Why do Autocrats Disclose?∗
James R. Hollyer† 1 , B. Peter Rosendorff2 , and James Raymond Vreeland3
1
University of Minnesota
2
3
New York University
Georgetown University
August 2015
Word Count (including bibliography, exlcuding appendix): 11,482
Word Count (excluding bibliogrpahy and appendix): 10,106
Abstract
Autocratic governments are often thought to hold a strong preference for opacity. Autocracies are,
on average, less transparent than democracies and a closed informational environment serves as a
mechanism for preventing mass unrest and preserving the autocratic regime. Yet, autocracies vary
widely in the extent to which they disclose economic information to their publics. In this paper, we
offer an explanation for why some autocrats choose to disclose. The disclosure of information to the
public benefits autocratic leaders in two ways: First, it encourages increased foreign direct investment,
boosting the resources available for disbursing rents to members of the regime. Second, precisely
because a more transparent informational environment facilitates mass unrest, it renders elites more
complacent. Members of the elite are less willing to take action to remove their leader as the regime
becomes less stable. In essence, transparency enables the leader to use mass politics to influence
elite behavior.
∗
Thanks to Allan Defoe, Anita Gohdes, Charles Hankla, Andy Harris, Jacqueline Sievert and participants in the 2014 Annual
EPSA Conference, Panel on the Elements of a General Theory of Peace; the 2014 APSA Conference, Panel on Accountability and
Development; the 2015 MPSA Conference, Panel on Decision-making in Autocracies; and the 2015 EPSA Conference, Panel on
Dictators and the Media for helpful comments and suggestions. Thanks also to Vanessa Hofman for valuable research assistance.
James Hollyer would also like to thank the Benjamin Evans Lippincott foundation and the Niehaus Center for Globalization and
Governance for research support. All remaining errors are our own. The HRV index and materials related to the HRV Transparency
Project are available from HRVTransparency.org
†
Corresponding author. 1414 Social Sciences Building, 267 19th Avenue South, Minneapolis, MN 55455. [email protected]
Why do autocratic leaders, free from electoral checks on their behavior, voluntarily choose to disclose
information their publics? Governments, it is often argued, have a natural taste for opacity. An absence
of information facilitates rent-seeking and increases leaders’ freedom of maneuver. Aside from these
benefits to opacity, disclosure entails costs in terms of finances and personnel hours. National statistical
offices must be staffed, press releases and other documentation crafted, all of which costs time, money
and attention. Presumably, leaders who face little pressure from the public to disclose would eschew such
costs.
Transparency’s role in facilitating mass mobilization under autocratic rule stacks the deck still further
against disclosure.1 Government disclosures – particularly of economic information – help members of the
populace form shared beliefs regarding their leaders’ performance, easing mass mobilization against the
regime. Hollyer, Rosendorff and Vreeland (forthcoming 2015a) demonstrate that autocratic regimes that
disclose large volumes of information are more likely to collapse, due to mass protest or processes leading
to democratization, than regimes that fail to disclose.
Yet autocratic governments do, sometimes, disclose information to their publics. The HRV Index (Hollyer, Rosendorff and Vreeland, 2014), which measures the extent to which governments disclose credible
economic information, ranks such autocratic regimes as pre-transition Korea and Mexico as more transparent than the mean democracy in their sample. Though democracies, on average, disclose substantially
more than autocracies; the latter vary considerably in the extent of their opacity (Hollyer, Rosendorff and
Vreeland, 2011).
In this paper, we provide an explanation for why autocrats choose to disclose. We contend that, even
as disclosure renders autocratic regimes more vulnerable to mass protest, it insulates autocratic leaders
from opposition that emerges within the regime. Indeed, transparency plays this role, in part, precisely
because it facilitates mobilization by the public.
To be more precise, we contend that transparency has two effects, both of which serve to insulate autocratic leaders: First, it encourages inward foreign direct investment. This investment improves economic
performance, the benefits of which disproportionately flow to members of the regime. Second, it increases
the mobilizational capacity of the populace. As noted above, disclosure enables the formation of common
beliefs and thus the coordination of mass anti-regime protest. These two effects render the institutional
status quo more valuable for the elite – they partake of increased economic returns – even as the regime’s
hold on power becomes more tenuous. Members of the regime thus become less likely to ‘rock the boat’
by acting against their leaders when as transparency rises.
Leaders, therefore, are most likely to disclose when they perceive threats as emerging from within their
ruling coalition, rather than from the populace. This is most likely to be the case when autocracies are
highly institutionalized and leaders are new to office. We empirically test these claims and find that they
enjoy support. We further empirically test the model primitive that transparency is associated with high
1
Here and throughout we use the term transparency and disclosure interchangeably. We adopt a narrow definition of transparency, which pertains to the disclosure of economic information by the government to the public. We elaborate on this definition
below.
1
levels of inward foreign investment, and find that this claim also holds in the data.
1
Argument
Our argument begins with the contention that authoritarian leaders must navigate two threats to their rule.
One threat emerges from within the regime itself, the other is the danger of mass mobilization on the part
of the public. We share this framework with much of the recent literature on autocratic institutions (see
particularly, Svolik, 2012). Models of autocratic rule often find these threats to be strategic substitutes –
policies aimed at alleviating the threat of mass revolt may increase the risk of a coup (Svolik, 2013). Here,
we contend that the reverse holds: policies that increase the mobilizational capacity of the populace may
force members of the regime to toe the line set by the leadership.
This contention rests on the assumption that attempts by elites to discipline their leaders tend to destabilize the regime as a whole. Attempts to unseat the leader, whether via a coup or more peaceable
procedures, may serve as a focal point for mobilizing mass unrest. This unrest has the potential to lead to
the upending of the regime more generally.
The extent to which removing an autocratic leader destabilizes the regime is a function of the institutional foundations of the autocracy and the time a leader has served in office (Besley and Kudamatsu,
2007; Francois, Rainer and Trebbi, 2014; Gehlbach and Malesky, 2010; Svolik, 2012). Highly institutionalized regimes, in which the leader is likely to have a designated successor, in which a formal voice is given
to regime officials, or in which legitimacy derived from institutional positions rather than the leader’s personal authority, face a low risk of mass unrest following a change in leadership. Leadership replacement
in such regimes tends to be regularized and is less informative as a signal to the public of intra-regime
conflict and weakness. By contrast, in personalized regimes with long-established leaders, a changing-ofthe-guard typically reflects intense intra-regime conflict and a moment of regime weakness. Under these
circumstances, leadership change is particularly likely to provide a focal point for unrest, producing mass
protest or to setting in motion conflicts likely to upend the regime as a whole (Geddes, 1999).
Similarly, the removal of a leader who recently assumed office is less likely to produce mass unrest than
the removal of a long-serving autocrat. New leaders are less likely to have had time to build up a patronage
network within the regime, hence their removal is less likely to require sweeping purges of official ranks.
New leaders are less likely to have built up personal authority or a cult of personality with the mass public
than leaders who have long been ensconced in power (Bueno de Mesquita et al., 2002).2 Moreover, the
time a leader has served in office may serve as a proxy for unobserved factors correlating with the extent
of institutionalized, as opposed to personalized, authority. Term limits and rotation in office are often key
components of regime institutionalization and, for instance, were an important aspect of Deng’s attempts
to institutionalize authority in the People’s Republic of China (see Shih, Whan and Liu, 2010; Svolik, 2012).
Thus, members of the regime, when faced with a leader that acts against their interest, face a choice
2
Francois, Rainer and Trebbi (2014) find that the hazard functions of African autocrats are monotonically decreasing over time.
2
of whether to remove this leader. Removing the leader, on the one hand, opens up the possibility that her
replacement will prove more amenable to the elite. But, this is a costly gamble – removing the leader increases the risk of regime collapse, potentially costing these same elite individuals their privileged positions
(Besley and Kudamatsu, 2007; Bueno de Mesquita et al., 2003; Gehlbach and Malesky, 2010).
We contend that transparency alters elites’ decision calculus in two ways: First, it enhances the value
of privilege. Transparent regimes attract higher levels of foreign investment, ensuring that a place in the
regime brings with it a greater access to resources. Second, transparency increases the mobilizational
capacity of the populace, boosting the probability that attempts to remove the leader cause the regime to
collapse. Both factors push members of the elite in the same direction – toward inaction. As the danger of
insurrection mounts and the value of regime membership rises, members of the elite grow more complacent
in the face of a recalcitrant leadership.3
Consider, first, the relationship between transparency and inward foreign investment. Multinational corporations may choose to invest in a given autocratic country – or they may choose invest their resources
elsewhere. This decision is determined by expectations regarding the rate of return on investment. Transparency may shift these expectations in two ways: First, the failure to disclose economic information increases the uncertainty of these expectations.4 Even holding mean expected returns constant, such an
increase in the variance will tend to drive away risk-averse investors. This effect is likely to be particularly large for foreign direct investment (FDI), as this form of investment is particularly difficult to reverse
(Jensen, 2003). Irreversibile investment is particularly vulnerable to uncertainty, since investors cannot
subsequently minimize their exposure if their bets turn bad (Bloom, 2009).5 Moreover, foreign investors
are particularly likely to lack insider information from the regime, and are hence most likely to depend on
public disclosures. Steps to alleviate this uncertainty should thus give rise to a more convivial investment
environment and attract greater inflows of FDI..
However, steps by the government to increase transparency may do more than simply reduce the
level of uncertainty (the variance of investor beliefs), they may also shift the mean value of these beliefs.
Increases in transparency may be interpreted as part of more general efforts at reform favorable to foreign
investors. As such, they may be interpreted as a signal of a government’s intentions or type – indicative
of more general political support for foreign investors and thus of a higher expected rate of return. If this
is true, reforms to boost transparency will increase investment still further – the effects on uncertainty and
3
In some circumstances, therefore, autocratic elites may be opposed to transparency and may attempt to prevent disclosure.
Note, however, that elite members both benefit, from increased access to rents, and are harmed, via the reduced accountability
of leaders, by disclosure. Our model is flexible as to which effect dominates. Moreover, transparency is only costly to the elite if
the leader is predisposed to act in a manner contrary to the elite’s interest.
4
This is not to contend that investors lack any sources of information absent government disclosures. Potential investors
can hire expert consultants or rely on in-house expertise to make judgments regarding the investment environment even absent
government disclosures – and are particularly likely to do so in opaque counties. However, such investments are costly for a firm
and may give rise to issues of public goods in information provision (Rodrik, 1995). Consequently, at the margin, investment is
deterred by government opacity.
5
An extensive theoretical and empirical literature links uncertainty to recessions (Baker, Bloom and Davis, 2013; Bloom, 2009),
currency attacks and financial crises (Carlsson and van Damme, 1993; Leblang and Satyanath, 2006; Morris and Shin, 1998),
and a general lack of international investment (Rodrik, 1991, 1995).
3
Figure 1: HRV Transparency Scores Time Series: China and Vietnam
Mean HRV transparency index scores (Hollyer, Rosendorff and Vreeland, 2014),
and 95 percent credible intervals, are plotted on the y-axis. Time, measured annually, is plotted on the x-axis. Mean scores are denoted by diamonds, credible
intervals are denoted by whiskers. Note that the y-axes in the two graphs are not
measured on the same scale. HRV index values range from -10.9 to 10, in the full
sample of 125 country years from 1980-2010, with higher scores denoting greater
disclosure.
on mean expectations reinforce one another (on the strategic complementarity of reform and transparency
see, Chen and Xu, 2015).
Indeed, prominent examples of economic reform and liberalization under autocratic rule are also associated with dramatic increases in government disclosure. China’s liberalization – beginning with Deng’s
introduction of reforms in 1978 – was marked by a concurrent increase in transparency scores, as measured by the HRV index. HRV index values are available from 1980, and indicate substantial increases in
the PRC’s level of disclosure throughout the early 1980s, particularly following the introduction of the 1982
constitution. Similarly, Vietnam’s economic opening, beginning with the 1986 Party Congress, coincided
with substantial increases in transparency. With regard to contentions later in the paper, both regimes
recently experienced the death of long-serving personalist leaders – Mao Zedong in the case of the PRC
and Lê Duân in the case of Vietnam – and had new leaderships dealing with internal opposition even as
they attempted to cement political and economic reforms (Esterline, 1987; Malesky, Abrami and Zheng,
2011). Figure 1 plots the time series of transparency scores for both autocracies.
The proceeds of increased investment under autocratic rule tend to disproportionately benefit those
affiliated with the regime. These benefits may be indirect. Autocratic governments may use their powers of taxation and redistribution to favor members of the ruling elite (Bueno de Mesquita et al., 2003).
Any increase in the size of the economy resulting from FDI flows will tend to generate greater opportunities for such redistribution. But, more direct interventions may also ensure that members of the regime
profit from foreign investment. The regime may create requirements that international investors establish
joint ventures, typically with established domestic elites; requirements for technology transfers to domestic
4
companies affiliated with the regime; or uncertainties in the political or legal environment may lead foreign
investors to choose to employ or partner with domestic officials or their families even absent overt government pressure to do so. Recent SEC investigations of JP Morgan Chase’s “Sons and Daughters” program
– which involved the selective hiring of the children of prominent Chinese officials – serve to underscore
the way in which foreign investment can selectively benefit ruling elites under autocratic rule.6 In the words
of Bao Tong, a former adviser to General Secretary Zhao Ziyang, “...if you don’t pay for the right to do
business, then you are likely to encounter interference from party officials.”7
Of course, public disclosure of economic information may influence not only foreign investors, but also
domestic. We focus on foreign direct investment in this paper for two reasons: First, foreign investors
are more likely to be reliant on publicly disclosed information than their domestic counterparts. Domestic
actors with the resources to invest in many autocratic systems are likely to be regime insiders, and may
have access to information that the government chooses to keep private. Second, FDI flows are likely
to be dominated by private investment. By contrast, measures of domestic investment under autocratic
rule are often dominated by government-controlled resource flows, whether direct or indirect via stateowned enterprises. The determinants of such flows are likely to differ from the factors for private investors
discussed above.
Disclosure has effects beyond its impact on investment. Transparency, under autocratic rule, also
tends to facilitate mass mobilization by the populace against the regime. This effect arises due to collective
action problems inherent in mass protest. Protests that draw widespread participation are likely to succeed
in forcing the regime to change policy or leadership, or even in bringing about regime change. But, smaller
protests are likely to be put down, often quite violently and at considerable cost to participants. Protest
is subject to strategic complementarities – the willingness of any one individual to protest rises in the
willingness of others to protest. Any individual’s decision to protest thus depends on her beliefs about
the willingness of others to similarly turn out. In contexts marked by strategic complementarities, public
information plays an outsized role in determining outcomes (Morris and Shin, 2002). Information that is
witnessed by all citizens, and known by all citizens to be publicly observed, allows individuals not only
to update their beliefs about the performance of government, but also their higher-order beliefs about
the expectations held by others. In the absence of such information, uncertainty about the willingness of
others to mobilize may render mass protest impossible, whereas, public disclosures by the government may
render protest feasible. Hollyer, Rosendorff and Vreeland (forthcoming 2015a) formalize this argument and
demonstrate, both theoretically and empirically, that transparency renders mass protest more likely under
autocratic rule.8 Transparent autocracies are more prone to collapse at the hands of mass protest than are
6
Silver-Greenberg, Jessica and Ben Protess, August 29, 2013.
“JP Morgan’s Hiring Put China’s Elite on
an Easy Track,” New York Times.
Last accessed June 5, 2014, http://dealbook.nytimes.com/2013/08/29/
jpmorgan-hiring-put-chinas-elite-on-an-easy-track/
Bao Tong. “How Deng Xiaoping Helped Create a Corrupt China,” New York Times Op-Ed Contribution. June 3, 2015. http:
//www.nytimes.com/2015/06/04/opinion/bao-tong-how-deng-xiaoping-helped-create-a-corrupt-china.
html?emc=edit_ty_20150603&nl=opinion&nlid=47910031&_r=0, last accessed June 6, 2015.
7
8
See also Bueno de Mesquita (2010), Edmond (2013), Little, Tucker and LaGatta (2013), Shadmehr and Bernhardt (N.d.) and
Shadmehr and Bernhardt (N.d.).
5
opaque autocracies.
Disclosure thus brings both costs and benefits for a leader. Transparency tends to spur investment,
which both directly benefits the leader and provides indirect returns in terms of elite complacency. Elites
are also rendered more complacent by virtue of the increased mobilizational capacity of the populace. Yet,
empowering the populace in this manner is risky for the leader: citizens may depose the regime even as
the elite toes the leadership’s line. Leaders are thus placed in the position of trading off the threat they
face from the populace against the threat from the elite. When the former is low and the latter high – as
in highly institutionalized regimes, with clear lines of succession and a new leadership – the leader will
choose to increase the popular threat to reduce that posed by the elite. Highly institutionalized regimes
and new leaders will tend to disclose more readily. By contrast, when the internal threat is low and the
popular threat relatively high – as in personalized regimes with long-established leaders – there is little
incentive to disclose. Any gains in internal regime cohesion are more than offset by increases in threat of
popular mobilization.
1.1
Glasnost and Perestroika in the USSR: An Illustrative Example
Perhaps the least intuitive aspect of our theory is the claim that autocratic leaders may deliberately take
steps that destabilize the regime so as to increase their control over members of their governing coalition.
To give these claims greater empirical grounding, we illustrate the mechanisms of our argument with an
example: Gorbachev’s implementation of the glasnost and perestroika reforms in the former Soviet Union
between 1985 and 1991. We discuss this case for illustrative purposes only. We do not contend that these
reforms were only the result of the mechanisms we describe in our model, nor do we seek to present a full
blown case study of the former USSR. We conduct large-N empirical tests of the propositions derived from
our theory below.9
Mikhail Gorbachev came to power in 1985 faced with a stagnating economy and popular ideological
disaffection (Dallin, 1992; Kotz and Weir, 1997). To address these threats, Gorbachev promoted a series of
economic and political reforms – labeled perestroika10 – which sought to reduce the authority of the Soviet
industrial ministries over state enterprises, introduce limited marketization of the economy, and decentralize
authority within the ruling Communist Party (Brown, 2007; Whitefield, 1993). Coupled with these structural
reforms, Gorbachev instituted a variety moves aimed at political liberalization. These policies, which were
grouped under the rubric of ‘socialist democracy,’ would come to include multi-candidate elections and
increased transparency and freedom to debate (glasnost) (Kotz and Weir, 1997).
Unsurprisingly, these ‘democratizing’ reforms appear in our transparency data as increases in disclosure by the Soviet government. We plot these scores, from 1984-1991, in Figure 2. Over this period, the
USSR substantially increases its transparency score, from the lowest levels in our sample of country-years
9
Support can also be found in the results of Hollyer, Rosendorff and Vreeland (forthcoming 2015a) – particularly their finding
that transparency is associated with an increased risk of autocratic collapse due to mass mobilization or democratization, but a
reduced risk of coup.
10
This term may be translated either as ‘restructuring’ or ‘reconstruction’.
6
Figure 2: HRV Transparency Scores Time Series: the USSR
Mean HRV transparency index scores (Hollyer, Rosendorff and
Vreeland, 2014), and 95 percent credible intervals, are plotted
on the y-axis. Time, measured annually, is plotted on the x-axis.
Mean scores are denoted by diamonds, credible intervals are denoted by whiskers. HRV index values range from -10.9 to 10, in
the full sample of 125 country years from 1980-2010, with higher
scores denoting greater disclosure.
to levels more consistent with the average non-democracy.
Most scholars have interpreted glasnost and socialist democracy as an attempt to overcome resistance among Party members to the economic restructuring entailed in perestroika. For instance, Kotz and
Weir (1997, 96) state that, “As resistance mounted to [the leadership’s] program of economic and social
reform, Gorbachev apparently concluded that democratization was the way to break this resistance and
prevent perestroika from being stopped in its tracks,” (see also Brown, 2007; Reddaway and Glinski, 2001;
Whitefield, 1993). Such ‘democratization’ helped to ensure the compliance of Party-members through
two methods. First, Gorbachev attempted to employ newly available avenues of mobilization to rally the
populace to his reformist cause and against recalcitrant elites. Second, the creation of such avenues of
mobilization created the risk that opposition groups might form within the populace and threaten continued
Soviet rule. As these threats emerged, conservative Party elites were less able to take action against
Gorbachev without endangering their own positions.
Scholars agree that Gorbachev’s liberalizing reforms played a critical role in giving rise to a popular
opposition. Aleksandr Yakovlev, Politburo member, chief of ideology and close associate of Gorbachev,
remarked that in their efforts at reform, “We [the leadership] created an opposition to ourselves,” (as quoted
in Reddaway and Glinski, 2001, 122). Brown (2007, 18-19) describes perestoika as a reform “from above,”
and notes that prior to these reforms “[n]either a mass movement for reform nor (still less) a revolution
was remotely in the cards.” Brown (2007, 92-3) further notes that glasnost was a “double-edged” sword
which aided reformers, led by Gorbachev, by publicizing the deficiencies of the Soviet state, but that greater
access to information also risked increasing public discontent and mobilization against the leadership.
7
Nor were these risks merely incidental – the dangers posed by a popular opposition increased Gorbachev’s authority within the Party. At times, it seems that Gorbachev acted to deliberately increase these
threats. For instance, one of the most radical opposition voices belonged to Boris Yeltsin, who would
come to play a pivotal role in the eventual collapse of the Soviet regime. Gorbachev had the opportunity
to sideline Yeltsin as early as 1987, when Yeltsin was forced to resign as the Moscow Party secretary.
Yet, Gorbachev merely reassigned Yeltsin to a somewhat less prominent position. Later, Gorbachev failed
to act to remove Yeltsin from electoral ballots, following the introduction of multi-candidate elections in
1988. Reddaway and Glinski (2001) (and Yeltsin himself) attribute Gorbachev’s actions to his desire to use
Yeltsin’s radicalism as a threat to ensure the quiescence of conservative opponents. Similarly, Reddaway
and Glinski (2001, 160) contend that Gorbachev “covertly encouraged” organizers of a rally by Democratic
Russia in 1990, as a means of threatening more conservative opponents and ensuring the passage of
reforms creating a Soviet presidency. As Brown (2007, 128) argued of the rise of a popular opposition,
“...Gorbachev and the progress of perestroika now have liberal as well as conservative critics.
While in some ways this makes life even tougher for the Soviet leader, on balance it is to his
political advantage.”
The presence of a popular opposition and, more generally, the opening of the informational environment
following Gorbachev’s reforms increased the mobilizational potential of the populace. As the public became
better able to mobilize, members of the Soviet elite faced a greater risk in moving against Gorbachev. Any
destabilization of the leadership posed an increased risk of giving rise to popular unrest and consequently
to the collapse of the regime as a whole. Despite these risks, conservative members of the elite did move
to depose Gorbachev through a coup in August of 1991.11 Consistent with the mechanisms of our theory,
this coup gave rise to a counter-coup led by Boris Yeltsin in his capacity as newly created president of the
Russian Republic. This counter-coup led to the ouster of the putschists and gave rise to processes leading
to the collapse of the Soviet regime and the dissolution of the Union.
2
Related Literature
Our theoretical analysis builds on a growing literature on autocratic government, which particularly emphasizes the role of institutions in shaping relations within the regime and between the regime and the mass
populace. The intuition that attempts by the elite to discipline autocratic leaders risk the continued stability
of the autocratic regime, and hence officials’ continued grasp their privileged positions, owes heavily to
Bueno de Mesquita et al. (2003) and Besley and Kudamatsu (2007). Gehlbach and Keefer (2011) also
build on this intuition to suggest that the institutionalization of autocratic regimes enhances the credibility of
commitments not to expropriate investors, and thus increases investment (see also Gehlbach and Keefer,
11
The proximate cause of the coup was the negotiation of a new union treaty giving increased independent authority to the 15
constituent republics of the USSR.
8
2012). Geddes (1999) argues that institutionalization – and particularly the discrepancy between personalistic and party-based structures – explains the survival of autocratic regimes. Party-based regimes, she
contends, rely less on the personal authority of the leader and incorporate groups that may act as a check
on the leadership, facilitating regime-survival after a given leader dies or is removed from office. Gandhi
(2008) also emphasizes the importance autocratic institutions, primarily focusing on the role of legislatures
in co-opting potential opposition groups (see also Gandhi and Przeworski, 2006, 2007). Our theoretical
focus is on institutionalization rather than the co-optation mechanism.
We also rely on insights from Svolik (2012, 2013) regarding the interaction between the twin threats
to authoritarian leaders – those from within the regime and those emerging from the populace. Bueno de
Mesquita and Smith (2009) also build on the insight that autocratic leaders must meet these twin threats,
and like this paper, they consider the role of public goods that enhance the ability of the populace to coordinate. However, while Svolik (2013) primarily focuses on the manner in which attempts to curb pressure
from the populace enhance the threat of military intervention in politics; we focus on the way in which
authoritarian leader may reduce internal threats by increasing the moblizational capacity of the populace.
Our paper also closely relates to a literature on various forms of transparency under autocratic rule.
Typically, these studies focus on the role of the mass media. Accounts by Egorov, Guriev and Sonin (2009)
and Lorentzen (2014) argue that autocratic regimes can effectively outsource the role of monitoring their
lower level agents to the media. Autocratic leaders can spare themselves some of the costs of ensuring
the compliance of regional or bureaucratic officials by instead relying on investigative media reports, which
may be used to curb corruption or other behavior which stands to harm the regime as a whole. These
benefits must be traded off against the risk that a free media may promote mass public opposition. King,
Pan and Roberts (2013), in an innovative study of the PRC’s ‘Great Firewall’, find supportive evidence for
these accounts – online censors permit criticism of local government corruption and other forms of misgovernance, but delete calls for protest. In complementary theoretical explorations of media control under
autocracy, Gehlbach and Sonin (2014) and Shadmehr and Bernhardt (forthcoming) examine autocrats’
incentive to engage in media censorship, given that citizens rationally update to discount good news about
the regime or interpret no news as bad news.12
While these papers share our focus on information dissemination under autocratic rule, the conception
of transparency used in these papers differs fundamentally from that herein. Rather than focusing on the
media, we emphasize the role of government disclosures of information to the public. Increasing transparency does little to enhance monitoring of lower-level public officials in our formulation, since there is
no outsourcing of information collection to private organizations. And while closer government monitoring
of subordinates may be associated with the collection of more information which can be disclosed, there
is no need for an autocratic government to make the results of such monitoring public. Indeed, there is
considerable evidence that they often do not – Hollyer, Rosendorff and Vreeland (2014) find little association between GDP per capita and disclosure in autocracies, presumably because while wealthy autocrats
12
For an excellent review of the literature on information problems in non-democracies, see Wallace (2014).
9
collect large amounts of information they choose not to disclose.
Boix and Svolik (2013) also examine the role of a form of transparency under autocratic rule, and –
like this paper – they conclude that higher levels of transparency are associated with a reduced risk of
coup. But, like studies that focus on the media, their conception of transparency differs fundamentally from
that used in this paper. In their model, transparency relates to the ability of members of the regime to
observe efforts by autocratic leaders to amass greater authority and power. Transparency is thus, in their
formulation, the clarity of the ‘rules of the game.’ As the authority vested in an autocratic leader becomes
more clearly defined, conflict is less likely to emerge between these leaders and members of their ruling
coalition. Their definition of transparency thus more closely relates to our notion of institutionalization,
rather than our concern with government disclosure.
This paper most closely relates to recent work on the measurement and implications of government
disclosure by Hollyer, Rosendorff and Vreeland (2014, forthcoming 2015a, 2015b). Our definition and – in
empirical sections – our measure of transparency is derived from Hollyer, Rosendorff and Vreeland (2014),
which focuses on the disclosure of economic information by the government to the broader public. We treat
the findings of Hollyer, Rosendorff and Vreeland (forthcoming 2015a), that transparency increases the risk
of mass public protest under autocratic rule, as a theoretical primitive. Hollyer, Rosendorff and Vreeland
(forthcoming 2015a) also find that transparency is associated with a reduced risk of coup in autocracies,
a finding which is consistent with our argument. However, their examination of coups was motivated as
a robustness check – a demonstration that transparency isn’t simply reflective the general weakness of
autocratic regimes – whereas, here the finding that transparency reduces the threat an internal opposition
poses to autocratic leaders is a central conclusion of the model. Unlike these pieces, however, this work
focuses on the conditions under which autocrats disclose.
3
Model
In the following section, we present a model of autocratic disclosure, incorporating the threats posed to
autocratic leaders from both members of the regime and the mass populace. Our model builds on the
theoretical framework introduced by Besley and Kudamatsu (2007), who construct a model of autocratic
accountability involving both moral hazard and adverse selection. Like us, they examine an interaction
in which steps by regime-members to discipline their leaders threaten the stability of regime. Our central
innovation is to incorporate disclosure into the model, which we treat as both increasing the risk of mass
mobilization and the pool of resources which may be distributed to regime members.
3.1
Model Primitives
Consider an interaction between an autocratic leader L, a group of regime elites R, and the populace
denoted O .
10
The game proceeds for two periods. In the first period, L must decide whether to disclose d ∈ {0, 1},
and in each period she must make a policy decision et ∈ {0, 1}, where t denotes the period of play
t ∈ {1, 2}. This policy decision has implications for the utility of both the leader and members of the
regime in a manner which will be described below.
Regime elites R, after witnessing the outcome of the leader’s policy decision, must determine whether
to retain her in office. We denote this decision v ∈ {0, 1}, where v = 1 denotes a decision to remove. v
may thus be thought of as a decision to launch a coup, or may describe administrative procedures through
which the leader may be impeached.
Leaders may be of one of two types θ ∈ {0, 1}. When θ = 1, leaders are ‘convergent’ – they have
a primitive preference for setting et equal to the value that maximizes the welfare of the group in power.
When θ = 0, leaders are ‘divergent’ – they have a primitive preference for setting et equal to a value
viewed as suboptimal by the group in power. The prior probability that L is a convergent type is defined as
π (P r(θ = 1) = π ).
Following the conclusion of the first period of play, a contest for power takes place between members
of the regime and the populace. If members of the regime chose to keep L in place, the probability that
the regime falls is given by p(d), where p(1) > p(0). We thus make a primitive assumption that greater
disclosure causes increased mobilizational capacity. This assumption is consistent with the findings of
Hollyer, Rosendorff and Vreeland (forthcoming 2015a). Define the effect of disclosure on mobilizational
capacity as ρ ≡ p(1) − p(0). If L was removed, the probability the regime falls is given by ωp(d) where
1
ω ∈ (1, p(1)
). The term ω thus reflects the tendency of internal strife to destabilize the regime. This
tendency, we argue, is reflective of the degree to which autocratic rule is institutionalized and the time a
leader has spent in office. ω thus rises with a leader’s time in office and falls in the degree of regime
institutionalization.
If L was removed, either by members of the regime or by the victory of the populace, we assume a
new leader is selected from the same distribution described above (i.e., P r(θ = 1) = π ). If the regime is
overthrown, we assume O takes over as a new regime.
Members of the regime derive utility both from the share of national income they consume λy(d) and
based on L’s policy decision et in the first period of play. λ ∈ ( 21 , 1) reflects the disproportionate flow
of resources to members of the regime. We assume y(1) > y(0) – increased transparency promotes
FDI and thus increases the resources that can be distributed to members of the regime. (We empirically
examine the relationship between disclosure and FDI flows in Section 4.2.)
Members of the regime also want L to set policy equal to a state variable st ∈ {0, 1}, which is randomly
determined and where P r(st = 1) =
1
2
∀ t, and st is independently drawn in each period. The utilities the
regime is thus given by:
(
uR,t (et , st , d) =
It [∆ + λy(d)] + (1 − It )(1 − λ)y(d) if et = st
It λy(d) + (1 − It )(1 − λ)y(d) otherwise
11
(1)
where ∆ > 0 and It ∈ {0, 1} is an indicator variable equal to 1 if R is in power at time t and equal to zero
if O is in power. Note that R always begins the game in power, hence I1 = 1, whereas I2 is determined
by the contest for power that takes place at the end of the first period of play.
Those out of power have no ability to directly sanction the autocratic leader. Hence, their preferences
over the policy variable et are irrelevant. We treat the utilities of those out of power as independent of et
to emphasize that accountability, in the autocratic context, means accountability to the winning coalition of
the regime. If I2 = 0, therefore, R has been removed from power, her share of national income declines
to (1 − λ)y(d) and she is no longer concerned about the value of the policy variable.
Members of the populace derive utility only based on their share of national income (1 − λ)y(d) in the
first period of play. Should they come to power in the second period, they will gain in their share of national
income – which will rise to λy(d) – and they become concerned about the leader’s choice of policy. The
utility of the populace is thus given by:
(
uO,t (et , st , d) =
It (1 − λ)y(d) + (1 − It )[∆ + λy(d)] if et = st
It (1 − λ)y(d) + (1 − It )λy(d) otherwise
(2)
where It is as defined above. Note that this implies that the ‘convergence’ or ‘divergence’ of the leader is
with reference to whichever group is in power at time t.
L’s utility depends on her type. Convergent types (θ = 1) share the policy preferences of the members
of those elites in power (hence ‘convergent’). Divergent types (θ = 0), however, have primitive preferences
opposed to sitting regime members. Specifically, we assume that divergent types derive a utility of rt from
setting et 6= st , where rt is a random variable drawn from a cdf G(. ). G(. ) has support on R+ such that
G(∆) = 0 (i.e., rt ∈ (∆, ∞)) and has expected value µ. Leaders also derive utility from the share of
national income flowing to the elite. Thus, leader utility is given by:


∆ + λy(d) if et = st and in power



 λy(d) if e 6= s , θ = 1 and in power
t
t
uL,t (et , st , y; θ) =

r
+
λy(d)
if
e
 t
t 6= st , θ = 0 and in power


 0 if out of power.
(3)
The order of play is as follows:
1. N ature draws the the leader’s type θ ∈ {0, 1}, the state variable s1 and the value of rents r1 , which
are revealed to the leader but not to any citizen.
2. The leader chooses d ∈ {0, 1} and the value of e1
3. Members of the regime observe the choice of d and the realization of the policy outcome. They
choose whether to unseat the leader v ∈ {0, 1}.
4. A contest for power between R and O takes place. O prevails with probability p if the leader was
12
previously retained and with probability ωp if the leader was previously removed.
5.
(a) If O prevails, it is in power in round 2 and a new leader is chosen by N ature. This leader is of
type θ = 1 with probability π .
(b) If R prevails after ousting the leader, a new leader is chosen by N ature. This leader is of type
θ = 1 with probability π .
(c) Otherwise, L remains in office.
6. N ature chooses values of s2 and r2 , which are revealed to the sitting leader, but not to any other
player.
7. The sitting leader chooses e2 . All payoffs are realized and the game ends.
3.2
Equilibrium
We characterize a perfect Bayesian equilibrium to this interaction (Fudenberg and Tirole, 1991). As is
common in signaling games, this interaction can give rise to many such equilibria. We further restrict attention to the unique equilibrium in which (1) the intuitive criterion refinement of Cho and Kreps (1987) is
satisfied, and (2) in which regime members believe convergent types (θ = 1) make disclosure decisions in
line with their primitive preferences over investment and regime stability. The second requirement ensures
that convergent types act in a manner consistent with their primitive preferences, which are also the preferences of regime members, and rules out perverse equilibria in which leaders pool on actions suboptimal
to members of the regime, under the belief that they will face removal for taking actions in R’s interest.
An equilibrium thus consists of a strategy profile and a set of posterior beliefs for members of the
regime. A strategy for L consists of a choice of et and d, each of which is a mapping from her type θ and
the realization of the value of rents, et : {0, 1} × (∆, ∞) → {0, 1}, d : {0, 1} × (∆, ∞) → {0, 1}. A
strategy for a member for R consists of a mapping from her posterior beliefs over L’s type and the choice
of disclosure into her decision of whether to mobilize against the regime, v : [0, 1] × {0, 1} → {0, 1}. And
the posterior beliefs of regime members are updated according to Bayes’ Rule based on the two signals
e1 , d.13
A complete characterization of the equilibrium to this game is tedious, and we leave a full exposition to
the appendix. In the main text we restrict our attention to the equilibrium disclosure decisions of all types
of L.
To characterize this decision, we first require several definitions. We first implicitly define two thresholds
in ω :
13
O is non-strategic in this game, given that the interaction terminates after two periods. Even if O comes to power, members
of the new regime cannot remove their sitting leader from power.
13
Definition 1. Define ω̄ and ω such that:
p(0)y(0)(ω̄ − 1)(2λ − 1)
1 − ω̄p(0)
p(1)y(1)(ω − 1)(2λ − 1)
π∆ =
.
1 − ωp(1)
π∆ =
Note that the right hand side of the equality π∆ =
p(d)y(d)(ω−1)(2λ−1)
,
1−ωp(d)
which defines these two terms,
is monotonic and increasing in ω and similarly monotonic and increasing in p(d) and y(d). Note further that
limω→1
p(d)y(d)(ω−1)(2λ−1)
1−ωp(d)
= 0, and limω→
1
p(d)
p(d)y(d)(ω−1)(2λ−1)
1−ωp(d)
= ∞. Thus, ω̄ and ω are well defined.
Moreover, since the expression is monotonically increasing and continuous in p, y , ω̄ > ω .
These thresholds are relevant for the following reason: for ω > ω̄ , no member of the regime will ever
seek the removal of the leader. This situation corresponds to an autocracy dominated by a personalist
leader long ensconced in office. The consequences of intra-elite strife for regime stability are sufficiently
high that removing a leader, even if she is known to be a divergent type, is never worth the risk. If ω <
ω , members of the regime strictly prefer to remove any leader who is revealed to be a divergent type.
The implications of so-doing for regime stability, even in the event of disclosure, are sufficiently small
that members of regime always prefer to remove types who reveal themselves to be divergent. This
corresponds to a highly institutionalized autocracy or to situations in which the leader is new to office
and has yet to establish a cult of personality. For ω ∈ [ω, ω̄] (an interval which is always non-empty),
members of the elite are wiling to remove types that are known to be divergent absent disclosure, but are
unwilling to remove known divergents when the leader chooses to disclose. The existence of this interval
substantiates our claim above that disclosure may stabilize autocratic leaders against threats that emerge
from within their own regimes.
Equilibrium strategies are also a function of the economic impact of disclosure, y(1) − y(0) ≡ ψ .
Intuitively, higher values of ψ imply that disclosure brings greater economic benefits. As we demonstrate
below, this then implies that leaders of all types are more willing to disclose as ψ rises. First, however, we
characterize two thresholds in ψ :
Definition 2. Define ψ̄ , and ψ such that:
ρ
µ
[ + y(0)]
2 − p(1) λ
ρ
∆
ψ=
[ + y(0)]
2 − p(1) λ
ψ̄ =
Notice that, given µ > ∆, ψ̄ > ψ .
We can now characterize equilibrium levels of disclosure as a function of model parameters, which we
present in Proposition 1.
14
Proposition 1. L’s equilibrium strategy over disclosure can be characterized in the following manner:
• For ψ > ψ̄ , d = 1 for all θ ∈ {0, 1}.
• For ψ ∈ [ψ, ψ̄], d = 0 iff θ = 0 and ω > ω̄ .
• For ψ < ψ d = 0 for all θ = 1. For θ = 0:
– d = 0 for ω > ω̄ .
– d = 1 for ω ∈ [ω, ω̄] iff r1 > ∆ + ρ[µ + λy(0)] − [2 − p(1)]λψ .
– d = 1 for ω < ω iff r1 > ∆ − λψ + [1 − p(0)][µ + λy(0)]
To illustrate the intuitions underlying this finding, consider the thresholds in ψ described above. ψ̄
denotes the economic returns necessary to induce a divergent type of leader to disclose when she is
certain of facing no opposition from within her regime (i.e., when ω > ω̄ ). ψ is the analogous value for a
convergent type. ψ̄ > ψ since, absent autocratic accountability, divergent types derive greater value from
office than convergent types, and divergent types are correspondingly less willing to risk their position in
power by increasing the mobilizational potential of the citizenry.
Now consider a situation where autocratic leaders are accountable to their regimes. Here, divergent
leaders always prefer to set d = 1 if the rents from adopting a divergent policy (setting e1 6= s1 ) are sufficiently high. If et 6= st , the regime will know with certainty that the leader is divergent. Transparency always
then ensures that the leader will enjoy a higher return from her first period in office (since y increases),
and may (if ω ∈ [ω, ω̄]) ensure that she can adopt divergent policies without fearing repercussions from
members of her regime.
Convergent types, by contrast, only wish to set d = 1 if ψ > ψ . When this holds, divergent types
who seek to masquerade as convent types must also set d = 1. And, as stated above, those who do not
seek to pool, and who face elite accountability, always strictly prefer to set d = 1. If ψ < ψ , convergent
types will never set d = 1. Divergent types will only do so if they are willing to run the resultant risk of
sanctioning from their regime – i.e., if the value of rents r1 and the economic returns from transparency ψ
are sufficiently high.
3.3
Comparative Statics
As is evident from Proposition 1, equilibrium levels of disclosure depend critically on two model parameters, ω and ψ . To reiterate, ψ represents the economic implications of transparency: higher values of ψ
correspond to a greater responsiveness of investment to transparency. ω represents the additional risks to
regime stability incurred by removing the leader from office. ω is lower in more institutionalized autocracies
and when a leader is newly installed in office.
Proposition 2. Equilibrium disclosure is rising in ψ .
15
Proposition 2 states that autocratic leaders are more likely to disclose the more responsive international
investment is to transparency. Increased investment is attractive to autocratic leaders for two reasons. First,
the leadership – like others within the regime – directly enjoys higher levels of consumption as investment
rises. Second, increased incomes stabilize the regime against internal threats. Regime members grow
less willing to risk their positions of privilege as y(d) rises.
Empirically, Proposition 2 implies that we have still greater reason to anticipate a positive association
between FDI inflows and transparency. Transparency is anticipated to have a causal effect on investment,
due to the primitive assumption that y(1) > y(0). However, if ψ varies across autocratic regimes, we would
anticipate an endogenous equilibrium relationship in which regimes with higher values of ψ are more likely
to disclose.
Proposition 3. Leaders disclose for a wider range of values when ω ≤ ω̄ than when ω > ω̄ .
Proposition 3 documents the role of autocratic institutions in encouraging disclosure. Leaders in highly
personalized regimes are relatively unlikely to disclose. Under these circumstances, disclosure has no
implications for internal threats against the leader – such threats are absent with or without transparency.
Leaders, therefore, only trade off the economic benefits of disclosure against the mobilizational costs –
there is no consideration of disclosure’s effect on regime coherence.
Empirically, we interpret Proposition 3 as stating that transparency should be systematically higher in
more institutionalized regimes and in regimes with new leaders.
4
Empirics
Hollyer, Rosendorff and Vreeland (forthcoming 2015a) document a positive association between transparency and the risk of mass unrest and a negative association between transparency and the risk of coup
in autocratic regimes. We do not seek to replicate these results here. Rather, we focus on testing the
model’s predictions of which autocrats disclose and on the relationship between disclosure and inflows of
foreign direct investment.
4.1
Autocracy and Transparency
Proposition 3 tells us that disclosure should be a function of the parameter ω . Disclosure should systematically be higher when ω < ω̄ than when ω > ω̄ .
We map the theoretical parameter ω into two sets of empirical covariates. The first relate to the institutionalization of the regime. ω is inversely related to the degree of institutionalization. Hence ω is higher in
personalist than in party based regimes, and is lower in regimes that possess institutions with characteristics such as elected legislatures than in regimes that lack such institutions. Second, ω is mapped into a
measure of the length of an autocratic leader’s tenure in office. ω is low for leaders who are new to office
and rises over time.
16
We draw on two sources for information on autocratic institutions. The first is the Autocratic Regimes
dataset of Geddes, Wright and Frantz (2012) (henceforth the GWF dataset). This dataset classifies autocratic regimes as either: party-based, personalistic, or military.14 Each type of regime is denoted by a
dichotomous indicator variable. Observations are available for 280 autocratic regimes from 1946-2010.
Our theory predicts that personalistic regimes should disclose less than other regime-types – ω is higher
for personalist rulers than for either military or party-based regimes. Our theory is rather more ambiguous
with respect to comparisons between military and party-based regimes.
The second such source is the Democracy and Development Revisited (DD) dataset (Cheibub, Gandhi
and Vreeland, 2010). We construct a series of dichotomous indicators based on the information therein.
Singleparty ∈ {0, 1} and M ultiparty ∈ {0, 1} are indicators for single-party regimes and autocracies
which permit the existence of multiple political parties, respectively.15 We additionally code a variable
Legislature ∈ {0, 1} which assume the value 1 if there exists an elected legislature.16 ω should fall
as the number of permitted parties rises, and should be lower in regimes with legislatures than in those
without. We additionally control for an indicator of whether the head of state is (or was) a member of
the military, and an indicator for communist regimes. The latter control is included given that communist
regimes may, for ideological reasons, have particularly strained relations with both international investors
and the World Bank.
We draw our definition of autocratic regimes and leaders’ time in office from Svolik (2012). Svolik
considers an authoritarian regime to have collapsed when (1) there is a transition of leadership (2) involving
the assumption of power by a leader unaffiliated with the ruling regime. We use these data to (1) identify
autocratic regimes (the autocratic regime-year is the unit of observation in these regressions), and (2) to
code an indicator variable N ew Leader ∈ {0, 1}, which takes the value of 1 if a leader has served five
years or less in office and a value of zero otherwise. Since newly installed autocrats are likely to face
greater threats of coup and intra-regime discipline (Bueno de Mesquita et al., 2002; Francois, Rainer and
Trebbi, 2014), we would expect that – consistent with Proposition 3 – new leaders disclose more readily
than entrenched leaders. ω rises with the amount of time served in office.
Our measure of transparency in these regressions is the HRV index (Hollyer, Rosendorff and Vreeland,
2014). The HRV index is a continuous measure which reflects the public disclosure, by governments, of
credible economic information. It does so by relying on the presence or absence of data from the World
Bank’s World Development Indicators (WDI) data series. Since data disclosed to the World Bank are, in
recent years, publicly available and, in the past, were widely available to researchers, the presence of such
data are reflective of a general tendency to disclose economic information. Since the World Bank actively
reviews these data for inaccuracies, and deletes reported observations it finds incredible, the availability of
such observations reflects a tendency to disclose credible economic information. The HRV index scales
14
The GWF dataset also includes a category for monarchies. However, there are only 8 such autocratic regime in our sample,
constituting 6.7 percent of observations and preliminary regressions indicated that these states disclose at similar rates to military
regimes. Hence we collapse military and monarchical regimes into one category.
15
These are based on recodings of the def acto variable in the original DD dataset.
16
This is based on a recoding of the legselec variable in the original DD dataset.
17
the presence/absence of 240 measures from the WDI using an item-response algorithm, thus producing
a continuous measure of transparency, measured at the country-year level, for 125 countries from 19802010.
We draw a number of economic controls from the Penn World Table, version 7.1 (Heston, Summers
and Aten, 2012). We include controls for both GDP per capita and chain-series GDP, both measured in
purchasing power parity adjusted 2005 US dollars. These controls are included given that more developed
states may simply be more able to disclose data than less developed ones. We additionally control for
economic openness ( Exports+Imports
), given that more open economies may be subject to more pressure
GDP
to disclose from international markets; economic growth (percent change in GDP), given the possibility that
governments may be more tempted to disclose when times are good; and government consumption as a
percent of GDP, given that statist economies may face less pressure to disclose information to the general
public.
All regressions additionally include an indicator for fuel exporters, drawn from Easterly and Sewadeh
(2001). We include this term given the findings of Egorov, Guriev and Sonin (2009), who find that natural
resource exporters are more opaque than other autocracies.
We asses the relationship between transparency, autocratic institutions, leaders’ time in office, and
economic factors via the following hierarchical varying-intercepts model:
transparencyi,t = ρtransparencyi,t−1 + αi + Xi,t−1 β + i,t
αi ∼ N (Zi γ, σα2 )
(4)
where i denotes regime, t denotes year, Xi,t−1 is a vector of time-varying controls including economic
data and the N ew Leader indicator, αi is a regime-specific intercept term, and Zi is a vector of regimelevel (time-invariant) controls. In addition to the controls described above, we include a cubic polynomial
of time in all specifications to control for general time trends in disclosure. The lagged dependent variable
is included to incorporate model dynamics, given that transparency evolves via a slow-moving process
(Beck and Katz, 2011). We standardize all continuous variables that are not time counters, by subtracting
the mean and dividing by the standard deviation, to ease interpretation of model coefficients and improve
performance of the Gibbs sampler. All indicator variables are left as {0, 1} values.
We estimate the model described in equation 4 via MCMC in JAGS 3.3.0. We place separate diffuse
multivariate normal priors on, respectively, the coefficients on regime-level variables γ and the regime-year
level variables β . We place an informative prior on ρ ∼ N (0, 1).17
The results from models employing the GWF data can be found in Table 1 and those employing the DD
data can be found in Table 2. In both tables, results from the baseline model described by equation 4 are
located in the leftmost three columns.
While the lagged dependent variable is necessary to incorporate dynamics into the model, its presence,
17
Further details are available in Appendixes B.1.1 and B.1.2, for the GWF and DD specifications, respectively.
18
19
Cubic Time
Polynomial
# Obs
# Regimes
New Leader
Gov’t Consumption
Growth
Ec. Openness
GDP
GDP per capita
Lag Transparency
Fuel Exporter
Personal
X
1530
119
X
1530
119
-0.010
[-0.023, 0.004]
0.024
[0.001, 0.048]
LDV Models
Model 2
0.002
[-0.039, 0.031]
-0.038
[-0.085, -0.007]
-0.036
[-0.073, 0.006]
0.961
[0.943, 0.977]
0.005
[-0.013, 0.020]
0.003
[-0.009, 0.016]
1530
119
X
0.024
[0.002, 0.049]
Model 3
0.002
[-0.037, 0.036]
-0.044
[-0.087, -0.008]
-0.033
[-0.070, 0.008]
0.964
[0.947, 0.980]
1411
111
X
1411
111
X
1411
111
X
Instrumented LDV Models
Model 1
Model 2
Model 3
0.002
-0.002
-0.003
[-0.031, 0.032]
[-0.037, 0.028]
[-0.034, 0.024]
-0.037
-0.037
-0.040
[-0.073, 4×10−4 ] [-0.070, -0.001] [-0.072, -0.007]
-0.029
-0.027
-0.027
[-0.065, 0.008]
[-0.061, 0.008]
[-0.059, 0.005]
0.645
0.647
0.648
[0.634, 0.656]
[0.636, 0.657]
[0.637, 0.657]
7×10−4
0.001
[-0.013, 0.015]
[-0.012, 0.014]
0.004
0.003
[-0.007, 0.014]
[-0.008, 0.013]
0.004
[-0.008, 0.017]
-0.006
[-0.013, 2×10−4 ]
-0.008
-0.007
[-0.019, 0.003]
[-0.018, 0.003]
0.007
0.007
0.008
[-0.011, 0.026]
[-0.010, 0.027]
[-0.011, 0.026]
Results from a hierarchical varying-intercepts linear regression of HRV transparency index scores on listed covariates. Covariates that shift the intercept term are described as
‘Regime Predictors’, while those that directly shift predicted transparency values are listed as ‘Regime-Year Predictors.’ Results in the left three columns are from the linear model
described by equation 4, while those in the right three columns are from the system of equations 5-7. All covariates that are neither indicators terms nor time counters have been
standardized by subtracting the mean and dividing by the standard deviation. 95 percent credible intervals are presented in brackets.
Regime-Year Predictors
Regime Predictors
Party
Model 1
0.002
[-0.033, 0.038]
-0.039
[-0.083, -0.001]
-0.037
[-0.082, 0.010]
0.960
[0.943, 0.978]
0.004
[-0.012, 0.021]
0.003
[-0.009, 0.017]
0.002
[-0.013, 0.017]
-4×10−4
[-0.011, 0.010]
-0.010
[-0.02, 0.003]
0.023
[-4×10−4 , 0.047]
Table 1: Models of Disclosure: GWF Data
20
Cubic Time
Polynomial
# Obs.
# Regimes
New Leader
Gov’t Consumption
Growth
Ec. Openness
GDP
GDP per capita
Lag Transparency
Fuel Exporter
Communist
Royal
Military
X
1623
135
X
1623
135
-0.015
[-0.027, -0.002]
0.030
[0.008, 0.052]
0.005
[-0.042, 0.055]
-0.031
[-0.076, 0.008]
0.962
[0.947, 0.979]
0.004
[-0.010, 0.020]
0.002
[-0.011, 0.014]
0.027
[-0.011, 0.063]
-0.034
[-0.061, -0.004]
LDV Models
Model 2
1623
135
X
0.031
[0.007, 0.051]
0.009
[-0.037, 0.055]
-0.029
[-0.069, 0.008]
0.966
[0.948, 0.981]
0.027
[-0.011, 0.069]
-0.034
[-0.062, -0.007]
Model 3
1486
121
X
1486
121
X
Instrumented LDV Models
Model 1
Model 2
-0.010
[-0.069, 0.053]
0.019
[-0.032, 0.070]
0.019
0.021
[-0.021, 0.057]
[-0.016, 0.054]
-0.026
-0.028
[-0.053, 0.004]
[-0.055, -0.003]
0.002
[-0.055, 0.057]
0.028
0.006
[-0.020, 0.086]
[-0.034, 0.047]
-0.019
-0.022
[-0.064, 0.018]
[-0.061, 0.012]
0.644
0.647
[0.632, 0.655]
[0.637, 0.657]
0.004
0.004
[-0.012, 0.020]
[-0.012, 0.014]
0.001
0.002
[-0.010, 0.013]
[-0.009, 0.012]
1×10−4
[-0.012, 0.012]
-0.009
[-0.016, -0.002]
-0.012
-0.012
[-0.023, -4×10−4 ] [-0.022, -4×10−4 ]
0.002
0.013
[-0.015, 0.021]
[-0.003, 0.030]
1486
121
X
0.014
[-0.004, 0.032]
0.009
[-0.030, 0.047]
-0.020
[-0.050, 0.013]
0.650
[0.639, 0.660]
0.020
[-0.013, 0.056]
-0.028
[-0.057, -0.006]
Model 3
Results from a hierarchical varying-intercepts linear regression of HRV transparency index scores on listed covariates. Covariates that shift the intercept term are described as
‘Regime Predictors’, while those that directly shift predicted transparency values are listed as ‘Regime-Year Predictors.’ Results in the left three columns are from the linear model
described by equation 4, while those in the right three columns are from the system of equations 5-7. All covariates that are neither indicators terms nor time counters have been
standardized by subtracting the mean and dividing by the standard deviation. 95 percent credible intervals are presented in brackets.
Regime-Year Predictors
Regime Predictors
Legislature
Multi-Party
Single Party
Model 1
-0.010
[-0.072,0.060]
0.024
[-0.033, 0.081]
0.027
[-0.013, 0.070]
-0.030
[-0.058, 0.003]
3×10−4
[-0.061, 0.063]
0.023
[-0.035, 0.087]
-0.024
[-0.069, 0.020]
0.959
[0.941, 0.976]
0.007
[-0.011, 0.027]
6×10−4
[-0.012, 0.013]
-0.004
[-0.019, 0.011]
-0.004
[-0.015, 0.007]
-0.014
[-0.027, 3×10−4 ]
0.028
[0.006, 0.050]
Table 2: Models of Disclosure: DD Data
when coupled with regime-specific random effects, raises a potential problem with this specification. Much
as with fixed-effects estimators, the presence of regime-specific variable intercepts induces a correlation
between the lagged dependent variable and the error process, resulting in bias. This bias is inversely
proportional to the number of time periods observed (Wawro, 2002). While our dataset covers a relatively
long period, autocratic regimes are highly heterogeneous in the time over which they survive, and thus
over which they are observed. On average, autocratic regimes are observed for 19 years in our dataset.
To correct for this bias, we employ the following procedure: We obtain unbiased estimates of timevarying coefficients (including ρ, the coefficient on the lagged dependent variable) through the use of the
Anderson-Hsiao estimator. That is, we first remove unit specific effects by first-differencing equation 4. We
then estimate the model in first-differences, instrumenting for the lagged dependent variable by using the
twice-lagged level of transparency (i.e., transaprencyi,t−2 ), which is independent of the first-differenced
error process. We then reestimate equation 4, constraining all coefficients on time-varying variables (ρ
and β ) to equal the estimates from the (unbiased) first-differenced model. This process then provides our
estimates on regime-level parameters γ .18 We thus estimate a series of equations of the following form:
∆transparencyi,t−1 = µ + ζtransparencyi,t−2 + ∆Xi,t−1 ψ + νi,t−1
(5)
ˆ
∆transparencyi,t = ρ̂∆transparency
i,t−1 + ∆Xi,t−1 β̂ + ηi,t
(6)
transparencyi,t = αi + ρ̂transparencyi,t−1 + Xi,t−1 β̂ + i,t
(7)
αi ∼ N (Zγ, σα )
(8)
ˆ
where ∆ is the first-difference operator, ∆transparency
i,t−1 is the predicted value of the lagged firstdifference of transparency obtained from equation 5, and ρ̂ and β̂ are obtained from equation 6. We
estimate all four equations in the same MCMC algorithm run from JAGS 3.3.0. Estimates using the GWF
measures of autocratic institutions and using the DD measures of autocratic institutions are reported,
respectively, in the three rightmost columns of Tables 1 and 2.19
In all specifications, regardless of the source of institutional data, the coefficient on the N ew Leader
indicator is positive. 95 percent credible intervals on this term are bounded away from zero in many of
these estimates – particularly when we estimate the baseline model as described by equation 4. In all
estimates, the bulk of the probability mass of the posterior density lies above zero.
Recall further that the presence of the lagged dependent variable renders these models dynamic. We
simulate the estimated marginal effect of a new leader over time in Figure 3. These figures are analogous
to an impulse response function from a VAR model – the system is assumed to be in equilibrium in time
t = 0 and a new leader is introduced in time t = 1. The graph to the left depicts the estimated marginal
18
This process bears some semblance to the fixed-effects vector decomposition method of Plümper and Troeger (2007), in that
estimates of coefficients on time-varying coefficients are obtained from one unbiased model and substituted into a (biased) model
to obtain estimates for time-invariant characteristics. Here, however, this process is meant to address Nickell bias rather than
discrepancies between fixed and random effects estimators.
19
Further details are available in Appendixes B.1.1 and B.1.2, for GWF and DD estimates, respectively.
21
Figure 3: Marginal Effect of a New Leader
0.25
0.20
0.15
0.10
Change in HRV Score (Standard Deviations)
0.00
0.05
0.25
0.20
0.15
0.10
0.05
0.00
Change in HRV Score (Standard Deviations)
0.30
Marginal Effect of New Leadership
0.30
Marginal Effect of New Leadership
2
4
6
8
10
2
Time (in years)
4
6
8
10
Time (in years)
Estimates of the marginal effect of the introduction of a new leader in time t = 1
over a 10 year period. Standardized HRV transparency index scores are plotted
on the y-axis. Time, measured in years, is plotted on the x-axis. Solid lines depict
mean estimated marginal effects, dotted lines depict 95 percent credible intervals.
The graph on the left depicts estimates from Model 1 of Table 1, using the GWF
institutional measures. The graph on the right depicts estimates from Model 1 of
Table 2, using the DD institutional measures.
effect of a new leader over time, and 95 percent credible intervals around this estimate, based on Model 1
from Table 1; while the graph to the right does the same for Model 1 from Table 2.
In both instances, the estimated marginal effect of a new leader is small in absolute terms. The introduction of a new leader in time t = 0 is anticipated to increase transparency scores by roughly 0.12
standard deviations by time t = 5. However, our transparency scores tend to vary little within autocratic
regimes over time. On average, the standard deviation of the (standardized) transparency measure within
an autocratic regime is 0.28. Thus, a new leader is predicted to increase transparency scores by roughly
to
1
2
1
3
their over-time standard deviation by the conclusion of her first five years in office. This result is con-
sistent with theoretical expectations, based on Proposition 3, that hold that leaders are particularly likely to
disclose.
In models using the GWF definitions of institutions, the coefficient on the indicator for personalist
regimes is consistently negative – again in line with theoretical expectations. Personalistic regimes receive a transparency score between 0.037 and 0.044 standard deviations lower than military regimes (and
a score roughly 0.04 standard deviations below party-based regimes). Given the dynamics of the model,
this estimate is equivalent to an equilibrium difference of roughly 0.97 standard deviations based of the
estimates using equation 4 and 0.10 standard deviations based of Anderson-Hsiao estimates. In all but
one specification, the 95 percent credible interval is bounded away from zero.
22
Similarly, regressions using the DD definitions of autocratic institutions find that autocracies with elected
legislatures receive higher transparency scores than those without; while those with military heads of state
receive lower transparency scores. The former result is not statistically significant, though the bulk of the
probability mass lies above zero. The latter estimate is significant at the 95 percent level in all but two
specifications.
Finally, the coefficient on the f uelexporter indicator is negative in every model estimated, as is consistent with Egorov, Guriev and Sonin (2009). Though, these results are not typically significant – p-values
always exceed 0.10 with the DD data and hover around this value when using the GWF data.
The other consistency which jumps out from these data is how little the time-varying economic covariates seem to matter for transparency. Only government consumption seems consistently (negatively)
related to transparency scores.
This finding echoes a point raised previously, that transparency primarily varies across rather than
within regimes. While a handful of autocracies witness substantial changes in transparency over time,
these incidences are few and far between.
With this reality in mind, we estimate an additional set of cross-sectional regressions of transparency
against regime characteristics. In this model, the unit of observation is the autocratic regime. We average
transparency scores for each regime over time. We then regress these scores on time-invariant institutional characteristics, and economic covariates taken from the first year the regime is observed in our
sample. (We use the first year of observation to help guard against endogeneity – the risk that changes in
transparency over time are driving changes in that regime’s economic performance.) We estimate a linear
model using MCMC as run from JAGS 3.3.0, and draw coefficients from a diffuse multivariate normal prior.
We present results from this model in Table 3.
Results from the cross-sectional model reveal that model covariates predict far more of the variation
in transparency levels across countries than they do over time. Moreover, results from this model are
strongly consistent with theoretical expectations. More strongly institutionalized autocracies are more likely
to disclose. Personalistic regimes (as defined by the GWF data) have average transparency levels roughly
1
3
of a standard deviation below those coded by the GWF as militaristic, though 90 percent credible intervals
on this estimate (just) encompass zero. By contrast, regimes with elected legislatures receive average
transparency scores roughly
1
2
a standard deviation above those without – a result that his highly significant.
Across all specifications, fuel exporters disclose substantially less (roughly
1
2
of a standard deviation less)
than non-fuel exporters. Again, this result is highly significant in all specifications.
4.2
Transparency and FDI
Our theory further predicts that autocratic governments that disclose more economic information will also
attract higher levels of foreign investment. This results both from a causal process and as an equilibrium
effect. The causal effect of transparency follows from the primitive assumption in our model that y(1) >
y(0). The equilibrium effect is stated in Proposition 2 – those leaders who anticipate the highest returns
23
Table 3: Collapsed Cross-Sectional Model of Disclosure
GWF Data
DD Data
Party
-0.243
[-0.582, 0.113]
Personal
-0.330
[-0.675,0.119]
Single Party
0.086
[-0.558, 0.693]
Multiparty
0.346
[-0.142, 0.871]
0.438
0.492
Legislature
[0.054, 0.766]
[0.174, 0.870]
Military
0.029
-0.040
[-0.288, 0.323]
[-0.306, 0.272]
Communist
-0.747
-0.936
[-1.281, -0.131] [-1.447, -0.381]
Fuel Exporter
-0.451
-0.466
-0.529
[-0.981, 0.027] [-0.960, -0.009] [-1.03, -0.064]
GDP
0.521
0.482
0.497
[0.376, 0.686]
[0.326, 0.618]
[0.356, 0.631]
GDP per capita
0.058
0.159
0.144
[-0.132, 0.217]
[0.008, 0.312]
[-0.017, 0.279]
-0.072
-0.087
-0.094
Gov’t Consumption
[-0.222, 0.090] [-0.237, 0.045]
[-0.219, 0.051]
# Obs
119
135
135
Results of a linear regression of transparency on listed covariates, wherein all observations have been collapsed to the autocratic regime level. Mean transparency
scores for each autocratic regime are regressed on time-invariant regime characteristics, and economic controls. Economic controls are taken from the first period
in which a given regime is under observation in the dataset. All variables that are
not dichotomous indicators have been standardized by subtracting the mean and
dividing by the standard deviation. 95 percent credible intervals are presented in
brackets.
24
to transparency in terms of increased FDI inflows are also those who are most likely to disclose. We
do not separate these two effects here, and instead simply report the associational relationship between
investment and transparency. These results should not be interpreted causally – they are predicted to
be an upwardly biased estimate of the causal effect of disclosure. Rather, we merely seek to assess the
model’s claim that higher levels of transparency should be associated with increased FDI inflows.
To test this claim, we rely on the UN Conference on Trade and Development’s (UNCTAD’s) data on
net (inward and outward) foreign direct investment (FDI), measured in current US dollars. These data are
measured at the country-year level and are available for the period between 1970 and 2012. All other
measures used in this analysis are as described above.
To assess the relationship between FDI and transparency, we regress current net FDI inflows on their
lag, lagged levels of transparency (as measured by the HRV index), and a host of economic and political
controls. The unit of observation is the autocratic country-year. We employ random intercept hierarchical
models, such that each autocratic country-year receives its own intercept. As in the above specifications,
all continuous covariates have been standardized by subtracting their means and dividing by their standard
deviations. Indicator variables are left as an element in {0, 1}. We thus run a model of the following form:
F DIi,t = αi + ρF DIi,t−1 + γtransparencyi,t−1 + Xi,t−1 β + i,t
where i denotes country and t denotes year. We estimate this model via MCMC, run from JAGS 3.3.0.20
We place diffuse multivariate normal priors on the αi , γ and β priors. We place a prior on ρ of ρ ∼ N (0, 1).
Of course, this specification runs into the same issues of bias as those presented above. However,
these problems are rather less severe. Because the unit of observation here is the country-year, nearly
all panels are observed for the full 27 year period. This stands in contrast to the above models, in which
the unit of observation is the autocratic regime, some of which are quite short-lived. The extent of Nickell
bias is inversely proportional to T , and the fixed-effects lagged dependent variable specification typically
outperforms alternatives (on the basis of mean squared error), when panels are more than 25 years in
length (Beck and Katz, 2011). We therefore present results of the simple linear hierarchical model here.21
Estimates using controls for political institutions drawn from the GWF dataset are presented in Table 4,
while those using the DD data are presented in Table 5.
These results confirm theoretical expectations – transparency is quite closely related to FDI inflows.
These results are remarkably consistent across all specifications. The point estimates reveal that a one
standard deviation increase in transparency is associated with an contemporaneous increase in FDI inflows
of roughly 0.02 standard deviations. 95 percent credible intervals indicate that a one standard deviation
20
Further details and robustness tests are included in Appendix B.2.1 for models employing GWF controls and B.2.2 for DD
controls.
21
In Appendixes B.2.1 and B.2.2, we additionally present results employing (1) a fixed-effects Bayesian estimator and (2) an
Arellano-Bond GMM estimator run from Stata. Results are substantively similar to those presented here, save that the marginal
effect of transparency is somewhat larger in (1) and standard errors are somewhat inflated in (2).
25
Table 4: Models of FDI: GWF Data on Institutions
Model 1
Model 2
Model 3
Lag FDI Inflows
0.784
0.798
0.804
[0.75, 0.817]
[0.768, 0.83]
[0.774, 0.831]
Transparency
0.021
0.022
0.023
[0.006, 0.036]
[0.006, 0.035] [0.008, 0.036]
GDP
0.195
0.181
0.175
[0.161, 0.227]
[0.151, 0.211] [0.145, 0.204]
GDP per capita
0.01
0.015
[-0.006, 0.026] [-0.002, 0.029]
0.003
0.003
Growth
[-0.011, 0.016] [-0.01, 0.016]
Ec. Openness
0.016
[-0.001, 0.033]
0.001
Gov’t Consumption
[-0.012, 0.015]
Party
-0.016
-0.015
[-0.05, 0.018] [-0.047, 0.019]
Personal
0.022
0.02
0.023
[-0.015, 0.063] [-0.016, 0.06] [0.008, 0.036]
Obs
1368
1368
1368
Countries
80
80
80
Results from a linear varying intercepts regression in which net
FDI inflows is the outcome variable. All variables that are neither
indicator terms, nor time counters, have been standardized by
subtracting the mean and dividing by the standard deviation. 95
percent credible intervals are presented in brackets.
26
Table 5: Models of FDI: DD Data on Institutions
Model 1
Model 2
Model 3
Lag FDI Inflows
0.793
0.803
0.808
[0.764, 0.823]
[0.774, 0.828]
[0.782, 0.834]
Transparency
0.017
0.017
0.018
[0.003, 0.031]
[0.004, 0.031]
[0.006, 0.031]
GDP
0.185
0.175
0.171
[0.157, 0.217]
[0.148, 0.203]
[0.145, 0.197]
GDP per capita
0.006
0.012
[-0.009, 0.021] [-0.001, 0.026]
Growth
0.004
0.004
[-0.008, 0.017] [-0.008, 0.017]
0.013
Ec. Openness
[-0.001, 0.03]
Gov’t Consumption
0
[-0.013, 0.013]
-0.004
-0.01
Legislature
[-0.045, 0.035] [-0.044, 0.022]
Single Party
-0.01
[-0.063, 0.041]
Multiparty
-0.024
[-0.066, 0.017]
-0.006
-0.008
-0.012
Military
[-0.035, 0.022] [-0.036, 0.019] [-0.038, 0.014]
Obs
1627
1627
1627
Countries
87
87
87
Results from a linear varying intercepts regression in which net
FDI inflows is the outcome variable. All variables that are neither
indicator terms, nor time counters, have been standardized by
subtracting the mean and dividing by the standard deviation. 95
percent credible intervals are presented in brackets.
27
Figure 4: Marginal Effect of a One Standard Deviation Increase in Transparency
1000
0
500
Change in FDI Inflows (Millions USD)
1000
500
0
Change in FDI Inflows (Millions USD)
1500
Marginal Effect of Transparency
1500
Marginal Effect of Transparency
2
4
6
8
10
12
14
2
Time (in years)
4
6
8
10
12
14
Time (in years)
Estimates of the marginal effect of a permanent one standard deviation increase
in transparency (in year t = 1), over a 15-year period. Net FDI inflows, measured
in millions of current US dollars, are plotted on the y-axis. Time, measured in
years, is plotted on the x-axis. Solid lines depict mean estimated marginal effects,
dotted lines depict 95 percent credible intervals. The graph on the left depicts
estimates using institutional controls from the DD dataset. The graph on the right
draws institutional controls from the GWF dataset.
is associated with between a 0.003 and 0.036 standard deviation increase in FDI inflows. Translated into
dollar terms, these estimates imply that a one standard deviation increase in transparency is associated
with a predicted increase in annual FDI flows of $154 million, with a 95 percent credible interval running
from $43-$265 million. Recall, however, that the inclusion of a lagged dependent variable renders this
model dynamic. The long term equilibrium impact of a one-standard deviation increase in transparency is
given by relevant coefficient on transparency multiplied by
1
1−ρ ,
or roughly four times the contemporaneous
effect. Thus a permanent one standard deviation increase in transparency is estimated to lead to a roughly
0.08 standard deviation increase in FDI inflows over the long term, or $700 million, with an associated 95
percent credible interval of $198 million - $1.2 billion.
Figure 4 graphically depicts these estimates. Each figure plots estimates of the marginal effect of a
one standard deviation increase in transparency over time. These figures are analogous to an impulse
response function from a VAR model – the system is assumed to be in equilibrium in time t = 0, and a
one standard deviation increase in transparency takes place in time t = 1. The graph to the left depicts
estimates from Model 1 in Table 5, while the graph to the right depicts estimates from Model 1 in Table 4.
Other than GDP, few other covariates are substantial or significant predictors of inflows. Interestingly,
once transparency is controlled for, elected legislatures have no significant association with FDI. This
finding speaks to the results of Jensen, Malesky and Weymouth (forthcoming), who find that authoritarian
28
legislatures improve corporate governance. These results suggest that authoritarian regimes with elected
legislatures may attract more investment because they tend to be more transparent.
5
Conclusion
Transparency is robustly associated with FDI inflows in authoritarian regimes. The desire to attract increased investment is part of what motivates autocratic governments to disclose. The other part of this
equation is the manner in which transparency helps to insure autocratic leaders against opposition that
emerges from within the regime itself. This latter effect arises in part because FDI inflows disproportionately benefit regime officials, and in part because transparency facilitates mass mobilization by the public,
which threatens the regime as a whole. Our theory therefore predicts that autocratic leaders are most
likely to disclose information to the public when the threat of sanctioning by elites within the regime is relatively high. Consistent with this prediction, we find empirically that disclosure is highest in autocracies
with elected legislatures and when leaders are newly appointed to office, and find that disclosure is less
common in personalistic regimes.
Our argument makes contributions to three literatures. First, it directly addresses a growing literature on
transparency in non-democratic regimes. Our argument broadens the conception of transparency typical
of this literature, which focuses primarily on the media environment (Egorov, Guriev and Sonin, 2009;
Gehlbach and Sonin, 2014) and censorship (King, Pan and Roberts, 2013; Lorentzen, 2014; Shadmehr
and Bernhardt, forthcoming). Instead, we focus on government disclosures. However, our argument may
pertain to government policies that facilitate mass mobilization more generally – at times, the threat of
unrest is actually beneficial for the autocratic leaders. Moreover, we provide an argument that helps to unify
the existing empirical findings of Hollyer, Rosendorff and Vreeland (forthcoming 2015a), that transparency
is associated with an increased threat of autocratic collapse via unrest, but a reduced risk of collapse due
to coups.
Second, we contribute to a literature on international FDI flows, particularly flows involving autocratic
regimes. The bulk of this literature focuses on the threat of expropriation: the relative inability of autocrats
to commit to respect property rights, when compared with democracies (Jensen, 2003, 2006); and the role
of autocratic institutions in facilitating credible commitments to investors (Gehlbach and Malesky, 2010).
Others (e.g. Jensen, Malesky and Weymouth, forthcoming) focus on the role autocratic institutions may play
in facilitating interactions between domestic an foreign actors. We, by contrast, emphasize the importance
of information to FDI flows. Investors are more willing to enter markets when governments are transparent,
allowing greater investor certainty over the anticipated rate of return.
Finally, we advance a more general argument about autocratic institutions, and particularly about the
manner in which threats to autocratic leaders that emerge from within the regime and those that emerge
from the populace interact. Like Svolik (2012, 2013), we contend that internal and external threat may
act as strategic substitutes. But, unlike these pieces, we contend that higher levels of external threat may
29
promote regime unity. Elites are less likely to sanction autocratic leaders when the perceive that the public
is highly capable of mobilizing against the regime, as any increase in regime instability comes at a high
cost. This tendency creates a somewhat perverse incentive for leaders, who may deliberately engage in
policies that increase the threat of public unrest in order to ensure regime unity. Disclosing information to
the public is one policy that reflects this more general mechanism.
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A
Proofs of Theoretical Propositions
Characterization of the Equilibrium
We characterize a perfect Bayesian equilibrium to this game. A perfect Bayesian equilibrium consists of
a strategy profile and a set of posterior beliefs for regime-members such that: (1) Each player adopts a
best response, given her beliefs. (2) Beliefs are consistent with the strategy profile. (3) Beliefs are updated
according to Bayes’ Rule, wherever possible (Fudenberg and Tirole, 1991).
33
To characterize an equilibrium, first note that the sitting leader in period t = 2 has a dominant strategy,
conditional on her type:
(
e2 (s2 , θ) =
s2 if θ = 1
¬s2 if θ = 0
(9)
Given L’s strategy, contingent on her type, in the second period, we can now turn our attention to the
strategy of regime elites R. Naturally, for a range of parameter values, this strategy will be contingent on
R’s beliefs over the type of leader she is facing θ. However, since removing the leader is costly – insofar as
it runs the risk of costing elites their position of privilege – there exist a range of values for which R has a
strictly dominant strategy of retaining L. More precisely, R has a strictly dominant strategy of setting v = 0
p(d)y(d)(ω−1)(2λ−1)
. This
1−ωp(d)
p(d)y(d)(ω−1)(2λ−1)
π∆ ≥
,
1−ωp(d)
if π∆ <
If
threshold characterizes the values of ω̄ and ω defined in Definition 1.
R’s best response is a function of her beliefs over L’s type θ, which –
in equilibrium – will be conditioned on the value of e1 and d. If P r(θ = 1|e1 , d) ≥ π , R’s best response
is to retain the leader (set v = 0), otherwise her best response is to remove the leader. R’s beliefs
are required to be weakly consistent with the strategy profile. We additionally require these beliefs to (1)
satisfy and intuitive criterion refinement and (2) to hold that convergent types act in a manner consistent
with their primitive preferences, which are also the primitive preferences of R. The latter requirement rules
out perverse pooling equilibria in which convergent types act against the interest of the regime in the fear
that acting in the regime’s interest (off the equilibrium path) would induce punishment. We define these
beliefs and L’s equilibrium strategy below.
We begin by defining equilibrium beliefs and strategies as a function of the leader’s policy decision e1 .
These are defined by the following lemma:
Lemma 1. In any perfect Bayesian equilibrium that survives the intuitive criterion refinement, e1 = s1 for
convergent types (θ = 1). R’s posterior beliefs must be such that P r(θ = 1|e1 6= s1 ) = 0, regardless of
the disclosure decision d.
Proof of Lemma 1. There does not exist a perfect Bayesian equilibrium in which all types of L pool on
setting e1 = s1 , since, for some realizations of r1 , divergent types of L have a strictly dominant strategy of
setting e1 6= s1 .
No perfect Bayesian equilibrium in which all types of L pool on setting e1 6= s1 can survive the intuitive
criterion refinement of Cho and Kreps (1987). In any such equilibrium, the message e1 6= s1 equilibrium
dominates the message e1 = s1 for divergent types (θ = 0) of L. Hence, R’s (off-path) posterior beliefs
on seeing e1 = s1 must be such that P r(θ = 1|e1 = s1 ) = 1. Yet, given such off-path beliefs, R will retain
any L that sets e1 = s1 – implying that convergent types strictly prefer to deviate from the equilibrium, and
that the equilibrium cannot survive the intuitive criterion refinement.
Hence, the only strategy for L that can survive an intuitive criterion refinement is one in which e1 = s1
for θ = 1. e1 6= s1 if θ = 0 and the realization of r1 is sufficiently high. Weak consistency then implies that
P r(θ = 1|e1 6= s1 ) = 0.
34
We can now turn our attention to L’s decision regarding disclosure d ∈ {0, 1}. This decision will be
a function of three concerns facing the leader: (1) The economic benefits of disclosure ψ . (2) The risks
disclosure poses for regime stability ρ. (3) The inferences R will draw over the leader’s type following
disclosure and the consequent implications for leader removal v ∈ {0, 1}. We can begin by stating the
following lemma:
Lemma 2. Whenever regime elites chose to remove the leader in equilibrium v = 1, the leader also choses
to disclose d = 1.
Proof of Lemma 2. Disclosure brings immediate economic returns to L, as y(1) > y(0). It is costly only
insofar as it has implications for L’s continued survival in office. Hence, in any circumstances in which L
anticipates being removed, disclosure brings only benefits and no costs.
The above beliefs and strategies must hold in any perfect Bayesian equilibrium that satisfies the intuitive
criterion refinement. We additionally restrict our attention to equilibria in which convergent types act (and
are believed to act) in the best interest of the elite – which is also their own primitive preference. This
rules out perverse equilibria in which convergent types adopt harmful behaviors in an attempt to convince
the elite to retain them in office. The intuitive criterion will not, itself, rule out such equilibria since, for
certain configurations of parameter values (i.e., for sufficiently high or low realizations of ψ ), convergent
and divergent types have identical primitive preferences over disclosure and the intuitive criterion will not
limit off-path beliefs.
Convergent types (and hence R) have a primitive preference for setting d = 1 iff:
ψ≥
ρ
∆
[ + y(0)].
2 − p(1) λ
(10)
Notice that Expression 10 defines ψ , as given in Definition 2.
We can now characterize the unique perfect Bayesian equilibrium satisfying the requirements laid out
above. We will do so in two lemmas, the first of which defines equilibrium strategies and beliefs if ψ ≥ ψ ,
the second of which defines strategies and beliefs if ψ < ψ .
Lemma 3. If ψ ≥ ψ , there exists a perfect Bayesian equilibrium in which et = st and d = 1 if θ = 1. If
θ = 0, L’s strategy is a function of ω , r1 , and ψ and is defined as follows:
(
d(ψ, ω) =
0 if ψ < ψ̄ and ω > ω̄
1 otherwise.


 ¬s1 if ω ≥ ω
e1 (ω, r1 ) =
¬s1 if ω < ω and r1 ≥ ∆ + [1 − p(1)][µ + λy(1)]


s1 if ω < ω and r1 < ∆ + [1 − p(1)][µ + λy(1)]
e2 6= s2
35
where ψ̄ is defined in Definition 2.
R’s beliefs and strategies are given by:
P r(θ = 1|d = 0, e1 ) = 0
P r(θ = 1|d, e1 6= s1 ) = 0
P r(θ = 1|d = 1, e1 = s1 ) > p


 1 if d = 0 and ω < ω̄
v=
1 if e1 6= s1 and ω < ω


0 otherwise
Proof of Lemma 3. The strategy for convergent (θ = 1) types of L is dictated by the conditions described
in Expression 10 and by Lemma 1.
Lemma 1 further dictates that R’s posterior beliefs on seeing e1 6= s1 must be such that P r(θ = 1|e1 6=
s1 ) = 0. As discussed above, R’s best response given P r(θ = 1|d, e1 ) < π is to set v = 1 if either ω < ω
and d = 1 or ω < ω̄ and d = 0. R will set v = 0 whenever these conditions are not satisfied.
Divergent types (θ = 0) of L have a primitive preference for disclosure whenever ψ ≥ ψ̄ , and have
a primitive preference for non-disclosure otherwise, where ψ̄ is defined in Definition 2. Whenever ψ ≥ ψ̄ ,
therefore, divergent types have a best response to set d = 1. This both satisfies their primitive preference
and mimics the behavior of convergent types. Given disclosure, L is insulated from regime-sanction for
any choice of e1 whenever ω > ω . Hence, divergent types of L will satisfy their primitive preference
over policy by setting e1 6= s1 whenever ω > ω and achieve rents r1 > ∆. If, however, ω < ω , setting
e1 6= s1 guarantees removal by the regime. L will only adopt this action if the realization of rents r1
is sufficiently high to outweigh the benefits of survival to the next period– or, more precisely, if r1 >
∆ + [1 − p(1)][µ + λy(1)]. (Lemma 2 guarantees that L discloses if she faces certain removal.)
Given this strategy by divergent types, the action d = 0 is off the equilibrium path, and – given ψ > ψ̄
– the message d = 1 equilibrium dominates d = 0 for both convergent and divergent types. R’s off-path
beliefs are therefore unrestricted, and we define these beliefs as P r(θ = 1|d = 0) = 0 ∀ e1 .
Now consider values of ψ ∈ [ψ, ψ̄], where both ψ and ψ̄ are as defined in Definition 2. The strategy
of convergent types and R’s beliefs given e1 are unchanged. However, divergent types no longer have a
primitive preference for disclosure.
Consider the strategy, therefore, for divergent types. If ω > ω̄ , then R must set v = 0 regardless of
the actions of L. Hence, divergent types of L will satisfy their primitive preference for setting d = 0 and
e1 6= s1 . If ω ∈ [ω, ω̄], divergent types of L may insulate themselves from removal by R if they set d = 1.
Moreover, they must set d = 1 to mimic convergent types. Hence, if ω ∈ [ω, ω̄] divergent types of L
will set d = 1 and – since R must set v = 0 whenever ω > ω and d = 1 – divergent types will satisfy
their primitive preference by setting e1 6= s1 . Finally, if ω < ω , divergent types are susceptible to removal
whenever P r(θ = 1|d, e1 ) < p. Hence, they must either mimic convergent types by setting d = 1, e1 = s1
36
– which is a best response for sufficiently low draws of r1 – or accept removal and set e1 6= s1 – which is
a best response whenever r1 > ∆ + [1 − p(1)][µ + λy(1)]. Given certain removal, Lemma 2 guarantees
disclosure.
Given this strategy for divergent types, d = 0 iff θ = 0 and ω > ω̄ . Hence weak consistency implies
P r(θ = 1|d = 0) = 0 whenever ω > ω̄ . If ω < ω̄ , the message d = 1 equilibrium dominates the
message d = 0 for convergent types (given ψ > ψ ), but does not equilibrium dominate for divergent types
(given ψ < ψ̄ ). Hence, the intuitive criterion implies P r(θ = 1|d = 0) = 0. Lemma 1 dictates that
P r(θ = 1|e1 6= s1 ) = 0. Hence, these beliefs constitute an equilibrium.
Lemma 4. If ψ < ψ , there exists a perfect Bayesian equilibrium in which et = st and d = 0 if θ = 1. If
θ = 0, L’s strategy is a function of ω , r1 and ψ and is defined as follows:


 1 if ω < ω and r1 ≥ ∆ − λψ + [1 − p(0)][µ + λy(0)]
d(ψ, r1 , ω) =
1 if ω ∈ [ω̄, ω] and r1 ≥ ∆ + ρ[µ + λy(0)] − [2 − p(1)]λψ


0 otherwise


 s1 if ω < ω and r1 < ∆ − λψ + [1 − p(0)][µ + λy(0)]
e(ψ, r1 , ω) =
s1 if ω ∈ [ω̄, ω] and r1 < ∆ + ρ[µ + λy(0)] − [2 − p(1)]λψ


¬s1 otherwise
e2 6= s2
where ψ is as defined in Definition 2.
R’s beliefs and strategies are given by:
P r(θ = 1|d = 1, e1 ) = 0
P r(θ = 1|d, e1 6= s1 ) = 0
P r(θ = 1|d = 0, e1 = s1 ) > p

6 {0, s1 }

 1 if ω < ω, and {d, e1 } =
v=
1 if ω < ω̄, d = 0, and e1 6= s1


0 otherwise
Proof of Lemma 4. The strategy for convergent (θ = 1) types of L is dictated by the conditions described
in Expression 10 and by Lemma 1.
Lemma 1 further dictates that R’s posterior beliefs on seeing e1 6= s1 must be such that P r(θ = 1|e1 6=
s1 ) = 0. As discussed above, R’s best response given P r(θ = 1|d, e1 ) < π is to set v = 1 if either ω < ω
and d = 1 or ω < ω̄ and d = 0. R will set v = 0 whenever these conditions are not satisfied.
Divergent types (θ = 0) have a primitive preference for non-disclosure (d = 0) given ψ < ψ < ψ̄ .
However, their strategy regarding disclosure will be influenced by (1) the considerations of Lemma 2 and
(2) the role of disclosure in insulating divergent types from removal if ω ∈ [ω, ω̄].
37
If ω > ω̄ , R must set v = 0 regardless of their posterior beliefs. Hence divergent types will always
satisfy their primitive preferences by setting d = 0 and e1 6= s1 . If ω ∈ [ω, ω̄], divergent types may insulate
themselves from regime sanction either by setting d = 1 or by mimicking convergent types and setting
d = 0, e1 = s1 . The former mechanism is preferred over the latter iff the rents from setting e1 6= s1 or the
economic returns of disclosure ψ are sufficiently high. More precisely, divergent types of L will set d = 1
when ω ∈ [ω, ω̄] iff r1 ≥ ∆ + ρ[µ + λy(0)] − [2 − p(1)]λψ . Finally, if ω < ω , divergent types can only
avoid sanction by R by mimicking convergent types and setting d = 1 and e1 = s1 . Alternatively, they may
accept certain removal by the regime and gain policy rents r1 – and, if they take this course of action –
they will also always set d = 1, in accordance with Lemma 2. Certain removal is preferred over mimicking
convergent types if r1 ≥ ∆ − λψ + [1 − p(0)][µ + λy(0)].
Given this strategy by divergent types, R only witnesses d = 1 when θ = 0 and ω < ω̄ . Hence weak
consistency implies P r(θ = 1|d = 1) = 0 whenever ω < ω̄ . When ω > ω̄ , d = 1 is off the equilibrium
path. Moreover, the message d = 0 equilibrium dominates the message d = 1 for both divergent and
convergent types. Off-path beliefs are thus unrestricted, and we confine these beliefs to equal those for
when ω < ω̄ . R’s beliefs on seeing e1 6= s1 are given by Lemma 1.
Proof of Proposition 1. The statement in Proposition 1 follows directly from the equilibrium characterized
in Lemmas 3 and 4.
Both Proposition 2 and Proposition 3 follow directly from this characterization.
B
Empirical Appendix
B.1
B.1.1
Autocracy and Transparecy Results
Time-Series Cross-Section: GWF Controls
We assess the relationship between transparency, autocratic institutionalization (as measured by the GWF
indicators for autocratic regime type), and the presence of a new leader through two sets of regressions.
The first of these, based on the model described in Equation 4, consist of Bayesian hierarchical regressions
of transparency on covariates. The unit of observation is the regime-year, and we estimate a varying
intercepts model, in which each regime receives its own intercept, which is a function of time-invariant
covariates. We estimate this model using an MCMC estimator run in JAGS 3.3.0. We run two chains of
20,000 iterations each, with the first 10,000 iterations used as a burn-in period. Gelman-Rubin diagnostics
on all model parameters – including regime specific intercepts – indicate that the MCMC estimator has
converged.
These regressions, however, may be biased by an induced correlation between the error process
and the random intercepts, due to the presence of a lagged dependent variable in the specification. We
therefore estimate an Anderson-Hsiao type correction. This approach requires the estimation of a series of
38
equations, which we will label as three stages, though we estimate all simultaneously in the same MCMC
estimator. To obtain unbiased estimates of the time-varying coefficients, we first difference the estimating
equation and instrument for the first-differenced value of the lagged dependent variable using the twicelagged level of the dependent variable. The instrumenting equation is the first-stage of our estimates,
and is described by Equation 5. The second stage regresses the first difference of transparency on the
predicted value of lagged first difference of transparency, and on the first differences of all time varying
covariates. This is described in Equation 6 and provides unbiased estimates of the coefficients on all timevarying variables. The third stage provides the estimates of coefficient on time-invariant coefficients. It
does so by reestimating Equation 4 in levels, but constraining all coefficients on time-varying covariates to
the (unbiased) values obtained from Equation 6. This stage is described in Equation 7.
We estimate this series of equations via MCMC, with all equations estimated in the same Gibbs sampler. To do so, we employ JAGS 3.3.0 and estimate two chains of 30,000 iterations each, where the first
20,000 iterations are used as a burn-in period. Gelman-Rubin diagnostics on all model parameters indicate
that the model has converged.
Table 1 in the main text provides coefficient estimates from the second- and third-stage of this system
of equations. Table 6, below, presents estimates from the first stage.
B.1.2
Time-Series Cross-Section: DD Controls
We additionally fit models analogous to those described above, using the DD measures of autocratic
institutionalization in place of the GWF measures. First, we describe the results from the hierarchical
model using these covariates, as described by Equation 4. We estimates this model via MCMC from JAGS
3.3.0, running 2 chains of 20,000 iterations each, with the first 10,000 iterations used as a burn-in period.
Gelman-Rubin diagnostics on all parameters indicate that the model has converged.
As with the GWF data, we estimate an Anderson-Hsiao correction using the DD data. We present the
first-stage estimates from this series of equations, in which the lagged first-difference of transparency is
regressed on its twice lagged level and the first-differences of time-varying covariates, in Table 7.
B.2
B.2.1
Transparency and FDI
FDI and Transparency: GWF Controls
We assess the relationship between FDI and transparency through the use of varying intercepts hierarchical linear models. In these regressions, we employ two sets of controls for autocratic institutions: one
drawn from the GWF dataset, the other from the DD dataset. We describe models employing the former in
this section and models employing the latter in the section that follows.
We estimate these models using an MCMC estimator run in JAGS 3.3.0. We run two chains of 50,000
iterations each, with the first 40,000 used as a burn-in period. Gelman-Rubin diagnostics for all parameters
indicate that the model has converged.
39
Table 6: First Stage Anderson-Hsiao Estimates, GWF Controls
Variable Name
Constant
GDP per capita
GDP
Ec. Openness
Growth
Gov’t Consumption
New Leader
Time
Time2
Time3
Transparencyt−2
Model 1
-0.386
(22.374)
-0.007
(0.052)
0.032
(0.03)
0.018
(0.019)
0.004
(0.004)
0.001
(0.016)
0.01
(0.015)
0.419
(22.375)
0.023
(0.132)
-0.018
(0.028)
-0.009
(0.005)
Model 2
0.146
(22.835)
0.002
(0.05)
0.033
(0.03)
0
(0.015)
0.01
(0.014)
-0.114
(22.834)
0.028
(0.136)
-0.019
(0.029)
-0.009
(0.005)
Model 3
-0.507
(22.451)
0.01
(0.014)
0.54
(22.451)
0.02
(0.135)
-0.016
(0.028)
-0.008
(0.005)
Coefficient estimates from the first stage regression of the lagged first-difference
of transparency on the twice lagged level of transparency and time-varying covariates. Standard errors are in parentheses.
40
Table 7: First Stage Anderson-Hsiao Estimates, DD Controls
Variable Name
Constant
GDP per capita
GDP
Ec. Openness
Growth
Gov’t Consumption
New Leader
Time
Time2
Time3
Transparencyt−2
Model 1
0.225
(22.345)
-0.013
(0.051)
0.03
(0.03)
0.011
(0.019)
0.006
(0.004)
0.006
(0.016)
0.007
(0.013)
-0.197
(22.344)
0.055
(0.133)
-0.023
(0.028)
-0.009
(0.005)
Model 2
0.291
(22.867)
-0.005
(0.05)
0.031
(0.03)
0.003
(0.015)
0.007
(0.013)
-0.264
(22.867)
0.055
(0.129)
-0.023
(0.027)
-0.009
(0.005)
Model 3
0.123
(22.245)
0.002
(0.014)
-0.094
(22.246)
0.047
(0.132)
-0.02
(0.028)
-0.008
(0.005)
Coefficients from the regression of the lagged first difference of transparency on
its twice lagged level and the first-differences of time-varying covariates. Standard
errors are presented in parentheses.
41
We additionally estimate fixed-effects LSDV models analogous to the ‘random effects’ models described above. We again estimate these models via MCMC, as run from JAGS 3.3.0. We estimate two
chains, of 20,000 iterations each, with the first 10,000 used as a burn-in period. Results from this specification are substantively similar to those above – save only that the marginal effect of transparency is
somewhat increased. We present these estimates in Table 8, below.
Finally, to guard against the risk that the lagged dependent variable induces a correlation between
the error process and the autocratic country-specific intercepts, we fit a series of Arellano-Bond estimates
of the relationship between transparency and FDI inflows. These results are obtained from Stata 11,
and are substantively similar to those above. (Note that we did not standardize outcome or explanatory
variables for these regressions, hence coefficient estimates look radically different than those in the text.)
These estimates do, however, have inflated standard errors relative to those above. 90 percent confidence
intervals on the transparency term are bounded away from zero, however 95 percent confidence intervals
include zero. We present these estimates in Table 9, below.
B.2.2
FDI and Transparency: DD Controls
In this section, we describe our varying intercept linear hierarchical models estimating the relationship
between FDI and transparency, when employing the DD set of controls.
We estimate these models using an MCMC estimator run in JAGS 3.3.0. We run two chains of 50,000
iterations each, with the first 40,000 used as a burn-in period. Gelman-Rubin diagnostic statistics for all
parameters indicate that the model has converged.
We additionally estimate fixed-effects LSDV models analogous to the ‘random effects’ models described above. We again estimate these models via MCMC, as run from JAGS 3.3.0. We estimate two
chains, of 20,000 iterations each, with the first 10,000 used as a burn-in period. Results from this specification are substantively similar to those above – save only that the marginal effect of transparency is
somewhat increased. We present these estimates in Table 10, below.
Finally, to guard against the risk that the lagged dependent variable induces a correlation between
the error process and the autocratic country-specific intercepts, we fit a series of Arellano-Bond estimates
of the relationship between transparency and FDI inflows. These results are obtained from Stata 11,
and are substantively similar to those above. (Note that we did not standardize outcome or explanatory
variables for these regressions, hence coefficient estimates look radically different than those in the text.)
These estimates do, however, have inflated standard errors relative to those above. 80 percent confidence
intervals on the transparency term are bounded away from zero, however 95 percent confidence intervals
include zero. We present these estimates in Table 11, below.
42
Table 8: Fixed Effects Models of FDI: GWF Data on Institutions
Model 1
Model 2
Model 3
Lag FDI Inflows
0.762
0.764
0.763
[0.725, 0.802]
[0.723, 0.804]
[0.729, 0.800]
Transparency
0.059
0.059
0.058
[0.027, 0.093]
[0.026, 0.092]
[0.028, 0.093]
GDP
0.223
0.222
0.222
[0.183, 0.263]
[0.182, 0.268]
[0.183, 0.262]
-0.012
-0.009
GDP per capita
[-0.114,0.077] [-0.010, 0.080]
Growth
0.001
0.001
[-0.013, 0.015] [-0.013, 0.016]
Ec. Openness
0.007
[-0.027, 0.043]
Gov’t Consumption
-0.001
[-0.036, 0.036]
Party
-0.013
-0.014
[-0.098, 0.078] [-0.101, 0.068]
Personal
-0.024
-0.025
-0.017
[-0.138,0.080] [-0.136, 0.085] [-0.111, 0.074]
Cubic Time
Polynomial
X
X
X
Country
Fixed-Effects
X
X
X
# Obs
1368
1368
1368
80
80
80
# Countries
Results from a linear regression in which net FDI inflows is the
outcome variable. All variables that are neither indicator terms,
nor time counters, have been standardized by subtracting the
mean and dividing by the standard deviation. Coefficients thus
reflect the relationship between a one standard deviation change
in explanatory term and standard deviations of FDI flows. 95 percent credible intervals are presented in brackets.
43
Table 9: Arellano-Bond Maximum Likelihood Estimates: Transparency and FDI
Model 1
Model 2
Model 3
Lag FDI Inflows
0.850
0.851
0.847
[0.434,1.267]
[0.448,1.255]
[0.511,1.184]
Transparency
838.819
841.483
845.469
[-138.343,1815.980] [-120.670,1803.636] [-128.769,1819.707]
3.954
3.939
3.979
GDP
[-0.387,8.295]
[-0.282,8.160]
[0.269,7.690]
GDP per capita
-49.445
-15.591
[-238.479,139.589]
[-295.407,264.225]
Growth
1.621
2.594
[-6.048,9.290]
[-7.922,13.110]
Ec. openness
5.771
[-31.174,42.716]
-14.032
Gov’t Consumption
[-67.441,39.376]
Party
14.037
-0.967
[-889.094,917.167]
[-709.883,707.949]
Personal
-185.469
-244.908
-240.366
[-1333.230,962.292] [-1102.884,613.068] [-1106.814,626.081]
Cubic Time
Polynomial
X
X
X
# of Obs.
1182
1182
1182
# of Countries
75
75
75
Results of an Arellano-Bond GMM estimate of the relationship between net FDI inflows, transparency, and controls. All models are
run from Stata 11. 95 percent confidence intervals are in brackets.
In these models, covariate values have not been standardized.
44
Table 10: Fixed Effects Models of FDI: DD Data on Institutions
Model 1
Model 2
Model 3
Lag FDI Inflows
0.768
0.768
0.768
[0.732, 0.804]
[0.733, 0.803]
[0.736, 0.803]
0.062
0.062
0.062
Transparency
[0.034, 0.090]
[0.033, 0.089]
[0.034, 0.090]
GDP
0.219
0.220
0.219
[0.182, 0.259]
[0.182, 0.258]
[0.181, 0.256]
GDP per capita
-0.006
-0.001
[-0.089, 0.077] [-0.080, 0.082]
0.002
0.002
Growth
[-0.011, 0.016] [-0.011, 0.015]
Ec. Openness
0.004
[-0.026, 0.038]
Gov’t Consumption
-0.003
[-0.037, 0.029]
Legislature
0.006
0.001
[-0.039, 0.061] [-0.046, 0.044]
Single Party
-0.018
[-0.089, 0.064]
Multiparty
-0.017
[-0.087, 0.047]
Military
-0.032
-0.031
-0.032
[-0.091, 0.021] [-0.080, 0.024] [-0.080, 0.018]
Cubic Time
Polynomial
X
X
X
Country
Fixed-Effects
X
X
X
# Obs
1627
1627
1627
# Countries
91
91
91
Results from a linear regression in which net FDI inflows is the
outcome variable. All variables that are neither indicator terms,
nor time counters, have been standardized by subtracting the
mean and dividing by the standard deviation. Coefficients thus
reflect the relationship between a one standard deviation change
in explanatory term and standard deviations of FDI flows. 95 percent credible intervals are presented in brackets.
45
Table 11: Arellano-Bond Maximum Likelihood Estimates: Transparency and FDI
Model 1
Model 2
Model 3
Lag FDI Inflows
0.826
0.830
0.826
[0.413,1.240]
[0.432,1.228]
[0.485,1.167]
Transparency
562.333
571.375
577.124
[-245.551,1370.216] [-155.596,1298.346] [-177.653,1331.902]
GDP
4.172
4.129
4.164
[-0.215,8.559]
[-0.124,8.382]
[0.418,7.910]
GDP per capita
-49.941
-15.219
[-208.107,108.225]
[-269.037,238.600]
Growth
3.952
3.016
[-0.070,7.973]
[-3.259,9.291]
Ec. openness
5.965
[-22.729,34.659]
11.784
Gov’t Consumption
[-44.058,67.627]
Legislature
130.301
34.983
[-117.515,378.116]
[-245.183,315.149]
-405.671
Single Party
[-1306.560,495.219]
Multiparty
-386.791
[-896.831,123.250]
Military
-343.728
-338.645
-364.943
[-1297.862,610.406] [-1266.062,588.773] [-1572.464,842.577]
Cubic Time
Polynomial
X
X
X
# of Obs.
1432
1432
1432
# of Countries
80
80
80
Results of an Arellano-Bond GMM estimate of the relationship
between net FDI inflows, transparency, and controls. All models
are run from Stata 11. 95 percent confidence intervals are in
brackets. Covariate values have not been standardized for these
estimates.
46