ERADICATING POVERTY IN FRAGILE STATES: PROSPECTS OF REACHING THE “HIGH-HANGING” FRUIT BY 2030 Optimistic and pessimistic growth rates are tied to the baseline rates from the Economic Intelligence Unit (EIU). The authors decrease the EIU growth rates by two percentage points per year in their pessimistic forecast, and increase the rate by two percentage points for the optimistic forecast.

The optimistic and pessimistic scenarios are applied to the entire developing world, and the authors also analyze fragile states separately in their work. However, their list of fragile states comes from the OECD DAC 2013 annual report on fragile states. This alternative list contains the subset of the World Bank fragile states list, as well as “alert” countries according to the Fund for Peace Failed States Index—resulting in a doubling of the poor population in fragile states relative to the FCS list that we use (see Table 2 below).

# _EDWARD AND SUMNER_

Edward and Sumner (2013) construct their alternative scenarios using three variations each on economic growth and inequality. For inequality, the authors use “static inequality,” which holds inequality constant over the entire time period, “dynamic inequality,” in which inequality changes based on recent trends, and “best ever distribution,” which sets each country’s level of inequality to its lowest historical level. Edward and Sumner address the issue of missing inequality data with their Growth, Inequality, Poverty Model (GrIP), which extrapolates trends and fills in missing income distributions. The different variations on economic growth are as follows: Optimistic economic growth is set to the average GDP growth rate given by the World Economic Outlook (WEO) from 2010 to 2017<sup>12</sup> , moderate economic growth is set to the WEO rates minus 1% each year from 2010 to 2017, and pessimistic growth is set to half of the growth rates given in the WEO from 2010 to 2017.

Edward and Sumner apply their optimistic and pessimistic scenarios to developing countries and analyze many subsets of these countries, including the World Bank Fragile and Conflict-Affected Situations (FY 2013).

# _RAVALLION_

Ravallion (2012; see also 2013) uses two separate methods to forecast poverty; first he uses simple optimistic and pessimistic linear projections of annual percentage point change in poverty rates. The underlying narrative of Ravallion’s linear projections is that developing countries will either continue to reduce poverty at the same impressive rates since the turn of the last century, or revert back to poverty reduction rates of the 1980s and 1990s. Working from this narrative, Ravallion uses a poverty reduction rate of 1% for the developing world to produce an optimistic trajectory. Ravallion then calculates a pessimistic trajectory using a 0.6% poverty reduction rate per year for the developing world while keeping poverty reduction rates in China around 1% per year.

In his second method, he uses a country-level simulation to determine the economic growth rates necessary to reach certain poverty goals. In the simulation, Ravallion works backward from these and other poverty targets to find the growth rate for mean household consumption per capita needed to achieve the poverty reduction target. For example, in the simulation-based analysis he calculates (Ravallion 2013: 14) that mean household consumption per capita would need to increase by 4.5% per year in the developing world, and inequality would need not to rise, to achieve a global poverty rate of 3% by 2027.

Like the works mentioned above, the geographic focus of Ravallion’s study is developing countries, but he does not include a separate analysis of his scenarios on any subset of developing countries and the analysis therefore does not include any focus on fragile or conflict-affected states.

> 12 Scenarios are based on scenarios used in Karver et al. (2012); WEO GDP growth rate estimates are assumed to be overly optimistic according to Leo (2009)

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