2. Assessing “Success” in Anti-Poverty Policy Lars Osberg Dalhousie University October 1, 2004

3. Helping People out of Poverty? Poverty reduction = “success” for social policy Implicitly – statistical criterion is poverty rate Policy focus: wages, jobs & poverty Problem: poorest have multiple problems, expensive to move out of poverty Implications of focus on rate of poverty: Can achieve apparent “success” by creaming off marginally poor Triage, time limits, “Opportunity Accounts” rhetoric The convenience of abandoning “no-hopers”

4. Is there a better Index of Poverty Policy Success? Sen (1976) & large literature since Depth of Poverty also matters Principle of transfers – transfer from poor person to richer person increases poverty NOT satisfied by Poverty Rate Axioms + Communicability => Useful + Used focus, monotonicity, symmetry, replication invariance, transfer sensitivity, continuity + communicable

5. Axioms to Measure Poverty ? (1) Focus: the poverty measure should be independent of the nonpoor population. (2) Weak monotonicity: a reduction in a poor person’s income, holding other incomes constant, must increase the value of the poverty measure. (3) Impartiality: A poverty measure should be insensitive to the order of incomes. (4) Weak transfer: An increase in a poverty measure should occur if the poorer of the two individuals involved in an upward transfer of income is poor and if the set of poor people does not change. (5) Strong upward transfer: An increase in a poverty measure should occur if the poorer of the two individuals involved in an upward transfer of income is poor.

6. Axioms Continued (6) Continuity : The poverty measure must vary continuously with incomes. (7) Replication invariance : The value of a poverty measure does not change if it is computed based on an income distribution that is generated by the k-fold replication of an original income distribution. Is there “something different” about being Poor Social Exclusion ?

7. Poverty Intensity Sen-Shorrocks-Thon (SST) Index. P = (RATE ) (GAP ) (1+G (g )). Empirically: inequality of poverty gaps [1+G (g)] is very nearly constant. SST = volume of Box, one dimension nearly constant. Changes over time, differences across jurisdictions can be approximated by. RATE x GAP. Poverty Box – in 2 dimensions. Poverty Intensity proportional to area RATE X GAP.

9. Foster, Greer,Thorbecke Index Based on weighted average of poverty gap ratios, where weights = poverty aversion parameter  Satisfies the transfer axiom for  > 1 and the transfer sensitivity axiom for  > 2 Higher values of  imply greater weight to deprivation of least well-off Ethically desirable – but is data on the most disadvantaged dependable?

10. What is the cost of simplicity ? Poverty rate FGT  = 0 Poverty Intensity Poverty Box = rate x % gap FGT  = 1 SST FGT  = 2…6 Simplest – ignores depth Fails Monotonicity, Transfer Fails transfer axiom Implicitly - $1 deprivation has constant value Formally Satisfies Axioms - insensitive in practice ? Satisfies Axioms - excess sensitivity to bad data on poorest ?

11. But does it matter ? Do we get the Same Results ? International variation > national changes over time Do alternative indices of poverty differ in ranking of social states ? Luxembourg Income Study data After-tax, after transfer equivalent income LIS equivalence scale: Y i = Y f / N 0.5 Relative poverty line = ½ median equivalent

12. Same Cross Sectional Country Rankings ? In Mid 1990s Luxembourg Income Study data, the average poverty gap differs significantly Implication: country rankings by poverty intensity frequently diverge from rankings by poverty rate

13. Qualitative Conclusions Re: Poverty Trends Often Differ Relative poverty in early 1990s ? Canada, UK, Sweden: intensity & rate disagree US & Germany: indices agree Over-all – 22 year/year comparisons 4 no change rate but change intensity 5 opposite direction change: intensity & rate 9/22 = 40% disagreement Absolute poverty line UK - poverty rate and poverty intensity change in opposite directions every time - 1974/79/86/91/95

14. Communicability ? The whole point of measuring poverty is to influence the policies that might affect poverty A poverty index not used or understood by the public & by policy makers is pointless How much of the variation in Higher Order Poverty (FGT  = 2…6 ) is explained by [rate*gap] ? To what extent do international rankings change for FGT  = 2…6 ? Do plausible corrections for bad data matter ?

15. How might measurement choices affect results ? Great Public Interest in “League Tables” but Very small changes may re-rank order of countries Unequal range of Indices implies scaling to unit interval desirable Linear Scaling = (Value – Min)/(Max – Min)

16. Sensitivity to “Bad” Data ? Very poorest lead lives where likely to find: Response error in income Sampling bias Homeless Unlikely to be able to report ‘in-kind’ income Unlikely to be recorded in sample surveys As   = 2…6, any measurement errors increase in importance

17. Plausible magnitudes of implications ? Assume all incomes less than $2,000 US (PPP) must be wrong Caution is in order !!!!! Assume Number of Unreported Homeless = [(r i / r US )* 0.002 Scale up number of very low income households Both

18. Better Measurement Matters Poverty Intensity / FGT 1 / Normalized Poverty Gap Makes a Difference to conclusions Often has different trend to poverty rate Easily communicated – visually and verbally Decomposable Useful for policy evaluation Count each $ reduction in poverty gap as part of “success” FGT  = 2…6 Less impact on rankings & much harder to communicate