Figure 5.13 Global and Regional Poverty Trends, 1981–2010akaraiva/tables_ch5.pdf · The impacts of natural disasters are dispro-portionately high among lower income groups, older

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Figure 5.13 Global and Regional Poverty Trends, 1981–2010

(either income or multidimensional) is largest in South Asia, Sub- Saharan Africa, and East Asia and the Pacific (table 3.1).

Multidimensional poverty has a strong geographical component, since it tends to be highest in rural areas. In Somalia it affects 60 percent of the population in urban house-holds and over 95 percent of the population in rural households. In Burkina Faso 43 per-cent and 94 percent, in Niger 56 percent and 96 percent and in Ethiopia 54 percent and 96 percent.

In many countries multidimensional poverty is also more likely among female-headed house-holds and those that include a person age 60 or older. Another important factor is the presence of young children. In Bolivia, for example, the overall proportion of the population in multidimensional poverty is 12 percent, but in households with at least one child under age 5 it is 34 percent. The corresponding propor-tions are 21 percent and 42 percent in Ghana,

7 percent and 19 percent in Peru and 4 percent and 11 percent in the Syrian Arab Republic.

Vulnerability increases when poverty inter-acts with other household conditions.114 Poor people are more likely to live in areas vulnera-ble to the impacts of climate change, whether in low-lying coastal regions at greatest risk of inundation from rising sea levels or on marginal land subject to increasing dryness and drought from climate change.115

Poor people are vulnerable since they general-ly lack access to savings, borrowed funds or oth-er assets they can draw on to meet unforeseen contingencies. Faced with a job loss or other income shock, they resort to more harmful coping strategies such as cutting back on food or reducing spending on health or children’s educa-tion.116 Even with a higher income, households may not be or even feel much less vulnerable, and despite progress over recent decades in both developed and developing countries, individuals feel economically less secure.117

FIGURE 3.10

Some 1.2 billion people live on less than $1.25 a day, and 1.5 billion people live in multidimensional poverty

6 7 9543 8 10210

MultidimensionalPoverty Index

3

2.5

2

1.5

1

0.5

0

Population(billions)

Less than $1.25a day

$1.25 - $2.50a day

$2.50 and more

a day

Multidimensional povertyIncome poverty

49.7%

21.9%

29.2%

15.5%

55.4%

Not multi-dimesionally

poor

Multi-dimesionally

poor

Near multi-dimesionally

poor

Povertycutoff3.33

Nearpovertycutoff2

28.4%

Source: Multidimensional poverty, Human Development Report Office calculations based on various household surveys, including ICF Macro Demographic and Health Surveys, United Nations Children’s Fund Multiple Indicator Cluster Surveys and several national household surveys; income poverty, Human Development Report Office calculations based on data from the World Bank’s World Development Indicators database.

72 | HUMAN DEVELOPMENT REPORT 2014

alex

Note

includes deprivations in health, education and standard of living

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Table 5.1 Typical Size Distribution of Personal Income in a Developing Country by Income Shares—Quintiles and Deciles


Figure 5.1 The Lorenz Curve


Figure 5.2 The Greater the Curvature of the Lorenz Line, the Greater the Relative Degree of Inequality


Figure 5.3 Estimating the GiniCoefficient


Figure 5.4 Four Possible Lorenz Curves


Figure 5.6 Measuring the Total Poverty Gap


The Multidimensional Poverty Index

(MPI)

• Identification of poverty status through a dual cutoff:

• First, cutoff levels within each dimension (analogous to falling below a poverty line for example $1.25 per day for income poverty);

• Second, cutoff in the number of dimensions in which a person must be deprived (below a line) to be deemed multidimensionally poor.

• MPI focuses on deprivations in health, education, and standard of living; and each receives equal (that is one-third of the overall total) weight.


MPI Indicators

• Health - two indicators with equal weight - whether any child has died in the family, and whether any adult or child in the family is malnourished –weighted equally (each counts as one-sixth toward the maximum deprivation in the MPI)

• Education - two indicators with equal weight - whether no household member completed 5 years of schooling, and whether any school-aged child is out of school for grades 1 through 8 (each counts one-sixth toward the MPI).

• Standard of Living, equal weight on 6 deprivations (each counts as 1/18 toward the maximum): lack of electricity; insufficiently safe drinking water; inadequate sanitation; inadequate flooring; unimproved cooking fuel; lack of more than one of 5 assets – telephone, radio, TV, bicycle, and motorbike.

2010

Region

East Asia and Pacific

Europe and Central Asia

Latin America and the Caribbean

Middle East and North Africa

South Asia

Sub-Saharan Africa

Total

1999

Region





South Asia

Sub-Saharan Africa

Total

1990

Region





South Asia

Sub-Saharan Africa

Total

1981

Region





South Asia

Sub-Saharan Africa

Total

Aggregation:

Note: Regions for which survey coverage is less than 50% are highlighted

Pov.line(PPP$/mo)

Headcount(%)

Pov. gap(%)

Squaredpov. gap

Num ofpoor (mil.)

Population(mil.)

Surveycoverage

38.00 12.48 2.82 0.93 250.90 2,010.44 93.50

38.00 0.66 0.21 0.13 3.15 477.06 85.30

38.00 5.53 2.89 2.12 32.29 583.89 86.90

38.00 2.41 0.55 0.23 7.98 331.26 30.80

38.00 31.03 7.09 2.36 506.77 1,633.15 94.50

38.00 48.47 20.95 11.85 413.73 853.57 64.10

38.00 20.63 6.30 2.92 1,214.98 5,889.37 84.70

Pov.line(PPP$/mo)

Headcount(%)

Pov. gap(%)

Squaredpov. gap

Num ofpoor (mil.)

Population(mil.)

Surveycoverage

38.00 35.47 10.63 4.31 653.56 1,842.57 93.40

38.00 3.82 1.00 0.42 17.97 470.32 93.90

38.00 11.86 5.55 3.81 60.10 506.76 97.70

38.00 5.01 0.96 0.31 13.64 272.22 70.00

38.00 45.11 12.19 4.53 619.46 1,373.21 20.10

38.00 58.01 25.98 14.94 376.75 649.46 53.10

38.00 34.05 11.09 5.10 1,741.50 5,114.55 68.20

Pov.line(PPP$/mo)

Headcount(%)

Pov. gap(%)

Squaredpov. gap

Num ofpoor (mil.)

Population(mil.)

Surveycoverage

38.00 56.24 19.07 8.52 926.42 1,647.27 92.40

38.00 1.86 0.53 0.26 8.64 464.30 81.50

38.00 12.24 5.31 3.43 53.43 436.56 94.90

38.00 5.75 1.08 0.36 12.96 225.41 76.80

38.00 53.81 16.13 6.51 617.26 1,147.11 96.50

38.00 56.53 25.36 14.74 289.68 512.44 46.00

38.00 43.05 14.82 6.94 1,908.45 4,433.09 86.40

Pov.line(PPP$/mo)

Headcount(%)

Pov. gap(%)

Squaredpov. gap

Num ofpoor (mil.)

Population(mil.)

Surveycoverage

38.00 77.18 34.86 19.00 1,096.50 1,420.70 73.40

38.00 1.91 0.50 0.24 8.21 429.66 0.00

38.00 11.89 4.36 2.48 43.33 364.45 55.90

38.00 9.56 1.87 0.58 16.48 172.40 0.00

38.00 61.14 20.46 9.06 568.38 929.64 87.50

38.00 51.45 21.80 12.17 204.93 398.31 11.30

38.00 52.16 21.36 11.14 1,937.83 3,715.17 56.50

Click on the region to get the country result.

Click on the heading to get its definition.

PovcalNet report... http://iresearch.worldbank.org/PovcalNet/PovCalServlet?povResultFile.jsp

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Table 5.5 Income Poverty Incidence in Selected Countries


Table 5.5 Income Poverty Incidence in Selected Countries (continued)


Table 5.6 Multidimensional Poverty Index, Data for 2007–2011

Recent austerity measures have increased poverty in more than half of European coun-tries, and the groups most at risk are children, immigrants and people from a migrant back-ground, ethnic minorities and people with disabilities.118

The impacts of natural disasters are dispro-portionately high among lower income groups, older people and people with disabilities. During the 2005 Mumbai floods the poorest households were the most vulnerable. Though the losses may not appear large in absolute terms, the average loss incurred by households roughly equalled the average household’s sav-ings. The ability to recover and reconstruct in the aftermath of the floods was impaired by the depletion of household savings and loss of household assets.119 The 2001 earthquakes in El Salvador reduced the income per capita of the most affected households by a third.120 In Bangladesh, in major flooding events in certain years, up to 7.5 million hectares of crops were damaged, hurting mostly the poor.121

During and after disasters children from poor households are particularly vulnerable to malnutrition and other long-term consequenc-es. The 1982–1984 drought in Zimbabwe increased the probability of child stunting and delayed the school enrolment of children by an average of 3.7 months, which worsened their performance at school up to 16 years after the disaster. In Ethiopia between 2002 and 2006, 90 percent of the households in the poorest

income quintile experienced at least one risk of shocks to adverse events, while many reported multiple risks, with an average of 4.2 risks per household.122 Family responses to shocks include eating less, reducing household assets and accumulating debt, all likely to have long-term consequences for children’s development. Income shocks have major impacts on the school attendance and performance of children from poor households.123

The effect of disasters on people and com-munities is conditioned not only by their capabilities and competences, but also by their asset base—their financial and natural capital. For example, considerable degradation of the ecosystem could threaten the livelihoods of the rural communities that depend directly on natural resources: access to marine biodiversity, nontimber forest products and small-scale or subsistence crop and stock farming. How vul-nerable these communities are is determined by the condition of the natural resource base for current and alternative economic activities, the regimes for managing those resources and how close natural ecosystems are to tipping points past which productivity can no longer be restored. Environmental degradation and natural resource impoverishment are major threats. In 2011 agricultural workers account-ed for 40 percent of the world’s economically active population, 60 percent of them in low Human Development Index countries. Two-thirds of the extreme poor are in rural areas,

TABLE 3.1

Income and multidimensional poverty, by region

Region

Number of countries in sample

Income poverty

headcount (%)

Near income poverty

(%)

Number of countries in sample

Multidimensional poverty

headcount (%)

Intensity of deprivation

(%)

Near multi-dimensional

poverty (%)

Arab States 10 6.5 36.4 9 15.5 48.4 8.7

East Asia and the Pacific 11 12.7 25.1 10 6.4 44.7 16.2

Europe and Central Asia 15 1.4 6.0 15 1.8 37.3 4.5

Latin America and the Caribbean 20 5.7 7.0 14 6.7 42.8 9.5

South Asia 8 30.6 44.4 7 53.4 50.8 17.9

Sub- Saharan Africa 40 50.9 27.8 36 59.6 55.0 16.2

Source: Multidimensional poverty, Human Development Report Office calculations based on various household surveys, including ICF Macro Demographic and Health Surveys, United Nations Children’s Fund Multiple Indicator Cluster Surveys and several national household surveys; income poverty, Human Development Report Office calculations based on data from the World Bank’s World Development Indicators database.

Chapter 3 Vulnerable people, vulnerable world | 73


Table 5.2 Selected Income Distribution Estimates


Figure 5.13 Long-Term Economic Growth and Income Inequality, 1965-1996

Source: Ravallion and Chen, Understanding China’s (uneven) progressagainst poverty, Finance and development, 2004

Example: China


Figure 5.14 Change in Inequality in Selected Countries, with or without Growth


Table 5.6 Poverty: Rural versus Urban


Table 5.7 Indigenous Poverty in Latin America

Source: Dollar & Kraay. 2002

Poverty

Typical measures of poverty are based on income or consumption, which register im-portant dimensions of deprivation but provide only a partial picture. People can be deprived of many things beyond income. They may have poor health and nutrition, low education and skills, inadequate livelihoods and poor household conditions, and they may be socially excluded.

Some of these broader aspects of poverty are captured in the concept of multidimensional poverty. In 104 developing countries 1.2 bil-lion people had an income of $1.25 or less a day.18 But the multidimensional poverty headcount for 91 developing countries was an estimated 1.5 billion people—as measured by the Multidimensional Poverty Index (MPI).19

According to the MPI, which was introduced in the 2010 HDR to measure deprivations in the three HDI dimensions—health, education and living standards—2.2 billion people live in multidimensional poverty or near-poverty (out of 10). The MPI measures not only the propor-tion of people deprived but also the intensity of deprivation for each poor household, providing a more comprehensive picture (see chapter 3).

The proportion of multidimensionally poor people is usually higher than the proportion living on less than $1.25 a day. In Cambodia 47 percent of the population were in multi-dimensional poverty in 2010, but only 19 per-cent lived on less than $1.25 a day. But in Brazil and Indonesia income poverty is higher. Moreover, while in many countries both multi-dimensional poverty and income poverty have decreased, the rate of progress varies widely

FIGURE 2.6

In countries where inequality has been high or rising, growth in consumption for the poorest 40 percent of the population has been slower than for the population as a whole

-4 0 4 8 12-4

0

4

8

12

BoliviaBrazil

Egypt

China

Malaysia

Growth in consumption for the population as a whole, 2005–2010 (%)

Growth in consumptionfor the poorest 40

percent, 2005–2010(%)

Growth in consumptionis positive but slower for the poorest 40%

than for the population as a whole

Growth in consumption is positiveand faster for the poorest 40%than for the population as a whole

Uganda

Philippines

Cambodia

Source: Narayan, Saavedra-Chanduvi and Tiwari 2013.

Chapter 2 State of human development | 41

Source: Louise Cord. Pro-poor growth in the 90’s. WB. AFD. DFID. GermanCooperation. Dfid

States of India (Besley. Burgess & Esteve-Volart. 2004. /www.dfid.gov.uk)

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Figure 5.13 Global and Regional Poverty Trends, 1981–2010akaraiva/tables_ch5.pdf · The impacts of natural disasters are dispro-portionately high among lower income groups, older