Property Rights Regimes, Technological Innovation, and Foreign Direct Investment
Mark David Nieman
Iowa State University
and
Cameron G. Thies
Arizona State University
Abstract
We argue that democratic institutions influence property rights in attracting
foreign direct investment (FDI) by providing: 1) a coherent logic to the property
rights regime that is created in a state, and 2) a legitimate way to manage conflicts
that arise in dynamic economies. We expect that the marginal effect of property
rights in attracting FDI has grown stronger over time as the rate of technological
dynamism has increased. We test this using a non-nested multilevel modeling
strategy with random coefficients on data from 1970-2008 Our results
demonstrate that the effect of property rights on attracting FDI is contingent on
democratic institutions and that this effect becomes more pronounced over time.
This effect holds for both developing and developed countries across all regions.
Introduction
The relationship between regime type and foreign direct investment (FDI) remains mired
in controversy. Some studies show a positive relationship between democracy and FDI, whereas
others do not (c.f. Jakobsen and de Soysa 2006, Li and Resnick 2003). We sort out the disparate
empirical findings in the literature by considering the theoretical relationship between regime
type and property rights protections and how this has changed over time. We expect that their
interplay, rather than one or the other in isolation, is responsible for attracting higher levels of
FDI. Moreover, we argue that this effect changes depending on the rate of technological
dynamism associated with an economy. In particular, we argue that the effect of property rights
protections is conditioned by democracy and that it has time-varying effects.
We argue that democracy influences the impact of property rights through two
intertwined mechanisms: 1) democratic institutions provide a coherent logic from which to frame
the property rights regime, and 2) democracies are better apt than autocratic regimes to
legitimately manage conflicts that emerge as different groups win and lose in a dynamic
economic setting (Chang 2003; Mousseau 2003; North, Wallis, and Weingast 2009; Sunstein
1997). The effect of democratic institutions is important because property rights protection is
more than simply stating that individuals have the right to private property; enforcement of
property rights under changing technological conditions and in dynamic economies, as well as
how property rights are truncated in the face of other rights, must also be addressed. That is,
firms consider not only the existing property rights of a country when deciding where to invest,
but also how property rights for goods that are not yet on the market will be treated.
This is especially important as the rate of technological dynamism increases and new
goods and services are created that do not fit neatly within existing legal definitions.
Technological dynamism has driven changing economic arrangements in many states, as
international business has responded to changing rates of return that increasingly privilege the
information and service sectors over natural resource procurement, thus emphasizing varying
degrees of skilled labor over cheap workforces (Blanton and Blanton 2007; Kozlow, Rutter, and
Walker 1978; Mody, Dasgupta, and Sinha 1999; Moran 2002). Democratic states, by creating a
coherent property rights regime and managing conflict, are able to provide both the stability and
flexibility that firms value when deciding where to invest (Zheng 2011).
We find that the effect of property rights on FDI is contingent on the strength of
democratic institutions in a state through the use of a non-nested multilevel statistical model. The
statistical model is able to capture the changing marginal effect of property rights over time
while also accounting for heteroskadasticity between political units (Bell and Albu 1999; Fujita,
Krugman, and Venables 1999). Our methodological approach allows us to move beyond simply
examining the average treatment effect, and instead to better visualize the individual cases to
more fully explore trends and possible outliers. We find that the effect of property rights varies
depending on the level of democracy. We also find that this effect has changed over time, with
the effect of property rights in the presence of democracy becoming stronger over time across all
regions.
Regime Type and FDI
FDI is defined as “a category of cross-border investment associated with a resident in one
economy having control or a significant degree of influence on the management of an enterprise
2
that is resident in another economy” (IMF 2007, 100).1 Since this definition requires that private
capital flows have a lasting management interest in the company that is acquired or created, it is
more than simple portfolio diversification.2 FDI is an important source of employment and
technology to host countries that is thought to spur economic growth (Borensztein, Gregorio, and
Lee 1998; Markusen and Vernables 1999), and encourage technological spillovers (Baldwin,
Braconier, and Forslid 2005; Findlay 1978; Glass and Saggi 1998). As a result, states frequently
compete for FDI inflows by adopting domestic policies and creating institutions that generate an
attractive investment climate (Biglaiser and Staats 2010; Jensen 2008; Li 2009a).
Previous literature on the relationship between democratic regimes and FDI has produced
conflicting outcomes. Li and Resnick (2003) claim that democracy has both positive and
negative effects on FDI inflows. These duel effects result from competing expectations regarding
whether the competitive political environment in democracies encourages FDI or whether it is
the presence of stronger property rights protections that is often associated with democracy that
drives the process. However, the interplay between democracy and property rights and how these
effects are conditioned by one anther are not clearly specified in the existing literature.
The argument that democracy reduces FDI inflows begins with the observation that
multinational corporations (MNCs) and domestic companies compete to seek preferential
policies from the government. Democracies must appease domestic interests to remain in power
and are therefore less likely in general to offer advantages to MNCs. Autocracies, on the other
hand, are better equipped to make more favorable entry-level arrangements with foreign
investors (O’Donnell 1978, 1988; Oneal 1994). Autocrats seek FDI inflows for their own private
economic benefits and the interests of the elites that they serve. Authoritarian institutions are
rarely constrained by either electoral consequences or structural veto players, which allows them
to ignore the domestic public’s concerns about welfare, wages and employment (Bueno de
Mesquita et al 2003).
Democratic regimes are generally more constrained in their ability to offer attractive
entry incentives to MNCs owing to their large number of veto players, but this resistance to
drastic policy change also provides a benefit. MNCs can use existing democratic institutions to
A firm is assumed to have “control of a significant degree of influence” when it holds ten percent or more of voting
stock” (World Bank 2011). This is the standard measure used throughout the literature.
2
Portfolio investment is defined as “crossborder transactions and positions involving debt or equity securities, other
than those included in direct investment or reserve assets” (IMF 2007, 110).
1
3
enforce favorable arrangements negotiated with the democratic government and make it more
difficult for the government to reverse policies (Jensen 2003). While authoritarian governments
can grant better entry-level agreements to MNCs, they provide MNCs with little room for
domestic recourse should the leader or leaders renege on their initial commitments. Though the
expropriation of firms has become less common over time, there is still a non-zero probability
that an authoritarian government may confiscate MNC property or engage in other infringements
on such property rights (Li 2009a; Zheng 2011). In addition, Buthe and Milner (2008, 741) note
that “more subtle government interventions that reduce the profitability of investments have
become the key political concern of investors.”
Finally, some argue that it is not democratic regime types that attract FDI, but property
rights protections (Li and Resnick 2003, Straats and Biglaiser 2012). For instance, Biglaiser and
Staats (2010) use surveys of chief executives of US corporations to show that property rights
protections are of key interest to actual investors. This suggests that any advantage that
democracies enjoy in attracting FDI is primarily due to their strong rule of law. Yet, Li (2006,
64) notes that democracies and autocracies differ systematically in terms of property rights
protection and policy credibility. In addition, Jensen (2008) argues that investors would prefer
participating in transparent legal systems to relying solely on an authoritarian government to
enforce contracts and intellectual property rights. To support this, he demonstrates that the
insurance premiums from private insurance companies for investments in states with
authoritarian regimes are greater than those with democratic regimes. Hence, the private market
associates autocratic regimes with greater risk.
Despite the differing expectations found in the literature regarding the impact of regime
type on attracting FDI, it is important to note that each position argues that regime type is
important. Yet, the relationship between regime type and property rights protections remains
unclear.
Democratic Institutions and Property Rights Regimes
The role of institutions is often discounted when discussing property rights; this is
unfortunate because “the delineation of property rights is not independent of what rights
members of a society accept as legitimate, and as a result most, if not all, property rights are
‘truncated’ in a most complex manner” (Chang 2003, 181; see also Barzel 1989, Chang 1994,
4
and Mousseau 2003). We argue that previous studies have ignored the true manner in which
democracy affects investment. Rather than acting as an independent factor, democracy enhances
the effects of property rights protections by creating institutions that: 1) provide a coherent logic
for the creation and enforcement of legal protections in a property rights regime, and 2) manage
conflicts that emerge as different groups win and lose in a dynamic economy. These two roles of
democracy are intertwined, as a coordinated economic vision is necessary to address the
concerns of those groups that lose as the economy changes and engage in subsequent
mobilization against the new economic arrangements (Chang 2003, 52-63; Chang and Singh
1993; Mousseau 2003; Sunstein 1997, Ch 6 and Ch 8).
A legal regime regarding insurance law, for example, is important given the degree of
influence such firms have over economic transactions.3 However, it can be unclear if and to what
degree insurance firms are liable to provide coverage to clients should technological changes
alter the existing legal structure within a specific field. Privacy laws, for instance, are undergoing
significant changes as companies (and governments) are able to gather and organize greater
quantities of individuals’ personal data. The possibility of medical side effects associated with
genetically modified foods, and even cellular phones, introduce new potential legal pitfalls,
where it is unclear how existing insurance companies would be libel to provide payouts on
behalf of their clients, or if these fall outside of their coverage agreement. For example, Margaret
Hamburg (2012), Director of the U.S. Food and Drug Administration, laments that technological
advances have made it easier for compounding pharmacies to create and sell large volumes of
combinational drugs or drugs in altered forms (e.g., change from a pill to a liquid), yet regulatory
agencies and government laws are designed to focus on traditional drug manufacturers.
In addition, private and public insurance programs are also called into question as
financial products become increasingly complicated. Clear legal regimes are especially important
for reducing expensive moral hazards in these cases, because governments frequently intervene
or subsidize major banking and insurance firms, as Dam and Koetter (2012) demonstrate in the
case of the German government and the its private banking sector.
Recent work shows that property rights protections work in conjunction with domestic
institutions to promote economic growth and attract FDI (Dreher, Gaston, and Martens 2008;
3
For a more in-depth description of the importance of insurance on market economies, see Knight (1921) or Strange
(1996, 122-146).
5
Soper et al 2012). Mousseau (2003, 489-490) argues that even in complex economies, market
norms fail to take hold in the absence of state intervention to enforce contracts. This contract
enforcement; moreover, must be equitably distributed by a legitimate governing institution
recognized by the majority of economic participants.
Chang (1994, 2003) goes even farther, arguing that the property rights on their own are a
minor determinate of FDI, lagging behind other factors such as large or growing markets, the
quality of the existing infrastructure, and the quality of labor (human capital). Instead, he points
to the need for strong institutions (e.g. contract laws) in order for property rights protections to
actually impact the economy. As evidence, Chang (2003, 14) observes that “during the last few
decades, many countries have tried to copy the Anglo-American model with little success
[which] is a testimony to the difficulty of building the ‘special’ institutions that are required for
an effective functioning of market-oriented trade and industrial policies.” Sunstein (1997, 209210) agrees, suggesting that well-drafted constitutions provide the foundation for the state to
create ownership rights in which all future private property rights are grounded. This is necessary
to prevent a system in which property rights definitions are open to reinterpretation and
readjustment.
Logic of a Property Rights Regime
A state needs institutions when creating property rights because it “cannot grant property
(and other) rights to people in a coherent way, unless it has a certain vision of what it regards as
the desirable future” (Chang 2003, 55-56). Democracies have important financial and legal
institutions that autocracies either do not have or do not permit to act outside the range of the
executive. These independent institutions provide checks on executive power and introduce
additional veto players into the process of economic policy formation, including the formation
and enforcement of property rights (Change and Singh 1993; Zheng 2011).
In addition, democratic institutions derive legitimacy from the process in which they are
formed, as constituents have at least some level of control over them or those who oversee them.
These institutions must meet and adhere to the principle of the existing legal framework (e.g.
constitution), ensuring philosophical and legal continuity (Sunstein 1997, Ch 8). Moreover, the
larger number of veto players make changes to these basic principles less likely and are thus
more likely to delineate property rights in the same manner over time (Putnam 1988; Tsebelis
6
2002). This creates a common vision of how property rights are assigned and in what way they
are enforced.
While exhibiting stability, democracies also provide a flexible framework to incorporate
changing technologies and financial developments within a coherent and consistent legal
framework (Chang and Singh 1993). Their decentralized nature allows them to more easily adapt
to technological and societal change and generate a coherent set of property laws (e.g. moving to
electronic based currency exchange). Thus, the state acts as a visionary that provides the
foundation and direction in goals that private firms then fulfill. Chang (2003, 53) argues that “by
providing such a vision at the early stage of change, the state can drive private sector agents into
a concerted action without making them spend resources on information gathering and
processing, bargaining and so on.” The role of government is crucial when, for example,
developing states seek to extend land tenure and property rights to vulnerable groups, such as
tenants and women (Payne 2004). By creating this clear vision, the state is able to lower
transaction costs and reduce uncertainty regarding future policy in a dynamic economy. This
promotes investment by strengthening the effectiveness of the state’s property rights protections.
Managing Emergent Conflicts
As has long been noted in the literature, democracies are more effective than autocracies
at managing conflict (Danilovic and Clare 2007; Liphart 1976; Wright 1942). This stems from
their ability resolve disputes via non-violent, legitimate processes. Democracies provide
legitimate methods for groups to raise their voice, air grievances, and seek compensation for
losses that do not exist in autocratic regimes, such as politically relevant legislatures and
independent courts. Democratic legislatures often feature compromises and tradeoffs among
political parties representing various domestic interests. Independent judiciaries provide a nonviolent outlet for conflicts to be settled between individuals regarding specific claims. This
becomes even more important as economies become more robust owing to complex divisions of
labor, as “most adults in a society obtain their incomes and consumer goods by interacting with
the norms of market competition: with free choice, bargaining, and regularized interactions
with strangers” (Mousseau 2003, 489). As interactions with strangers become more regularized,
formal legal institutions--in contrast to non-government institutions, and traditional societal
means of conflict resolution--take on even greater importance.
7
The ability to manage conflict enhances property rights protections under democratic
regimes. Conflict management is an important consideration for long-term economic growth and
attracting foreign investment. Under static conditions, the market is often thought to be the most
efficient mechanism to resolve conflict (Friedman 2002; Hayek 1976). Yet, in practice markets
are not static and considerations of dynamic efficiency should also be addressed (Joskow and
Rose 1989). In particular, technological changes affect how existing property rights protections
are interpreted and applied. The manner in which new property rights protections are allocated
changes the relationships between production factors, increasing the likelihood of conflictual
outcomes (Chang 2003, 56-57; Kuznets 1973).
Thus, states can realistically rely on the market solution to resolve conflicts only where
technological change is gradual, when the costs paid by the losing parties are low, or they are
prevented from organizing politically. In the absence of these conditions, other institutions must
exist to manage conflicts. Chang (2003, 58) argues that the state itself is best suited to undertake
this task since “it is the ultimate guarantor of property (and other) rights and (normally) the most
important actor in setting and executing the public agenda for changes in rights and institutions.”
It does this by creating institutions, such as courts, regulatory agencies, and legislative oversight
that can quickly respond to technological change and address emergent conflicts in a consistent
and coherent manner.
When property rights change either in the face of technological change or owing to other
structural factors, the subsequent uncertainty faced by international firm results in increased
transaction costs for doing business. Transaction costs increase because uncertainty implies
increased financial risk (and insurance premiums) as firms do not know how courts will treat
future cases, if existing contracts are less likely to be fulfilled, or if their costs will change.4
Moreover, “those who are to lose will try to mobilize against the new institutional arrangements
and sometimes will succeed in doing so” (Chang 2003, 56; see also Garrett 1998, Fordham 2008,
and Hays 2009). If losing parties are able to succeed in overturning the new institutional
arrangements, the state’s economy will suffer since it will be less competitive globally. However,
if losing parties mobilize and institutional structures do not exist for these groups to legitimately
4
Technological changes may cause changes in costs of production. While changes in production costs may result in
savings for firm A, it may represent an opportunity cost for firm B, as the latter would want to adjust their current
business terms. If the costs change too much, firm B may consider voiding the contract, even if this action incurs
penalties, if the gain from a new contract with an alternative firm outweighs the penalties, potentially hurting firm A.
8
air their grievances, they may turn to illegitimate means, up to and including violence (Barber
1995; Nieman 2011; Rosenau 2003). The outcome of this is that “in societies where the state
fails to manage conflict in an appropriate way, people will be reluctant to take risks or commit
their resources in specific investments and therefore the dynamism of the economy may suffer”
(Chang 2003, 61-62; see also Keynes 1997, 373). Thus, strong conflict management institutions
encourage long-term investment, such as FDI, and improve economic dynamism.
Our theoretical argument that democratic institutions are better than their autocratic
counterparts at creating a property rights regime and managing conflict produces the following
hypothesis:
Hypothesis 1 (H1): The effect of property rights on FDI inflows is conditioned by a state’s
level of democracy.
Temporal Change
We argue that one of the reasons for the ambiguity in the effect of democracy on FDI is
that it is not direct; instead, democracy works through property rights protections by providing a
coherent logic for the creation and enforcement of legal protections and by managing emerging
conflicts that occur in a dynamic economy as different societal groups “win” and “lose”
economically. Moreover, this effect is time varying; when technological dynamism is low there
is little need for firms to care about anything other than existing property rights. When
technological dynamism is high; however, firms care not only about existing property rights, but
also about what property rights will look like in the future, including for products that do not
exist yet or whose application is likely to change (Bell and Albu 1999; Ferguson and Mansbach
2004, 276). Moreover, technological dynamism also affects the relative costs of industries. This
is most evident when analyzing changes in business sectors and its effect on the global
workforce: investment has shifted away from natural resource procurement towards consumer
products, manufacturing, and the information and service sectors (Blanton and Blanton 2007;
Kozlow, Rutter, and Walker 1978; Mody, Dasgupta, and Sinha 1999; Moran 2002). Finally, the
uncertainty introduced by technological dynamism, moreover, increases transaction costs for
firms.
Previous scholarship has found that the effect of political regimes has changed over time.
For example, Huggard and Kaufman (1992, 1996) argued that while democratic governments of
9
the 1960s and 1970s frequently intervened in the economy and focused on consumption--hurting
economic growth--these same regimes increasingly rejected such policies in the 1980s and
1990s. Krieckhaus (2004) finds that the effect of democracy on economic growth varies over
time, exerting a negative effect in the 1960s, a positive effect in the 1980s, and no effect in the
1970s and 1990s. We argue that property right regimes--and hence democratic institutions-exhibit an increasingly positive effect as technological dynamism increases.
The rate of technological dynamism over the past 100 years is unprecedented.
Technological advances, especially those in information and communication, have made the
world smaller as information between individuals has become increasingly simple and
instantaneous to exchange (Castells 1997; Harvey 1989; Langhorne 2001). This has had an
important effect on capital markets (Keohane and Nye 2000). For instance, Dreher, Gaston, and
Martens (2008, 8-9) argue, “innovations and applications of the microchip have led to the
emergence and widespread use of the internet and other computer communication systems. More
importantly, the invention of computer technology and the microchip made it possible to
construct global data networks that function as the hardware for the global financial capital
market” (see also Strange 1996, 103). Moreover, technological dynamism affects all sectors,
from agriculture to services, particularly in terms of the capital costs associated with financing
innovations (Strange 1996, 9).
Ferguson and Mansbach (2004, 277) highlight the central concern of firms when they
warn, “the rate of technological change has accelerated, complicating the task of making sense of
what is taking place and increasing the probability of wrongheaded public policy.” As noted
previously, democracies are uniquely able to address this concern as they can provide a stable,
yet flexible property rights regime. Thus, while uncertainty increases firms’ transaction costs, a
coherent property rights regime can help offset these, thus increasing a state’s international
competitiveness. This implies that the effect of democratic property rights regimes should
increase over time as technological innovation has increased.
Hypothesis 2 (H2): The effect of democratic property rights regimes on FDI inflows has
increased over time.
10
Methodology
This paper examines the effects of democracy, property rights, and their interactive effect
on attracting FDI in 126 countries between 1970 and 2008. While the theory posited above
expects that property rights and democracy are determinants of investment, there are likely
country and time specific effects as well. As Strange (1996, 10) points out, “the supply of capital
to finance technological innovation (and for other purposes) has been as important in the
international political economy as the demand from the innovators for more money to produce
ever more sophisticated products by more capital-intensive processes of production.”5
It is clear from looking at Figure 1 that FDI has changed greatly over time. This means
that while states continue to compete for FDI, the supply has increased substantially as firms are
more willing to create lasting investments in markets outside their home nation’s borders.
Ignoring this temporal variation would lead to biased estimates of our theoretically relevant
variables.
< Figure 1 about here >
We use a non-nested multilevel model to account for the cross-sectional time-series
structure of the data (Beck and Katz 2006; Shor et al 2007). A non-nested model, also referred to
as a crossed random effects model, is appropriate since neither state nor year is nested within the
other (Gelman and Hill 2007, 2; Snijders and Bosker 1999, Ch 11). Thus, we model three
sources of variation in order to efficiently estimate the model: at the individual observation-level,
the state-level, and the year-level. The individual observation-level is the fixed, or pooled,
portion of the model that is common to all units. The multilevel approach adopted here allows us
to also account for common sources of variation across states and years by modeling these as
random effects. As Gill (2009, 395) notes, “ignoring the aggregate information excludes
potentially important effects and treating the aggregate information as individual level effects
confuses covariance in the model.” Our modeling approach is able to capture unobserved
heterogeneity within states, as well as system-wide temporal changes common to all units, while
simultaneously keeping information and efficiency gains from the partially pooled data (Gelman
and Hill 2007, 254-275). Our model takes the form:
5
Italics in original.
11
p
q
m
h 1
h 1
h 1
yijt    hi xhi   i    hj xhj   j    ht xht   t
where y is the dependent variable, xh is an independent variable, β, γ, and δ are parameters, ε, 𝜂,
and 𝜈 are random components, p, q, and m are the number of h independent variables at each
level, and i, j, and t are subscripts for individual, state and year, respectively. Each of the random
components are assumed to be distributed standard normal.
The inclusion of γhjxhj and δhtxht permits time- and country-varying effects for specified
variables. While theoretically all explanatory variables specified in the fixed portion of the nonnested model--i.e., βhixhi--can also be included in the temporal- and spatial-varying random
effect portion of the model, in practice doing so dramatically increases the number of parameters
that require estimation and is very computationally intensive (Beck and Katz 2006, 191-192).6
Therefore, we include only the theoretically relevant property rights regime interaction variable
and the property rights constitutive term in this portion of the model. Diagnostics reveal that the
democracy variable--the other constitutive term in the property rights regime interaction-exhibits little temporal variation in its effect.7 Finally, we center all independent variables at their
means. This makes the interaction and intercept terms substantively interpretable.
The use of a non-nested multilevel model produces several benefits over other commonly
used approaches, such as panel corrected standard errors (PCSEs) with fixed effects. First, the
modeling strategy employed here makes no assumption about temporal trends while also
accounting for unit heteroskedasticity. This is important because non-linear temporal trends and
heteroskedasticity can mask the relationship between political factors and FDI. For instance,
King and Roberts (2014, 15-17) re-examine a recent study by Buthe and Milner (2008) and find
that their data exhibits non-linear temporal trends that vary considerably by country, affecting
empirical inferences drawn from their analysis. Non-nested multilevel models, however, allow us
to account for unit level variation, even if the unit-to-unit heterogeneity is not normally
distributed (Beck and Katz 2006, 189-190).
An additional benefit of our approach is that we can explicitly model time rather than
simply treat it as a nuisance (Shor et al. 2007, 171). A benefit of this approach is that standard
6
As the number of time-varying parameters increases, the model becomes much less likely to converge on the
global maximum.
7
As a practical matter, the model is unable to converge if the interaction and all constituent parameters are free to
vary across both time and space, as we estimate in Model 3.
12
errors are able to vary over time, and are thus estimated more precisely, which in turn permits
more accurate hypothesis testing (Gelman and Hill 2007, 289-292; Shor et al 2007, 172).
Finally, our approach allows us to investigate the effect of property rights and democracy
on each country by over-parameterizing the random effect (spatial and temporal) portions of the
model rather than resorting to the use of a reference category, as is true with models employing
fixed effects (Gelman and Hill 2007, 68).8 This allows us to avoid the dangers noted by Buthe
and Milner (2008) of including developed and developing countries in the data set as coefficients
for each country can easily be recovered and displayed to verify trends identified in the statistical
analysis and to note outliers.9 Nevertheless, we conduct an additional set of analyses looking at a
sample of non-OECD states to evaluate the robustness of our argument. The results, available in
the Appendix, demonstrate that similar temporal patterns affect how property rights regimes
behave in both developed and developing countries.
Dependent Variable
Some of the ambiguity regarding the effect of democracy on FDI may result from
different operational measures of FDI (cf. Choi 2009, Li 2009b). Jensen (2003), for example,
measures FDI as net FDI inflows as a percent of GDP and finds a positive relationship between
democracy and FDI. Li and Resnick (2003), on the other hand, operationalize FDI as net FDI
inflows and find that democracy has both positive and negative effects on FDI, with democracy
exerting no statistical influence when accounting for the rule of law. Choi (2009) replicates the
Li and Resnick study and finds that when FDI is measured as net FDI inflows as a percent of
GDP, democracy has a positive impact on FDI. Moreover, Choi’s study, as well as work by
Jakobsen and de Soysa (2006), highlights the strong effect of outliers as contributing to the
regime type-FDI controversy. Jakobsen and de Soysa argue that China attracts a
disproportionate level of FDI and masks the general effect of democracy on attracting FDI. 10
They find that when China is controlled for, democracy attracts FDI even in the presence of
property rights protections.
8
Note that group level intercepts are estimated in part by the group level predictors (Gelman and Hill 2007, 269).
See Gelman and Hill (2007) for identification of outliers using multilevel models.
10
Jakobsen and de Soysa (2006) contend that while China is highly authoritarian, it has strong property right
protections and thus attracts a large sum of FDI inflows. Chang (2003, 11) suggests that China accounts for ten
percent of the world’s total FDI inflows
9
13
While FDI studies using the net FDI inflows as a percent of GDP as the dependent
variable generally find consistent results, measures constructed from ratios are problematic. 11
Hegre (2009), for example, stresses that ratio measures are unable to identify which of the two
components they are comprised of actually drives results, as a change in one or both of the
components can produce statistically significant results. Li also makes this point, while adding
that the measures are conceptually distinct: net FDI inflows measures the amount of FDI a state
attracts while net FDI inflows as a percent of GDP demonstrates the importance of FDI to the
economy (Li 2009b, 173).
We address these issues by measuring FDI as the natural log of net inflows. Net inflows
are appropriate because we are interested in the changing effect that property rights regimes have
on the amount of FDI that a state attracts while the natural log transformation reduces the effect
of outliers.12 Data for net FDI inflows are obtained from the World Development Indicators
(World Bank 2011).
Independent Variables
Two independent variables are tested in this study: Democracy and Property Rights. The
Democracy variable is operationalized as the Xconst component of the polity2 score acquired
from the Polity IV project (Marshall and Jaggers 2008). This means that our Democracy variable
ranges from 1-7. We use this component to represent democracy for a number of reasons. First,
and most importantly, the Xconst component most closely operationalizes our theoretical
depiction of democracy. Democratic institutions are expected to provide a coherent logical basis
for the creation, interpretation, and enforcement of property rights laws. Any such institutions
necessarily restrict the executive’s ability to unilaterally expropriate foreign property or to
change the domestic property rights regime structure on their own. Further, Gleditsch and Ward
(1997, 371) find that in the post-1969 period, Xconst is the driving force behind the polity2
democracy score. Choi (2011) and Starr (1992) suggest that this is because executive constraints
are the most easily identifiable signal of a liberal democracy. Moreover, Xconst is the best
indicator of a regime’s concentration of power--which is associated with differences in behavior
among autocratic regimes’ tendencies to adhere their agreements--with oligarchic regimes
11
See Aykut et al (2003), Jensen (2003), and Choi and Samy (2008) for examples of studies using net FDI inflows
as a percent of GDP.
12
As net inflows can sometimes assume a negative value, a constant is added prior to logging to make all values
positive. Because this value is a constant, it has no effect on the estimated coefficients.
14
behaving more like democracies than personalist regimes (Chyzh 2014). Finally, by using a
single component, we reduce statistical noise generated by including several related
components.13
In order to measure a state’s legal structure and respect for property rights, we use the
Legal Structure and Security of Property Rights measure from the 2011 Economic Freedom of
the World data set (Gwartney, Hall and Lawson 2011).14 This variable ranges from 0-10, with
higher values reflecting an increasingly strong legal structure that enforces property rights.
Property rights data are only available every five years during the period prior to 2000, meaning
data exist for 1970, 1975, 1980, 1985, 1990, 1995, and 2000-2008.15
As was noted earlier, though often conflated (e.g., Cao and Ward 2014), democracy and
property rights are conceptually distinct. However, these terms are often treated as closely
related, with some even implying that private property rights are nested within democracies
(Jensen 2003; Jakobsen and de Soysa 2006). As Gibler and Randazzo (2011) discuss, however,
protection of property rights is neither a necessary nor sufficient condition for the presence of
democracy. In fact, they find that approximately 40% of democracies lack independent
judiciaries and that some non-democracies have strong property right protections (e.g.,
Singapore). Within our data set, it is evident that the correlation between the democracy and
property rights is moderately high (r = .44) and is statistically significant (p < .001). While this
raises some collinearity concerns, it should actually make finding a statistically significant
relationship between either variable and FDI more difficult.
Like Li and Resnick (2003), we expect that democracy may have both positive and
negative effects on FDI. As Simmons (2000) suggests, firms may avoid democracies with weak
property rights protections. However, we theorize that property rights affects FDI conditionally
through democracy; democracy increases the effectiveness of property rights protections by
promoting continuity in a property rights regime and that this effect has increased over time.
13
Analyses using the 21-point polity2 democracy measure produce similar results.
The variable is conceptually the same as the International Country Risk Guide, used by Choi (2010) and Powell
and Staton (2009), but is available for a wider time frame. This data set is commonly used within the economic
literature. See, for example, Burkhard (2002), Knack and Heckelman (2005), Ovaska and Takashima (2006), and de
Soysa and Vadlammanati (2011),.Doucouliagos and Ulubasoglu (2006) employ a meta-analysis of different
economic freedom indicators and find no statistical significant difference between studies employing these data and
those produced by the Heritage Foundation or Freedom House. For a more in-depth discussion of the how economic
freedom is conceptualized and measured, see Gwartney and Hall (2003).
15
Rather than interpolating data, we rely only on this available data for our results.
14
15
While firms seek states with strong property rights protections, they prefer democracies with
strong property rights protections, especially in dynamic technological and economic settings.
To account for this conditional effect, we include an interaction between Democracy and
Property Rights to represent a state’s Property Rights Regime. In order to test our hypotheses-that the effect of property rights is conditioned by regime type, and that this effect has increased
over time--we allow the slopes for Property Rights Regime to vary temporally in one of our
models. In addition, we conduct additional analyses with temporally and spatial random slopes to
test and account for unit heterogeneity. The temporal range of the data allows for presence of
several democracy-property rights combinations to ensure that no one type is driving the results:
the absence of both democracy and property rights (e.g., the Philippines during the Fourth
Republic), democratization prior to the property right protections (e.g., Poland), property rights
prior to democratization (e.g., Chile), and both democracy and property rights (e.g., Great
Britain), as well as oscillations for each (e.g., Turkey). This concern is also mitigated by our
inclusion of time-varying effects, as discussed above.
Control Variables
Several variables are included to account for alternative determinants of FDI. These
variables include measures of economic and population size, economic growth, resource
endowments, level of economic development, and physical security. While there are a number of
other potentially confounding factors, most alternative theoretical explanations are accounted for
by this set of control variables.16 Control variables are obtained from the World Development
Indicators unless otherwise noted (World Bank 2011).
To control for the effects of country size on attracting FDI, we include control variables
such as economic and population size (Busse and Hefeker 2007, 403; Hegre 2009). The size of
the economy also has an effect on FDI inflows. Larger markets are likely to produce higher FDI
inflows, since they provide greater probability for future returns. Economic Size is
operationalized as gross domestic product (GDP) in constant 2000 US dollars. Economic Size is
logged to control for skewness. Population figures are taken from the Correlates of War’s
National Material Capabilities (V3.02) data set (Singer 1987).
16
See Blonigen and Piger (2011) for a review of the plethora of independent and control variables used to
model FDI in the economics literature. The most striking feature of this review is how very little overlap there is
from one study to the next.
16
GDP Growth rates are used to demonstrate growing economies. High rates of GDP
Growth demonstrate expanding markets. Growth attracts more FDI investment as foreign
investors seek to maximize returns on future markets.
Economic Development is represented by the inverse of rural percentage of the
population. It is expected that populations that are rurally based are less economically developed
than those with more urban development. The measures for population size and economic
development are not highly correlated within the sample (r = 0.06).
Resource endowments are thought to attract FDI. Resource is the sum of mineral, gas,
and oil rents as a percent of GDP. These are logged to control for skewness. Because the
measure of Resource for a country can be equal to zero, a constant is added before logging the
value to make all values positive and non-zero. Since this value is a constant, it has no effect on
the estimated coefficient.
Physical Insecurity is represented by the number of battle deaths within a state’s
territorial borders. This variable accounts for instability generated from either intra- or inter-state
conflict. The number of battle deaths is logged to control for skewness. As was the case for the
previous variable, a constant is added to all countries to prevent zero values.
Empirical Analysis
Table 1 reports the estimated fixed and random effects for the non-nested multilevel
model. The random effects portion of the table demonstrates the importance of accounting for
variation in space and time. This can be seen by looking at the random effects parameters for
country (σj) and year (σt). These values of these parameters represent the amount of variation
accounted for, or ‘soaked up,’ by including multiple levels in the statistical model that would
otherwise be attributed to the estimates of the parameters in the fixed portion of the model,
which is represented by σi. Both the country and year random intercepts are able to account for a
significant portion of variation across the three models, suggesting that a non-nested model is
appropriate. Model 1 includes only country and year random intercepts, while model 2 and 3
include random slopes for Property Rights Regime. The random slopes of the latter two models
are presented graphically and discussed below.
< Table 1 about here >
17
Model 1 reports parameter estimates for the fixed portion of the model with random
intercepts at each level within the data. Model 1 is a baseline model that assume no temporal nor
spatially varying effects associated with Property Rights Regime; rather, it takes the average
individual effect from the fixed portion of the multilevel model while also estimating the group
effects associated with the non-nested levels to provide the average treatment effect for each
independent variable. This baseline model allows us to identify if there exists a conditional
relationship between Property Rights and Democracy when attracting FDI.
When examining the primary explanatory variables of interest--the interaction and its
constitutive parts--it is important not to simply look at the results and interpret them as we would
additive regression models. This is because interactive terms are multiplicative and non-linear in
nature (Brambor, Clark and Golder 2006; Braumoeller 2004; Kam and Franzese 2007). Thus, the
interactive and constitutive terms provide limited information on their own; however, because
the variables are centered at their means, the estimated parameters are at the average level of
each constitutive term (Gelman and Hill 2007, 93). Estimates of the marginal effects are
calculated to identify what the effect (and level of certainty) of property rights are at varying
levels of democracy in each of the models and are displayed graphically for ease of
interpretation.
Figure 2 displays the marginal effect of property rights protections on FDI over different
levels of democracy. This means that the effect of property rights is conditional on a state’s level
of democracy, providing support for H1. At the lowest value of Democracy, the effect of
Property Rights protections towards attracting FDI is negative and statistically significant.
However, this effect becomes statistically insignificant once Democracy reaches values of 5 or
more. This means that marginal effect of property rights is negative in autocracies and
insignificant in democracies; that is, the effect of property rights changes depending on the level
of Democracy. A kernel density estimate is included on the right hand side of the plot to display
the proportion of observations at each level of democracy.
< Figure 2 about here >
Model 2 adds a random slope for Property Rights Regime, allowing us to consider the
marginal effect of Property Rights on FDI over time, while Model 3 includes random slopes for
both country and year. As evidence from the Akaike information criterion (AIC)--where lower
values indicate better model fit--the models that include temporal variation significantly
18
outperform Model 1, the baseline model which does not account for any temporal or spatial
variation.17 This result provides initiation support for H2, that there is temporal variation in the
effect of Property Rights Regimes on FDI. In addition, based on the AIC, it seems that Model 2-which includes only temporal variation--provides the best fit for the data. This suggests that
while the effect of Property Rights Regimes have changed over time, their effect is relatively
consistent across states.
In order to more fully test H2, we must identify any temporal trend of the effect of
Property Rights Regimes on FDI. To do so, we provide visualizations of our interaction term
rather than simply report the average treatment effect. Figure 3 displays the marginal effect of
Property Rights for each year at varying levels of Democracy, with darker lines indicating
greater levels of Democracy. The change in the coefficient between years is not constant, but
instead experiences significant variation over time. This helps to demonstrate the value of
treating each year individually rather than assuming a common structure across all years. It is
clear that there is an upward trend in the marginal effect of Property Rights for all regimes types
over the period 1970 to 2008.
< Figure 3 about here >
What is striking, however, is that between 1990 and 2000, there appears to be a changepoint regarding the effect of regime type on attracting FDI. Prior to 1995, while all regime types
are associated with a negative effect of how much property rights attract FDI, autocratic regimes
appear to have exerted a less negative effect. Conversely, after 1995, more democratic regimes
are associated with a greater marginal effect coefficient. This result is consistent with the
implementation of economic liberalization initiatives, particularly in Eastern Europe. The post1995 period is also associated with an increasing rate of technological development, particularly
within in information and communication technologies, which has had a dramatic effect on
investment and economic growth (Colecchia and Schreyer 2002; Seo, Lee, and Oh 2009; Soper
et al 2012). The upward trend and positive effects associated with increased levels of democracy
in latter period, when technological dynamism has become increasingly fast paced and taken
even greater levels of importance, provides support for H2. These results hold even after
17
It is important to note that while adding parameters is expected to increase model fit, AIC penalizes models for
each additional parameter included (including time- or country-varying parameters).
19
accounting (via temporal random effects) for the significant increase in available FDI in the
twenty-first century.
Time-varying effects provide a possible explanation, in addition to the measurement
controversy, for the disparate findings within the literature regarding the effects of democracy on
FDI. Figure 3 suggests that studies employing large numbers of observations from earlier time
frames are more likely to find either a negative or insignificant relationship while those including
more recent years find a positive relationship. These results are consistent with recent work
highlighting the importance of modeling the changing impact of explanatory variables, rather
than treating them in a static manner (De Boef and Keele 2008; Park 2010, 2012).
Model 3 examines the marginal effect of Property Rights on FDI with random slopes for
both year and country. Figure 4 displays the marginal effect of Property Rights for each country
in every year; thus, each line represents a specific country and each dot is an observation point.
Random slopes for each country and time period allow us to investigate the marginal effects of
while accounting from temporal and unit specific heteroskedasticity (Beck and Katz 2006; King
and Roberts 2014). For ease of interepretation, these results are displayed at the regional level.
The graphical presentation makes it easy to identify outliers--such as Greece and Turkey, the line
with the lowest coefficient values in “Europe” and the “Middle East,” respectively--that have
especially extreme coefficients.
< Figure 4 about here >
It is clear from the figure that the marginal effect coefficients change over time and by
country. There are, however, some clear tends: in all regions, the coefficient associated with
most countries is negative at the beginning of the period and becomes positive by the end of the
period. This suggests a surprising degree of sameness in the general trend of increasing marginal
effects associated with Property Rights between developed and developing countries. Yet, the
coefficient associated with the marginal effect of Property Rights is not the same for countries in
all regions nor is the upward trend of the same trajectory. It is also clear that some regions
experience substantially greater variation, with most Middle East and Southeast Asian countries
behaving in similar ways while European countries vary widely.
These results suggest that spatial effects also have some influence on the marginal effect
of Property Rights. This is not altogether surprising, because “although technology has
contributed to the transformation of global politics around the world and in practically all issue
20
areas, its impact has been uneven” (Ferguson and Mansbach 2004, 277). Dreher, Gaston, and
Martens (2008) provide empirical measures highlighting these uneven effects, noting that
technological and economic dynamism often go hand in hand. Likewise, Ciravegna (2011)
demonstrates that social embeddedness and the number of social ties are important factors for
knowledge spillovers and technology learning to take place. While not the focus of the present
study, the results are suggestive of a need for theorizing that explicitly accounts for spatial
variation in the effect of explanatory variables.
Finally, Figure 5 displays the proportion of countries with a positive marginal effect
coefficient associated with Property Rights. This highlights the temporal changes in the marginal
effect of Property Rights, even when controlling for country-specific random slopes. The graph
demonstrates that, after an initial decrease from 1970 to 1975, the proportion of countries with a
positive marginal effect increases dramatically over time. While there is a drop in the early2000s, this setback is quickly reversed.
< Figure 5 about here >
The empirical results confirm our theoretical expectations. Each of the models
demonstrates that the effect of property rights is conditioned by the level of democracy.
Moreover, the results from models 2 indicate that this effect has increased over time,
corresponding to increases in technological innovation. Model 3 confirms that the marginal
effect of property rights has increased over time, but reveals that these increases exhibit
significant spatial and temporal heterogeneity. The latter results show that the effect of stronger
property rights regimes began earlier and is stronger in more heavily technologically dependent
and economically dynamic regions. The positive and growing effect, however, permeates all
geographical regions and is global in scope.
We conduct two additional sets of analyses to test the robustness of our results: the first
includes bilateral investment treaties (BITs), and the second looks at only developing states. We
find that our hypotheses and substantive claims are robust to these alternative specifications (see
Appendix).
Conclusion
The disparate empirical findings in the literature are largely reconciled through our
analytical and empirical approach. Analytically, both those arguing for democracy and
21
autocracy as having a privileged position for enticing FDI are in some measure correct.
However, we argue that the effect of property rights protections on FDI is conditioned by the
institutional structure and legitimacy provided by a country’s regime type and, furthermore, that
this effect varies over time. Despite claims in the literature that democracy attracts FDI because
of its high levels of property rights protections, we know empirically that democracy and
property rights protections are separate concepts. Yet, these concepts are intertwined as
democratic institutions provide a coherent logic from which to frame the property rights regime
and are better apt than autocratic regimes to legitimately manage conflicts that emerge as
different groups win and lose in dynamic settings. Thus, democracies are able to provide stability
and flexibility in dynamic settings, making them more attractive for FDI.
Our empirical approach also represents an advance in the literature on regime type and
FDI. The non-nested multilevel model allows us to more appropriately account for countryspecific and temporal effects. We can examine the effect of property rights and regime type on
FDI inflows in each country by over-parameterizing the model rather than resorting to a
reference category, as is the case with fixed effects models. Moreover, we can explicitly model
potential time- and country-level random effects on our primary explanatory variable. This
approach also allows us to avoid well-known problems resulting from pooling developed and
developing countries in the data set, while simultaneously examining temporal and spatial trends
as the focus of the analysis rather than treat them as a nuisance. Thus, we are able to observe
whether the interaction of property rights and political regime becomes stronger over time, as
technological and economic dynamism has increased.
In terms of policy, the results of our analysis indicate that neither democracy nor property
rights protections alone are sufficient to attract FDI. Instead, democracies help promote a strong
and stable property rights regime. Such property rights regimes provide institutionalized,
legitimate means of establishing and modifying property rights as technology and the economy
change over time. Only democracies are capable of ensuring such protections, and when they
do, they are rewarded handsomely through increased FDI inflows.
22
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30
Table 1. Effects of Property Rights Regime on FDI using a Non-nested Multi-level Model of Country and Year.
Property Rights Regime Random Slopes
None
Year
Country and Year
Property Rights Regime
0.004*
(Property Rights * Democracy)
(0.002)
see Figure 3
see Figure 4
Property Rights
-0.010
see Figure 3
see Figure 4
-0.012**
-0.005
-0.009*
(0.005)
(0.005)
(0.005)
0.103***
0.065***
0.062***
(0.013)
(0.014)
(0.010)
-0.037**
0.005
0.009
(0.016)
(0.016)
(0.011)
0.001
0.001
0.001
(0.001)
(0.001)
(0.001)
-0.001*
0.001
-0.001
(0.001)
(0.001)
(0.001)
-0.024***
-0.022***
-0.008
(0.008)
(0.008)
(0.007)
-0.005*
-0.006*
-0.008**
(0.003)
(0.003)
(0.003)
10.632***
10.629***
10.630***
(0.025)
(0.023)
(0.021)
0.128***
0.129***
0.017
(0.010)
(0.009)
(0.015)
0.080***
0.070***
0.072***
(0.017)
(0.015)
(0.015)
0.178***
0.165***
0.163***
(0.004)
(0.015)
(0.003)
Observations
1435
1435
1435
Countries
126
126
126
Log-Likelihood
295.271
397.195
538.460
(0.006)
Democracy
GDP
Population
GDP Growth
Economic Development
Resources
Physical Insecurity
Constant
Random Effect Parameters
Country (σj)
Year (σt)
Individual (σi)
-572.542
-720.389
-612.919
AIC
Note: * p < 0.10, ** p < 0.05, *** p < 0.01. Coefficients are displayed above the standard errors, which are in
parentheses. Random effects parameters display the estimated standard error associated with each non-nested group
variable, where j is country-, t is year- and i is individual-level variation. Coefficients for the Country and Year level
random slopes in models 2 and 3 are not displayed in the table and instead are represented graphically in Figure 3
and Figure 4.
31
0
500
1000 FDI 1500
2000
2500
Figure 1. Aggregated World FDI Over Time.
1970
1980
1990
Year
Note: Foreign Direct Investment figures are in billions of US dollars.
32
2000
2010
.2
.15
.1
.05
0
-.03
-.02
-.01
0
.01
Kernal Density Estimate of Democracy
.25
.02
Figure 2. Marginal Effects of Property Rights on FDI.
0
2
4
Level of Democracy
6
8
Note: Dashed lines give 95% confidence interval. Light dashed-dot line displays the Kernal density
estimate. Displayed value is from model 1 of Table 1.
33
-.3
-.2
-.1
0
.1
.2
Figure 3. Time-varying Marginal Effect of Property
Rights at Different Levels of Democracy on FDI.
1970
1980
1990
Year
2000
2010
Note: Displayed value is the marginal effect of Property Rights when interacted with Democracy from
model 2 of Table 1. Darker lines indicate greater levels of Democracy.
34
Figure 4. Time-varying and Country-varying
Marginal Effect of Property Rights on FDI.
1970
1980
1990
2000
0
-1.5
-1
-.5
0
-.5
-1
-1.5
-1.5
-1
-.5
0
.5
Africa
.5
Europe
.5
Americas
2010
1970
1990
2000
2010
1970
1980
1990
2000
2010
1990
2000
2010
0
.5
-1.5
-1
-.5
0
-1.5
-1
-.5
0
-.5
-1
-1.5
1970
1980
Southeast Asia & Oceania
.5
Central & North Asia
.5
Middle East
1980
1970
1980
1990
2000
2010
1970
1980
1990
2000
Year
Note: Displayed value is the marginal effect of Property Rights when interacted with Democracy from
model 3 of Table 1.
35
2010
0
.2
.4
.6
.8
1
Figure 5. Proportion of Countries with a Positive
Property Rights Regime Coefficient.
1970
1980
1990
Year
2000
2010
Note: Proportion based on marginal effect of Property Rights when interacted with Democracy from
sample in model 3 of Table 1.
36
Appendix
Property Rights Regimes, Technological Innovation, and Foreign Direct Investment
We conduct two additional sets of analyses to test the robustness of our results: the first
includes bilateral investment treaties (BITs), and the second looks at only developing (nonOECD) states. We first describe why we include BITs before moving on to interpret the results.
We do the same for the analyses of developing states sample. In each set of analyses, we run
analogous models to Table 1 in the main text; that is, we display a model including the Property
Rights Regime interaction, a second model with time-varying parameters for Property Rights
Regime and Property Rights, and finally a model with both time- and country-varying parameters
for Property Rights Regime and Property Rights.
Bilateral Investment Treaties
BITs are an increasingly used policy tool that, if ignored, may provide an alternative
explanation for any changing temporal patterns of the relationship between property rights
regimes and FDI (Elkins, Guzman, and Simmons 2006). This is especially true if democratic
states with strong property right protections are more likely than others to sign BITs. The
empirical record of how influential BITs are on attracting FDI; however, is mixed (cf. Neumayer
and Spess 2005; Tobin and Rose-Ackerman 2005). Moreover, data availability on BITs severely
restricts the temporal range of our analysis, reducing our number of observations to 525 for 111
countries. Given their limited empirical support and limited time frame, we do not include BITs
in our primary analysis.
We obtain BIT data from Elkins, Guzman, and Simmons (2006). We operationalize BITS
as the natural log of the total number of BITs that a country is party to. As the case for previous
variables, we add a constant to all observations.18
Table R.1 reports the results of a non-nested multilevel model which includes BITs in
additional to the variables reported in Table 1. It is clear from the first model, which includes on
country and year random intercepts, that the effect of BITs on FDI is not statistically significant.
This result is consistent with much of the existing literature. In two of the models, however, a
model with time-varying parameters and a model with both time- and country-varying
18
We estimated an alternative specification where we included a dummy variable indicating whether a state had a
BIT with a G-7 country (Canada, France Germany, Italy, USA, UK). This variable was insignificant in all models.
37
parameters, BITs is statistically significant. While interesting on its own, these results warrant
closer examination in order to tell if they affect our primary claims, i.e. that the marginal effect
of Property Rights is time-varying and that this effect has increased over time.
To do this, we first examine the marginal effect of Property Rights on FDI at different
levels of Democracy. Figure R.1 shows that, when including BITs, the marginal effect of
Property Rights is negative at low levels of Democracy, statistically insignificant at moderate
levels of Democracy, and positive at high levels of Democracy. The result differs slightly from
Figure 2, but provides the same general trend: the marginal effect of property right protections in
countries with low levels of democracy is negative, while it is either insignificant or positive in
countries with high levels of democracy.
Table R.1 again shows that the model with time-varying parameters is a better fit than the
model without, according to AIC. Of greater importance to the present analysis are the results
presented in Figure R.2, which reflect the parameter estimates of the marginal effect of Property
Rights while allowing for with time-varying effects (model 2). This figure presents results that
are very similar to those presented in Figure 3, albeit with a shorter time frame (ending in 2000).
That is, the marginal effect of Property Rights is negative in the until approximately 1990, after
which all regime types experience a positive effect. As was the case in Figure 3, there appears to
be a change-point where the conditional effect of Democracy on Property Rights changes: prior
to 1990, autocratic regimes exert a less negative effect than democratic regimes, while after 1990
democratic regimes exert a greater positive effect than autocratic regimes.
Figure R.3 present the results from Table R.1 with both time- and country-varying effects
on the marginal effects of Property Rights, displayed by region. As was the case above, Figure
R.3 is similar to the analogous Figure 4 from the main text, though here the y-axis displays a
more narrow range. In all regions, the coefficient associated with most countries is negative at
the beginning of the period and becomes positive by the end of the period. This again suggests a
surprising degree of sameness in the general trend of increasing marginal effects associated with
Property Rights between developed and developing countries, even after accounting for the role
of BITs. Thus, we find that including BITs has no substantive effect on our primary results of
interest.
38
Developed Compared to Developing States
It may also be the case that developing states attract FDI according to a different data
generating process than developed states and that we may draw incorrect inferences if these
distinct processes are conflated (Buthe and Milner 2008). It is worth mentioning, however, that
in order to invalidate the substantive claims in the paper, the data generating process of
developed and developing states must differ in respect to the effect of democracy on property
rights, rather than simply in terms the influence of control variables.
To explore this potential dynamic, we re-run the previous estimation on a sample of
developing countries. We operationalize “developing” as all non-OECD countries. This provides
a sample of 394 observations for 93 countries.19
Table R.2 displays the results of three models. As was the case with Table R.1, BITs are a
statistically significant determinant of FDI inflows. In contrast to Table 1, factors such as GDP
do not appear to be significant in the sample. In addition, if it is only the time-varying random
slopes for Property Rights Regime are included, then the random effect parameters for country
and year are statistically significant. Unfortunately, we are unable to see if the random effect
parameters are statistically significant in the time- and country-varying model, as their standard
errors could not be calculated given the number large number of parameters relative to
observations.20 We are able to display the coefficients of the time- and country-varying
parameters from these models in Figure R.5 and R.6, respectfully, which provides some
information towards determining their general trends. Finally, the AIC from the time-varying
model (though not the time- and country-varying model) outperforms that of the model without
any varying-slopes for Property Rights Regime.
Turning to more substantively meaning results given the hypotheses in the main text,
Figure R.4 presents the marginal effect of Property Rights from the interaction in model 1 of
Table R.1. Figure R.4 shows that the marginal effect of Property Rights is negative at low levels
of Democracy, and statistically insignificant at higher levels of Democracy. This result is
consistent with Figure 2 from the main text, and reveals the same trend as noted previously: the
marginal effect of property right protections in countries with low levels of democracy is
negative, while it is either insignificant or positive in countries with high levels of democracy.
19
20
Similar results are obtained if BITs are removed from the non-OECD sample.
See fn 5 in the main text.
39
Figure R.5 displays the time-varying parameter estimates for the marginal effect of
Property Rights at varying levels of democracy. The effects displayed here show the same trends
as Figure 3 in the main text and Figure R.2 described above, albeit at much reduced overall
effect. That is, in the case of non-OECD countries, the marginal effect of Property Rights is
generally fairly weak at any level of Democracy, at least until 1995. In 1995, we again see the
change-point where democracies are associated with increased Property Rights effects and
autocracies have a lesser, though still positive effect. In addition, we begin to see greater
variation in the strength of the condition effect for different regime types. While weaker than the
sample pooling developed and developing countries, the general trend remains the same as in
Figure 3 and is consistent with the interpretation provided in the main text.
Finally, Figure R.6 displays parameter estimates for the marginal effect of Property
Rights for varying countries and over time for different regions. The results are again consistent
with Figure 4 from the main text, and Figure R.4 above. We again see a slight upward trend
across all regions, though Europe and Central and North Asia experience the greatest changes.
While this indicates some spatial variation, recall that model 2 from Table R.2 (time-varying
parameters) is more consistent with the data than model 3 (time- and country-varying
parameters), according to AIC. In sum, we find that the effect of Property Rights Regimes does
not differ in any substantively meaningful way between developed and developing countries.
40
Table R.1. Effects of Property Rights Regime on FDI using a Non-nested Multi-level Model of Country and Year
(with BITs).
Property Rights Regime Random Slopes
None
Year
Country and Year
Property Rights Regime
0.008***
(Property Rights * Democracy)
(0.002)
see Figure R.2
see Figure R.3
Property Rights
0.11*
see Figure R.2
see Figure R.3
-0.005
-0.001
-0.003
(0.005)
(0.004)
(0.005)
0.020
0.025**
0.022**
(0.012)
(0.010)
(0.010)
0.032***
0.019
0.015
(0.012)
(0.012)
(0.011)
0.004
0.017
0.011
(0.013)
(0.013)
(0.011)
0.001
0.001
0.002
(0.002)
(0.001)
(0.001)
0.001
0.001
-0.001
(0.001)
(0.001)
(0.001)
-0.008
-0.008
0.004
(0.007)
(0.007)
(0.006)
-0.004
-0.005**
-0.005*
(0.003)
(0.003)
(0.003)
10.580***
10.585***
10.579***
(0.021)
(0.014)
(0.013)
0.053***
0.069***
0.001
(0.009)
(0.008)
0.049***
0.027**
(0.016)
(0.012)
0.144***
0.119***
(0.005)
(0.004)
Observations
525
525
525
Countries
111
111
111
Log-Likelihood
238.313
314.533
482.550
(0.006)
Democracy
BITs
GDP
Population
GDP Growth
Economic Development
Resources
Physical Insecurity
Constant
Random Effect Parameters
Country (σj)
Year (σt)
Individual (σi)
0.026
0.095
-456.627
-585.066
-581.100
AIC
Note: * p < 0.10, ** p < 0.05, *** p < 0.01. Coefficients are displayed above the standard errors in parentheses.
Coefficients for the Country and Year level random slopes in models 2 and 3 are not displayed in the table and
instead are represented graphically in Figure R.2 and Figure R.3. Standard errors for random effects in model 3 were
unable to be calculated given the number of free parameters relative to observations.
41
.2
.15
.1
.05
0
-.04
-.02
0
.02
Kernal Density Estimate of Democracy
.25
.04
Figure R.1. Marginal Effects of Property Rights on FDI (with BITs).
0
2
4
Level of Democracy
6
8
Note: Dashed lines give 95% confidence interval. Light dashed-dot line displays the Kernal density
estimate. Displayed value is from model 1 of Table R.1.
42
-.05
0
.05
.1
.15
Figure R.2. Time-varying Marginal Effect of Property
Rights at Different Levels of Democracy on FDI (with BITs).
1970
1980
1990
2000
Year
Note: Displayed value is the marginal effect of Property Rights when interacted with Democracy from
model 2 of Table R.1. Darker lines indicate greater levels of Democracy.
43
Figure R.3. Time-varying and Country-varying
Marginal Effect of Property Rights on FDI (with BITs).
1970
1980
1990
.2
-.4 -.2
0
.2
0
-.4 -.2
-.4 -.2
0
.2
.4
Africa
.4
Europe
.4
Americas
2000
1970
1990
2000
1970
1980
1990
2000
1990
2000
.4
.2
-.4 -.2
0
.2
-.4 -.2
0
.2
0
-.4 -.2
1970
1980
Southeast Asia & Oceania
.4
Central & North Asia
.4
Middle East
1980
1970
1980
1990
2000
1970
1980
1990
Year
Note: Displayed value is the marginal effect of Property Rights when interacted with Democracy from
model 3 of Table R.1.
44
2000
Table R.2. Effects of Property Rights Regime on FDI using a Non-nested Multi-level Model of Country and Year
(Non-OECD).
Property Rights Regime Random Slopes
None
Year
Country and Year
Property Rights Regime
0.003**
(Property Rights * Democracy)
(0.002)
see Figure R.5
see Figure R.6
Property Rights
0.008
see Figure R.5
see Figure R.6
-0.002
-0.003
-0.004
(0.003)
(0.003)
(0.004)
0.022**
0.021**
0.025***
(0.009)
(0.009)
(0.010)
0.020**
0.015
0.020*
(0.009)
(0.010)
(0.011)
0.001
0.007
0.011
(0.009)
(0.001)
(0.011)
0.001
0.001
0.002
(0.001)
(0.001)
(0.001)
0.001
0.001
-0.001
(0.001)
(0.001)
(0.001)
-0.010**
-0.008
0.008
(0.005)
(0.005)
(0.006)
-0.003
-0.004*
-0.006**
(0.002)
(0.002)
(0.003)
10.598***
10.592***
10.577***
(0.008)
(0.010)
(0.013)
0.015
0.030***
0.16
(0.013)
(0.008)
0.012
0.017**
(0.008)
(0.008)
0.103***
0.095***
(0.004)
(0.004)
Observations
394
394
394
Countries
93
93
93
Log-Likelihood
329.390
351.027
432.992
(0.005)
Democracy
BITs
GDP
Population
GDP Growth
Economic Development
Resources
Physical Insecurity
Constant
Random Effect Parameters
Country (σj)
Year (σt)
Individual (σi)
0.027
0.078
-618.78
-658.055
-527.985
AIC
Note: * p < 0.10, ** p < 0.05, *** p < 0.01. Coefficients are displayed above the standard errors in parentheses.
Coefficients for the Country and Year level random slopes in models 2 and 3 are not displayed in the table and
instead are represented graphically in Figure R.5 and Figure R.6. Standard errors for random effects in model 3 were
unable to be calculated given the number of free parameters relative to observations.
45
0
2
.25
.2
.15
.1
.05
0
-.02
-.01
0
.01
Kernal Density Estimate of Democracy
.02
Figure R.4. Marginal Effects of Property Rights on FDI
(non-OECD).
4
Level of Democracy
6
8
Note: Dashed lines give 95% confidence interval. Light dashed-dot line displays the Kernal density
estimate. Displayed value is from model 1 of Table R.2.
46
-.05
0
.05
.1
.15
Figure R.5. Time-varying Marginal Effect of Property
Rights at Different Levels of Democracy on FDI
(non-OECD).
1970
1980
1990
2000
Year
Note: Displayed value is the marginal effect of Property Rights when interacted with Democracy from
model 2 of Table R.2. Darker lines indicate greater levels of Democracy.
47
Robustness R.6. Time-varying and Country-varying
Marginal Effect of Property Rights on FDI (non-OECD).
1970
1980
1990
.2
-.4 -.2
0
.2
0
-.4 -.2
-.4 -.2
0
.2
.4
Africa
.4
Europe
.4
Americas
2000
1970
1990
2000
1970
1980
1990
2000
1990
2000
.4
.2
-.4 -.2
0
.2
-.4 -.2
0
.2
0
-.4 -.2
1970
1980
Southeast Asia & Oceania
.4
Central & North Asia
.4
Middle East
1980
1970
1980
1990
2000
1970
1980
1990
Year
Note: Displayed value is the marginal effect of Property Rights when interacted with Democracy from
model 3 of Table R.2.
48
2000