Measurement – session 5

1. Measurement – session 5 Wages and Income

2. Issues in mst of wages and income • Wages = individual • Self-employment, capital : how to measure their income? • Other (non-monetary) resources? • How to compare households of different size and composition? • Income = a household level variable?

3. Issues in mst of wages and income • Why do we want to measure income? • We assume income = money = a “universal equivalent” with which each consumer can buy whatever s/he wants • Should we not be measuring consumption, or “capabilities”? Or (subjective) welfare/happiness?

4. Income or consumption? • Consumption reflects income as well as past savings, access to credit markets, and seasonal variation in income • No records of income or seasonal fluctuations • Large informal sectors • Consumption data helps in deriving the poverty line

5. Income or consumption? • Measuring income is a theoretical, ethical choice: income is supposed to offer potentially anything, whereas consumption is the outcome of a choice • It’a a liberal point of view : we want to measure situations before choices, and not care about the outcomes

6. The measurement time-frame • There is no good reason to choose the year as unit • Some consensus that poverty is real when deprivation for 3 years (consumers smooth their consumption, save or borrow…) • But panel data is so noisy it doesn’t improve measurement!

7. Data needs for poverty analysis • National level data • National accounts – GDP, consumption, savings, investment, imports, exports, etc. • Ministry of Finance, Central Statistical Agency • Budgets, price surveys, and data collection • Monthly, quarterly, and yearly

8. Data needs for poverty analysis • Household – Individual level data • Household income, consumption, employment, assets, production, demography, etc. • NSIs, sectoral ministries, NGOs, academics • Household survey, rapid assessments, monitoring and evaluation • Yearly, 2-3 years, every 5 years…

9. Available data • Administrative data: taxes and payrolls, mainly • Population Census • Household surveys – Labor Force Survey, HBS, SILC (European Panel) • Qualitative and Participatory Assessments – ethnographic, village studies, beneficiary assessments, etc.

10. (1) Wages • What’s in a wage? • Much is excluded: all the in-kind payments • Time frame: hourly, monthly, yearly wage? • Statistical sources • Wages: employer’s tax declarations • Wages and employment: Labor Force Survey • Income: households’ income tax declarations

11. (1) Wages : average annual wages (full-time workers) Private sector Civil servants

12. (1) Wages : annual earnings • But define earnings as the sum of wages earned over one year by all those who have worked at least 1 day • The diagnosis is quite different

13. (1) Wages : annual earnings That is because earnings are a composition of wages earned * number of days worked Here are the average number of worked days for men and women

14. (1) Wages : annual earnings Earnings are a composition of wages earned * number of days worked Here are the average number of worked days for men and women, by age group

15. (1) Wages : annual earnings The average yearly wage from national accoutning sources can also look very different depending on the numerator and denominator you choose

16. (1) Wages : annual earnings • National accounting: • all wages/ ”average labor force”: (number of employed at beginning of year+ at end of year) /2 • Alternative denominator: all those who have worked at least 1 day during the year • Gross wages have increased much more than net wages because taxes on wages have increased • The diagnosis is definitely not the same!

17. (2) Other income • Self-employed • Taxes are an unreliable source • Depend on the legal status of the business • More fundamental problem: for themselves, there is no conceptual difference between their household budget and their business’

18. (2) Other income • Survey data: • Finally, European comparisons are made from survey data • SILC: survey on income and living conditions • Many questions on income • Survey effect: the more numerous the questions, the richer the respondents!

19. What’s in an income • Things that are ill-measured: • Income from capital (wealth): would have little impact on poverty since almost entirely above the median • Yet changes a lot when considering inequality

20. Taxes • Again, not simple: • Income tax = is removed from disposable income • But what about local taxes? • Again, it depends on what you consider a choice or not • Ex: is living in Paris a choice? Yes  you use your income to pay local taxes. No  local taxes should be removed from income

21. Transfers between HH • Alimonies and the money transfers of migrants to their homeland are removed from disposable income • But it may underestimate income: migrants send money for their own future use, too

22. What’s in an income • Choices of what to include are often made for no good theoretical reason but practical ones

23. Recent pushes towards better income measurement • French official report (CNIS)+ general Eurostat tendency • Goal = take better account of non monetary resources

24. Recent pushes towards better income measurement • Imputed rent • Owning your home

25. Recent pushes towards better income measurement • Home production

26. Recent pushes towards better income measurement • Public goods (that can be individualized)

27. Niveaux de vie • D’abord faut le définir: • Income (gross – net – including non market goods?) • By consumption unit

28. (1) Wages : annual earnings

29. Equivalence scales • Are used in setting level of allowances • Ex « RMI » in France • 425,40 € for 1 single person • 638,10 € for 1 couple • 765,72 € for 1 couple + 1 child. • Underlying hypothesis: 425,40 € buys same quality of life when single than 765,72 € when 2 parents + 1 child • 638,1= 425,40 + 0,5* 425,40 • 765,72= 425,40 + 0,5* 425,40 +0,3* 425,40 • Implicit equivalence scale: 1st adult = 1; 2nd adult = 0,5; child = 0,3

30. Equivalence scales : Where do they come from? • No consensus: example of number of consumption unit / child? • Household budget surveys: ‘direct’ child expenses ~ 8%. But how to “split” food, housing… expenses? -> comparing parents / non-parents (but: unobserved taste differences). Much noise! • Lechêne, 1993 : between .2 and .7 • Subjective measurements (“how much do you need to…”) -> even wider dispersion

31. Equivalence scales : example on 4 variants • The 4 variants Source: “Du bon usage des échelles d’équivalence L’impact du choix de la mesure”, Jérôme Accardo. http://www.cairn.info/revue-informations-sociales-2007-1-page-36.htm

32. Equivalence scales : example on 4 variants • Results All figures calculated on fiscal data for 2001 Source: “Du bon usage des échelles d’équivalence L’impact du choix de la mesure”, Jérôme Accardo. http://www.cairn.info/revue-informations-sociales-2007-1-page-36.htm

33. Equivalence scales : example on 4 variants Composition of “the poor” depending of equivalence scale used From top to bottom: • Couple w. children • Couple w.out children • Single parent w. children • Single adult Source: “Du bon usage des échelles d’équivalence L’impact du choix de la mesure”, Jérôme Accardo. http://www.cairn.info/revue-informations-sociales-2007-1-page-36.htm

34. Intra-household allocation • Duflo and Udry (2004) [NBER Working Paper 10498] • Data : the Côte d’Ivoire Living Standards Measurement Survey (CILSS). 1985-1988. 1,500 HH • Some crops are cultivated by men, others by women • They do not benefit equally from rain

35. Intra-household allocation • Results: • Rainfall shocks associated with high yields of women’s crops shift expenditure towards food • Rainfall-induced fluctuations in income from yams are transmitted to expenditures on education and food, not to expenditures on private goods • Other crops fluctuations are associated with more consumption of private goods

36. Intra-household allocation • Evidence from sociology in the US and France • Income is not 100% shared among household members • There are intra-household variations in disposable income and consumption • But we know way too little to take them into account statistically… so far.

37. In 2001 in a few European countries

38. Lessons to be learnt • On child poverty: policy impact of measurement linked with ideology of those you want to convince (figures for advocacy)