The shapes and shades of poverty Pali Lehohla Statistics South Africa

South Africa Made Great Strides In A Number Of Areas Impacting The Lives Of Its People

However studies on satisfaction with social provision show disparities

Outright Satisfaction with services provided Affordable Housing ranks lowest amongst all MIIF categories High Satisfaction with Electricity services almost universal B3 and B4 Municipality have particular concerns with Quality of water provision Percentage Outright Satisfied Source KZN:CSS

SA legacy of Apartheid Spatial Planning still impacts the development of our communities

Youth Multidimensional Poverty Index with former homeland boundaries, by municipality, 2011 Source: Measuring multidimensional poverty among youth in South Africa at the sub-national level, SALDRU Working paper

Investigating the promise of a demographic dividend to accelerate economic growth

The demographic dividend is the accelerated economic growth that may result from a decline in a country's mortality and fertility …..and the subsequent change in the age structure of the population. decline

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

reflections on current economic realities

Current state vs NDP target: Economic growth Source: Gross domestic product (GDP), Q4 2016 Gross domestic product (GDP), Q4 2016

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

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

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

Can we address economic imbalance, unemployment and inequality without the right skills?

25% Source CS 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 challenges facing SA are also seen in the drivers of poverty

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

What are the key perceived municipal challenges as seen by households?

Perceived municipal challenges Cost of electricity Access to reliable and safe water Lack of/ Inadequate employment opportunities Rank #2 Rank #1 Rank #3 Source: Community Survey 2016 Rank #15 Lack of/ Inadequate educational facilities

Diversified Dynamic Economy NDP Aims for a Diversified Dynamic Economy

Planning Tools such as the Growth Accounting Framework (GAF) and Social Accounting Matrix (SAM) assist in understanding the structure of the economy

The GAF is used to measure the contribution of different factors to economic growth such and monitor the change of average incomes. The SAM Identifies beneficiaries using salaries, wages and household expenditure

but what type of growth are we aiming for?

Snapshot of transmission-mechanism

What about being helpful from Stats Background Successful economic transformation needs knowledge and planning instruments that support strategic decision-making. Over the last two decades, Stats SA has developed its capacity to provide analysts and decision makers access to a wide range of raw data intelligence and basic analytical tools. Last year we began exploring the possibility of facilitating government access to more advanced analytical tools that are built upon Stats SA raw data intelligence and its basic analytical tools.

Objectives To introduce a unique system of web-based economic models built for South Africa over the last 15 years. To show the potential of these models to enhance the capability of the South African government to Design coordinated policies Conduct impact analyses Generate forecasts of key economic and development indicators that are necessary for strategic forward looking decision making. Monitor progress Identify future demand for skills under alternative economic and development scenarios Establish baseline forecasts to monitor and evaluate future paths of economic growth, employment, poverty alleviation, and inequality reduction in the economy and provinces. To propose access to the suite of models and build internal capacity to use them effectively in the government

STATS SA’S LEVELS OF SERVICES

Provision of Raw Data Intelligence (Level 1) Post 1994, Stats SA developed the internal capacity to provide raw data intelligence to government and the public. As a result, today we collect and make available extensive economic and demographic data. Government and the public have used Stats SA data for research, policy analysis, and policy development. Financial statistics   Labour force quarterly survey Employment and Earnings Census Industry and trade statistics Price data Service delivery and poverty Population profile Community Survey

Provision of Basic Analytical Intelligence (Level 2) Beyond data collection, Stats SA has used its statistical pool to built a number of basic economic policy tools. These tools reflect accounting relationships that underlie the current structure of the economy in significant detail based on our collected economic and demographic data. However, as accounting frameworks they are unable to forecast economic performance because they are static and do not include dynamic properties of the economy. Input-Output Table Supply & Use Table Social Accounting Matrix (SAM) Growth Accounting Framework (GAF)

The Suite of South African Models: Range of Data and Methodologies For the construction and the regular update of the suite of models, ADRS has extensively used Stats SA data releases over the last 15 years that include: Quarterly Labour Force Survey Census 10 Percent Sample General Household Surveys Income and Expenditure Survey Living Conditions Survey Consumer Price Index Manufacturing Capacity Utilisation Various economic time series data Time series and survey data regression techniques that have been used in the suite of models include: Autoregressive Distributed Lag (ARDL) cointegration techniques Ordinary least square Multinomial logistic regressions Probit analysis Principal components regression Cohort component techniques Why is Scen planning useful? Scenario planning fosters better strategic decisions by discovering and framing uncertainties and enhanced understanding of risks, prior to making substantial, irreversible commitments. Needs reference to Focus Question

The Suite of South African Models: Range of Forecasts The suite of models, based on South African data, captures the accounting relationships in the economy and the behaviour of private sector, household, and government to produce annual forecasts of: National income and product account Macroeconomic and industry indicators (growth, inflation, trade, per capita GDP, debt/GDP, deficit/GDP, etc.) Employment for 45 sectors and its distribution among 21 SETAs Demand and supply of skills Provincial macroeconomic and industry indicators Poverty and inequality by gender, race, province, age, and quintile Sector demand for five categories of energy and emissions Demand for and cost of various social grants Direct and indirect taxes by gender, race, province, age, and quintile Distributional impact of public employment Why is Scen planning useful? Scenario planning fosters better strategic decisions by discovering and framing uncertainties and enhanced understanding of risks, prior to making substantial, irreversible commitments. Needs reference to Focus Question

The Suite of South African Models: Range of Policy The utility of the suite of models is highlighted by the scope of its output and its ability to: Quantify the economic and social impact of current policies and programmes Generate medium and long term forecasts of a comprehensive set of social and economic indicators that take account of the diverse national and provincial government short, medium and long term development plans and policies. For example, the suite of models can quantify the impact of one or more of the following: The National Development Plan (the NDP) The 9 Point Plan The New Growth Path (the NGP) The latest Industrial Policy Action Plan (IPAP) Provincial growth and development plans The localisation policy The latest Integrated Resource Plan (IRP) The proposed carbon tax policy The adopted National Minimum Wage policy The Comprehensive Social Security system The Davis Tax Committee recommendations The MTEF tax and expenditure proposals South Africa target of reducing its greenhouse gas (GHS) emissions Why is Scen planning useful? Scenario planning fosters better strategic decisions by discovering and framing uncertainties and enhanced understanding of risks, prior to making substantial, irreversible commitments. Needs reference to Focus Question

Suite of SA Models and Government Departments The suite of SA models are directly linked to the government structure. Most government departments are directly or indirectly represented by one ore more of the SA models. For example, the Multi-Sector Economy-Energy-Emissions model is directly relevant for challenges facing the Departments of Energy, Environmental Affairs, Science and Technology, SARS, and the Presidency.

Provision of Advanced Analytical Intelligence (Level 3)

Groundwork: Stats SA’s Role Stats SA has explored the possibility of securing access to the suite of models for the South African government through a special licensing agreement that includes a significant volume discount. Stats SA will introduce a multi-phase training programme that includes building the skills of a selected inter-departmental team to train others within the government on how to effectively use the suite of models. Stats SA will actively participate in the process of ensuring that the suite of models is kept up-to-date at all times, using the latest statistics.

Way Forward Stats SA has laid the groundwork to support and facilitate access by the government to a suite of economic models that can potentially enhance the policy design, forecasting, and monitoring capability of the government. Forward thinking, commitment, and support is required to make the most of this initiative. Forward Thinking: to envision enhanced use of knowledge and planning instruments by government policy analysts to support strategic decision-making. Commitment: Commitment to build capacity to effectively integrate the suite of models in policy development, planning and monitoring processes. Support: Provision of institutional and financial support for the system whose infrastructure needs to be maintained, updated and upgraded regularly.