Angus Deaton, Princeton University 4 th OECD World Forum, Delhi, October 16 th, 2012 MATERIAL CONDITIONS PROGRESS AND PUZZLES IN MEASUREMENT

Transcription

1 Angus Deaton, Princeton University 4 th OECD World Forum, Delhi, October 16 th, 2012 MATERIAL CONDITIONS PROGRESS AND PUZZLES IN MEASUREMENT

2 This talk Measurement in three areas Material well-being: purchasing power priceadjusted incomes for international comparisons Understanding PPPs Implications for global poverty & inequality Outstanding puzzles Information gaps in international data Material wellbeing and broader measures of wellbeing How do they fit together

3 National accounts Remain as central as ever Pillars on which everything else depends New frontiers exciting, but much remains to be done with the old We must not neglect Refocusing towards well-being GNP and NNP better than GDP Drilling down: not only aggregates but who received what Rethinking imputations Tension between comprehensibility and completeness Examples: FISIM and actual consumption Lots of work to be done here Extensions to what we have now As well as improving NAS in many countries where they are weak

4 PRICE INDEXES AND GLOBAL INCOME COMPARISONS

5 PPP exchange rates The Penn World Table is the basis for the income comparisons in almost all global calculations Or other numbers based on ICP price collection The ICP collects prices of individual goods around the world Rounds in : PWT 6.X 2005: PWT (not yet released) Used to construct multilateral price indexes Which deflate local estimates of income, consumption, etc. measured in local currencies Between rounds, and waiting for rounds, e.g. post 2005 Local CPIs (or implicit price deflators) are used to update PPP between, say, US and India, should track relative growth rates of CPIs in US and India

6 Ratio of new to old PPP for Congo, DR Burundi Guinea Bissau Ethiopia Guinea Togo Tanzania Sao Tome & Principe Lesotho Ghana Cambodia Bangladesh India Cape Verde Philippines China Namibia Vietnam Nigeria Bolivia Fiji Angola Tonga Yemen Congo, R Lebanon Large upward revisions for many low income countries Gabon Revisions down for oil producers and high oil prices in 2005 Kuwait Logarithm of per capita GDP in 2005 international $ 6

7 PWT 6.2, 1993 prices Gini coefficient for per capita GDP, weighted by population PWT 5.6, 1985 prices WDI 2008, 2005 prices WDI 2007, 1993 prices year

8 PWT 6.2, 1993 prices Gini coefficient for per capita GDP, weighted by population PWT 5.6, 1985 prices WDI 2008, 2005 prices WDI 2007, 1993 prices year

9 Why? We do not understand, yet it is crucial for understanding changes in global income inequality Not important for global poverty, because $1 a day line is average of poor country lines & depends only on PPP rates among poor countries, which are not much affect World Bank switch from $1 to $1.25 is an increase in the line, and not consequence of PPP revision Note this is not Balassa-Samuelson BS says ER and CPI change at different rates with growth Because of non-traded goods, and rising productivity in tradedgoods sector But PPPs are updated by CPIs, not exchange rates Number of technical, tedious, but important details E.g. treatment of trade-balance differs in ICP and NIPA Which is why oil exporters got to be relatively richer in 2005

10 National v international Local price indexes use local weights to weight price changes PPP comparing two or more countries uses weights from two or more countries Rate of growth of PPP is not the same as differential rate of growth of CPIs Except under exceptional conditions CPI updating between rounds will lead to discontinuities when new data become available As from 1985 to 1993 And 1993 to 2005 The aggregation bias causes largest revisions in fast growing relatively poor countries

11 Where are we now? Data from the ICP2011 is currently in house Being cleaned and processed Final results in December 2013 Updating will be done at a disaggregated level Tested from 2005 to 2011 using data from 2005 Much higher quality than 1993 or earlier Methodology much improved Since World Bank has been running this, overall quality much improved Will begin to resolve these issues By December 2013 some idea about world inequality and world poverty

12 Precision of PPPs How precise are PPPs and other numbers based on them? Large standard errors? Need a concept Richard Stone (1949) Why do we need to compare the U.S. with, say, India or China? Everybody knows that one country is very rich and another country very poor, does it matter whether the factor is thirty or fifty or what? We have learned a lot since, or have we?

13 A star system of PPPs Compared with a star country, e.g. US, each country has a N-vector of price relatives, one for each good Variance of log price ratios between pairs of countries source of PPP index uncertainty Theorem: log of Laspeyres to Paasche ratio for A relative to B (divided by N) is approximately equal to the variance over goods of log price ratios for A to B Standard error of PPP comes from thinking about log price ratios as drawn from a distribution PPP distribution calculated using (fixed) weights applied to price draws

14 Square root of log Laspyeres-Paasche ratio over N Group is Canada, Austria, Germany Belgium, France, Finland, Luxemburg, Denmark, Britain, Ireland, Switzerland, Italy, Norway, Australia, Sweden, Iceland, Portugal, Spain, Netherlands, New Zealand, Slovenia, Greece, Cyprus, Israel, Chile (from left to right) USA India Kyrgyzstan China Tanzania Tajikistan Azerbaijan Equatorial Guinea s.e. of log Tornquist or log Fisher

15 Notes on the figure The standard errors are large 2 s.e. for China and India is around 30 percent Much smaller for the group on the left But still substantial, ten percent Stone again in 1949 I do not expect a very rapid resolution of the intellectual problems of making welfare comparisons between widely different communities But ICP indexes are multilateral, not bilateral, and we need standard errors for those Essence of multilateral is that transitivity is enforced, and bilateral indexes are not transitive So what happens?

16 s.e. of log multilateral GEKS index Bahrain Qatar Kuwait Fiji Saudi Tajikistan Chad s.e. of bilateral log Törnquist or log Fisher

17 Multilateral s.e. s Multilateral standard errors are typically larger Average 15 percent instead of 12 percent Dispersion of ML standard errors smaller Transitivity is spreading the errors Poor bilateral is buttressed by ML comparisons Close countries have much larger s.e. s ML is a bad idea for them Bringing Tajikistan into the Canada US comparison is not necessarily a good idea Middle group of countries where costs of transitivity are balanced by the gains Still substantial uncertainty, big standard errors

18 INTERNATIONAL DATA

19 Bricks without straw ICP and others accept national estimates of GDP PPP only deflates them by price indexes ICP offers some technical assistance Many countries national accounts remain very weak Particularly but not exclusively in Africa Household surveys are inconsistent with national accounts Different growth rates of mean income/consumption Allows much mischief with poverty estimates Much more rapid poverty decline from NAS plus assumptions about inequality than directly measured from surveys India is the classic but far from only case International drilling down efforts in NAS statistics should help here Multipurpose surveys should include health and income

20 Data gaps International system has very little funding to collect new data Limited interest in funding governments to improve systems No international control, in contrast to often elaborate domestic controls International agencies produce numbers with little crosscheck: appearance of self-serving data production Compare domestic CPI and unemployment Perhaps high quality global statistics, like global justice, is a cosmopolitan fantasy Certainly no political constituency that will punish egregious errors/use of data for self-promotion Who needs these numbers?

21 MATERIAL V BROADER CONCEPTS OF WELLBEING

22 GDP v Happiness Has GDP been replaced by happiness measures? Perhaps we don t need income numbers of we can measure happiness Happiness has everything that GDP has, except for the mistakes! In particular, income doesn t matter beyond some point We can ignore material well-being

23 New information Gallup s World Poll samples all of the citizens of the world 1,000 people in each country in each year 160 countries now covered, since 2006 Identical questionnaires Data allow examination of many questions about SWB Are richer countries better off? At least beyond some point 23

24 Cantril s ladder question Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally stand at this time? Life evaluation measure, like life satisfaction, but hedonically neutral Not happiness Happiness proper (mood, hedonic experience) Did you experience a lot of happiness yesterday Distinction between experiencing life (hedonics, like happiness, sadness, enjoyment, etc) and thinking about life is crucial, conceptually and empirically Different correlates, different adaptation, etc.

25 (mean) ladder Cantril Ladder, pc GDP, 2005 international constant $

26 (mean) ladder Cantril Ladder, pc GDP, 2005 international constant $

27 Cantril Ladder, pc GDP, 2005 international constant $

28 Average ladder score Liberia Burundi Mozambique Bangladesh Afghanistan Sierra Leone Togo Benin Guatemala India Kenya Pakistan Georgia Congo Costa Rica Panama Brazil Colombia Thailand China Angola Mexico Hungary Lebanon Denmark Norway Finland Singapore Venezuela Argentina Germany Russia Korea Japan Hong Kong 800 3,200 12,800 51,200 GDP per capita, 2005 international constant $

29 GDP v life evaluation Life evaluation is closely linked to per capita income True within countries as well as across countries Growing evidence that this is true over time too No evidence that income is misleading about wellbeing GDP has many problems of its own Well understood and good work going on to improve it

30 Measuring wellbeing Happiness measures also have many problems Becoming more apparent as we go Different measures of happiness are very different things Are you happy? How satisfied are you with your life? Which is the right one for policy? Very different policy correlated Increasing evidence that people have great difficulty answering the questions in a coherent way Context effects are very important Cannot use to track over time Context effects change group rankings Much work to be done here before these can form a firm basis for policy

31 THANK YOU!