Ekonomika ISSN 1392-1258 eISSN 2424-6166

2026, vol. 105(1), pp. 92–107 DOI: https://doi.org/10.15388/Ekon.2026.105.1.6

Policy Uncertainty and Foreign Direct Investment: The Moderating Role of Institutional Quality

Zeki Akbakay
Bingöl University, Faculty of Economics and Administrative Sciences,
Department of Economics, Bingöl, Turkey
E-mail: zekiakbakay@gmail.com
ORCID ID: https://orcid.org/0000-0002-6736-6483

Abstract. This study investigates the impact of economic policy uncertainty (EPU) on foreign direct investment (FDI) inflows and examines how this relationship is moderated by institutional quality. The analysis is based on data from 37 developing countries over the period of 2002–2021. The World Uncertainty Index (WUI) and Worldwide Governance Indicators (WGI) serve as the primary data sources. Employing a two-step system Generalised Method of Moments (GMM) estimation for dynamic panel data, the study yields two key findings. First, policy uncertainty significantly reduces FDI inflows. Second, institutional quality – measured by the average of WGI dimensions – alleviates this negative effect. Robustness checks, including OLS estimation and analyses based on individual WGI components, confirm these results. Overall, the findings suggest that the adverse impact of policy uncertainty on FDI is conditional on the host country’s institutional strength; in countries with stronger institutions, FDI inflows are less susceptible to uncertainty.
Keywords: Policy uncertainty, foreign direct investment, institutional quality.

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Received: 17/12/2024. Accepted: 30/01/2026
Copyright © 2026 Zeki Akbakay. Published by Vilnius University Press
This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

1. Introduction

Developing countries¹ consider FDI as a source of economic development, growth and employment and therefore try to make their national policies favourable for an environment that can maximise foreign investment inflows. After the 1990s, traditional factors such as the exchange rate (Ayenew, 2022; Lajevardi & Chowdhury, 2024; Sultana et al., 2024), growth (Sokhanvar & Jenskin, 2021; Wondimu, 2023, Shinwari et al., 2024), inflation (Agudze & Ibhaghui, 2021; Mensah et al., 2024) and openness (Akorsu & Okyere, 2023; Xiao et al., 2024) have become insufficient in explaining FDI volatility, primarily due to their relative stability over time. This has prompted researchers to explore alternative determinants of FDI. For example, Albulescu and Ionescu (2018) argue that changes in the financial environment of the host country can better explain the fluctuations in FDI. However, many studies provide evidence that FDI fluctuations are more related to global and domestic policy uncertainties (Chen et al., 2018; Nguyen & Lee, 2021). In response to the changing dynamics in this field, the current research poses the following questions: How does EPU influence FDI inflows in developing countries? Does institutional quality moderate the impact of EPU on FDI? Accordingly, the research hypotheses derived from both theoretical considerations and empirical findings are examined by using panel data modeling techniques.

EPU, which refers to the uncertainty about the future economic policies of governments, affects firms’ decisions regarding FDI. EPU can influence FDI both directly and indirectly through the above-mentioned traditional determinants of FDI (Canh et al., 2020). This issue has gained scholarly attention. For instance, Andrikopoulos et al. (2022) argue that the impact of EPU on FDI can be realised through three primary transmission channels. The first is the ‘wait-and-see’ effect, whereby investors delay investment decisions during periods of heightened uncertainty. The second channel is related to investors’ risk-taking behaviour. Accordingly, due to risk aversion, international capital flows tend to decline during periods of an increased policy uncertainty. The third channel involves the adverse relationship between policy uncertainty and expected investment returns in financial markets.

Because investment decisions are typically irreversible, they tend to be the most vulnerable element of aggregate output under conditions of uncertainty (Jardet et al., 2022). This irreversibility hampers the mobility of capital across borders, leading to a shift of bargaining power to the host country (Inada & Jinji, 2023). Moreover, since foreign investments generally incur higher costs than domestic ones (Julio & Yook, 2016), and because foreign investors often lack sufficient information about the political environment and receive limited protection from host country institutions (Bénassy-Quéré et al., 2007), FDI dynamics have become increasingly sensitive to uncertainty.

Compared to developed countries, developing economies are more reliant on foreign resources and exhibit greater economic fragility, making them more vulnerable to global shocks. In this context, policy uncertainties caused by global developments – such as wars and economic crises – affect developing countries more, and ultimately lead to the interruption of international capital flows. However, the impact of EPU on FDI may not be the same for every country. At this point, country-specific structural characteristics – such as the degree of openness and institutional structure – may play a decisive role in the impact of EPU on FDI. Numerous studies explaining the causes of growth and productivity differences between countries emphasize the critical role of institutional quality (Keefer & Knack, 1997; Acemoglu et al., 2005; Durguti et al., 2024; Tmava et al., 2025). Corruption, inefficient bureaucracy, and a lack of legal robustness significantly undermine a country’s ability to attract foreign investment. According to Daude and Stein (2007), weak institutional structures discourage investment by both elevating transaction costs and creating ambiguity about future profitability. Investors tend to avoid countries where institutional frameworks foster bureaucracy, nepotism, and corruption, as these factors raise the overall cost of conducting business. Conversely, the presence of a robust governance structure can instill investor confidence, not only encouraging greater foreign investment (Globerman & Shapiro, 2002) but also mitigating the negative impact of EPU on FDI.

Despite contributions from studies like Julio and Yook (2016) and Choi et al. (2020) regarding the mediating influence of institutional environment on the policy uncertainty–FDI relationship, scholarly research in this area remains insufficient. This study contributes to the literature by focusing on the role of institutional quality in the relationship between policy uncertainty and FDI in developing countries. Another contribution of the study is that, unlike previous studies which used a limited number of indicators for institutional quality, it uses six sub-indices to examine the role of institutional quality in the policy uncertainty–FDI relationship. Thus, it becomes possible to compare how various aspects of a country’s institutional framework influence this relationship.

At this point, it is observed that studies on the relationship between uncertainty and FDI are limited in the literature, and that empirical findings on the regulatory role of institutional quality in this relationship are insufficient. However, strong institutions can support foreign capital inflows by increasing investor confidence in times of uncertainty. Building on this gap, the present study aims to examine how economic policy uncertainty affects FDI in developing countries, and whether this effect is contingent upon the strength of institutional structures. In the study, institutional quality is considered as a regulatory variable that mitigates the possible adverse effects of EPU on FDI and is analysed by using the GMM with data from 37 developing countries for the period of 2002–2021. The findings indicate that institutional quality plays an important moderating role in the EPU-FDI relationship and point to the importance of strong institutional frameworks for sustainable and long-run growth.

2. Literature

2.1. Literature on the Impact of İnstitutional Quality on FDI

Schneider and Frey (1985) were among the pioneers in examining how institutional quality influences foreign direct investment; they concluded that political instability tends to reduce FDI inflows. Similarly, Wei (2000) reaches the conclusion that corruption in the host country represents significant impediments to FDI. Subsequent studies employing disparate measures of institutional quality have reached a consensus that institutional quality is a crucial driver of FDI flows.

As Globerman and Shapiro (2002) note, the characteristics of a country’s institutional environment significantly shape foreign direct investment inflows. In this context, the authors highlight the significance of institutional quality in attracting foreign capital and facilitating the emergence of multinational companies capable of investing abroad. Bénassy-Quéré et al. (2007) also highlight that institutional indicators exert an effect on FDI. Daude and Stein (2007) demonstrate that robust institutions exert a significant effect on FDI. In a similar vein, Buchanan et al. (2012) show that institutional quality not only has a positive impact on FDI but also reduces its volatility. Aziz (2018) supports these findings, highlighting the positive effects of multiple institutional dimensions on FDI inflows. Sabir et al. (2019), by using panel data and the GMM approach, analysed both developed and developing countries and concluded that stronger institutional frameworks consistently promote FDI across country categories. Antonietti and Mondolo (2023) present evidence of a bidirectional relationship, suggesting that while better institutions attract more FDI, FDI can also contribute to improving institutional quality in host countries. Khan et al. (2024) found that the voice and accountability, control of corruption and political stability increase FDI inflows in developing countries, whereas government effectiveness and regulatory quality significantly reduce FDI inflows. The findings of Bhujobal et al. (2024) and Tabash et al. (2024) further support the notion that institutional strength plays a crucial role in attracting FDI.

2.2. Literature on the Impact of Economic Policy Uncertainty on FDI

Although capital is relatively more mobile than other factors of production, its international mobility is constrained during periods of heightened uncertainty. Given that uncertainty is not directly observable, it may prove challenging to empirically test the effects of uncertainty. Nevertheless, the existing literature on this subject finds a negative relationship between EPU and FDI.

Azzimonti (2019), by using the Partisan Conflict Index, demonstrates that a partisan conflict over trade policy uncertainty in the United States is associated with a reduction in FDI inflows. Similarly, the timing of elections has been identified as another factor influencing FDI decisions. Julio and Yook (2016) find that US firms’ investment in foreign countries decreases during election years in the US or destination countries. Nevertheless, the authors demonstrate that the impact of election-related uncertainty on FDI is transient, with these effects dissipating over time. In a large panel study comprising 216 countries, Chen et al. (2018) observed a notable decline in FDI during electoral periods characterised by a heightened policy uncertainty. However, this decline was particularly pronounced in democratic countries. The authors posit that this decline is attributable to an increase in policy uncertainty associated with elections. Likewise, using election periods as an indicator of policy uncertainty, Jahn and Stricker (2022) find a negative relationship between policy uncertainty and short-term capital flows. However, they observe that higher institutional quality diminishes the negative effect of uncertainty.

Recent studies increasingly use new uncertainty indices such as the Economic Policy Uncertainty index (EPU) developed by Baker et al. (2016) and the WUI developed by Ahir et al. (2022). Inspired by the real options theory, these studies consistently point to a negative relationship between policy uncertainty and FDI (Jahn & Stricker, 2022). Gao et al. (2024), by utilizing data from 264 Chinese cities, identified a negative correlation between EPU and FDI inflows. Their findings also indicate that FDI inflows in more developed cities exhibit greater sensitivity to EPU.

The literature discussed above has inspired us to formulate the following hypotheses.

H₁: Policy uncertainty reduces FDI inflows;

H₂: Institutional quality moderates the negative impact of policy uncertainty on FDI.

3. Data and Methodology

3.1. Data

The panel of this study includes 37 developing countries and covers the period of 2002–2021. The dependent variable is the net inflow of FDI as a percentage of GDP, obtained from the World Bank’s World Development Indicators (WDI). FDI net inflow consists of equity capital, reinvestment of earnings, and the sum of long- and short-term capital (WB, 2024a). To investigate the influence of policy-related uncertainty on FDI, WUI is adopted as the main independent variable. Comprising 143 developed and developing countries’ uncertainty measures, the WUI is constructed using Economist Intelligence Unit (EIU) country reports. The index is based on how often the term ‘uncertainty’ appears in country reports published by the EIU. The WUI not only measures domestic economic policy uncertainty for each country, but also provides analyses and forecasts of key economic and political issues and conditions. Apart from the WUI, many researchers have measured policy uncertainty using different methods (Baker et al., 2016; Altig et al., 2021; Caldara & Iacoviello, 2022). However, these indices are limited to developed countries and most of them use data after 1990. WUI is important in terms of eliminating this deficiency (Ahir et al., 2022).

This analysis uses the WGI created by Kaufmann et al. (2010) as a measure of institutional quality. It is not possible to use all governance indicators in the same equation due to the high correlation between them (Globerman & Shapiro, 2002; Daude & Stein, 2007; Buchanan et al., 2012). Therefore, as in Canh et al. (2020), the average institutional quality indicator (IQ_AVR) obtained from the average of these indicators is used as a regressor. On the other hand, in order to test the robustness of the model estimation results for IQ_AVR, the impact of each indicator on FDI is analysed in different models. Moreover, in order to determine the role of institutional quality in the impact of policy uncertainty on FDI, interaction terms consisting of the interaction of existing quality indicators and WUI are included in the models.

In addition, the models incorporate control variables that are theoretically expected to affect FDI inflows. Among these, the Financial Globalisation index (FG) measures a country’s openness to international financial flows and investments (Gygly et al., 2019). Fixed Capital formation (FC) represents the domestic investment climate (Buchanan et al., 2012). The Real Effective Exchange Rate (RER) measures the relative prices of goods. In the analysis, the real exchange rate (RER) is derived by dividing the domestic price index by the foreign price index, while ensuring that both are denominated in local currency. Thus, when the real exchange rate increases, it implies that the purchasing power of the domestic currency has strengthened. (Darvas, 2021). In order to enhance normality, this study employed the logarithm of the real exchange rate and the financial globalisation index. Table 1 shows the definitions and sources of the data used in the analysis.

Table 1. Definitions of variables, data sources and references

Source: Author’s own calculations.

3.2. Methodology

In contrast to static models, dynamic panel models include past values of the dependent variable as regressors. This structure leads to endogeneity issues, as these lagged values tend to be correlated with the model’s error term. (Barros et al., 2020). Endogeneity, which is seen as an an important problem in econometrics, can be caused by neglecting the relevant variable, measurement error, simultaneity or sample selectivity. Estimation methods such as GLS and OLS neglect endogeneity, resulting in biased estimates (Baltagi, 2005). The System GMM, developed by Arellano and Bover (1995) and Blundell and Bond (1998), is very effective in eliminating the endogeneity problem. Since the lagged levels of the series in finite samples provide poor instruments for first differences, the forecasting power of the difference GMM may be weakened, leading to biased results. Unlike the difference GMM, the system GMM can provide relatively more reliable results by allowing the use of more instrumental variables. However, using too many instrumental variables may lead to overfitting and biased results (Roodman, 2009). Overfitting reduces the reliability of the Hansen test, which tests the validity of instrumental variables. To overcome this problem, it is appropriate to equalise or reduce the number of instrumental variables to the number of groups (Soto, 2009).

Dynamic panel data models are defined by the inclusion of lagged dependent variables among the regressors, reflecting the dynamic nature of the relationships being studied. This relationship in dynamic panel data models can be estimated by the following equation (Baltagi, 2005):

Y_it = αY_i,t–1 + βX_i + ε_it (1)

ε_it = μ_i + v_it (2)

In Equation (1), i is the number of units, t is the time, Y is the dependent variable, Y_i,t–1 is a lagged value of the dependent variable, and Xi represents control variables. In Equation (2), ε_it, μ_i, and v_it denote the error term, fixed effects, and shocks, respectively.

In this study, the effect of policy uncertainty on FDI and the role of institutional quality in this effect are estimated by using a two-step system GMM approach. The two-step GMM estimator yields a smaller asymptotic variance, which enhances the efficiency and asymptotic power of associated statistical tests relative to the one-step estimator. To improve the efficiency of the GMM estimator and the statistical power of its associated tests, researchers frequently adopt a two-step approach within the commonly applied GMM framework (Hwang & Sun, 2018). To this end, the following FDI models are constructed:

FDI_it = αFDI_it–1 +βWUI_it + γX_it + ε_it (3)

FGI_it = α FDI_it–1 + β(IQ_it * WUI_it) + γX_it + ε_it (4)

In analyses with the two-step system GMM, standard errors can be biased downwards if the sample is small. This study follows Windmeijer (2005) in its undertaking to obtain robust standard errors.

4. Results and Discussion

Table 2 provides summary statistics for the variables included in the empirical analysis. The results reveal that fixed capital formation has the highest average value, while average institutional quality demonstrates the lowest value. Furthermore, FC and FDI exhibit considerable variability across countries, as indicated by their high standard deviations. This indicates a high degree of heterogeneity across countries with regard to both FDI and FC. In contrast, the low standard deviation of the real effective exchange rate and policy uncertainty suggests that these variables are relatively stable.

Table 2. Descriptive statistics of variables

Source: Author’s own calculations.

Table 3 reports the correlation coefficients among the variables. The absolute values of the coefficients indicate that the highest correlation is 0.5729 whereas the lowest value is 0.0027, with the remaining coefficients falling between these two opposites. Low correlation coefficients suggest the absence of multicollinearity issues within the model. This increases the reliability and validity of the results (Naimoğlu, 2023).

Table 3. Correlation matrix

Source: Author’s own calculations.

Table 4 presents the results of the Levin-Lin-Chu (LLC), Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) panel unit root tests. According to the p-values of the tests, the null hypothesis (H₀), which states that there is a unit root in the panel, is rejected, while the alternative hypothesis (H₁), which states that there is no unit root, is accepted.

Table 4. Panel unit root test

Note. LLC, ADF and PP denote Levin-Lin-Chu, Augmented Dickey-Fuller, and Phillips- Perron tests, respectively.

Source: Author’s own calculations.

Table 5 presents the results of estimations of both systems, i.e., GMM and OLS. In this study, a two-step system GMM is preferred as the research method. However, in order to test the robustness of the results, the findings of the one-step system GMM and OLS estimators are also reported. Columns 4 to 6 show the two-step system GMM estimation results. To identify the effect of policy uncertainty on FDI inflows, Equation (3) is initially estimated. Consistently with previous studies, column 4 indicates that policy uncertainty (WUI) is associated with a meaningful negative effect on the inflows of FDI, thereby supporting hypothesis H₁. Subsequently, the model is estimated with the inclusion of institutional quality (IQ_AVR), and the results in column 5 reveal a positive and statistically significant effect of institutional quality on FDI. Finally, in order to investigate the effect of institutional quality on the relationship between policy uncertainty and FDI, an interaction term between IQ_AVR and WUI is added in Equation (4). As the interaction term does not represent the level of policy uncertainty or institutional quality, the approach of Alfaro et al. (2004) is followed by including both variables independently in the model. As seen in column 6, the interaction term’s coefficient is both positive and statistically meaningful. These results indicate that institutional quality mitigates the negative impact of policy uncertainty on FDI. This finding, which demonstrates that the impact of policy uncertainty on FDI is conditional on the level of institutional quality, is consistent with previous empirical studies (Julio & Yook, 2016; Choi et al., 2020; Jahn & Stricker, 2022), thus supporting hypothesis H₂ proposed in this study.

Table 5. Policy uncertainty and FDI: the role of average institutional quality (GMM and OLS estimation results)

Note. Parentheses show the robust standard errors. ***, ** , * denote significance at the 1%, 5% and 10 % significance levels, respectively.

Source: Author’s own calculations.

As shown in Table 5, the result of one-step system GMM and OLS estimations clearly support those obtained from the two-step system GMM, thereby reinforcing the robustness of the findings. Table 5 also illustrates the impact of control variables on FDI. The results indicate that financial globalisation has a significant and positive effect on FDI inflows across all models. As noted by Poelhekke (2016), financial globalisation facilitates FDI inflows by reducing transaction costs. Fixed capital formation (FC), which is another control variable representing the domestic investment climate, also positively influences FDI inflows. Buchanan et al. (2012) posit that fixed capital formation serves a key signal for foreign investors. Consistent with earlier studies (Kok & Acikgoz Ersoy, 2009), growth in fixed capital formation is found to positively impact FDI inflows across all model specifications. Lastly, the real effective exchange rate (RER) exerts a negative influence on FDI. An appreciation of the exchange rate may result in a reduction in FDI inflows, due to the increased costs associated with local assets for foreign investors. As evidenced in Table 5, the real effective exchange rate has a significantly adverse impact on FDI inflows.

For the GMM estimator to produce consistent results, the error term must not exhibit second-order serial correlation (AR (2)), the instrumental variables should be valid, and the overall model must be stable. The p-values of AR (2), Hansen test and Difference-in-Hansen test (p>0.05) indicate that there is no second order autocorrelation, and that the instrumental variables and the subset of instrumental variables are valid, respectively. In addition, the Wald test p-value (p<0.05) indicates the stability of the system GMM model, while the F-test p-value (p<0.05) verifies the reliability of the OLS model.

The findings presented in Table 5 indicate that in countries with stronger institutional frameworks, the negative impact of policy uncertainty on FDI inflows is significantly reduced. In these models, IQ_AVR serves as a proxy for institutional quality. To assess the reliability of this result, a robustness test is required. To this end, new models are estimated in which each of the WGI indicators is used as an independent variable instead of IQ_AVR. Table 6 illustrates whether the WGI indicators exert a moderating effect on the impact of policy uncertainty on FDI.

As illustrated in Table 6, all governance indicators exhibit a positive effect on FDI. Among these, the rule of law indicator has the greatest impact. This finding is consistent with prior studies, including Hossain and Rahman (2017), who employed WGI indicators in their analysis. Studies that find that regulatory quality has positive effects on FDI emphasise the ease of starting a business (Hasan et al., 2024). Sabir et al. (2019) also found that government effectiveness encourages FDI inflows in both high-income and upper-middle-income countries. The impact of political stability on FDI inflows has been a topic of considerable debate in previous studies. Political instability can discourage investment as it is often associated with expropriation. While numerous studies, such as Rashid et al. (2017), have identified a positive correlation between political stability and foreign direct investment (FDI), some research, including Shan et al. (2018), presents conflicting findings suggesting that political stability may not always lead to increased FDI inflows. Column 3 shows that the voice and accountability indicator also positively influences FDI, which is consistent with the findings of Lacroix et al. (2021). However, Mathur and Singh (2013) found a negative relationship between democracy and FDI, arguing that political freedoms without accompanying economic freedoms cannot attracts FDI inflows. One of the institutional factors affecting FDI inflows is corruption. It is often viewed as an additional cost of production or an implicit tax (Al-Sadig, 2009), which discourages investment. As can be seen in Table 6, the positive coefficient of the control of corruption (CC) supports these expectations. Nevertheless, Jetin et al. (2024) have concluded that there is a non-linear relationship between corruption and FDI.

Table 6. Policy uncertainty and FDI: the role of WGI indicators

Note. Parentheses show the Windmeijer-corrected standard errors. ***, **, * denote significance at the 1%, 5% and 10% significance levels, respectively.

Source: Author’s own calculations.

The effect of governance indicators on FDI also shapes the policy uncertainty-FDI relationship. With the exception of Government Effectiveness (IQ_GE), the remaining five governance indicators serve to moderate the effect of policy uncertainty on FDI. These findings lend support to the results presented in Table 5.

5. Conclusion and Policy Implications

While most studies confirm the adverse effect of EPU on FDI inflows, limited attention has so far been given to the potential moderating role of institutional quality in this relationship. This study contributes to the existing literature by empirically examining how institutional quality shapes the impact of policy uncertainty on FDI inflows in developing countries. To achieve this, the study conducted a dynamic panel data analysis. The findings indicate that policy uncertainty and institutional quality are important determinants of FDI inflows in developing countries. In line with expectations, policy uncertainty has a negative impact on FDI inflows. Institutional strength plays a buffering role against the adverse consequences of policy uncertainty for FDI. The moderating role of governance quality is statistically confirmed through both average and component-level governance indicators. The findings imply that the influence of EPU on FDI inflows is contingent upon the strength of institutional frameworks. Therefore, the quality of institutions is an important factor determining the economic effects of policy uncertainty on capital flows.

These findings offer several implications for policymakers. Given the irreversibility of FDI and its dependence on future expectations, it remains highly sensitive to economic policy uncertainty. Despite its contribution to economic growth, FDI does not occur automatically; thus, policies must be implemented to ensure a favourable investment environment. In this context, uncertainty can be reduced through the implementation of rule-based monetary and fiscal policies. Such frameworks reduce discretion and increase predictability, which are key in investor decision-making. Nevertheless, a rule-based policy framework alone may not be sufficient for FDI inflows. Beyond predictability, the success of such policies also depends on policymakers’ genuine commitment and willingness to implement them consistently and wholeheartedly. For example, although central bank independence and fiscal discipline are legally established in many countries, policymakers often disregard these regulations in practice. Therefore, an institutional culture, i.e., sound institutions, is needed to support these policies. Differences in institutional quality are a key reason for the varying impact of policy uncertainty across countries. In countries with high institutional quality, the negative impact of EPU on FDI and thus on the economy is relatively weaker. Therefore, building sound institutions is essential along the way towards mitigating the adverse effects of policy uncertainty on FDI. A sound institutional structure can only be created through institutional reforms. Accordingly, reduction of bureaucratic complexity is a key step towards lowering barriers to foreign investment. It is also necessary to implement a fair judicial system with provisions to protect individual and property rights. Finally, it is essential to establish a transparent and democratic system of governance. Transparency backed by strong institutions can encourage FDI inflows by increasing predictability and credibility.

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