# Public Health as a Buffer for FDI: The Role of Healthcare Services in Economic Stability

Zahra Khalilzadeh Silabi

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Document de Recherche du Laboratoire d'Économie d'Orléans

Working Paper Series, Economic Research Department of the University of Orléans, University Of Tours
and University Clermont-Auvergne, France

DR LEO 2025-25

<figure><img src="image_2.png" /></figure>

# Public Health as a Buffer for FDI: The Role of Healthcare Services in Economic Stability

Zahra Khalilzadeh silabi

MISE EN LIGNE / ONLINE | 31/12/2025

<figure><img src="image_3.png" /></figure>

Laboratoire d'Économie d'Orléans
Collegium DEG
Rue de Blois - BP 26739
45067 ORLÉANS Cedex 2

TÉL | (33) (0)2 38 41 70 37

MAIL | leo@univ-orleans.fr

www.leo-univ-orleans.fr
# Public Health as a Buffer for FDI: The Role of Healthcare Services in Economic Stability

Zahra Khalilzadeh Silabi, *

**Abstract**

This study examines how epidemic outbreaks influence foreign direct investment (FDI) inflows in developing countries, with a particular focus on whether healthcare systems can act as buffers during such shocks. Using a panel dataset of 98 countries from 2000 to 2022, the analysis combines two-way fixed effects (FE) models and the Local Projection Method (LPM). The analysis is structured in three parts: First, fixed-effects regressions assess the average effect of 20 major epidemics on FDI, revealing that diseases such as Ebola, MERS, Lassa Fever, and Leptospirosis significantly reduce investment inflows. Second, local projection methods trace the short- and medium-term responses of FDI to health shocks by transmission type. The results show varying recovery patterns: while FDI tends to rebound after direct contact or mosquito-borne outbreaks, airborne diseases cause more persistent declines. Third, the study explores whether stronger healthcare systems can mitigate these negative effects. Results suggest that countries with a higher density of nurses experience less severe FDI losses during outbreaks, particularly for diseases transmitted through direct contact or bodily fluids.

These findings underline the importance of healthcare investment not only for public health but also for economic resilience. By distinguishing effects across disease types and highlighting the moderating role of health infrastructure, this study offers practical insights for policymakers seeking to safeguard investment flows during times of crisis.

JEL Codes: F21; F35; H51; I15; O11

Keywords: Foreign Direct Investment; Epidemic Shocks; Healthcare Systems; Developing Countries

<footnote>*Univ. Orléans, LEO, Rue de Blois-BP 26739, 45067 Orléans Cedex 2, France; ay.khalilzadeh@gmail.com</footnote>
# 1 Introduction

Infectious disease outbreaks no longer belong only to the realm of public health; they have become powerful global economic shocks. From localized epidemics to pandemics, these events increasingly disrupt trade, production, mobility, and investment flows. In the 21st century alone, dozens of outbreaks have tested the ability of countries not only to save lives, but also to maintain their economies.

One of the most affected channels is foreign direct investment (FDI), a critical engine of economic growth, technology diffusion, and global integration (Fund [2020]). Given its sensitivity to perceived risk, institutional strength, and uncertainty, FDI is especially vulnerable to health-related shocks (Lee and McKibbin [2004]; Bloom et al. [2014]). Historical evidence bears this out: across multiple health crises since 2000, including SARS, Ebola, and COVID-19, global FDI has contracted sharply. In 2020, inflows fell by 42% following the COVID-19 outbreak, dropping below levels seen during the 2008–2009 financial crisis (UNCTAD [2020b]). The impact has been highly uneven: Latin America and the Caribbean saw a 37% fall, Africa’s inflows declined by 18%, while developing Asia experienced only a mild 4% drop, with East Asia even showing modest resilience (Sheng et al. [2023]). Similar declines were observed during the 2014–2016 Ebola outbreak in West Africa, particularly in countries with limited healthcare capacity and weak institutions. These are not just numbers; they reflect how quickly an epidemic can bring economic activity to a halt. Empirical research supports this trend, showing that disease outbreaks tend to reduce FDI inflows in the short term (Fu et al. [2021]; Oh et al. [2020]; Yu et al. [2023a]).

Health shocks, whether sudden like fast-moving epidemics or slower moving local outbreaks affect much more than public health systems. They send shock waves through entire economies, disrupting how people work, move, consume, and invest. As Smith et al. [2019] remind us, good health is the foundation of any functioning society. When fear and illness spread, they do not just burden hospitals; they weaken consumer confidence, interrupt supply chains, and destabilize institutions. These disruptions extend beyond immediate output losses, influencing longer-term economic decisions, especially in FDI, which tends to retreat under heightened uncertainty. While traditional determinants of FDI, such as market size, institutional quality, and trade openness, remain central (Aisbett et al. [2018]; Frenkel and Walter [2019]; Li and Vashchilko [2010]; Li et al. [2018]), epidemic outbreaks introduce new layers of risk that can alter investment behavior.

According to the World Health Organization report, in 2019, the WHO African Region spent only 5.3% of its GDP on health the second lowest among WHO regions. Although per capita health spending rose between 2002 and 2011, it began to decline around the time of the 2014–2016 Ebola outbreak. Most African countries dedicate less than 10% of their total government budget to the health sector, with many falling below 5%. This reflects persistent underinvestment in epidemic preparedness (WHO [2022]). In addition to financial constraints, basic healthcare infrastructure remains limited in many parts of the region. With an average of just 10 beds per 10,000 population, well below the global average of 27, many countries are ill-equipped to handle sudden surges in demand during health crises (WHO [2022]). Such limitations have been shown to undermine investor confidence and to amplify the economic impact of health shocks (OECD [2023]; Fu et al. [2021]). Weak health systems and limited infrastructure create a lot of uncertainty for investors. When countries are not equipped to handle outbreaks properly, investors see greater risks, not just in reality
but also in perception. By “reality,” we mean concrete operational risks, service disruptions, overwhelmed hospitals, and abrupt policy shifts, whereas “perception” reflects uncertainty amplified by limited or noisy information and media signals; this perceived risk often spikes ahead of fundamentals, prompting a wait-and-see stance ([Ahir et al., 2022]). During COVID-19, this perception-driven caution coincided with a marked pullback in global FDI ([UNCTAD, 2020b]). This uncertainty makes them more likely to pull back their investments or hold off on new ones, which only makes the economic damage from health crises worse.

Past crises have shown that economies with weaker health systems and lower institutional capacity suffer sharper and more prolonged declines in FDI. Limited epidemic preparedness heightens both perceived and actual risks, making investors more likely to withdraw or avoid high-risk markets altogether. The dominant explanation for these declines centers on the uncertainty mechanism, whereby health shocks amplify perceived risks, disrupt investor confidence, and destabilize host economies (OECD [2023]; Yu et al. [2023b]). Some research has begun to explore heterogeneity in FDI responses by industry, showing that sectors with lower redeployability and countries with weaker institutions suffer more pronounced investment losses during outbreaks (Moosa and Merza [2022]; Nawo and Njangang [2022]). There is also evidence that the negative effects of epidemics on FDI can persist beyond the immediate crisis, but recovery patterns vary and remain poorly understood. This study documents the FDI losses associated with outbreaks and examines whether stronger health infrastructure, proxied by nurse and midwife density, can buffer these effects by stabilizing investor expectations.

Despite valuable contributions from previous studies, the literature on the relationship between FDI and health shocks remains surprisingly limited in scope. Most studies treat epidemics as uniform events, documenting that FDI tends to fall during outbreaks but overlooking important differences between epidemics. This statistical simplification ignores how epidemics vary widely in severity, duration, geographic reach, transmission modes, and affected sectors. Treating all outbreaks as the same risks masks the true heterogeneity in their economic impact and can lead to misleading conclusions about FDI responses.

This leaves critical questions unanswered: Why do some epidemic outbreaks trigger stronger declines in FDI inflows than others? To what extent do these health shocks have persistent effects on FDI over time, and how long does it take for FDI to recover? What features of a country's healthcare system and institutional capacity help limit the economic damage from epidemics?

To address these gaps, this paper contributes to the literature in three key ways. First, while previous studies have shown that epidemics reduce FDI, I go further by examining a wider range of disease outbreaks across 98 countries over two decades. This comprehensive approach allows me to capture variations in outbreak characteristics, such as severity, duration, and transmission modes, and how these differences affect FDI differently across regions and time. By broadening the scope beyond a single epidemic or region, I can identify patterns and heterogeneity in the economic impact that earlier studies may have missed. Second, I use dynamic methods to capture persistent effects, moving beyond contemporaneous associations. This study systematically quantifies the moderating role of health infrastructure in the FDI response to epidemics and adds new evidence to the literature.

First, I study the short-term effect of epidemics on FDI. Then, recognizing that investment is key for long-term growth, I examine the dynamic response over time. Finally, I consider the mitigating effect of healthcare
policy on a country's attractiveness for FDI. Building on the literature and theoretical expectations, I test three central hypotheses. First (H1), epidemic outbreaks are associated with a significant decline in FDI inflows. While this paper does not directly test the uncertainty channel, previous research identifies increased investment uncertainty and perceived risks as primary reasons investors delay or withdraw capital during health crises. Additionally, other economic channels such as disruptions to supply chains, declines in consumer demand, and rising operational costs may also explain the drop in FDI inflows. Second (H2), the effects of health shocks are dynamic, with FDI declining even after the immediate outbreak. This reflects both lingering uncertainty and institutional disruption. Third (H3), countries with stronger healthcare systems (proxied by nurse and midwife density) experience smaller declines in FDI during and after epidemics, due to better outbreak control and investor confidence in public infrastructure.

Using a balanced panel of 98 countries from 2000 to 2022, this study provides new evidence on how epidemic shocks affect FDI. The results reveal three key insights. First, epidemic outbreaks are associated with short-term declines in FDI inflows, particularly for high-fatality diseases such as Ebola, MERS, Lassa Fever, and Marburg. These diseases appear to trigger stronger investor reactions due to their severity, uncertainty, and visibility. Second, the effects are not confined to the outbreak period. A dynamic analysis using local projection methods shows that FDI often continues to decline for one to two years after a shock, though recovery patterns vary by transmission type and perceived severity. Third, and perhaps most importantly, countries with stronger healthcare systems, proxied by nurse and midwife density, experience significantly smaller investment losses during health crises. Nurse and midwife density is not just a health statistic; it signals a country's ability to contain outbreaks quickly, stabilize local conditions, and maintain business continuity. This buffering effect is especially pronounced for outbreaks transmitted through direct contact or vector-borne flea. Together, these findings underscore the importance of epidemic preparedness not only for public health, but also for safeguarding economic stability.

These findings answer an important question: why are some countries able to withstand the economic fallout of epidemics more effectively than others? The answer appears to lie not only in the nature of the disease itself, but in the strength of a country's healthcare system and its institutional ability to respond. Exploring these sources of resilience is key to understanding how health shocks affect investor behavior and how governments can limit the economic damage. At its core, this paper makes a simple but often overlooked point: epidemics are not just public health emergencies; they are economic shocks that test a country's institutional and structural resilience. While FDI is known to respond quickly to perceived risk (Alsan et al. [2006]), what matters in the longer run is a country's capacity to manage that risk.

The remainder of the paper is structured as follows. Section 2 stylised facts and describes the data and key variables. Section 3 presents the methodology. Section 4 discusses the main findings, including heterogeneity by disease characteristics and healthcare capacity. Section 5 concludes with policy implications and directions for future research.
## 2 Stylised Facts

Understanding cross-country differences in public health capacity is essential to contextualize how epidemic shocks may affect foreign direct investment.

<figure><img src="image_4.png" /><figcaption>Figure 1: Public healthcare spending as a share of GDP, 2022</figcaption></figure>

Note: Values report general government health expenditure (% of GDP). Source: WHO Global Health Expenditure Database, via World Bank WDI (2025)

Figure 1 shows a wide variation in public healthcare spending across countries. In much of Sub-Saharan Africa, South Asia, and parts of Latin America, government health expenditure is below 4% of GDP, with some countries spending less than 2%. In contrast, higher-income countries in Western Europe, North America, and Oceania exceed 6%, with a few surpassing 8%. These disparities point to large differences in fiscal capacity and public health readiness, which may shape how economies absorb epidemic shocks.
<figure><img src="image_5.png" /><figcaption>Figure 2: Number of nursing and midwifery personnel (per 10,000 people)</figcaption></figure>

Note: Values report the distribution of nurses and midwives per 10,000 people. Source: World Health Organization (2023)

Figure 2 shows the distribution of nurses and midwives per 10,000 people. In 31 countries and territories, there are under 10 nurses and midwifery personnel for every 10,000 people. As this chart shows, these inequalities are regional, with figures lowest in countries in sub-Saharan Africa. Sub-regional differences are clear too. These structural weaknesses may amplify the disruptive effects of epidemics and deter foreign investors in affected regions.

Figures 3 and 4 provide a descriptive overview of the global distribution and intensity of epidemic outbreaks during the period 1996–2022.
<figure><img src="image_6.png" /><figcaption>Figure 3: Number of Epidemic Outbreaks affecting each Countries</figcaption></figure>

*Note:* This figure shows the geographic distribution of global infectious disease outbreaks (2001–2021). *Source:* Authors’ calculations based on data from the global dataset compiled by Torres Munguía et al. [2022]

<figure><img src="image_7.png" /><figcaption>Figure 4: Total Number of Years for each Outbreak by Region</figcaption></figure>

*Note:* This Figure shows how many times each region was affected by each disease in each region during the study period. *Source:* Authors’ calculations based on data from the global dataset compiled by Torres Munguía et al. [2022]

Figure 3 displays a world map indicating the number of recorded outbreaks in each country, based on the global dataset compiled by Torres Munguía et al. [2022]. The map uses two distinct color scales: countries included in this study sample are shaded from yellow to red, while all other countries appear in green tones. Although all countries represented have experienced at least one epidemic outbreak, the yellow-to-red gradient highlights the frequency of outbreaks specifically among the 98 countries included in the empirical analysis. As illustrated, a higher number of outbreaks is concentrated in parts of Sub-Saharan Africa and South Asia, particularly in countries such as the Democratic Republic of Congo and Nigeria.

Figure 4 complements this spatial representation by showing the total number of years each region has been affected by specific diseases. The results indicate that Sub-Saharan Africa has experienced repeated exposure to high-mortality diseases such as Ebola, Lassa fever, and Marburg virus over extended periods. The heatmap also reveals that certain diseases, such as cholera, measles, and zoonotic influenza are more
widespread across regions, while others, including MERS and Marburg, are more geographically concentrated. These patterns provide important context for understanding the regional variation in epidemic risks, which may influence foreign direct investment (FDI) dynamics in the subsequent analysis.

Together, these four figures paint a clear picture; the regions hit hardest by epidemic outbreaks often have the weakest healthcare systems. Low government spending on health (Figure 1) and serious shortages of nurses and midwives (Figure 2) leave these countries vulnerable and less able to respond quickly when crises strike. This gap between high epidemic risk and limited health capacity matters a lot for foreign investment, since investors look closely at a country's ability to handle shocks and maintain stability. Strengthening health systems in these regions is not just a public health issue; it is essential for building economic resilience and attracting investment.

# 3 Data Description and Variable Construction

This study combines data about FDI inflow percentage of GDP, epidemic outbreaks, and unilateral country characteristics. After cleaning and merging the data sets from different sources, I come up with a panel ranging from 2000 to 2022 with 1,944 observations for 98 countries. Following the World Bank classification, the sampled countries are grouped into four regions (Sub-Saharan Africa, Asia and Pacific, Middle East and North Africa, Latin America and Caribbean), (see Table A.19 in Appendix A). The definition and sources of the variables are listed in Appendix A, Table A.1, which includes both the economic and epidemic-related indicators used in the analysis. This table shows that the epidemic variables are constructed as binary indicators, while macroeconomic controls come from standard sources like the World Bank.

Table 1 presents the summary statistics. The average FDI inflow across the sample is 3.85% of GDP, but the range is wide, with some countries experiencing negative FDI and others exceeding 100% in a given year. Political stability, GDP growth, and natural resource rents also exhibit substantial variation. Trade openness averages 73% of GDP, with a maximum value exceeding 400%, highlighting large differences in global integration. Government expenditure accounts for about 13.7% of GDP on average.

## 3.1 Epidemic Outbreaks Data

Data on epidemic-prone disease outbreaks were obtained from the global dataset compiled by Torres Munguía et al. [2022], which integrates information from the World Health Organization's Disease Outbreak News (DONs) and the WHO Coronavirus Dashboard. The dataset provides standardized records of 2,227 outbreaks of 70 infectious diseases across 233 countries and territories from January 1996 to March 2022. The DONs are based on official epidemiological, clinical, and laboratory investigations conducted by WHO member states and partners. The authors processed and harmonized these data using ISO country codes and ICD disease classifications, ensuring consistency and comparability across countries and years. This comprehensive dataset enables researchers to examine the spatial and temporal distribution of disease outbreaks globally and has been made openly accessible.

While several global databases track epidemic events such as EM-DAT (The Emergency Events Database)
and WHO's original Disease Outbreak News (DONs) this study uses the refined dataset compiled by Torres Munguía et al. [2022], which offers significant methodological and practical advantages. EM-DAT, though widely used, tends to focus primarily on large-scale natural disasters and often lacks granular, standardized information about disease-specific epidemic events, particularly when they are localized or not classified as disasters under EM-DAT criteria. In addition, reported damages in EM-DAT may be overestimated, as countries sometimes have incentives to exaggerate losses in order to secure greater international aid or insurance-based compensation (Cavallo and Noy [2011]). On the other hand, the raw DONs produced by WHO are not originally designed for statistical analysis; they are unstructured, event-driven bulletins that vary in format and detail, making them difficult to harmonize across countries and years without extensive manual processing.

The data set constructed by Torres et al. (2022) addresses these shortcomings by systematically collecting, cleaning, and harmonizing WHO's DONs and Coronavirus Dashboard data using ISO-3166 country codes and ICD-10 disease classifications. The authors tackle several data quality issues, including duplicates, multicountry reporting, and disease mutation grouping, and apply consistent criteria for defining outbreaks. This approach not only improves statistical reliability but also aligns with FAIR data principles (Findability, Accessibility, Interoperability, and Reusability), making it highly suitable for empirical research. As a result, this dataset enables more accurate identification of meaningful, transnational epidemic events with epidemiological and economic significance, particularly in the context of studying global FDI inflows.

To keep the scope of the analysis clear, it is important to define what I mean by outbreaks. According to the WHO, infectious diseases are caused by harmful microorganisms such as bacteria, viruses, parasites, or fungi and can spread either directly or indirectly between people. The Centers for Disease Control and Prevention (CDC) defines an epidemic as a sudden rise in the number of cases of a disease, going beyond what would normally be expected in a particular place and time. Based on these definitions, this study treats epidemic outbreaks as clear spikes in reported infectious disease cases that go beyond typical baseline levels.

Focusing on the period between 2000 and 2022, I refined the dataset by excluding outbreaks that affected only a single country, unless those outbreaks were classified by the WHO or CDC as particularly significant due to their severity, scale, or public health implications. This decision was made to ensure that the analysis captures epidemic events with broader relevance or greater epidemiological importance, rather than isolated or minor outbreaks. Additionally, I filtered the dataset based on the case fatality rate (CFR) to focus on diseases with meaningful mortality risk. CFR is defined as the number of deaths divided by the number of confirmed cases for each outbreak, expressed as a percentage. Outbreaks with a CFR below 0.01% were excluded.
<table><caption>Table 1: Descriptive Statistics</caption><thead><tr><th>Variable</th><th>Mean</th><th>SD</th><th>Min</th><th>Median</th><th>Max</th></tr></thead><tbody><tr><td>FDI Inflow</td><td>3.8546</td><td>6.2998</td><td>-37.1727</td><td>2.4242</td><td>103.3374</td></tr><tr><td>Political Stability</td><td>-0.5335</td><td>0.9132</td><td>-3.3130</td><td>-0.4609</td><td>1.5991</td></tr><tr><td>GDP Growth</td><td>4.1183</td><td>4.9676</td><td>-36.6568</td><td>4.3368</td><td>53.3856</td></tr><tr><td>Resource rent</td><td>10.0340</td><td>11.6884</td><td>0</td><td>5.5363</td><td>65.3185</td></tr><tr><td>lnGDP per Capital</td><td>7.879576</td><td>1.256237</td><td>5.451178</td><td>7.797576</td><td>11.30969</td></tr><tr><td>Trade Openness</td><td>73.3533</td><td>43.9049</td><td>2.6988</td><td>64.5191</td><td>437.3267</td></tr><tr><td>Government expenditure to GDP ratio</td><td>13.7164</td><td>4.7080</td><td>2.0471</td><td>13.5556</td><td>31.3443</td></tr><tr><td>Hepatitis E</td><td>0.0033</td><td>0.0575</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Chikungunya</td><td>0.0033</td><td>0.0575</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Cholera</td><td>0.0331</td><td>0.1790</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Dengue</td><td>0.0066</td><td>0.0811</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Diphtheria</td><td>0.0012</td><td>0.0352</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Ebola</td><td>0.0104</td><td>0.1012</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Zoonotic Influenza</td><td>0.0903</td><td>0.2866</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Japanese Encephalitis</td><td>0.0004</td><td>0.0203</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Lassa Fever</td><td>0.0066</td><td>0.0811</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Leptospirosis</td><td>0.0004</td><td>0.0203</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Marburg</td><td>0.0021</td><td>0.0455</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Measles</td><td>0.0219</td><td>0.1465</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Meningococcal Meningitis</td><td>0.0327</td><td>0.1779</td><td>0</td><td>0</td><td>1</td></tr><tr><td>MERS</td><td>0.0224</td><td>0.1479</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Plague</td><td>0.0050</td><td>0.0703</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Rift Valley Fever</td><td>0.0070</td><td>0.0836</td><td>0</td><td>0</td><td>1</td></tr><tr><td>SARS</td><td>0.0058</td><td>0.0759</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Typhoid</td><td>0.0012</td><td>0.0352</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Zika</td><td>0.0099</td><td>0.0992</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Viral Hemorrhagic Fever</td><td>0.0116</td><td>0.1071</td><td>0</td><td>0</td><td>1</td></tr></tbody></table>

Note: Summary statistics for the main variables used in the analysis. The sample extends from 2000 to 2022. Source: WDI, CDC, and WHO.

Given the above selection criteria, I include 20 epidemic outbreaks from 2000 to 2022 across 98 countries in my sample.

## 3.2 Other Control Variables

To account for other key determinants of foreign direct investment (FDI) inflows, I include a set of standard control variables based on the existing literature on FDI drivers (Yu et al. [2023b], Azzimonti [2019]). These variables reflect fundamental characteristics of the host economy that are widely recognized to influence investment decisions. Specifically, I control for GDP per capita (as a proxy for market size and development level), GDP growth (to capture the dynamism and growth prospects of the economy), and total natural resource rents (to account for resource-seeking FDI motivations). Political stability is included to reflect institutional quality and the perceived risk environment for investors. In addition, I incorporate trade openness—measured as the sum of exports and imports relative to GDP to capture the degree of integration into the global economy. Finally, government expenditure as a share of GDP is used to reflect the state's
fiscal capacity and its potential to influence economic stability and public investment. All control variables
were collected from the World Bank's World Development Indicators (WDI) database.

To classify countries based on healthcare system strength, I use nurse and midwife density (per 1,000 people) as a proxy for public health capacity. Countries with values above the first quartile of the distribution in a given year are considered to have a relatively strong healthcare system, while those below are classified as weak. This binary classification is used to construct an interaction term with epidemic outbreak variables in order to test whether stronger healthcare systems buffer the adverse impact of health shocks on FDI.

# 4 Methodology

This study investigates how epidemic outbreaks affect foreign direct investment (FDI) inflows and whether the strength of a country's healthcare system moderates this relationship. Using a panel dataset of 98 countries over the period 2000-2021, the empirical analysis proceeds in three steps, corresponding to the following hypotheses:

* **Hypothesis 1:** Epidemic disease outbreaks negatively affect FDI inflows in the short run.

* **Hypothesis 2:** The impact of epidemics on FDI is dynamic, involving short-term declines and potential recovery over time.

* **Hypothesis 3:** Countries with stronger healthcare systems are more resilient to epidemic-induced FDI shocks.

These hypotheses build on established theory and empirical findings discussed in the introduction. Hypothesis 1 reflects evidence that epidemic outbreaks generate immediate uncertainty and risk, leading to short-term declines in investment. Hypothesis 2 acknowledges that the impact of epidemics often extends beyond the initial outbreak period, as lingering uncertainty and institutional disruptions continue to affect FDI. Hypothesis 3 is based on the premise that stronger healthcare systems improve epidemic preparedness and response, thereby reducing economic disruptions and preserving investor confidence

To test these hypotheses, I employ two-way fixed effects regression models and the local projection method developed by Jordà [2005], using robust standard errors clustered at the country level. These methods are well-suited for analyzing the short-run and dynamic effects of shocks in panel data while accounting for unobserved country heterogeneity and time-specific global influences.

## 4.1 Baseline Estimation: Short-Run Effects of Epidemics on FDI (Hypothesis 1)

To test Hypothesis 1, I estimate a two-way fixed effects model separately for each epidemic disease. For each regression, the dependent variable is FDI inflows as a percentage of GDP, and the main explanatory variable is a dummy indicating the presence of a specific outbreak in a given country-year (e.g., Ebola, SARS, Zika). This setup allows me to capture the short-run effect of each outbreak individually, rather than averaging across all epidemics. The model includes country fixed effects to control for time-invariant characteristics
like geography or long-term institutions, and year fixed effects to account for global shocks. All control variables, such as political stability, economic growth, and trade openness are included with a one-year lag to help reduce potential reverse causality. Standard errors are clustered at the country level.

The estimated equation is as follows:

$$FDI_{it} = \alpha + \beta \cdot \text{Epidemic}_{it} + \gamma \cdot \mathbf{X}_{it} + \mu_i + \lambda_t + \epsilon_{it} \quad (1)$$

where FDI$_{it}$ denotes foreign direct investment inflows as a percentage of GDP in country i and year t, Epidemic$_{it}$ is a dummy equal to 1 if a specific outbreak occurred in year t in country i, and X$_{it}$ is a vector of time-varying economic and institutional controls. Country fixed effects (μ$_{i}$) capture unobserved heterogeneity across countries, while year fixed effects (λ$_{t}$) control for common global shocks.

## 4.2 Dynamic Response of FDI to Epidemic Shocks (Hypothesis 2)

Hypothesis 2 explores whether the effects of epidemic outbreaks on FDI are short lived or persist over time. In particular, I investigate how FDI behaves in the years following an outbreak does it gradually recover, remain depressed, or continue to decline?

To analyze this, I employ the local projection method developed by Jordà [2005], which provides a flexible way to estimate the dynamic path of FDI after a shock. Rather than relying on a single regression with lagged dependent variables, this approach estimates a separate equation for each time horizon h = 0, 1, 2, ..., H, where h represents the number of years after the epidemic year

The estimated equation is as follows:

$$FDI_{i,t+h} = \alpha_h + \beta_h \cdot \text{Epidemic}_{it} + \gamma_h \cdot \mathbf{X}_{it} + \mu_i + \lambda_t + \epsilon_{i,t+h} \quad (2)$$

The coefficients β$_{h}$ trace the effect of an epidemic in year t on FDI inflows in subsequent years. By plotting these estimates over time, I can assess whether FDI drops temporarily and recovers, or whether the shock has more lasting effects. As in the baseline model, I include country and year fixed effects to control for unobserved heterogeneity and global shocks. Standard errors are clustered at the country level.

This approach is particularly useful in this context, as it allows the data to speak freely about the duration and shape of the FDI response—without assuming in advance whether the effect is short-term, persistent, or reversible.

## 4.3 Moderation Analysis: Health System Capacity as a Buffer (Hypothesis 3)

The third hypothesis examines whether the strength of a country's healthcare system can cushion the negative effects of epidemic outbreaks on foreign investment. The idea is that stronger public health infrastructure may improve outbreak control, reduce uncertainty, and boost investor confidence during crises. To test this, I estimate an interaction model between epidemic shocks and a proxy for healthcare system strength. The regression specification is as follows:

$$FDI_{it} = \alpha + \beta_1 \cdot \text{Epidemic}_{it} + \beta_2 \cdot \text{GoodHealth}_{it} + \beta_3 \cdot (\text{Epidemic}_{it} \times \text{GoodHealth}_{it}) + \gamma \cdot \mathbf{X}_{it} + \mu_i + \lambda_t + \epsilon_{it} \quad (3)$$
The variable GoodHealth<sub>it</sub> is a binary indicator based on the distribution of nurses and midwives per 1,000 people. The threshold is defined as the median (50th percentile) within each year, so that countries above the median in a given year are considered to have relatively stronger healthcare systems. This year-specific threshold captures the idea that resilience depends on relative healthcare strength at the time of the outbreak, allowing countries to shift categories as their health capacity evolves.

The coefficient $\beta_3$ captures the moderating effect: a positive and statistically significant $\beta_3$ would indicate that countries with better healthcare systems experience smaller declines in FDI during epidemics, suggesting a buffering role.

As a robustness check, I also apply a fixed threshold calculated over the pooled sample period. This alternative approach emphasizes persistent structural differences between countries. Comparing results across both definitions helps ensure that the findings are not driven by the choice of threshold. (see Appendix A Table ).

## 4.4 Control Variables and Data Sources

To ensure that the estimated effects of epidemics on FDI are not confounded by other relevant factors, I include a standard set of control variables drawn from the literature on FDI determinants. These variables capture key economic and institutional characteristics of the host country that are widely recognized to influence investment decisions:

* **GDPpc**: GDP per capita (constant 2015 USD), which serves as a proxy for market size and development level. A positive relationship is expected, as higher income levels are generally associated with greater market potential and tend to attract market-seeking FDI (Asiedu [2006]; Nunnenkamp [2002]).

* **GDPg**: GDP growth rate (annual %), which reflects the economy's growth prospects. A positive relationship is expected, as faster-growing economies tend to signal greater future profitability and thus attract more FDI (Chakrabarti [2001]; Alfaro et al. [2003]).

* **Natres**: Total natural resource rents (as % of GDP), measuring the relative weight of extractive industries in the economy. While natural wealth can attract foreign investment, especially in mining and energy sectors, it may also deter investors due to exposure to price volatility, political risk, or weak institutions. Therefore, the expected sign is theoretically ambiguous (Poelhekke and van der Ploeg [2010]; Kolstad and Wiig [2009]).

* **Trade**: Trade openness, defined as the ratio of total trade (exports plus imports) to GDP, reflects a country's level of integration with the global economy. Greater openness is generally associated with a positive effect on FDI, as it reduces entry barriers, enhances market access, and signals a liberal economic environment (Edwards [1998]).

* **GovEx**: Government expenditure as a percentage of GDP, serving as an indicator of fiscal capacity and the scope of public sector involvement. Its expected effect on FDI is ambiguous, as public spending may promote investment when directed toward infrastructure or human capital, but may deter it if
associated with inefficient allocation, excessive bureaucracy, or crowding out of private sector activity
(Barro [1991]).

* **Politic:** Political Stability and Absence of Violence/Terrorism index, sourced from the Worldwide Governance Indicators, which captures perceptions of the likelihood of political instability or politically motivated violence. A positive relationship is expected, as political risk increases uncertainty and discourages long-term commitments such as FDI (Busse and Hefeker [2007]).

All economic indicators are sourced from the World Bank World Development Indicators (WDI).

# 5 Results and Discussions

I present my main findings of this study in three parts, each addressing a distinct component of the research question. First, I estimate the short-run effects of epidemic outbreaks on foreign direct investment (FDI) inflows using country and time fixed effects models (Hypothesis 1). This allows me to identify whether the immediate presence of specific infectious disease outbreaks leads to significant disruptions in FDI activity. Second, I use a local projection model to examine the dynamic response of FDI to epidemic shocks over time, identifying the persistence and potential recovery trajectories following an outbreak (Hypothesis 2). Finally, I investigate whether healthcare system capacity can mitigate the negative impact of epidemics on FDI, testing the hypothesis that stronger public health infrastructure acts as a buffer during health crises (Hypothesis 3).

## 5.1 Short-Run Effects of Epidemic Outbreaks on FDI inflows (Hypothesis 1)

### 5.1.1 Main Results(Hypothesis 1)

Epidemics are not all alike in the way they affect economies. The route through which a disease spreads can shape both its public health impact and the way investors perceive risk. Some, like airborne diseases, can travel quickly and widely, while others, such as direct-contact or certain vector-borne diseases, are often linked to higher fatality rates or more severe symptoms. These differences may influence how strongly foreign investors react, particularly in the short run.

To examine these effects, I estimate the two-way fixed effects model described in Equation (1), where the dependent variable is FDI inflows as a share of GDP and the main explanatory variable is an indicator for epidemic outbreaks by transmission type. All regressions include country and year fixed effects, and standard errors are clustered at the country level to account for serial correlation and heteroskedasticity within countries over time.

Outbreaks are grouped into five transmission categories, following CDC and WHO classifications: air-borne, water/food borne, vector-borne (mosquito), vector-borne (flea), and direct contact. The complete list of diseases by transmission type is provided in Appendix A, Table A.5.
<table><caption>Table 2: Country and Time Fixed Effect results for Transmission Type: Only Control Variables</caption><thead><tr><th rowspan="3">Variable</th><th colspan="5">Impact of Disease Outbreaks by Transmission Type on FDI</th></tr><tr><th>Airborne Outbreak</th><th>Water-Food Outbreak</th><th>Vector-borne Mosquito Outbreak</th><th>Vector-borne-Flea Outbreak</th><th>Direct Contact Outbreak</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Political Stability <sup>a</sup></td><td>0.184<br>(0.327)</td><td>0.179<br>(0.326)</td><td>0.177<br>(0.327)</td><td>0.171<br>(0.328)</td><td>0.146<br>(0.336)</td></tr><tr><td>GDP Growth <sup>a</sup></td><td>0.067**<br>(0.0328)</td><td>0.069**<br>(0.0327)</td><td>0.067**<br>(0.0329)</td><td>0.068**<br>(0.0330)</td><td>0.069**<br>(0.0327)</td></tr><tr><td>Natural Resources <sup>a</sup></td><td>-0.0132<br>(0.074)</td><td>-0.0133<br>(0.074)</td><td>-0.0129<br>(0.074)</td><td>-0.0126<br>(0.074)</td><td>-0.0146<br>(0.074)</td></tr><tr><td>Trade Openness <sup>a</sup></td><td>0.043 **<br>(0.019)</td><td>0.043 **<br>(0.019)</td><td>0.043 **<br>(0.019)</td><td>0.043 **<br>(0.019)</td><td>0.044 **<br>(0.019)</td></tr><tr><td>GDP per Capita <sup>a,b</sup></td><td>-2.102<br>(2.799)</td><td>-2.060<br>(2.772)</td><td>-2.045<br>(2.769)</td><td>-2.071<br>(2.771)</td><td>-1.979<br>(2.786)</td></tr><tr><td>General Expenditure <sup>a</sup></td><td>0.151 **<br>(0.067)</td><td>0.153 **<br>(0.067)</td><td>0.151 **<br>(0.067)</td><td>0.150 **<br>(0.067)</td><td>0.151 **<br>(0.067)</td></tr><tr><td>Observations</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td></tr><tr><td>R<sup>2</sup></td><td>0.071</td><td>0.071</td><td>0.071</td><td>0.071</td><td>0.074</td></tr><tr><td>Countries</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td></tr></tbody></table>

Notes: Results for control variables used in this study from two-way fixed effects regressions with robust standard errors. All regressions include country and time fixed effects. Source: Authors’ calculations based on WDI, CDC, and WHO.

a Lagged one period.

b Natural log transformation.

* p < 0.10, ** p < 0.05, *** p < 0.01.

Table 2 first presents the estimates for the control variables under different transmission-type specifications. The results confirm the expected positive association of GDP growth, trade openness, and government expenditure with FDI inflows (Chakrabarti [2001]; Aldashev and Verdier [2010]; Asiedu [2002]), while political stability, natural resource rents, and GDP per capita do not show consistent short-run effects. These controls are included to account for macroeconomic and institutional factors that typically influence investment decisions, ensuring that the estimated outbreak effects are not driven by broader economic trends

<table><caption>Table 3: Country and Time Fixed Effect results for Transmission Type</caption><thead><tr><th rowspan="3">Variable</th><th colspan="5">Impact of Disease Outbreaks by Transmission Type on FDI</th></tr><tr><th>Airborne Outbreak</th><th>Water-Food Outbreak</th><th>Vector-borne Mosquito Outbreak</th><th>Vector-borne-Flea Outbreak</th><th>Direct Contact Outbreak</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>FDI/GDP</td><td>-0.169<br>(0.233)</td><td>0.525<br>(0.644)</td><td>0.337<br>(0.315)</td><td>-1.344*<br>(0.721)</td><td>-1.718**<br>(0.750)</td></tr><tr><td>Observations</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td></tr><tr><td>R-squared</td><td>0.071</td><td>0.071</td><td>0.071</td><td>0.071</td><td>0.071</td></tr><tr><td>Number of Countries</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td></tr></tbody></table>

Notes: Results from two-way fixed effects regressions with robust standard errors, examining the differential impact of outbreaks by disease transmission route. All regressions include country and time fixed effects. Source: Authors’ calculations based on WDI, CDC, and WHO.

* p < 0.10, ** p < 0.05, *** p < 0.01.
Table 3 reports the estimated short-run effects of outbreaks, broken down by transmission type, on FDI inflows. Turning to the outbreak coefficients, the results indicate that the magnitude and statistical significance of the impact vary across transmission routes, with the most pronounced declines occurring for direct-contact diseases and vector-borne (flea) diseases. Direct-contact outbreaks such as Ebola, Lassa Fever, and Marburg virus are associated with an average reduction of about 1.7 percentage points in FDI relative to GDP. Vector-borne (flea) diseases, which are often unpredictable and harder to contain, are linked to a decline of roughly 1.3 percentage points. These outbreaks share characteristics such as high fatality rates, severe symptoms, and containment challenges, which tend to heighten public fear and investor caution. Moreover, many of these outbreaks are highly regional in scope (see Table 4). While this can limit their immediate global spread, it may also result in lower international visibility and weaker global mobilization of treatment and containment resources. As a result, affected countries may face prolonged disruptions with limited external support, further amplifying investor concerns. Consistent with previous studies, such disease profiles tend to magnify perceived operational and economic risks (McKibbin and Sidorenko [2006]; Goel et al. [2022]; Alam et al. [2021]).

### 5.1.2 Other Results (Hypothesis 1)

To complement the transmission-type analysis, I also estimated the short-run impact of each individual epidemic outbreak on FDI inflows. The results, summarized in Table 4.

<table><caption>Table 4: Significant Disease Outbreak Effects on FDI Inflows</caption><thead><tr><td>Disease</td><td>Coefficient</td><td>Std. Error</td></tr></thead><tbody><tr><td>Leptospirosis</td><td>-1.613***</td><td>(0.608)</td></tr><tr><td>Lassa Fever</td><td>-1.907**</td><td>(0.795)</td></tr><tr><td>Ebola</td><td>-1.508**</td><td>(0.702)</td></tr><tr><td>MERS</td><td>-1.202**</td><td>(0.533)</td></tr><tr><td>Plague</td><td>-1.344*</td><td>(0.721)</td></tr></tbody></table>

*Notes:* Results from two-way fixed effects regressions with robust standard errors. All regressions include country and year fixed effects. *Source:* Authors’ calculations based on WDI, CDC, and WHO.

*p* < 0.10, **p* < 0.05, ***p* < 0.01.

Table 4 shows a summary of diseases that are associated with statistically significant declines in FDI, ranging from approximately 1.2 to 1.9 percentage points of GDP. While the majority of the outbreaks in our sample also show negative estimated effects on FDI, these are not statistically significant at conventional levels.

These diseases are characterized by high case fatality rates, severe symptoms, and limited treatment options (WHO [2016], CDC [2020]), all of which heighten investor perceptions of risk (UNCTAD [2020a]; Adeleye et al. [2022]; Initiative [2014]; Yu et al. [2023a]). Many of these outbreaks have been regionally concentrated, which, while restricting their immediate global spread, often results in lower international visibility and a reduced mobilization of treatment and containment resources. This limited global attention can
prolong local disruptions and intensify the perceived vulnerability of affected economies, further discouraging investment.

In contrast, more frequent or less lethal diseases such as Cholera, Measles, Zika, and Typhoid do not significantly deter investment. Their relatively predictable epidemiological patterns, wider geographic familiarity, and more established treatment responses may lead investors to perceive them as manageable risks. This selective reaction underscores that investor responses are shaped not only by disease severity but also by its geographic profile, media coverage, and the extent of international engagement in the outbreak response (Smith et al. [2019]; Baker et al. [2020]). Full estimates for all diseases are provided in Appendix A, Table A.2.

To further examine how disease characteristics influence investor reactions, I classify epidemic outbreaks according to two key epidemiological indicators: the case fatality rate (CFR) and the basic reproduction number ($R_0$). CFR measures the proportion of deaths among confirmed cases and reflects the lethality of a disease. Following the WHO risk severity scale, I group diseases into five CFR categories: Very Low (<1%), Low (1-4.99%), Moderate (5-19.99%), High (20-49.99%), and Very High (≥50%) (see Appendix A, Table A.6).

The basic reproduction number ($R_0$) measures how many secondary cases one infected individual is expected to cause in a fully susceptible population, serving as an indicator of transmissibility. Based on ranges used in epidemiological modeling and emergency planning ([Delamater et al., 2019, Guerra et al., 2017, Lipsitch et al., 2003, Althaus, 2014, Van Kerkhove et al., 2019, Liu et al., 2020, Mukandavire et al., 2011, Nishiura et al., 2006]), I classify outbreaks into four categories: Low (<2), Moderate (2-3.49), High (3.5-5.49), and Very High (≥5.5) (see Appendix A, Table A.7).
<figure><img src="image_8.png" /><figcaption>Figure 5: Impact of Epidemic Outbreaks on FDI, by CFR and Reproduction Number</figcaption></figure>

Note: Each bubble is positioned by its average CFR (vertical axis) and average R₀ (horizontal axis). Bubble color indicates statistical significance from the regression: red = significant ($p \le 0.05$), orange = marginal significance ($0.05 < p \le 0.10$), grey = not significant ($p > 0.10$). Source: Author's calculations based on WDI, CDC, and WHO
The scatterplots show a clear pattern: diseases with very high fatality rates, such as Ebola, Marburg, and MERS, tend to spread less easily, with relatively low $R_0$ values. This is a common trade-off in epidemiology, diseases that kill quickly often have fewer opportunities to spread widely, both for biological reasons and because control measures are introduced earlier. On the other hand, the most contagious diseases in the sample, like Measles and Zika, generally have much lower fatality rates.

From an investment point of view, the outbreaks in the high-CFR, low-$R_0$ group are the ones most clearly linked to large drops in FDI. Figure 5 (a), Ebola, Lassa Fever, MERS, Leptospirosis, and Plague, are severe in their health impact and often limited to specific regions, which can mean less international attention and slower support. For the countries affected, this combination of serious health impact and weaker outside help makes investors more cautious, even if the disease is unlikely to spread globally.

The transmission-type view in Figure 5 (b) reinforces this finding. Direct-contact and vector-borne (flea) diseases, which sit in the high-fatality but moderate-transmissibility range, show the largest and most consistent negative effects on FDI. By contrast, categories dominated by low-fatality, more contagious diseases, like airborne or mosquito-borne outbreaks, tend to show smaller and less significant effects. This suggests investors react more to the combination of how deadly a disease is and how difficult it is to control, rather than to contagiousness alone (Breban et al. [2007]; World Bank [2020]; Hong et al. [2020]; Lesmanawati et al. [2020]).

## 5.2 Dynamic Response of FDI to Epidemic Shocks Over Time (Hypothesis 2)

### 5.2.1 Main Results (Hypothesis 2)

This section tests Hypothesis 2, which suggests that epidemic shocks can have dynamic and potentially lasting impacts on foreign direct investment (FDI). To examine this, I use the Local Projection Method (LPM) in Equation (2) introduced by Jordà [2005], which estimates impulse response functions (IRFs) without relying on strict assumptions about the data structure. The analysis focuses on the years following an outbreak, tracking the path of FDI for up to five years to determine whether investment levels recover quickly, remain depressed, or adjust only gradually.

Importantly, the immediate effects of epidemics are already captured in the two-way fixed effects models discussed earlier (Hypothesis 1). The purpose of this dynamic analysis is to examine the medium-term recovery path of FDI and understand whether health shocks trigger temporary disruptions or lead to more enduring consequences. All models control for relevant macroeconomic variables and include country and year fixed effects to account for unobserved heterogeneity across time and space.

Figure 6 illustrates the evolution of foreign direct investment (FDI) inflows in the five years following the end of epidemic outbreaks, disaggregated by the mode of disease transmission. The estimates are derived using a local projection model and reflect dynamic post-crisis responses.
<figure><img src="image_9.png" /><figcaption>Figure 6: Impulse Response of FDI to Infectious Diseases outbreaks by Transmission Type</figcaption></figure>

*Note:* Each panel shows the impulse response of FDI inflows to different types of infectious disease outbreaks, classified by transmission mode. The solid line indicates the estimated response, and vertical bars represent 90% confidence intervals. Source: Author's calculations based on WDI, CDC, and WHO

The results show that all transmission types lead to a decline in FDI inflows in the aftermath of an outbreak, typically ranging from 0.5 to 1.1 percentage points of GDP. However, the depth and duration of these declines as well as the pace of recovery vary considerably depending on how the disease spreads (see Appendix A Table A.5).

**Direct contact outbreaks**, such as those involving Ebola or Marburg, are associated with a sharp decline in FDI immediately following the epidemic. Importantly, FDI begins to recover as early as one year after the end of the outbreak, and by years two and three, investment flows not only stabilize but turn positive. This recovery suggests that although these outbreaks generate severe investor uncertainty in the short term, they may also trigger a rebound once the crisis subsides and confidence is gradually restored.

**Vector-borne (Flea) outbreaks** show a quicker recovery. After a short drop in FDI, inflows start to bounce back by the second year and even rise above pre-outbreak levels in some cases. Although these positive effects are not always statistically strong, this pattern may suggest that investors adjust more easily in situations where such outbreaks are familiar or expected. The ups and downs seen in years four and five could be due to other outbreaks or unrelated shocks happening around the same time.

**Airborne outbreaks** such as SARS or MERS cause a longer-lasting negative effect. FDI inflows continue to fall during the first two years after the epidemic, showing that investors remain hesitant for a longer time. Signs of recovery appear only after the second year, suggesting that airborne diseases, because of their fast
spread and wider disruptions, create deeper and longer economic uncertainty. This is consistent with the idea that investment decisions are often delayed when risks are systemic and hard to control.

**Vector-borne (Mosquito) outbreaks** cause moderate and short-lived drops in FDI. Investment inflows start to recover after the first year following the outbreak. Although the initial decline can be sharp, the recovery pattern suggests that investors see these epidemics as manageable or familiar risks, particularly in regions where such diseases are common.

**Water/Food-born outbreaks** show a sharp initial decline in FDI around 1% of GDP that remains persistent, with only partial recovery after the fourth year. This slower rebound may reflect deeper structural vulnerabilities, such as inadequate water and sanitation infrastructure, or recurring outbreaks that reinforce investor concerns. It may also indicate lasting reputational effects, as these diseases signal broader health and institutional challenges that take time to address.

These findings align well with the broader literature on FDI fluctuations following epidemics Yu et al. [2023b] report a 21.5% FDI decline during outbreaks followed by compensatory increases in the three years after, while preparedness measures (e.g., Global Health Security Index) have been shown to buffer investment losses (Ait Soussane and Ibourk [2024]). Global trends also support a U-shaped recovery UNCTAD observed FDI plunging in 2020 before a partial rebound in 2021–22 (UN [2020]). The heterogeneity by transmission type underscores that investor responses are influenced not only by outbreak presence but also by the perceived control and spatial scope of spread. These insights can inform how public health strategies tailored by transmission mode may help accelerate investment restoration.

### 5.2.2 Other Results (Hypothesis 2)

To complement the transmission analyses, I also estimate the dynamic response of FDI to each of the 20 epidemic outbreaks individually. This allows for a closer look at how investment flows evolve over time following specific health crises, highlighting differences that might be obscured in grouped analyses. The estimations are based on the local projection model described in Equation(2), with country and year fixed effects, and standard errors clustered at the country level.

Given the large number of epidemics, the results are presented in Appendix A (Figures A.1-A.2–A.3) rather than in the main text. Each figure shows the estimated impulse response of FDI inflows to a subset of outbreaks. These figures provide a comprehensive overview of medium-term FDI adjustments across a wide range of epidemic contexts.

Overall, most outbreaks are associated with an initial decline in FDI inflows, typically within the first one to two years after the event. While the magnitude and duration of these declines vary, the general pattern shows partial to full recovery by years three or four for the majority of epidemics. A few high-fatality outbreaks, such as Ebola and MERS, exhibit more persistent negative effects, consistent with the results reported in the first hypothesis's section (see Table 4). These findings are in line with the broader literature, which emphasizes that health shocks tend to disrupt capital flows in the short term but that recovery is often faster when economies have stronger fundamentals and effective policy responses (Ma et al. [2020]; ElFayoumi and Hengge [2021]; MIT Research Group [2020]; Beirne and et al. [2021]; Eurasian Research Institute [2021]).
To illustrate these dynamics more clearly, Figure 7 focuses on the regional dimension of two transmission types: direct-contact outbreaks and water/food-borne outbreaks. These are chosen as representative examples, while results for other transmission channels (airborne, mosquito-borne, flea-borne) are consistent with the same general message; the economic shock of an epidemic depends heavily on where it strikes, with regions facing structural public health challenges bearing the greatest burden (see Appendix A, Figure A.4).

<figure><img src="image_10.png" /><figcaption>Figure 7: Impact of Epidemic Outbreaks on FDI by Transmission Type and Region</figcaption></figure>

Note: Results are based on local projection estimates of the response of FDI inflows to epidemic shocks. Each line represents the average impulse response up to five years after the outbreak, with 95% confidence intervals. Source: Author's calculations based on WDI, CDC, and WHO.

The regional patterns in Figure 7 show that epidemics do not hit all parts of the world equally. For direct-contact diseases, the sharpest and most lasting declines in FDI are found in Sub-Saharan Africa, where outbreaks such as Ebola and Marburg have been most severe. Here, limited containment capacity, scarce treatment resources, and the fear surrounding person-to-person transmission translate quickly into investor retreat.

For water- and food-borne diseases, the strongest disruptions also appear in regions where access to clean water and sanitation remains fragile, especially in parts of Africa and South Asia. Outbreaks like cholera can therefore carry not only health consequences but also serious economic costs, as weak infrastructure
magnifies their impact.

## 5.3 Moderating Role of Healthcare System Capacity (Hypothesis 3)

### 5.3.1 Main Results (Hypothesis 3)

Building on the baseline specification in Equation (3), I extend the model by interacting epidemic outbreak dummies (classified by transmission type) with an indicator for healthcare workforce strength. The latter equals 1 if a country's nurse and midwife density per 1,000 people is above the annual median, capturing whether stronger frontline capacity moderates the effect of epidemics on FDI inflows.

Table 5 first presents the estimates for the control variables under different transmission-type specifications.

<table><caption>Table 5: Healthcare Workforce Capacity and FDI: Nurse and Midwife density as Buffers by Transmission Route, Only Control Variables</caption><thead><tr><th rowspan="3">Variable</th><th colspan="5">Impact of Disease Outbreaks by Transmission Type on FDI</th></tr><tr><th>Airborne Outbreak</th><th>Water-Food Outbreak</th><th>Vector-borne Mosquito Outbreak</th><th>Vector-borne-Flea Outbreak</th><th>Direct Contact Outbreak</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Political Stability <sup>a</sup></td><td>0.186<br>(0.391)</td><td>0.196<br>(0.398)</td><td>0.188<br>(0.389)</td><td>0.188<br>(0.390)</td><td>0.163<br>(0.390)</td></tr><tr><td>GDP Growth <sup>a</sup></td><td>0.062*<br>(0.035)</td><td>0.065*<br>(0.034)</td><td>0.062*<br>(0.034)</td><td>0.063*<br>(0.035)</td><td>0.065*<br>(0.035)</td></tr><tr><td>Natural Resources <sup>a</sup></td><td>-0.0163<br>(0.079)</td><td>-0.0163<br>(0.079)</td><td>-0.0195<br>(0.079)</td><td>-0.0161<br>(0.080)</td><td>-0.0162<br>(0.079)</td></tr><tr><td>Trade Openness <sup>a</sup></td><td>0.049 **<br>(0.019)</td><td>0.049 **<br>(0.019)</td><td>0.049 **<br>(0.019)</td><td>0.048 **<br>(0.019)</td><td>0.059 **<br>(0.019)</td></tr><tr><td>GDP per Capita <sup>a,b</sup></td><td>-2.669<br>(3.611)</td><td>-2.668<br>(3.571)</td><td>-2.624<br>(3.557)</td><td>-2.678<br>(3.557)</td><td>-2.575<br>(3.573)</td></tr><tr><td>General Expenditure <sup>a</sup></td><td>0.177 **<br>(0.080)</td><td>0.178 **<br>(0.079)</td><td>0.178 **<br>(0.079)</td><td>0.175 **<br>(0.079)</td><td>0.174 **<br>(0.080)</td></tr><tr><td>Observations</td><td>1,670</td><td>1,670</td><td>1,670</td><td>1,670</td><td>1,670</td></tr><tr><td>R<sup>2</sup></td><td>0.076</td><td>0.076</td><td>0.076</td><td>0.076</td><td>0.078</td></tr><tr><td>Countries</td><td>97</td><td>97</td><td>97</td><td>97</td><td>97</td></tr></tbody></table>

Notes: Table reports coefficients on the control variables from the two-way fixed-effects specification that includes epidemic-outbreak by transmission route indicators and their interaction with a “good healthcare system” dummy (based on the annual median (50th percentile) in a given year). All regressions include country and time fixed effects. All regressions include country and time fixed effects. Source: Authors’ calculations based on WDI, CDC, and WHO.

<sup>a</sup> Lagged one period.

<sup>b</sup> Natural log transformation.

* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01.

The Table shows the results from the specification, including only control variables, before introducing the interaction between epidemics and nurse density. The coefficients behave broadly as expected. GDP growth, trade openness, and government expenditure are positively and significantly associated with FDI inflows, consistent with the idea that stronger macroeconomic performance and openness create favorable conditions for foreign investors. Political stability and natural resource rents show no systematic effects, while GDP per capita enters negatively but remains insignificant. These results provide a consistent baseline against which the moderating role of healthcare capacity can be assessed in the subsequent models.
<table><caption>Table 6: Healthcare Workforce Capacity and FDI: Nurse and Midwife as Buffers by Transmission Route</caption><thead><tr><th>Variables</th><th>Airborne Outbreak</th><th>Water/Food-Borne Outbreak</th><th>Mosquito-Borne Outbreak</th><th>Vector-borne Flea Outbreak</th><th>Direct Contact Outbreak</th></tr></thead><tbody><tr><td>FDI/GDP</td><td>-0.0618<br>(0.376)</td><td>0.610<br>(0.850)</td><td>0.616<br>(0.397)</td><td>-2.208***<br>(0.583)</td><td>-1.745**<br>(0.804)</td></tr><tr><td>Good Healthcare system</td><td>-0.139<br>(0.388)</td><td>-710<br>(1.277)</td><td>-0.710<br>(0.617)</td><td>3.295***<br>(0.894)</td><td>2.251**<br>(0.994)</td></tr><tr><td>Observations</td><td>1,670</td><td>1,670</td><td>1,670</td><td>1,670</td><td>1,670</td></tr><tr><td>R-squared</td><td>0.076</td><td>0.076</td><td>0.078</td><td>0.076</td><td>0.076</td></tr><tr><td>Number of Countries</td><td>97</td><td>97</td><td>97</td><td>97</td><td>97</td></tr></tbody></table>

Notes: This table reports two-way fixed-effects regression estimates of the moderating role of healthcare capacity, proxied by nurse and midwife density, on transmission route. The indicator for “Good Healthcare System” equals 1 if a country’s nurse density is above the annual median (50th percentile) in a given year. Interaction terms capture how epidemic outbreaks (by transmission type) affect FDI inflows in countries with relatively stronger healthcare systems. All regressions include country and time fixed effects. Source: Authors’ calculations based on WDI, CDC, and WHO.

The results in Table 6 reveal important heterogeneity in how nursing system capacity moderates the FDI response to different Transmission types (see Table A.5 in Appendix A). Specifically, frontline health workforce strength appears particularly critical in mitigating investor retreat during severe health shocks transmitted via direct contact or animal vectors.

The most pronounced effects are observed for direct contact and Vector-borne Flea outbreaks, where FDI declines sharply in countries with weaker health systems (-1.745 and -2.208 percentage points, respectively). In contrast, countries with stronger nursing coverage see these effects significantly softened (+2.251 and +3.295 percentage points). These results align with evidence that timely, community-based care and trust-building roles often led by nurses are central to controlling outbreaks with person-to-person transmission (Bloom et al. [2015]; Kruk et al. [2017]). Strong nursing capacity also reduces transmission duration, hospital overload, and panic, key drivers of economic disruption during hemorrhagic epidemics (Evans et al. [2015]; Moon et al. [2015]).

For mosquito-borne diseases (e.g., Dengue, Zika), the baseline effect on FDI is weakly negative, with no statistically significant moderating role for nurse density. These outbreaks tend to be seasonal and spatially concentrated, and health system preparedness relies more on vector control than on facility-based interventions (Fitzpatrick et al. [2017]; Faria et al. [2016]). Thus, the limited buffering effect may reflect the relatively peripheral role of clinical care in influencing investor perceptions.

For fecal-oral and airborne transmission diseases, results are less conclusive. While FDI responses are statistically insignificant in both baseline and interaction terms, the direction of the coefficients suggests potential economic vulnerability that may not be adequately addressed by nursing capacity alone. Outbreaks such as cholera or airborne influenza may require infrastructure-based solutions like water sanitation or large-scale mobility restrictions domains where nurse density is a less decisive factor (Marcos-Marcos et al. [2018]; World Health Organization. Regional Office for Europe [2020]; Townson et al. [2005]).

Taken together, these findings reinforce the idea that healthcare system strength is not uniformly protective across all types of outbreaks. Its mitigating power depends both on how a disease spreads and on how central clinical response is to containment. In contexts where frontline care and public trust are pivotal, nursing workforce density is not only a health asset, but a strategic economic safeguard.

<footnote>***p < 0.01, **p < 0.05, *p < 0.10.</footnote>
5.3.2 Other Results (Hypothesis 3)

Figure 8 summarizes outbreak-specific estimates from two-way fixed effects regressions. Each bar reflects the change in FDI inflows associated with a given epidemic shock, both in countries with weaker health systems (No good HC) and in those with stronger nursing capacity (With good HC). The contrast between these two categories reveals how frontline healthcare strength reshapes investor responses to health crises.

<figure><img src="image_11.png" /><figcaption>Figure 8: Impact of Epidemic Outbreaks on FDI Inflows with and without Strong Healthcare Systems</figcaption></figure>

Note: This heatmap reports two-way fixed effects regression estimates of the effect of epidemic outbreaks on FDI inflows, distinguishing between countries with weaker healthcare systems (“No good HC”) and those with stronger healthcare capacity (“With good HC”), proxied by nurse and midwife density above the annual median (50th percentile). Numbers inside cells represent estimated coefficients, with stars denoting statistical significance (***$p < 0.01$, **$p < 0.05$, *$p < 0.10$). Source: Author’s calculations based on WDI, CDC, and WHO

Results indicate that several of the most severe and high-fatality outbreaks, particularly Ebola, Plague, and Marburg, are associated with sharp FDI declines in countries with weaker systems (−2.07, −2.31, and
−2.56 percentage points, respectively). However, when nurse density is above the median, these effects are substantially mitigated or even reversed, with positive coefficients of +2.78 for Ebola, +3.32 for Plague, and +3.78 for Marburg. This contrast illustrates that investor behavior depends not only on the occurrence of the outbreak itself but also on the credibility and visibility of the health system response.

For other diseases, such as Chikungunya and Viral Hemorrhagic Fever, the buffering effect of healthcare strength is also evident. FDI responses shift from strongly negative (−3.08 and −1.49) to positive values (+2.52 and +2.60), suggesting that stronger nursing coverage can transform investor expectations even in the face of less well-studied or emerging pathogens. In contrast, for outbreaks such as Dengue or Typhoid, where prevention relies more on vector control and broader public health infrastructure than on clinical workforce capacity, no consistent buffering effect is observed.

Taken together, these results reinforce the idea that nurses and midwives act as both health responders and economic stabilizers. Their presence ensures faster isolation, community-level communication, and continuity of essential care, thereby reducing the likelihood of systemic disruptions in economic activity. These findings are consistent with literature emphasizing the role of frontline healthcare capacity in containing epidemics and stabilizing societal functioning (World Health Organization [2020]; Aiken et al. [2014]; Kruk et al. [2017]; Evans et al. [2015]; Moon et al. [2015]). They also align with studies showing that resilient health systems can bolster investor confidence by signaling institutional preparedness and governance capacity (Yu et al. [2023b]; Correia et al. [2022]; Adda [2016]).

# 6 Robustness test

To ensure the reliability of the main findings, I conduct a series of robustness tests corresponding to each of the three core hypotheses. These checks use alternative model specifications and estimation strategies to assess whether the results are sensitive to sample selection, fixed effects, or model dynamics.<fnref n="1" />

## 6.1 Robustness for H1: Short-Term Impact of Epidemics on FDI

To assess the robustness of the main results, Table A.8 in Appendix A reports estimates from a model using only country fixed effects, excluding time fixed effects. This specification controls for time-invariant country characteristics but does not capture global shocks or trends common across countries in the same year (e.g., global recessions or pandemics). As expected, the overall $R^2$ is slightly lower compared to the baseline model, reflecting the omission of time-specific variation. Nevertheless, the direction and significance of most coefficients remain consistent with the main findings.

Key outbreaks like Ebola, MERS, Lassa Fever, Plague, and Leptospirosis still show significant negative effects on FDI, and the direction of estimates remains stable overall. Interestingly, in this version SARS and Japanese Encephalitis become statistically significant, with a negative effect on FDI inflows. This result suggests that when global shocks are not explicitly accounted for, the outbreak appears to have had a more severe country-specific impact on investment. This highlights the importance of controlling for time

<footnote n="1">¹All robustness regressions include the full set of control variables used in the main specification. Coefficients for these variables are reported in Appendix Tables A.9, A.10, A.12, A.13, A.17, and A.18.</footnote>
effects. Nonetheless, the direction and significance of key coefficients remain largely consistent, supporting
the robustness of the main findings.

As another robustness check, Appendix A Table A.11 shows the results from models that include time
fixed effects only, without country fixed effects. This approach enables me to control for global shocks
that may affect all countries in a given year, such as financial crises, global trade slowdowns, or large-scale
pandemics. However, it does not account for unobserved differences across countries, including political
systems, geography, or long-term investment environments. Even with this change, the core story holds.
Diseases like MERS, Lassa Fever, and Leptospirosis continue to show significant negative impacts on FDI
inflows. In fact, the effect of MERS and Lassa Fever appears even stronger in this version, reinforcing the
idea that these outbreaks had serious economic consequences beyond just health.
Some differences also emerge, for instance, Plague and Ebola, which were both significant in the baseline
model, still negative but no longer show statistically significant effects here. This suggests that their impact
may have been more country-specific potentially tied to the domestic context or the institutional response
rather than driven by global investor sentiment. On the other hand, Diphtheria, which was not significant
in the main model, now emerges as negatively associated with FDI. This shift may reflect certain country-
year episodes where outbreaks of Diphtheria coincided with weakened investor confidence, particularly when
global factors are held constant.

Overall, while this model does not capture differences between countries, the fact that several diseases still
have strong and significant effects gives us more confidence in the robustness of the results. The relatively
high R² (0.246) also shows that a good share of the variation in FDI can be explained by year-to-year global
factors, which makes sense in a world of increasingly integrated capital markets.

To make sure the results are not overly sensitive to how I define an epidemic year, I built a more flexible
version of the outbreak indicators. Instead of marking only the exact year when a disease appears, this
version captures broader outbreak windows. In this case, if a country experienced multiple outbreaks of the
same disease within five years of each other, I group those into a single episode and mark all years in that
window with a 1. This helps account for the fact that in many countries, especially those with weaker health
systems epidemics are not always short-lived. Their effects can linger and even resurface over several years.
This new approach better reflects the reality on the ground: health shocks often stretch across more than
one calendar year and can disrupt investment decisions over a longer horizon.

Table A.14 in Appendix A shows the results from a robustness check using wider outbreak windows that
include multiple years of disease activity, rather than just single-year outbreaks. The model still includes
both country and time fixed effects, just like the main analysis. The main results still hold. Key outbreaks
like Ebola, MERS, Plague, Lassa Fever, and Leptospirosis continue to have a significant negative effect on
FDI inflows, even with the broader definition. This suggests that my findings are not just driven by how I
define outbreak timing. There are a few small changes: for example, MERS remains significant but at the
10% level instead of 5%. Overall, this robustness test confirms that the main conclusions are solid. Even
when I allow for longer periods of disruption, the same diseases still show up as harmful to investment.
## 6.2 Robustness for H2: Dynamic Response of FDI to Epidemic Shocks

To test the robustness of the dynamic FDI response to epidemic shocks, I construct a general epidemic indicator coded as 1 if a country experiences at least one outbreak in a given year, and 0 otherwise. This binary measure captures overall exposure to health shocks, abstracting from disease-specific characteristics to focus on broader macroeconomic effects.

Following the approach of Jordà et al. [2022], I use a Local Projection Method to trace the average trajectory of FDI inflows over five years after an epidemic shock. As shown in Figure A.5, the impulse response exhibits a U-shaped pattern: FDI declines in the first year, reaches a trough in year one, and begins to recover by year two, peaking around year three before stabilizing.

These dynamics are consistent with the main results for Hypothesis 2, confirming that the negative impact of epidemics on FDI is transitory rather than permanent. While initial disruptions reflect uncertainty and delayed investment decisions, recovery generally occurs within a few years, highlighting the resilience of capital flows to non-financial shocks over the medium term (Kose et al. [2020]; United Nations Conference on Trade and Development (UNCTAD) [2022]; Kleinert and Sturm [2021]).

Following the baseline results using the general epidemic shock variable, I further explore whether this average global response masks important regional differences. While the global impulse response reveals a temporary U-shaped pattern in FDI flows, countries are not affected equally. To investigate this heterogeneity, I disaggregate the Local Projection estimates by region, as shown in Figure A.6. This approach helps capture shared institutional, economic, and geographic characteristics within regions that may shape how epidemics influence investment behavior.

The results highlight striking variation. Sub-Saharan Africa faces the steepest and most persistent FDI decline, likely due to limited health infrastructure and heightened investor sensitivity. The Middle East and North Africa display a modest and short-lived dip, while Asia and the Pacific show a shallow contraction followed by partial recovery. Latin America and the Caribbean, in contrast, exhibit a delayed but stronger rebound after three years of decline. These differences underscore the importance of regional resilience and institutional capacity in shaping FDI dynamics following epidemic shocks.

## 6.3 Robustness for H3: Moderating Role of Healthcare System Capacity

To further assess the robustness of the buffering role of healthcare system capacity, I re-estimate the baseline specification using alternative definitions of healthcare quality.

First, I apply a stricter threshold for physician availability, classifying countries as having a well-developed healthcare system if physician density exceeds the annual median (50th percentile). The results, reported in Table A.15, remain broadly consistent with the baseline estimates. In particular, the interaction terms between epidemic outbreaks and healthcare capacity continue to be positive and statistically significant for Vector-borne Flea and Direct Contact diseases, confirming the robustness of the moderating effect.

Second, I replace physician density with the Global Health Security Index (GHS Index)<fnref n="2" />, a composite

<footnote n="2">²The Global Health Security (GHS) Index is the first comprehensive assessment and benchmarking of health security and related capabilities across the 195 countries that are States Parties to the International Health Regulations (IHR, 2005). The GHS Index aims to promote measurable improvements in national health security and to strengthen international capacity</footnote>
measure capturing countries' preparedness for infectious disease prevention, detection, and response. Using this alternative proxy yields qualitatively similar results (Table A.16): countries with higher health security experience a significantly weaker adverse impact of epidemic outbreaks on FDI inflows. The magnitude and statistical significance of the interaction terms are comparable to those obtained in the main specification, reinforcing the conclusion that stronger healthcare systems mitigate the negative effects of epidemic shocks.

# 7 Conclusion

This study examined the impact of epidemic outbreaks on foreign direct investment (FDI) inflows, with particular attention to how health system capacity shapes resilience. Using a panel of 98 countries from 2000-2022 and a dataset covering 20 outbreaks, three main hypotheses were tested, and the results provide consistent and policy-relevant insights.

First (H1), epidemic outbreaks are found to reduce FDI inflows in the short run, confirming that health shocks act as economic shocks. The negative effect is particularly pronounced for severe outbreaks such as Ebola, MERS, and Lassa Fever. These results remain robust across different specifications, reinforcing the idea that epidemics heighten risk and uncertainty in ways that deter foreign investors.

Second (H2), the local projection analysis revealed that the impact of epidemics on FDI is not uniform but unfolds dynamically depending on the mode of transmission. Outbreaks spread through airborne transmission lead to sharp immediate declines in FDI, with recovery beginning only after the second year. Direct-contact diseases show an initial drop followed by signs of rebound after one year, though this recovery is often interrupted by overlapping outbreaks. By contrast, water- and food-borne diseases exhibit the most persistent decline, with losses extending into the fourth year, reflecting the difficulty of controlling these outbreaks in regions with weak infrastructure. This heterogeneity underscores that investors react not only to the presence of an epidemic but also to its perceived manageability, regional concentration, and the capacity of affected countries to contain it. Importantly, many of the long-lasting outbreaks (e.g., water- and food-borne, or direct contact) are concentrated in Sub-Saharan Africa and low-income countries, suggesting that regional vulnerabilities amplify the duration and depth of FDI losses.

Third (H3), the moderation analysis demonstrated that stronger healthcare systems, particularly higher nurse density, significantly buffer the negative impact of epidemics on FDI. The consistent results for nursing density suggest that the frontline health workforce plays a critical role not only in outbreak management but also in sustaining investor confidence during crises.

Taken together, these findings highlight three key messages. First, epidemics are not merely public health events but also economic shocks that can deter investment for years. Second, the economic impact of epidemics varies by disease transmission mode and is shaped by regional contexts. Third, strengthening healthcare systems, especially the health workforce, offers a viable pathway to reducing the economic costs of epidemics, complementing their core role in protecting lives.

Looking forward, future research could expand along several dimensions: examining how sectoral FDI is differently affected, assessing cross-border spillovers in highly integrated regions, or exploring how institu-

<footnote>to prevent, detect, and respond to infectious disease threats that may lead to epidemics and pandemics. Further details are available at https://ghsindex.org/.</footnote>
tional quality interacts with health capacity to moderate shocks. As global connectivity and climate change heighten epidemic risks, integrating health system strengthening into broader economic resilience strategies will remain an urgent priority for policymakers.
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# A Additional tables and figures

<table><caption>Table A.1: Definition of Variables</caption><thead><tr><td>Variables</td><td>Definition</td><td>Source</td></tr></thead><tbody><tr><td>FDIGDP</td><td>FDI inflow (% of GDP)</td><td>World Bank WDI database</td></tr><tr><td>GDPpc</td><td>GDP per capita (constant 2015 US$)</td><td>World Bank WDI database</td></tr><tr><td>Natres</td><td>Total natural resource rents (% of GDP)</td><td>World Bank WDI database</td></tr><tr><td>GDPg</td><td>GDP growth (annual %)</td><td>World Bank WDI database</td></tr><tr><td>Politic</td><td>Political Stability and Absence of Violence/Terrorism</td><td>World Bank WDI database</td></tr><tr><td>Trade</td><td>Trade (% of GDP)</td><td>World Bank WDI database</td></tr><tr><td>GovEx</td><td>Government expenditure to GDP ratio</td><td>World Bank WDI database</td></tr><tr><td>Hepatitis_E</td><td></td><td>Global Dataset</td></tr><tr><td>Chikungunya</td><td></td><td>Global Dataset</td></tr><tr><td>Cholera</td><td></td><td>Global Dataset</td></tr><tr><td>Dengue</td><td></td><td>Global Dataset</td></tr><tr><td>Diphtheria</td><td></td><td>Global Dataset</td></tr><tr><td>Ebola</td><td></td><td>Global Dataset</td></tr><tr><td>Zoonotic_Influenza</td><td></td><td>Global Dataset</td></tr><tr><td>Japanese_Encephalitis</td><td></td><td>Global Dataset</td></tr><tr><td>Lassa_Fever</td><td></td><td>Global Dataset</td></tr><tr><td>Leptospirosis</td><td></td><td>Global Dataset</td></tr><tr><td>Marburg</td><td></td><td>Global Dataset</td></tr><tr><td>Measles</td><td></td><td>Global Dataset</td></tr><tr><td>Meningococcal_Meningitis</td><td></td><td>Global Dataset</td></tr><tr><td>MERS</td><td></td><td>Global Dataset</td></tr><tr><td>Plague</td><td></td><td>Global Dataset</td></tr><tr><td>Rift_Valley_Fever</td><td></td><td>Global Dataset</td></tr><tr><td>SARS</td><td></td><td>Global Dataset</td></tr><tr><td>Typhoid</td><td></td><td>Global Dataset</td></tr><tr><td>Zika</td><td></td><td>Global Dataset</td></tr><tr><td>Viral_Hemorrhagic_Fever</td><td></td><td>Global Dataset</td></tr></tbody></table>

Notes: This table provides definitions and sources of the variables. All disease variables are binary indicators equal to 1 for country and year observations in which a laboratory-confirmed outbreak of the named disease was reported; 0 otherwise. Outbreak identification follows the Global Dataset of pandemic- and epidemic-prone diseases (Torres Munguía et al. [2022]), complemented with WHO and CDC reports. Sample: 98 countries, 2000–2021.
<table><caption>Table A.2: Country and Time Fixed Effects Result</caption><thead><tr><th colspan="6">Disease Outbreak Effects on FDI Inflows</th></tr><tr><th>Disease</th><th>Coefficient</th><th>Std. Error</th><th>Disease</th><th>Coefficient</th><th>Std. Error</th></tr></thead><tbody><tr><td>Hepatitis E</td><td>0.090</td><td>(0.845)</td><td>SARS</td><td>-1.587</td><td>(1.167)</td></tr><tr><td>Chikungunya</td><td>-2.242</td><td>(2.327)</td><td>Marburg</td><td>-1.365</td><td>(1.778)</td></tr><tr><td>Cholera</td><td>0.705</td><td>(0.737)</td><td>Measles</td><td>0.488</td><td>(0.581)</td></tr><tr><td>Dengue</td><td>-0.403</td><td>(0.465)</td><td>Meningococcal Disease</td><td>0.187</td><td>(0.496)</td></tr><tr><td>Diphtheria</td><td>-0.301</td><td>(0.859)</td><td>MERS</td><td>-1.202**</td><td>(0.533)</td></tr><tr><td>Ebola</td><td>-1.508**</td><td>(0.702)</td><td>Plague</td><td>-1.344*</td><td>(0.721)</td></tr><tr><td>Viral Haemorrhagic Fever</td><td>-1.869</td><td>(1.290)</td><td>Rift Valley Fever</td><td>0.666</td><td>(1.289)</td></tr><tr><td>Japanese Encephalitis</td><td>-0.829</td><td>(0.798)</td><td>Typhoid</td><td>-0.231</td><td>(0.317)</td></tr><tr><td>Lassa Fever</td><td>-1.907**</td><td>(0.795)</td><td>Zika</td><td>0.489</td><td>(0.425)</td></tr><tr><td>Leptospirosis</td><td>-1.613***</td><td>(0.608)</td><td></td><td></td><td></td></tr><tr><td>Zoonotic Influenza</td><td>-0.011</td><td>(0.331)</td><td></td><td></td><td></td></tr></tbody><tfoot><tr><td colspan="6">Total Observations: 1,944 across 98 countries</td></tr><tr><td colspan="6">(R<sup>2</sup>) for all: 0.071 - 0.073</td></tr></tfoot></table>

*Notes*: Table reports coefficients on all 20 disease outbreak dummies from two-way (country and year) fixed-effects regressions of FDI inflows. All regressions include country and time fixed effects. *Source*: Author's calculations based on WDI, CDC, and WHO.

<footnote>* $p < 0.10$, ** $p < 0.05$, *** $p < 0.01$.</footnote>

<footnote>a Lagged one period.</footnote>

<footnote>b Natural log transformation.</footnote>
Tables A.3 and A.4 present the estimated effects of key host country characteristics on FDI inflows, controlling for time and country fixed effects.

<table><caption>Table A.3: Country and Time Fixed effects result, Effect of Outbreaks on FDI: Only Control Variables</caption><thead><tr><th rowspan="2">Control Variables</th><th colspan="11">Disease-Specific Models (Part 1)</th></tr><tr><th>Hepatitis E</th><th>Chikungunya</th><th>Cholera</th><th>Dengue</th><th>Diphtheria</th><th>Ebola</th><th>Zoonotic Influenza</th><th>Japanese Encephalitis</th><th>Lassa Fever</th><th>Leptospirosis</th><th>Marburg</th></tr></thead><tbody><tr><td>Political Stability<sup>a</sup></td><td>0.180<br>(0.328)</td><td>0.191<br>(0.325)</td><td>0.176<br>(0.325)</td><td>0.179<br>(0.327)</td><td>0.179<br>(0.327)</td><td>0.181<br>(0.327)</td><td>0.179<br>(0.327)</td><td>0.179<br>(0.327)</td><td>0.169<br>(0.325)</td><td>0.179<br>(0.327)</td><td>0.185<br>(0.326)</td></tr><tr><td>GDP Growth<sup>a</sup></td><td>0.068**<br>(0.033)</td><td>0.068**<br>(0.033)</td><td>0.068**<br>(0.033)</td><td>0.081**<br>(0.033)</td><td>0.081**<br>(0.033)</td><td>0.096**<br>(0.033)</td><td>0.081**<br>(0.033)</td><td>0.081**<br>(0.033)</td><td>0.084**<br>(0.033)</td><td>0.080**<br>(0.033)</td><td>0.081**<br>(0.033)</td></tr><tr><td>Natural Resources<sup>a</sup></td><td>-0.012<br>(0.074)</td><td>-0.012<br>(0.073)</td><td>-0.013<br>(0.074)</td><td>-0.012<br>(0.074)</td><td>-0.013<br>(0.074)</td><td>-0.014<br>(0.074)</td><td>-0.012<br>(0.074)</td><td>-0.011<br>(0.074)</td><td>-0.012<br>(0.074)</td><td>-0.013<br>(0.074)</td><td>-0.012<br>(0.074)</td></tr><tr><td>Trade Openness<sup>a</sup></td><td>0.043**<br>(0.019)</td><td>0.043**<br>(0.019)</td><td>0.043**<br>(0.019)</td><td>0.043**<br>(0.019)</td><td>0.043**<br>(0.019)</td><td>0.044**<br>(0.019)</td><td>0.043**<br>(0.019)</td><td>0.043**<br>(0.019)</td><td>0.043**<br>(0.019)</td><td>0.043**<br>(0.019)</td><td>0.043**<br>(0.019)</td></tr><tr><td>GDP per Capita<sup>a,b</sup></td><td>-2.055<br>(2.771)</td><td>-2.128<br>(2.760)</td><td>-2.059<br>(2.773)</td><td>-2.065<br>(2.774)</td><td>-2.059<br>(2.774)</td><td>-2.029<br>(2.806)</td><td>-2.057<br>(2.775)</td><td>-2.060<br>(2.770)</td><td>-2.067<br>(2.770)</td><td>-2.053<br>(2.770)</td><td>-2.051<br>(2.771)</td></tr><tr><td>General expenditure<sup>a</sup></td><td>0.150**<br>(0.067)</td><td>0.149**<br>(0.067)</td><td>0.154**<br>(0.065)</td><td>0.150**<br>(0.067)</td><td>0.150**<br>(0.067)</td><td>0.151**<br>(0.067)</td><td>0.150**<br>(0.067)</td><td>0.150**<br>(0.067)</td><td>0.151**<br>(0.067)</td><td>0.151**<br>(0.067)</td><td>0.150**<br>(0.067)</td></tr><tr><td>Observations</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td></tr><tr><td>R<sup>2</sup></td><td>0.071</td><td>0.072</td><td>0.071</td><td>0.071</td><td>0.071</td><td>0.071</td><td>0.071</td><td>0.071</td><td>0.071</td><td>0.071</td><td>0.071</td></tr><tr><td>Countries</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td></tr></tbody></table>

*Notes:* Table reports first part of coefficients on the control variables only from two-way (country and year) fixed-effects regressions estimating the effect of 20 epidemic outbreaks on FDI inflows. All regressions include country and time fixed effects. *Source:* Author's calculations based on WDI, CDC, and WHO.

<footnote>* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01.</footnote>

<footnote>a Lagged one period.</footnote>

<footnote>b Natural log transformation.</footnote>
<table><caption>Table A.4: Country and Time Fixed effects result, Effect of Outbreaks on FDI: Only Control Variables</caption><thead><tr><th rowspan="2">Control Variables</th><th colspan="9">Disease-Specific Models (Part 2)</th></tr><tr><th>Measles</th><th>Meningococcal</th><th>MERS</th><th>Plague</th><th>Rift Valley</th><th>SARS</th><th>Typhoid</th><th>Haemorrhagic</th><th>Zika</th></tr></thead><tbody><tr><td>Political Stability<sup>a</sup></td><td>0.177<br>(0.328)</td><td>0.171<br>(0.326)</td><td>0.164<br>(0.328)</td><td>0.171<br>(0.328)</td><td>0.180<br>(0.327)</td><td>0.194<br>(0.331)</td><td>0.179<br>(0.327)</td><td>0.176<br>(0.327)</td><td>0.151<br>(0.340)</td></tr><tr><td>GDP Growth<sup>a</sup></td><td>0.068<sup>**</sup><br>(0.033)</td><td>0.068<sup>**</sup><br>(0.033)</td><td>0.068<sup>**</sup><br>(0.033)</td><td>0.068<sup>**</sup><br>(0.033)</td><td>0.068<sup>**</sup><br>(0.033)</td><td>0.068<sup>**</sup><br>(0.033)</td><td>0.068<sup>**</sup><br>(0.033)</td><td>0.068<sup>**</sup><br>(0.033)</td><td>0.068<sup>**</sup><br>(0.033)</td></tr><tr><td>Natural Resources<sup>a</sup></td><td>-0.012<br>(0.074)</td><td>-0.012<br>(0.074)</td><td>-0.016<br>(0.075)</td><td>-0.012<br>(0.074)</td><td>-0.013<br>(0.074)</td><td>-0.012<br>(0.074)</td><td>-0.012<br>(0.074)</td><td>-0.013<br>(0.074)</td><td>-0.013<br>(0.074)</td></tr><tr><td>Trade Openness<sup>a</sup></td><td>0.043<sup>**</sup><br>(0.019)</td><td>0.043<sup>**</sup><br>(0.019)</td><td>0.044<sup>**</sup><br>(0.019)</td><td>0.043<sup>**</sup><br>(0.019)</td><td>0.043<sup>**</sup><br>(0.019)</td><td>0.043<sup>**</sup><br>(0.019)</td><td>0.043<sup>**</sup><br>(0.019)</td><td>0.043<sup>**</sup><br>(0.019)</td><td>0.043<sup>**</sup><br>(0.019)</td></tr><tr><td>GDP per Capita<sup>a,b</sup></td><td>-2.054<br>(2.770)</td><td>-2.060<br>(2.773)</td><td>-2.151<br>(2.768)</td><td>-2.071<br>(2.771)</td><td>-2.059<br>(2.771)</td><td>-2.164<br>(2.832)</td><td>-2.055<br>(2.771)</td><td>-2.061<br>(2.771)</td><td>-1.964<br>(32.793)</td></tr><tr><td>General expenditure<sup>a</sup></td><td>0.150<sup>**</sup><br>(0.067)</td><td>0.149<sup>**</sup><br>(0.067)</td><td>0.152<sup>**</sup><br>(0.065)</td><td>0.150<sup>**</sup><br>(0.067)</td><td>0.150<sup>**</sup><br>(0.067)</td><td>0.149<sup>**</sup><br>(0.067)</td><td>0.150<sup>**</sup><br>(0.067)</td><td>0.150<sup>**</sup><br>(0.067)</td><td>0.154<sup>**</sup><br>(0.068)</td></tr><tr><td>Observations</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td></tr><tr><td>R<sup>2</sup></td><td>0.071</td><td>0.071</td><td>0.072</td><td>0.071</td><td>0.071</td><td>0.071</td><td>0.071</td><td>0.071</td><td>0.073</td></tr><tr><td>Countries</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td></tr></tbody></table>

*Notes:* Table reports second part of coefficients on the control variables only from two-way (country and year) fixed-effects regressions estimating the effect of 20 epidemic outbreaks on FDI inflows. All regressions include country and time fixed effects.

*Source:* Author’s calculations based on WDI, CDC, and WHO.* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01.

a Lagged one period.

b Natural log transformation.
<figure><img src="image_12.png" /><figcaption>Figure A.1: Response of FDI to Global Epidemic Outbreaks: Evidence from LPM – Part 1</figcaption></figure>

Note: This figure displays the estimated impulse responses of FDI to 20 epidemic outbreaks using the local projection method. Shaded areas represent 90% confidence intervals. Source: Author's calculations based on WDI, CDC, and WHO.
<figure><img src="image_13.png" /><figcaption>Figure A.2: Response of FDI to Global Epidemic Outbreaks: Evidence from LPM – Part 2</figcaption></figure>

Note: This figure displays the estimated impulse responses of FDI to 20 epidemic outbreaks using the local projection method. Shaded areas represent 90% confidence intervals. Source: Author's calculations based on WDI, CDC, and WHO.
<figure><img src="image_14.png" /><figcaption>Figure A.3: Response of FDI to Global Epidemic Outbreaks: Evidence from LPM – Part 3</figcaption></figure>

*Note:* This figure displays the estimated impulse responses of FDI to 20 epidemic outbreaks using the local projection method. Shaded areas represent 90% confidence intervals. Source: Author's calculations based on WDI, CDC, and WHO.
<caption>Figure A.4: Impact of Epidemic Outbreaks on FDI by Transmission Type and Region</caption>

<figure><img src="image_15.png" /><figcaption>(b) Mosquito-borne outbreaks</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>(c) Flea-borne outbreaks</figcaption></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

Note: This figure displays the estimated impulse responses of FDI by Transmission Type and Region using the local projection method. Shaded areas represent 90% confidence intervals. Source: Author's calculations based on WDI, CDC, and WHO.
<table><caption>Table A.5: Classification of Epidemic Outbreaks by Transmission Route</caption><thead><tr><td>Transmission Type</td><td>Diseases Included</td></tr></thead><tbody><tr><td>Water/Food-borne</td><td>Hepatitis E, Cholera, Typhoid, Leptospirosis</td></tr><tr><td>Vector-borne (Mosquito)</td><td>Chikungunya, Dengue, Japanese Encephalitis, Rift Valley Fever, Zika</td></tr><tr><td>Direct Contact</td><td>Ebola, Marburg, Viral Hemorrhagic Fever, Lassa Fever, Diphtheria</td></tr><tr><td>Airborne</td><td>Zoonotic Influenza, Measles, Meningococcal Meningitis, MERS, SARS</td></tr><tr><td>Vector-borne (Flea)</td><td>Plague</td></tr></tbody></table>

*Notes*: Diseases are grouped by their *predominant transmission route during human outbreaks* (how people acquire infection), not by ecological origin or spillover pathway. Multi-route diseases are assigned to the primary route observed in outbreaks, following WHO/CDC guidance.

<table><caption>Table A.6: Classification of Epidemics by Case Fatality Rate (CFR)</caption><thead><tr><td>CFR Class</td><td>CFR Range</td><td>Example Diseases</td></tr></thead><tbody><tr><td>Very Low</td><td>&lt; 1%</td><td>Chikungunya, Zika</td></tr><tr><td>Low</td><td>1–4.99%</td><td>Cholera, Dengue, Measles, Hepatitis E</td></tr><tr><td>Moderate</td><td>5–19.99%</td><td>SARS, Lassa Fever, Meningitis, Typhoid, Leptospirosis</td></tr><tr><td>High</td><td>20–49.99%</td><td>MERS, Plague, Japanese Encephalitis, Zoonotic Influenza</td></tr><tr><td>Very High</td><td>≥ 50%</td><td>Ebola, Marburg, Viral Hemorrhagic Fevers</td></tr></tbody></table>

*Notes*: The table shows the case fatality ratio (CFR). CFR is the proportion of deaths among confirmed cases, reflecting disease lethality. Following the WHO risk severity scale, diseases are grouped into five CFR categories.

<table><caption>Table A.7: Classification of Epidemics by Basic Reproduction Number ($R_0$)</caption><thead><tr><td>$R_0$ Class</td><td>$R_0$ Range</td><td>Example Diseases</td></tr></thead><tbody><tr><td>Low</td><td>&lt;2</td><td>MERS, Marburg, Lassa Fever, Japanese Encephalitis</td></tr><tr><td>Moderate</td><td>2–3.49</td><td>Ebola, Typhoid, Plague, Rift Valley Fever</td></tr><tr><td>High</td><td>3.5–5.49</td><td>SARS, Cholera, Chikungunya, Dengue</td></tr><tr><td>Very High</td><td>≥5.5</td><td>Measles, Zika</td></tr></tbody></table>

*Notes*: The table shows the average number of secondary cases generated by one primary case in a fully susceptible population. The bins shown are illustrative; example diseases are assigned based on typical literature estimates. Actual values vary by setting and intervention timing
# Robustness

<table><caption>Table A.8: Country Fixed Effect Results (Robustness)</caption><caption>Disease Outbreak Effects on FDI Inflows</caption><thead><tr><th>Disease</th><th>Coefficient</th><th>Std. Error</th><th>Disease</th><th>Coefficient</th><th>Std. Error</th></tr></thead><tbody><tr><td>Hepatitis E</td><td>-0.0293</td><td>(0.949)</td><td>Marburg</td><td>-0.873</td><td>(1.677)</td></tr><tr><td>Chikungunya</td><td>-2.383</td><td>(2.375)</td><td>Measles</td><td>0.148</td><td>(0.529)</td></tr><tr><td>Cholera</td><td>0.601</td><td>(0.741)</td><td>Meningococcal Meningitis</td><td>0.290</td><td>(0.513)</td></tr><tr><td>Dengue</td><td>-0.547</td><td>(0.424)</td><td>MERS</td><td>-1.172**</td><td>(0.491)</td></tr><tr><td>Diphtheria</td><td>0.356</td><td>(0.773)</td><td>Plague</td><td>-1.335**</td><td>(0.649)</td></tr><tr><td>Ebola</td><td>-1.450**</td><td>(0.718)</td><td>Rift Valley Fever</td><td>0.864</td><td>(1.187)</td></tr><tr><td>Zoonotic Influenza</td><td>-0.0576</td><td>(0.299)</td><td>SARS</td><td>-1.709**</td><td>(0.791)</td></tr><tr><td>Japanese Encephalitis</td><td>-1.259*</td><td>(0.689)</td><td>Typhoid</td><td>-0.294</td><td>(0.251)</td></tr><tr><td>Lassa Fever</td><td>-1.928**</td><td>(0.777)</td><td>Zika</td><td>0.368</td><td>(0.339)</td></tr><tr><td>Leptospirosis</td><td>-1.819***</td><td>(0.498)</td><td>Viral Hemorrhagic Fever</td><td>-2.078</td><td>(1.388)</td></tr></tbody><tfoot><tr><td colspan="6">Total Observations: 1,944 across 98 countries</td></tr><tr><td colspan="6">Model Fit (R<sup>2</sup>): 0.051</td></tr></tfoot></table>

*Notes:* The table shows results of the robustness check using country fixed effects only (no year fixed effects) for all 20 epidemic outbreaks. Standard errors in parentheses are heteroskedasticity-robust and clustered by country. *Source:* Author's calculations based on WDI, CDC, and WHO.

<table><caption>Table A.9: Country Fixed Effect Results (Robustness): Control Variables Only</caption><caption>Disease-Specific Models (Part 1)</caption><thead><tr><th>Control Variables</th><th>Hepatitis E</th><th>Chikungunya</th><th>Cholera</th><th>Dengue</th><th>Diphtheria</th><th>Ebola</th><th>Zoonotic Influenza</th><th>Japanese Encephalitis</th><th>Lassa Fever</th><th>Leptospirosis</th></tr></thead><tbody><tr><td>Political Stability <sup>a</sup></td><td>0.0504<br>[0.304]</td><td>0.0562<br>[0.303]</td><td>0.0427<br>[0.298]</td><td>0.0484<br>[0.303]</td><td>0.0515<br>[0.303]</td><td>0.0507<br>[0.303]</td><td>0.0503<br>[0.303]</td><td>0.0498<br>[0.304]</td><td>0.0384<br>[0.303]</td><td>0.0506<br>[0.303]</td></tr><tr><td>GDP Growth <sup>a</sup></td><td>0.0643**<br>[0.0265]</td><td>0.0643**<br>[0.0265]</td><td>0.0647**<br>[0.0264]</td><td>0.0641**<br>[0.0265]</td><td>0.0643**<br>[0.0265]</td><td>0.0652**<br>[0.0264]</td><td>0.0640**<br>[0.0264]</td><td>0.0643**<br>[0.0265]</td><td>0.0645**<br>[0.0265]</td><td>0.0642**<br>[0.0265]</td></tr><tr><td>Natural Resources <sup>a</sup></td><td>0.00328<br>[0.0668]</td><td>0.00301<br>[0.0668]</td><td>0.00316<br>[0.0668]</td><td>0.00372<br>[0.0669]</td><td>0.00348<br>[0.0672]</td><td>0.00153<br>[0.0669]</td><td>0.00374<br>[0.0680]</td><td>0.00330<br>[0.0668]</td><td>0.00370<br>[0.0668]</td><td>0.00350<br>[0.0668]</td></tr><tr><td>Trade Openness <sup>a</sup></td><td>0.0458**<br>[0.0187]</td><td>0.0454**<br>[0.0187]</td><td>0.0460**<br>[0.0187]</td><td>0.0457**<br>[0.0187]</td><td>0.0458**<br>[0.0187]</td><td>0.0464**<br>[0.0188]</td><td>0.0459**<br>[0.0188]</td><td>0.0457**<br>[0.0187]</td><td>0.0458**<br>[0.0187]</td><td>0.0458**<br>[0.0187]</td></tr><tr><td>GDP per Capita <sup>a,b</sup></td><td>-1.612<br>[1.819]</td><td>-1.617<br>[1.818]</td><td>-1.575<br>[1.809]</td><td>-1.611<br>[1.818]</td><td>-1.611<br>[1.819]</td><td>-1.575<br>[1.820]</td><td>-1.625<br>[1.854]</td><td>-1.619<br>[1.822]</td><td>-1.601<br>[1.818]</td><td>-1.618<br>[1.819]</td></tr><tr><td>General Expenditure <sup>a</sup></td><td>0.148**<br>[0.0687]</td><td>0.147**<br>[0.0686]</td><td>0.151**<br>[0.0670]</td><td>0.148**<br>[0.0687]</td><td>0.148**<br>[0.0688]</td><td>0.149**<br>[0.0686]</td><td>0.148**<br>[0.0681]</td><td>0.148**<br>[0.0687]</td><td>0.150**<br>[0.0687]</td><td>0.149**<br>[0.0688]</td></tr><tr><td>Observations</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td></tr><tr><td>R<sup>2</sup></td><td>0.049</td><td>0.049</td><td>0.049</td><td>0.049</td><td>0.050</td><td>0.049</td><td>0.049</td><td>0.049</td><td>0.049</td><td>0.049</td></tr><tr><td>Countries</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td></tr></tbody></table>

*Notes:* The table reports the first part of the coefficients of the robustness check on the control variables only from the country fixed effect for all 20 epidemic outbreaks. *Source:* Author's calculations based on WDI, CDC, and WHO.

<footnote>*p* < 0.10, **p* < 0.05, ***p* < 0.01.</footnote>

<footnote><sup>a</sup> Lagged one period.</footnote>

<footnote><sup>b</sup> Natural log transformation.</footnote>

<footnote>*p* < 0.10, **p* < 0.05, ***p* < 0.01.</footnote>
<table><caption>Table A.10: Country Fixed Effect Results (Robustness): Control Variables Only</caption><thead><tr><th rowspan="2">Control Variables</th><th colspan="10">Disease-Specific Models (Part 2)</th></tr><tr><th>Marburg</th><th>Measles</th><th>Meningococcal</th><th>MERS</th><th>Plague</th><th>Rift Valley</th><th>SARS</th><th>Typhoid</th><th>Zika</th><th>Hemorrhagic</th></tr></thead><tbody><tr><td>Political Stability <sup>a</sup></td><td>0.0538<br>[0.303]</td><td>0.0511<br>[0.303]</td><td>0.0376<br>[0.302]</td><td>0.0214<br>[0.303]</td><td>0.0415<br>[0.305]</td><td>0.0517<br>[0.303]</td><td>0.0700<br>[0.306]</td><td>0.0502<br>[0.303]</td><td>0.0482<br>[0.304]</td><td>0.0383<br>[0.307]</td></tr><tr><td>GDP Growth <sup>a</sup></td><td>0.0643**<br>[0.0265]</td><td>0.0643**<br>[0.0265]</td><td>0.0640**<br>[0.0267]</td><td>0.0638**<br>[0.0263]</td><td>0.0642**<br>[0.0265]</td><td>0.0643**<br>[0.0265]</td><td>0.0641**<br>[0.0264]</td><td>0.0643**<br>[0.0265]</td><td>0.0643**<br>[0.0265]</td><td>0.0651**<br>[0.0267]</td></tr><tr><td>Natural Resources <sup>a</sup></td><td>0.00299<br>[0.0668]</td><td>0.00354<br>[0.0669]</td><td>0.00312<br>[0.0669]</td><td>-0.00150<br>[0.0678]</td><td>0.00330<br>[0.0668]</td><td>0.00320<br>[0.0668]</td><td>0.00294<br>[0.0669]</td><td>0.00332<br>[0.0668]</td><td>0.00350<br>[0.0668]</td><td>0.00432<br>[0.0661]</td></tr><tr><td>Trade Openness <sup>a</sup></td><td>0.0459**<br>[0.0187]</td><td>0.0457**<br>[0.0187]</td><td>0.0459**<br>[0.0188]</td><td>0.0466**<br>[0.0188]</td><td>0.0456**<br>[0.0187]</td><td>0.0456**<br>[0.0187]</td><td>0.0458**<br>[0.0188]</td><td>0.0457**<br>[0.0187]</td><td>0.0458**<br>[0.0187]</td><td>0.0460**<br>[0.0189]</td></tr><tr><td>GDP per Capita <sup>a,b</sup></td><td>-1.607<br>[1.818]</td><td>-1.624<br>[1.834]</td><td>-1.588<br>[1.830]</td><td>-1.575<br>[1.820]</td><td>-1.619<br>[1.819]</td><td>-1.621<br>[1.819]</td><td>-1.734<br>[1.861]</td><td>-1.612<br>[1.818]</td><td>-1.626<br>[1.826]</td><td>-1.658<br>[1.819]</td></tr><tr><td>General Expenditure <sup>a</sup></td><td>0.148**<br>[0.0687]</td><td>0.149**<br>[0.0687]</td><td>0.147**<br>[0.0689]</td><td>0.151**<br>[0.0691]</td><td>0.148**<br>[0.0687]</td><td>0.148**<br>[0.0688]</td><td>0.149**<br>[0.0690]</td><td>0.148**<br>[0.0688]</td><td>0.148**<br>[0.0687]</td><td>0.151**<br>[0.0694]</td></tr><tr><td>Observations</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td></tr><tr><td>R<sup>2</sup></td><td>0.049</td><td>0.049</td><td>0.049</td><td>0.051</td><td>0.049</td><td>0.049</td><td>0.050</td><td>0.049</td><td>0.049</td><td>0.051</td></tr><tr><td>Countries</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td></tr></tbody></table>

Notes: The table reports the second part of the coefficients of the robustness check on the control variables only from the country fixed effect for all 20 epidemic outbreaks. Source: Author's calculations based on WDI, CDC, and WHO.

a Lagged one period.

b Natural log transformation.

* p < 0.10, ** p < 0.05, *** p < 0.01.

<table><caption>Table A.11: Time Fixed Effect Results (Robustness)</caption><thead><tr><th colspan="6">Disease Outbreak Effects on FDI Inflows</th></tr><tr><th>Disease</th><th>Coefficient</th><th>Std. Error</th><th>Disease</th><th>Coefficient</th><th>Std. Error</th></tr></thead><tbody><tr><td>Hepatitis E</td><td>0.707</td><td>(1.136)</td><td>Marburg</td><td>-0.773</td><td>(2.172)</td></tr><tr><td>Chikungunya</td><td>-1.596</td><td>(1.865)</td><td>Measles</td><td>1.097**</td><td>(0.551)</td></tr><tr><td>Cholera</td><td>0.453</td><td>(0.712)</td><td>Meningococcal Meningitis</td><td>-0.00931</td><td>(0.746)</td></tr><tr><td>Dengue</td><td>-0.194</td><td>(0.553)</td><td>MERS</td><td>-2.232***</td><td>(0.682)</td></tr><tr><td>Diphtheria</td><td>-1.461**</td><td>(0.723)</td><td>Plague</td><td>-0.435</td><td>(0.829)</td></tr><tr><td>Ebola</td><td>-0.591</td><td>(0.815)</td><td>Rift Valley Fever</td><td>1.558</td><td>(1.665)</td></tr><tr><td>Zoonotic Influenza</td><td>0.0345</td><td>(0.329)</td><td>SARS</td><td>-2.093**</td><td>(1.047)</td></tr><tr><td>Japanese Encephalitis</td><td>-1.208**</td><td>(0.604)</td><td>Typhoid</td><td>0.274</td><td>(0.613)</td></tr><tr><td>Lassa Fever</td><td>-3.433***</td><td>(0.962)</td><td>Zika</td><td>0.555</td><td>(0.562)</td></tr><tr><td>Leptospirosis</td><td>-2.670***</td><td>(0.750)</td><td>Viral Hemorrhagic Fever</td><td>0.150</td><td>(0.954)</td></tr></tbody><tfoot><tr><td colspan="6">Total Observations: 1,944 across 98 countries</td></tr><tr><td colspan="6">Model Fit (R<sup>2</sup>): 0.246</td></tr></tfoot></table>

Notes: The table shows results of the robustness check using time fixed effects only. Standard errors in parentheses are heteroskedasticity-robust and clustered by country. Source: Author's calculations based on WDI, CDC, and WHO.

* p < 0.10, ** p < 0.05, *** p < 0.01.
<table><caption>Table A.12: Time Fixed Effect Results (Robustness): Control Variables Only (Part 1)</caption><thead><tr><th rowspan="2">Control Variables</th><th colspan="10">Disease-Specific Models (Part 1)</th></tr><tr><th>Hepatitis E</th><th>Chikungunya</th><th>Cholera</th><th>Dengue</th><th>Diphtheria</th><th>Ebola</th><th>Zoonotic Influenza</th><th>Japanese Encephalitis</th><th>Lassa Fever</th><th>Leptospirosis</th></tr></thead><tbody><tr><td>Political Stability <sup>a</sup></td><td>0.602*<br>(0.344)</td><td>0.600*<br>(0.344)</td><td>0.601*<br>(0.345)</td><td>0.600*<br>(0.344)</td><td>0.598*<br>(0.344)</td><td>0.597*<br>(0.344)</td><td>0.601*<br>(0.345)</td><td>0.600*<br>(0.344)</td><td>0.613*<br>(0.343)</td><td>0.599*<br>(0.344)</td></tr><tr><td>GDP Growth <sup>a</sup></td><td>0.0808*<br>(0.0422)</td><td>0.0805*<br>(0.0424)</td><td>0.0812*<br>(0.0418)</td><td>0.0807*<br>(0.0423)</td><td>0.0803*<br>(0.0423)</td><td>0.0813*<br>(0.0425)</td><td>0.0806*<br>(0.0423)</td><td>0.0807*<br>(0.0423)</td><td>0.0812*<br>(0.0422)</td><td>0.0806*<br>(0.0422)</td></tr><tr><td>Natural Resources <sup>a</sup></td><td>-0.0177<br>(0.0250)</td><td>-0.0175<br>(0.0251)</td><td>-0.0182<br>(0.0253)</td><td>-0.0177<br>(0.0250)</td><td>-0.0177<br>(0.0250)</td><td>-0.0175<br>(0.0251)</td><td>-0.0176<br>(0.0251)</td><td>-0.0177<br>(0.0250)</td><td>-0.0172<br>(0.0250)</td><td>-0.0177<br>(0.0250)</td></tr><tr><td>Trade Openness <sup>a</sup></td><td>0.0557***<br>(0.00671)</td><td>0.0556***<br>(0.00671)</td><td>0.0557***<br>(0.00668)</td><td>0.0557***<br>(0.00670)</td><td>0.0557***<br>(0.00671)</td><td>0.0557***<br>(0.00672)</td><td>0.0557***<br>(0.00671)</td><td>0.0557***<br>(0.00671)</td><td>0.0557***<br>(0.00671)</td><td>0.0557***<br>(0.00671)</td></tr><tr><td>GDP per Capita <sup>a,b</sup></td><td>-0.760**<br>(0.317)</td><td>-0.763**<br>(0.316)</td><td>-0.753**<br>(0.316)</td><td>-0.761**<br>(0.316)</td><td>-0.761**<br>(0.316)</td><td>-0.766**<br>(0.318)</td><td>-0.762**<br>(0.319)</td><td>-0.762**<br>(0.316)</td><td>-0.776**<br>(0.316)</td><td>-0.762**<br>(0.316)</td></tr><tr><td>General Expenditure <sup>a</sup></td><td>0.0101<br>(0.0654)</td><td>0.00934<br>(0.0652)</td><td>0.0107<br>(0.0652)</td><td>0.00989<br>(0.0654)</td><td>0.00956<br>(0.0654)</td><td>0.00937<br>(0.0652)</td><td>0.0101<br>(0.0655)</td><td>0.00996<br>(0.0654)</td><td>0.00936<br>(0.0652)</td><td>0.0104<br>(0.0654)</td></tr><tr><td>Observations</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td></tr><tr><td>R<sup>2</sup></td><td>0.049</td><td>0.049</td><td>0.049</td><td>0.049</td><td>0.049</td><td>0.049</td><td>0.049</td><td>0.049</td><td>0.049</td><td>0.049</td></tr><tr><td>Countries</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td></tr></tbody></table>

Notes: The table reports the first part of the coefficients of the robustness check on the control variables only from the time fixed effect for all 20 epidemic outbreaks. Source: Author's calculations based on WDI, CDC, and WHO.

<sup>a</sup> Lagged one period.

<sup>b</sup> Natural log transformation.

* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01.

<table><caption>Table A.13: Time Fixed Effect Results (Robustness): Control Variables Only (Part 2)</caption><thead><tr><th rowspan="2">Control Variables</th><th colspan="10">Disease-Specific Models (Part 2)</th></tr><tr><th>Marburg</th><th>Measles</th><th>Meningococcal</th><th>MERS</th><th>Plague</th><th>Rift Valley</th><th>SARS</th><th>Typhoid</th><th>Zika</th><th>Hemorrhagic Fever</th></tr></thead><tbody><tr><td>Political Stability<sup>a</sup></td><td>0.601*<br>(0.344)</td><td>0.615*<br>(0.343)</td><td>0.601*<br>(0.345)</td><td>0.586*<br>(0.347)</td><td>0.600*<br>(0.344)</td><td>0.605*<br>(0.343)</td><td>0.596*<br>(0.345)</td><td>0.601*<br>(0.344)</td><td>0.599*<br>(0.344)</td><td>0.602*<br>(0.345)</td></tr><tr><td>GDP Growth<sup>a</sup></td><td>0.0809*<br>(0.0422)</td><td>0.0810*<br>(0.0422)</td><td>0.0807*<br>(0.0421)</td><td>0.0796*<br>(0.0419)</td><td>0.0806*<br>(0.0422)</td><td>0.0806*<br>(0.0423)</td><td>0.0817*<br>(0.0425)</td><td>0.0807*<br>(0.0423)</td><td>0.0807*<br>(0.0422)</td><td>0.0807*<br>(0.0423)</td></tr><tr><td>Natural Resources<sup>a</sup></td><td>-0.0176<br>(0.0250)</td><td>-0.0172<br>(0.0249)</td><td>-0.0177<br>(0.0251)</td><td>-0.0143<br>(0.0254)</td><td>-0.0176<br>(0.0250)</td><td>-0.0175<br>(0.0250)</td><td>-0.0184<br>(0.0249)</td><td>-0.0177<br>(0.0250)</td><td>-0.0175<br>(0.0251)</td><td>-0.0178<br>(0.0252)</td></tr><tr><td>Trade Openness<sup>a</sup></td><td>0.0557***<br>(0.00670)</td><td>0.0558***<br>(0.00670)</td><td>0.0557***<br>(0.00670)</td><td>0.0559***<br>(0.00647)</td><td>0.0557***<br>(0.00671)</td><td>0.0558***<br>(0.00669)</td><td>0.0559***<br>(0.00670)</td><td>0.0557***<br>(0.00670)</td><td>0.0557***<br>(0.00670)</td><td>0.0557***<br>(0.00672)</td></tr><tr><td>GDP per Capita<sup>a,b</sup></td><td>-0.763**<br>(0.317)</td><td>-0.770**<br>(0.315)</td><td>-0.762**<br>(0.322)</td><td>-0.695**<br>(0.314)</td><td>-0.763**<br>(0.318)</td><td>-0.758**<br>(0.316)</td><td>-0.757**<br>(0.316)</td><td>-0.761**<br>(0.316)</td><td>-0.764**<br>(0.316)</td><td>-0.761**<br>(0.317)</td></tr><tr><td>General Expenditure<sup>a</sup></td><td>0.00989<br>(0.0653)</td><td>0.00955<br>(0.0654)</td><td>0.01000<br>(0.0653)</td><td>0.0139<br>(0.0654)</td><td>0.00994<br>(0.0653)</td><td>0.00971<br>(0.0653)</td><td>0.00959<br>(0.0654)</td><td>0.0101<br>(0.0654)</td><td>0.0103<br>(0.0653)</td><td>0.0100<br>(0.0653)</td></tr><tr><td>Observations</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td></tr><tr><td>R<sup>2</sup></td><td>0.049</td><td>0.049</td><td>0.049</td><td>0.051</td><td>0.049</td><td>0.049</td><td>0.050</td><td>0.049</td><td>0.049</td><td>0.051</td></tr><tr><td>Countries</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td><td>98</td></tr></tbody></table>

Notes: The table reports the second part of the coefficients of the robustness check on the control variables only from the time fixed effect for all 20 epidemic outbreaks. Source: Author's calculations based on WDI, CDC, and WHO.

<sup>a</sup> Lagged one period.

<sup>b</sup> Natural log transformation.

* *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01.
<table><caption>Table A.14: Country and Time Fixed Effects, window- Robustness</caption><caption>Robustness: Disease Outbreak Effects on FDI Inflows</caption><thead><tr><th>Disease</th><th>Coefficient</th><th>Std. Error</th><th>Disease</th><th>Coefficient</th><th>Std. Error</th></tr></thead><tbody><tr><td>Hepatitis E</td><td>0.560</td><td>(0.818)</td><td>SARS</td><td>-1.587</td><td>(1.167)</td></tr><tr><td>Chikungunya</td><td>-1.876</td><td>(2.237)</td><td>Marburg</td><td>-1.284</td><td>(1.371)</td></tr><tr><td>Cholera</td><td>0.545</td><td>(0.834)</td><td>Measles</td><td>0.502</td><td>(0.449)</td></tr><tr><td>Dengue</td><td>-0.399</td><td>(0.485)</td><td>Meningococcal Disease</td><td>0.153</td><td>(0.625)</td></tr><tr><td>Diphtheria</td><td>-0.301</td><td>(0.859)</td><td>MERS</td><td>-1.153*</td><td>(0.583)</td></tr><tr><td>Ebola</td><td>-1.340**</td><td>(0.660)</td><td>Plague</td><td>-1.406**</td><td>(0.681)</td></tr><tr><td>Viral Haemorrhagic Fever</td><td>-0.532</td><td>(0.549)</td><td>Rift Valley Fever</td><td>0.783</td><td>(1.194)</td></tr><tr><td>Japanese Encephalitis</td><td>-0.829</td><td>(0.798)</td><td>Typhoid</td><td>-0.231</td><td>(0.317)</td></tr><tr><td>Lassa Fever</td><td>-2.176***</td><td>(0.731)</td><td>Zika</td><td>0.669</td><td>(0.438)</td></tr><tr><td>Leptospirosis</td><td>-2.176***</td><td>(0.731)</td><td></td><td></td><td></td></tr><tr><td>Zoonotic Influenza</td><td>-0.126</td><td>(0.407)</td><td></td><td></td><td></td></tr></tbody><tfoot><tr><td colspan="6">Total Observations: 1,944 across 98 countries</td></tr><tr><td colspan="6">(R<sup>2</sup>) for all: 0.071 - 0.073</td></tr></tfoot></table>

*Notes*: Each coefficient represents the estimated effect of a disease outbreak on FDI inflows from separate regression models using 5-year outbreak windows. All regressions include country and time fixed effects.

* p < 0.10, ** p < 0.05, *** p < 0.01.

<figure><img src="image_22.png" /><figcaption>Figure A.5: Impulse Response of FDI to all Infectious Diseases Outbreaks as a single Shock</figcaption></figure>

*Note*: This Figure shows the impulse response of FDI inflows to all infectious disease outbreaks as a single shock. The solid line indicates the estimated response, vertical bars represent 90% confidence intervals. Source: Author's calculations based on WDI, CDC, and WHO
<figure><img src="image_23.png" /><figcaption>Figure A.6: Region-Specific response of FDI after pandemics</figcaption></figure>

Note: This Figure shows the Region-Specific response of FDI after pandemics. The solid line indicates the estimated response, and vertical bars represents 90% confidence intervals. Source: Author's calculations based on WDI, CDC, and WHO

<table><caption>Table A.15: Healthcare Workforce Capacity and FDI: Physicians as Buffers by Transmission Route</caption><thead><tr><th>Variables</th><th>Airborne Outbreak</th><th>Water/Food-Borne Outbreak</th><th>Mosquito-Borne Outbreak</th><th>Vector-borne Flea Outbreak</th><th>Direct Contact Outbreak</th></tr></thead><tbody><tr><td>FDI/GDP</td><td>-0.0441<br>(0.315)</td><td>0.292<br>(0.857)</td><td>0.346<br>(0.498)</td><td>-2.277***<br>(0.541)</td><td>-1.318*<br>(0.672)</td></tr><tr><td>Good Healthcare system</td><td>-0.342<br>(0.384)</td><td>0.521<br>(0.953)</td><td>-0.270<br>(0.568)</td><td>3.355***<br>(0.818)</td><td>1.649**<br>(0.830)</td></tr><tr><td>Observations</td><td>1,743</td><td>1,743</td><td>1,743</td><td>1,743</td><td>1,743</td></tr><tr><td>R-squared</td><td>0.061</td><td>0.062</td><td>0.062</td><td>0.061</td><td>0.061</td></tr><tr><td>Number of Countries</td><td>97</td><td>97</td><td>97</td><td>97</td><td>97</td></tr></tbody></table>

Notes: This table reports fixed-effects regression estimates of the moderating role of healthcare capacity, proxied by physicians' density. The indicator for "Good Healthcare System" equals 1 if a country's Physicians density is above the annual median (50th percentile) in a given year. Interaction terms capture how epidemic outbreaks (by transmission type) affect FDI inflows in countries with relatively stronger healthcare systems. All regressions include country and time fixed effects. Statistical significance: ***p < 0.01, **p < 0.05, *p < 0.10.
<table><caption>Table A.16: Healthcare Workforce Capacity and FDI: GHS index as Buffers by Transmission Route</caption><thead><tr><th rowspan="2">Dependent Variable<br>Variables</th><th colspan="5">FDI/GDP</th></tr><tr><th>Airborne<br>Outbreak</th><th>Water/Food-Borne<br>Outbreak</th><th>Mosquito-Borne<br>Outbreak</th><th>Vector-borne<br>Flea<br>Outbreak</th><th>Direct Contact<br>Outbreak</th></tr></thead><tbody><tr><td>Epidemic Outbreak</td><td>-0.0282<br>(0.261)</td><td>0.487<br>(0.756)</td><td>0.556<br>(0.481)</td><td>-1.836***<br>(0.479)</td><td>-2.143**<br>(0.893)</td></tr><tr><td>Health Security Index<sup>+</sup></td><td>-0.245<br>(0.381)</td><td>0.271<br>(0.969)</td><td>-0.536<br>(0.510)</td><td>3.990***<br>(0.601)</td><td>1.914**<br>(0.865)</td></tr><tr><td>Observations</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td><td>1,944</td></tr><tr><td>R-squared</td><td>0.071</td><td>0.071</td><td>0.071</td><td>0.072</td><td>0.075</td></tr><tr><td>Number of Countries</td><td>98</td><td>98</td><td>97</td><td>98</td><td>98</td></tr></tbody></table>

*Notes:* This table reports two-way fixed-effects regression estimates of the moderating role of healthcare capacity, proxied by GHS index, on transmission route. The indicator for Health Security Index<sup>+</sup> equals 1 if a country's GHS index is above the annual median (50th percentile) in a given year. Interaction terms capture how epidemic outbreaks (by transmission type) affect FDI inflows in countries with relatively stronger healthcare systems. All regressions include country and time fixed effects.

\*\*\* *p* < 0.01, \*\* *p* < 0.05, \* *p* < 0.10.

*Source:* Authors' calculations based on WDI, CDC, and WHO.

<table><caption>Table A.17: Country and Time Fixed effects result, Effect of Outbreaks on FDI: window-Robustness-Only Control Variables</caption><thead><tr><th rowspan="2">Control Variables</th><th colspan="10">Disease-Specific Models (Part 1)</th></tr><tr><th>Hepatitis E</th><th>Chikungunya</th><th>Cholera</th><th>Dengue</th><th>Diphtheria</th><th>Ebola</th><th>Zoonotic Influenza</th><th>Japanese Encephalitis</th><th>Lassa Fever</th><th>Leptospirosis</th><th>Marburg</th></tr></thead><tbody><tr><td>Political Stability<sup>a</sup></td><td>0.179<br>[0.326]</td><td>0.188<br>[0.325]</td><td>0.172<br>[0.325]</td><td>0.178<br>[0.327]</td><td>0.179<br>[0.327]</td><td>0.183<br>[0.326]</td><td>0.180<br>[0.328]</td><td>0.179<br>[0.327]</td><td>0.149<br>[0.326]</td><td>0.179<br>[0.327]</td><td>0.186<br>[0.326]</td></tr><tr><td>GDP Growth<sup>a</sup></td><td>0.0684**<br>[0.0329]</td><td>0.0684**<br>[0.0330]</td><td>0.0687**<br>[0.0327]</td><td>0.0681**<br>[0.0330]</td><td>0.0681**<br>[0.0330]</td><td>0.0699**<br>[0.0329]</td><td>0.0681**<br>[0.0331]</td><td>0.0681**<br>[0.0330]</td><td>0.0694**<br>[0.0330]</td><td>0.0680**<br>[0.0330]</td><td>0.0683**<br>[0.0329]</td></tr><tr><td>Natural Resources<sup>a</sup></td><td>-0.0127<br>[0.0742]</td><td>-0.0120<br>[0.0738]</td><td>-0.0132<br>[0.0743]</td><td>-0.0122<br>[0.0747]</td><td>-0.0130<br>[0.0746]</td><td>-0.0146<br>[0.0744]</td><td>-0.0124<br>[0.0751]</td><td>-0.0128<br>[0.0744]</td><td>-0.0121<br>[0.0743]</td><td>-0.0127<br>[0.0744]</td><td>-0.0133<br>[0.0744]</td></tr><tr><td>Trade Openness<sup>a</sup></td><td>0.0434**<br>[0.0191]</td><td>0.0431**<br>[0.0191]</td><td>0.0435**<br>[0.0191]</td><td>0.0433**<br>[0.0191]</td><td>0.0434**<br>[0.0191]</td><td>0.0442**<br>[0.0192]</td><td>0.0434**<br>[0.0191]</td><td>0.0434**<br>[0.0191]</td><td>0.0435**<br>[0.0191]</td><td>0.0434**<br>[0.0191]</td><td>0.0437**<br>[0.0191]</td></tr><tr><td>GDP per Capita<sup>a,b</sup></td><td>-2.055<br>[2.771]</td><td>-2.128<br>[2.760]</td><td>-2.059<br>[2.773]</td><td>-2.065<br>[2.774]</td><td>-2.059<br>[2.774]</td><td>-2.029<br>[2.806]</td><td>-2.057<br>[2.775]</td><td>-2.060<br>[2.770]</td><td>-2.067<br>[2.770]</td><td>-2.053<br>[2.770]</td><td>-2.051<br>[2.771]</td></tr><tr><td>General expenditure<sup>a</sup></td><td>0.150**<br>[0.067]</td><td>0.149**<br>[0.067]</td><td>0.154**<br>[0.065]</td><td>0.150**<br>[0.067]</td><td>0.150**<br>[0.067]</td><td>0.151**<br>[0.067]</td><td>0.150**<br>[0.067]</td><td>0.150**<br>[0.067]</td><td>0.151**<br>[0.067]</td><td>0.151**<br>[0.067]</td><td>0.150**<br>[0.067]</td></tr></tbody></table>

*Notes:* Robust standard errors in parentheses. Each column represents a separate regression model examining the effect of a specific disease outbreak on FDI flows. All regressions include country and time fixed effects.

<sup>a</sup> Lagged one period.

<sup>b</sup> Natural log transformation.

\* *p* < 0.10, \*\* *p* < 0.05, \*\*\* *p* < 0.01.
<table><caption>Table A.18: Country and Time Fixed effects result, Effect of Outbreaks on FDI: window-Robustness-Only Control Variables</caption><thead><tr><th rowspan="2">Control Variables</th><th colspan="9">Disease-Specific Models (Part 2)</th></tr><tr><th>Measles</th><th>Meningococcal</th><th>MERS</th><th>Plague</th><th>Rift Valley</th><th>SARS</th><th>Typhoid</th><th>Zika</th><th>Haemorrhagic</th></tr></thead><tbody><tr><td>Political Stability<sup>a</sup></td><td>0.178<br>[0.329]</td><td>0.171<br>[0.328]</td><td>0.155<br>[0.328]</td><td>0.171<br>[0.327]</td><td>0.180<br>[0.327]</td><td>0.194<br>[0.331]</td><td>0.179<br>[0.327]</td><td>0.174<br>[0.327]</td><td>0.170<br>[0.331]</td></tr><tr><td>GDP Growth<sup>a</sup></td><td>0.0685**<br>[0.0330]</td><td>0.0681**<br>[0.0330]</td><td>0.0676**<br>[0.0328]</td><td>0.0686**<br>[0.0331]</td><td>0.0679**<br>[0.0330]</td><td>0.0685**<br>[0.0331]</td><td>0.0681**<br>[0.0330]</td><td>0.0681**<br>[0.0330]</td><td>0.0681**<br>[0.0330]</td></tr><tr><td>Natural Resources<sup>a</sup></td><td>-0.0125<br>[0.0744]</td><td>-0.0125<br>[0.0745]</td><td>-0.0155<br>[0.0749]</td><td>-0.0122<br>[0.0744]</td><td>-0.0131<br>[0.0744]</td><td>-0.0123<br>[0.0745]</td><td>-0.0128<br>[0.0744]</td><td>-0.0130<br>[0.0743]</td><td>-0.0126<br>[0.0741]</td></tr><tr><td>Trade Openness<sup>a</sup></td><td>0.0431**<br>[0.0191]</td><td>0.0436**<br>[0.0192]</td><td>0.0437**<br>[0.0191]</td><td>0.0434**<br>[0.0191]</td><td>0.0434**<br>[0.0191]</td><td>0.0437**<br>[0.0191]</td><td>0.0434**<br>[0.0191]</td><td>0.0434**<br>[0.0191]</td><td>0.0436**<br>[0.0192]</td></tr><tr><td>GDP per Capita<sup>a,b</sup></td><td>-2.065<br>[2.770]</td><td>-2.060<br>[2.773]</td><td>-2.151<br>[2.768]</td><td>-2.071<br>[2.771]</td><td>-2.059<br>[2.771]</td><td>-2.164<br>[2.832]</td><td>-2.055<br>[2.771]</td><td>-2.078<br>[2.776]</td><td>-2.024<br>[2.775]</td></tr><tr><td>General expenditure<sup>a</sup></td><td>0.150**<br>[0.067]</td><td>0.149**<br>[0.067]</td><td>0.152**<br>[0.065]</td><td>0.150**<br>[0.067]</td><td>0.150**<br>[0.067]</td><td>0.149**<br>[0.067]</td><td>0.150**<br>[0.067]</td><td>0.150**<br>[0.067]</td><td>0.151**<br>[0.0673]</td></tr></tbody></table>

Notes: Robust standard errors in parentheses. Each column represents a separate regression model examining the effect of a specific disease outbreak on FDI flows. This table continues the results from Part 1. All regressions include country and time fixed effects.

a Lagged one period.

b Natural log transformation.

* p < 0.10, ** p < 0.05, *** p < 0.01.
<table><caption>Table A.19: Countries Included in Analysis by Regional Classification</caption><thead><tr><th>Country</th><th>Region</th><th>Country</th><th>Region</th></tr></thead><tbody><tr><td>Angola</td><td>Sub-Saharan Africa</td><td>Mauritania</td><td>Sub-Saharan Africa</td></tr><tr><td>Benin</td><td>Sub-Saharan Africa</td><td>Mozambique</td><td>Sub-Saharan Africa</td></tr><tr><td>Burkina Faso</td><td>Sub-Saharan Africa</td><td>Namibia</td><td>Sub-Saharan Africa</td></tr><tr><td>Burundi</td><td>Sub-Saharan Africa</td><td>Niger</td><td>Sub-Saharan Africa</td></tr><tr><td>Cabo Verde</td><td>Sub-Saharan Africa</td><td>Nigeria</td><td>Sub-Saharan Africa</td></tr><tr><td>Cameroon</td><td>Sub-Saharan Africa</td><td>Rwanda</td><td>Sub-Saharan Africa</td></tr><tr><td>Central African Rep.</td><td>Sub-Saharan Africa</td><td>Senegal</td><td>Sub-Saharan Africa</td></tr><tr><td>Chad</td><td>Sub-Saharan Africa</td><td>South Africa</td><td>Sub-Saharan Africa</td></tr><tr><td>Congo, Dem. Rep.</td><td>Sub-Saharan Africa</td><td>Sudan</td><td>Sub-Saharan Africa</td></tr><tr><td>Congo, Rep.</td><td>Sub-Saharan Africa</td><td>Togo</td><td>Sub-Saharan Africa</td></tr><tr><td>Côte d'Ivoire</td><td>Sub-Saharan Africa</td><td>Uganda</td><td>Sub-Saharan Africa</td></tr><tr><td>Ethiopia</td><td>Sub-Saharan Africa</td><td>Zambia</td><td>Sub-Saharan Africa</td></tr><tr><td>Gabon</td><td>Sub-Saharan Africa</td><td>Zimbabwe</td><td>Sub-Saharan Africa</td></tr><tr><td>Gambia, The</td><td>Sub-Saharan Africa</td><td></td><td></td></tr><tr><td>Ghana</td><td>Sub-Saharan Africa</td><td></td><td></td></tr><tr><td>Guinea</td><td>Sub-Saharan Africa</td><td></td><td></td></tr><tr><td>Guinea-Bissau</td><td>Sub-Saharan Africa</td><td></td><td></td></tr><tr><td>Kenya</td><td>Sub-Saharan Africa</td><td></td><td></td></tr><tr><td>Liberia</td><td>Sub-Saharan Africa</td><td></td><td></td></tr><tr><td>Madagascar</td><td>Sub-Saharan Africa</td><td></td><td></td></tr><tr><td>Malawi</td><td>Sub-Saharan Africa</td><td></td><td></td></tr><tr><td>Mali</td><td>Sub-Saharan Africa</td><td></td><td></td></tr><tr><td colspan="4" style="text-align: center; font-weight: bold;">Middle East & North Africa</td></tr><tr><td>Algeria</td><td>MENA</td><td>Lebanon</td><td>MENA</td></tr><tr><td>Bahrain</td><td>MENA</td><td>Morocco</td><td>MENA</td></tr><tr><td>Egypt, Arab Rep.</td><td>MENA</td><td>Oman</td><td>MENA</td></tr><tr><td>Iran, Islamic Rep.</td><td>MENA</td><td>Qatar</td><td>MENA</td></tr><tr><td>Iraq</td><td>MENA</td><td>Saudi Arabia</td><td>MENA</td></tr><tr><td>Jordan</td><td>MENA</td><td>Syrian Arab Rep.</td><td>MENA</td></tr><tr><td>Kuwait</td><td>MENA</td><td>Tunisia</td><td>MENA</td></tr><tr><td></td><td></td><td>UAE</td><td>MENA</td></tr><tr><td></td><td></td><td>West Bank and Gaza</td><td>MENA</td></tr><tr><td></td><td></td><td>Yemen, Rep.</td><td>MENA</td></tr><tr><td colspan="4" style="text-align: center; font-weight: bold;">Asia & Pacific</td></tr><tr><td>Afghanistan</td><td>Asia & Pacific</td><td>Myanmar</td><td>Asia & Pacific</td></tr><tr><td>Azerbaijan</td><td>Asia & Pacific</td><td>Nepal</td><td>Asia & Pacific</td></tr><tr><td>Bangladesh</td><td>Asia & Pacific</td><td>Pakistan</td><td>Asia & Pacific</td></tr><tr><td>Cambodia</td><td>Asia & Pacific</td><td>Philippines</td><td>Asia & Pacific</td></tr><tr><td>China</td><td>Asia & Pacific</td><td>Russian Federation</td><td>Asia & Pacific</td></tr><tr><td>Fiji</td><td>Asia & Pacific</td><td>Singapore</td><td>Asia & Pacific</td></tr><tr><td>India</td><td>Asia & Pacific</td><td>Thailand</td><td>Asia & Pacific</td></tr><tr><td>Indonesia</td><td>Asia & Pacific</td><td>Türkiye</td><td>Asia & Pacific</td></tr><tr><td>Kazakhstan</td><td>Asia & Pacific</td><td>Viet Nam</td><td>Asia & Pacific</td></tr><tr><td>Korea, Rep.</td><td>Asia & Pacific</td><td></td><td></td></tr><tr><td>Kyrgyz Republic</td><td>Asia & Pacific</td><td></td><td></td></tr><tr><td>Lao PDR</td><td>Asia & Pacific</td><td></td><td></td></tr><tr><td>Malaysia</td><td>Asia & Pacific</td><td></td><td></td></tr><tr><td>Mongolia</td><td>Asia & Pacific</td><td></td><td></td></tr><tr><td colspan="4" style="text-align: center; font-weight: bold;">Latin America & Caribbean</td></tr><tr><td>Argentina</td><td>LAC</td><td>Honduras</td><td>LAC</td></tr><tr><td>Bolivia</td><td>LAC</td><td>Jamaica</td><td>LAC</td></tr><tr><td>Brazil</td><td>LAC</td><td>Mexico</td><td>LAC</td></tr><tr><td>Chile</td><td>LAC</td><td>Nicaragua</td><td>LAC</td></tr><tr><td>Colombia</td><td>LAC</td><td>Panama</td><td>LAC</td></tr><tr><td>Costa Rica</td><td>LAC</td><td>Paraguay</td><td>LAC</td></tr><tr><td>Ecuador</td><td>LAC</td><td>Peru</td><td>LAC</td></tr><tr><td>El Salvador</td><td>LAC</td><td>Uruguay</td><td>LAC</td></tr><tr><td>Guatemala</td><td>LAC</td><td>Venezuela, RB</td><td>LAC</td></tr><tr><td>Haiti</td><td>LAC</td><td></td><td></td></tr></tbody></table>

Note: MENA = Middle East & North Africa; LAC = Latin America & Caribbean
<figure><img src="image_24.png" /><figcaption>Figure A.7: Correlation matrix of Outbreak Variables</figcaption></figure>

*Note:*This figure displays the correlation matrix of Outbreak Variables

<figure><img src="image_25.png" /><figcaption>Figure A.8: Correlation matrix of Control Variables</figcaption></figure>

*Note:*This figure displays the correlation matrix of Control Variables.