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Government Checks and Balances, Policy Credibility, and Foreign Direct Investment: A Cross-National Investigation

Department of Political Science, College of Arts and Sciences, University of Miami, Coral Gables, FL 33146, USA
Economies 2023, 11(10), 249;
Submission received: 25 August 2023 / Revised: 1 October 2023 / Accepted: 4 October 2023 / Published: 8 October 2023


Placing constraints on elected officials is thought to bind their hands and render government policies credible. In turn, credible policies attract investment because investors can extend their policy and regulatory time horizons. Yet, a scholarship on LMICs suggests that too many constraints on policymakers may preclude necessary reforms, which repels capital. I motivate the study with an example from Liberia. Then, I evaluate political constraints and FDI for 182 countries between 1996 and 2022 and demonstrate that the rule of law conditions relationships between constraints on governments and FDI; in places with high rule of law, constraining government attracts FDI. In contrast, high constraints push investment away in contexts where the rule of law is lower. The logic is that constraining government makes credible high-income governments’ previous commitments to property rights and contract enforcement. However, the very same constraints on government may prevent LMICs from making credible commitments in the first place. Constraining government thus prevents desirable reforms under many circumstances, which I illustrate by returning to the Liberia example in the discussion. Ultimately, this study raises questions about universal benefits emerging from policy credibility and extends our understanding of political institutions, credible commitment, and FDI.

1. Introduction

Over half of Liberia’s population does not have legal title to the land on which they live. The lack of legal rights to ancestral land holdings limit residents’ ability to borrow money using land as collateral, creates disincentives to invest in projects that depend on land ownership, and fosters uncertainty surrounding future income. A lack of property rights also undermines investment and growth at the macro level. Insecure property rights and unenforced contracts hinder national and international firms’ ability to forge partnerships with the government and other firms, to pursue long-term investment projects, and to repatriate profits. Liberia’s political leaders recognize the ways that missing property rights undermine economic development. Members of Liberia’s Senate and House of Representatives have introduced more than 25 pieces of legislation designed to improve property rights since 2014.1 Yet, these bills have languished for years in Liberia’s divided Senate and House of Representatives, despite good economic intentions and strong presidential support.
Liberia’s legislative–executive stalemate is evidence that the country’s hard-fought transition to democracy has established checks on presidential authority. A motivated, empowered opposition presence in the House and the Senate limits unilateral presidential action and theoretically guards against a return to presidential authoritarianism. Effective constraints on executive authority also adds credibility to existing policies because any legislation to alter the policy status quo would require considerable opposition support in the legislature. This support is costlier to acquire from opposition politicians than from those within the governing coalition, on average, and thus renders policy change less likely when opposition politicians dominate the legislature than when allied politicians hold a majority.
Effective checks on authority lock-in the status quo and make existing Liberian policy (or lack thereof) credible. Benefits and detractions both emerge from this newfound credibility. A strong opposition presence in Liberia’s Senate and House of Representatives constrains the President, but also prevents the passage of necessary legislation to establish judicial independence, and enforce contracts and property rights. Solving one problem, that of unconstrained executives, thus creates another; reforms to establish new, desirable policy regimes can become prohibitively difficult. In turn, Liberia’s failure to establish property rights hinders investment and economic development—both critical losses in a country where most legal disputes center on property, the mean per capita income is only USD 700 per year, 68% of the population is employed in the informal economy, and only 14% of households have access to electricity (Liberia Institute for Statistics and Geoinformation Services 2020).
As in Liberia, many Low- and Middle-Income Countries (LMICs) struggle to establish and protect property rights following regime change and democratic transition. Anticipated investment and economic growth fail to materialize following democratic reforms due to the lack of rule of law. Slow growth then harms prospects for economic development and democratic consolidation. Many scholars and policymakers recognize the gravity of missing property rights for development; investment is low in countries without secure property rights or enforceable contracts and capital flight follows expropriation of property (De Soto 2000; North and Weingast 1989; Keefer and Khemani 2005; Besley and Persson 2009). This research generally explains countries’ (in)credible commitments to enforce contracts and secure property rights by focusing on political institutions that do not constrain politicians through checks and balances or separation of powers, and cannot hold them accountable through viable opposition parties and competitive elections. Yet, property rights and other beneficial polices must be established before they can be locked in place. Constraining authority before achieving these important reforms may therefore generate credible commitments but to harmful legal circumstances awaiting reform.
I question the benefits of constraining government in pursuit of credible policy in this paper. Specifically, I ask whether constrained government is associated with credible property rights and capital flows, and if so, under what circumstances. The stakes surrounding these questions are high; how investors distribute capital throughout the Global South has an extraordinary impact on economic and human development (World Bank 2023; Touchton 2016; Touchton and Tyburski 2022). I address these questions by first building an argument for why policy credibility only offers conditional and not universal investment benefits for countries. I contend that the desirability of credible policy commitments depends on whether desirable policies have been established in the first place, which, in many cases, they have not. I then build statistical models of political constraints and capital inflows using panel data on FDI for 182 countries between 1996 and 2022 to examine my hypotheses.
I find only sparse support for the argument that placing high levels of political and institutional constraints on elected officials is associated with high levels of foreign direct investment. Instead, I find that highly constrained politics correlate with lower FDI levels, all else equal. I investigate this result further and show that the relationship between political constraints and FDI is conditional on countries’ rule-of-law levels, with low rule-of-law countries featuring a strong negative connection between political constraints and FDI. In contrast, greater political constraints are associated with higher FDI levels in high rule-of-law countries. These results show how the conventional wisdom surrounding constrained government, protection of property rights, and investment flows does not travel well in the developing world, where many countries have yet to establish property rights and the rule of law. Instead of fostering economic growth, constraining government precludes necessary reforms and undermines foreign direct investment in countries where credible commitments are least established, hardest to make, most difficult to assess, and arguably most important for investors.
This article contributes to our understanding of FDI in three ways. First, I contribute to the debate on assessing the credibility of contract enforcement and property rights by using FDI inflows as the dependent variable in my analysis. Several previous studies evaluating the arguments surrounding constrained government use perceptual measures that depend on expert surveys to evaluate property rights (Keefer and Khemani 2005), corruption (Brown et al. 2011), and the rule of law (Andrews and Montinola 2004; Touchton 2015a, 2015b) cross-nationally. These studies assume connections between perceptions and the frequently unobservable underlying concepts. Then, these and many similar studies argue for the relevance of perceived property rights and the rule of law due to an expected connection with investor behavior. Yet, this connection is rarely tested directly in terms of capital flows. I use FDI as a dependent variable to avoid perceptual expert-based measures that might only reflect general country perceptions rather than concrete outcomes (Haggard and Tiede 2011; Kurtz and Schrank 2007). I thus provide a more direct test of hypotheses connecting political constraints to economic performance than previous studies.
Second, I demonstrate the conditional impact of political constraints on FDI. Finally, I demonstrate the importance of including comparative evidence from LMICs for evaluating arguments generated from industrialized, wealthy democracies.
This article begins by providing a discussion of the literature surrounding policy credibility and political risk. I then present arguments for why the standard model for how governments achieve desirable, credible policy is unlikely to apply to much of the developing world. Next, I describe the variables and methodology I use to test my hypotheses against the conventional wisdom. I describe these results as well as those from a variety of robustness checks and conclude with an analysis of what these results mean for our general understanding of policy credibility and its utility.

2. Policy Credibility and FDI

Foreign investment is predicated on the search for higher returns than domestic markets offer. Fundamental market conditions underlie much of the way investors distribute investment capital around the world. Levels of industrial productivity, the rate of economic expansion, the cost and availability of labor, and the distance to consumer markets are just some of the factors that drive FDI decisions. But returns on foreign investment are often higher than for domestic investment, in part because foreign investments are riskier than domestic projects. FDI is riskier still for several reasons. FDI generally takes longer to pay off than portfolio investment because of costs surrounding the construction of a plant, training a workforce, developing necessary infrastructure, and other costs required for production.
Political risk surrounding direct investments also motivates investment decisions (Biglaiser and DeRouen 2006; Braithwaite et al. 2014; Busse and Hefeker 2007; Henisz and Zelner 2010; Jensen 2008; Jensen et al. 2012). Investors will not devote capital to a market if they believe the risk of confiscation or exorbitant taxation is too high. Political unrest also harms FDI because of its potential to disrupt local market operations through protests, strikes, service interruptions, etc. Foreign direct investors therefore pay careful attention to political risk factors that might undermine their investments, which are much less liquid than portfolio investments in currencies, bonds, or equities. The result is caution before investing directly in a country, reluctance to exit the country, and a longer-term investment outlook that includes greater incentives to assess political risk relative to portfolio investors.
The credibility of property rights and contract enforcement are critical elements for attracting FDI (North and Weingast 1989; North 1990; Henisz and Zelner 2010; Henisz 2002; Hicken et al. 2005; Andrews and Montinola 2004; Keefer 2004). Institutional arguments suggest that checks and balances or separation of powers between branches of government will make credible politicians promise to respect property rights and enforce contracts (North and Weingast 1989; Weingast 1997; Tsebelis 2002; Keefer and Stasavage 2003; Persson and Tabellini 2003). These institutions are thought to constrain politicians and prevent them from violating the rules that the government sets for itself. The probability of government action in violation of such pre-existing policies is then thought to decrease as the number of institutional bottlenecks or checks and balances between branches of government increases (North and Weingast 1989; Tsebelis 2002; Stasavage 2002; Persson and Tabellini 2003). This is because institutional bottlenecks represent opportunities to derail government action to alter policy and thus protect the promises, laws, or rules that are already in place. Furthermore, politicians in different government branches have opportunities to prevent damaging actions through horizontal accountability embedded within the institutional framework (O’Donnell 2004; Campos et al. 2011).
The partisan composition of politicians within each branch of government is another important aspect of policy constraints. Heterogeneous legislative bodies theoretically constrain executives more than homogeneous legislatures because executives must please more audiences when politicians have disparate preferences than when oppositions are unified through one political party. Striking a policy bargain suitable to all parties will be more difficult as more preferences are represented and the costs of such a bargain automatically increase. An executive is therefore more constrained when legislatures are fractionalized than when they are unified.
It is important to note that elected officials who oppose each other will not always protect the rule of law or even a specific policy at all. Instead, I argue that officials in opposition to one another are more likely to oppose rather than support each other’s policies, which includes policies in violation of the rule of law. The implication is that expropriation may be less likely, on average, as political gaps among public officials widen and policy change becomes more difficult.
Low policy volatility and high accountability through constrained government is attractive in countries that have already established property rights. In this case, the government is effectively promising to not act; not to expropriate property, not to tax it at very high levels, and not to intervene in judicial decisions to bias contract enforcement. The argument that constraining authority fosters both policy credibility and government performance is not new. James Madison wrote that “ambition must be made to counteract ambition” to govern successfully in Federalist #51 (Madison 1788). For Madison, institutional constraints would protect constitutional rights and the public from capricious, volatile policy changes. These arguments suggest that every government’s commitment to existing laws becomes more credible when elected officials in opposition to one another are placed in offices with institutional veto power.
Many less affluent and new democracies feature constitutions and political institutions designed to constrain governments. The rationale for limiting government in new, less-affluent democracies is similar to that in liberal polities: opposition politicians with incentives to exercise any influence. They are likely to decrease the probability that politicians from the party in power can change policy and violate property rights.
Unfortunately for many countries around the world, institutions and rules translated from old to new democracies or from wealthy to less-affluent countries rarely function as advertised. The theoretical literature on policy credibility and commitments to property rights identifies important differences between the form of political institutions and their function (Haggard and Tiede 2011; Haggard et al. 2008; O’Donnell 2004; Andrews and Montinola 2004; Keefer and Stasavage 2003; Maravall and Przeworski 2003; North and Weingast 1989). However, comparative tests of these arguments have been somewhat rare. It has therefore been difficult to evaluate how credibility might function differently in different political, legal, and economic contexts.

3. Sequencing: A Missing Aspect of Credible Policy

I argue that credibility works similarly across political contexts, but the outcomes it promotes are different, as are investors’ interpretations of credibility. Constraining governments may undermine property rights and hinder investment in LMICs because constraints render the lack of property rights credible. Countries that have not established property rights may fail to do so, which can, in turn, limit the potential for growth in LMICs. In turn, this possibility highlights the potential tension between democracy, with its many constraints on government, the rule of law, and FDI.
Democratic deficits in the International Financial Institutions’ Washington Consensus during the 1980s and 1990s highlight the tension between democracy and policy credibility. The IMF and the World Bank generally excluded demands for vibrant political competition, divided government, or additional constitutional constraints on authority from loan conditions. Instead, the Washington Consensus position was that passing structural adjustment reforms as soon as possible—including over popular objections—was essential before they could be locked in place. The omission of democratic conditionality on financial bailout and development loans suggests that the International Financial Institutions (IFIs) feared the gridlock and inability to achieve economic reforms that political constraints might bring. The result is an unspoken logic in favor of first achieving economic and legal reforms and then using democracy to constrain politicians and secure those reforms.
Returning to the Liberia example above, the country’s institutional constraints preceded the establishment of property rights and contract enforcement, as evidenced by low rule-of-law scores. The optimal sequencing would be the reverse; Liberia’s constrained government has emerged prior to the establishment of strong property rights, which contributes to low FDI in the country. Of course, these issues of sequencing are not the only factors that influence FDI in Liberia but reflect a new argument surrounding the importance of sequencing for political institutions that deserves testing and has implications for policy, especially in LMICs.
I test hypotheses on political constraints, property rights, and contract enforcement, and FDI for 182 countries from 1996 to 2022 below. First, I estimate a model using matching to evaluate relationships between political constraints, the occupants of political institutions, property rights, and FDI. Additional models of FDI in states with low and high rule of law then show how countries’ relative levels of the rule of law condition these relationships.

4. Data and Methods

Cross-national data on FDI allow me to assess whether the conventional wisdom surrounding credible policy commitments does indeed explain capital flows, which are a more concrete measure of investors’ response to political conditions than expert ratings of property rights. These indicators are from the World Development Indicators dataset (World Bank 2023), which report FDI inflows in current U.S. dollars.2 Multi-year moving averages of investment are common in the literature, but I use annual data for the year following annual observations of a country’s political constraints and economic context. This strategy allows me to capture potential market responses to new information on relative policy credibility closer to shifts in the political context brought about through elections or other means.
I also compile data on the number of institutional checks and balances in each country as well as the relative alignment of policy preferences among officials occupying these institutions. Furthermore, I include data on different political factors thought to influence property rights and attract investment, such as countries’ general commitments to political rights and civil liberties, as indicated by their Freedom House (2023) Score; regime stability, as measured by the time in years since regime change; and the ideological orientation of the chief executive’s party (on a left–center–right scale). Additionally, I account for one of the central competing explanations for secure property rights and FDI, namely, that relationships between institutions, politicians, perceptions of governance, and investment flows simply reflect the underlying influence of wealth (as measured by the log of GDP per capita in constant 2010 dollars). I address this consideration when estimating relationships between political constraints and FDI and also account and control for other economic factors thought to attract investment in previous studies, such as openness to trade and economic growth (Buthe and Milner 2008; Blanton and Blanton 2007; Jensen et al. 2012).
Finally, I incorporate the World Bank’s Rule of Law (Kaufmann et al. 2016) measure to evaluate the extent to which a state’s rule-of-law levels, which include large property rights and contract enforcement component, which are condition arguments connecting policy credibility to investment. I present these variables and discuss their sources and methodology for measuring them below.
The central explanatory variable is from Henisz (2000). Henisz developed a comparative indicator for how different political configurations constrain government. This indicator, Polcon, records the degree of constraints on each elected official in a national political system based on the political institutions in that system and the partisan orientation of elected officials seated in office. The measure begins by identifying the institutions’ de jure vetoes. Veto points are critical junctures in the policymaking process where agreement from the occupant of an office is necessary to alter policy from a status quo position (Tsebelis 2002). For example, the U.S. House and Senate must pass legislation that the President then signs into law. Failure to gain agreement from any one of these entities “vetoes” a proposed policy change and maintains the policy status quo.
Henisz then counts the countries’ government branches that can theoretically exercise de jure vetoes. Henisz records information on political parties and where politicians from each party are seated across government branches. Then, Henisz describes the parties’ alignment (or lack thereof) across the branches of government. The Polcon score rises when politicians in seats with de jure veto points are members of the political opposition to the chief executive. Polcon scores rise again as members of the political opposition occupy more legislative seats, as policy change is deemed harder as more disparate interests require satisfaction to pass legislation. The mean score in my dataset is 0.22, with a standard deviation of 0.21. Belgium has the maximum, multi-year scores at 0.70 from 1996 to 2007. Cuba is tied for the minimum score with scores of 0.00. The U.S. mean score is 0.40 for the timeframe under investigation. No country exceeds a score of 0.73 in the data.
For the rule of law variable, there is a question as to whether high or low scores for the rule of law simply reflect the distinction between high and low income. The indicator for the rule of law correlates with GDP per capita, where high rule of law equates to wealth and low rule of law to low income. However, the variable emphasizes contract enforcement and property rights, thus the relationship is not perfect. For instance, China has low rule of law and a large economy, similar to many oil-rich countries. High taxation in the Nordic and other European countries also equates to lower ratings of property rights than in many other contexts where the GDP per capita is lower. Thus, the rule of law for contract enforcement and property rights covers different institutional aspects that may relate to economic development, but the variable does not measure economic development itself.

5. Control Variables

Democracy. Democratic regimes have a larger number of veto points than authoritarian regimes due to competitive elections, separating powers, and creating accountability mechanisms. Maravall and Przeworski (2003), Diamond (1999), Cox and Morganstern (2001), Chavez (2004), and others contend that constraining presidents/PMs and contesting elections increase the public’s ability to monitor elected officials and prevent their malfeasant behavior. If one conceives of property rights as a public good, many studies show that democracy improves service delivery, which could also include provisions like judicial services for enforcing contracts and protecting property rights (Keefer and Khemani 2005; Olken 2008). Yet, some authoritarian regimes have presided over high rates of economic growth, especially in East Asia, which implies that democracy is not a necessary condition for committing credibly to contract enforcement and property rights (Knutsen 2010; Gehlbach and Keefer 2011; Halperin et al. 2009; Montinola et al. 1995). I bring evidence to bear on this debate by including Freedom House’s (2023) measure of democratic practice as a control variable in my models of FDI. The score I use is the mean of the Political Rights and Civil Liberties scores, which ranges from 1 (most free) to 7 (least free).
Partisan Orientation. Many believe that parties on the political right tend to support business allies more than those on the left. Investors evaluating right-wing governments’ commitment to property rights may therefore rate property rights protection as greater under a right-leaning government than a left-leaning government. Parties on the right also frequently have platforms that emphasize general “law and order” policies. The business community’s presumed identification of property rights with right-wing parties suggests that constraining politicians may improve property rights if parties on the right are out of power but limit them if right wing parties are dominant. I use an indicator measuring the chief executive’s left-right political position from Keefer and Stasavage (2003) to evaluate this possibility. This indicator codes parties as belonging to the political left, right, or center based on stated policy preferences in official party platforms from the previous election.
Regime Stability. Frequent changes in national-level political regimes reflect political instability, which influences investment. The security of investors’ contracts with governments increases as prospects for a regime change decrease (Svensson 1998). I evaluate this possibility by including a variable that records the time since the previous regime change in years (Beck et al. 2001).
Ethnic Fractionalization. Ethnolinguistic polarization can potentially undermine investment by limiting legal protections and hindering societal cooperation. Specifically, scholars connect ethnic fractionalization to lower growth, income, equality, and investment (Touchton 2013; Campos et al. 2011; Scheuler and Weisbrod 2010; Easterly and Levine 1997). The indicator I include in my models of FDI is originally from Alesina et al. (2003). Alesina et al. estimate the likelihood of co-ethnicity across a country’s population. These data were collected in 2003, but ethnicity changes very slowly.
Log of Gross Domestic Product per Capita (in constant 2010 dollars). I include a measure of wealth to account for systematic differences in the economies and legal systems of wealthy countries. These systems are often correlated with democracy, which includes more veto points than authoritarian regimes and could be a function of economics. In other words, controlling for wealth is important because it could be driving both independent and dependent variables. I include the base 10 log of per capita GDP for each country-year in the models of FDI. Per capita GDP is in constant 2010 dollars (International Monetary Fund 2023).
Trade Openness. Governments may make different commitments to multi-national corporations than to investors. FDI tends to come from wealthy sources and governments might attract these sources by going beyond the standard legal system that applies to smaller-scale domestic investors. I use an indicator from the United Nations Conference on Trade and Development Statistics Division (2022) as a control variable in my models of FDI. This variable records the mean value of (exports + imports)/GDP. I use the base 10 logarithm of this data to better align it with assumptions of a normal distribution.
Economic Growth. I include data on countries’ annual growth rate of per capita GDP to account for the prospect that expanding economies will attract investment, whereas contracting economies will repel it. These data come from the World Bank’s World Development Indicators (World Bank 2023).
It is important to note that there are many additional variables that could be included in models of FDI. For example, some recent literature emphasizes regime type, such as democracy vs. autocracy (Moon 2019; Hamid et al. 2022), regulatory components and business environment (Contractor et al. 2020), and specific political institutions, such as term limits, including in authoritarian countries (Wang et al. 2020). Infrastructure and regulatory quality in a general sense and political stability are also commonly discussed indicators shown to influence FDI inflows (Paul and Jadhav 2019; Touchton 2015a, 2015b, 2016). Gravity models, neighbor country effects, and membership in trading blocs are also frequent ways to estimate and account for variation in FDI (Dorakh 2020). Moreover, the creation of indices, such as the political constraints and rule-of-law measures above, which are comprised of responses to dozens of survey questions administered to country-experts, are also useful ways to examine a variety of factors thought to influence FDI. For example, Maza and Villaverde (2015) created an index of FDI potential that includes economic potential, market size, labor strength, access to basic technology, labor regulation, and the competitiveness of markets for the EU.
Many of the variables described above are incorporated into the rule-of-law variable, particularly the business climate and quality of regulation. Others, such as regime type, are incorporated in the political constraints variable. In all cases, the goal of this paper is not to exhaustively explain variation in FDI relative to GDP, but rather to estimate new relationships between political constraints, the rule of law, and FDI. These variables form part of the conventional wisdom on FDI and are included in many broader indices. However, these variables require testing individually before they are added to aggregate indices. The identification strategy described below fills this gap and addresses endogeneity problems with previous estimations to add value to the modeling exercise.

6. Identification Strategy

I use several different techniques to estimate relationships between political constraints and FDI. First, I use coarsened exact matching to estimate the average treatment effects of political constraints on FDI. Matching is one way to address potential endogeneity problems associated with evaluating the impact of political constraints (Ho et al. 2007). States’ previous FDI levels may influence officeholders’ popularity based on economic performance while in office. Subsequently, previous support for politicians might alter their electoral prospects, the representation of parties within political institutions, and ultimately, constraints on government. Additionally, other, unobserved, variables may influence both configurations of public officials and FDI. Elected officials in states with low rule of law might allow incumbents in these countries to bar opposition politicians from office, where they could exercise vetoes in decision-making processes. Weak rule of law may also drive low FDI due to the rule of law’s connection to property rights. Drawing inferences from any statistical relationships between political constraints and FDI might be unwarranted because these relationships might only reflect the state’s predisposition to establish and enforce the law, rather than any direct effect of constrained leaders themselves. Additionally, the current year’s per capita GDP may stem from FDI in the previous year because FDI is designed to produce goods or services that factor into GDP directly.
Matching represents one way I account for potential endogenous regressors in my data. Matching lets me align annual observations in pairs based on similarities to one another and on indicators that might influence FDI, such as the size of their economies, their openness to trade, their recent economic growth, the partisan orientation of their chief executive, their level of ethnolinguistic fractionalization, and their Freedom House score. The key difference among the pairs is one country-observation has high levels of political constraints in the year in question, while the other has low or medium constraints. These differences then allow for the estimation of average treatment effects of high political constraints on FDI in the next year relative to the control of low or medium political constraints on hundreds of matched observations.
I estimate the average treatment effects of both high and low political constraints on FDI in different legal and investment climates.3 I perform this first for the full dataset to test the general argument connecting political constraints to FDI. I then split the dataset to estimate models of FDI for country-year observations that are above the mean for World Bank rule-of-law scores as well as for those country-years that are below the mean. This strategy allows me to test the hypothesis that the rule of law conditions political constraints’ influence on FDI. Constraints may promote FDI in states that have already established property rights and must now enforce them but undermine FDI in those that depend on reforms to first establish the rule of law or have fallen to such low levels that they would have to be re-established. The results of average treatment effects estimation appear in Table 1 below.
The equation for average treatment effect on the treated is as follows:
Τ = E[FDIi (1) − FDIi ()|Polconi = 1]
where high values of Polcon is the treatment received, assignment to treatment is unconfounded, and the probability of assignment to treatment is not bounded toward one and zero.

7. Results and Discussion: Political Constraints, Policy Credibility, and FDI

The results in Table 1 are opposite to a considerable amount of the literature on political institutions, policy credibility, and FDI. Treatment effect results for the full sample show negative relationships between high political constraints and FDI, while the relationship between low political constraints and FDI is not significant. Results for the split samples shed light on this unexpected result: high political constraints have no association with FDI in high rule-of-law countries in which property rights are more established, but low levels of constraints are correlated with dramatically lower investment levels for these observations. More importantly, these relationships are reversed for country-years with below-average rule of law scores. High political constraints are associated with low FDI levels and low political constraints with high FDI levels. This final set of results provides support for my hypothesis that political constraints can impede necessary reform and drive away investment in states that have yet to establish the rule of law. Of course, an alternative explanation is that low political constraints on government allow for policies that are simply favorable for foreign investors, such as selective property rights or other regulatory benefits. The data does not present opportunities to adjudicate between these possibilities, but high political constraints are associated with lower investment levels in low rule-of-law environments. These results support the argument that a constrained government may prevent meaningful reforms, increase investors’ wariness of deadlock, and undermine FDI under certain circumstances.

8. Full Models of FDI

I supplement coarsened exact matching with several different techniques to test hypotheses connecting political constraints with FDI while accounting for potential endogeneity as well as covariates’ independent influence on FDI. Specifically, I use dynamic panel estimation from Arellano and Bond with Roodman’s xtabond2 (Roodman 2013) option and time series, cross sectional, and OLS, including country- and year-fixed effects to model FDI in this section (Arellano and Bond 1988, 1991). I employ the “System” Generalized Method of Moments (GMM) technique, which provides two central advantages in modeling FDI. First, these estimators are intended for panel data with short timeframes, like mine, and a large number of cross-sectional observations. Many of the indicators in the data change slowly, which the technique is also designed to address (Roodman 2013; Arellano and Bond 1988, 1991; Holtz-Eakin et al. 1988). Second, I use Arellano–Bond estimation as another way to account for potential endogeneity problems while also generating independent estimates of other variables’ relationships with FDI. The GMM technique creates a series of instruments from lags of potentially endogenous regressors. In this case, I use lagged values of the political constraints indicator and per capita GDP as instruments for dependent variables in the regression models, beginning with the second lag and continuing as far as possible. I also use annual country dummy variables to verify that the key assumption of zero correlation across units functions (Roodman 2013). Results for the models, including an interaction between the rule of law and Polcon, are available in Table 2.
The equation of the primary model is as follows:
Y1 = f(αyi(t−1)) + β1 (FDI/GDP L1) + β1 (FDI/GDP L2) + β1 Polconit + β1 + β1 GDPit + β1 Partyit + β1 Stabilityit + β1 Growthit + ηi + υit,
where υit are not serially correlated.
FDI/GDP L1 and L2 represent one- and two-year lags of the primary dependent variable. Furthermore, these controls in the model are part of the Arellano–Bond estimation technique.
The results of estimation using Arellano–Bond and fixed effects estimators are mixed. Political constraints are not correlated with FDI at statistically significant levels in either model, and interactions between rule of law and Polcon offer mixed evidence, with some models supporting the results using average treatment effects, while relationships between political constraints and FDI are not statistically significant in the models. At a minimum, full models of FDI imply that political constraints do not influence investment in nearly as strong or direct a way as the literature suggests. Simultaneously, Arellano–Bond and fixed effects models showcase expected relationships between a state’s economic productivity, its openness to trade, its recent economic growth, and FDI. Diagnostics for the suitability of the GMM estimator provide support for its selection. First, the Sargan test of over-identification restrictions demonstrates that we cannot reject the null hypothesis of exogenous instruments for the estimator (Roodman 2013). The test for autocorrelation in first differences shows that we can reject the null hypothesis of no autocorrelation in first differences, which is expected in Arellano–Bond estimation and is usually ignored. However, we cannot reject the null hypothesis of no serial correlation in the residuals based on the AR (2) test. This suggests that the Arellano–Bond estimator is appropriate for these data.
My results here agree with Andrews and Montinola’s (2004) for the economic determinants of the rule of law, only with a much larger dataset testing several additional hypotheses directly against FDI—an outcome that is much more concrete than property rights or rule-of-law indices that depend on expert evaluations. My results differ from those of Andrews and Montinola (2004) as well as other cross-national studies of political constraints’ influence on governance and investment (Brown et al. 2011; Henisz and Zelner 2010) regarding political constraints, albeit with FDI as a dependent variable rather than the rule of law. Finally, the chief executive’s ideological orientation is not a statistically significant determinant of FDI. This suggests that the political right does not have a monopoly on attracting investment once a broad range of covariates are included in FDI models.
Table 3 presents the estimates from models with interactions of Polcon and the rule of law. Each column presents conditional coefficients for different levels of political constraints and the rule of law.
The equation of the secondary model is as follows:
Y1 = f(αyi(t−1)) + β1 (FDI/GDP L1) + β1 (FDI/GDP L2) + β1 Polconit * Rule of Lawit + β1 Polconit + β1 Rule of Lawit + β1 GDPit + β1 Partyit + β1 Stabilityit + β1 Growthit + ηi + υit
where υit is not serially correlated.
FDI/GDP L1 and L2 represent one- and two-year lags of the primary dependent variable. Furthermore, these controls in the model are part of the Arellano–Bond estimation technique.
The results showcase the differential impact of political constraints on FDI at different levels of the rule of law. High political constraints are correlated with high FDI at high levels of the rule of law but low FDI at low levels of the rule of law. The results also provide evidence for the corollary hypotheses: low political constraints are correlated with low FDI at high levels of the rule of law but higher FDI at low levels of the rule of law. The years 1997–2005, 2008–2010, and 2020–2021 are significant and negative for FDI relative to 1996, which is the baseline year. The earlier significant era likely reflects the Asian financial crisis, the later global financial crisis of the late 2000s, and the global COVID-19 pandemic. The years 2012–2014, 2018, and 2019 are significant and positive, which likely reflects economic recovery following the downturns mentioned above. Asia is significant and positive compared to North America, while Africa and Latin America are significant and negative. The results are similar for all variables in magnitude, direction, and statistical significance.
Similar to the models in Table 2 above, diagnostics assessing the GMM estimator support its selection. Based on the Sargan test of over-identification restrictions, we cannot reject the null hypothesis of exogenous instruments (Roodman 2013). The test for an AR (1) process shows that we can reject the null hypothesis of no autocorrelation in first differences, as shown in Table 2. We cannot reject the null hypothesis of no serial correlation in the residuals based on the AR (2) test. This suggests that the Arellano–Bond estimator is appropriate for these data, as shown in Table 2 above.
From the perspective of LMICs, such as Liberia, these results strongly suggest that political constraints are not universally beneficial as a signal of policy credibility for FDI. Instead, sequencing is important to establish the rule of law in terms of property rights and contract enforcement prior to “locking in” those policies by constraining politicians. Instead of fostering a credible commitment to contract enforcement, property rights, and the rule of law, Liberia’s institutional checks and balances hinder FDI inflows because the government faces difficulty in passing legislation and implementing regulations that are attractive to investors.

9. Limitations

There are several limitations to this research, principally, that neither political constraints nor levels of the rule of law are exogenous. The Arellano–Bond technique helps to account for potential endogeneity and estimate these variables’ joint influence on FDI, but there always remains the possibility that these estimates reflect the influence of an unobserved set of confounders on FDI, rather than the influence of the primary independent variables. As such, we should be cautious and not take the results of a statistical modeling exercise as fact, but rather to use the results as suggestive with implications for policy that can be judiciously applied to real contexts. Additionally, this study examines new variables that should be included in models of FDI inflows, but it does not create a new index of FDI potential or examine these new variables across all potential models of FDI. Such efforts remain for future research on the subject based on the results of the analysis in this paper.

10. Conclusions

I use political constraints and the rule of law to model FDI, which includes two key aspects of politics: institutions and politicians’ preferences within those institutions. The results of the analysis demonstrate that political constraints undermine foreign direct investment in countries with low rule of law, where property rights, and by extension policy credibility, is not yet established. Low political constraints do repel FDI, but only in countries where the rule of law is high and commitments to contract enforcement and property rights has already been made.
These results are important for credible commitments and FDI. The conventional wisdom on political constraints has a new caveat: constraints can undermine FDI in LMICs with low rule of law. Policy credibility is not desirable if the underlying policies are not themselves desirable. In such cases, necessary reforms may become very difficult to push through because counterproductive policies have been locked in. Limiting government in all contexts is therefore likely a misplaced ideological sentiment, particularly in LMICs. The effect of limiting government in such cases, may also unintentionally limit FDI, growth, and development.


This research was supported by the Institute for Advanced Study of the Americas at the University of Miami Grant number 2021A5.

Data Availability Statement

Replication data and code are available at the author’s Harvard Dataverse site:, accessed on 10 June 2023.

Conflicts of Interest

The author declares no conflict of interest.


The Land Rights Act of 2014, the Financing Agreement for the Youth Opportunity Project, the Bill to Establish the Rubber Development Fund submitted 20 June 2014; the bill to Ratify the Concession Agreement Between the Republic of Liberia and the Liberia Cocoa Corporation, submitted 8 July 2014; the Mineral Development Agreement with Hummingbird Resources (Liberia), Inc. and the Amendment to the Mineral Development Agreement among the Government of the Republic of Liberia, Western Cluster Limited, Sesa Goa Limited, and Bloom Fountain Limited are all designed to establish property rights and the conditions for enforceable contracts for individual citizens and multinational firms alike.
I convert this data to constant 2010 dollars to impart consistency across the covariates.
I identify high political constraints in country years where Polcon scores are more than one standard deviation above the mean. Similarly, low political constraints occur in country years where Polcon scores are more than one standard deviation below the mean.


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Table 1. Political constraints and FDI. 1996–2022. This model uses coarsened exact matching with average treatment effects.
Table 1. Political constraints and FDI. 1996–2022. This model uses coarsened exact matching with average treatment effects.
Treatment VariableLog of FDI
(Full Sample)
Coefficient (SE)
Log of FDI
(High Rule of Law)
Coefficient (SE)
Log of FDI
(Low Rule of Law)
Coefficient (SE)
High Political Constraints
(vs. Low and Medium Constraints)
−0.43 **
−0.90 **
Low Political Constraints
(vs. Medium and High Constraints)
−4.48 **
1.15 **
** indicates significance at greater than 0.01 (two-tailed test).
Table 2. FDI regressed on political constraints, 1996–2022. The first model uses Arellano–Bond Dynamic Panel Estimation, while the second uses time-series, cross-sectional OLS with country and year fixed effects.
Table 2. FDI regressed on political constraints, 1996–2022. The first model uses Arellano–Bond Dynamic Panel Estimation, while the second uses time-series, cross-sectional OLS with country and year fixed effects.
Coefficient (SE)
Fixed Effects
Coefficient (SE)
FDI (L1)0.08
FDI (L2)−0.09 *
Political Constraints−0.55
Per Capita GDP, PPP (logged)1.26 **
1.31 **
Party Orientation0.04
Trade Openness (logged)0.53
0.73 **
Freedom House Score−0.18
Regime Stability0.0002
Economic Growth0.02 **
0.03 **
Ethnic FractionalizationOmitted
ConstantOmitted7.90 **
Number of Groups (Country_Dummy)52
Wald Chi2 (9)255.12
Prob > Chi20.00
F 28.76
Prob > F 0.00
Sargan Test of Overid. RestrictionsChi2 (9) = 8.48
Prob > Chi2 = 0.51
Arellano–Bond Test for AR (1) in First Differencesz = −4.78
Pr > z = 0.000
Arellano–Bond Test for AR (2) in First Differencesz = −1.39
Pr > z = 0.17
* indicates significance at greater than 0.05 (two-tailed test). ** indicates significance at greater than 0.01 (two-tailed test).
Table 3. Share of FDI/GDP regressed on political constraints, 1996–2022, using Arellano–Bond Dynamic Panel Estimation. This model uses an interaction term between the rule of law and Polcon.
Table 3. Share of FDI/GDP regressed on political constraints, 1996–2022, using Arellano–Bond Dynamic Panel Estimation. This model uses an interaction term between the rule of law and Polcon.
VariableHigh Polcon/High Rule-of-Law
Coefficient (SE)
High Polcon/Low Rule-of-Law
Coefficient (SE)
Low Polcon/High Rule-of-Law
Coefficient (SE)
Low Polcon/Low Rule-of-Law
Coefficient (SE)
FDI/GDP (L1)0.16
0.08 **
FDI/GDP (L2)−0.04
−0.03 **
−0.07 *
Political Constraints*ROL0.18 *
−0.20 **
−0.12 **
0.27 **
Political Constraints0.15
0.17 *
Rule of Law0.22 *
Per Capita GDP, PPP (logged)0.79 **
0.85 **
1.04 **
1.20 **
Party Orientation0.13
Regime Stability−0.07
−0.06 *
Economic Growth0.02 **
0.03 **
0.04 **
0.05 **
Number of Groups (Country Dummy)26292022
Number of Instruments189181115147
Wald Chi2 (9)259.05224.66292.30248.17
Prob > Chi20.
Sargan Test of Overid. RestrictionsChi2 (7) = 11.68
Prob > Chi2 = 0.43
Arellano–Bond Test for AR (1) in First Differencesz = −5.14
Pr > z = 0.000
Arellano–Bond Test for AR (2) in First Differencesz = −1.49
Pr > z = 0.15
* indicates significance at greater than 0.05 (two-tailed test). ** indicates significance at greater than 0.01 (two-tailed test).
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