The Relationship Between Happiness and Foreign Direct Investment in African Countries

Abstract

This research looks into the relationship between happiness and foreign direct investment (FDI) in African countries so as to shed light on whether or not the inward FDI stock in African countries has an influence on happiness in the region. The study utilises annual panel data from 2006 to 2022 for 46 African countries and uses the Cantril life ladder data as a measure of happiness. The findings from this research reveal that there is a positive but statistically insignificant relationship between inward FDI stock and happiness in Africa. The results suggest that FDI alone does not directly contribute to economic development in African countries, but rather the benefits of FDI may be contingent on other factors in the country. Nonetheless, the positive relationship observed provides a foundation for policymakers to encourage FDI, as it has the potential to improve happiness levels in African nations. Additionally, the results from this study show that the level of happiness in the previous year and social support have a positive and statistically significant effect on happiness in African countries.

1. Introduction

1.1. Background on Happiness

The Greek Philosopher Aristotle put forward in his writings that the ultimate goal of human existence is happiness (Pursuit of Happiness 2024). Happiness may be described as a sense of wellbeing or contentment with one’s quality of life (Veenhoven 2012). Happiness has both affective and cognitive elements. While the cognitive aspect relates to life satisfaction based on an individual’s standards, the affective element, often known as the emotional component, refers to the balance between pleasant and unpleasant feelings (Diener et al. 2009). Happiness can be assessed through self-reporting at both the group and individual level. There is no sole determinant of happiness, but some habits are linked to happiness. These include cultivating social relationships; practising kindness, forgiveness, and spirituality; working towards genuine goals; exercising; and meditating (as cited in Fisher 2010).

With regard to computing happiness for a population, the Gross National Happiness (GNH) Index was conceptualised by King Jigme Singye Wangchuck of Bhutan in the early 1970s. The GNH index has nine domains: health, education, governance, community vitality, good governance, time use, cultural diversity and resilience, psychological wellbeing, and ecological diversity and resilience. It categorises people as unhappy, narrowly happy, extensively happy, and deeply happy (Oxford Poverty & Human Development Initiative (OPHI) 2024). The GNH index was designed to better represent the general wellbeing of the people of Bhutan rather than relying on a monetary indicator, such as Gross Domestic Product (GDP). It has been argued that GDP, which estimates the resources available to the average person to achieve their goals, is a poor measure of wellbeing because it does not take into consideration the value of the production by-products. These include positive and negative externalities such as infrastructural improvement and pollution, respectively. Moreover, GDP underestimates the value of the citizens’ work, for example, the unpaid labour of stay-at-home partners (Aitken 2019). Although the GNH index is only available for Bhutan, there are measures that have been inspired by the GNH index such as the World Happiness Report, the Organization for Economic Cooperation and Development (OECD) Better Life Index, and the Happy Planet Index. Moreover, in 2012, the United Nations held a meeting to encourage the spread of Bhutan’s GNH philosophy, and in that same year, the 20th of March was declared to be the International Day of Happiness by the United Nations (Action for Happiness 2021).

A more widely available measure of happiness for a majority of countries is available from the Gallup World Poll. The poll generally measures individuals’ attitudes towards notable matters in their country such as GDP per capita, healthy life expectancy, generosity, freedom, social support, and perceptions of corruption. The survey uses a sample size of around 1000 or 2000 individuals in larger countries such as Russia and China (Helliwell et al. 2024). Further, the poll adopts the Cantril life ladder survey question. The question is worded as “Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?” (Helliwell et al. 2024).

1.2. Happiness and Foreign Direct Investment in Africa

The World Happiness Report, which has been published since 2012, indicates that compared to other regions of the world, Africa has lower levels of happiness. For example, in 2017, the average happiness levels were below average for over four in five African countries (Helliwell et al. 2017). In 2022, the average level of happiness based on 32 countries in Africa was 4.39 points, while the average level of happiness globally was 5.54 points using data from 134 countries (The Global Economy 2024). Unfortunately, these findings point at African citizens being less satisfied with their quality of life, a condition that should be improved upon so as to give individuals the chance to lead better and more fulfilled lives.

Over the years, African countries have continued to pursue FDI since it results in increased employment, consumer choice, transfer of capital, new technology, management skills, access to international markets, and economic growth (Gionea 2005). The benefits obtained from FDI inflows would be valuable in helping African countries achieve higher levels of living, leading to increased sustenance, self-esteem, and freedom of choice. This would ultimately increase the level of happiness for people living in the continent. Moreover, Amartya Sen’s Capability Approach highlights that true development and wellbeing for individuals depend on increasing their capabilities. In their book, Todaro and Smith (2020) explain that capabilities as defined by Sen are the “freedoms that people have, given their personal features and their command over commodities” (p. 12). The Stanford Encyclopedia of Philosophy (2024) describes capabilities as the actions and things that one can accomplish if they so choose. For example, being able to attain an education, being well nourished, and being able to participate in community life. In this research, we put forward that FDI has the potential to increase people’s capabilities. This is because the benefits provided by FDI such as increased employment, economic growth, new technology, and access to international markets provide individuals with the freedoms to achieve such things like being educated, being healthy, having decent housing, and having access to economic opportunities. As such, by enhancing people’s freedom to attain what they value, FDI is likely to improve their happiness.

Additionally, past studies such as Stevenson and Wolfers (2013) have found that there is significant correlation between income and happiness across countries and over time. When individuals earn a living and can afford their basic needs, their happiness increases. Unfortunately, within the African continent, there are approximately 534 million people that live in extreme poverty as they live on less than USD 2.15 per person, per day (United Nations Development Programme and Oxford Poverty and Human Development Initiative 2023). Increased incomes, which would lead to increased happiness, are needed for individuals living in the continent, and FDI could provide an opportunity for higher incomes. However, it should be noted that the relationship between income and happiness increases at a decreasing rate. Moreover, after a high enough level of income is reached, when people are relatively well nourished, healthy and educated, happiness is no higher on average with higher income (Todaro and Smith 2020).

In 2022, Africa received 3.5% of the global inward FDI flows, which were valued at USD 45 billion. Specifically, in 2022, FDI inflows to Northern Africa were USD 15 billion, while those to Southern Africa were valued at USD 7 billion. West Africa received FDI inflows worth USD 8 billion, East Africa received FDI inflows amounting to USD 9 billion, while FDI inflows to Central Africa totalled USD 6 billion (UNCTAD World Investment Report 2024). In 2023, Northern Africa had the highest FDI stock1 in the continent valued at USD 355 billion, followed by Western Africa, which had a value of USD 211 billion. In Eastern Africa, the value was USD 196 billion, and Southern Africa had a value of USD 140 billion, while Central Africa had the lowest FDI stock valued at USD 134 billion (UNCTAD Data Centre 2024). In the context of the source of FDI, European investors are the largest holders of FDI stock in the African continent. Specifically, the United Kingdom has an FDI stock valued at USD 60 billion, while France and the Netherlands each have an FDI stock with a value of USD 54 billion. Within the continent, intraregional investments have increased over the last five years but still remain quite small in comparison to investments coming from outside of the continent (UNCTAD World Investment Report 2024).

Given the increasing FDI stock in the African continent, it would be valuable to look into the effect that FDI has on the overall level of happiness, an indicator of economic development. For example, research carried out by Kanweri (2015) found that at the global level, inward FDI is positively correlated with the level of happiness in a country. However, the study further indicates that the relationship between FDI and happiness is not significant for middle income and low-income countries. The research by Kanweri (2015) utilised data from various countries across the world, including only 14 African countries. However, our study specifically examines the effect of FDI on happiness using data from 46 African countries. Focusing only on the relationship between FDI and happiness in African countries will shed light on any differences or similarities that these African nations might share with countries in other regions across the globe. Additionally, this study controls for the relationship between happiness and other factors such as income per capita, social support, life expectancy, education, governance, and the COVID-19 pandemic. As a measure of happiness in African countries, this research used the Cantril life ladder2 data from the Gallup World Poll, while FDI was measured using the natural log of foreign direct investment stocks in USD. Additionally, for robustness, we also used data from the Happy Planet Index3 as a measure of happiness in African countries.

This study is important to policymakers and researchers as it enables a deeper understanding of how happiness, a measure of wellbeing and satisfaction with one’s quality of life, is influenced by the FDI stock in Africa. Moreover, a positive relationship between happiness and FDI would provide policymakers with a stronger basis to encourage the overall agenda of regional integration across the continent as this tends to bring with it increased intra-regional FDI, which would eventually lead to higher levels of happiness for African citizens.

2. Review of Related Literature

2.1. Happiness and Foreign Direct Investment

Past studies on the relationship between happiness and FDI show that FDI influences happiness both directly and indirectly (through factors such as GDP and unemployment) and that the nature of the impact varies across regions. For example, a study by Kanweri (2015) found that globally, inward FDI had a significant and positive effect on happiness. Outward FDI also had a positive effect on happiness, though the effect was not statistically significant. Further, the impact of FDI on happiness varied depending on the income levels of the various countries. Specifically, the study by Kanweri (2015) found a significant and direct relationship between FDI and the happiness of high-income OECD and high-income non-OECD, North American, Asian and African countries. However, this direct association was not found in the low-income and lower- and upper-middle income European and South American countries.

In other research, Piteli (2017) reported mixed evidence on the potential benefits of inward FDI flows, suggesting that the hypothesised positive impact of FDI inflows seems to be dependent on several factors, including the institutional environment, human capital endowments, and the absorptive capacity of host countries. This may provide some insights as to why the research by Kanweri (2015) did not find a correlation between FDI and happiness in the low-income and lower- and upper-middle-income European and South American countries.

2.2. Happiness and Income

Richard Easterlin, after whom the ”Easterlin paradox” was coined, was among the first to conduct empirical research on the happiness–income relationship. In 2001, Easterlin conducted a survey and found that, while happiness increases as income increases, this effect only lasts up to a certain level, beyond which the additional gain in happiness begins to decline, both among and within countries. This was named the ”Easterlin paradox”. According to this paradox, those earning a high income are happier than those earning a low income, but over the life cycle, despite significant income gain, happiness remains constant. Therefore, he implies that people are happy if they earn enough to meet their basic needs.

Later, Stevenson and Wolfers (2013) reassessed the Easterlin paradox and established that there is no fixed point of satiation for both wealthy and low-income individuals and nations. They also found that the happiness–income relationship is approximately log linear, such that a higher increase in happiness is correlated with each additional unit (USD) of income for the low-income earners than for the high-income earners. Similarly, Frey and Stutzer (2002) concluded that national income is significantly linked to overall life satisfaction until a certain level, after which income ceases to be important. Elsewhere, Shrotryia (2023) argues that after fulfilling basic needs, the role of money does not necessarily translate to an increase in happiness.

2.3. Happiness and Social Factors

In addition to income, past research suggests that social factors such as social support, education, life expectancy, and health all have an effect on wellbeing. For example, findings from Chen and Feeley (2014) indicated that higher support and lower strain from a partner, children, friends, and family improve wellbeing. More recently, research by Poudel et al. (2020) revealed that adolescents who perceive good social support have higher self-esteem, which in turn contributes to their psychological wellbeing. Additionally, both boys and girls tend to reach out more to family for social support compared to friends and others.

In relation to education, research carried out in the U.S. found that adults who have education beyond the high school level have better health and wellbeing outcomes. Additionally, these adults are more likely to have jobs that align with their talents and interests and tend to engage more with their community (Lumina Foundation 2023). However, there are also studies that have found a negative relationship between education and wellbeing. For example, research using Chinese data by Wu et al. (2022) shows that higher education on average is significantly negatively correlated with happiness for people in urban China. The authors go on to discuss that individuals with higher education are likely to have “acceptable” lives rather than “extremely happy” lives. Specifically, the study highlights that in urban China, highly educated people generally have more unpaid housing debt.

When it comes to life expectancy, longitudinal studies of normal populations show that a variety of subjective wellbeing factors, such as the positive affect element, can predict health and longevity (Diener and Chan 2011). Further, when combined with experimental research, the case that subjective wellbeing influences health and longevity in healthy populations is compelling. Furthermore, a study by Gimenez et al. (2021) argues that among Chilean senior citizens, happiness is the most important determinant of life expectancy.

Poor health in general is expected to have a detrimental effect on happiness. The recent COVID-19 pandemic, which affected billions of people and resulted in millions of lives being lost across the globe, was a time when many suffered poor health. However, according to the World Happiness Report (Helliwell et al. 2022), findings show that COVID-19 led to only modest changes in the overall happiness rankings, reflecting both the global nature of the pandemic and a widely shared resilience in the face of it. Nonetheless, findings from the Gallup World Poll reported increased frequency of stress among individuals in 2020 as a result of the heightened pressure during the pandemic.

2.4. Happiness and Governance

The government also has a critical role to play when it comes to the happiness of its citizens. For example, research findings indicate that those living in Nordic countries tend to have high levels of happiness even though these may not be the world’s wealthiest nations. Instead, they score high in terms of well-functioning democratic institutions, levels of trust, mutual respect, and support in society. Similarly, nations that have low levels of happiness tend to have higher levels of corruption, distrust towards others in society, and weaker institutions (Cotofan 2023). In addition, research using data from ten South American countries found that even after controlling for sociodemographic and macroeconomic factors, there was a strong positive correlation between confidence in national institutions and the current and expected life satisfaction (Macchia and Plagnol 2019).

3. Data and Methodology

3.1. Data

This research used data from 464 out of the 55 countries in the African continent to investigate the relationship between happiness and FDI in African countries. Annual panel data from 2006 to 2022 were used in this study, as this is when the Cantril life ladder, as well as the FDI stock data, are available for the 46 African countries. The dependent variable used in this study is the Cantril life ladder, which is the measure of happiness. Data for the Cantril life ladder were obtained from the 2024 World Happiness Report Appendix and were calibrated on a scale of 0 to 10; where 0 is very unhappy and 10 is very happy. The main independent variable for this study is the annual FDI stock data in USD, which were obtained from the United Conference on Trade and Development (UNCTAD) data centre.

The control variables included in this study are social support5, which is measured as either 0 or 1; income, which is measured as the natural log of GDP per capita; health, which is measured as the healthy life expectancy at birth; education, which is measured using the United Nations Development Programme (UNDP) education index on a scale between 0 and 1; governance index, which is measured on a scale between −2.5 (weak) and 2.5 (strong); and the COVID-19 pandemic, which is a dummy variable included to control for the COVID-19 pandemic in the years 2020, 2021, and 2022.

Data for social support were obtained from the 2024 World Happiness Report Appendix. The education index data were obtained from the United Nations Development Programme (UNDP), while the income and healthy life expectancy data were obtained from the World Bank Development Indicators database. The variables (i.e., voice and accountability, political stability and absence of violence/terrorism, rule of law, regulatory quality, government effectiveness, and control of corruption) used to construct the governance index were obtained from The World Bank Worldwide Governance Indicators database.

3.2. Constructing the Governance Index

The set of governance indicators used in constructing the index for this study were voice and accountability, political stability and absence of violence/terrorism, rule of law, government effectiveness, control of corruption, and regulatory quality. The governance index was constructed using the principal component analysis (PCA) method, which was deemed suitable as the indicators used are strongly correlated. Governance was captured as an index rather than using the individual indicators so as to provide for a multidimensional concept that cannot be sufficiently described by an individual indicator. The index ranges from −2.5 to 2.5 such that higher values are related to better governance.

3.3. Methodology

To estimate the effect of FDI stock on happiness in Africa, the econometric model below was used:

$$\begin{array}{ll}{Life ladder}_{i,t}=& k+{\beta }_1 {Life ladder}_{i,t-1}+{\beta }_2 log {FDI}_{i,t}+{\beta }_3 {log GDP per capita}_{i,t}\\ & +{{\beta }_4 life expectancy}_{i,t}+{\beta }_5 {education index}_{i,t}+{\beta }_6 {social support}_{i,t}\\ & +{\beta }_7 {governance index}_{i,t}+{\beta }_8 {Covid-19}_{i,t}+{\epsilon }_{i,t}\end{array}$$

(1)

The subscript t refers to the years ranging from 2006 to 2022, and i refers to an individual country. k is the common intercept, which is the same for all the cross-sectional countries and over time. ${\beta }_1$ is the coefficient for the lag of the dependent variable life ladder, which is the measure for happiness. ${\beta }_2$ is the coefficient to the independent variable, log of FDI stock, and illustrates how much happiness changes when FDI stock changes by a percent, all else being equal. ${\beta }_3-{\beta }_8$ represent the coefficients of the control variables, which are income per capita, life expectancy, education, social support, governance, and the COVID-19 pandemic. ${\epsilon }_{i,t}={\omega }_i+u_{it}$. ${\omega }_i$ measures the random deviation between each country’s intercept term and the common intercept term $k$, while $u_{it}$ is the residual term for country $i$ at time $t$.

The econometric model (1) above shows that the life ladder is influenced by its lag, log of FDI stock, a set of control variables, and a set of country-specific effects and time-fixed effects. Hence, this study employed the Generalised Methods of Moments (GMM) technique to address any endogeneity or reverse causality concerns. For example, there might be a correlation between regressors such as FDI stock, GDP per capita, social support, and the error term. There could also be reverse causality because happier populations might attract more FDI. Specifically, the system GMM was used rather than the difference GMM because, according to Blundell and Bond (1998), the latter produces biased and inefficient estimates when working with finite samples due to the use of poor instruments. Hence, the system GMM is preferred.

4. Results and Discussions

4.1. Summary Statistics

Table 1. Summary statistics.

Variable	Obs.	Mean	Std. Dev	Min.	Max.
Life ladder	539	4.384	0.664	2.560	6.355
Happy Planet Index (HPI)	536	28.564	8.129	8.3	56.54
FDI inward stock	782	15,539.18	2.30 × 109	−7.40 × 109	4.07 × 1010
GDP per capita	782	2215.609	2498.081	166.276	1576.42
Social support	536	0.707	0.116	0.290	0.922
Life expectancy	782	60.883	6.586	42.914	77.129
Education index	736	0.457	0.132	0.152	0.769
Governance index	782	−1.24 × 10−9	1.000	−2.344	2.301
COVID-19	782	0.177	0.381	0	1

reports the summary statistics. The life ladder averages around 4.38. This is lower than the 5.0 mid-point of the 0 to 10 ladder. The lowest happiness score over the period of study is 2.56, recorded in Sierra Leone in 2022, and the highest is 6.35, recorded in Algeria in 2014. On average, Mauritius has the highest happiness score at 5.86 followed by Algeria with 5.39, whereas the least happy country is Central African Republic, with an average score of 3.52.

FDI inward stock (current USD) averages around USD 15.5 billion while varying considerably from USD 9.0 million in Burundi (2007) to USD 179.5 billion in South Africa (2010). Variables that showed the greatest variation throughout the sample as measured by standard deviation were FDI and GDP per capita. Over the study period, the total number of observations varies for the various variables, ranging from the highest value at 782 observations recorded for inward FDI stock, GDP per capita, life expectancy, and the governance index to 543 observations for the social support variable. The difference in the number of observations is a result of missing data. As such, the data were classified as an unbalanced panel data set.

4.2. Correlation Matrix

Table 2. Correlation matrix.

	Life Ladder	Log of FDI Stock	Log of GDP per Capita	Social Support	Education Index	Life Expectancy	(d)Governance Index	(d)COVID-19
Life ladder	1.000
Log of FDI	0.395	1.000
Log of GDP per capita	0.440	0.524	1.000
Social support	0.3027	0.352	0.389	1.000
Education index	0.279	0.486	0.739	0.280	1.000
Life expectancy	0.273	0.380	0.518	0.116	0.516	1.000
(d)Governance index	−0.048	−0.010	0.037	0.015	0.036	0.069	1.000
(d)COVID-19	0.137	0.141	0.092	−0.010	0.115	0.115	−0.114	1.000

below reports the correlation coefficients of all the variables used in this study and shows that the correlation among the variables is within the acceptable range.

4.3. Stationarity Tests

As the study utilised an unbalanced panel data set, the Fisher-type tests were used to test for the stationarity of the variables. Except for the governance index and the COVID-19 control variable, the results showed that at least one panel of each variable examined was stationary at their level forms. The governance index and the COVID-19 variables were stationary at first difference. Given this, the governance index and the COVID-19 variables were used at their first difference, while all the other variables in this study were used in their level forms.

4.4. Model Results and Discussion

Table 3. Two-step system GMM (robust) results.

Life Ladder	Coefficient and Std. Error
Life ladder(t−1)	0.439 *** (0.118)
Log inward FDI stock	0.198 (0.264)
Log GDP per capita	0.168 (0.492)
Social support	2.613 ** (1.173)
Life expectancy	0.001 (0.022)
Education index	−1.960 (2.643)
(d)Governance index	0.123 (0.117)
(d)COVID-19	0.218 * (0.123)
Constant	−1.478 (2.673)
p-value of overall model	0.000
p-value of AR(1)	0.001
p-value of AR(2)	1.000
p-value of Hansen test	0.578

Dependent variable is the life ladder. Robust standard errors (in parentheses). Statistical significance: *** at the 1% level, ** at the 5% level, * at the 10% level.

below illustrates the effect of inward FDI stock on happiness in Africa using the System GMM estimation method. The model controls for factors including log of GDP per capita, education, life expectancy, social support, governance, and the COVID-19 pandemic.

The p-value of 0.578 from the Hansen test demonstrates that the instruments used in the model were valid. Additionally, given the p-value of 1.000 for the AR (2) test, the study failed to reject the null hypothesis, confirming that the model is specified correctly and that there is no second order serial correlation.

The study notes that the current year’s life ladder score is positively influenced by the previous year’s score such that a 1-point increase in the previous period’s life ladder score would increase the current year’s happiness by 0.44 points.

Additionally, the results suggest that a 1% increase in inward FDI stock results in a 0.20-point increase in happiness in African countries, holding all other factors constant. However, the results indicate that inward FDI does not have a statistically significant impact on happiness in African countries at either the 90%, 95%, or 99% confidence level. There have been mixed findings on the benefits of inward FDI to various countries, and some posit that FDI alone does not sufficiently contribute to economic growth or development and thus may not directly influence wellbeing. Piteli (2017) finds this to be conditional on several factors, including the absorptive capacity, institutional environment, and human capital endowments of host countries. In their study, Alfaro et al. (2004) find that factors such as the level of financial market development could significantly determine how countries gain from FDI.

To further confirm whether there is a relationship between inward FDI and happiness in Africa, this study used an alternative measure of happiness, the Happy Planet Index (HPI). This further confirmed that at the continental level, the relationship between inward FDI and happiness is positive but statistically insignificant. The results are shown in

Table A1. Two-step system GMM (robust) results, Happy Planet Index (HPI).

HPI	Coefficient and Std. Error
HPI(t−1)	0.496 *** (0.124)
Log inward FDI stock	0.748 (2.395)
Log GDP per capita	5.388 (5.935)
Social support	15.042 * (8.125)
Life expectancy	0.346 (0.219)
Education index	−33.450 (37.151)
(d)Governance index	0.345 (0.978)
(d)COVID-19	1.895 * (0.991)
Constant	−47.103 (37.805)
p-value of overall model	0.000
p-value of AR(1)	0.000
p-value of AR(2)	0.765
p-value of Hansen test	0.471

Dependent variable is the Happy Planet Index. Robust standard errors (in parentheses). Statistical significance: *** at the 1% level, ** at the 5% level, * at the 10% level.

in the Appendix A.

The variables social support and COVID-19 were found to positively influence happiness at the 95% and 90% confidence intervals, respectively. Family embeddedness as well as perceived and experienced support are determinants of social support. This study finds that the presence of social support increases happiness by 2.61 points, holding all other factors constant. This is consistent with Siedlecki et al. (2014), who found that people are happier when they feel valued and that they have family and/or friends to lean on in times of need. The study also controlled for the COVID-19 pandemic experienced in 2020 to 2022 and found a positive relationship with happiness. According to the World Happiness Report (Helliwell et al. 2021), although many countries experienced a decline in life satisfaction, some countries were notably resilient owing to strong social support systems, among other factors.

Income, as measured by GDP per capita, education, life expectancy, and governance, was not found to be statistically significant in influencing happiness in Africa as a whole.

5. Conclusions and Recommendations

This study concludes that the impact of inward FDI on happiness in Africa as a whole is not statistically significant. This indicates that the impact of inward FDI on happiness is a complex issue that can vary depending on several factors. In general, inward FDI can affect life satisfaction either directly or indirectly through its influence on factors such as economic growth, employment opportunities, and social inequality. This may also vary depending on how well the FDI flows are managed to ensure that the benefits are distributed equitably across the country’s citizens so as to avoid the benefits from being offset due to factors such as economic inequality.

The results of this study may suggest that with enhanced management of inward FDI to the betterment of citizens through improvements in areas such as infrastructure, access to healthcare, high-quality education systems, creating enabling environments for businesses to thrive as well as job creation, the quality of life could be improved, resulting in higher life satisfaction or happiness. For example, research findings from Hermes and Lensink (2003) indicate that the level of development of the recipient nation’s financial system is a crucial prerequisite for FDI to positively impact economic growth, which is likely to lead to increased happiness. Further, a study by Meyer and Sinani (2009) highlights that there is a relationship between spill-overs and the degree of development of the host country in terms of income, institutional structure, and human capital.

This study encourages further research to examine other direct and indirect factors that could influence the relationship between inward FDI and happiness such as whether or not the impact of FDI on happiness varies with national income levels. Additionally, this research encourages further investigation into whether the results may differ across the various regions in the continent (i.e., Central Africa, Eastern Africa, Northern Africa, Southern Africa, and Western Africa). Factors such as level of human development, democratisation, and natural resource wealth differ across the continent and may influence the relationship between inward FDI and happiness.