Insights into Poverty and Inequality Pali Lehohla Context and Reality Pali Lehohla Statistics South Africa

Beyond cyclical events such as economic boom and busts and matters of weather, The matters of demography and poverty have generational effects

Age structure based on CS 2016 Source: Community Survey 2016

Age structure based on CS 2016 The life circumstances of first demographic wave have not achieved full potential High Unemployment/Poor Educational outcomes Need to invest in second demographic wave to achieve outcomes not seen in their parents generation First demographic wave: Children of 1996 Second demographic wave Source: Community Survey 2016

Population pyramid, South Africa, Source: Stats SA and calculations Male There was a narrowing of the population pyramid base, and the working-age ratio increased from 55% in 1960 to 65% in 2010 Female

Demographic Transition: % Working Age Population South Africa along with East Asian and Latin American Countries experienced a demographic transition but Economic Growth varied Percentage * Population aged 15–64 as a percentage of the total population

Real GDP per capita, average annual growth (%), 1961–2015 Growth Rates for Selected Countries A demographic transition on its own does may not result in extended periods of high growth South Africa’s per capita growth rate from 1961 to 2015 averaged just 1%, which was well below the East Asian growth rates and below five of the six Latin American growth rates. Source: World Bank and calculations

Economic Growth 1995-2017 Recent performance in South Africa’s economic growth stands in sharp contrast to the target indicated in the National Development Plan (NDP). South Africa’s historical GDP growth performance suggests that its demographic transition has very little, if anything, to show for itself in terms of producing a demographic dividend.

Portrait of the Growth Accounting Framework Income Per Capita Labour Productivity Total Factor Productivity Capital Deepening Economies of Scale Absorption Rate Participation Rate Employment Rate Job Creation Terms of Trade Gainfully Employed Working Poor Knowledge Generating Employees Knowledge Processing Employees Young Dependency Ratio Demographic Dividend Elderly Dependency Ratio Demographic Dividend

The outcomes at higher levels of education, show great disparity both by population group and age

2016: Proportion of Matric Graduates who attain a bachelor degree 1975 1980 1985 1990 1995 2000 2005 2010 2015 16 Source: Community Survey 2016

Absolute vs Proportions Source: Adapted from HE Broekhuizen (Hemis Aggregate Figures) Excludes undergraduate diplomas and certificates Source: Adapted from HE Broekhuizen (Hemis Aggregate Figures ) with addition of population estimates for time points Excludes undergraduate diplomas and certificates

The disparities are played out within the employment realm

Not economically active* The labour market Q2:2017 37,2 million People of working age in South Africa (15 – 64 year olds) Labour force Not economically active* 22,3 million 14,9 million Employed Unemployed discouraged work seekers M M M M Employed Unemployed ILO hierarchy – Employed first then unemployed and the remainder is NEA (including discouraged job-seekers). 3 mutually exclusive groups. Cannot be in two groups at the same time

Unemployment Rate by Population Group Significant variation in Unemployment by Population Group White Indian/Asian Coloured Black African Source QLFS Q2:2017

Unemployment Rates: TVET vs University For older Age Groups there is a less than 5% point difference between TVET and University Graduates 20% Point Difference 11,7 % Point Difference *University includes those whose post-school qualification was obtained from University, Technikon or College. Source QLFS Q2:2017

Skilled employment as a percentage of total employment within each population group, by age group (South Africa) Amongst the youth, i.e. age groups 15–24 and 25–34, there were increases in skilled employment (as a proportion of corresponding total employment) between 1994 and 2016 in all population groups except black African

Education and Unemployment continue to drive Multidimensional Poverty

Multidimensional Poverty by Municipalities 2001-2016 Msinga Headcount 24,5% Msinga Headcount 37,2% Msinga Headcount 59,8% Intsika Yethu Headcount 27,7% In 2001 wide dispersion of Poverty with Msinga having a poverty Headcount of around 60% Between 2001 and 2011 poverty generally declines for all municipalities However between 2011 and 2016 poverty trends diverge between municipalities

Poverty Drivers 40 52 33 10 Years 5 Years CS 2016

Main contributors to poverty amongst Youth (15-24) The major contributor to the poverty situation of the youth in South Africa is educational attainment. Source CS 2016

The effects of the economic and labour situation is not felt equally Investigating inequality and poverty

Based on the results of the 2011 Census, South Africans were shocked at the distribution of income

Census 2011

Census 2011

Census 2011

Census 2011

No Income by Level of Education and age Source: Census 2011

What does this picture look like today?

Average Expenditure Average Income Average annual household consumption expenditure and income by population group of household head White-headed households (R350 937) spent five times more than black African-headed households (R67 828) and three times more than the national average White Indian Coloured Black African Average Expenditure Average Income Source LCS 2014/15

Inequality is also found within Population Groups

Average annual household consumption expenditure by population group of household head Median vs Mean R124 445 Coloured Mean Median R65975 R 350 937 White Median Mean R256 159 Mean R67 828 Black African Median R36 501 Mean R195 336 Indian Median R122 476 Rands ‘000

Highest Fourth Middle Second Lowest Percentage distribution of households by expenditure per capita quintiles and population group of the household head Highest Fourth Middle Second Lowest Almost half of black African-headed households (46,58%) fell within the lowest two expenditure quintiles combined Source LCS 2014/15

Share of bottom 40% of households income = 8,34% Household Income, LCS 2015 100% % Share of bottom 40% of households income = 8,34% households Income 40% Source LCS 2014/15

Significant differences exist in proportions of certain categories between the lowest and highest expenditure deciles Expenditure Categories Decile 10 Decile 1 Source LCS 2014/15

Proportion of total annual household consumption expenditure on food, beverages & tobacco by selected population group of household head Average for All Population Groups 13,75% (R14 202) Black African 17,9% White 7,1% (R12 163) R24 998 Black African-headed households had the highest proportion (17,93%) across all population groups, while white-headed households spent the most in monetary terms (R 24 998)

The money metric poverty headcounts, provide an overview of the current poverty landscape

R992 R647 R441 Upper-Bound Poverty Line Lower-Bound Poverty Line Threshold of relative deprivation below which people cannot afford the minimum desired lifestyle by most South Africans R992 Lower-Bound Poverty Line R647 Austere threshold below which one has to choose between food and important non-food items Food Poverty Line Threshold of absolute deprivation. The amount of money required to purchase the minimum required daily energy intake R441 * Based on 2015 prices

Poverty headcounts in 2015 In 2015, more than a quarter of the population were living below the food poverty line Upper-Bound Poverty Line Lower-Bound Poverty Line Food Poverty Line Source: Stats SA Poverty Trends in South Africa

Poverty headcounts based on the FPL, LBPL and UBPL Upper-Bound Poverty Line Lower-Bound Poverty Line Food Poverty Line Approximately 13,8 million South Africans were living below the FPL in 2015, down from a peak of 16,7 million in 2009. Source: Stats SA Poverty Trends in South Africa

Individual poverty by level of education (UBPL) An individual’s educational level is closely related to poverty Percentage No Education Some Primary Primary Some Secondary 79,2% of individuals with no formal education were poor compared to only 8,4% of individuals who had a post-matric qualification in 2015 Matric Higher Education For Periods 2006 / 2009 / 2011 / 2015 Data applies to persons age 18+ Source: Stats SA Poverty Trends in South Africa

“ significant progress is possible and is within our reach as we gain better handle on planning through the planning tools.  Minister in the Presidency: Planning, Monitoring and Evaluation, Mr Jeffrey Thamsanqa Radebe

Level 3: Provision of Advanced Analytical Intelligence System-wide infusion and intelligence Description - Diagnosis - Prediction - Prescription - Adaptation Linked National-Provincial Macroeconomic Model Modelling Level 3: Provision of Advanced Analytical Intelligence Multi-sector Macroeconomic Model Linked Macro-Education Model Public Employment Model Poverty-Inequality Model Input-Output Table Supply & Use Table Social Accounting Matrix (SAM) Growth Accounting Framework (GAF) Level 2: Provision of Basic Analytical Intelligence STATS SA'S PROVISION OF DATA AND ANALYTICAL INTELLIGENCE Financial statistics Labour force Trade Statistics Employment and Earnings Census Household Survey Price data Community Survey Service delivery Level 1:Provision of Raw Data Intelligence to plan we need 5 capabilities in our data systems, namely descriptive, diagnostic, predictive, prescriptive and adaptive capability.