Factors influencing the comparability of poverty estimates across household surveys and censuses Derek Yu Department of Economics

Income and expenditure data in RSA surveys and censuses (60-second) Review of poverty concepts (60-second) Literature review on poverty trends Critical factors (we tend to ignore) affecting the reliability of poverty estimates and trends Poverty trends before / after addressing these issues Brief discussion, due to time constraint, sorry… Outline

Survey data in South Africa (until 2012) Survey data Statistics South Africa Census 1996 Census 2001 CS 2007 Census 2012 IES 1995, 2000, 2005/2006, 2010/2011 OHS LFS QLFS GHS SALDRU of UCT PSLSD 1993 NIDS 2008, 2010, 2012 SAARF AMPS

4 Survey data in South Africa (until 2012) Census 1996, Census 2001, CS 2007, Census 2011 –Income collected in intervals/bands/categories –Recall method

5 Survey data in South Africa (until 2012) IES 1995, 2000, 2005/2006, 2010/2011 –Income and expenditure collected in exact amounts –Detailed questions asked on each income and expenditure item, before the aggregate household income and expenditure were derived –Recall method in IES 1995 and IES 2000 –Recall method and diary method in IES 2005/2006 & 2010/2011 Income: Recall only Durable good expenditure: Recall + Diary Semi-durable good expenditure: Recall + Diary Non-durable good expenditure: Diary only

6 Survey data in South Africa (until 2012) OHS , LFS , QLFS –Expenditure collected in 4 OHSs and 4 LFSs –Income question only asked in OHS 1999 –Reporting the relevant intervals –Recall method

7 Survey data in South Africa (until 2012) GHS –Expenditure collected in all GHSs –Same intervals as in the OHSs/LFSs, except the slight improvement in GHS –Reporting the relevant intervals –Recall method

8 Survey data in South Africa (until 2012) Project for Statistics on Living Standards and Development (PSLSD 1993) –Conducted by SALDRU (UCT) –Aggregation method (just like IESs) –Exact amounts –Recall method

9 Survey data in South Africa (until 2012) National Income Dynamics Study (NIDS) –Conducted by SALDRU (UCT) –Panel data –Aggregation method (just like IESs) –Exact amounts –Recall method

10 Survey data in South Africa (until 2012) All Media Products Survey (AMPS ) –Conducted by South African Advertising Research Foundation (SAARF) –Income collected in intervals/bands/categories –Recall method

11 Poverty concepts Poverty dimensions: –Income / Expenditure poverty: by using an income or expenditure variable and an appropriate poverty line, if someone’s income level is at or below the poverty line, he/she is defined as poor –Health and education: poor health and level of education –Vulnerability: risks such as violence, crime, natural disasters, etc. –Voicelessness and powerlessness: lack of access to physical assets (e.g., household equipments and facilities, dwelling type, etc.), links to networks (e.g., family-based networks, occupation-based groups of mutual help, savings and credit groups, etc.), and access to credit markets.

12 Poverty concepts Money-metric approach to measure poverty: –Using per capita variable, converted to real prices (e.g., 2013 prices) –Foster-Greer-Thorbecke (FGT) approach Poverty headcount ratio Poverty gap ratio Squared poverty gap ratio –Poverty line in 2013 prices, per capita per month – proposed by Woolard and Leibbrandt (2006): R665: expenditure on food items and essential non-food items for survival…

13 Poverty concepts Per capita income Poverty line (R665) Poverty headcount ratio = 3/5 = 0.6 or 60% Main sources of poverty reduction: Increase in earnings of employed Increase in number of employed Expansion of social grants (discouraged...) R1 100 R850 R550 R400 R250

14 (60-second) literature review Differences amongst the recent studies: –Poverty lines used differed in the studies, but the poverty line proposed by Woolard & Leibbrandt (R665) were used more often in recent studies General conclusion from these studies: –Poverty: headcount ratio increased moderately between 1994 and 2000, before a strong downward trend took place since 2000

15 Critical issues (we ignore) behind the figures and trends

16 #1: Income vs. Consumption / Expenditure Poor / Developing countries: –Income is harder to measure as much of it comes from informal, self-employment & agricultural activities –Expenditure is more straightforward and easier to estimate The matter is not that simple (after reading those thick literature…)

17 #1: Income vs. Consumption / Expenditure

18 Issue #2: Recall method vs. Diary method Recall method: Pros –A relatively straightforward approach Recall method: Cons –Recall bias –Telescoping

19 Issue #2: Recall method vs. Diary method Recall bias: –Respondents no longer remember the expenditure correctly/clearly, if reference period is too long. –Example #1: How much did you spend on airtime in yesterday? –Example #2: How much did you spend on airtime in the last month? –Example #3: How much did you spend on airtime in the last 12 months?

20 Issue #2: Recall method vs. Diary method Telescoping: –Respondents tend to include consumption events that took place before the beginning of the recall period –Example: Person A took part in IES 2010 on 31/12/2010, and claimed he spent R1000 to purchase a new dog in the 12 months (i.e., 1/1/2010 – 31/12/2010), but… Hi, my name is Vuvuzela! I was purchased at a price of R1 000 by my owner 13 months ago, on 30/11/2009.

21 Issue #2: Recall method vs. Diary method Diary method: Pros –Reduce the likelihood of recall bias and telescoping (hopefully…) –Feel more comfortable to record information without the presence of interviewers Example: I never remember how many cigarettes I took in the last month (recall method), but I remember I took 20 cigarettes today, so my cigarette spending is recorded as R15.95 on the diary.

22 Issue #2: Recall method vs. Diary method Diary method: Cons –Less appropriate where literacy levels are low –First-day effect (fatigue and/or loss of interest) –Time + budget constraints to process the data

23 Issue #2: Recall method vs. Diary method

24 Issue #3: Actual amount vs. Intervals/Categories Actual amount: –Too sensitive? –Respondents really don’t remember the answer? Declaring the relevant interval/category: –Intervals need to be adjusted if applied continuously, due to the impact of inflation as survey year progresses. –Something needs to be done to make the data continuous (e.g., midpoint method, etc.)

25 Issue #3: Actual amount vs. Intervals/Categories

26 Issue #4: Actual amount: ‘One-shot’ overall amount or aggregation of amounts from sub-items ‘One-shot’ amount / Single estimate: –Suitable for people who don’t want to spend too much time participating in the survey –Does respondent really know what income/expenditure items to be included in this ‘one-shot’ amount? Aggregation of amounts from sub-items –More precise method –Time-consuming and costly –Interviewer and interviewee fatigue

27 Issue #5: How to make the interval dataset continuous? Midpoint method –Midpoint method can be easily applied to derive the income of almost all intervals (e.g., midpoint is R1 000 for the “R500 – R1 500” interval) –The income of the open interval is 1.1 × lower bound of this interval, e.g., in the AMPS 2000 “R ” interval, the mean is R22 000

28 Issue #6: Number of intervals and width of each interval Number of intervals are as few as 8 in GHS, but 32 in AMPS Width of an interval is as narrow as R100 in AMPS, but R in Census 2001 How would the number and width of intervals affect poverty and inequality estimates: –There are virtually no studies done in South Africa (unless I miss something) –International study: Seiver (1979) found that fewer, wider brackets result in over-estimation of inequality measures

29 Issue #6: Number of intervals and width of each interval Figure 4: Poverty headcount ratios in 2007 in selected surveys

30 Issue #7: Households with zero or unspecified household income / expenditure % of households with zero income: 13.0% in Census 1996, 21.0% in Census 2001, 8.2% in CS 2007, 15.1% in Census 2011 –Leaving the zero-income households unadjusted would seriously over-estimate poverty, especially if these households clearly contain employed members who earn non-zero income % of households with unspecified income: 11.5% in 1996, 16.4% in 2001, 11.1% in 2007, 0.1% in 2011 –Ignoring these households would under- or over-estimate poverty, depending on whether they are rich or poor households

31 Issue #7: Households with zero or unspecified household income / expenditure Commonly used methods to deal with zero or unspecified data: –Simply exclude them from analysis… –Single imputation –Multiple imputation (e.g., Sequential regression multiple imputation (SRMI)) – Econometrics involved. Cannot discuss this issue further due to time constraint, sorry…

32 Poverty levels and trends (Poverty line: R665 per capita per month, 2013 prices)