Financial Diaries Project 2003-2004 - South Africa

Abstract
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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.

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

Analysis unit
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Households and individuals

Universe
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The survey covered households in the three geographic areas.

Kind of data
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Sample survey data

Sampling procedure
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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.

Mode of data collection
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Face-to-face [f2f]

摘要
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南非政策制定者正致力于确保贫困人口能够更好地获得金融服务。然而，对贫困家庭金融需求的缺乏理解阻碍了应对这一需求的广泛策略。金融日记研究通过审视农村和城市家庭的财务管理来解决这一知识差距。该研究是一项为期一年的家庭调查，基于在Diepsloot（豪登省）、Langa（西开普省）和Lugangeni（东开普省）进行的每两周一次的访谈。总共有160户家庭参与了这一开创性研究，该研究有望提供关于贫困人口如何管理金钱以及贫困人口作出金融决策的背景的重要见解。该研究描绘了城镇金融市场的丰富图景，突出了非正式金融产品的普遍性、生存型商业的作用以及社会援助的贡献。金融日记数据集包括高度详细、按日现金流数据，涵盖了收入、支出和金融流动，包括家庭和个人层面。
地理覆盖范围
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开普敦的Langa、约翰内斯堡的Diepsloot以及东开普省的农村村庄Lugangeni。
分析单位
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家庭和个人
总体
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调查涵盖了三个地理区域的家庭。
数据类型
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样本调查数据
抽样程序
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为了创建金融日记的抽样框架，研究人员借鉴了Rutherford（2002）和Ruthven（2002）所使用的方法，即参与式财富排名（PWR）。在南非洲，参与式财富排名方法由位于农村Limpopo省的知名非政府组织小额贷款机构Small Enterprise Foundation（SEF）采用。Simanowitz（1999）将PWR方法与视觉贫困指标（VIP）进行了比较，发现VIP测试与PWR测试的最佳一致性为70%。有时，VIP测试中被定义为最贫困的家庭列表中的三分之一实际上是根据PWR测试被认为是最富有的。PWR方法在van der Ruit、May和Roberts（2001）的研究中也得到了隐含的评估，他们通过将其与CGAP使用的用于评估客户贫困的主成分分析（PCA）进行比较。他们发现，PCA定义的75%的贫困人口也被PWR定义为贫困。我们严格遵循SEF手册进行财富排名，并就如何将该方法应用于城市地区咨询SEF。
第一步是与社区领导者进行咨询，询问他们如何划分他们的社区。在每个类型的地区中，随机选择了约100户家庭的代表性街区。南非的城镇按街道组织——每个街道或区域都有自己的街道委员会。街道委员会的目的是了解其街道上的每个人，并作为街道内所有活动的监护人。每个地区的每个街道委员会都被邀请参加一个中央会议，并要求绘制其地区的地图并提供家庭名单。在绘制地图后，每个地区都进行了访问，并通过与街道委员会一起逐户走访来检查地图和名单。
然后从街道委员会和社区高级成员中选出了两个参考组，每个参考组有4至8人。每个参考组首先被要求表明他们如何定义贫困家庭与富裕家庭。这次讨论具有双重目的。首先，它传达了每个社区认为何为富裕或贫困的信息。其次，它促使参考组开始考虑哪些家庭属于哪个类别。
在这次讨论之后，每个参考组然后根据他们感知的财富对街区内的每个家庭进行排名。SEF的财富排名方法是去规范化的，即参考组被邀请将家庭放入他们认为合适的尽可能多的不同财富堆中。只有被两个参考组都知的家庭才保留在样本中。
使用SEF指南为特定堆中的每个家庭分配一个分数。这些分数是通过将100除以堆的数量乘以堆的水平来创建的。这意味着如果最贫困的堆是编号1，那么该堆中的每个家庭都被分配了100分的分数，代表100%的贫困。如果最富裕的堆是编号6，那么该堆中的每个家庭收到16.7分的分数，而编号5的堆中的每个家庭收到33.3分的分数。然后取两个参考组的平均分数作为分布。
评估结果好坏的一种方法是通过分析两个参考组之间排名的一致性。根据SEF的方法，如果两个参考组之间的分数差异不超过25分，则结果是一致的。如果分数差异在26至50分之间，则结果是不一致的；如果分数差异超过50分，则结果是不可靠的。SEF使用一致和不一致的排名，只要它们使用两个参考组的平均值——这意味着可以使用91%的样本。然而，因为只使用了两个参考组，所以只有最终样本选择中的一致家庭被考虑。
为了进一步测试，计算了参考组将家庭放入完全相同类别的次数。还评估了两个参考组在财富光谱两端的一致程度。这个结果不会被参考组将家庭放入多少个类别所偏颇。
遵循在印度和孟加拉国使用的例子，样本根据家庭的总分数分为三个不同的财富类别。区分三个不同的财富类别允许遵循与孟加拉国和印度相似的财富排名，同时也避免了样本过度分层。然后从每个地区随机抽取了60户家庭的样本。如果根据每个财富排名在人口中的比例代表性来抽取样本，可能会使样本缺乏某些排名中的富裕家庭，从而无法得出结论。因此，研究人员从每个排名中均等地抽取。
数据收集方式
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面对面 [f2f]