Assessing “Success” in Anti-Poverty Policy

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

2. 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”

3. 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

4. (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. Axioms to Measure Poverty ?

5. (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 ? Axioms Continued

6. 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.

8. 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?

9. 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 ? What is the cost of simplicity ?

10. 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: Yi =Yf / N 0.5 • Relative poverty line = ½ median equivalent

11. 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

12. 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

13. 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 ?

14. 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)

15. 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

16. 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 = [(ri / rUS )* 0.002 • Scale up number of very low income households • Both

17. Better Measurement Matters • Poverty Intensity / FGT1 / 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