Financial Diaries Project 2003-2004

South Africa, 2003 - 2004

Get Microdata

Reference ID

zaf-saldru-fdp-2003-2004-v1

Producer(s)

Southern Africa Labour and Developement Research Unit (SALDRU)

Collections

Financial Sector Deepening Zambia

Metadata

Documentation in PDF DDI/XML JSON

Study website

Created on

Nov 07, 2008

Last modified

Jun 02, 2020

Page views

886102

Downloads

2544

Identification

Survey ID number

zaf-saldru-fdp-2003-2004-v1

Title

Financial Diaries Project 2003-2004

Abbreviation or Acronym

FDP 2003-2004

Country

Name	Country code
South Africa	zaf

Study type

Household Survey [hh]

Series Information

Southern Africa Labour and Development Research Unit (SALDRU). Financial Diaries Project 2003-2004 [dataset]. Version 1. Cape Town: SALDRU [producer], 2006. Cape Town: DataFirst [distributor], 2010. DOI: https://doi.org/10.25828/gegg-sk25

Abstract

South African policymakers are endeavouring to ensure that the poor have better access to financial services. However, a lack of understanding of the financial needs of poor households impedes a broad strategy to attend to this need. The Financial Diaries study addresses this knowledge gap by examining financial management in rural and urban households. The study is a year-long household survey based on fortnightly interviews in Diepsloot (Gauteng), Langa (Western Cape) and Lugangeni (Eastern Cape). In total, 160 households were involved in this pioneering study which promises to offer important insights into how poor people manage their money as well as the context in which poor people make financial decisions. The study paints a rich picture of the texture of financial markets in townships, highlighting the prevalence of informal financial products, the role of survivalist business and the contribution made by social grants. The Financial Diaries dataset includes highly detailed, daily cash flow data on income, expenditure and financial flows on both a household and individual basis.

Kind of Data

Sample survey data

Unit of Analysis

Households and individuals

Version

Version Description

v1: Edited anonymised dataset for public distribution

Version Date

2006

Scope

Notes

The scope of the Financial Diaries study 2003-2004 includes the daily income, expenditure and financial exchanges of poor households. These include: employment, cash flows, incomes, remittances/lobola, bank accounts, pensions or provident funds, stokvels (social savings: gooi-gooi, savings clubs, umgelelos), informal burial societies, funeral plans, retirement annuities, and other types of insurance. Also investigated are loans from a banks//employers/cash loans and informal group loans from stokvels or individuals, credit/accounts and borrowing, money guarding (looking after other’s money) and informal individual savings. Other topics dealt with are rent arrears, wage advances. income arrears, giving credit, credit cards/ store cards, salary timing and debts under administration. The survey also covered living standards of the households covered, food habits, and tenure.

Keywords

income expenditure living standards

Coverage

Geographic Coverage

Langa in Cape Town, Diepsloot in Johannesburg and Lugangeni, a rural village in the Eastern Cape.

Geographic Unit

The lowest level of geographic aggregation for the data is "township" or village.

Universe

The survey covered households in the three geographic areas.

Producers and sponsors

Primary investigators

Name	Affiliation
Southern Africa Labour and Developement Research Unit (SALDRU)	University of Cape Town

Funding Agency/Sponsor

Name	Abbreviation	Role
Ford Foundation	FF	funder
FinMark Trust	FMT	funder
Micro Finance Regulatory Council of South Africa	MFRCSA	funder

Sampling

Sampling Procedure

To create the sampling frame for the Financial Diaries, the researchers echoed the method used in the Rutherford (2002) and Ruthven (2002), a participatory wealth ranking (PWR). Within South Africa, the participatory wealth ranking method is used by the Small Enterprise Foundation (SEF), a prominent NGO microlender based in the rural Limpopo Province. Simanowitz (1999) compared the PWR method to the Visual Indicator of Poverty (VIP) and found that the VIP test was seen to be at best 70% consistent with the PWR tests. At times one third of the list of households that were defined as the poorest by the VIP test was actually some of the richest according to the PWR. The PWR method was also implicitly assessed in van der Ruit, May and Roberts (2001) by comparing it to the Principle Components Analysis (PCA) used by CGAP as a means to assess client poverty. They found that three quarters of those defined as poor by the PCA were also defined as poor by the PWR. We closely followed the SEF manual to conduct our wealth rankings, and consulted with SEF on adapting the method to urban areas.

The first step is to consult with community leaders and ask how they would divide their community. Within each type of areas, representative neighbourhoods of about 100 households each were randomly chosen. Townships in South Africa are organised by street - with each street or zone having its own street committee. The street committees are meant to know everyone on their street and to serve as stewards of all activity within the street. Each street committee in each area was invited to a central meeting and asked to map their area and give a roster of household names. Following the mapping, each area was visited and the maps and rosters were checked by going door to door with the street committee.

Two references groups were then selected from the street committee and senior members of the community with between four and eight people in each reference group. Each reference group was first asked to indicate how they define a poor household versus those that are well off. This discussion had a dual purpose. First, it relayed information about what each community believes is rich or poor. Second, it started the reference group thinking about which households belong under which heading.

Following this discussion, each reference group then ranked each household in the neighbourhood according to their perceived wealth. The SEF methodology of wealth ranking is de-normalised in that reference groups are invited to put households into as many different wealth piles as they feel in appropriate. Only households that are known by both reference groups were kept in the sample.

The SEF guidelines were used to assign a score to each household in a particular pile. The scores were created by dividing 100 by the number of piles multiplied by the level of the pile. This means that if the poorest pile was number 1, then every household in the pile was assigned a score of 100, representing 100% poverty. If the wealthiest pile was pile number 6, then every household in that pile received a score of 16.7 and every household in pile 5 received a score of 33.3. An average score for both reference groups was taken for the distribution.

One way of assessing how good the results are is to analyse how consistent the rankings were between the two reference groups. According to the SEF methodology, a result is consistent if the scores between the two reference groups have no more than a 25 points difference. A result is inconsistent if the difference between the scores is between 26 and 50 points while a result is unreliable is the difference between the scores is above 50 points. SEF uses both consistent and inconsistent rankings, as long as they use the average across two reference groups - this would mean that 91% of the sample could be used. However, because only used two reference groups were used, only the consistent household for the final sample selection was considered.

To test this further,the number of times that the reference groups put a household in the exact same category was counted. The extent of agreement at either end of the wealth spectrum between the two reference groups was also assessed. This result would be unbiased by how many categories the reference groups put households into.

Following the example used in India and Bangladesh, the sample was divided into three different wealth categories depending on the household's overall score. Making a distinction between three different categories of wealth allowed the following of a similar ranking of wealth to Bangladesh and India, but also it kept the sample from being over-stratified. A sample of 60 households each was then drawn randomly from each area. To draw the sample based on a proportion representation of each wealth ranking within the population would likely leave the sample lacking in wealthier households of some rankings to draw conclusions. Therefore the researchers drew equally from each ranking.

Data collection

Dates of Data Collection

Start	End
2003	2004

Data Collection Mode

Face-to-face [f2f]

access_policy

Access authority

Name	Affiliation	URL	Email
DataFirst	University of Cape Town		support@data1st.org

Access conditions

Public use files, available to all

Citation requirements

Contacts

Name	Affiliation	Email	URL
DataFirst Helpdesk	University of Cape Town	support@data1st.org

Metadata production

Producers

Name	Affiliation	Role
DataFrist	University of Cape Town	Metadata producer

Date of Metadata Production

2020-05-18

Metadata version

DDI Document version

Version 3

Back to Catalog