Do our low income indicators also capture poverty? Using longitudinal data to evaluate Canada s leading poverty indicators

Transcription

1 Do our low income indicators also capture poverty? Using longitudinal data to evaluate Canada s leading poverty indicators Charles Plante, McGill University/University of Saskatchewan CRDCN Conference The Many Faces of Inequality: From Measurement to Policy Montreal, November 15th, 2017

2 Introduction

3 Where do we draw the line? In recent years, the LIM has emerged as a premier measure of poverty because it s easy to calculate, understand, and use for comparisons. Why not 60%, as favoured by the European Union, or 30%? Or 49.5%? And why not use the provincial median or the CMA? Oh, and in what year should we base it? Should we update it annually?

4 Why does this matter? Poverty is becoming an increasingly central concept in theories of of development (Sen 2000) and human action (Mani et al. 2013) a. We need to measure poverty well if we are going to know how to lift people out of poverty b. We need to measure poverty well if we are going to capture its effects on people

5 A further note on the spirit of this project Historically, we have been largely limited to cross-sectional measurement and thinking about concepts This is changing radically as new forms of longitudinal data are becoming available, particularly administrative This project revisits a familiar concept and recasts it for the N-spoiled years to come

6 Context

7 Toward an external criteria Few poverty researchers are satisfied with the measures we use Weaknesses in common income measures have invited the: a. capability approach b. multi-dimensional measures But these introduce their own challenges (Ravallion 2011)

8 Plus ça change... In fact, differences between approaches are not as great as they seem (Duclos 2002) The indicator/threshold problem persists

9 External criteria could do the trick The primary trouble is the functioning transformation curves If we could settle on a standard in functioning, we could project this back into income We could estimate this threshold for different populations as needed But which functioning?

10 Poverty

11 Toward a longitudinal definition of poverty Deprivation is a, damaging lack of material benefits considered to be basic necessities in a society (my italics) (OED). Strong claim: poverty is the experience of deprivation Weaker claim: deprivation is a really important functioning and indicator of poverty

12 Deprivation in time Deprivation is having so little that this results in having less in the long run (i.e. damaging ) This implies a specific autoregressive pattern in well-being In ordinary English: the trajectories of the poor should look different from the trajectories of the non-poor They should be muted

13 Static vs. dynamic approaches We can think of deprivation either in static or dynamic terms: Static: if you are at the bottom, you are less likely to move up Dynamic: if you move down, you are less likely to move up E.g. the differential life impacts of a speeding ticket

14 In favour of dynamics The dynamic approach is prefered for several reasons: a. It implies a within person approach b. It controls for people s positions in the distribution c. It leads to an overall summary measure of dynamics that can be decomposed later d. It allows for randomness sometimes people just get lucky

15 A model with deprivation This is what this kind of process will look like mathematically: where w_it is well-being, k_it capital, and X_it, controls z_it is the absolute value of the last change in ln w_it if it was negative Theory posits γ < 0 υ_it is an error term structured by a constant and a person fixed effect

16 This is what this looks like The average of the green line is 10 The average of the other lines lie below this

17 A reparameterization The ECM version of the above ADL model is more intuitive: When γ(ln w_it + δ)z_it + κk_it-2 < 0, low levels of well-being penalize overall levels of well-being in the long run When γ(ln w_it + δ)z_it + κk_it-2 > 0, this damage is compensated by capital

18 Measuring Deprivation

19 Well-being is difficult to observe Observing well-being is difficult The best we can do at present is identify variables that are a. correlated with wellbeing b. variable from one period to the next c. consistently well measured Unfortunately, options are slim

20 The case for earnings Earnings change from year to year and can go up and down Earnings are an important indicator of status and achievement in modern societies All else being equal, people tend to prefer greater earnings Autoregressive patterns in earnings are interesting for other reasons, specifically, as they relate to productivity generally Welfare states strive to maximize earnings

21 Confirming deprivation effects with earnings We essentially substitute earnings, y_it, for well-being and estimate the following model: We include Mincer controls and year in X_it, and leave out k_it for now

22 Dynamic panel data estimator Dynamic panel data methods are designed to estimate models with endogenous regressors in small T, large N situations (Bond 2002) Use the first difference instead of within estimator Use lags of transformed endogenous variable as instruments Fit with Generalized Method of Moments (GMM) Readily implemented in Stata with xtabond2 (Roodman 2015)

23 Table 1. Dynamic panel model of ln-earnings on past changes in earnings and Mincer regressors, 6 year trajectories in SLID

24 Table 2. Dynamic panel model of ln-earnings on past changes in earnings and Mincer regressors, 6 year trajectories in PSID

25 Deprivation in relative and absolute terms Note that after a loss: Deprivation effects are constant in relative terms But, in absolute terms, inflated at the bottom of the income distribution

26 This is what this looks like The larger the shock, effect sizes are greater and felt higher in earnings All converge back to the intercept: δ

27 Indicating Poverty

28 Including material capital The above models included controls for human capital, in the next we include controls for material I operationalize material capital as relative household adjusted after-tax total income (RHAATI) RHAATI is the dimension in which we ordinarily define poverty thresholds

29 Transforming RHAATI Taking the square-root reduces skew Taking the log does not work

30 How much capital is needed? We essentially substitute RHAATI, r_it, for capital above: Theory posits κ > 0 Note the subscript on κ: this only applies to RHAATI offset

31 Table 3. Dynamic panel model of ln-earnings on past changes in earnings, Mincer regressors, and RHAATI, 6 year trajectories in PSID

32 Minimizing the deprivation function Suppose that a person should be able to cope with a typical loss ~$4,500 is the median loss in PSID (this is actually roughly the same for rich and poor) ~$10,000 is the mean loss in PSID (and also the same over income) Taking the median and minimizing the deprivation function, γ(ln w_it + δ)z_it, results in a maximum long-run penalty of

33 Income poverty Now that we know the extent of the potential impact of losses over time, how much capital is needed to offset? Divide the maximum long-run penalty by the RHAATI effect size of 0.36: Then square to undo the root transform...

34 = 37% of the median* *Not far off acute poverty threshold in van den Berg et al. (2017)

35 = 51% when mean is used* *Believe me, I am as surprised as you are

36 Other kinds of poverty The method implies opportunities to explore poverty in other dimensions of capital, including human In rich countries like Canada and the US, most people are not poor in education Obviously, people that are poor in multiple dimensions are more hard done by than those only poor in a few

37 Conclusion

38 Falsifiability We re not in the habit of thinking about falsifiability in the social sciences nonetheless: Hypothesis 1: Deprivation effects occur in other dimensions correlated with well-being (like health) Hypothesis 2: The same model and methods applied in other dimensions will define similar thresholds in income

39 Verifiability Deprivation effects prevalent in both Canadian and US survey data They should also arise in new administrative data sources like the LAD Deprivation effects should not be the same for all populations: They should be more pronounced in the US Also, among women and more vulnerable populations generally

40 Further avenues and applications 1. I have not yet to used this information to assess extant poverty thresholds 2. Earnings and RHAATI are not independent of one another, how does this affect things? 3. Measuring deprivation effects provides us with an opportunity to asses the cost benefit of transfers designed to mitigate them 4. Income poverty is only one of many poverty s that can be explored

41 Thank you

42 References Bond, Stephen R Dynamic Panel Data Models: A Guide to Micro Data Methods and Practice. Portuguese Economic Journal 1(2): Duclos, Jean-Yves Poverty and Equity: Theory and Estimation. Département d économique and CRÉFA. Mani, Anandi, Sendhil Mullainathan, Eldar Shafir, and Jiaying Zhao Poverty Impedes Cognitive Function. Science 341(6149): Ravallion, Martin On Multidimensional Indices of Poverty. Journal of Economic Inequality 9(2): Roodman, David xtabond2: Stata Module to Extend Xtabond Dynamic Panel Data Estimator. Boston College Department of Economics. Sen, Amartya Development as Freedom. Reprint. Anchor. van den Berg, Axel, Charles Plante, Hicham Raïq, Christine Proulx, and Samuel Faustmann Combatting Poverty: Quebec s Pursuit of a Distinctive Welfare State. Toronto: University of Toronto Press.