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Vladimir Bartenev — leading research fellow at the Institute for Advanced Strategic Studies of the HSE University.

SPIN-RSCI: 9801-1158

ORCID: 0000-0001-9804-0630

Researcher ID: AAE-4219-2019

Scopus Author ID: 57188561575

For citation: Bartenev, V., 2024. Mitigating Effect of Official Development Assistance on Political Risks to Foreign Direct Investment: Limits of Measurability. Contemporary World Economy, Vol. 2, No. 4.

Keywords: official development assistance, international development cooperation, political risks, foreign direct investment, statistics

The research was supported by the Russian Science Foundation grant No. 23-28-01060, https://rscf.ru/project/23-28-01060/.

Abstract

Mitigating political risk is a critical condition for mobilizing private capital need to achieve the Sustainable Development Goals. However, the issue of measuring the mitigating effect of various financial and non-financial instruments has not yet been conceptually or technically elaborated. This article aims to reveal the problem of measuring the effect of official development assistance (ODA) in minimizing political risks to foreign direct investment (FDI) and to provoke an academic discussion on the key limitations that complicate such measurement, as well as ways to overcome them.

Research has demonstrated that private businesses and government authorities in donor countries share a mutual interest in using development cooperation toolkit to manage political risks associated with investment in developing countries. For this purpose, home country governments can use both conventional development assistance instruments and guarantees, for which the OECD Development Assistance Committee has recently authorized the calculation of grant equivalents and their reporting as ODA.

The mitigating effect of traditional forms of development assistance can theoretically be measured by correlating data on the volume of funding for certain relevant sectors with the values of the relevant components of the most authoritative political risk ratings. However, the implementation of this idea is impeded by the reciprocal influence of the explanatory and dependent variables. A high degree of political instability in the recipient country inherently constrains the allocation of aid. Furthermore, both parameters are influenced by a multitude of endogenous and exogenous factors, which can be expressed only through dummy variables.

In the case of guarantee instruments, there is a theoretical possibility of assessing the strength of both their catalytic effect and the “halo effect,” which additionally protects investments from political risks of “legal-governmental” origin. The catalytic effect could be measured based on the exact conditions of the guarantee coverage and the amount of private capital mobilized, and the “halo effect”—based on the total number of projects supported by guarantees against political risks and the number of projects where this effect did not work. However, the practical realization of this idea is hampered by a fundamental lack of statistical data of adequate quality.

The aforementioned limitations underscore the necessity of employing qualitative methods when investigating the stated topic. This approach necessitates the utilization of a diverse array of complementary sources of various types.

Introduction

The engagement of the private sector in financing the Sustainable Development Goals (SDGs) has emerged as a pivotal topic in global discourse. Initially, it was evident that political risks for foreign direct investment (FDI) could impede a qualitative leap in this direction. Historically, the majority of investment has been directed toward stable, high-income countries rather than toward states with lower incomes, which are more susceptible to internal and external challenges.

The mitigation of such risks has been identified by experts and policymakers as a priority. However, the issue of measuring the mitigating effect of various financial and non-financial instruments has not been sufficiently elaborated either conceptually or technically. The article's objective is twofold: first, to elucidate the fundamental problem of measuring the effect of official development assistance (ODA) on minimizing political risks for foreign direct investment; and second, to stimulate an academic debate on the key limitations that complicate such measurement and potential strategies for overcoming them.

No examples of similar goal setting have been found in the scientific literature. Despite the fact that commercial interests are invariably mentioned in the research on the motives for aid and its effectiveness, both foreign [Berthélemy 2006; van Veen 2011; Dreher, Lang, Reinsberg 2024, etc.] and domestic [Degterev 2012; Baranovskii 2018, etc., Morozkina 2018, etc.] studies almost always focus on export promotion rather than investment. The endeavors to evaluate the efficacy of official development assistance (ODA) in influencing political risk factors that impact foreign direct investment (FDI) are sporadic and tend to concentrate on individual risks, such as expropriation [Asiedu, Jin, Nandwa 2009; Jin, Zeng 2017, et al.], terrorism [Bandyopadhyay, Sandler, Younas 2014; Efobi, Asongu, Beecroft 2018, etc.], corruption [Bahoo et al. 2023], or the policies of individual donors (mainly the PRC [Lu, Huang, Muchiri 2017; Wang, Yang, Li, Zhang 2022; Gomboin 2023, etc.]). The depth of scientific exploration of the problem does not correspond to the level of attention paid to it in political discourse [Bartenev 2023]. This finding underscores the existence of a discernible research niche, this article aiming to expand the knowledge in this area.

The article is organized into three sections. The initial section provides a concise overview of the general conceptual framework. The subsequent section delineates a series of issues pertinent to the evaluation of the mitigating effect of “conventional” ODA instruments on political risk. The third section delineates the challenges associated with the quantification of the mitigating effect of guarantee instruments, for which the OECD Development Assistance Committee (OECD DAC) has recently authorized the calculation of grant equivalents and their reporting as ODA.

1. Conceptual framework

Political risk constitutes an inherent and well-studied type of risk for foreign economic activity (FEA) in general and FDI in particular. In its most general form, it represents the probability of incurring financial losses due to the actions (or inaction) of various political actors, such as public authorities and representatives of states in international organizations, capable of generating “legal-governmental risks,” and various non-state actors, capable of generating “extra-legal” risks.¹

Entities that provide protection for investments against political risks utilize lists that, while not identical, exhibit a high degree of similarity. The comparison of the current lists utilized by pertinent international organizations was made, including the Multilateral Investment Guarantee Agency (MIGA), the Asian Development Bank, the African Trade Insurance Agency, the Arab Investment and Export Credit Guarantee Corp., national development finance institutions (DFIs) or export credit agencies (ECAs) of the world’s largest economies (the United States, China, Japan, Germany), and leading private insurance companies (AIG, Chubb, Lloyd’s, Sovereign, Zurich Insurance). This comparison helped formulate a consolidated shortlist of political risks of various origins against which FDI is most often protected. This shortlist includes nationalization and expropriation, restrictions on currency transfers, breach of contract, and various forms of political violence, such as wars, revolutions, coups d’état, and terrorist attacks. The majority of these risks may affect either the host country as a whole, a particular region, a specific industry of that country, or even a particular foreign investor.

To measure political risks one can use both statistics on the political process, the state of the business, investment and regulatory climate, and political risk ratings calculated by specialized consultancies (AON, Control Risks, Economist Intelligence Unit, Marsh, PRS Group, etc.) based on their proprietary methodologies. The most widely recognized of these is the methodology of the International Country Risk Guide (ICRG) by The PRS Group, which calculates the level of political risk based on 12 components: 1) government stability; 2) socio-economic conditions; 3) investment profile; 4) internal conflict; 5) external conflict; 6) corruption; 7) military in politics; 8) religious tensions; 9) law and order; 10) ethnic tensions; 11) democratic accountability; 12) bureaucracy quality [The PRS Group 2022]. This rating does not encompass all jurisdictions (approximately 150 countries are covered) but it has several notable advantages, particularly its continuous compilation over a span of four decades (since 1984). This extensive history contributes to its extensive utilization in academic literature, including works addressing similar issues to those outlined herein [Asiedu, Jin, Nandwa 2009; Bandyopadhyay, Sandler, Younas 2014; Fon, Alon 2022, among others]. An additional argument in favor of the utilization of political risk indices (despite the inherent imperfections in their methodologies) is that international companies, when formulating their own investment strategies, frequently adhere to these indices rather than the original statistical data on which these indices are calculated.

The key parameters of political risk, akin to any other type of risk, are the likelihood of its materialization and the scale of possible losses (impact). These parameters are instrumental in the ranking of risks and the development of risk management strategies.

All traditional risk management strategies have been shown to be applicable to political risks for FEA. Among them: Firstly, risk avoidance refers to the cessation of risky activities, which serves to reduce the probability of risk materialization and, consequently, losses to zero. Secondly, risk reduction, which includes diversification, is designed to minimize the probability of risk materialization and the extent of its negative impact. Thirdly, risk insurance, through its transfer to a third party, aims primarily to minimize damage by ensuring that losses are covered by a guarantor or insurer. Of the aforementioned behaviors, our primary interest lies in the reduction and insurance of risk.

Obviously, a private company focused on foreign markets,² in its endeavor to manage political risks for FDI, could hypothetically do without interacting with home country government institutions (See for more details: [Godfrey, Merill and Hansen 2009; Ali et al. 2021; Choi, Chung and Wang 2022]). First, it can go down the path of establishing joint ventures. Second, lobby the host country authorities. Third, it can turn to private insurance companies and buy insurance against individual risks of one origin or another, or some kind of bundled product. Fourth, bet on the implementation of social projects in the country where it places its investments. The latter makes it possible to minimize, on the one hand, the risks of “legal-governmental” origin (with the authorities of the host country adopting targeted restrictive measures against it) and, on the other hand, the risks of damage to its property in the course of revolutions, protests, etc.

Concurrently, private enterprises may pursue a range of strategies to manage political risks, including the engagement with official institutions within the country of origin. Some strategies entail the cultivation of relationships with the state as a provider of international development cooperation (for more details, see [Bartenev 2023b]). It is evident that such interactions are also executed by state-owned enterprises. Concurrently, the parameters of political risks they confront in the host countries, particularly in the context of escalating interstate rivalry, and the strategies for mitigating such risks will exhibit distinct variations that merit independent consideration (beyond the scope of this article).

On the one hand, a company can seek financial coverage from the home country authorities for losses from political risks associated with planned (or already made) investments in developing countries. This process entails the submission of an application to either an ECA or a DFI to get the aforementioned protection for a fee. Consequently, such company benefits from substantial coverage of the invested funds (typically at least 90%), in addition to political support from the state. This confluence of factors serves to mitigate the likelihood of materialization of pivotal “legal-governmental” risks, chief among them being nationalization and expropriation. The so-called “halo effect” emerges: the authorities of the host country may refrain from taking any restrictive measures against foreign companies associated with the guarantor, due to concerns over the potential deterioration of bilateral relations, although such behavior is by no means predetermined.

Conversely, a private company may engage in lobbying authorities of the country of origin, with the objective of persuading them to provide assistance to the host country. This assistance would serve to mitigate any potential political risks that could adversely impact the company’s operations in the host country. Such assistance could take various forms, including but not limited to: improving the investment and business climate; improving legislation; combating corruption (mitigating risks of “legal-government” origin) on a nationwide scale; streamlining regulatory practices in specific economic sectors; or providing support aimed at reducing conflict potential in a society (designed to mitigate risks of “extra-legal” origin). The aforementioned forms of assistance have been operational for multiple decades, constituting a significant component of ODA flows.

A distinct strategy entails the host country’s government providing backing for capital investments by specific companies through the provision of concessional preferential financing. This approach is intended to dissuade the authorities of the recipient country from engaging in actions that could potentially result in the expropriation of the investor’s assets or other detrimental actions. The latter method is currently widely employed by the People’s Republic of China [Gomboin 2023]. Evidently, the provision of such assistance will serve to mitigate political risks for companies from the provider country, yet it will not extend to investors from third countries.

Finally, another option available to companies from the donor country is to implement international development projects and programs as contractors, being selected either through  “tied aid” mechanisms (when the range of suppliers of goods and services is formally restricted to companies from a donor country) or through procurement processes open to companies from other countries.³ In this way, a company from a donor state may enter new, untapped foreign markets at zero risk without making its own capital investments. Although in recent years the practice of “tying” aid has been associated primarily with the PRC, it is common to many DAC donors. Regardless of the OECD DAC Recommendation on Untying ODA, meant to reduce the share of tied aid for the most vulnerable categories of countries, no meaningful progress has been made in its implementation: since the turn of the century share of the de jure tied aid in total ODA from the DAC donors has even increased from 16.9% in 2000 to 19.1% in 2022. De facto, in only 13% of projects, developing countries were the suppliers of goods and services, and in only 9% donors contracted with firms from the recipient country itself [OECD 2022a].

Private companies are taking proactive steps to utilize the aforementioned options. They send their representatives to parliamentary hearings on development policy and to various advisory bodies set up under national development agencies, provide grants to various think tanks to conduct research on development assistance issues,⁴ and so on. The active exchange of personnel between the private sector and development agencies is also of great importance, with representatives of the corporate world joining development agencies and acting as advocates for private business.

For their part, home country authorities may also be interested in addressing the political risk mitigation needs of domestic companies in all the ways described above.

On the one hand, they are driven by foreign and foreign economic policy imperatives, such as the desire to support the expansion of national export-oriented businesses, which can help strengthen political influence in the countries where they operate. Moreover, the provision of guarantee protection against political risks generates income, which in some cases can be quite substantial⁵ and can be used by a DFI for subsequent reinvestment and expansion of its guarantee and loan portfolios.

On the other hand, the authorities of donor states can also be driven by the desire to comply with modern international development “standards,” which today include the creation of favorable conditions for the mobilization of private capital. Many key international documents adopted in the first quarter of the 21st century, including the 2030 Agenda for Sustainable Development itself and the 2015 Addis Ababa Action Agenda, guide providers to this end.

Based on the logic described above, there is a hypothetical possibility of “quantifying” the mitigating effect of various ODA-reported flows on political risks using data from official international statistics, but there are a number of important limitations.

2. Challenges in assessing the mitigating effect of conventional ODA instruments

In the domain of international development cooperation, the challenge of operationalizing specific quantitative metrics is typically addressed through the agreements among representatives of prominent state-owned entities, facilitated under the aegis of international organizations. The OECD DAC has served as the primary multilateral platform for more than half a century, having been established in 1961 on the basis of the Development Assistance Group of the Organization for European Economic Cooperation. In 1969 this body adopted the still highly relevant technical and statistical metric “official development assistance” (ODA), designed to ensure the comparability of contributions from different states, and the principles of ODA-eligibility.

According to the most recent iteration of the OECD DAC’s converged statistical reporting directives, as presented in September 2024, ODA encompasses “grants and loans to the official sector of countries and territories on the DAC List of ODA Recipients, INGOs and multilateral development institutions which are: a) provided by official agencies, including state and local governments, or by their executive agencies; and b) each transaction of which:  is administered with the promotion of the economic development and welfare of developing countries as its main objective; and is concessional in character” [OECD 2024a. P. 17].

Today the ODA metric encompasses a substantially broader array of flows than a half a century ago. This expansion of the ODA persists despite the deliberations on the obsolescence of the metric. Approximately 50 countries report ODA to the OECD in one way or another, including 32 OECD DAC members and about 20 non-DAC countries, including major providers such as Turkey and the Arab monarchies from the Gulf. It is noteworthy that neither the People’s Republic of China (PRC) nor the majority of other non-Western providers of South-South cooperation report ODA to the OECD. However, there are documented instances of scrupulous estimates of “ODA-like flows” from the latter category, particularly as reported by the AidData research lab at the College of William and Mary (United States) [Custer et al. 2023].

The data on ODA contained in the OECD databases is quite detailed. For the majority of providers, comprehensive data on the allocation of financial assistance among diverse measures, modalities and channels is accessible. A critical aspect of addressing the task outlined in this article is the sectoral differentiation of ODA flows with each sector being assigned a distinct code, known as a “purpose code.” The OECD code lists used in the Creditor Reporting System (CRS) contain a total of more than two hundred such sectors (in addition, donors can voluntarily report on their activities in even greater detail using an additional set of codes) [OECD 2024c].

These purpose codes have been used several times to identify a specific set of areas deserving special monitoring. For example, in 2006, the Working Group established by the WTO to implement the Aid for Trade initiative,⁶ composed a very broad setlist of the CRS purpose codes to track the dynamics of the aid for trade flows [OECD/WTO 2015. P. 455–459]. Similarly, the OECD Secretariat agreed in 2023 on a list of 22 purpose codes for the new umbrella category “Peace ODA”, the calculation of which was based on the approach proposed by the Institute for Economics and Peace in 2017 [Institute for Economics and Peace 2017]. The category combines “core” peacebuilding areas from subsection 152 of the OECD CRS list of purpose codes (“Conflict, peace and security”) and “secondary” peacebuilding areas from subsection 151 (“Government and civil society, general”) [OECD 2023a].

Despite several precedents of creating a list of purpose codes for a specific task, no analogous list of “sectors” significant for mitigating political risks for business has been proposed at the international level (which is generally logical, given the declared focus of ODA on promoting the development in recipient countries, rather than on protecting the investments of companies from donor countries). Consequently, in the development of metrics for quantifying the contribution of ODA in mitigating political risks for business, it is essential to adhere to the conventional logic.

Consequently, the predictor should be the amount of ODA of one or another type (in absolute terms) sent by the provider state A to the recipient country B, which is at the same time a host country for FDI from state A. The extant scholarly literature contains numerous examples of the utilization of aggregate metrics, including those expressed as a percentage of GNI, in conjunction with various categories of ODA. These categories include bilateral aid, multilateral aid, loans from individual international organizations, conflict-oriented ODA coinciding in scope with the aforementioned “core” peacebuilding subset of peace ODA, aid for infrastructure, and others—as predictors in assessing the mitigating effect of foreign aid.⁷

The OECD CRS code lists help identify sectors deemed most effective in mitigating political risks faced by businesses from donor countries in recipient countries. These sectors are determined based on prevailing assumptions about FDI activities. These areas can be readily correlated with the parameters that comprise the most prominent political risk indices, particularly the rating calculated by The PRS Group according to the ICRG methodology (see Table 1).

 Table 1. Results of correlating the ODA sectors of high risk mitigation potential with subcomponents of The PRS ICRG political risk rating

 

ODA sector, OECD CRS purpose code		Political risk components, The PRS ICRG methodology
Code	Description	Component  and its weight (%)
15110	Public sector policy and administrative management	Bureaucracy quality (4)
15111	Public finance management (PFM)
15113	Anti-corruption organizations and institutions	Corruption (6)
15130	Legal and judicial development	Law and order (6)
15150	Democratic participation and civil society	Democratic accountability (6)
15151	Elections
15152	Legislatures and political parties	Government stability (12)
15160	Human rights	Law and order (6)
15210	Security sector management and reform	Military in politics (6)
15220	Civilian peacebuilding, conflict prevention and resolution	Internal conflict (12) External conflict (12) Religious tensions (6) Ethnic tensions (6)
15230	Participation in international peacekeeping operations
15240	Reintegration and small arms and light weapons control
15250	Removal of land mines and explosive remnants of war
15261	Child soldiers (prevention and demobilization)
16010	Social protection	Socio-economic conditions (12)
16020	Employment creation
16070	Labour rights
24010	Financial policy and administrative management	Investment profile (12)
25010	Business policy and administration
51010	General budget support-related aid
600	Action relating to debt

Source: compiled by the author on the basis of: [OECD 2024b]; [The PRS Group 2022].

As we can see, 10 of the 12 components of this political risk rating correlate with the CRS purpose codes from code 150, Government & Civil Society, which are included in the above-mentioned “Peace ODA” category already used by the OECD Secretariat for monitoring purposes. However, it does not make sense to calculate correlation coefficients between the aggregate amounts of “peace ODA” provided by country A to country B and country B’s aggregate political risk score from The PRS Group. No donor distributes its aid to an individual recipient evenly across all sectors, which means that different political risks will be mitigated to varying degrees. This clearly indicates the need to calculate correlations between the directly linked ODA sectors and the components of the political risk rating. Such an analysis could hypothetically allow for comparing the mitigation potential of aid to different sectors (or their clusters) with each other. However, there are very serious obstacles to a realization of this idea.

First of all, we note the problem of simultaneity bias, which complicates the application of econometric methods.

Not only can development assistance policies influence the level of political risk in recipient countries, but vice versa: there is a pronounced parallel relationship between ODA levels and the level of political risk in recipient countries and the factors which generate that risk.

According to the latest data collected by the International Network on Conflict and Fragility (INCAF), ODA to the 60 countries categorized as “fragile contexts”⁸ decreased by 10% from 2010 to 2022, and in 2022 amounted to only 48.8% of ODA reported to the OECD DAC, the lowest figure over the last decade [OECD 2024d. P. 3]. Equally important is the fact that “peace ODA” is declining: in 2022—against the background of a sharp increase in ODA to Ukraine—this indicator amounted to $4.9 billion, and its share in the total amount—to only 9.9% (the lowest figure since 2006) [OECD 2024d. P. 10]. Separately, the fragmentation of aid increases along with the level of “fragility” of recipients: for example, in an “extremely fragile contexts” there are on average 27 bilateral and 14 multilateral donors, in others there are 22 bilateral and 11–14 multilateral donors [Hoeffler and Justino 2023. P. 8]. This trend seems particularly alarming, since donor proliferation increases the burden on the already weak institutions in recipient countries, thus becoming an additional factor of political risk, including for foreign investment.

Moreover, there are myriad endogenous and exogenous political risk factors that affect their level (and hence FDI flows) beyond inward ODA.

Let us imagine just one of the many specific situations that can occur in the “donor-recipient” dyad. For most of the year, donor country A sends quite significant amounts of aid to recipient country B, whose domestic and foreign policy is generally in line with the donor’s interests, including in the areas outlined in Table 1. Then, in the last quarter, country B undergoes a coup d’état due to domestic factors, and a government comes to power that is much less loyal to country A and the presence of its public and private companies. In response, country A decides to temporarily suspend or substantially reduce aid to country B.

This situation will be reflected in statistics in a very specific way: the annual value of the key components of The PRS Group’s political risk rating, calculated according to the ICRG methodology as the arithmetic mean of the sum of monthly values, is likely to exceed the previous calendar year, while the numerical value of ODA provided is likely to be lower than in the previous year. Numerical indicators taken on their own, without taking into account the described radical change in the political context, would indicate a negative mitigating effect (less aid, more risks), but in reality the parameters would change independently of each other.

Even if these two problems had a conceptual solution, the quality of the numerical data available for running regressions on the basis of the proposed methodology could hardly be recognized as satisfactory. Unfortunately, the representatives of donor countries do not always assign a numerical purpose code to a development assistance project in a methodologically sound manner.

Let us illustrate this problem with the example of the sector “Business policy and administration.” Its formal description, contained in the OECD DAC materials, allows to characterize it as a sector with a high potential of mitigating political risks of “legal-governmental” origin: “Public sector policies and institution support to the business environment and investment climate, including business regulations, property rights, non-discrimination, investment promotion, competition policy, enterprises law, private-public partnerships.” In reality, this code is assigned to projects of various types. Thus, for example, the OECD project-level data on the ODA from the largest world donor—the United States—in the area of “business policy and administration” in 2022 includes, among other things, the following activities:

- KosovoUp To Youth project in Kosovo, aimed at reaching out to vulnerable youth exposed to social exclusion, by mobilizing and empowering them to affect positive change;

- English language training for local government employees in Serbia;

- “Visit Tunisia” program, aimed at “capitalizing on the country’s rich natural, cultural and historical endowments to develop a diversified and high-quality sector that supports broad-based economic growth”;

- support for ICT centers of excellence and high profit agro-productions in Moldova, etc. [OECD 2024b].

Similar discrepancies (between the nature of the implemented activity and the CRS purpose code assigned to it) can be found in quite a large number for any other relevant sector. This means that in order to calculate correlations between aid volumes and the level of political risk in recipient countries, it would be necessary to double-check manually the correctness of sectoral coding of each individual project by those responsible for reporting ODA to the OECD. This is an extremely time-consuming exercise, the value of which is also highly questionable.

3. Challenges in assessing the mitigating effect of development guarantees

It is hypothetically possible to measure statistically the mitigating effect of development guarantees, for which, as already noted, it has been decided to calculate an ODA-reportable grant equivalent. In order to make such a measurement, it is important, first of all, to distinguish between the parameters of possible impact (damage) from risk and the likelihood of its materialization.

It is obvious that the parameters of political risks perceived by the investor will be influenced by the very fact of guaranteeing compensation for possible losses, which, in fact, determines the catalytic effect of guarantee instruments. In purely financial terms, the mitigating effect of guaranteeing for one investment project worth $1 billion and for 10 projects worth $100 million each—under identical coverage conditions—will be the same in our understanding. However, in order to correctly interpret the data on the volume of such guarantees (and/or capital mobilized with them) in terms of their impact on investors’ perception of risks, it is necessary to know the specific terms and conditions of guarantees. This applies both to the scope of the risks covered and to the proportion of losses compensated by the guarantor (which may differ by many percentage points).

The issue of quantifying the contribution of guarantee instruments to reducing the probability of materialization of political risks through the so-called “halo effect” deserves special attention. This effect affects risks of “legal-governmental” origin, and, accordingly, it is crucial for the researcher to know not only the volume of guarantees but also its exact terms (coverage).

However, a correlation of a sheer volume of guarantees extended with The PRS Group’s ICRG political risk rating, such as that described in the previous section for conventional ODA instruments, is hardly possible. Although ICRG’s political risk rating contains an “investment profile” component (comprising three elements: contract viability/expropriation; profits repatriation; and payment delays), calculating the correlation between it and a volume of investment guarantee makes no sense.

Guaranteeing creates a “halo effect” for a specific project, while The PRS Group’s ICRG rating (or any other rating of the same type) assesses the relevant risks on a country-wide scale and without regard to the industry specifics.

The most accurate information on the strength of the “halo effect” can be provided by information on activated (called) guarantees. It is important to know what proportion of supported projects faced such serious risks that the guarantee was activated (called), and how many conflicts with host country authorities (if the risks had materialized) were resolved through the intervention of the home country authorities.

However, such techniques for assessing the mitigating effect of guarantees/insurance remains impractical. The issue in this case is, regrettably, considerably more trivial than in the case of conventional ODA instruments. It consists of the fundamental absence of the statistical data of adequate volume and quality.

For an extended period of time, the OECD data on the utilization of guarantee instruments reflected only activated guarantees (in case of materialization of the risks covered by them), which were reported as other official flows [OECD 2023c. P. 5]. Since 2012, the volume of capital mobilized by guarantee instruments has been systematically documented, including detailed data on beneficiary countries and sectors. However, the data does not allow for the determination of the country of origin of the company to which the guarantee coverage was provided, or—most importantly—the type of risk against which the investor (or the lending institution) is protected. In 2023, a series of modifications to statistical reporting were made. In addition to the amount of funds mobilized, it was decided to calculate the grant-equivalent of those guarantees that support projects that are primarily focused on promoting development in ODA recipient countries, meet the criteria of financial and developmental additionality, and have a maturity of more than one year.

A consensus was reached about the need for enhanced reporting from guarantee providers. To this end, the template to be completed by donors has been expanded to encompass more than 50 items.

However, even when the transition to more granular reporting is implemented, and assuming that most donors will provide data in the new format, the raw data cannot be used to assess the contribution of guarantees to mitigating political risks to business. The new template does not make investors indicate which specific risks a particular guarantee protects them against. Available data indicates that a considerable proportion of guarantees encompass either commercial risks or both commercial and political risks. Consequently, quantitative data on the amount of guarantee coverage and the amount of funds mobilized through guarantees cannot be used “as is.” The only solution is to rely on data from national agencies. However, such data is characterized by a high degree of fragmentation, including with regard to the specific terms and conditions of guarantees.

Despite the availability of the necessary numerical indicators in international statistics, the issue of the simultaneity bias remains unavoidable, particularly in the context of guarantee instruments. On the one hand, a higher percentage of FDI in fragile and conflict-affected states has insurance coverage than in other low-income countries (6.2% vs. 3.8%) [US International Development Finance Corporation 2023, p. 24]. Conversely, the volume of guarantee coverage for projects in the most high-risk jurisdictions remains minimal, as does the volume of capital mobilized through guarantees. According to the OECD data from 2018 to 2020, this group of recipients accounted for only 24% of total funds mobilized [OECD 2023b. P. 17].

The current international environment engenders uncertainty regarding the trajectory of these indicators. On the one hand, companies themselves remain cautious about investing in high-risk jurisdictions. However, we observe that providers of development cooperation are making concerted efforts to attract private capital to regions, countries, and sectors that are deemed to be of geopolitical and geo-economic importance. This includes regions and countries with high political risk, where private capital is particularly scarce. These efforts include both persuading national investors and subsidizing guarantee fees.

Therefore, it is not inconceivable that the magnitude of ODA-reportable development guarantee expenditures by donor states would be predetermined by the level of political risks to a greater extent than vice versa.

Conclusion

Private companies and their respective countries of origin share a vested interest in optimizing the utilization of international development assistance resources to mitigate the political risks that render investment in developing countries particularly challenging. The specific instruments employed in this process, such as conventional ODA instruments and guarantee instruments, are determined by the risk management strategy selected.

The mitigating effect of conventional ODA instruments could be theoretically quantified. The predictor could be the individual sectors of ODA most relevant in terms of their impact on political processes in the recipient country and the factors determining them. The regressor could be some numerical indicators from political risk ratings correlated with these sectors. In the context of guarantee instruments, it is possible to measure their direct catalytic effect, based on the amount of capital mobilized, and the “halo effect”—protection against risks of “legal-governmental” origin—by calculating the share of projects where such an effect did not work and investors suffered losses.

However, there are several insurmountable limitations to the application of quantitative methods. In the context of conventional ODA instruments, the primary challenge lies in simultaneity bias, as well as the presence of a multitude of exogenous and endogenous factors influencing both aid volumes and political risk parameters. A significant proportion of these factors can only be captured through the use of dummy variables. As for the guarantee instruments, the measurement of their direct catalytic effect and the magnitude of the “halo effect” is predominantly impeded by the substantial fragmentation and imprecision inherent in source data. The determination to calculate the grant-equivalent for guarantees and make them partly ODA-reportable has not yet rectified this issue.

This observation underscores the complexity of assessing the mitigating effect of ODA-eligible flows on political risks solely based on quantitative data. It highlights the necessity of employing qualitative methods that encompass a wide array of complementary sources of various types. A comprehensive case study can facilitate the identification of the most suitable combination of analytical tools. This analysis should encompass not only the numerical data on ODA from the OECD databases or from political risk ratings, but also the subtle characteristics of the development finance provided, its terms and conditions, and the dynamic changes in the context and political risk factors in the host country and its relationship with the outside world. The selection of genuinely interesting and representative cases is a non-trivial task and may help identify new solutions that could pave new paths in the study of this topic.  

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Notes

¹ The differentiation between “legal-governmental” and “extra-legal” risks was proposed in [Kennedy 1987].

² In this article, private companies are defined as business organizations in which private ownership exceeds 50%. This definition is consistent with the distinction between official and private cross-border flows to developing countries as used in the OECD statistics.

³ “Linking” is also possible when donor states implement targeted projects through international organizations.

⁴ A typical example of this kind of research program is the Project on US Leadership in Development, implemented by the influential Washington-based Center for Strategic and International Studies (CSIS; the organization is recognized as undesirable in Russia) since 2011 and funded by the oil giant Chevron.

⁵ For example, in FY 2020–2023, political risk insurance accounted for 35% of all US International Development Finance Corporation revenues (about $600 million). Expenditures amounted to only $95 million (about 9.6%), of which $45 million were insurance payments related to the escalation of the conflict in Ukraine in 2022 [US International Development Finance Corporation 2023. P. 81].

⁶ Launched at the Ministerial Conference in Hong Kong in December 2005.

⁷ See our earlier review of relevant foreign studies using various techniques and published up to and including 2023 [Bartenev 2023a], as well as recent publications that assess the catalytic effect of ODA and consider country risk indicators as variables (e.g., [Bertrand and Betschinger 2024]).

⁸ As of 2022, “fragile contexts” accounted for 24% of the world’s population, but nearly 73% of people in extreme poverty, with this figure projected to rise to 86% by 2030 [OECD 2022b].