How does food insecurity influence GDP per capita?

Andrew Wolski & Andy Southerland

Introduction

Food insecurity is a persistent global issue, affecting millions of individuals and posing significant challenges to economic development. Defined as the lack of consistent access to enough safe and nutritious food for a healthy and active life, food insecurity undermines workforce productivity and limits economic potential. When individuals do not receive adequate nutrition, their ability to work and contribute effectively to the economy diminishes, creating a ripple effect that hinders national growth.

This project investigates how food insecurity, as measured by the Food Insecurity Experience Scale (FIES), impacts GDP, focusing on labor productivity as a mediating factor. Additionally, the role of education in mitigating these effects is explored, providing a holistic view of how human capital can offset some of the adverse impacts of food insecurity on economic performance. By leveraging global data on food insecurity, productivity, and GDP, this analysis aims to identify actionable insights for policymakers and stakeholders. Through case studies of six countries representing varying levels of food insecurity—mild, moderate, and severe—the project highlights the multifaceted challenges and opportunities for addressing this pressing issue.

Data Explanation

This study relies on globally recognized datasets to examine the relationship between food insecurity and GDP, with labor productivity as a key mediator variable. Food insecurity levels are measured using the Food Insecurity Experience Scale (FIES), a standardized metric provided by the Food and Agriculture Organization (FAO). This measure captures the percentage of a country’s population experiencing moderate or severe food insecurity, enabling consistent comparisons across nations. GDP, a critical indicator of economic output, is sourced from the World Bank and reported in constant U.S. dollars to account for inflation and facilitate comparability over time. Labor productivity, another central variable, is represented by GDP per worker, sourced from the World Bank as well. This metric reflects the efficiency of the workforce and its contribution to economic performance.

To ensure data quality and consistency, the dataset was rigorously cleaned and standardized. Missing values were addressed through interpolation or averaging, while countries with significant data gaps were excluded. Variables such as GDP per capita and labor productivity were log-transformed to normalize data ranges and minimize the influence of outliers. Countries were categorized into three levels of food insecurity: mild (less than 20% of the population affected), moderate (20% to 50%), and severe (more than 50%). Representative countries were selected from each category to provide case studies that complement the global analysis, offering deeper insights into the diverse impacts of food insecurity on economic outcomes.

This correlation matrix shows the relationships between food insecurity levels, labor productivity, and GDP per capita. Each number in the matrix represents the strength and direction of the relationship between two variables. A value closer to 1 or -1 means a stronger relationship, while values closer to 0 mean a weaker connection. Let’s break it down.

First, there is a strong negative relationship between food insecurity and labor productivity, with a correlation coefficient of -0.62. This means that as food insecurity increases, labor productivity decreases significantly. This makes sense because food insecurity often leads to poor nutrition and health, which lowers workers’ ability to perform well, especially in physically demanding jobs.

Second, the relationship between food insecurity and GDP per capita is also negative, but weaker, with a correlation of -0.27. This suggests that higher levels of food insecurity are associated with lower GDP per capita, but the connection is not as strong as with labor productivity. This is logical because food insecurity impacts GDP both directly and indirectly, with labor productivity acting as a key pathway.

Lastly, there is a modest positive relationship between labor productivity and GDP per capita, with a correlation of 0.16. This shows that higher labor productivity contributes to higher GDP per capita, but it also indicates that GDP is influenced by many other factors beyond labor productivity.

The empirical analysis explores the relationships between food insecurity, labor productivity, and GDP per capita using three regression models. Each model examines a specific link in the proposed causal pathway, shedding light on the mechanisms through which food insecurity impacts economic outcomes.

Regression 1: Effect of Food Insecurity Level on Labor Productivity The first regression examines how food insecurity affects labor productivity. The results indicate a strong and statistically significant negative relationship between these two variables. The coefficient for food insecurity is -1306.82, meaning that a 1% increase in the level of food insecurity is associated with a reduction of approximately 1306 units in labor productivity. With an R-squared value of 0.372, the model explains 37% of the variation in labor productivity, suggesting that food insecurity is a major factor influencing workforce efficiency. The high F-statistic and near-zero p-value confirm the robustness of this relationship. These findings highlight how insufficient access to adequate nutrition can severely diminish worker productivity, particularly in labor-intensive sectors.

Regression 2: Effect of Labor Productivity on GDP per Capita The second regression explores the relationship between labor productivity and GDP per capita. The results reveal a positive and statistically significant relationship, with a coefficient of 0.0013. This implies that an increase of one unit in labor productivity corresponds to a small but meaningful increase in GDP per capita. However, the R-squared value of 0.098 indicates that labor productivity alone explains only about 10% of the variation in GDP per capita, suggesting that other factors, such as infrastructure or political stability, also play significant roles. Despite this, the significant p-value and t-statistic underscore the importance of labor productivity as a driver of economic output.

Regression 3: Effect of Food Insecurity Level on GDP per Capita The final regression assesses the direct effect of food insecurity on GDP per capita. The results show a negative and statistically significant relationship, with a coefficient of -2.46. This means that a 1% increase in food insecurity leads to a decrease of approximately 2.46 units in GDP per capita. The R-squared value for this model is 0.074, indicating that food insecurity directly explains only a small fraction of the variation in GDP per capita. However, this relationship remains meaningful, as food insecurity likely impacts GDP through other mediators, such as labor productivity, as shown in Regression 1. These findings highlight the multifaceted ways in which food insecurity can hinder economic performance.

Effect of Food Insecurity Level on Labor Productivity The first chart illustrates the relationship between food insecurity levels and labor productivity across countries. The negative slope of the regression line indicates that as food insecurity levels increase, labor productivity declines. This finding aligns with the regression analysis, where a negative coefficient for food insecurity was observed. The data points are scattered widely, reflecting variability in productivity across countries, but the trend remains consistent: food insecurity is associated with lower productivity levels. This highlights the critical role of food security in maintaining an efficient workforce and supporting economic development.

Effect of Labor Productivity on GDP The second chart shows how labor productivity impacts GDP. The regression line is positively sloped, illustrating that higher levels of labor productivity are correlated with increases in GDP. While the data points are dispersed, the upward trend is evident, reinforcing the idea that productive labor forces are a key driver of economic output. The regression supports this observation with a significant positive coefficient for labor productivity, emphasizing the importance of investing in workforce efficiency to boost economic growth.

Effect of Food Insecurity Level on GDP The third chart examines the direct effect of food insecurity on GDP. Similar to the first chart, the regression line has a negative slope, indicating that higher levels of food insecurity correspond to lower GDP. This relationship suggests that food insecurity not only hampers individual productivity but also has broader economic implications. Countries with high food insecurity struggle to sustain strong economic performance, underscoring the need for policies aimed at improving food availability and access.

This heat map illustrates the correlations between food insecurity levels, labor productivity, and adjusted GDP. The values in the heat map range from -1 to 1, with negative values indicating an inverse relationship and positive values representing a direct relationship. Each variable’s correlation is color-coded for clarity, with darker red and blue tones signifying stronger relationships.

The relationship between food insecurity levels and labor productivity is notably strong and negative, with a correlation coefficient of -0.62. This indicates that higher food insecurity is associated with significantly lower labor productivity. The negative correlation aligns with the understanding that food insecurity leads to poor worker health and reduced economic output.

The correlation between food insecurity levels and adjusted GDP is also negative, though weaker, at -0.28. This suggests that as food insecurity increases, adjusted GDP tends to decrease, but the relationship is less direct than the one with labor productivity. This finding supports the idea that food insecurity impacts GDP both directly and indirectly through other mediating factors, such as labor productivity.

Lastly, the correlation between labor productivity and adjusted GDP is positive, at 0.17. This indicates that higher labor productivity contributes to higher GDP, but the relatively weak correlation suggests that GDP is influenced by additional factors beyond labor productivity.

This bar chart compares labor productivity and adjusted GDP across two categories: "Food Insecure" and "Not Food Insecure." The blue bars represent labor productivity, measured in USD, while the orange bars show adjusted GDP, measured in trillions of USD. By presenting these two metrics side by side, the chart highlights the disparities between countries with varying levels of food insecurity.

The "Food Insecure" category displays noticeably lower labor productivity and adjusted GDP compared to the "Not Food Insecure" category. This visual underscores the significant economic disadvantages faced by food-insecure populations. Specifically, countries with food insecurity experience lower workforce efficiency (labor productivity) and smaller overall economic output (adjusted GDP).

The orange bars further emphasize the ripple effect of food insecurity on GDP. The gap between the two categories suggests that food insecurity not only affects individual workers but also has broader implications for national economic performance. The alignment between labor productivity and adjusted GDP bars supports the idea that labor productivity serves as a key mediator in the relationship between food insecurity and economic outcomes.

This bar chart compares the country-level GDP and labor productivity for two groups: the 20 countries with the highest food insecurity and the 20 countries with the lowest food insecurity. The blue bars represent adjusted GDP in billions of USD, while the orange bars represent labor productivity. This dual-axis visualization provides a clear contrast between the economic performance of these two groups.

For the 20 countries with the highest food insecurity, both adjusted GDP and labor productivity are significantly lower. The blue bar for GDP shows a stark economic disadvantage, with total economic output far below that of the 20 countries with the lowest food insecurity. Similarly, the orange bar for labor productivity illustrates that countries with severe food insecurity have a much less efficient workforce compared to their counterparts.

In contrast, the group of 20 countries with the lowest food insecurity demonstrates much higher adjusted GDP and labor productivity. This disparity highlights the critical role food security plays in fostering economic growth and workforce efficiency.

The alignment between the two metrics suggests a strong connection between labor productivity and GDP, reinforcing the idea that food insecurity impacts national economies through its effects on worker productivity. This chart provides compelling evidence of the economic benefits of addressing food insecurity and underscores the importance of improving food security as a pathway to enhanced economic performance.

This scatter plot provides a combined visualization of food insecurity levels (FIES), labor productivity, and adjusted GDP. Each data point represents a country, with the x-axis showing labor productivity, the y-axis showing food insecurity levels as a percentage, and the color gradient representing the logarithm of adjusted GDP.

The chart reveals a clear pattern: countries with higher food insecurity levels (upper part of the graph) tend to have lower labor productivity (left side of the graph) and lower adjusted GDP (darker colors). In contrast, countries with lower food insecurity levels (closer to the bottom of the graph) generally exhibit higher labor productivity (toward the right side of the graph) and higher adjusted GDP (brighter yellow colors).

This visualization effectively demonstrates the interconnectedness of food insecurity, labor productivity, and GDP. The clustering of darker data points in the upper-left corner highlights the negative impact of food insecurity on both labor productivity and overall economic performance. Meanwhile, the brighter, lower-right data points emphasize the economic advantages enjoyed by countries with secure food systems.

This graph provides a comparative analysis of Average GDP (scaled to trillions of USD) and Average Labor Productivity (in USD) across different global regions. The x-axis categorizes regions such as Africa, Asia, Europe, Latin America, and Oceania, with two bars representing each region. The blue bars on the left correspond to average labor productivity, measured as economic output per worker, which reflects workforce efficiency. The orange bars on the right represent average GDP, the total economic output, scaled to trillions for readability. These dual metrics highlight significant regional economic disparities.

The chart reveals that Europe outperforms other regions with the highest labor productivity and GDP, indicating its strong economic efficiency and output. Conversely, Africa shows the lowest values for both metrics, reflecting challenges in workforce efficiency and overall economic performance. Asia demonstrates moderate levels of both GDP and labor productivity, capturing its regional diversity. Meanwhile, Latin America and Oceania have relatively low values compared to Europe, but rank above Africa, indicating room for growth.

This visualization underscores the correlation between labor productivity and GDP—regions with higher workforce efficiency tend to have stronger economies. It also offers critical insights for policymakers and international organizations. For instance, Africa’s low metrics may highlight the need for targeted investments in education, infrastructure, and industrial development to stimulate economic growth. On the other hand, Europe’s high values emphasize the benefits of technological advancements and institutional stability. Overall, the graph serves as a valuable tool for identifying regional disparities and prioritizing economic development efforts.

This visual analysis shows trends in food insecurity levels (FIES), labor productivity, and adjusted GDP for selected countries. Each country is represented by a different color, with the data shown across three different time periods, from 1980 to 2019.

The top graph displays the trends in food insecurity levels over time for each country. The data suggests that food insecurity in some countries, such as South Sudan, remains high with little improvement, while others, such as Switzerland, show a clear reduction in food insecurity levels.

The middle graph illustrates trends in labor productivity (measured in USD per worker). The results show large fluctuations, particularly for countries such as South Sudan, which has a significant increase in labor productivity in the most recent years. Meanwhile, countries like Switzerland maintain a steadier, more consistent labor productivity trend.

Finally, the bottom graph shows trends in adjusted GDP (in trillions USD) for the same countries. A clear upward trend can be seen in many countries, such as Switzerland and the Republic of Moldova, with a sharp rise in adjusted GDP beginning in the mid-2000s. South Sudan and some of the lower food insecurity countries exhibit volatile growth patterns, especially in the late 20th century and early 21st century.

The Causal Directed Acyclic Graph (DAG) presented above illustrates the hypothesized relationships between food insecurity, labor productivity, and GDP per capita. Food insecurity is positioned as a key independent variable that directly impacts both labor productivity and GDP. The arrow pointing from food insecurity to labor productivity indicates that higher levels of food insecurity are expected to reduce worker efficiency and output. This reduction in labor productivity, in turn, mediates the relationship between food insecurity and GDP, as shown by the arrow from labor productivity to GDP. Simultaneously, the direct link from food insecurity to GDP captures additional effects that are not fully explained through labor productivity, such as economic instability or healthcare burdens associated with food insecurity. This simplified DAG effectively visualizes the pathways through which food insecurity influences economic outcomes, emphasizing labor productivity as a crucial mediator in the broader causal framework. It aligns with the empirical findings of this study, reinforcing the need for targeted interventions to address food insecurity as a means to promote economic development

Discussion

The analysis reveals a significant relationship between food insecurity, labor productivity, and GDP. Food insecurity strongly reduces labor productivity, as evidenced by the regression results and visualizations. Higher food insecurity levels correlate with lower worker productivity, a statistically and economically meaningful effect. The scatter plot underscores this negative impact on labor output. Labor productivity also mediates the link between food insecurity and GDP, as increased productivity substantially boosts GDP, while food insecurity exerts a less pronounced direct negative effect. Although the regressions show low R-squared values, suggesting unaccounted factors, the findings remain robust and emphasize the economic impact of food insecurity.

These insights carry critical policy implications. Addressing food insecurity must be a priority for economic development. Policies should enhance access to affordable, nutritious food, strengthen agriculture, and provide social safety nets in highly insecure regions. Such measures would improve workforce health, boost productivity, and positively influence GDP. Governments and international organizations must integrate food security into broader development strategies to achieve sustainable growth.

However, this study has limitations. Low R-squared values highlight unexplored factors like political stability or healthcare access. Missing or incomplete FIES data and reliance on self-reported metrics may reduce reliability. The assumption of linear relationships oversimplifies complex dynamics, and reverse causality, where lower GDP exacerbates food insecurity, was not addressed. These limitations warrant further investigation.

Conclusion

This study demonstrates the significant impact of food insecurity on economic outcomes, particularly through its influence on labor productivity. Food insecurity directly lowers labor productivity, which, in turn, mediates its negative impact on GDP. These findings emphasize the importance of addressing food insecurity as a critical step toward fostering economic development. Investments in food security programs, such as improving access to nutritious food and enhancing agricultural systems, are essential. Coupled with educational initiatives to boost workforce skills, these measures can mitigate the adverse effects of food insecurity and drive sustainable economic growth. Policymakers and development agencies must prioritize strategies that recognize the interconnected nature of food security, labor productivity, and GDP. By addressing these challenges simultaneously, countries can create a path toward greater economic resilience and prosperity, improving the well-being of their populations and fostering long-term stability.