Moss-Leo, 2011 6.1 (graduaters)a n.a. n.a. 32
Kharas-Rogerson, 2012 8.2 (convergers)b recent HH surveys 562mn 5/6
Africa Edward-Sumner, 2013 5.1 (moderate)c current/static/historic 305-1309mn n.a.
Ravallion, 2013 5.6 (HH expenditure per capita)d
2008 level & 1999 level 243mn (=3%)e n.a.
Chandy et al., 2013 0.81xEIU private consump-tion growth
static 386mn n.a.
ODI, 2013 n.a. n.a. 624mn 63f
Yoshida, Uematsu &
Sobrado, 2014g
Country-specific growth rates (HH expenditure per capita) between 2002-2010
=4.7 on average
Observed changes in distri-bution between 2002-2010
695mn n.a.
Notes: a) IMF-WEO projections 2010: 6.1% p.a.; b) based on growth assumption for converging countries in Kharas 2010; c) IMF-WEO projec-tions minus 1% for moderate scenario; d) household/capita consumption in baseline scenario (4.5%) + projected population growth (1.1%); e) USD 1.25/cap, central scenario; f) number of countries with highest, high and moderate poverty vulnerability (latter defined as >10% or >1mn <
USD 1.25 cap). g) reported results are from the baseline scenario which estimates a poverty headcount of 8.6% in 2030.
which will cross the operational threshold of USD 1,205 and their per capita GNIs in 2012 and projected GNIs in 2025 are listed in Table 3.3. These countries are quite di-studies relied on overoptimistic IMF growth projections
from a few years ago. To better reflect the emerging consensus on the “new normal” of slower growth in the developing world, we simply adopt the more recent IMF WEO projections from October 2013, which are less op-timistic than the 2009 projections used by Moss and Leo.
The second chief difference with the Leo and Moss study is that we do not automatically exclude consideration of the small-island states, and we also consider the results of the projections for countries above the operational threshold, which may still be below the historical IDA ceiling. This is intended to better highlight IDA gradua-tion as a transigradua-tion process.
We apply the growth projections to 2012 GNI per capita figures to derive GNI per capita for 2025. The IMF pro-jections cover the period 2013-2018 and we assume the projected 2018 per capita growth rates represent medium term growth estimates, which remain constant 2018-2025. As the IDA operational threshold is set in nominal terms, and then subsequently revised for inflation, we can keep the USD 1,205 GNI per capita operational cutoff for the fiscal year (FY) 2013 (and the USD 1,965 GNI per capita historical ceiling) constant going forward to 2025.
Applying the real growth per capita projections provided by the IMF database therefore leads to 2025 GNI per capita values in constant 2012 dollars that can be com-pared with both, the FY2013 operational and historical thresholds.
Our initial projection considers the 39 countries below the operational threshold in 2012. According to our projec-tions, this number declines to 26 in 2025. The 11 countries
Table 3.2: Shifting Distribution of IDA Eligible Countries across Income Categories Number of countries
Below cutoffs Above cutoffs
operational historical
Low-income
<USD 1,035
Lower-middle-income
<USD 1,205
Lower-middle-income USD 1,206 – 1,965
Lower- and upper- middle-income
>USD 1,965
2012 35 4 16 27
2025* 20 6 15 <39**
Note: *These 2025 totals exclude several countries for which 2012 base data is unavailable.
**This upper bound is based on the extreme assumption that all eligible countries maintain some type of transitional or exceptional eligibil-ity, similar to blend status or the small island exclusion – which undoubtedly will not be the case.
Source: Calculations based on IMF WEO (2013)
Table 3.3:
Projected Graduates from Operational Eligibility Gross national income per capita, 2012 constant USD
2012 2025
Bangladesh 840 1,708
Cambodia 880 1,950
Cameroon 1,170 1,643
Guinea 440 1,592
Haiti 760 1,218
Kenya 860 1,275
Kyrgyz Republic 990 1,607
Mauritania 1,110 2,114
Senegal 1,030 1,346
Solomon Islands 1,130 1,353
Tajikistan 860 1,399
Note: Suitable GNI per capita data was unavailable for two countries below the operational cutoff, Somalia and Myanmar. Whether they would be above or below the cutoff in 2025 could therefore not be determined.
Source: Calculations based on IMF WEO (2013)
This group is a much more sizable component of current IDA-eligible countries, including among them both the most populous South Asian and Sub-Saharan African countries, India and Nigeria, respectively, and accounting for a total of approximately USD 4.7bn – or 28.9 percent of IDA’s USD 16.3bn in approved FY2013 projects. In the 2025 projections, only one country – Mauritania – man-ages to cross the sizable lower-middle-income “gulf”
between IDA operational and historical thresholds, as it is projected to increase from a GNI per capita of just over USD 1,100 to just over USD 2,000 in the coming decade.
This is partly because its starting point is just shy of the operational threshold, but is also helped by a projected medium-term average growth projection of nearly 4.6%
from the period 2018 to 2025.
3.2 Extreme Poverty
It is indeed conceivable that global poverty could be eliminated by 2030, as discussed by Ravallion (2012), Edward and Sumner (2013), Kharas and Rogerson (2012), and others, but it is vital to keep in mind the ambition that such a goal entails, as pointed out by Yoshida et al. (2014). Future poverty trends are highly dependent on future trends in growth and the future shape of the income or consumption distribution. For poverty to be eliminated in 2030, both of these trends will need to follow almost an ideal trajectory, and it will need to be nearly eliminated by 2025. The past two decades of high growth will need to continue and inequality will need to decrease in many countries – in many cases through redistributive efforts.
The important issue for IDA shareholders to consider is how IDA resources can contribute to this ambitious agenda, as IDA itself is an important global redistribu-tive mechanism. The key question is how to compare the poverty challenges faced by IDA’s low and lower-middle-income clients below the operational and historical cutoff to that of poverty in the higher income categories.
This becomes particularly important as the relative size of the higher categories grows. While today 12 IDA-eligible countries are small island states and therefore do not require substantial magnitude of resources to achieve growth and poverty reduction, as shown in Table 3.4, in 2025 that group potentially could grow to more than 30 countries, and would include the most populous countries in Africa and South Asia. Will those countries still face a significant poverty challenge in 2025 and exhibit significant “relative poverty” compared to other countries, therefore suggesting the need for continuing concessional finance?
verse geographically – five from Asia and the Pacific, five from Africa and one from the Americas. Interestingly, the projected 2025 GNI per capita values are all – with the exception of Mauritania – still below the historical IDA eligibility threshold of USD 1,965 – and in most cases, well below it. In terms of their share of IDA resources, together these 11 countries accounted for nearly USD 2.9bn – approximately 18 percent – of the USD 16.3bn in IDA projects, which were approved in FY2013.
Table 3.4:
Projected Graduates from Historical Eligibility Gross national income per capita, 2012 constant USD
2012 2025
Papua New Guinea 1,790 2,587
São Tomé and Príncipe 1,310 2,542
Uzbekistan 1,720 3,132
Vietnam 1,550 2,698
Note: Calculations do not yet include the 2014 rebasing of Nigeria´s GNI.
Source: Calculations based on IMF WEO (2013).
Given that the projected graduates from operational eligibility still mostly remain below the historical eligi-bility cutoff in 2025, it is natural to wonder whether this will lead to a shift in the bimodal distribution of IDA countries across income categories. If we then consider the broader group of 56 countries that include all IDA-eligible LICs and lower MICs below the historical cutoff of USD 1,965, we can further forecast how many coun-tries will cross the historical IDA eligibility threshold by 2025. In fact, 12 IDA-eligible countries will eventually exceed the historical eligibility ceiling in 2025, as shown in Table 3.4.
tion in our simulation is driven solely by growth. It is the case, however, in a great many countries that inequality has actually increased over recent years. Still, almost as many countries have experienced inequality decreases, albeit often from high initial levels. After presenting the results below, we will return to the issue of alternative distributional trajectories leading up to 2025.
Our poverty reduction results find that in 2025 more than 515 million people will still be living on less than one dollar per day – the largest single group of which will be the more than 140 million people in India. The other large poor potential IDA graduate, Nigeria, will have 62 million poor people – almost exactly as many poor as the 64 million who will be in the Democratic Republic of the Congo (Congo, D.R.). These three countries alone – two of which potentially will have become ineligible for IDA re-sources – will constitute half of global extreme poverty in 2025. Global poverty will thus constitute approximately 6-7% of the global population, and poverty will be con-centrated in 42 countries, which will have populations of poor larger than one million, as shown in Figure 3.1 and as detailed in Table A.2 in the appendix.
Table 3.5 expands these above results by looking at each country with a poverty headcount above one million poor people, region by region. Indeed, Africa accounts for 293 million out of the 515 million extremely poor people in 2025, followed by South Asia with 159 million. More than 60 million people, however, will be spread across East Asia, Latin American and the Middle East. And many of those poor populations will be in countries that are not IDA-eligible – such as for example, Indonesia, which will still be home to nearly 20 million extreme poor and will thus have the largest concentration of people liv-ing in extreme poverty outside Sub-Saharan Africa and South Asia.
Table 3.5 also goes one step further beyond most prior global poverty reduction simulations in providing pro-jected relative poverty headcount ratios for 2025 in all countries where it will be higher than the USD 1.25 PPP per day headcount. Drawing a relative poverty line at 60 percent of mean income or consumption, i.e. very similar to how OECD countries measure poverty, it is clear that even as the number of extreme poor popula-tions will have been substantially reduced globally by 2025, most countries will still face sizable problems with respect to social exclusion and relative deprivation, as measured by relative poverty lines.
To forecast the future trajectory of poverty reduction, we shall look at how the distribution of poor popula-tions globally will look in 2025. To do so, we need to combine reasonable growth projections through 2025 with data on the relative distribution of mean incomes or consumption of the households who constitute the population in the countries we are interested in. To do so we adopt some of the key methods used in the ear-lier studies. First, in order to ensure compatibility with our IDA graduation projections, we adapt the same IMF growth projections used above in order to account for future growth in household expenditure per capita. We use these growth projections to update the country-specific household survey means available from the WB PovcalNet database and then apply the most recent dis-tributional parameters from PovcalNet.
On the growth side, we must make reasonable assump-tions about future growth in household welfare, which takes into account the discrepancy between national accounts data and survey means. As we wish to project poverty levels in 2025, we are interested in growth of household income or consumption, rather than growth of gross national product per capita, so we must adjust the IMF projections to account for the fact that house-hold welfare tends to grow more slowly than that of the economy as a whole. This follows from the discussion in Edward and Sumner (2013) of the considerable – and in many cases growing – discrepancy between national ac-counts means, like GDP or GNI per capita, and household survey means, such as those provided by the PovcalNet database. It is therefore an important departure from other future projections of poverty, such as Kharas and Rogerson (2012), who assume that growth in GDP per capita passes through entirely to growth in the means used to calculate poverty. Deaton (2005) and Ravallion (2003) have demonstrated the overoptimistic nature of this approach, and thus, following Edward and Sumner (2013), we apply a discount factor of 0.81 on the growth rates for countries with expenditure surveys, and a factor of 0.91 on the growth rates for countries using income surveys. Given the particularly sizable discrepancy that has emerged between the national accounts and survey data in India over the last two decades, we also follow Edward and Sumner (2013) and previous WB practice in treating India separately, discounting projected growth in the national accounts by a factor of 0.54.
With respect to distributional parameters, we simply hold constant each country’s most recent distributional parameters, as recorded in the PovcalNet database. This assumes that inequality neither worsens nor improves in each country going forward to 2025. Thus, poverty
reduc-Table 3.5: Extreme and Relative Poverty Headcount Ratios in 2025 Ordered by region and size of poor population
Country
Mean monthly income or consumption
Share of population living on <1.25, 2005