serbia national poverty analysis workshop march 31 –...
TRANSCRIPT
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POVERTY PROFILES
Serbia National Poverty Analysis WorkshopMarch 31 – April 4, 2008
Giovanni VecchiUniversita’ di Roma “Tor Vergata”[email protected]
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Giovanni Vecchi - Apr 20082
PLAN OF THE LECTURE
1) The Many Facets of a Poverty Profile2) Robustness Analysis3) Poverty Comparisons
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Giovanni Vecchi - Apr 20083
POVERTY PROFILEa definition
A poverty profile shows how a measure of poverty varies across subgroups of a population (e.g. region of residence) and compares key characteristics of the poor versus non-poor.
Main purposes: 1. to identify poverty patterns2. to formulate poverty reduction strategies3. to monitor poverty changes
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Giovanni Vecchi - Apr 20084
POVERTY PROFILEquestions addressed
Poverty profiles help answer questions such as:1. how many are the poor?2. who are the poor?3. where do they live?4. what economic sectors they depend on?5. do they have access to social services?6. ...
Poverty profiles are highly sensitive to the choice of the method for setting poverty lines and poverty measures...
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Giovanni Vecchi - Apr 20085
POVERTY PROFILESmethods matter
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Giovanni Vecchi - Apr 20086
POVERTY PROFILEthe cover
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Giovanni Vecchi - Apr 20087
POVERTY PROFILEfacet 1: graphs (poverty rates by pop. subgroup)
Eastern
Central
Adriatic South
Adriatic North
ZagrebNational Average
0 5 10 11.1 15 20Headcount Poverty Ratio
(%)
Figure 6 – Poverty Incidence in Croatia by Region
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Giovanni Vecchi - Apr 20088
POVERTY PROFILEfacet 2: graphs (pop. shares accounted for)
Figure 7 – Distribution of Poverty by Region
38%
34%
7%
5%
15%
38%
CentralEasternZagrebAdriatic NorthAdriatic South
Regions:
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Giovanni Vecchi - Apr 20089
POVERTY PROFILEfacet 3: poor versus non-poor persons
Figure 7 – Expenditure Patterns of the Poor and the Nonpoor
452
53
7
20
13
8
15
10
17
53
37
0
10
20
30
40
50
Bud
get s
hare
s (%
)
liqueurand
tobacco
durablesclothingutilitiestransportand
communication
otherfood
nonpoorpoor
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POVERTY PROFILEfacet 4: tables
Source: Nestic (2008), Welfare Analysis in Montenegro using the Household Budget Survey Series, mimeo.
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POVERTY PROFILEfacet 5: words
In 2006, 8.8% of the population of Serbia was classified as poor.
Central Serbia accounts for 63% of national poverty incidence: Vojvodina 26.5%, Belgrade 10.5%.
Poor hh tend to have larger-than-average size, high child-adult ratios, illiterate breadwinners.
And so forth.
Source: Republic Statistical Office (2008), Poverty in Serbia fir the year 2006. Preliminary results, mimeo.
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Giovanni Vecchi - Apr 200812
POVERTY PROFILEfacet 6: poverty risks
poverty risk ...
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POVERTY PROFILEfacet 7: special reports
After identifying the poor, in-depth analysis can focus on specific population groups
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Giovanni Vecchi - Apr 200814
POVERTY PROFILEfacet 8: regression analysis
From simple correlations (two-way tables and graphs) to partial correlations.Estimate (= regress) an econometric model for household expenditure and use it to predict poverty measures.Steps: 1. Estimate regression: Log(Ch)=βXh+εh2. Predict consumption: E(Ch | Xh)=Exp(βXh+σ2/2)3. Calculate poverty rates based on predicted consumption,
or calculate probability of being poor.Simulations
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ROOM FOR DISAGREEMENT
The process of measuring poverty requires a number of assumptions and decisions to be made (on the welfare aggregate, on poverty lines, and on poverty indices).Those sceptical as to the conclusion that poverty has increased, for instance, may argue that the choice of a different poverty line could lead to a reversal of the conclusion.Two solutions:1) sensitivity analysis2) stochastic dominance
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SENSITIVITY ANALYSISbosnia and herzegovina, 2003 (vol. II)
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SENSITIVITY ANALYSIS excerpt from the index
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SENSITIVITY ANALYSISconclusions
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ATKINSON (1987)
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ATKINSON (1987)stochastic dominance
Atkinson (1987) explored the use of stochastic dominance.
Dominance methods test whether one income distribution has more poverty than another for a broad class of poverty measures and a wide range of poverty lines.
Take two income distributions A and B, characterized by cdfs FA and FB, respectively…
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FIRST-ORDER STOCHASTIC DOMINANCE(FOD)
We say that FA first-order stochastically dominates FB if and only if, for all positive x:
( ) ( )xFxF BA ≤
For instance, A could be the distribution of PCE for urbanhouseholds, B for rural. 0
.2
.4
.6
.8
1
0 5000 10000 15000 20000 25000 30000PCE
URBANRURAL
Source:2001 Nicaragua LSMS
A
B
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ATKINSON (1987) “Condition I”
The poverty ranking of two distributions according to headcount ratio does NOT depend on the choice of the poverty line if and only if one distribution FOD the other.
We are interested in comparing two distributions, F and F1, denoting the difference ΔF = F – F1.
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FOD“Condition I” in practice
1. All we have to do to test the robustness of the headcount ratio is to plot the CDFs of the two distributions that we are interested in comparing.
2. If one lies above the other over the range of relevant poverty lines, then the choice of poverty line within that range will make no difference to the outcome.
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FODpoverty incidence curves
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ATKINSON (1987) “Condition I”
If two distributions cross within the range of poverty lines [Z−, Z+], then FOD does not hold: the choice of different poverty lines combined with the use of the headcount poverty ratio will lead to different rankings of the two distributions.
Can we do any better by adopting a different poverty measure?
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Giovanni Vecchi - Apr 200826
SECOND-ORDERSTOCHASTIC DOMINANCE (SOD)
To define SOD, we start by defining the poverty deficit curve D(z;F):
( ) ( )dxxFFzDz
∫= 0;
The poverty deficit curve is the area under the CDF up to some poverty line z.
If DA ≤ DB for all x (i.e. the area under A up to x is less the area under B up to x), then distribution A is said to second-order stochastically dominate distribution B.
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Giovanni Vecchi - Apr 200827
SECOND-ORDER STOCHASTIC DOMINANCE (SOD)
Remember the definition: ( ) ( )dxxFFzDz
∫=0
;
DB ≤ DA
then
B SOD A
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ATKINSON (1987)“Condition II”
If the poverty deficit curve for one distribution lies above the poverty deficit curve of another, the first distribution will always have more poverty as measured by the poverty gap measure.
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Giovanni Vecchi - Apr 200829
ATKINSON (1987)“Condition II” (In Practice)
All we have to do to test the robustness of the poverty gap index is to plot the PDCs (poverty deficit curves) of the two distributions that we are interested in comparing.
If one lies above the other over the range of relevant poverty lines, then the choice of poverty line within that range will make no difference to the outcome: the first distribution will always have more poverty according to the poverty-gap measure.
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POVERTY DEFICIT CURVESin practice
Deaton (1997:166) shows that: D(z;F) = z×PG
The PG ratio is higher in 2001 than in 2005, regardless of the poverty line
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COMPARING THE DIFFERENT ORDERS OF DOMINANCE
FOD ⇒ SOD ⇒ TOD
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LIST OF PAPERS CITED
Atkinson, A.B. (1987), “On the Measurement of Poverty”, Econometrica, 55(4): 749-764.Ravallion, M. and B. Bidani (1994), “How Robust Is a Poverty Profile”, The World Bank Economic Review, 8(1): 75-102.