Wave 1 Discussion Papers

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NIDS Discussion Paper 2009/01

Title:  Migration:  Analysis of the NIDS Wave 1 Dataset

Author(s):  Dorrit Posel

Date:  July 2009


This paper investigates measures of migration captured in the National Income Dynamics Study (NIDS), and it compares these measures, and how information is collected, with other household surveys conducted in South Africa. The paper is divided into five sections. The first section describes households with non-resident (or absent) household members. A key reason why individuals may be absent from a household for much of the year is because they are migrant workers. The second section therefore looks specifically at households which report migrant workers as absent household members. Section three describes inter-household transfers received by households, and it considers the relationship between households that report transfers and households that report migrant workers. The fourth section investigates migration more generally as the movement of people across space and the change in an individual’s place of residence. The last section summarises the main similarities and differences between NIDS and other household surveys in South Africa.

NIDS Discussion Paper 2009/02

ITitle:  Health:  Analysis of the NIDS Wave 1 Dataset

Author(s):  Cally Ardington and Anne Case

Date:  July 2009


Health status and socioeconomic status are important determinants of individuals’ wellbeing. Information on income alone, or on health alone, provides a less complete picture. Better health can lead to higher income, and higher income can lead to better health, so that we cannot fully understand the dynamics of either process without understanding both. Much of the research on international health and income has focused on the cross-country relationships between population health and national income. Starting from Preston (1975, 1980), these relationships have been used to investigate the causes of mortality decline, particularly the relative roles of income and of medical knowledge. And data on adult height have been used to investigate the causes of the historical decline in mortality, see in particular Fogel (1997, 2004), Floud, Wachter, and Gregory (1990), and Steckel (1995).

NIDS Discussion Paper 2009/03

Title:  Education:  Analysis of the NIDS Wave 1 Dataset

Author(s):  Nicola Branson and David Lam

Date:  July 2009


The National Income Dynamics Study (NIDS) questionnaires devote considerable attention to education. This report analyzes Wave 1 data corresponding to the sections of the questionnaires that are most specifically related to education – Module B of the household questionnaire, Module C of the child questionnaire, and Module H of the adult questionnaire. Many of the questions in these modules are similar to questions on other national surveys such as the Labour Force Survey (LFS), the General Household Survey (GHS), and the Community Survey (CS). In the first part of this report we compare estimates from NIDS to estimates from the 2007 Community Survey and the LFS 2007 for variables that are captured in both surveys. NIDS also includes a number of education variables that are not captured in other national surveys. These include measures of grade repetition, age of starting and ending school, school fees, reasons for not attending school, and plans for post-secondary schooling. While we do not have space to analyze all of the education variables that are unique to NIDS, we will analyze some of the variables that we believe may be of major interest to researchers and policy makers.

NIDS Discussion Paper 2009/04

Title:  Access to Household Services and Assets: Analysis using the NIDS Wave 1 Dataset

Author(s):  Haroon Bhorat, Carlene van der Westhuizen and Aalia Cassim

Date:  July 2009


The National Income Dynamics Study (NIDS) is the first national household panel study in South Africa, covering topics such as income and expenditure dynamics, determinants of changes in poverty and well-being; household composition and structure; fertility and mortality; migrancy and migrant strategies; labour market participation and economic activity; human capital formation, health and education; vulnerability and social capital. In 2008, about 7305 households and approximately 28 255 people across South Africa were interviewed as part of the NIDS Wave 1.

The main objective of this paper is to provide an overview of the findings from the first wave of NIDS in terms of access to household services and assets. A secondary objective is to provide a comparison of the results from NIDS with those from comparable household surveys. At the time of writing, the 2008 General Household Survey (GHS) has not yet been released and the results from NIDS are therefore compared with findings from the 2006 and 2007 GHS. The GHS is a nationally representative household survey conducted annually since 2002 by Statistics South Africa. The aim of this survey is to capture information on living conditions of South African households in order to evaluate government programmes and projects. The survey covers education, health, the labour market, housing and household access to services and facilities, as well as household assets.


NIDS Discussion Paper 2009/05

Title:  Income and Expenditure Inequality:  Analysis of the NIDS Wave 1 Dataset

Author(s):  Arden Finn, Murray Leibbrandt and Ingrid Woolard

Date:  July 2009


The purpose of this report is to provide a snapshot of income and expenditure inequality in South Africa as measured by the 2008 data from the National Income Dynamics Study. While there are many ways in which to decompose and analyse inequality, the most common feature of post-Apartheid studies is the focus on changes in inequality by racial group. We remain with this precedent here although some mention is also made of inter-provincial inequality and inequality by geo-type.

Section 2 of this report presents an overview of aggregate income and expenditure inequality while Section 3 analyses inequality through the prism of race. Finally, Section 4 discusses two spatial dimensions of South African inequality by briefly looking at provincial and geo-type-level inequality.

All income and expenditure figures in the report refer to monthly household income/expenditure per capita. The figures of per capita household income and expenditure were constructed by dividing the final derived figures in the data by the number of people living in the household. All of the analysis below makes use of post-stratified sampling weights in order to make the results reflective of the South African population, rather than the NIDS sample.


NIDS Discussion Paper 2009/06

Title:  Agriculture:  Analysis of the NIDS Wave 1 Dataset

Author(s):  Julian May and Michael Carter

Date:  July 2009


The agricultural sector in South Africa is starkly dualistic, comprising a highly capitalised well-integrated commercial sector, and a subsistence sector that is mostly to be found in the former ‘homeland’ areas. Although only about 12 percent of South Africa can be used for crop production, with areas of high-potential making up 22 percent of this land, South Africa is virtually self-sufficient in all major agricultural products and is usually a net food exporter. Further, while agriculture production contributes less than 3 percent to GDP and 7.2 percent of formal employment, downstream linkages into agro-industrial processing increases this contribution to 15 percent of GDP. The Western Cape, KwaZulu-Natal and Free State are the largest provinces in terms of the number of commercial farms, although Gauteng displaces the Free State in terms of gross farming income. Estimates of the contribution of the subsistence sector in terms of value, employment and impact on food security are scanty and prone to measurement error. Official statistics show employment in ‘informal sector agriculture’ to be highly variable, but attribute some 470 000 workers to this sector, mostly concentrated in KwaZulu-Natal (42.6 percent) and the Eastern Cape (37.3 percent) (Stats SA, 2007: xiv).

In this discussion paper, we will first discuss the potential role that can be played by agricultural production and by government support for this sector. We will then note methodological decisions concerning weighting and the selection of variables that have been used. We then go onto discuss data contained in the Adult Questionnaire which can be used to show the demographic profile of those who are employed in informal or subsistence agriculture. The information in the household questionnaire is then discussed and these data are combined in a preliminary analysis of the outcomes from agricultural production on household well-being. We end by noting some data quality concerns and make suggestions for amendments to the Wave 2 questionnaire.

NIDS Discussion Paper 2009/07

Title:  Wellbeing and Social Cohesion:  Analysis of the NIDS Wave 1 Dataset

Author(s):  Justine Burns

Date:  July 2009


Individuals and households often participate in a myriad of organisational activities and engage socially with others in their communities on a range of issues. The term "social cohesion" refers to these forms of social capital. This conceptualisation follows from the work of Robert Putnam, and refers to the different ways that members of a community interact with one another, thereby providing "a map of a community's associational life, and thus with it a sense of its civic health." (Grootaert et al, 2004:3). Collecting this kind of data allows one to examine the extent to which social capital contributes towards household welfare and poverty reduction, as well as examining the determinants of social capital. The National Income Dynamics Study (NIDS) provides an important opportunity to examine the impact of social capital on well-being and social cohesion since data on participation in community and civic organisations has been collected in Wave 1 in Section M of the adult questionnaire, along with information on life satisfaction, happiness, trust, perceived income status of the household and expectations concerning economic mobility in the future.

NIDS Discussion Paper 2009/08

Title:  Personal Debt and Financial Access:  Analysis of the NIDS Wave 1 Dataset

Author(s):  Garikai Nyaruwata and Murray Leibbrandt

Date:  July 2009


This report provides an overview of the data on personal debt and financial access that is collected in section G of the adult questionnaire. The data generally look promising as there are relatively low non-response rates and the indebtedness and financial access measures are similar to earlier findings from the Income and Expenditure Surveys and other sources. There is some concern however, as to whether the levels of indebtedness have been slightly under-reported in lower income households. Also, these comparisons are conducted at the level of the household. This requires that the NIDS adult level data are aggregated. Given that there is no debt section in the adult proxy questionnaire, such proxy adults are omitted from the aggregation.

In section 2 we focus on item non-response for the variables in the debt section of the questionnaire. Section 3 then offers some descriptive findings on financial access by race and income. We move on to analyse indebtedness in section 4; with a discussion of indebtedness by income, race, gender and province and conclude the paper with a brief section on equity. This concluding section serves to draw attention to this unique aspect of the NIDS data set.

NIDS Discussion Paper 2009/09

Title:  Demography:  Analysis of the NIDS Wave 1 Dataset

Author(s):  Tom Moultrie and Rob Dorrington

Date:  July 2009


This report sets out the major findings from investigations into the demographic data collected as part of the National Income Dynamics Study (NIDS). The report concerns itself primarily with three distinct investigations: fertility; child mortality and adult mortality. Each of these will be presented in turn. We have not prepared a section on the basic demography (age and sex structure of the population), as we presume that this will be covered in the report on the realisation of the sample, and the derivation of weights used or on migration, which is the topic of a separate report. However, where our findings relate to that material, comment will be offered.

NIDS Discussion Paper 2009/10

Title:  Social Assistance Grants: Analysis of the NIDS Wave 1 Dataset

Author(s):  Hayley McEwen, Catherine Kannemeyer and Ingrid Woolard

Date:  July 2009


This report summarizes the initial findings of the National Income Dynamics Survey (NIDS) regarding social assistance grants in South Africa. A comparison to various other data sources is also included in order to highlight any shortfalls or strengths in the NIDS data in comparison to previous surveys.

To date there has been no publicly available nationally representative survey which includes detailed information on social assistance received. The October Household Surveys from 1995 to 1999 include questions on whether social assistance is received and what form it takes (child support grant, disability grant etc.), and sometimes including questions on how much is received. The General Household Surveys from 2003 to 2007 include questions on what form of social assistance is received, if any. Demographic statistics on grant recipients can also be obtained from the South African Social Security Agency (SASSA) which is responsible for managing grant payments.

NIDS contributes to knowledge by including questions such as who receives the payments, how much do they receive, how long the grant has been received, whether the respondent has ever applied for a grant, why it was rejected or why they never applied. This will hopefully provide some insight into the effectiveness of the social security system and encourage further research on the topic.

This report considers social assistance for children, the elderly and the disabled in turn. It then provides some simple analysis of the importance of social assistance to poor households.

NIDS Discussion Paper 2009/11

Title:  Intra-household Decision Making & Development: Analysis of the NIDS Wave 1 Dataset

Author(s):  Kamilla Gumede

Date:  July 2009


People make choices every day, and those choices affect their living standards and life outcomes. Recent research suggests that most people do not always make the best choices for themselves: As an example, it is widely recognized that most of us tend to succumb to short-term impulses at the expense of long-term interests (Benabou and Tirole, 2004). Most people don’t save as much money as they ought to (especially not for old age), they spend more money on curative care than preventive care despite the fact that preventive care often is a more cost effective investment, and they procrastinate on economic and other choices in their life.

NIDS Discussion Paper 2009/12

Title:  Labour Market:  Analysis of the NIDS Wave 1 Dataset

Author(s):  Vimal Ranchhod

Date:  July 2009


The purpose of this paper is to provide a brief summary of the labour market subset of the NIDS data. Most sections in this paper relate to the various sections in the adult questionnaire. We further include a short section on labour market information obtained from the proxy questionnaires, as well as the impact that this data has on the aggregate measures. We provide some personal commentary on our findings in each section, and highlight any findings or data related characteristics that may prove to be of interest to subsequent users of the data. All results are obtained using the post-stratification weights, unless specified otherwise.

The remainder of this paper is structured as follows: Section 2 provides a summary of the data at a broad level. Section 3 explores employment in more detail, including wage employment, self employment, casual employment and other types of non-wage employment. Section 4 describes working conditions, with an emphasis on hours worked and earnings. Section 5 relates to the unemployed, their experiences and expectations. Section 6 analyses search methods, both those used by people searching for jobs as well as those that were used by people who are currently employed. Section 7 considers the group who are not economically active. Section 8 incorporates information from the proxy questionnaires. Section 9 does a data quality consistency check by comparing summary statistics from NIDS with corresponding statistics obtained from the South African Labour Force Surveys conducted at approximately the same time. Section 10 considers the impact of various forms of interviewee non-response on the sample and Section 11 provides a concluding discussion.

NIDS Discussion Paper 2009/13

Title:  Poverty:  Analysis of the NIDS Wave 1 Dataset

Author(s):  Jonathan Argent, Arden Finn, Murray Leibbrandt and Ingrid Woolard

Date:  July 2009


This report offers a brief application of the 2008 income and expenditure data from the National Income Dynamics Study (NIDS) to the analysis of poverty in South Africa.  The per capita figures of household income and expenditure were constructed by dividing the final derived figures for total income and total expenditure in the data by the number of people living in the household.  All of the analysis below makes use of post-stratified sampling weights in order to make the results reflective of the South African population, rather than the NIDS sample. Conventional poverty measures and dominance analyses are presented and interpreted in aggregate as well as across race and geographical area.


Section 2 of this report provides a racial overview of poverty in South Africa while Section 3 adopts a spatial approach. Sections 4 and 5 discuss poverty dominance in South African society and some salient features of poor households respectively. Section 6 provides a brief comparison between the NIDS 2008 findings and other research on contemporary poverty in South Africa and Section 7 concludes. 

NIDS Discussion Paper 2009/14

Title: Subjective Welfare: Analysis of the NIDS Wave 1 Dataset

Author(s): Benjamin Roberts

Date: July 2009


"This is an interesting approach...future household surveys for developing countries, such as the LSMS [Living Standards Measurement Study], should consider including subjective poverty line questions" - Martin Ravallion (1992:33-34)

"We are all in the gutter, but some of us are looking at the stars." – Oscar Wilde, Lady Windermere’s Fan (1892), Act III

Over the last two decades, recognition of the multi-dimensional nature of poverty among the research community and policymakers, together with the rapid proliferation of nationally-representative survey data in developing countries, has provided the impetus for a more inclusive approach to measuring and addressing poverty. In consequence, there have been a number of exciting new developments in the field of economic measurement. This new research agenda has included, inter alia, the use of mixed qualitative and quantitative (Q-Squared) poverty appraisal (Kanbur, 2005; Kanbur & Shaffer, 2007), experimentation with multidimensional poverty measures, renewed interest in the so-called "economics of happiness", as well as renewed interest in the derivation of subjective poverty measures and poverty lines.

NIDS Discussion Paper 2009/15

Title:  Intergenerational Mobility:  Analysis of the NIDS Wave 1 Dataset

Author(s):  Sarah Girdwood and Murray Leibbrandt

Date:  July 2009


Intergenerational mobility measures the degree to which an individual’s socio-economic status depends on his or her parents’ status. Mobility matters in countries, such as South Africa, with high inequality and poverty as the consequences of remaining stuck at the bottom are serious.

This report provides a very brief overview of the 2008 data from the National Income Dynamics Study (NIDS) for conducting intergenerational mobility research on education, occupation and income. It then goes on to undertake a preliminary analysis of intergenerational educational and occupational mobility as well as a cursory look at income mobility for co-residing parents and children.

Mobility analysis is technically the domain of panel data. However, intergenerational mobility is one of the chief themes in NIDS and special attention was given to this theme in the Wave 1 questionnaire. Even in the cross-section of NIDS Wave 1, it is possible to compare parents and their children in terms of their education and occupation status. Indeed, NIDS provides rich data for these topics. There are other dimensions to intergenerational mobility that are in NIDS but that we do not explore in this report. Examples include residential, consumption and health mobility.

This report is structured as follows. Section 2 focuses on intergenerational education mobility, first examining item non-response of parental education and then proceeding to a description of educational mobility results. Section 3 proceeds, in a similar manner, with intergenerational occupation mobility. These two sections are revisited in Section 4 where correlations are used to measure the degree of intergenerational mobility. Section 5 provides a cursory inspection of intergenerational income mobility of co-resident parents and children before Section 6 concludes.

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