income generation. In fact, it is the variable with the single highest impact on income developments of the bottom 40 percent. This result holds even when the possible effect on mean income is factored in.

Improvements in school enrollment seem to be associated with faster income growth for the two poorest quintiles than better infrastructure, given a certain growth in mean income. However, physical capital seems to be correlated with faster mean income growth than education, so once the indirect effects through average growth are included, this ranking is reversed. The importance of a stable macroeconomic environment is also enhanced once its indirect association with average income growth is included. A stable political environment, as measured by the democratic accountability index, is associated with faster overall income growth, but not with any change in the income distribution.

An alternative approach would be to define a meaningful reference group to implement the benchmarking exercise. Several previous studies performed a benchmarking exercise of overall growth rates. For example, Loayza et al. (2005) aim to explain differences in Latin American and Caribbean growth rates over the 1990s by benchmarking key policies against the best performing country over that period, Chile. Building on this idea, Araujo et al. (2014) developed a benchmarking exercise that serves as the key reference point of our analysis.

The main idea in Araujo et al. (2014) is to derive a benchmark in key policy areas (such as education, infrastructure, and stabilization policies) which is defined as the 90<sup>th</sup> percentile best performer in this variable in the region i.e., the benchmark is set in a way that only 10 percent of regional peers perform better in that particular policy dimension. The growth impetus of these policy variables is identified using a cross-country regression model. By multiplying the difference in policy variables between the country of interest and the benchmark (policy gap) with the estimated growth regression parameter, this exercise provides a counterfactual per capita income for the country of interest, assuming it would catch up to the benchmark level. This idea thus combines identifying areas where policy gaps are large (and thus where progress is considered more realistic to achieve) with the potential growth/income dividend from closing existent gaps.<sup>18</sup> So, what level of policy variables (our _X_ ) could or should Haiti aim for? One way to obtain a reasonable benchmark is to relate to top performers of regional peers. This could, for example, be the 90<sup>th</sup> percentile of the distribution of _X_ among regional peers (as in the case of Araujo et al., 2014) or their top three performers (as in the case of Varga and Veld, 2014). Another approach would be to look at the income level the country wants to achieve in the future with its development strategy and look at the levels of _X_ for countries that currently display this income level (Moller and Wacker, 2015, take this approach, together with a regional perspective).

> 18 For alternative benchmarking methodologies see, for example, Varga and Veld (2014), the OECD’s “Going for Growth” report (first published in 2005), or D’Acosta et al. (2013).

-16-