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Not Separate, Not Equal: Poverty and Inequality in post-Apartheid South Africa. Berk Özler January, 2007. Introduction. Introduction. Introduction. These figures are interesting and powerful for South Africa, however…

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Not separate not equal poverty and inequality in post apartheid south africa l.jpg

Not Separate, Not Equal:Poverty and Inequality in post-Apartheid South Africa

Berk Özler

January, 2007


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Introduction


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Introduction


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Introduction

  • These figures are interesting and powerful for South Africa, however…

  • If both your data and your analysis are not credible, your results may never see the light of day.

  • If they do, they may be assailed and shoved aside by those who have an incentive to do so…


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Data: Can we afford to let “perfect” be the enemy of “good”?

  • Surveys used for our analysis of the South African expenditure distribution suffer from various problems:

    • Sampling frames biased and outdated

    • Expenditure, not consumption surveys (with implications for the consumption aggregate)

    • No quantity information (i.e. no unit values) and no community price surveys

    • No rural price data

    • Low quality data on home production of food…


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Analysis: Blunders at the Stats Office

  • A rapid descriptive analysis of the results disseminated too quickly to the national media caused immediate concerns with the data.

  • On the other hand, the primary data were not made available to the public for a long time, preventing the replication or debunking of the results.

  • Multiple revisions to the data (especially to sampling weights) only increased data concerns.


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How to do poverty work that receives wide-spread acceptance?

  • Under ideal circumstances, the data collection effort should be planned very carefully.

    • Sampling frame issues

    • Panel vs. cross-sectional data collection

    • Comparability over time vs. improvements in methodology

    • Diary vs. recall

    • Etc.


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How to do poverty work that receives wide-spread acceptance?

  • Under less than ideal circumstances, do every sensitivity analysis possible under the sky, exploit every data source available, and anticipate all the criticism ahead of time and prepare.

  • The work on South Africa is an example of this latter approach.


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Basic data work

  • Carefully document all of your adjustments to the data:

    • Cleaning

    • Merges

    • Trimming

  • Carefully examine survey design to understand sampling weights, stratification, clustering, etc.


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Building a consumption aggregate

  • Decide what will be included in your consumption aggregate, which will be your welfare indicator:

    • What should be included, what can be included?

    • What is the comparability of survey design over time

  • Make these decisions based on careful arguments that are based in theory and empirics

  • If the choice ends up being somewhat subjective (which will, to a certain extent, always be the case), then test the robustness of your results later to your inclusion/exclusion of certain items.


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South Africa’s consumption aggregate

  • The consumption aggregate includes the following expenditure categories:

    • food, beverages, and cigarettes (excluding home-grown foods);

    • housing (imputed rental value of residence and utilities);

    • compensation for domestic workers; personal care, household services, and other household consumer goods;

    • fuel (excluding firewood and dung);

    • clothing and footwear; transport (excluding cost of purchased vehicles);

    • Communication, education, reading matter, cost of licenses and other rental charges, and cost of insurance.


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South Africa’s consumption aggregate

  • Important categories of expenditures we have excluded from the consumption aggregate are:

    • water; firewood and dung;

    • health;

    • imputed value of household durables;

    • food consumption from home production;

    • lobola/dowry, funerals, religious or traditional ceremonies, gambling;

    • lumpy expenditures, such as furniture, appliances, vehicles, sound and video equipment, etc.


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Poverty Lines

  • For South Africa, we chose to draw normative poverty lines for our analysis, using the “cost-of-basic-needs” method.

  • This method stipulates a consumption bundle deemed to be adequate for basic consumption needs, and then estimates its cost for each province (Ravallion, 2001).

  • The basic needs bundle is typically anchored to food-energy requirements consistent with common diets in the specific context.


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Poverty Lines

  • The food basket we have selected consists of the mean per capita quantities of each food item consumed by the third quintile of the (nominal) expenditure distribution in 2000.

  • Using the nutritional value information for each food item obtained from the Medical Research Council (MRC) in South Africa, we calculated that this bundle would provide the average household with roughly 1927 kilocalories per capita per day.


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Prices

  • To calculate quantities from expenditure data (which was not asked in our surveys), we need price data.

  • Nor did our surveys collect information on prices of various food items from the markets in the sampled communities.

  • However, STATS SA has been collecting monthly price data for practically all the items in the food module of the IES surveys from metropolitan and urban areas of the nine South African provinces.


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Prices

  • The Laspeyres Food Price Index was calculated using these prices from January 2001.

  • To derive an overall price index, we had to also derive a price index for non-food and housing (Lanjouw et al., 1996)

  • We derived a housing price index by predicting the “rental value” of a house in an urban area that has 4 rooms, brick walls, a flush toilet, and access to electricity and street lighting in each province.

  • Finally, we took a weighted average of the food and the housing index to estimate our non-food (non-housing) price index.


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Food Poverty Line

  • The “average” representative bundle of the third quintile…

    • costs 180 Rand in 2000 prices, and

    • provides 1927 kilocalories per person.

  • Using recommended average energy allowances, we calculated that the consumption in kilocalories recommended for an average South African household per capita is 2261.

  • Linearly adjusting the 180 Rand figure by 2261/1927, we arrived at a food poverty line of 211 Rand – the amount necessary to purchase enough food to meet the basic daily food-energy requirements.


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Upper- and Lower-Bound Poverty Lines

  • To derive the overall poverty line, we set a lower bound and an upper bound for cost-of-basic-needs poverty lines in South Africa, following Ravallion (1994).

  • We calculate the mean non-food expenditure of those households whose total consumption expenditures lie in small, but increasing intervals around the food poverty line.

  • The simple average of these mean non-food expenditures plus the food poverty line yields a lower bound poverty line of 322 Rand.

  • The basic idea here is that if a household’s total expenditure is equal to the food poverty line, then any non-food expenditure for that household must be absolutely necessary as the household is giving up basic food needs for those non-food consumption goods.


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Upper- and Lower-Bound Poverty Lines

  • Using the same technique, but this time calculating the mean total expenditure of households whose food consumption expenditures are equal to the food poverty line, we derive an upper bound poverty line of 593 Rand.

  • If the basic needs norms that are anchored to food-energy requirements of South African households are deemed reasonable, then the poverty line for South Africa must lie between 322 and 593 Rand in 2000 prices.

  • We also briefly discuss results using two more poverty lines: 87 Rand and 174 Rand per capita per month. These are equivalent to the commonly used international poverty lines of $1/day and $2/day, adjusted for purchasing power parity. The $2/day poverty line is close to that used by Deaton (1997), and the food poverty line of 211 Rand described above. In this sense, the $2/day poverty line can be thought of as an extreme poverty line.


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Poverty Lines: Will people buy into them?

  • First, the poverty lines should be internally consistent.

  • Second, it helps if the poverty lines are consistent with popular benchmarks (for example in South Africa, earning 1000 Rands per month)

  • Third, it does not help if your poverty line defines everyone (or very few) as poor.

  • Finally, in any case, all of your distributional analysis should include stochastic dominance analysis (as in the figures shown above).

  • For analysis over time, simply inflate/deflate your poverty line using your inter-temporal price indices (do not reconstruct it!)


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Potential Areas of Concern

  • Sampling weights due to problems with sampling frame

  • Lack the necessary information to impute a comparable value for consumption of home-grown products

  • The lack of rural price data

  • Excluded items from the consumption aggregate


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Sensitivity Analysis: Sampling Frame

  • Following the end of apartheid, internal migration and emigration might have led to rapid shifts in demographic composition in South Africa, possibly making the 1996 Population Census – the sampling frame for the 2000 IES – somewhat outdated.

  • Comparing respective population shares of racial groups in the IES with those from the recent 2001 Census (STATS SA, 2003) confirms that this may be true to some extent.


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Sensitivity Analysis: Sampling Frame


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Sensitivity Analysis: Rural/Urban Price Differentials

  • Go to another data source to get an idea of rural/urban cost-of-living differences.

  • Go outside the country to get an idea regarding whether urban/rural price differentials are increasing or decreasing.

  • Go to other parts of your data to see where people purchase their products and whether they purchase bulk.

  • Finally, if you don’t have data, devise scenarios that would overturn your main conclusions. Then, discuss whether those scenarios are realistic. If not, you are safe.


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Sensitivity Analysis: Rural/Urban Price Differentials


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What Do Other Data Sources Tell Us?

  • The results were challenged by the government and other researchers.

  • Data from the Quarterly Bulletin Time Series of the South African Reserve Bank shows that the total ‘Final consumption expenditure by households’ grew by approximately 3% per year between 1995 and 2000.

  • Census figures from Statistics South Africa put the annual population growth rate in South Africa at exactly 2%, i.e. the per capita growth rate at approximately 1%.

  • In comparison, using household survey data to carefully construct comparable expenditure aggregates in this study, we find that the annual per capita growth rate of per capita household expenditures is 0.5%.


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What Do Other Data Sources Tell Us?


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“What would we expect given what is known about this period in South Africa?”

  • Low growth rate (National Accounts Data)

  • Rising unemployment (Klasen & Woolard, 2000)

  • Job losses for lower-paid, less-skilled persons (Whiteford and van Seventer, 2000)

  • An increase in poverty “…is not unexpected, given the large increase in the number of unemployed.” (Meth & Diaz, 2003).


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Final Lessons?

  • Plan ahead

    • Do not skimp on questionnaire development, training and pre-testing.

    • Put great care into documentation of survey design

  • Don’t do it yourself – it is a lot of work!

  • Cooperate with (and be nice to) all the people you might need data from.


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Final Lessons?

  • Be honest

    • Admit the possibility that results could be different under certain assumptions

  • Be open

    • Make sure the data are available and your results are replicable (make your codes available to others)

  • Be thorough, leave no holes in your work

  • Hire someone to independently review your work

  • Have written agreements regarding data release, release of reports, etc.

  • Be patient, don’t lose your cool, but also be determined and do not back down.


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