ERADICATING POVERTY IN FRAGILE STATES: PROSPECTS OF REACHING THE “HIGH-HANGING” FRUIT BY 2030 although means from national accounts most often differ from and are typically higher than those from household surveys. Analysts vary in their use of one or the other mean, most often relying on survey values (e.g. Ravallion 2012 and 2013; Chandy, Ledlie and Penciakova 2013: 16); the estimates of Edward and Sumner (2013: 9) use both means separately. The IFs model and this paper initialize forecasts with poverty estimates drawn from the distributions and associated means of the PovcalNet data.

# 3.2 COMPARATIVE FORECASTING METHODOLOGIES

Forecasts may build on surveys for initial conditions, but involve aggregate variables representing changes in distribution and/or average values of consumption (or income). Focus tends to be on growth of consumption (or of national income or GDP as a proxy) and most analysts use forecasts generated by others (e.g. Chandy, Ledlie and Penciakova, 2013 use those of the Economist Intelligence Unit) or they build upon extrapolations of recent growth rates. For instance, Edward and Sumner (2013: 11-12) look to the recent IMF estimates and forecasts and develop scenarios that extend and/or shift those.<sup>11</sup> Forecasting changes in income distribution is even more difficult than forecasting economic growth and almost all projections rely on purely exogenous, self-generated assumptions, typically assuming no change (e.g. Ravallion 2012; Chandy, Ledlie and Penciakova, 2013: 17). Because income inequality within countries has been increasing in more cases than not, this may be an optimistic assumption.

Poverty forecasting also provides not just rates of poverty, but headcounts. Most authors use the UNDP medium variant population forecast for their population projections. The IFs system includes its own economic model and forecasts of mean consumption. It assumes log-normal functional distributions of consumption that can also be summarized with Gini coefficients.

# 3.3 GLOBAL POVERTY FORECASTS: BASELINE AND ALTERNATIVE SCENARIOS

As a result of differences in structure, but also very much depending on the assumptions built into alternative scenarios, global poverty forecasts in 2030 vary by an order of magnitude, from 100 million (1.5%) to 1300 million (15.5%) poor. To better locate the IFs base case forecasts in the current literature, we discuss below each of the approaches, the assumptions underlying each approach and their respective results. Each of the authors gives a baseline, or “business-asusual” scenario, which is tied to recent trends in inequality and economic growth; the authors also typically provide optimistic and pessimistic scenarios based on varying expectations for future economic growth and inequality.

> 11 Given a common tendency to initialize forecasts with survey data and to drive them with values rooted in national account statistics, there needs to be some attention both to the adjustment of initial discrepancies between the means provided by the two methods (a simple ratio can suffice) and to whether or not those discrepancies will remain constant or change.  A common approach is to explore the historical ratios _of growth_ in the two sets of means and to posit that ratio's continuation.  For instance, Ravallion (2012: 7, footnote 16) discusses forecasts that pass through 90 percent of growth in private consumption per capita from national accounts to survey means, with a much lower number for India (see also Edward and Sumner 2013: 7). Chandy, Ledlie, and Penciakova (2013: 16-17) calculate a general discount coefficient of 0.81 for consumption (0.54 for India). There is an obvious difficulty, however, in assuming that the discrepancy between underlying consumption by households and the value built-into national accounts will continue to grow, and it is a generally pessimistic assumption with respect to future poverty reduction.

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