Inequality of Opportunity: New Measurements Reveal the Consequences of Unequal Life Chances

Over the last decade, the world has seen increasingly bitter political debates around growing levels of inequality. According to a growing body of research, effectively addressing this trend requires a more rigorous toolkit to measure the varieties of inequality societies face and the different consequences these types of inequality may have.    

“When an inventor creates a blockbuster technology that benefits everyone, she rightly reaps the rewards,” said Asli Demirguc-Kunt, former Director of Research at the World Bank. “But not all inequality is the result of hard work or ingenuity. While we’ve known about inequality of opportunity for a long time, we’re only now reaching the point where we can rigorously measure it.”  

At a recent Policy Research Talk, World Bank Senior Advisor Francisco Ferreira delivered a wide-ranging overview of the theory and empirical evidence on inequality of opportunity, taking his audience on a journey starting with the philosophical foundations laid down by John Rawls and Amartya Sen and ending at the cutting-edge of machine learning.

As Ferreira explained, inequality of opportunity is not a new concept. In 1937—during another period of great political upheaval—American President Franklin D. Roosevelt argued in his second inaugural address for the need to create the conditions for equality of opportunity.

To translate this aspiration into something concrete, economists have typically followed two principles. First, the principle of compensation holds that individuals should be compensated for circumstances outside their control. Second, the principle of reward seeks to preserve differential rewards that are the result of individual responsibility and effort.

To separate out inequalities that result from circumstance versus effort, economists have had to gather much more data than is needed to simply measure inequality of outcomes. In one early example of this type of research, Ferreira and a colleague gathered data on ethnicity, father’s occupation, parents’ education, and birth region in six Latin American countries. The researchers were able to tease out the minimum contribution that circumstances make to consumption inequality and found that this figure ranges from 25 percent in Colombia to 51 percent in Guatemala.

Zooming out to a broader view, Ferreira also compiled the work of multiple researchers to compare the extent of inequality of opportunity across 51 different countries around the world. This broader view produced a larger range of estimates of the minimum contribution of circumstances to income inequality, ranging from 3 percent in Norway to 40 percent in Mali.

While this first wave of research helped lay important groundwork, it has limitations, said Ferreira. The fact that it only identifies the minimum contribution of circumstance to inequality means there is still much uncertainty about just how significant inequality of opportunity really is.

A second wave of research has sought to overcome these limitations by expanding the universe of data on the circumstances individuals face. In one example of research on the United States and the United Kingdom, researchers looked not only at factors like race and parents’ education, but also issues like childhood behavioral problems, time spent with parents, and parents’ smoking and drinking habits. With this expanded universe of data, researchers were able to conclude that circumstances are responsible for 31 percent of inequality in the United Kingdom and a striking 45 percent in the United States.

According to Ferreira, these advances in measuring inequality of opportunity have finally reached a stage where they can make a difference in concrete policy issues. Two examples include a better understanding of the impact of Mexico's Oportunidades program on children's future opportunities and the impact of child care reform in Norway. 

Another area with real-world consequences is machine learning. The role of algorithms in decision making is rapidly spreading to everything from online ad delivery to who is granted parole, and researchers are already using inequality of opportunity measurement to find ways to ensure machine learning tools produce non-discriminatory outcomes.   

But perhaps even more importantly, measuring inequality of opportunity is helping break a logjam in a longstanding debate about whether inequality has a negative or positive impact on growth. In a study of differences in economic growth between U.S. states, researchers found a negative relationship between inequality of opportunity and growth and a positive relationship between inequality of effort and growth.

“The kind of inequality that is bad for growth is the inequality that takes opportunities away from people,” said Ferreira. “If many potential scientists, engineers, and artists cannot access a decent education or basic healthcare early on because of the circumstances of their birth, that ultimately creates a cost for everyone in terms of wasted human and economic potential.”

Yet that still doesn’t remove all the blame on other types of inequality. The sum of the choices and circumstances that leads to a certain level of inequality in one generation in turn shapes the unequal circumstances faced by the next generation. Data have shown that there is a strong negative correlation between inequality in a society and the degree of mobility up the economic ladder—underlining the urgency of implementing policies that can help everyone have an equal chance at achieving their potential.