months that domestic fuel prices remained the same. If a
country has a fixedness of zero, it has a completely flexible price regime in which
prices change every month, while if it has a fixedness of 100 then its price regime is
completely rigid with no changes in prices at all.
Of the 157 countries for which we have monthly domestic price data, 73 have
regimes in which the price changes every month. By contrast, only two countries had
no price changes at all over the period. All other countries kept prices fixed at least
for some months. However, most let prices adjust regularly. Over three-quarters of
the countries adjusted prices at least every two months, while only around a fifth
of countries adjusted domestic prices infrequently. To simplify, we define a country
as having a ‘fixed price regime’ if it keeps domestic prices the same more than 80
percent of the time across all months for which we have data. If the country fixes
prices less than this, we define it as having a ‘flexible price regime’.
To answer our question about the pass-through of international prices to the
local market in each country, we estimate the following model:
6For the moment, we have assumed that the policy regime remains fixed over the period for which
we have data.
7

gpdom
im =β0gpint
im +β1gpint
i,m−1+β2gpint
i,m−2+im (2)
where gpdom
im is the growth of the domestic fuel price of country ibetween month
mand the preceding month; gpint
im is the growth in the international fuel price during
the same period; gpint
i,m−1and gpint
i,m−2are the growth in international prices in the
preceding months; and im represents a random error term.
Our model reflects the fact that international prices are unlikely to pass through
immediately to domestic prices, but may do so with some lag. Thus β0represents
the short-run pass-through of international prices, while the sum β0+β1+β2pro-
vides an estimate of the long-run pass-through of prices. Since countries have quite
different approaches to regulating domestic prices, it is likely that the value of these
coefficients will differ substantially by country. We therefore estimate this model
separately for all countries. We find that – for the median country, the short-run
pass through is around 0.1 - that is around 10 percent of the change in the interna-
tional price is passed through to domestic markets in the same month; the long-run
(3 month) pass through is around 0.3.
Our hypothesis is that a policy of fixing local prices should reduce the pass
through of international prices. Figure 3 shows the range of estimates of short-run
and long-run pass through coefficients for countries with flexible and fixed price
regimes. As anticipated both short-run and long-run pass through coefficients in
fixed price regime countries are significantly below those in countries with a flexible
price regimes. The median short-run pass through for countries with flexible price
regimes is 0.12 and the median long-run pass through is 0.33; however, for countries
with fixed price regimes the equivalent figures are 0.005 and 0.08. As one might
expect, countries which fix prices most of the time pass through international price
shocks much less.
However, while fixing prices does appear to reduce domestic price volatility in
the short term, it also has a major impact on the size of domestic price increases
when they do occur. Figure 4 shows the mean price change for the months in which
price changes occurred for all countries plotted against the extent to which they fix
prices. Countries that adjusted prices frequently (low fixedness), tended to have
relatively small adjustments. However, those that fixed prices and held them for
longer, tended to have much larger price increases when prices did change.
Dividing countries again into flexible and fixed price regimes as above, we find
that the mean price change for countries with flexible price regimes was 0.7 percent
(the standard deviation of price changes was also 0.7); but for fixed price regimes,
the mean price change was 17.3 percent, almost 24 times larger. The standard
deviation of price changes was 27, almost 40 times larger. Even if we include all
of the months in which there is no change in price in the calculation of the mean
price change and standard deviation, the mean price change for fixed price regimes
is 68 percent higher than that of flexible price regimes and the standard deviation
more than doubles. In short, fixed price regimes may protect populations from
international price changes over the relatively short term, but when price changes
do happen, they are much larger.
Why does fixing prices result in large domestic price shocks? The discussion in
the introduction to the paper suggests that this is because countries that fix prices
tend to have larger subsidies. When such subsidies become fiscally unsustainable,
governments choose to raise the domestic price. If this is true, we would expect to
8

-.2 0 .2 .4 .6 .8
Flexible price regime Fixed price regime
Pass through (long-run) Pass through (short-run)
Figure 3: Pass through of international prices under flexible and fixed price regimes
9

0 50 100 150
Mean price change (%)
0 20 40 60 80 100
Percentage of months with no change in price
Figure 4: The impact of fixed prices on average price changes
10

find evidence of a strong relationship between fixing prices and subsidies. To test
this, we use annual data on subsidies for all countries. To see the impact of fixing
domestic prices on subsidies, we regress subsidies on international oil prices and our
measure of price fixedness. Table 2 shows that, as expected, higher oil prices make
subsidies larger – but fixing domestic prices also increases subsidies significantly. 7
Table 2: Subsidies, oil prices and fixed price regimes
(1)
Log subsidies
Log of real oil price 0.271∗∗∗
(9.23)
Interaction of oil price and price fixedness 0.0151∗∗
(2.16)
Log GDP per capita 0.00634
(0.07)
Log population 0.464∗∗
(2.57)
Observations 922
tstatistics in parentheses
Panel regression with country level fixed effects
∗p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.001
Finally, we are now able to test the effect price regimes on fuel riots. We have
established a connection between local price shocks and fuel riots. However, the
evidence above suggests that fixing prices tends to lead to large subsidies, which adds
to the fiscal pressure for a price adjustment. Reducing or removing such subsidies
may abrogate a social contract which could lead to violent protest. If this is the case,
we would expect to see that subsidies have an independent impact on the likelihood
of fuel riots. Table 3 shows that this is indeed the case. Regressing fuel riots against
domestic price changes and lagged subsidies shows that subsidies have a large and
statistically significant impact on the likelihood of a fuel riot.8
Taken together, these results strongly suggest that fuel riots are driven by changes
in fuel prices but these are mediated by the price regimes in place in each country.
Fuel riots are more likely in countries with large implicit price subsidies that are
not capable of maintaining them when faced with large increases in international oil
prices.
7Again, we control for country level fixed effects which might influence the size of subsidies. We also
include controls for time-varying factors which could influence the size of subsidies, notably GDP per
capita and population.
8Since we are now using annual data, we take the largest monthly price change during the year as
our domestic price change variable.
11

Table 3: Riots and subsidies
(1)
Fuel riot
Max monthly growth of domestic gasoline price 0.182∗∗∗
(3.32)
L.Log subsidies 0.146∗∗
(2.85)
Log GDP per capita -0.199
(-1.45)
Log population -0.428
(-1.23)
Observations 768
tstatistics in parentheses
Panel regression with country level fixed effects and year dummies
∗p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.001
3.3 Robustness tests
To assess the robustness of our result that subsidies are associated with fuel riots
we take three approaches. First, although we have already controlled for country
and year fixed effects, and for GDP per capita and population, it is possible that
there might be other, time-varying country characteristics that might give rise to
fuel riots which are correlated with subsidies. In particular, political characteristics
change over time and such changes could give rise to riots and might plausibly also be
correlated with subsidies. We therefore draw on the Varieties of Democracy (VDEM)
(Coppedge et al., 2019) and the Polity IV datasets (Marshall, 2019) which provide
a comprehensive set of variables measuring different aspects of the political makeup
of each country over time. We look at three measures: regime type; corruption; and
civil society repression.
Annex Table 1 shows the same regression as Table 3 except each regression
includes an additional political variable. In the first column we include regime type.
9The results suggest that more democratic regimes do have fewer fuel riots – but the
size and significance of the coefficient on subsidies remains the same. In the second
column we use an alternative measure for regime time – polity2 from the Polity
IV dataset. 10 Contrary to the VDEM regime variable, we find no statistically
significant effect of the polity2 variable on fuel riots; but the coefficient on subsidies
remains sizeable and significant. Column 3 considers the possibility that it might
be perceptions of corruption, rather than type of regime, that affects riots. Citizens
might be angry about corruption, which is also correlated with subsidies, leading to
riots. However, we find no statistically significant effect of corruption on fuel riots,
9This takes the values: 0-autocracy; 1-electoral autocracy; 2-electoral democracy; 3-liberal democracy.
See Coppedge et al. (2019) for details.
10Polity2 is a scale from -10 (strongly autocratic) to +10 (strongly democratic).
12

while the coefficient on subsidies remains much the same. Finally, we consider the
impact of civil society freedom. We would expect this to increase fuel riots and so it
is possible that the effect of subsidies could simply be reflecting a correlation with a
lack of civil society freedom. However, Column 4 shows that, the coefficient is not
statistically significant and has no influence on the size of the subsidies coefficient.
Second, we examine the possibility that our dependent variable may be biased.
Our dependent variable is based on media reports of violence associated with fuel
related protests, but what constitutes violence is a subjective matter. We therefore
draw on a separate database constructed for the Global Chaos Mapping Project 11
which uses a narrower, but more precise, measure that records fuel related protests
that led to at least one death. Annex Table 2 shows the same set of regressions as
Annex Table 1, but using the new dependent variable. Although the coefficient on
subsidies is smaller, it remains positive and statistically significant throughout.
Finally, we test the robustness of our results to the estimation method used.
Annex Table 3 shows our original regression from Table 3, alongside estimations of
the same regression using a panel logit specification, as well as a Probit with country
dummies. In both cases, the results confirm a strong and statistically significant
association between subsidies and fuelriots.
4 Why countries fix prices and create subsidies
Finally, given the damaging impact of fixing domestic prices and thereby creating
fuel subsidies, as well as their propensity to prompt riots, we explore why so many
governments use this policy instrument. The literature points towards two possible
motivations for adopting subsidies by fixing prices. First, people living in countries
with oil may feel that they are entitled to a share of the benefits. Knowing this,
states choose to subsidize fuel as a way of providing a benefit to the population
that is directly linked to the resource (McCulloch, Moerenhout, & Yang, 2020). In
a sense, this is a basic social contract, but one not based on service delivery, but
rather simply sharing out, in an easy and conspicuous way, some of the proceeds of
oil wealth. If this is the case, we would expect the adoption of a fixed price regime
to be strongly associated with being a net energy exporter.
Second, Victor (2009) argues that some countries subsidize fuel because they
lack the capacity to implement more sophisticated forms of social protection. If
this is the case, we would expect to see a negative association between government
effectiveness and the size of subsidies.
We separately regress the size of subsidies and whether a country has a fixed
price regime against whether the country is a net energy exporter and measures of
government effectiveness.12
Table 4 and Table 5 show that the data support both of the hypotheses above.
Net energy exporters are much more likely to adopt a fixed price regime and much
more likely to have large subsidies. Similarly, countries with more effective admin-
istration are much less likely to adopt such policies. However, we find that several
11See https://aru.ac.uk/global-sustainability-institute-gsi/research/global-risk-and-resilience/global-
chaos-map-project for details
12We cannot use a fixed effects panel regression because whether a country is a net energy exporter is
almost always a fixed characteristic; we therefore estimate an OLS regression controlling for heterogeneity
with year and region dummies as well as GDP per capita and population as before.
13

of our measures of governance quality are associated with subsidies. If we substi-
tute our measure of government effectiveness with the measures described above for
regime type, corruption, and civil society freedom, our results suggest that more
democratic and less corrupt regimes, as well as those with greater civil society free-
dom, also tend to have fewer subsidies, while those with more corruption and less
effective governments are more likely to adopt fixed price regimes. While we are
not able to assert causality, our results are consistent with the hypotheses that such
policies are often introduced in resource abundant countries with relatively weak
governance.
Table 4: Structural determinants of subsidies
(1) (2) (3) (4)
Log subsidies Log subsidies Log subsidies Log subsidies
Net energy exporter 0.612∗∗∗ 0.632∗∗∗ 0.604∗∗∗ 0.552∗∗∗
(3.96) (4.24) (4.12) (3.51)
Government effectiveness -0.348∗∗
(-2.72)
Regime type -0.266∗∗
(-3.28)
Extent of corruption 0.956∗∗∗
(3.49)
Civil society freedom -0.217∗∗∗
(-3.60)
Log GDP per capita 0.215∗∗ 0.103∗0.175∗∗ 0.0956
(2.25) (1.71) (2.44) (1.62)
Log population 0.302∗∗∗ 0.316∗∗∗ 0.301∗∗∗ 0.302∗∗∗
(6.80) (7.03) (6.65) (6.77)
Observations 703 698 698 698
tstatistics in parentheses
OLS regression with year and region dummies
∗p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.001
14

Table 5: Structural determinants of fixing prices
(1) (2) (3) (4)
Fixed price regime Fixed price regime Fixed price regime Fixed price regime
Net energy exporter 0.136∗∗ 0.173∗∗ 0.146∗∗ 0.185∗∗
(2.48) (2.99) (2.64) (2.96)
Government effectiveness -0.0981∗
(-1.70)
Regime type -0.0191
(-0.47)
Extent of corruption 0.220∗
(1.75)
Civil society freedom 0.00102
(0.04)
Log GDP per capita 0.0213 -0.0302 0.00296 -0.0383
(0.48) (-1.01) (0.09) (-1.57)
Log population 0.00720 0.00855 0.00638 0.00860
(0.48) (0.56) (0.42) (0.54)
Observations 1305 1305 1305 1305
tstatistics in parentheses
OLS regression with year and region dummies
∗p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.001
15

5 Conclusions
Fuel riots are common and can have major implications for ordinary people and
for entire countries. They are violent – often leading to deaths – and are highly
disruptive. Moreover, fuel riots often pre-empt or prevent further attempts at policy
dialogue and reform – at least for a while (Hossain, 2020).
Our findings suggest that fuel riots are primarily driven by domestic price in-
creases. To some extent, these reflect changes in the international oil price, but these
effects are mediated by how countries attempt to protect their populations by fixing
domestic prices for periods of time. However, fixed price policies tend to result in
large fuel subsidies which can create fiscal strains. Our results show that large fuel
subsidies may make fuel riots more likely. This is because when these subsidies are
no longer sustainable, the resulting price increases are much larger than those that
typically occur in countries with more flexible price regimes, potentially triggering
riots.
We also find that net countries which are energy exporters are much more likely
to fix prices and more likely to have large subsidies as a result. Countries will low
levels of government capability and effectiveness are also more likely to have large
subsidies, supporting the idea that subsidies are used as an administratively easy
way of providing a social transfer. Ironically, we find that the large subsidies that
such policies produce do not protect populations from price shocks and make fuel
riots more likely.
Our findings further emphasize the value of removing fuel subsidies and shifting
to flexible price regimes. However, this naturally begs the question of why countries
have not already done so. The answer is likely to lie in the complex politics of social
contracts in energy net exporter countries. Nonetheless, our results should give
policymakers further pause for thought about the wisdom of policies that perpetuate
large subsidies. Subsidies may provide short-term political gains but, by making riots
more likely, they may have large long-term political costs. Going forward, researchers
may wish to focus more on building a better understanding of the political, and not
just the economic, dimensions of subsidy reform.
Data availability statement
The authors declare that the data supporting the findings of this study are available
within the paper and its supplementary information files.
Code availablility statement
The authors declare that the Stata do files used to undertake the analysis are in-
cluded in the supplementary information files.
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Annex 1: Robustness tests
Table 6: Robustness to political variables
(1) (2) (3) (4)
Fuel riot Fuel riot Fuel riot Fuel riot
Max monthly growth of domestic gasoline price 0.180∗∗ 0.179∗∗ 0.183∗∗∗ 0.178∗∗
(3.28) (3.19) (3.32) (3.22)
L.Log subsidies 0.154∗∗ 0.154∗∗ 0.145∗∗ 0.150∗∗
(2.99) (2.90) (2.82) (2.91)
Regime type -0.0647∗∗
(-2.01)
Polity2 -0.00618
(-0.70)
Extent of corruption 0.143
(0.63)
Civil society freedom -0.0257
(-0.95)
Log GDP per capita -0.161 -0.203 -0.196 -0.185
(-1.16) (-1.45) (-1.42) (-1.33)
Log population -0.390 -0.443 -0.430 -0.408
(-1.12) (-1.23) (-1.23) (-1.17)
Observations 763 750 763 763
tstatistics in parentheses
Panel regression with country level fixed effects and year dummies
∗p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.001
19

Table 7: Robustness to alternative dependent variables
(1) (2) (3) (4)
GCMP fuel GCMP fuel GCMP fuel GCMP fuel
Max monthly growth of domestic gasoline price 0.149∗∗∗ 0.143∗∗∗ 0.151∗∗∗ 0.145∗∗∗
(4.49) (4.25) (4.52) (4.34)
L.Log subsidies 0.0844∗∗ 0.0934∗∗ 0.0807∗∗ 0.0860∗∗
(2.72) (2.92) (2.60) (2.77)
Regime type -0.0325∗
(-1.67)
Polity2 -0.0103∗
(-1.94)
Extent of corruption -0.0899
(-0.65)
Civil society freedom -0.0323∗∗
(-1.99)
Log GDP per capita 0.0789 0.0583 0.0575 0.0771
(0.94) (0.69) (0.69) (0.92)
Log population -0.403∗-0.431∗∗ -0.423∗∗ -0.396∗
(-1.91) (-1.98) (-2.00) (-1.88)
Observations 763 750 763 763
tstatistics in parentheses
Panel regression with country level fixed effects and year dummies
∗p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.001
20

Table 8: Robustness to estmation method
(1) (2) (3)
Fuel riot Fuel riot Fuel riot
main
Max monthly growth of domestic gasoline price 0.182∗∗∗ 1.482 1.321
(3.32) (1.25) (1.54)
L.Log subsidies 0.146∗∗ 4.073∗∗ 3.291∗∗
(2.85) (2.32) (2.74)
Log GDP per capita -0.199 -3.610 -2.554
(-1.45) (-0.83) (-0.88)
Log population -0.428 -15.45 -10.93
(-1.23) (-1.16) (-1.28)
Observations 768 124 124
tstatistics in parentheses
(1) Panel regression with country level fixed effects and year dummies
(2) Panel logit with country level fixed effects and year dummies
(3) Probit with year and country dummies
∗p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.001
21