level and within-group diversity. As an example, Table 7 presents OTE means and standard deviations by geographic region for each of the indicators. Let’s consider a few comparisons. Regarding life expectancy, Europe and Central Asia is the region that shows the highest average and lowest dispersion in OTE, while Sub-Saharan Africa is at the other end of the spectrum, with the lowest average and highest standard deviation. Knowing these regions’ levels of development, this result may not be very surprising. More surprising may be the similarity in average OTE across all regions regarding fiscal balance, although with differences regarding within-region dispersion. Also surprising may be the result regarding measles immunization, for which the Americas is the region with the highest average and lowest dispersion in OTE. This good regional performance contrasts with its poor performance regarding other indicators, such as trade openness or income inequality.

So far, we have concentrated the discussion on overall technical efficiency (OTE). The Stochastic Frontier approach allows for separation of persistent and time-varying components. While persistent components are associated with longer-run factors requiring deeper reforms to change, time-varying components are linked to shorter-run developments. As mentioned before, these are presented in the Appendix Tables, following the way they are introduced in the methodological section. For illustration, consider the following example applied to access to finance. For Australia, the persistent component of _inefficiency_ is 35.1% and the time-varying component of inefficiency increased from 8.8% to 18.8%. The country worsened over the period considered, with the overall potential for improvement for Australia rising from 43.9% (35.1% + 8.8%) in the initial year to 53.9% (35.1%+18.8%) in the final year. In contrast, for Indonesia, persistent inefficiency at 60.4% is higher than Australia’s, but Indonesia’s time-varying component went down from 26.4% to 7.7%, signaling an improvement over time.

# **7. Conclusion**

To assess a country’s performance in terms of a relevant set of development indicators, we need a benchmark against which these indicators can be compared. If the benchmark is measured as a feasible frontier, then departure from the benchmark can be viewed as inefficiency, slack, or potential for improvement. This paper focuses on international benchmarking for assessing a country’s realistic potential for improvement on various social and economic dimensions.

We use the stochastic frontier (SF) approach to estimate a country-, time-, and indicator-specific benchmark and (one-sided) inefficiency. Our approach contrasts with the literature that compare countries by looking at raw indicators without taking into account the structural endowments or predetermined conditions that constrain the maximum performance that countries can achieve in a policy-relevant horizon. The SF approach also improves on the literature that uses OLS regression residuals to measure performance. Although the residuals can give some idea about a country’s performance, they cannot be interpreted as inefficiency because they are mixed with noise and can take both positive and negative values.

To illustrate the SF methodology, we apply it to 10 indicators of economic and social performance, using panel data of 142 countries for the period 2005-2014. We compute benchmarks, as well as persistent and time-varying inefficiency measures. To facilitate the discussion of results, we introduce the concept of overall technical efficiency (OTE), the inverse of inefficiency. Not surprisingly, we find that countries’ overall technical efficiency varies across indicators, with no country or region dominating the rest. More interestingly, we find that the potential for improvement does not follow a simple relationship with 14