Tem-se falado demasiadamente sobre a distribuição espacial do pobre. Quanto mais desagregado o mapa, mais perfeita é a sua visualização, maior a evidência da heterogeneidade da pobreza e melhor o entendimento da maneira com que ela é formada e como pode ser combatida a partir de políticas públicas localmente específicas. O Rio Grande do Sul apresenta a pobreza distribuída de forma heterogênea por todo o território gaúcho (medida pela proporção de pobres - PP - e pelo índice de pobreza humana municipal - IPH-M), o que é corroborado a partir dos mapas de pobreza apresentados. A hipótese de que há aglomeração ('clusterização') da pobreza e não-pobreza no Rio Grande do Sul é confirmada para os dados fornecidos pelo IPEAdata para o ano 2000. Para tanto se buscou apresentar as definições de pobreza e as formas mais usadas para a sua mensuração e as dificuldades e os benefícios do mapeamento da pobreza, chamando a devida atenção ao fato que as dimensões e características da pobreza se manifestam de forma diferente quando o espaço é considerado. / There has been a lot of debate about the space distribution of the poor. The more disaggregated the map, the more perfect the evidence of poverty; aggregated national-level poverty data may obscure considerable regional variation and can bias public policies to fight poverty. Rio Grande do Sul State has its poverty (measured by the headcount index and by the human poverty index) distributed in a heterogeneous way throughout the 'gaucho' territory, what is corroborated by the presented maps of poverty displayed in this study. The hypotheses that there is a cluster of poverty and non-poverty in Rio Grande do Sul is confirmed when using the data supplied by IPEAdata for the year 2000. The confirmation was possible thanks to the use of spatial econometrics tools. To achieve this goal we presented the definitions of poverty and the most used ways to measure it and also the difficulties and the benefits of poverty mapping, giving proper attention to the fact that the dimensions and the characteristics of poverty occur in a different way when space is considered.

Tem-se falado demasiadamente sobre a distribuição espacial do pobre. Quanto mais desagregado o mapa, mais perfeita é a sua visualização, maior a evidência da heterogeneidade da pobreza e melhor o entendimento da maneira com que ela é formada e como pode ser combatida a partir de políticas públicas localmente específicas. O Rio Grande do Sul apresenta a pobreza distribuída de forma heterogênea por todo o território gaúcho (medida pela proporção de pobres - PP - e pelo índice de pobreza humana municipal - IPH-M), o que é corroborado a partir dos mapas de pobreza apresentados. A hipótese de que há aglomeração ('clusterização') da pobreza e não-pobreza no Rio Grande do Sul é confirmada para os dados fornecidos pelo IPEAdata para o ano 2000. Para tanto se buscou apresentar as definições de pobreza e as formas mais usadas para a sua mensuração e as dificuldades e os benefícios do mapeamento da pobreza, chamando a devida atenção ao fato que as dimensões e características da pobreza se manifestam de forma diferente quando o espaço é considerado. / There has been a lot of debate about the space distribution of the poor. The more disaggregated the map, the more perfect the evidence of poverty; aggregated national-level poverty data may obscure considerable regional variation and can bias public policies to fight poverty. Rio Grande do Sul State has its poverty (measured by the headcount index and by the human poverty index) distributed in a heterogeneous way throughout the 'gaucho' territory, what is corroborated by the presented maps of poverty displayed in this study. The hypotheses that there is a cluster of poverty and non-poverty in Rio Grande do Sul is confirmed when using the data supplied by IPEAdata for the year 2000. The confirmation was possible thanks to the use of spatial econometrics tools. To achieve this goal we presented the definitions of poverty and the most used ways to measure it and also the difficulties and the benefits of poverty mapping, giving proper attention to the fact that the dimensions and the characteristics of poverty occur in a different way when space is considered.

Tem-se falado demasiadamente sobre a distribuição espacial do pobre. Quanto mais desagregado o mapa, mais perfeita é a sua visualização, maior a evidência da heterogeneidade da pobreza e melhor o entendimento da maneira com que ela é formada e como pode ser combatida a partir de políticas públicas localmente específicas. O Rio Grande do Sul apresenta a pobreza distribuída de forma heterogênea por todo o território gaúcho (medida pela proporção de pobres - PP - e pelo índice de pobreza humana municipal - IPH-M), o que é corroborado a partir dos mapas de pobreza apresentados. A hipótese de que há aglomeração ('clusterização') da pobreza e não-pobreza no Rio Grande do Sul é confirmada para os dados fornecidos pelo IPEAdata para o ano 2000. Para tanto se buscou apresentar as definições de pobreza e as formas mais usadas para a sua mensuração e as dificuldades e os benefícios do mapeamento da pobreza, chamando a devida atenção ao fato que as dimensões e características da pobreza se manifestam de forma diferente quando o espaço é considerado. / There has been a lot of debate about the space distribution of the poor. The more disaggregated the map, the more perfect the evidence of poverty; aggregated national-level poverty data may obscure considerable regional variation and can bias public policies to fight poverty. Rio Grande do Sul State has its poverty (measured by the headcount index and by the human poverty index) distributed in a heterogeneous way throughout the 'gaucho' territory, what is corroborated by the presented maps of poverty displayed in this study. The hypotheses that there is a cluster of poverty and non-poverty in Rio Grande do Sul is confirmed when using the data supplied by IPEAdata for the year 2000. The confirmation was possible thanks to the use of spatial econometrics tools. To achieve this goal we presented the definitions of poverty and the most used ways to measure it and also the difficulties and the benefits of poverty mapping, giving proper attention to the fact that the dimensions and the characteristics of poverty occur in a different way when space is considered.

The aim of this dissertation was to analyse the effect of socio-economic factors on poverty in Nyakallong. Nyakallong is a former Black township in the Free State Province of South Africa. The effect of the socio economic factors on poverty was analysed using an econometric model. The analysis was based on data collected by the researcher and three fieldworkers who conducted a survey of 412 households in Nyakallong in 2009. To calculate poverty rates and the effect of socio-economic factors, data relating to the area was used. Poverty was defined and then measured for the township, and the profile of both the whole and the poor population was determined. The following poverty lines are used in South Africa – PDL, MSL, MLL, SLL, HSL and HEL. The HSL, which is defined as an estimate of the theoretical income needed by an individual household to maintain a defined minimum level of health and decency in the short term, was used as a measure of poverty in the area. The headcount index, poverty gap ratio and the dependency ratio were also used to measure poverty. The headcount index was found to be 0.472 for Nyakallong, meaning that 47.2% of all household’s income is below their respective poverty line. Poverty rate in Nyakallong was found to be 48.5% which is almost similar to the poverty rate of 49.1% for the Free State province, while poverty rate in Kwakwatsi was found to be 62.1%. The analysis of the sources of income of the poor showed that government grants constitute 64% of household income, with the old state pension grant alone contributing 16% to household income for a poor family. In Kwakwatsi, government grants contributed 38.4% of poor household’s income, with the old state pension grant having contributed 40.6%. On average, the whole population has a monthly income of R2 938, 35 compared to R1 140 which is received by the poor population; while in Kwakwatsi, the poor population received a monthly income of R688 and the whole population received an average of R1401.01. The expenditure patterns for the whole sampled population show that 39.7% of household income goes to buying food, compared to 44.3% for the poor sampled population of Nyakallong. In Kwakwatsi, poor population spent 49.2% of income on food and the whole population spent 33.4%. In Nyakallong, 50% of the whole population and 53% of the poor population was found to be economically inactive. In Kwakwatsi, 44% of the whole population and 56% of the poor population was found to be economically inactive. The unemployment rate of the poor in Nyakallong is 95.6% compared to 69.9% of the whole population. In Kwakwatsi 86.9% of the poor population and 79% of the whole population were unemployed. The dependency ratio was found to be 6 among the poor population and 2 for the whole population of Nyakallong, while in Kwakwatsi it was found to be 7 among the poor population and 4 among the whole population. The study analysed the socio-economic determinants of poverty in the area. The data was evaluated using hypothesis testing for statistical significance of the parameters. It was established that there is a positive relationship between education and the poverty gap ratio although it is statistically insignificant. It was also found that there is an inverse relationship between employment and poverty ratio. This complies with theory. The results also showed a positive relationship between household expenditure and the poverty gap – this is what was expected, because expenditure is the reduction of resources. On gender, the results confirm the generally held hypothesis that female headed households are poorer compared to their male counterparts. The results show that poverty is high among female headed households compared to male headed households. Household size was measured by the number of people staying in a given house. The household size was found to range from one to eleven members per household. The average household size was found to be 4.2 in Nyakallong, 3.9% in Kwakwatsi and 3.4% in the Free State. Household size is an important variable in determining poverty – increasing the household size by 10% is likely to increase the poverty gap of the household by about 1%. This might seem not significant, but this is a result that must be noted and handled with caution. More people in households also mean more expenditure on food items, medical expenses, clothing and education. In order to reduce the level of poverty in Nyakallong, job creation and employment opportunities should be targeted. The nearby university of technology and FET College should inform learners at secondary schools about funds (NFSAS) available to help them in furthering their studies. Educators should also engage learners to realise the disadvantages of large household size. Large organisations such as ESCOM and Harmony Gold could help by means of skills development, especially among youth and females, in order to make them employable. Unemployment can also be reduced by putting back into operation the closed mine shaft and Allanridge Sanatorium hospital. A food garden community programme should be established in order to reduce the level of poverty. People who are involved should be trained on how to manage and develop the programme. / MCom, Economics, North-West University, Vaal Triangle Campus, 2012

The aim of this dissertation was to analyse the effect of socio-economic factors on poverty in Nyakallong. Nyakallong is a former Black township in the Free State Province of South Africa. The effect of the socio economic factors on poverty was analysed using an econometric model. The analysis was based on data collected by the researcher and three fieldworkers who conducted a survey of 412 households in Nyakallong in 2009. To calculate poverty rates and the effect of socio-economic factors, data relating to the area was used. Poverty was defined and then measured for the township, and the profile of both the whole and the poor population was determined. The following poverty lines are used in South Africa – PDL, MSL, MLL, SLL, HSL and HEL. The HSL, which is defined as an estimate of the theoretical income needed by an individual household to maintain a defined minimum level of health and decency in the short term, was used as a measure of poverty in the area. The headcount index, poverty gap ratio and the dependency ratio were also used to measure poverty. The headcount index was found to be 0.472 for Nyakallong, meaning that 47.2% of all household’s income is below their respective poverty line. Poverty rate in Nyakallong was found to be 48.5% which is almost similar to the poverty rate of 49.1% for the Free State province, while poverty rate in Kwakwatsi was found to be 62.1%. The analysis of the sources of income of the poor showed that government grants constitute 64% of household income, with the old state pension grant alone contributing 16% to household income for a poor family. In Kwakwatsi, government grants contributed 38.4% of poor household’s income, with the old state pension grant having contributed 40.6%. On average, the whole population has a monthly income of R2 938, 35 compared to R1 140 which is received by the poor population; while in Kwakwatsi, the poor population received a monthly income of R688 and the whole population received an average of R1401.01. The expenditure patterns for the whole sampled population show that 39.7% of household income goes to buying food, compared to 44.3% for the poor sampled population of Nyakallong. In Kwakwatsi, poor population spent 49.2% of income on food and the whole population spent 33.4%. In Nyakallong, 50% of the whole population and 53% of the poor population was found to be economically inactive. In Kwakwatsi, 44% of the whole population and 56% of the poor population was found to be economically inactive. The unemployment rate of the poor in Nyakallong is 95.6% compared to 69.9% of the whole population. In Kwakwatsi 86.9% of the poor population and 79% of the whole population were unemployed. The dependency ratio was found to be 6 among the poor population and 2 for the whole population of Nyakallong, while in Kwakwatsi it was found to be 7 among the poor population and 4 among the whole population. The study analysed the socio-economic determinants of poverty in the area. The data was evaluated using hypothesis testing for statistical significance of the parameters. It was established that there is a positive relationship between education and the poverty gap ratio although it is statistically insignificant. It was also found that there is an inverse relationship between employment and poverty ratio. This complies with theory. The results also showed a positive relationship between household expenditure and the poverty gap – this is what was expected, because expenditure is the reduction of resources. On gender, the results confirm the generally held hypothesis that female headed households are poorer compared to their male counterparts. The results show that poverty is high among female headed households compared to male headed households. Household size was measured by the number of people staying in a given house. The household size was found to range from one to eleven members per household. The average household size was found to be 4.2 in Nyakallong, 3.9% in Kwakwatsi and 3.4% in the Free State. Household size is an important variable in determining poverty – increasing the household size by 10% is likely to increase the poverty gap of the household by about 1%. This might seem not significant, but this is a result that must be noted and handled with caution. More people in households also mean more expenditure on food items, medical expenses, clothing and education. In order to reduce the level of poverty in Nyakallong, job creation and employment opportunities should be targeted. The nearby university of technology and FET College should inform learners at secondary schools about funds (NFSAS) available to help them in furthering their studies. Educators should also engage learners to realise the disadvantages of large household size. Large organisations such as ESCOM and Harmony Gold could help by means of skills development, especially among youth and females, in order to make them employable. Unemployment can also be reduced by putting back into operation the closed mine shaft and Allanridge Sanatorium hospital. A food garden community programme should be established in order to reduce the level of poverty. People who are involved should be trained on how to manage and develop the programme. / MCom, Economics, North-West University, Vaal Triangle Campus, 2012

Thesis (M.Com. (Economics))--North-West University, Vaal Triangle Campus, 2005.

Thesis (M.Com. (Economics))--North-West University, Vaal Triangle Campus, 2005.

Thesis (M.Com. (Economics))--North-West University, Vaal Triangle Campus, 2005.

Thesis (Ph.D. (Economics))--North-West University, Vaal Triangle Campus, 2004.

Thesis (M.Com. (Economics))--North-West University, Vaal Triangle Campus, 2006.