to Ending Poverty and Boosting Shared Prosperity...Middle East & North Africa Latin America & the Caribbean East Asia & Pacific South Asia Sub-Saharan Africa 415 million 399 million

A Measured Approach to Ending Poverty and Boosting Shared Prosperity Concepts, Data, and the Twin Goals

Policy Research Report Presentation Tokyo, December 8, 2014

Dean Jolliffe, Peter Lanjouw; Shaohua Chen, Aart Kraay, Christian Meyer, Mario Negre, Espen Prydz, Renos Vakis, and Kyla Wethli

Background: The World Bank Twin Goals

PRR focuses on concepts, empirical evidence, strengths & weaknesses, and measurement related to the new twin goals

2

Ending extreme poverty by 2030 (< 3% of global pop. below $1.25 a day)

Boosting shared prosperity (Growth of incomes of bottom 40% of population in every country)

Presenter

Presentation Notes

The PRR will try to inform at two levels: First, given the articulation of the two institutional goals, we will look at them in detail and attempt to provide guidance as to their derivation and interpretation. Second, we will take advantage of the renewed attention to the concerns embodied in the two goals, to step back and provide a broader perspective on the kind of indicators and issues that one might wish to examine. This broader objective will be somewhat partial and incomplete. We will focus on those areas that are particularly pertinent to our, and the World Bank’s objectives, and where we feel research has something useful to say.

Part I: Defining and assessing the goals > Ending Poverty by 2030

> Understanding Shared Prosperity

Part II: The twin goals in a broader context > Alternative Notions of Poverty and Shared Prosperity

> Challenges Posed by Uncertainty

Part III: Data and measurement challenges > Tracking Poverty and Shared Prosperity, Nationally

> Tracking Poverty and Shared Prosperity, Globally

Overview of PRR structure

DEC Policy Research Talk

Measuring Global Poverty: The Basic Approach

• Builds on established practice of the past 25 years • starting with 1990 WDR on Poverty

• 3 key ingredients

• Constructing a global database

• Assembling and ‘cleaning’ country-level household survey data • PovcalNet + Global Poverty Working Group

DAC Development Debate 4

Indicator of economic wellbeing

Selection of poverty line

Aggregation to single summary

index

6 key takeaways from the report

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Assessing and decoding the goals • Reaching the poverty goal is ambitious: Business as usual will not get us

there by 2030 • Shared prosperity is one articulation of inclusive growth with historical

antecedents in the literature • Synergies to “twinning” the goals – Progress in boosting shared prosperity is

critical to eliminate extreme poverty. Data challenges • Progress is made at the country level – Policies need to be informed by high-

quality household surveys. • Research in data collection methods improves data quality and comparability. • “More frequent data” is not the right call to action – PRR calls for greater

emphasis on data quality and data systems.

Presenter

Presentation Notes


Global Poverty in 2011: Headcount and Number of Poor By Developing Region

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Europe & Central Asia

Middle East & North Africa

Latin America & the Caribbean

East Asia & Pacific

South Asia

Sub-Saharan Africa

415 million

399 million

161 million

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6

2

Headcount at $1.25 a day (2005 PPP), percent

Developing World 17.0% 1,010.7 million

World 14.5% 1,010.7 million

Presenter

Presentation Notes

Why 2011? Why 2005 PPPs? Any more recent line up year would involve a great deal of extrapolation based on national accounts growth data 2011 PPPs merit the same scrutiny as earlier rounds of the ICP. How suited are they to purpose of global poverty measurement?

3% by 2030 is far from assured: Business as usual will not get us there

Scenario Headcount (percent)

Number of poor (million)

Average income growth of 4% p.a. in each country 3 252

Each country sustains avg per capita growth during past 20 years 6.8 573

Each country sustains avg per capita growth during past 10 years 4.8 405.4

Each country sustains avg per capita growth during past 10 years (survey-based growth) 6.7 564.8

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Global Poverty in 2030 at $1.25 per day (2005 PPP), assuming unchanged inequality

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) Forecasts of future growth and thus poverty reduction are highly uncertain

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2001-2011

1991-2001

1981-1991

Approach 1: Projections based on actual past average growth rates

Approach 2: Probabilistic scenarios based on random draws from past variation in growth rates between 2001 and 2011

median

1st to 99th percentile 5th to 95th percentile

7.1%

3.8% 3% target

Global headcount:

3% target

Presenter

Presentation Notes

To simulate uncertainty about future growth one can draw on observed variation of growth in the past. It is assumed that future growth is uncertain, but that uncertainty about future growth can be captured by taking draws from the historical distribution of developing country growth rates. Specifically, it is assumed that the historical data on growth is generated by the following very simple empirical model: 𝑔 𝑖,𝑡 = 𝜇 𝑖 + 𝜃 𝑖 𝑔 𝑡 + 𝜀 𝑖,𝑡 Real GDP growth in country i at time t, 𝑔 𝑖,𝑡 , consists of a country fixed effect, 𝜇 𝑖 ; the country's response to global shocks, 𝜃 𝑖 𝑔 𝑡 ; and an idiosyncratic component 𝜀 𝑖,𝑡 . Note that each country's growth may respond differently to the global shock. It is simply assume that the global shock is adequately proxied by historic world average GDP growth retrieved from the Penn World Table. More elaborate versions of a model like this could replace global growth with an unobserved common factor, whose distribution can be retrieved from the data using an unobserved components model. This equation is estimated by Ordinary Least Squares regressions for each of the 122 countries represented in the PovcalNet database, i.e. estimates for 𝜇 𝑖 , 𝜃 𝑖 , and the country-specific variance of the error term, 𝜎 𝑖 2 , are obtained for each country. With these estimates in hand, draws from the distribution of country growth rates are generated for each year between 2011 and 2030. Specifically, for each country we generate the draws from a normal distribution with mean 𝜇 𝑖 and variance 𝜎 𝑖 2 corresponding to the country-specific component of the growth rate. In addition, identical draws are deployed for all countries from the distribution of historical global average growth rates, 𝑔 𝑡 , however, this is multiplied by a global shock by the country-specific response 𝜃 𝑖 . Adding the country and global components gives annual growth rate projections that are cumulated forward to obtain a path for mean income for each country. We then calculate the country-level and global headcounts using the same lognormal distributional assumptions about income distributions. Finally, this process is repeated 1,000 times, resulting in 1,000 trajectories of poverty for the global headcount.

Sources of uncertainty about progress towards the twin goals

Economic and financial crises, food price shocks • Unrealistic to postulate stable growth for all countries • Crises can affect the sustainability of programs that assist the poor

Climate change and extreme weather patterns • Effect on global poverty up to 2030 may be muted • Key impact may be on sustainability of progress beyond 2030

State fragility, political, social, and armed conflict • Up to 1/3 of the world’s poor live in FCS • Complicated by link with climate change

Global disease risk (pandemics) • Pandemics have generated episodes of profound disruption • Globalization can hasten the spread of pathogens

9 DAC Development Debate

What does past country experience suggest about the likely pace of poverty reduction in the future?

DAC Development Debate 10

In countries where poverty has “ended” poverty decline did not always slow


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Presenter

Presentation Notes


How do we define “Economic Development”?

PRR Shared Prosperity 12

Historically: • Growth in average income

– Usually defined in terms of GDP per capita.

• This is far from egalitarian (Chenery et al, 1974) Average income assigns greater weight to those in richer percentiles of the income distribution.

0 0.2 0.4 0.6 0.8 1

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Percentiles of income or consumption distribution

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Percentiles of income or consumption distribution

Beyond the poverty line: Classes of functions that do not distinguish between poor and non-poor

13 Weights normalized to sum to one; drawn for hypothetical lognormal income distribution (mean $2000, Gini .30).

𝜶𝜶 = 𝟏𝟏

𝜶𝜶 = 𝟎𝟎

(mean income)

𝜶𝜶 = 𝟐𝟐 Sen real national income

Bottom 40 percent

w y p = 1 − 𝑔𝑔 y p

𝑤𝑤 𝑦𝑦 𝑝𝑝 = 𝐼𝐼𝑝𝑝<0.4 𝑦𝑦(𝑝𝑝) 𝑤𝑤 𝑦𝑦 𝑝𝑝 = 𝑦𝑦(𝑝𝑝)

1−𝛼𝛼 Atkinson

Higher 𝛼𝛼 higher inequality aversion

Presenter

Presentation Notes

A second class of functions does not distinguish between the poor and non-poor, but considers the welfare of all people – with a choice about what weight to place on different groups in society.

Growth and social welfare: Decomposing growth in social welfare into (a) change in average incomes, and (b) change in inequality

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Atkinson (1) Sen index

Poverty headcount Bottom 40%

DAC Development Debate

Presenter

Presentation Notes

Panels a through c of figure 4.7 show the relationship between growth in these three social welfare functions and growth in average incomes. Each data point represents an episode or “spell” between two household surveys for a given country. Spells are defined so that they are non-overlapping, and at least five years long. Average annual growth in social welfare and average annual growth in the survey mean income (or consumption) is calculated for each spell, and then graphed against each other. This graph suggests two key stylized facts. First, the contribution of changes in inequality to changes in social welfare are, on average, much smaller than the contribution of growth itself. Consider, for example, the contrasting cases of Kenya between 1997 and 2005 on the one hand, and China between 2002 and 2008 on the other. Growth in average incomes in the bottom 40 percent was minus 6.9 percent in Kenya, while growth in average incomes in the bottom 40 percent was 7.1 percent in China. These differences are largely due to differences in average growth performance: growth in the survey mean was minus 4.9 percent in Kenya but 7.6 percent in China. These examples highlight a more general pattern. Changes in the inequality measures relevant for social welfare growth (that is the vertical distances between each data point in these panels and the 45-degree line) are much smaller than the dispersion in countries’ average growth performance (that is the variation along the horizontal axis in these figures). Second, social welfare on average increases more or less equiproportionately with average incomes. This can be seen from the fact that the slope of the estimated relationships is close to one. This reflects the fact that the contribution of changes in inequality to changes in social welfare are not correlated with the contribution to growth in average incomes: on average, episodes of fast growth are not systematically associated with particularly fast increases in inequality, nor are episodes of slow growth associated with declines in inequality. As a result, if average incomes are growing, it is likely that social welfare is growing at more or less the same rate. For a more systematic documentation of these stylized facts in different time periods and country samples, see Dollar, Kraay, and Kleineberg (2014). This conclusion can be based on the evidence shown for the specific social welfare functions discussed in this chapter. But do the same conclusions hold for other social welfare functions not considered here? A useful tool to answer this question can be found in the Shorrocks (1984) concept of generalized Lorenz dominance. Shorrocks (1984) shows that for any increasing and concave social welfare function, social welfare unambiguously increases between two points in time if the growth rates of all of the cumulative percentile shares of income are positive over the same period. Dollar, Kraay, and Kleineberg (2014) consider a large set of spells similar to those studied here, and document that in over 80 percent of spells generalized Lorenz dominance holds, in other words any increasing and concave social welfare function would be higher during these positive growth spells. Panel d of figure 4.7 shows the relationship between average annual growth in the headcount measure of poverty (on the vertical axis) and average annual growth in mean income (on the horizontal axis). The graph shows a well-known and clear relationship: poverty reduction and growth are strongly correlated. Consider, for example, the contrast between Pakistan over the period 2002 to 2008 versus Kenya over the period 1997 to 2005 discussed earlier. In Kenya, the period of decline in the survey mean coincided with sharp increases in poverty: the growth rate of the headcount measure of poverty was 9.9 percent. On the other hand, in Pakistan growth in the survey mean was a healthy 3.1 percent per year, and poverty declined at the rate of 8.9 percent per year.

Shared Prosperity: towards “inclusive growth” “Boosting the per capita income or consumption growth of the poorest 40 percent in a given country”

PRR Shared Prosperity | DECRG Departmental Review Meeting 15

Simplicity • Closely linked to average

income per capita Focus • 40% cut-off is arbitrary but with some precedence

…the poorest 40 percent of the citizenry is of immense urgency since their condition is in fact far worse than national averages suggest. […]

Robert S. McNamara: 1972 Annual meetings

9011

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South Africa

9011

013

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(198

9 =

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1980 1990 2000 2010Year

Uganda

Comparing the bottom 40% to the national average

DEC Policy Research Report

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Population mean

Population mean Bottom 40 mean

Bottom 40 mean

This provides a means to assess changes in inequality of outcomes – even though shared prosperity goal is not in and of itself an inequality goal.

Presenter

Presentation Notes

Another possibility is to compare the performance of the bottom 40 percent with that of other parts of the income distribution (for example the top 60 percent of the population) or overall national average performance. Alongside trends in average income of the bottom 40 percent, figure 3.4 also shows annualized growth rates for the population as a whole. In addition to providing a means to compare performance of shared prosperity across countries, this comparison also allows an assessment of the evolution of income inequality (this point is discussed further below). For example, the bottom 40 percent in South Africa did better than average during the mid-1990s (suggesting not only that incomes at the bottom 40 grew but also that there was some catching up). By contrast, by the 2000s, income growth for the bottom 40 increased compared to the mid-1990s, but was significantly slower than average income growth, implying increased inequality. So even though shared prosperity was boosted over this period in South Africa (average incomes of the bottom 40 percent increased), the bottom 40 percent underperformed relative to the rest of the population. In Uganda, on the other hand, the trends suggest not only that shared prosperity has been increasing over time, but the bottom 40 percent also did at least as well as the rest of the population (the growth rate was the same as or exceeded the overall average). Note, examining inequality in this way takes us to an assessment of inequality of outcomes WB has historically focused on inequality of opportunity

Shared Prosperity: Country-level indicator

DEC Policy Research Report | Source: Lakner and Milanovic (2013) 17

income distribution United States

income distribution Brazil

consumption distribution India

Mean bottom 40%

Mean bottom 40%

Mean bottom 40%

Average US household in bottom 40% would be in the richest 10% in Brazil. Average Brazilian household in bottom 40% would be at about the 90th percentile in India.

Presenter

Presentation Notes

The people who constitute the bottom 40 percent are likely to vary substantially across countries. In low- and lower-middle-income countries there will likely be significant overlap between those living in poverty and the bottom 40 percent of the population: tracking shared prosperity can thus reinforce poverty reduction efforts in these countries. By contrast, the bottom 40 percent of the population in upper-middle-income countries are likely to be non-poor: in these countries tracking shared prosperity can bring attention to those not covered by poverty policies but who might otherwise be relatively left behind.


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Presenter

Presentation Notes


Twinning the goals: Three scenarios for global poverty under differential shared prosperity

Source: Lakner, C., Negre, M., & Prydz, E. B. (2014). Twinning the goals: how can promoting shared prosperity help to reduce global poverty?. World Bank Policy Research Working Paper, (7106). 19

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3% goal for 2030

Historic 2002-2011 Simulations 2012-2030


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critical to eliminate extreme poverty. Data challenges • Research important both for policy & for improving data collection methods.

The importance of country level policy and poverty profiles. • Progress is made at the country level – Policies need to be informed by high-

quality household surveys. • “More frequent data” is not the right call to action – PRR calls for greater


Presenter

Presentation Notes


Spatial price differences within countries matter for sub-national poverty profiles

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In the US, official poverty estimates do not account for cost of living differences

• In official estimates, poverty in non-metropolitan areas is higher than in metropolitan areas.

• Once adjusted for cost-of-living differences, poverty in non-metro areas is 15% lower than in metro areas (Jolliffe, 2004)

Adjusted poverty estimates

Official poverty estimates

Non-metro poverty less metro poverty in the US


Research in data collection methods Questionnaire changes => changes in cons., poverty, & shared prosperity

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Source: Adapted from Beegle et al. (2012).

• Household consumption surveys vary widely (over time & countries)

• Beegle et al. (2012) provide experimental evidence on the effect

• Exact same instrument except increase recall period

=> 12% drop in avg consumption => 8 point (%) increase in poverty

• Same recall period, but long list collapsed to comprehensive groups => 24% drop in avg consumption

• => 32% drop in shared prosperity

• Research can inform questionnaire design & provide bridges across otherwise noncomparable data

change from benchmark personal diary

-0.136***

-0.173***

-0.207***

-0.283***

-0.071*

-0.039

-0.161***

Diary: HH, infreq.

Diary: HH, freq.

Recall: Usual, 12 month

Recall: Collapse, 7 day

Recall: Subset, 7 day

Recall: Long, 7 day

Recall: Long, 14 day

Presenter

Presentation Notes

Chart plots results of a regression of log consumption on dummies indicating module assignment. Personal diary omitted. We absorb EA fixed effects but do not include any other controls. *** significant at 1%, ** at 5% level.


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critical to eliminate extreme poverty. Data challenges • Research in data collection methods improves data quality and comparability. • Progress is made at the country level – Policies need to be informed by high-

quality household surveys. • “More frequent data” is not the right call to action – PRR calls for greater


Presenter

Presentation Notes


Poverty and Prosperity Policies, made at the country level, need high-quality data

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1999 2009

Vietnam poverty maps Pockets of poverty & dynamics of Poverty Profiles require research & country context & data


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Presenter

Presentation Notes


Complementary data needed to estimate poverty and shared prosperity Household surveys are a necessary input to measuring global poverty and shared prosperity, but they are not sufficient.

Several complementary data sources are also needed

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Purchasing power parity (PPP) indices

Population (census) data

Inflation and national accounts growth

• to estimate total number of the poor (as product of poverty rate and population)

• population frame for survey samples

• Make poverty line comparable across countries

• Inflation data to keep measures of wellbeing in real terms

• NA data to “line up” surveys into reference years

Error in population counts => error in poverty estimates

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UN WPP 2012 Rev. / WDI 2014 and later

UN WPP 2008 Rev. / WDI 2011 and earlier

UN WPP 2010 Rev. / WDI 2012 and 2013

71m poor

65m poor

64m poor

Poverty rate in 2010 43%

Bangladesh, 2005 to 2015 • US National Research Council:

4.8% average absolute error in UN/WB 5-year projections

=> 50 million mis-identified

• UN World Population Prospects estimates serve as inputs to WDI and for poverty estimates.

• One example, Bangladesh: • Census in 2011 • UN WPP pre-censal

estimates significantly higher than post-censal

• => 6-7 million fewer poor.

• Good population counts relies on good data systems (vital statistics systems, construction/housing, fertility, mortality, etc.)

26% 25%

51%

24%

15% 10%

43% 42% 47%

39%

50%

57%

0%

10%

20%

30%

40%

50%

60%

1985 ICP 1993 ICP 2005 ICP

East Asia and the Pacific Latin America and the CaribbeanSouth Asia Sub-Saharan Africa

New PPPs can have substantial implications for overall level & regional profile of global poverty

| Source: Deaton (2010) 28

1993 poverty headcount based on three PPP Indices

Reg

iona

l pov

erty

hea

dcou

nt

$1.01 a day $1.08 a day $1.25 a day

1.35 bn total 1.3 bn total 1.8 bn total • 2005 ICP PPP: “the

developing world is poorer than we thought”

• Release of 2011 ICP brings same challenges

• Our view: “…additional research will be necessary before international poverty rates can be estimated using the ICP PPPs” (International Comparison Program, 2014).

Projection errors in poverty estimates due to divergence between national accounts and surveys: Let’s pretend we didn’t have India’s 2009/2010 survey…

| Sources: Povcal, India NSS, WDI, Ravallion (2008) 29

04/05 scaled to 09/10 using survey means

actual

04/05 scaled to 09/10 using national accounts

04/05 actual

04/05 scaled to 09/10 using survey means

04/05 scaled to 09/10 using national accounts

09/10 actual

222 m people

Income distribution (density estimate) Poverty headcount, percent (at $1.25 a day, 2005 PPP)

41.6

Daily consumption per capita (2005 PPP)

Critical to better understand divergence between NA and surveys

Concluding observations and looking forward

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• There is a merit to twinning the goals

• While shared prosperity indicator is not in and of itself an inequality measure, it does open avenues to broadening the discussion to include inequality of outcomes

• Introduction of shared prosperity reasserts need to focus on types of growth

• Quality of data needs as much attention as frequency

• Data systems architectures at the country level are needed not only to support credible measurement of twin goals, but also for effective national development policy


Thank you

www.worldbank.org/AmeasuredApproach A Measured Approach to Ending Poverty and Boosting Shared Prosperity Concepts, Data, and the Twin Goals World Bank Policy Research Report

http://www.worldbank.org/AmeasuredApproach

Documents

to Ending Poverty and Boosting Shared Prosperity...Middle East & North Africa Latin America & the Caribbean East Asia & Pacific South Asia Sub-Saharan Africa 415 million 399 million