1. How sensitive are estimates of the marginal propensity to consume to measurement error in survey data in South Africa Reza C. Daniels UCT reza.daniels@uct.ac.za Vimal Ranchhod UCT vimal.ranchhod@gmail.com

2. Outline Context Question Econometric problem Proposed solution Data Results Caveats Conclusion

3. Context Best micro level data in SA for incomes and expenditures comes from StatsSA income and expenditure surveys (ies 95, 00 and 05) From here, we can estimate the marginal propensity to consume (MPC), i.e. what proportion of every rand of disposable income do households spend. This can be broken up into various categories of expenditure. The marginal propensity to save (MPS) is defined as 1 – MPC, with corresponding definition.

4. Context (2) A common problem in survey data is measurement error in responses. i.e. The data captured might not truly reflect the financial reality being measured. In the `classical measurement error’ case, this leads to attenuation bias. Estimated relationships are weaker than the true relationships. For other types of measurement error, even being able to sign this bias may not be possible.

5. Question How sensitive are estimates of the MPC to measurement error (m.e.) in the IES data. – We propose to estimate this sensitivity using an instrumental variables approach, with wage data from the same households, but from a different survey, namely the LFS 2000:2, as our candidate instrument.

6. Econometric Problem Suppose the “true” relationship is: – Y i = B 0 + B 1 X i * + u i, where: Y is the outcome variable of interest, X * is the ‘true’ value of the dependent variable, And u is a mean zero error term. The subscript i refers to person i, where i=1, …, n – It can be shown that, asymptotically, the OLS estimator of B1 obtained from a regression of Y on X * will be consistent if and only if: Cov(X *, u) = 0

7. OLS estimator with measurement error Suppose that we observe X instead of X*, – Where X = X* + e, E[e]=0, cov(X *, e) = 0 and cov(u, e)=0 By regressing Y on X, we can show that the probability limit of our estimate of B 1 from an OLS regression = B 1 + cov(X, u-B 1 e)/Var(X) = B 1 – B 1 (Var(e)/[Var(X * ) + Var(e)]) This is known as attenuation bias. – In essence, regardless of the type of m.e. we are considering, the crucial question to ask is whether or not the covariance between the observed X and the composite error term is zero.

8. M.E: An IV solution Suppose we had another variable, Z, which is: – Correlated with our observed X, and – Uncorrelated with the composite error term, v=(u-B 1 e) Then Z would provide a valid instrument for the endogenous regressor X. In particular, another noisy measure of X *, eg. Z=X * + k would suffice, if k is uncorrelated with both u and e.

9. Asymptotically plimB hat1,IV = B 1 + (corr(Z,v)/corr(Z,X 1 ))*(var(v)/var(X 1 )) 0.5 (Recall that v=(u-B 1 e)) Now, var(v) ≠0, var(X 1 )≠0 and corr(Z,X 1 )≠0, therefore the IV works iff corr(Z,v)=0. i.e corr(X 1 * + k, u-B 1 e)=0, So the crucial requirement is that k and e are uncorrelated.

10. Implementing the solution We match data on individuals from the IES 2000 and LFS 2000:2. The data were obtained in October and September respectively. IES contains more detailed information on multiple sources of income and categories of expenditure, expenditure at HH level. LFS contains information on employment status and wage income.

11. Summarizing the Data Table 1: Summary statistics of sample Variable # of individuals103214 # of HHs25964 Mean HH size3.96 % of sampleMean HH Yd p.a. Mean HH Exp p.a. Ratio of exp/Yd African79.421006209330.997 Coloured10.435837355330.992 Indian2.075232690100.917 White8.11249991299381.040 Total32058322441.006 Male47.540198402771.002 Female52.519590199371.018 Notes: 1. Means do not include sampling weights. 2. Means are obtained by race or gender of HH head.

12. Mean Expenditure by Category CategoryMean ExpenditureAs % of Yd alcohol & cigs634.02.0 beverages533.31.7 clothes1384.64.3 durable goods1097.93.4 education980.83.1 food6706.120.9 health2241.77.0 housing4792.614.9 insurance3578.611.2 other non durable955.53.0 own production679.32.1 recreation47.90.1 hh services1002.13.1 transport2944.79.2 utilities1025.83.2 TOTAL EXPENDITURE32300.1100.8 Total Disposable Income32058 Notes: 1. Proportion in sub-category do not necessarily add to 100, due to omitted categories such as debt servicing.

13. OLS and IV coefficients OLSIV COEFFICIENTyds.eyds.e alcohol & cigs0.00362***-0.000140.00235***-0.00072 beverages0.00321***-6.5E-050.00499***-0.00035 clothes0.00978***-0.000170.00998***-0.00091 durable goods0.0142***-0.000280.0184***-0.0014 education0.0153***-0.000290.0208***-0.0014 food0.0355***-0.000450.0461***-0.003 health0.0274***-0.000350.0378***-0.0018 housing0.0747***-0.00160.0688***-0.0077 insurance0.217***-0.00190.242***-0.0094 other non durable0.0828***-0.00110.00574-0.0057 own production0.315***-0.00330.325***-0.016 recreation0.00214***-0.000110.00244***-0.00054 hh services0.0317***-0.000370.0513***-0.0019 transport0.0639***-0.000820.105***-0.0042 utilities0.0119***-0.000180.0124***-0.00092 TOTAL0.981***-0.00541.049***-0.026

14. Caveats Sensitive to imputation method of wage data from categories. Conceptual difficulties on how to treat debt financing and dissaving or borrowing. IVs are fairly weak, many HH’s get income from grants. If m.e. correlated with income levels, eg. Rich HHs always understate, then its not clear that the solution is valid. Non-response also not accounted for.

15. Conclusion Promising avenue of investigation IV’s do have significant first stages Lots more to be done … – Do by income quintile, – And by age of HH head, or some form of HH composition.