The scope and extent of home-based business income relative to employment earnings in financing household expenditures: a study undertaken in a subeconomic housing area within the Cape Metropole

Abstract

This study was undertaken to examine the scope of home-based businesses in poor neighbourhoods and the extent to which household income is derived from them. The aim was to determine the ratio of home-based business income to wage earnings, in order to understand its relative importance in augmenting primary employment (wage-based) income. The size, necessity and importance of self-generated (business-derived) income in augmenting primary income was measured in a selected subeconomic housing area within the Cape Metropole. The significance of this analytical research is the determination of income data and the levels of poverty. This paper provides the primary data (base information) for policy formulation relating to social and economic development in this subeconomic area. The findings add to the debate for the provision of a Basic Income Allowance (grant) for those people experiencing poverty. The results correlate with findings of national longitudinal studies. The level of job creation through businesses is minimal and the extent of unemployment is much greater than anticipated.

1. Introduction and background

The purpose of the study was to determine the scope of home-based businesses and the extent to which household income is derived from these; in other words, to determine the ratio of home-based business income to wage earnings and its relative importance in augmenting primary employment (wage-based) income.

The study was conducted among households in the subeconomic housing area of Kleinvlei, Eerste River, in the Oostenberg region of the Metropole of Cape Town, South Africa. (See Appendices 1, 2 and 3 for the geographic location and size of the area.) These are previously rented council-built dwellings specifically built for the very low-income group households which, generally, are households not able to obtain any form of mortgage loan.

The high unemployment rate in this geographic area, which is estimated at over 50 per cent by the local community health-care centre, supports the hypothesis that the majority of households would be supplementing their wage-based earnings, unemployment transfers and retirement benefits to finance household expenditures. A further hypothesis was that the business-generated income comprised more than 7 per cent of the total household income, thereby financing a significant proportion of household consumption (expenditure).

The question often arises as to how the very low income-earners (referred to as the poor), in areas with no visible commercial and industrial activity and having a high level of unemployment, are able to cover their basic cost of existence. Normally studies of this nature identify the extent of poverty or the level of income without reflecting the exact amount of income derived from home-based business activities, as households are reluctant to declare such income. Previous studies (Strassman, 1987; Budlender & Theron, 1995; Soldressen et al., 1998) did attempt to determine the extent of businesses without linking these to total household earnings.

This study separates home-based business earnings from employment-based earnings, with the express purpose of demonstrating its importance in financing household expenditure and that these householders are obliged to acquire basic entrepreneurial skills partly because of low income, the pressures of poverty and the survival instinct.

2. Review of the related literature

Traditionally, it is assumed that the levels of income in poor areas are the result of poor education and low skills levels. Furthermore, it is implied that the high poverty levels in these geographic areas are indicative of the low level of entrepreneurial talent and skills of people living in these areas.

Levin, in his report on employment creation, inversely links poverty and low levels of income to educational opportunities, services infrastructure and targeted employment schemes. He supports the notion of low educational levels in the impoverished areas. However, he also develops a case for a meaningful approach to poverty eradication and employment-strengthening policies, arguing that employment creation could be stimulated through informal sector activities, a focus on self-employment and an improvement in the entrepreneurial climate (Levin, 1994: 101).

A relationship exists between income and expenditure and it is believed that those households with businesses are indeed better off than those without this form of income. Bradbury (1996: 1) argues that within the poor segments of the community, those with business income are in fact ‘poorer than what is estimated or assumed’. It is also assumed that the ability to generate income within the poorer levels will be comparatively high given the low asset-base.

In line with Bradbury's arguments, Leibbrandt et al. (2000: 31–51) reveal that self-employment income is 5.6 per cent of wage income in the ‘below poverty line’ category and the equivalent of 25.9 per cent in the ‘above the poverty line’ category, whereas for the total number of households it is 24.8 per cent. This statistic shows that a small percentage of the household income of the poor, who are those below the poverty line, is derived from businesses. The research conducted by Leibbrandt et al. (2000), was concerned with the inequality in the distribution of income commonly depicted by the Lorenz curve. Certain aspects of their research relate to this study in that they show the proportion of household income by source.

Simkins (1998), McGrath (1990) and Whiteford & McGrath (1994) have produced extensive research on inequality and income distributions in South Africa. Economists focus mainly on the degree of income inequality in the distribution as measured by the Gini coefficient or index. In South Africa it is argued that the overall level of income inequality, although still very unequal, has decreased during the last decade. This applies to the population as a whole; however, significantly, the income disparity has increased among black households (Robert, 1997: 21; Pearce, 1999: 21).

This study places itself within the broader parameter of income equality within the black household groups, as outlined in the previous paragraph. The majority of the households in this study were considered to be within the category referred to as the poor. The research undertaken provides a profile of the poor, the reasons for self-employment, the characteristics of businesses among the poor and the size and extent of home-based business income.

A consumer expenditure survey (Bureau of Labour Statistics, 2001) undertaken in the United States shows the existence of a distinctive positive correlation between income derived and household expenditure incurred by those who have home-based business to augment their employment income, among the consumers in the lowest-spending decile.

In general, there are few studies that document accurately the extent of home-based business income. The sensitivity of this type of information and the diverse reasons for households not to disclose or to under-declare home-based business income may be the reason for the limited success of these studies. One of the reasons for non-disclosure in the low-income categories is that grants are issued to households on the express condition that no other income is being generated. The premise of this study is that the home-based business income percentage will be lower in the poorer segments of the population than in the richer or middle class segments. However, on a comparative basis the earnings from business activity relative to employment (wage-related) activities are higher in poorer areas.

3. Research approach and methodology

The Kleinvlei area consists of 2,245 (N = 2,245) residential plots with household dwellings. A random sample of 500 (n = 500) households was extracted from this population. This is a statistically significant unstratified random sample of 0.1 (p = 0.1). Excluded from the sample were municipal dwellings used for non-residential purposes and inhabitants in informal dwelling structures (‘squatters’). In cases where more than one family was located in a single dwelling, the combined families were recorded as one household. However, where families were living in separate dwellings on the same erf (plot), they were recorded as separate households.

A questionnaire survey design was adopted for this research. The questionnaire, consisting of 43 closed-ended questions, was completed through interviews. The data were collected from 485 households, which reflected a response rate of 97 per cent. However, 27 were deemed incomplete, leaving 92 per cent of the questionnaires as admissible.

The questionnaire was divided into the following sections:

•	Biographical details, including educational attainment and mode of transport used
•	Employment earnings
•	Benefits-related data
•	Home-based business information
•	Household expenditure data

In addition to the questionnaire, an observation sheet was designed and used to capture information about the visible wealth status of the household, such as durable goods in the home and an estimation of their values. This was used to verify the size of income declared in the survey.

The data of household expenses and expenditure patterns, although not analysed in the findings, were used to validate the self-employment proportion of income. The amount of profit (or net business income) was extracted and extrapolated from the income and expenditure data provided by the respondents. Personal drawings (or direct consumption) from the businesses were distinctly excluded from the data collected. The business income was used as the basis for determining the ratio of home-based business income to wage earnings and the proportion it represented of household earnings.

4. Analysis

The findings drawn from the analysis are presented under separate headings.

4.1 Household size and population demographics

The study revealed that the average household size is 5.5 people. The household size of the bottom quintile was substantially smaller than the top quintile of inhabitants; namely, 4.19 members per household compared to 6.35 members per household. The top quintile had 51 per cent more members within the household. Furthermore, the female to male ratio was 55 per cent female to 45 per cent male. The national population gender breakdown has the same tendency.

In terms of age, the mean age of the total households was 27.4 years. This reflects the international tendency within developing communities who have a very youthful population. The trend towards a young population is further reflected in the mode which shows that the category of 10-year-old people is the biggest age frequency. Given this mode, it is natural that the central tendency or the median will be well below the mean, in this case 23 years old.

The study further revealed that a large proportion were single-parent households. This is considered high, given that the average household size is 5.5 members. If the married, divorced and widowed groupings should be aggregated (referred to as ‘house parents’), then the ratio of single people to ‘house parents’ was 3.8 to 1.6. Therefore, 20 per cent of the households were single parent households compared to 5 per cent within the economic housing sector households.

4.2 Qualification profile

Educational attainment within economically active groupings is an important indicator of employability. Table 1 in Appendix 4 depicts the qualification profile for the area. The statistics reveal that 1.8 per cent of the population (over the age of 18) had no formal school education. If functional literacy is deemed to be having had schooling up to grade 7 (standard 5), then 35 per cent of the economically active population are functionally illiterate.

Given the current high level of unemployment—according to Stats SA (2002), 37.5 per cent of the country's economically active population (EAP)—the demand for labour will dictate that the employability criterion include a minimum education level of grade 12. Should this criterion be applied within this population segment, then only 15 per cent of the economically active will be employable.

The study revealed that 52.1 per cent of the population attained a minimum qualification of grade 9, which is significant when compared to the legislative compulsory school-going grade. The employment rate in the area is determined at 48 per cent as per the expanded definition of unemployment.

The study also revealed that 646 respondents were presently at school, of which 65.3 per cent were at primary school and 34.7 per cent at secondary school. (Only six of these learners attended private schools, which included Model C schools.) There were three adult workers attending evening school and 33 respondents attended tertiary educational institutions.

4.3 The size and extent of employment and unemployment trends

The data and findings about the employed were critical in determining the extent of unemployment and underemployment. These were factors within the employment environment that could contribute to households or individuals electing to start a business. There was the assumption, given the nature of the area, that the unemployment figure would be high. A finding from the observations reveals that there is a strong feature of underemployment and non-gainful employment within the households.

To facilitate the analysis of the data, those respondents operating or involved in a home business industry were regarded as employed, albeit self-employed. Many of those who were operating businesses were unemployable, received no transfer benefits, and would rather be employed than eke out a living from self-employment.

Five of the unemployed (less than 1 per cent) derived benefits from the unemployment insurance fund (UIF). The unemployment rate for the whole area was 48 per cent. The unemployment rate in the economic housing sector was 7 percentage points lower than in the subeconomic housing sector (49 per cent). The unemployment figures within this study include new job entrants (see Table 2 in Appendix 4). This is high for a peri-urban area when compared with international trends.

The study showed that 46 per cent of the population were economically inactive. This relates to the fact that it was a youthful population with the highest frequency (mode) being the 10-year-old category.

4.4 Modes of transport

The mode of transport used shows a correlation to income earnings. This correlation is also present in South Africa. Low-income earners use public transport, whereas middle and upper income groups use cars. The use of the different modes of transport is reflected in Table 3 of Appendix 4.

4.5 Classification of income data

The income data are discussed under the following classifications:

•	Type of transfer benefits received
•	Employment-related (wage) earnings
•	Business-related income

4.5.1 Type of transfer benefits received

The dependency of households on transfer income was evident from the number receiving transfer benefits. Table 4, Appendix 4, depicts the different types of transfer income received. The major categories were state transfers (pensions and grants)—75.6 per cent, and private transfers—12 per cent. Significantly, only four people were receiving unemployment benefits.

4.5.2 Employment-related (wage) earnings

Employment-related earnings or simply wages remain the major contributor to income. On average, the portion of income attributed to employment earnings (wages) was 81 per cent. The mean wages were well above the household subsistence level (HSL). However, neither the mean nor the quintile distribution shows that 17 per cent of all households had no wage earnings. This is significant because of the fact that the bottom decile of the population, which has no income at all, is not brought to the fore. Table 5, Appendix 4, reflects the earnings relative to transfer benefits and proportionate to total income.

The data revealed that the bottom quintile of the households on average received less from employment than they did through transfers. The mean wage income of the bottom quintile was R53 per month compared to the R636 mean benefit income per month. The bottom decile of the total households received no wage income but had a mean benefit income of R282 per month. A decrease in the average transfer receipts occurs as the wage earnings increase.

4.5.3 Business-related income

Prior to the analysis the assumption was made that most of the home-based business income would be understated. However, in this study, when correlating the household expenditure with incomes declared by households operating a home business, it was found that the respondents were accurate in their declared income.

The different kinds of businesses have been categorised into five groupings; namely, entertainment, food outlets, transport-related, building and engineering and personal services. A ‘rapper’, i.e. a music-maker, has been listed separately because of the uniqueness of this service. Within the entertainment category three illegal liquor traders, commonly referred to shebeens, were observed. The average net profit of the shebeens was higher than the other businesses in this category. The average profit for the building and engineering category was improved by the building subcontracting and house renovations businesses. The likelihood of these contracts being repeated is remote, according to the owners. The highly profitable engineering businesses were located in the economic dwelling households. The business income of the subeconomic households was just over R1 000 per month.

A detailed breakdown of the weighted average profit per business type and business category can be found in Table 6, Appendix 4, which shows that 63 businesses were reported in 60 households. The main ratios are tabled in Table 7, Appendix 4. This Table shows the average business income as R1,023 per month, the mode R217 and the median R433. The business profit is as low as R50 per month.

The mean reflected above is the mean per total number of recipients within that particular category, as opposed to the average per household. The mean (average) per household required an additional calculation where the number of recipients within a household was considered. The participants per household ratios are reflected in Table 8, Appendix 4. Further analysis will require consideration and comparison of the household sizes.

The central tendency measurement within each economic grouping and across the three types of income, namely transfer receipts (benefits), wage or employment earnings (earnings) and business income or profit (business), are illustrated in Table 9 of Appendix 4. The average home-based household business income relative to wage earnings is shown in Table 10, Appendix 4.

The household business mean income was just over 7 per cent of waged-based earnings income. Within the subeconomic sector it was 6.3 per cent, compared to the 11.2 per cent of the economic sector. Per household, the percentage contribution of transfer income was 12.6 per cent, which was twice that of the percentage of business income reflected as 5.9 per cent. The subeconomic sector showed disparities of contribution, per income segment.

Table 11 of Appendix 4 summarises the measures of central tendency and the distributions of income generated; namely, transfer receipts (columns 1 and 8), employment earnings (columns 2 and 9), business profits (columns 3 and 10), the ratio or percentage of business income to wages income (columns 5 and 12) and the ratio or percentage of business income to total income (column 6 and 13). The quintiles and deciles show the distribution of the different types of income. The main ratio is that of business to wage earnings ratio being 6.2 per cent. This is presented primarily for the purpose of making comparisons. However, it illustrates the depth of the prevailing poverty.

5. Study findings

A significant number of the lower income earners who are transfer income-dependent are pensioners and mostly single income, single parent (specifically single female parent) households. The community has low educational attainment levels associated with high unemployment. Except for the fact that about 2 per cent of the economically active had no formal schooling, only about 15 per cent have attained grade 12 (standard 10). There is a coincidental correlation between the 52 per cent employment figure of the economically active and the 52 per cent having attained grade 9.

A small percentage of unemployed, 0.7 per cent of households, receive unemployment benefits, which shows that the duration of unemployment of most of the unemployed exceeds the time period allowed for collecting unemployment benefits.

Commensurate with unemployment is the mode of transport used. The fact that only 16 per cent of the population use a form of transport is indicative of the condition of poverty and weak consumer spending power. Significantly, a large percentage of the population walk to their required destinations. Transport (public or private) is deemed to be a luxury. In South Africa, it is known that the poorer communities mainly use public transport, especially rail transport, with 54.4 per cent of the households using trains.

The values of the different categories of income are shown in Table 12 of Appendix 4. It is evident that wage earnings were by far the major contributor (81.5 per cent) to total household income, followed by transfer benefits (13.4 per cent). The income quintiles reveal a high dependency on transfer benefits (73.2 per cent) in the bottom 20 per cent of the population, while the top 40 per cent derive about 80 per cent of their income from wage earnings. The income deciles provide a finer analysis of the income groups. The bottom decile has no wage earnings while the top decile derives 94 per cent of their income from wage earnings.

The most common ‘poverty-line’ measurement used in South Africa is the Household Subsistence Level (HSL) calculated by the Institute for Development Planning Research of the University of Port Elizabeth (Potgieter, 1993). The HSL is a spending line and represents the minimum level of spending needed to meet the basic needs of a family. The minimum level of income needed for an urban family of two adults and three children (a household of five) was estimated at R825.10 per month in September 1993. If this figure were adjusted by the annual inflation rates to September 2002 then the HSL value would be R1,571.57. This figure would place more than 30 per cent of the subeconomic households in this study below the poverty line.

The bottom 30 per cent of the population has an average total income of R1,368 per month. The bottom decile has a mean income of R533—less than the 1993 HSL. Using the $1 per day (at the exchange rate of R8.50 = $1 as a crude measurement instead of the purchasing power parity index), per capita, as the minimum poverty level (R1,275 for a household of five), then the same percentage (just above 30 per cent) will be categorised at an income level below the poverty line.

The study revealed that 13 of the households in the subeconomic dwelling category (3.3 per cent) have no income at all and 65 (or 16.5 per cent) have no wage earnings. Table 13 in Appendix 4 shows that a substantial number of households are totally reliant on the income of pensioners and income grants. These ‘no income’ residents survive through direct non-monetary assistance and support from neighbours, community organisations and family outside of the neighbourhood. Many of the poor households contain clusters of non-earners and low-earners with low school education.

The assumption that business profit will be understated, could lead to a further assumption that a number of the businesses are seasonal and therefore the profits would be overstated as they were not annualised. However, it must also be stated that many businesses were only in operation to ‘keep the pot boiling’.

The mean of 7 per cent income derived from business correlates with longitudinal surveys. The hypothesis that business income would be substantially more than 7 per cent as predicted in longitudinal studies because of the push from unemployment and the lack of other income within such a poor community was not proved. The business income in the poorer sector was less than anticipated and those with businesses in the lower deciles were poorer than estimated.

It was observed that the household business income mean for the whole area was just over 7 per cent of waged-based earnings income, but 6.3 per cent within the subeconomic sector. This correlates well with national surveys. The fact that the business income in the poorer segment was less than the total area mean substantiates Bradbury's point that those with business income in the ‘poorer communities are in fact poorer than’ at first estimated (1996: 1).

6. Conclusion and recommendations

The fact that the household transfer income exceeds the home-business income shows how small an impact home-based business has on household expenditures. The competition from major retail outlets that are in close proximity to householders could be a contributory factor for the minimal success of home-based businesses. The study concludes that the actual households who derive income from conducting home-based business do derive a distinct benefit from conducting the business but the primary condition towards improving the overall standard of living index of the community is minimal.

The lack of Unemployment Insurance Fund (UIF) benefits indicates that the unemployed have been unemployed for longer than one year, which exceeds the UIF benefit window. The fact that the unemployment rate is 48 per cent underscores the necessity for the majority of households to supplement (or augment) their wage-based earnings and transfer benefits with income derived from some form of household business, no matter how small, to finance basic household expenditures.

Furthermore, the lack of mobility and travel indirectly reveals the lack of exposure to other influences that could include work opportunities. This is basically a scenario of poverty entrapment. It shows that the majority of the population is trapped within the Kleinvlei area, decreasing the scope of growth, employment and other stimuli. There is no doubt that it would require a huge amount of non-local investment to get this geographic economy to grow fast in order to create more jobs. The need for non-local investment stems from the fact that the geographic domestic savings is virtually nil, as most of the income is used for consumption purposes.

An extension of this research could well be the formulation of such an investment strategy. A further extension would be to reproduce this survey in similar type of areas in the Western Cape and other parts in South Africa, which will also facilitate comparative analyses.

In view of the foregoing it is recommended that:

•	based on the findings of the research, local government set explicit targets to eradicate inequality and poverty and develop a framework for the compilation of primary baseline data
•	the contributing factors of poverty manifested in subeconomic dwelling areas be analysed so as to determine the type of investment needed to develop an environment conducive to raising consumption income and savings for reinvestment through incentives
•	basic educational attainment and employment opportunities be developed, and thereafter assistance (informational, educational, training and mentoring) in business development, especially turnaround strategies for liberation from locational (geographic) economic entrapment.

Additional information

Notes on contributors

Harry Herbert Ballard

Respectively, Senior Lecturer and Head of Department, Faculty of Business, Peninsula Technikon. Special appreciation is expressed to the Technical and Business Entrepreneurship Education Initiative in South Africa (Tabeisa) for their financial support in the initial study.

Appendix 1

Appendix 2

Appendix 3

Appendix 4

Table 1: Educational attainment

	#	%	Cum %	Cum % (Reversed)
No schooling	29	1.80	1.80	100.00
Grade 1	22	1.30	3.10	98.20
Grade 2	25	1.50	4.60	96.90
Grade 3	53	3.20	7.90	95.40
Grade 4	66	4.00	11.90	92.10
Grade 5	101	6.20	18.10	88.10
Grade 6	99	6.00	24.10	81.90
Grade 7	188	11.50	35.60	75.90
Grade 8	201	12.30	47.90	64.40
Grade 9	221	13.50	61.40	52.10
Grade 10	239	14.60	76.00	38.60
Grade 11	142	8.70	84.70	24.00
Grade 12	241	14.70	99.40	15.30
N4	1	0.10	99.50	0.60
Certificate	2	0.10	99.60	0.50
Diploma	7	0.40	100.00	0.40
Subtotal	1,637	66.50
Scholars & students	679	27.60
Not specified	145	5.90

Table 2: The magnitude of employment and unemployment

	Total	%	Subeconomic	%	Economic	%
Econ. inactive	1,148	46	980	45	168	49
Econ. active	1,361	54	1,188	55	173	51
Employed	711	52	610	51	101	58
Unemployed	650	48	578	49	72	42

Table 3: Mode of transport

	Mode of transport
	Total		Subeconomic		Economic
	Pop	Users	Pop	Users	Pop	Users
	%	%	%	%	%	%
	Train	19.9	50.6	19.0	54.4	25.2	37.7
Taxi	7.9	20.2	6.2	17.6	19.2	28.7
Car	5.5	14.0	4.1	11.7	14.4	21.5
Bus	1.7	4.3	1.6	4.6	2.4	3.6
Motorcycle	0.2	0.6	0.2	0.7	0.3	0.4
Bicycle	0.2	0.4	0.2	0.5	0.0	0.0
Other	3.9	9.9	3.7	10.5	5.4	8.1
Total	39	100	35	100	67	100

Table 4: Breakdown of transfer benefits

	Total			Subeconomic
	#	%	%	#	%	%
	UIF benefits	5	1.7		4	1.6
Social (Govt) pension	106	36.2		96	37.2
Social (Govt) grant	108	36.9	73	99	38.4	76
Private pension	16	5.5		14	5.4
Private grant	18	6.1	12	17	6.6	12
Other benefits	40	13.7		28	10.9

Table 5: Wage comparisons

	All households		Wages % of Tot-earn	Subeconomic		Wages % of Tot-earn
	Benefits	Wages		Benefits	Wages
	Mean	349		2,328	81.8		349	2,120	81.5
Mode	0	0		0	0
Median	0	1992		0	1,800
Max	5,000	19,000		5,000	16,120
Min	0	0		0	0
Quintiles
Bottom	636	53	5.6	650	46	5.2
Second	339	1,023	69.1	328	972	69.0
Third	332	1,903	82.3	369	1,817	79.9
Fourth	194	2,923	88.7	201	2,738	88.6
Top	242	5,730	92.7	200	5,029	94.0
Mean	349	2,326	81.8	350	2,120	81.5
Deciles
Bottom	282	0	0.0	317	0	0.0
Second	982	104	8.5	975	102	8.2
Third	350	734	57.2	394	714	52.2
Fourth	329	1,305	78.0	352	1,275	77.7
Fifth	388	1,702	80.7	265	1,641	83.9
Sixth	278	2,099	83.7	381	2,010	79.8
Seventh	190	2,601	88.0	167	2,468	86.4
Eight	198	3,238	89.3	239	3,023	90.4
Ninth	282	4,298	90.8	197	3,948	93.6
Top	202	7,147	93.9	203	6,110	94.2
Ave	348	2,323	81.8	349	2,129	81.5

Table 6: Weighted average profit per business type

	All households				Subeconomic only
	Monthly		Group		Monthly		Group
Business description	#	Total	Ave	Ave	#	Total	Ave	Ave
Entertainment	6			726	5			1,673
Video games	1	300	300		1	300	300
Games shop	1	1,300	1,300		1	1,300	1,300
Sports café	1	500	500		0
Shebeen (Liquor outlet)	3	6,767	2,256		3	6,767	2,256
Food outlets	27			307	25			971
Vegetables	2	867	433		2	867	433
Selling: Hampers	1	3,000	3,000		1	3,000	3,000
Tuck shop	21	21,762	1,036		20	19,595	980
Catering	1	217	217		1	217	217
Butchery	1	3,000	3,000		0
Chicken & clothing	1	600	600		1	600	600
Transport & related	3			1,889	2			1,083
Transport	2	2,167	1,083		2	2,167	1,083
Driving school	1	3,500	3,500		0
Building & engineering	10			2,357	5			1,740
Mechanical repairs	1	2,500	2,500		1	2,500	2,500
Repairing of bikes	1	1,000	1,000		1	1,000	1,000
House renovations	1	6,500	6,500		0
Bricklayer	1	867	867		0
Plumber	2	1,367	683		1	867	867
Subcontractor	1	4,333	4,333		1	4,333	4,333
Electrical engineer	1	0	0		1		0
Furniture repairs	1	1,000	1,000		0
Air-conditioning	1	6,000	6,000		0
Personal services	13			581	12			593
Hair	5	4,983	997		5	4,983	997
Dressmaking	1	450	450		1	450	450
Day care	5	1,033	207		5	1,033	207
Curtains	2	1,083	542		1	650	650
Rappers (Music)	1	1,265	1,265	1,265	1	1,265	1,265	1,265
Total	60	76,361		1,273	50	51,894		1,038

Table 7: Business income ratios

	Total	Subeconomic	Economic
Mean	2103	1023	2447
Mode	217	217	500
Median	500	433	1583
Minimum	50	50	533
Maximum	6500	4333	6500

Table 8: Participants per household ratios

	Per household ratios
Per household	Total	Subeconomic	Economic
Transfer beneficiaries	0.62	0.63	0.53
Wage earners	1.45	1.45	1.47
Businesses	0.13	0.13	0.16

Table 9: Income comparisons by type of income & households

		Maximum	Minimum	Mean per income category	Mean per household	Mode	Median
Total	Benefits	3,400		579	357	620	620
	Earnings	15,000		1,590	2,309	1,200	1,400
	Business	6,500	50	1,273	167	217	550
Sub-economic	Benefits	3,000		563	356	620	620
	Earnings	15,000		1,463	2,120	1,200	1,300
	Business	4,333	50	1,038	133	217	433
Economic	Benefits	3,400		703	374	620	620
	Earnings	10,000		2,327	3,418	2,000	1,550
	Business	6,500	433	2,447	382	500	1,583

Table 10: Business income mean relative to wage earnings mean

	Total %	Sub- economic%	Economic%
Household business income mean
As a % H/H wage income	7.2	6.3	11.2
As a % H/H wage & transfer income	7.2	6.3	11.2
As a % Total income	5.9	5.1	9.2
Transfer income as a total income	12.6	13.7	9.0

Table 11: Summary – Business income relative to wage earnings

	All households (Subeconomic & economic dwellings)						Subeconomic dwellings only
	1	2	3	4	5	6	7	8	9	10	11	12
	Trsfers	Wages	Bus	Total	B:W %	B:Tot %	Trsfers	Wages	Bus	Total	B:W %	B:Tot %
Mean	349	2,328	168	2,844	7.2		349	2,120	132	2,601	6.2
Mode	0	0	0	620			0	0	0	620
Median	0	1,992	0	2,370			0	1,800	0	2,210
Max	5,000	19,000	6,500	2,2000			5,000	16,120	4,333	16,370
Min	0	0	0	0			0	0	0	0
Quintiles
Bottom	636	53	259	948	488.7	27.3	650	46	192	888	4,17.4	21.6
2	339	1,023	119	1,481	11.6	8.0	328	972	108	1,408	11.1	7.7
3	332	1,903	76	2,311	4.0	3.3	369	1817	89	2,275	4.9	3.9
4	194	2,923	179	3,296	6.1	5.4	201	2738	151	3,090	5.5	4.9
Top	242	5,730	206	6,178	3.6	3.3	200	5029	122	5,351	2.4	2.3
Mean	349	2,326	168	2,843	7.2	5.9	350	2120	132	2,602	6.2	5.1
Deciles
Bottom	282	0	383	665	a	57.6	317	0	216	533	a	40.5
2	982	104	137	1,223	131.7	11.2	975	102	162	1,239	1,58.8	13.1
3	350	734	200	1,284	27.2	15.6	394	714	260	1,368	36.4	19.0
4	329	1,305	40	1,674	3.1	2.4	352	1,275	13	1,640	1.0	0.8
5	388	1,702	20	2,110	1.2	0.9	265	1,641	49	1,955	3.0	2.5
6	278	2,099	131	2,508	6.2	5.2	381	2,010	129	2,520	6.4	5.1
7	190	2,601	165	2,956	6.3	5.6	167	2,468	223	2,858	9.0	7.8
8	198	3,238	192	3,628	5.9	5.3	239	3,023	82	3,344	2.7	2.5
9	282	4,298	153	4,733	3.6	3.2	197	3,948	72	4,217	1.8	1.7
Top	202	7,147	259	7,608	3.6	3.4	203	6,110	173	6,486	2.8	2.7
Ave	348	2,323	168	2,839	7.2	5.9	349	2,129	138	2,616	6.5	5.3

Table 12: Contribution household income per income category

	Transfers		Wages		Business		Total
Subeconomic dwellings only
Mean	349	13.4%	2,120	81.5%	132	5.1%	2,601	100%
Mode	0		0		0		620
Median	0		1,800		0		2,210
Max	5,000		16,120		4,333		16,370
Min	0		0		0		0
Subeconomic dwellings only
Quintiles
Bottom	650	73.2%	46	5.2%	192	21.6%	888	100%
Second	328	23.3%	972	69.0%	108	7.7%	1,408	100%
Third	369	16.2%	1,817	79.9%	89	3.9%	2,275	100%
Fourth	201	6.5%	2,738	88.6%	151	4.9%	3,090	100%
Top	200	3.7%	5,029	94.0%	122	2.3%	5,351	100%
	350	13.5%	2120	81.5%	132	5.1%	2,602	100%
Subeconomic dwellings only
Deciles
Bottom	317	59.5%	0	0.0%	216	40.5%	533	100%
Second	975	78.7%	102	8.2%	162	13.1%	1,239	100%
Third	394	28.8%	714	52.2%	260	19.0%	1,368	100%
Fourth	352	21.5%	1,275	77.7%	13	0.8%	1,640	100%
Fifth	265	13.6%	1,641	83.9%	49	2.5%	1,955	100%
Sixth	381	15.1%	2,010	79.8%	129	5.1%	2,520	100%
Seventh	167	5.8%	2,468	86.4%	223	7.8%	2,858	100%
Eight	239	7.1%	3,023	90.4%	82	2.5%	3,344	100%
Ninth	197	4.7%	3,948	93.6%	72	1.7%	4,217	100%
Top	203	3.1%	6,110	94.2%	173	2.7%	6,486	100%
Ave	349	13.3%	2,129	81.4%	138	5.3%	2,616	100%

Table 13: Households in abject poverty

		No income	No wage
	Subeconomic households
1.1	No. of households with income	13
1.2	No. of residents* with no income	71.5
2.1	No. of households with wage earnings		65
2.2	No. of residents* with no wage earnings		357.5
3.1	No. of unemployed (U)	578	578
3.2	No. of households (HHs) in sample	394	394
4.1	Households with no income as a % of HHs	3.3%
4.2	Households with no wage income as a % of HHs		16.5%
4.3	No wage** recipients as a % of U	12.40%	22.49%
*For “no income” based on 5.5 residents per household.
** For “no wage” based on 2 economically active persons per household.