and medium for the rest. The lower the correlation, the more informational value can be obtained from the SF approach to determine efficiency with respect to own benchmark.

In Table 5, we report the top 5 and bottom 5 countries according to OTE for each indicator. For illustration, let’s consider some specific examples. For access to finance, the SF model places Mongolia at the top 1, while the raw indicator places Norway as the best. The raw indicator is right in absolute terms, since almost 100% of the population in Norway has an account at a financial institution. However, Mongolia appears to be the country that is doing its best considering its structural characteristics: 85% of the population in Mongolia has an account at a financial institution, which is outstanding considering that Mongolia’s GDP per capita is only 2% of Norway’s and that Mongolia is the second country with lowest population density. Mongolia’s high OTE and ranking does not mean that it is better than, say, Norway. Rather, it tells us that given its structural characteristics, Mongolia is operating close to the frontier and does not have much slack (scope for improvement) regarding financial inclusion.

Consider another example. While for educational quality there is high correlation between the OTE and the raw indicator, inspection of the SF outcomes reveals some surprising results. The SF model places Vietnam as second while Korea is ranked seventh. However, according to the raw PISA indicators for reading performance, this second position is occupied by Korea, while Vietnam is number 17. The SF model tells us that Vietnam has been more efficient in reaching its frontier for educational quality considering that its overall level of development is way below Korea – Vietnam’s GDP per capita is only about 5% of Korea’s GDP per capita. Note again that we are not comparing the performance of one country with another; rather, we are comparing each country with its potential.

A third example relates to the indicator of financial depth. The raw indicator suggests that the United States is top 2 in the world with a financial depth ratio of 190% of GDP, while Fiji is placed number 39 with a financial depth ratio of 79% of GDP. In contrast, the SF model places Fiji as the top 2. That is, considering that Fiji is a small state with GDP per capita only 7% that of the United States, Fiji is operating close to its potential.

The ranking comparisons can be done for different country groupings. These comparisons may add some insight regarding a country’s OTE relative to its peers. For instance, Table 6 presents top 5 and bottom 5 countries for each indicator by geographic location. Alternative groupings –by income level, for example—are also possible.

Next, we present a broad perspective on where countries stand regarding their OTE for each indicator. We do so by showing a color-coded world map for each indicator. We group countries into five categories according to OTE (high, medium-high, medium, medium-low, and low) and assign a color for each category. Figure 2 presents this collection of world maps, one for each indicator. Let’s consider a couple of examples. The measles immunization map shows that countries with more scope for improvement are concentrated in Africa and South Asia. The same pattern is observed for life expectancy. The income inequality map shows that countries with more scope for improvement are in Latin America and SubSaharan Africa. Within Latin America, the countries with more scope for improvement are Honduras, Colombia, Guatemala and Brazil. While in Sub-Saharan, the countries with more scope for improvement are South Africa, Eswatini, Lesotho, and Rwanda.

Not only individual countries but also groups of countries can be compared using the outcomes from the SF approach. One possibility is to explore how particular groups of countries fare regarding their average 13