Tag: International relations

Jurisdictional Competition for FDI in Developing and Developed Countries

20210531 Jurisdictional Competition FREE Network Image 01

This brief is based on research studying jurisdictional competition between countries and its influence on the inflow of foreign direct investments (FDI). The study compares jurisdictional competition among the developing Central and Eastern European (CEE) countries with competition among developed EU countries. As instruments of jurisdictional competition for FDI, we consider governments’ efforts to improve the rule of law, corporate governance, and tax policies. The results suggest the presence of proactive jurisdictional competition via the quality of corporate governance regulation both in the CEE and the EU countries. The CEE states also attract FDI by competing in tax policies.


The determinants of FDI inflows have been examined in numerous studies. A substantial number of them consider the influence of institutions, which are defined as particular organizational entities, procedural devices, and regulatory frameworks (IMF, 2003).

The quality of institutions is a particularly important FDI determinant for less-developed countries because the poor institutional quality and weak law enforcement increase the costs of running a business, create barriers for financial market efficiency, and increase the probability of foreign assets expropriation (Blonigen, 2005).

However, governments interested in attracting FDI to boost job creation, new technologies, and tax revenues to their countries are not only concerned about the internal institutional environment. They are also competing with other countries in attracting foreign investments, engaging in what is often referred to as “jurisdictional competition”. In a broad sense,  this can be thought of as governments’ efforts to outcompete one another in offering foreign companies more favorable institutional and fiscal conditions for capital placements.

This brief summarizes the results of a study on the jurisdictional competition for FDI among the developing CEE and among developed EU countries (Mazol and Mazol, 2021). The research explores the precondition for proactive jurisdictional competition between economies for FDI – namely, how the economic and institutional environment within a country impacts the inflow of FDI both domestically and to its neighboring states, – by using a spatial econometric approach. The brief emphasizes the difference in the FDI policy responses implemented by developing CEE and developed EU countries.

Data and Methodology

In our econometric analysis, we use the FDI inward stock (i.e., the value of capital and reserves in the economy attributable to a parent enterprise resident in a different economy) as the dependent variable. The explanatory variables indicating jurisdictional competition include quality of corporate governance, rule of law, political stability, and tax policy. We employ balanced panel datasets for 26 developing CEE countries and 15 developed EU countries for the period 2006-2018. The dataset is derived from the World Bank and UNCTAD databases.

The analysis is based on a panel spatial Durbin error model (SDEM) with fixed effects (LeSage, 2014). Parameter estimates in the SDEM contain a range of information on the relationships between spatial units (in our case, countries). A change in a single observation associated with any given explanatory variable will affect the spatial unit itself (a direct effect) and potentially affect all other spatial units indirectly (a spillover effect) (Elhorst, 2014). The spatial spillover effect is viewed here as the impact of the change in the institutional or economic factor in one country on the performance of other economies (LeSage & Pace, 2009).

In our case, the direct effect is the effect on the FDI in country i of the changes in the studied instrument of jurisdictional competition in country i. The spillover effect is the change in FDI in country j following a change in the studied instrument of jurisdictional competition in country i.


The results of our estimation are suggestive of a proactive jurisdictional competition in taxes among the CEE countries and in corporate governance quality both among the CEE and EU countries. Analyses of other factors (i.e., political stability and rule of law) show no significant interrelation between policy measures implemented by neighboring countries in order to attract FDI.

The precondition for the presence of proactive jurisdictional competition in a particular factor is to have statistical significance in both its direct and spillover effects (Elhorst and Freret, 2009). Such findings may indicate that policy measures in one economy trigger a policy response in a neighboring economy, which, in turn, influences the level of FDI in both countries.

Table 1. Estimation results of SDEM models – direct effects

Notes: *** – significance at 1% level, **  – significance at 5% level, *  – significance at 10% level. ln – denotes the logarithm of the underlying variable. lagt – denotes lagged underlying variable by one period (year) in time. Values of t statistics in parenthesis. CEE countries: Albania, Armenia, Azerbaijan, Belarus, Estonia, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Georgia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Macedonia, Moldova, Poland, Romania, Russia, Serbia, Slovakia, Slovenia, Tajikistan, Ukraine, Uzbekistan. EU countries: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Italy, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland. Source: Author’s estimates based on World Bank and UNCTAD databases.

Our results for the direct and indirect response to a tax policy in CEE countries illustrate this logic. Decreasing tax_rateincreases FDI to the CEE economy enacting this change (see Table 1), as well as to its neighboring countries (see Table 2). This finding is consistent with jurisdictional competition in taxes. That is, a reduction in domestic tax_rate may entail a decrease in the tax rate of a neighboring economy, resulting in a subsequent increase in FDI. (To explicitly confirm the suggested channel, further tax policy analysis would be needed). Interestingly, our results suggest that jurisdictional competition in taxes is only present among CEE economies, but not among EU countries.

In turn, an increase in corp_governance, a measure of corporate governance quality, increases FDI in neighboring countries both in the EU and in the CEE region (see Table 2).  A possible interpretation is that an increase in corp_governance in one country may entail an increase in corp_governance in its neighboring economies, resulting in a subsequent increase in FDI.  This result suggests proactive competition via corporate governance policy both among the EU countries and the CEE countries.

However, the direct effect differs between the regions. In the EU, an increase in corp_governance increases FDI to the EU economy in question, in line with common wisdom (see Table 1). At the same time, in the CEE region, an increase in corp_governance is followed by a decrease in FDI to that country.

Table 2. Estimation results of SDEM models – spillover effects

Notes: ***  – significance at 1% level, **  – significance at 5% level, *  – significance at 10% level. ln – denotes the logarithm of the underlying variable. Values of t statistics in parenthesis. lagt_lags – denotes spatially lagged underlying variable (multiplied by spatial weight matrix) lagged by one period (year) in time. Source: Author’s estimates based on World Bank and UNCTAD databases.

One potential explanation for the negative direct effect of corporate governance quality on FDI in the CEE economies is that improved corporate governance practices can block certain types of FDI, leaving behind foreign investors with a lower “threshold for corruption”. This may decrease FDI to the CEE country in question. However, once the jurisdictional competition results in an improvement of corporate governance across the region, it ultimately has a positive spillover effect.

The above explanation is in line with the theory of regulatory capture (Stigler, 1971), which suggests that the decisions made by public officials might be shaped and sometimes distorted by the efforts of rent-seeking interest groups to increase their influence.

Finally, the estimates do not indicate that the other studied institutional factors, rule of law and political stability, are applied as instruments of jurisdictional competition as neither groups of countries show significant spillover effects. The results, however, show that these factors influence the FDI inflow via the direct effect. More specifically, an increase in political_stability positively influences the FDI inflow to the economies in question, both in CEE and the EU, while rule_of_law is positive and significant only for the CEE countries. If investors are not as responsive to changes in rule_of_law when the initial level is high, the fact that EU countries typically have a higher rule_of_law value compared to CEE countries might explain why this estimate is insignificant for the EU countries.


This brief, first, presents new evidence on the relationship between different economic and institutional factors and FDI using a spatial econometric approach; second, it analyzes the possible existence of jurisdictional competition among developing CEE countries and developed EU countries as well as its effect on FDI.

The results suggest proactive jurisdictional competition in FDI determinants such as corporate governance quality and tax rates. CEE countries competing with one another use both these instruments of jurisdictional competition, while EU countries compete only via corporate governance quality. Furthermore, foreign investors are not sensitive to the quality of rule of law in the EU countries, while this instrument is more important for the FDI inflow to CEE economies.

Our results stress that officials responsible for the FDI policy implementation should pay more attention to the policies undertaken by neighboring governments as such external policies can make their own strategies to attract FDI to their economy less effective.


  • Blanton, S., and R. Blanton. (2007). What Attracts Foreign Investors? An Examination of Human Rights and Foreign Direct Investment. The Journal of Politics, 69(1), 143-155.
  • Blonigen, B. (2005). A Review of the Empirical Literature on FDI Determinants. Atlantic Economic Journal, 33(4), 383-403.
  • Elhorst, J. (2014). Spatial Econometrics from Cross-Sectional Data to Spatial Panels. Berlin: Springer.
  • Elhorst, J., and S. Freret. (2009). Evidence of Political Yardstick Competition in France Using a Two-Regime Spatial Durbin Model with Fixed Effects December. Journal of Regional Science, 49(5), 931-951.
  • IMF (2003). World Economic Outlook 2003. International Monetary Fund: Washington DC.
  • LeSage, J. (2014). What Regional Scientists Need to Know About Spatial Econometrics? Working Paper, Texas State University-San Marcos, San Marcos.
  • LeSage, J., and R. Pace. (2009). Introduction to Spatial Econometrics. Boca Raton, FL: CRC Press, Taylor and Francis Group.
  • Mazol, A., and S. Mazol. (2021). Competition of Jurisdictions for FDI: Does Developing and Developed Countries Response Different to Economic Challenges? BEROC Working Paper Series, WP no. 73.
  • Stigler, G. (1971). The Theory of Economic Regulation. Bell Journal of Economic and Management Science, 2, 3-21.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

A Decade of Russian Cross-Domain Coercion Towards Ukraine: Letting the Data Speak

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Russia’s coercion towards Ukraine has been a topic of major international events, meetings and conferences. It regularly makes the headlines of influential news outlets. But the question remains open – do we really understand it? We diligently collect and analyze data to make informed decisions in practically all domestic issues but is the same done for international relations? This research paper introduces a number of tools and methods that could be used to study Russia’s coercion towards Ukraine beyond its most visible manifestation, looking into latent trends and relations that could reveal more.


For the past decade, Ukraine has been in the headlines of the major world news outlets more frequently than ever before. Ukrainian-Russian relations have been and still remain the topic of international summits and other events. The occupation of a part of Ukraine’s territory has been denounced and Russia’s coercion towards Ukraine is now generally accepted as a fact. But what do we really know about the underlying empirics and dynamics and how can this multi-domain assertiveness be measured and tracked? This paper presents a number of data-driven approaches that allow looking beyond the headline stories to identify and track various dimensions of Russia’s coercion towards Ukraine and the dynamics of its development.

Academic Interest

Mapping the landscape of scholarly literature reveals a number of interesting results. First, the body of works studying Russia’s coercion towards Ukraine remains relatively modest. It quintupled in 2014 but afterwards the interest started tapering off. A search for papers on this topic in Scopus and Web of Science with a very precise query (to increase the accuracy of search) and publication time of 2009-2019 returned 155 papers most of which were published in or after 2014.

Figure 1. Scholarly publications on Russian-Ukrainian relations.

Source: WoS and Scopus, 2009-2019

A closer look at the content of these works with the use of a bibliometric software called CiteSpace shows that the majority of papers focus on Putin, once again emphasizing the significant role of his personality in Russia’s coercion towards Ukraine. The second largest cluster has the “great power identity” as its main theme and presumably looks beyond actions of Russia to identify its ideological grounds. Another group of publications is devoted to sanctions, pointing to their important role in studying Russian-Ukrainian relations.

Figure 2. The landscape of topics in scholarly publications on Russian-Ukrainian Relations.

Expressions of Coercion

The “practical” side of Russia’s coercion towards Ukraine is also frequently associated with the personality of Vladimir Putin and his attitudes towards Ukraine. To analyze this perception further, we created a corpus of speeches of Russian presidents published on the Kremlin website, filtered them to keep only those that mention Ukraine, divided them into pre-2014 and 2014 and after, and then analyzed them using an LDA topic modeling algorithm. This algorithm is based on the assumption that documents on similar topics use similar words. So, the latent topics that a certain document covers can be identified on the basis of probability distributions over words. Each document covers a number of topics that are derived by analyzing the words that are used in it. In simple terms, the model assigns each word from the document a probabilistic score of the most probable topic that this word could belong to and then groups the documents accordingly.

Figure 3. Speeches of Russian presidents before 2014, LDA topic modeling.

Figure 4. Speeches of Russian presidents in 2014 and after, LDA topic modeling.

Quite surprisingly, we discovered that the overall rhetoric of speeches is very similar for the two periods. Although some speeches do differ and the later corpus includes new vocabulary to reflect some changes (i.e “Crimea”, “war”) the most common words remain practically the same. Thus, regardless of the apparent shift in relations between the two countries, Russian leadership still relies on the same notions of collaboration, interaction, joint activities, etc. The narrative of “brotherhood” between the nations persists despite and beyond the obvious narrative of conflict.

To include a broader circle of Russia’s leadership we also looked at the surveys of the Russian elite conducted regularly by a group of researchers led by William Zimmerman and supported by various funders over the years (in 2016 – the National Science Foundation and the Arthur Levitt Public Affairs Center at Hamilton College). Seven waves of the survey already took place; the most recent one in 2016. The respondents were the representatives of several elite groups (government, including executive and legislative branches, security institutions, such as federal security service, army, militia, private business and state-owned enterprises, media, science and education; for practical reasons from Moscow only).

The survey revealed a number of interesting observations. For instance, while the prevailing Russian opinion on Russia’s occupation of Crimea had been that it was not a violation of international law, a closer look at the demographic characteristics of respondents shows that they were not as coherent as it might seem from the outside. While the “green” answers from respondents with backgrounds such as media or private business may have been anticipated, the number of members of the legislative and especially executive branch and the military that had at least some doubt on the legality was surprisingly quite sizable, and they even demonstrated some support of the “violation of law” interpretation.

Figure 5. Elite and public opinion on Russia’s annexation of Crimea.

Comparing these elite opinions to the public opinion poll by Levada Center conducted in the same year shows that even the general public is slightly more likely to choose the most extreme “full legality” option than the respondents from the executive branch.

Beyond the elite or general opinion polls, we tried to develop a metric that might allow us to track Russian sensitivities towards Ukraine. For that, we examined two different ways of expressing “in Ukraine” in Russian language: ‘на Украине (the ‘official’ Russian expression) vs. ‘в Украине (the version preferred by Ukrainians). [In English, this can be compared so saying ‘Ukraine’ vs ‘the Ukraine’.]

Our first visual plots how many search queries were done on Google Search with both versions over the last decade.

Figure 6. Search queries for “в Украине” (green) versus “на Украине” (red), Google Trends, 2009-2019.

We can clearly observe that during less turbulent times the more politically sensitive version is much more common. This however drastically changes during the peaks of Russia’s coercion towards Ukraine when the number of searches with the less politically correct term increases significantly.

A different trend can be observed if we look at official media publications stored in the Factiva database. We estimated the ratio of search volumes for each term and observed that until the beginning of 2013, about a third of articles and news reports used “in Ukraine”. This changed around January 2013 when the ratio starts to decrease for “in Ukraine” searches and plummets to a mere 10% of outlets still preferring this term.

Figure 7. The ratio of “в Украине” to “на Украине” occurrences in large Russia media (2009 – 2019), Factiva.

Tracking Coercion Itself

What is the track record of Russia’s actual coercion over this decade? For this, we turn to a few recent datasets that try to systematically capture verbal and material actions (words and deeds): the automated event datasets. The largest one of those, called GDELT (Global Database of Events, Language, and Tone), covers the period from 1979 to the present, and contains over three quarters of a billion events. It is updated every fifteen minutes to include all “events” reported in the world’s various news outlets. To exclude multiple mentions of the same event by one newswire, the events are “internally” deduplicated. The events are not compared across newswires.

An event consists of a “triple” coded automatically to represent the actor (who?), the action (what?) and the target (to whom?) as well as a number of other parameters such as type (verbal or material; conflict or cooperation; diplomatic, informational, security, military, economic), degree of conflict vs cooperation etc. Other similar datasets include ICEWS (Integrated Crisis Early Warning System) and TERRIER (Temporally Extended, Regular, Reproducible International Events Records). For this analysis, we filtered out only those events in which Russia was the source actor and Ukraine was the target country. We present two metrics: (1) the percentage of all world events that this subset of events represents and (2) the monthly averages of the Goldstein score, which captures the degree of cooperation or conflict of an event and can take a value from -10 (most conflict) to +10 (most cooperation). Also, to add a broader temporal perspective, we looked beyond the last decade. It can be clearly seen that the number of events before 2013 was significantly lower, especially in “material” domains. Some verbal assertions from Russia towards Ukraine happened during the Orange Revolution and so-called “gas wars”.

The situation changes radically starting from 2013. The proportion of events increases with some especially evident peaks (i.e. during the occupation of Crimea). The verbal events remain quite neutral while the actions towards Ukraine move from some fluctuations to steadily conflictual.

Figure 8. Russia’s negative assertiveness towards Ukraine, 2000-2019.

Measuring Influence

We have seen that the past decade was exceptional in the scale of Russian assertiveness towards Ukraine. But what do we know about Russia’s influence on Ukraine and Ukraine’s dependence on Russia? Influence measures the capacity of one actor to change the behavior of the other actor in a desired direction. In an international context this often concerns the relations between countries. Influence can be achieved by various means, one of which is to increase the dependence of the target country upon the coercive one. This strategy is frequently employed by Russia willing to regain and/or increase control over the former post-Soviet countries. The Formal Bilateral Influence Capacity (FBIC) Index developed by Frederick S. Pardee (Center for International Future) looks at several diplomatic (i.e. intergovernmental membership), economic (trade, aid) and security (military alliances, arms import) indicators allowing to identify the level of dependence of one country upon another. This is especially interesting from a comparative perspective. Figure 9 shows that countries such as Armenia and Belarus remain highly dependent on Russia. For half of the decade, Ukraine was number three on this list. Today the situation has changed. Ukraine’s dependence on Russia has gradually decreased and has become even smaller than Moldova’s, moving closer to the steadily low level of dependence of Georgia. This may signify a positive trend and a break of a decade-long relationship of dependence.

Figure 9. Dependence of post-Soviet countries on Russia, FBIC.


Consequently, Russia and Ukraine have become much more visible in the international academic and policy research efforts. This can be measured through a number of instruments, including a comprehensive mapping of the academic landscape itself with regard to salience and topics that are being studied, analysis of the word choice (that could be represented by the use of the terms to describe events in Ukraine by the government media and Google search users (“на Украине” versus “в Украине”); speeches of Russian presidents that use the same rhetoric of collaboration when talking about Ukraine despite the obvious change in relationships) and material coercion (significant increase in number of assertive conflictual Russia’s actions towards Ukraine). Some findings do give hope for change: the opinions of the Russian elite on recent Russian actions towards Ukraine while remaining generally unfavourable are not as cohesive as it might appear and Ukraine’s dependence on Russia has decreased significantly.


This research is a part of a larger research effort titled RuBase funded by the Carnegie Foundation of New York and implemented jointly by The Hague Centre for Strategic Studies and Georgia Tech with the help of the Kyiv School of Economics StratBase team in Ukraine. The ‘Ru’ part of the title stands for Russia; and ‘base’ has a double meaning – both the knowledge base built during the project, and the (aspirationally) foundational nature of this effort. The project intends to look beyond the often-shallow traditional understanding of coercion and apply innovative tools and instruments to study coercion in its multifaceted form. This is only a small selection of the tools that have been successfully tested in the course of this (ongoing) research project and applied to the study of Russia’s coercion in different domains. The prospects of any progress in resolving the Russian-Ukrainian conflict are currently slim, thus further work that would allow identifying patterns and trends that the human eye may oversee to understand Russia better and develop an informed foreign policy strategy both for Ukraine and the West is crucially important.