This research study explored livelihood strategies that female headed households adopt in Magaso village of Mutoko district in Zimbabwe. The study intended to achieve the following objectives: (i) examine the existing livelihood strategies of female headed households (ii) explore the various challenges faced by female headed households and (iii) establish the support mechanisms in place for female headed households to cope with life challenges .The literature reviewed in the study was drawn from several researchers, and the study was shaped by the strengths perspectives and the liberal feminism perspective. The study was qualitative in nature and used interviews to collect data from fifteen (15) female headed households. The data collection process used an interview guide. The research employed a qualitative research design in the form of a case study cum a phenomenological study design. Data was analysed qualitatively using the content thematic data analysis which used interpretive approaches and presentation is textual rather than statistical. The study findings were the following: engaging in subsistence farming was found to be the main livelihood activity of the female heads; engaging in home gardens; exchanging labour for food; involvement in business; reliance on temporary employment from different agencies; reliance on handouts from government and other bodies; and household heads sanctioning child labour that compromises school attendance. Moreover, these female heads faced numerous difficulties ranging from emotional, social to financial problems that resulted in worsening the condition of women, and hence validating feminization of poverty among them. Several support mechanisms were discovered to be available for the female heads but they fail to produce to fruitful results to the lives of the female heads. The study made the following recommendations: mainstreaming gender education from childhood stage; efforts aimed at job creation; financial empowerment through setting up of micro schemes for rural women amongst; seeking the services of agricultural extension services to the female head farmers; improving the social services delivery in Zimbabwe equitably across genders and strengthening informal strategies to improve women‘s social capital. Lastly, the study concluded that little is being done in terms of policy formulation to make the support structures responsive to the female headed households especially in rural areas, hence the need for sustainable development through empowerment.

Thesis (M.Sc. (Statistics)) -- University of Limpopo, 2015 / Introduction: The high number of variables from the 2009 General Household Survey is prohibitive to do holistic analysis of data due to high correlations that exist among many variables, making it virtually impractical to apply traditional methods such as multinomial logistic regression. The purpose of this study to identify observed variables that can be explained by a few unobservable quantities called factors, using factor analysis. Methods: Factor analysis is used to describe covariance relationships among 162 variables of interest in the 2009 General Household Survey (GHS) and 2009 Quarterly Labour Force Survey of South Africa (QLFS). Data for the respondents aged 15 years and above was analysed by first applying factor analysis to the 162 variables to produce factor scores and develop models for five core areas: education, health, housing, labour force and social development. Multinomial logistic regression was then used to model educational levels and service satisfaction using identified factor sores. Results: The variability among the 162 variables of interest was described by only 29 factors identified using factor analysis, even though these factors are not measured directly. Multinomial logistic regression (MLR) analysis showed negative and significant impact of education factors (fees too high, violence and absence of parental care) on levels of educational attainment. “Historically advantaged” factor is the only factor significant and positively affects educational levels. Housing and social development factors were regressed against service satisfaction. Housing factors such as the home owners, age of a house and male household heads were found to be significant. Social development factors such as “no problem with health”, sufficient water, high income, household size and telephone access were found to be significant. Labour force factors such as employment, industrial business and occupation, employment history and long-term unemployment have positive and significant impact on levels of education. Conclusion: It can be concluded that factor analysis as a data reduction technique has managed to describe the variability among the 162 variables in terms of just 29 unobservable variables. Using MLR in subsequent analysis, this study has managed to identify factors positively or negatively associated with educational levels and service satisfaction. The study suggests that educational, housing, social development and labour force facilities should be improved and education should be used to improve life circumstances. Keywords: factor analysis, factors, multinomial logistic regression, logits, educational levels of attainment, service satisfaction, quality of service delivery. / DST-NRF, Centre of Excellence in Mathematical and Statistical Sciences (MaSS)

Performing health surveys in developing countries and humanitarian emergencies can be challenging work because the resources in these settings are often quite limited and information needs to be gathered quickly. The Expanded Program on Immunization (EPI) sampling method provides one way of selecting subjects for a survey. It involves having field workers proceed on a random walk guided by a path of nearest household neighbours until they have met their quota for interviews. Due to its simplicity, the EPI sampling method has been utilized by many surveys. However, some concerns have been raised over the quality of estimates resulting from such samples because of possible selection bias inherent to the sampling procedure. We present an algorithm for obtaining the probability of selecting a household from a cluster under several variations of the EPI sampling plan. These probabilities are used to assess the sampling plans and compute estimator properties. In addition to the typical estimator for a proportion, we also investigate the Horvitz-Thompson (HT) estimator, an estimator that assigns weights to individual responses. We conduct our study on computer-generated populations having different settlement types, different prevalence rates for the characteristic of interest and different spatial distributions of the characteristic of interest. Our results indicate that within a cluster, selection probabilities can vary largely from household to household. The largest probability was over 10 times greater than the smallest probability in 78% of the scenarios that were tested. Despite this, the properties of the estimator with equally weighted observations (EQW) were similar to what would be expected from simple random sampling (SRS) given that cases of the characteristic of interest were evenly distributed throughout the cluster area. When this was not true, we found absolute biases as large as 0.20. While the HT estimator was always unbiased, the trade off was a substantial increase in the variability of the estimator where the design effect relative to SRS reached a high of 92. Overall, the HT estimator did not perform better than the EQW estimator under EPI sampling, and it involves calculations that may be difficult to do for actual surveys. Although we recommend continuing to use the EQW estimator, caution should be taken when cases of the characteristic of interest are potentially concentrated in certain regions of the cluster. In these situations, alternative sampling methods should be sought. / Thesis / Master of Science (MSc)

Thesis (PhD)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: In order to evaluate the extent to which South Africa achieve the objectives of poverty and inequality reduction as well as job creation, up-to-date and reliable data are required. Since the transition, various survey data have been commonly used for these analyses, namely Census, Community Survey (CS) 2007, Income and Expenditure Survey (IES), October Household Survey (OHS), Labour Force Survey (LFS), Quarterly Labour Force Survey (QLFS), General Household Survey (GHS), Project for Statistics on Living Standards and Development (PSLSD), National Income Dynamics Study (NIDS) and All Media Products Survey (AMPS). However, these datasets are not fully comparable, due to differences in the sampling design, sample size, questionnaire structure, methodology to derive labour market status, as well as the way the income and expenditure information was collected. Hence, this dissertation begins by analysing these issues in each survey in Chapter 2. With regard to the income and expenditure information, it was collected differently in the surveys: the recall method was used in all surveys except IES 2005/2006, the only survey that adopted the diary method; respondents were asked to report the actual amount in some surveys but only asked to declare the relevant interval in others; for the former approach, respondents could either declare the single estimate amount or amounts for sub-categories that were then aggregated; for interval data, various methods can be used to determine the amount in each interval. Thus, Chapter 3 begins by discussing the merits and drawbacks of these approaches, as well as how they would affect the reliability and comparability of income and expenditure variables across the surveys. In some surveys (e.g., the two censuses and CS 2007), quite high proportions of households incorrectly reported zero income or expenditure or did not specify their income or expenditure. Poverty and inequality estimates could be influenced by either including or excluding these households from the analyses. Hence, various approaches to deal with these households are examined in Chapter 3. As the surveys typically under-captured income or expenditure when compared with the national accounts income, the validity of the resultant poverty and inequality estimates might be affected. Hence, arguments for and against adjusting the survey means in line with the national accounts mean (e.g. by shifting the survey distribution rightwards) are discussed. As the survey data are, strictly speaking, crosssectional and not designed for time-series labour market, poverty and inequality analyses, it is sometimes argued that the data should be re-weighted to be consistent with demographic and geographic numbers presented by the Actuarial Society of South Africa (ASSA) and Census data. This cross entropy re-weighting approach is discussed in Chapter 3. Finally, the chapter examines the labour market status derivation methodology in all OHSs, LFSs and QLFSs in greater detail, and investigates how the changes across the surveys could possibly affect the comparability of labour market estimates throughout the years. The dissertation then examines the labour market trends since the transition by using the OHS, LFS and QLFS data, and it is found that both the labour force and employment numbers increased in general since the transition, but the latter increase was not rapid enough to absorb the expanding labour force. In addition, the number of narrow unemployed doubled between 1994 and 2009, and the narrow unemployment rate showed an upward trend and peaked at just above 30% in 2003. It decreased between 2004 and 2007, before rising again in 2008- 2009 due to the impact of global recession. Application of the cross entropy approach does not substantially affect labour market trends, suggesting that the trends (including the abrupt increase in labour market estimates during the changeover from OHS to LFS) were either real or took place due to the improvement of the questionnaire to capture the labour market status of the respondents better. Furthermore, the application of the LFS 2000b-LFS 2007b methodology on the earlier surveys reduced the extent of the abrupt increase of the number of broad unemployed and broad unemployment rates during the changeover between OHS and LFS. Finally, the use of the QLFS methodology (which required minor revisions) on the LFSs greatly reduced the extent of the abrupt decrease of unemployment aggregates between LFS 2007b and QLFS 2008Q1, thereby improving the comparability of these aggregates across the surveys. In Chapter 5 poverty and inequality concepts are reviewed, followed by a detailed explanation of the sequential regression multiple imputation (SRMI) technique to deal with households with zero or missing income or expenditure, as well as the derivation of real income, expenditure and consumption variables in each survey. Poverty and inequality trends since the transition are examined in Chapter 6. With regard to poverty, with the exception of AMPS, the poverty trends were very similar across the surveys, that is, poverty increased since the transition, before a downward trend took place since 2000. As far as inequality is concerned, both the levels and trends in the Gini coefficients differed a lot amongst the surveys, as the estimates were very stable in the AMPSs, showed an upward trend in surveys like IESs and GHSs, but first increased until 2000 before a downward trend took place in others (e.g., the two censuses and CS 2007). The levels of inequality also differed when comparing the surveys. The abovementioned poverty and inequality estimates and trends could in part be affected by the various issues discussed in Chapter 3, thus there is a need for careful analysis. The impact of the number and width of intervals in which income or expenditure data are recorded on poverty and inequality estimates and trends are dealt with in greater detail in Chapter 6 by applying various intervals on the three IESs and NIDS 2008. It is found that the number and width of intervals only had some impact on these estimates and trends in some surveys. The effect of adjusting the survey means in line with the national accounts mean is also investigated. Finally, the application of the cross entropy re-weighting technique did not have any significant impact on the poverty and inequality estimates and trends. / AFRIKAANSE OPSOMMING: Data wat op datum en betroubaar is word vereis om te kan evalueer in watter mate Suid- Afrika sy doelwitte rakende die vermindering van armoede en ongelykheid en die skepping van werkgeleenthede bereik. Sedert die politieke oorgang word verskeie opnamedatastelle gewoonlik vir sulke ontledings gebruik, byvoorbeeld Sensusse, die Gemeenskapsopname van 2007, Inkomste- en Bestedingsopnames, Oktober-huishoudingsopnames, Arbeidsmagopnames, Kwartaallikse Arbeidsmagopnames, Algemene-Huishoudingsopnames, die Nasionale-Inkomste-Dinamika-Studie en die Alle-Media-en-Produkte-opnames. Weens verskille in steekproef-ontwerp, struktuur van die vraelyste, metodologie om arbeidsmarkstatus te klassifiseer, asook maniere waarop inligting oor inkomste en besteding ingewin is, is hierdie datastelle egter nie ten volle vergelykbaar nie, Gevolglik begin hierdie proefskrif in Hoofstuk 2 om elk van hierdie kwessies in elke opname te ontleed. Inkomste- en bestedingsinligting is in die opnames verskillend ingewin: In die meeste opnames is respondente gevra om aan te dui hoeveel hulle in die verlede bestee of verdien het, maar in die Inkomste- en Bestedingsopname van 2005/2006 is die dagboekmetode gebruik; respondente is in party opnames gevra om die presiese bedrag te vermeld, terwyl hulle in ander opnames die betrokke inkomste- of bestedingsinterval moes aandui; vir eersgenoemde is hulle gevra om òf die enkelbedrag te verklaar, òf hulle moes ‘n aantal sub-komponente onderskei; vir intervaldata kan verskillende metodes gebruik word om skattings van die inkomste in elke interval te maak. Dus begin Hoofstuk 3 met ‘n oorsig van die voor- en nadele van die verskillende benaderings en ‘n bespreking van hoe dit die betroubaarheid en vergelykbaarheid van inkomste- en bestedingsveranderlikes oor die opnames beïnvloed. In party opnames (bv. die twee sensusse en die Gemeenskapsopname van 2007) dui heelwat huishoudings verkeerdelik aan dat hulle geen inkomste verdien of uitgawes aangaan nie, of hulle spesifiseer nie hoeveel hulle verdien of bestee nie. Ramings van armoede en ongelykheid kan geraak word deur sulke respondent in te sluit of deur hulle uit te laat in die ontledings. Gevolglik word verskeie benaderings in Hoofstuk 3 bespreek om hiermee om te gaan. Omdat opnames vergeleke met die nasionale rekeninge tipies inkomste of besteding onderskat, mag dit die geldigheid van daaruitvoortspruitende armoede- en ongelykheidsramings raak. Gevolglik word argumente vir en teen die aanpsssing van die opname-data om dit in ooreenstemming te bring met die nasionale rekeninge (d.w.s. deur die verdeling na regs te verskuif) bespreek. Ten slotte, omdat die opnamedata streng gesproke kruissnitdata is en nie ontwerp is vir tydreekse van die arbeidsmag, armoede en ongelykheid nie, word soms aangevoer dat die gewigte van die data herweeg moet word om in ooreenstemming te wees met demografiese en geografiese data soos verkry van die Aktuariële Vereniging van Suid-Afrika en sensusdata. Hierdie kruisentropie herwegingsmetode word in Hoofstuk 3 bespreek. Ten slotte ondersoek die laaste hoofstuk die metodologie vir die bepaling van arbeidsmarkstatus in all die OHS, LFS en QLFS opnames in groter besonderhede, en ook hoe die veranderings oor die verskillende opname-reekse heen dalk die vergelykbaarheid van arbeidsmarkramings deur die jare kan beïnvloed. Die proefskrif ontleed daarna arbeidsmarktendense sedert die politieke oorgang met gebruik van die Oktober-huishoudingsoponames, Arbeidsmagopnames en Kwartaallikse Arbeidsmagopnames. Beide die arbeidsmag en indiensneming het sedert die transisie toegeneem, maar die toename in indiensneming was onvoldoende om die uitbreiding van die arbeidsmag te absorbeer. Verder het die getal eng-gedefinieerde werkloses tussen 1994 en 2009 verdubbel, en die eng werkloosheidskoers het ‘n toename getoon en in 2003 ‘n toppunt van 30% bereik. Dit het daarna tussen 2004 en 2007 gedaal voordat dit weer in 2008-2009 gestyg het weens die wêreldreseessie. Die toepassing van die kruisentropie-benadering het arbeidsmarktendense nie noemenswaardig beïnvloed nie, wat daarop dui dat hierdie tendense (insluitende die skielike toename in arbeidsmagramings in die oorgang van die Oktoberhuishoudingsopname- data na die Arbeidsmarkopname-data) werklik was, of anders plaasgevind het weens veranderings in die opnamevraelyste om respondente se arbeidsmarkstatus beter te probeer bepaal. Verder het die toepassing van die LFS2000b tot LFS 2007B metodologie op die vroeëre opnames die abrupte verlaging in die oorgang tussen die OHS en LFS in die getal breed-gedefineerde werkloses en breë werkloosheidkoerse verminder. Ten slotte het die gebruik van die QLFS-metodologie op die LFS (wat kleiner hersienings benodig het) die abrupte verlaging tussen LFS2007b en QLFS2008Q1 aansienlik verminder, en dus die vergelykbaarheid van hierdie groothede oor die opnames heen verbeter. In Hoofstuk 5 word eers ‘n oorsig van armoede- en ongelykheidsbegrippe gegee, waarma die sekwensiële-regressie-veelvoudige-imputasie-tegniek in besonderhede bespreek word. Hierdie tegniek word veral gebruik vir gevalle waar huishoudings aandui dat hulle inkomste of besteding nul is, of waar hulle nie antwoord nie. Daar is ook ‘n bespreking van die bepaling van reële inkomste, besteding of verbruiksveranderlikes in elke opname. Armoedeen ongeleykheidstendense word in Hoofstul 6 bespreek. Rakende armoede is daar, met uitsondering van die Alle-Media-en-Produkte-opname, eenstemmigheid dat dit sedert die politieke oorgang eers gestyg het voor dit sedert 2000 begin daal het. Sover dit ongelykheid aanbetref verskil neigings in die Gini-koëffissiënt baie tussen die opnames, want die ramings is stabiel oor die periode vir die Alle-Media-en-Produkte-opname, styg vir die Inkomste- en Bestedingsopname en die Algemene-Huishoudingsopnames, en styg tot 2000 voordat dit afneem in ander opnames (bv. die twee sensusse en die Gemeenskapsopname van 2007). Vlakke van ongelykheid verskil ook tussen die opnames. Deels kan die genoemde tendense in armoede- en ongelykheid dalk toegeskryf word aan die kwessies wat in Hoofstuk 3 bespreek is. Die effek van die getal en wydte van die intervalle waarin inkomste- en bestedingsdata ingewin word op ramings van armoede en ongelykheid word in meer besonderheid in Hoofstuk 6 bespreek. Deur die toepassing van verskillende intervalle op data van die drie Inkomste- en Bestedingsopnames en die Nasionale-Inkomste-Dinamika-studie word bevind dat die getal en wydte van intervalle ‘n beperkte effek op hierdie ramings en tendense het. Verder word gekyk na die effek van die aanpssing van die opname-data om dit in ooreenstemming met die nasionale rekeninge te bring. Ten slotte word getoon dat die gebruik van die kruisentropie-metode nie enige beduidende uitwerking op armoede- en ongeleykheidsramings en -tendense het nie.

The social conditions in the rural areas of South Africa are by now widely known and numerous researchers have documented the poverty stricken quality of life which generally prevails amongst black families in these areas (Wilson and Ramphele, 1989). The vast majority of rural households have incomes which are below subsistence levels and in the Homelands, agricultural productive ability has become so eroded that rural household income is now chiefly derived from remittances from migrants in the towns or from the wages of farm labourers (Nattrass and May, 1986). As such, at present the majority of black rural households living in Region E make up consumer communities which must purchase the majority of their subsistence needs, rather than producer communities in which subsistence needs can be met from the utilisation of local resources (Derman and Poultney, 1983). Despite this unpromising situation, the diminishing importance of agricultural production to the South African national economy (Bethlehem, 1989), and the dominance of urbanisation as a social force, it can be argued that the rural areas of Region E will be directly and substantially affected by efforts to restructure the South African economy as a whole. Consequently, revitalising the rural economy in a restructured social and economic system would be a concern in itself, even though the effect of this for a future growth path for South Africa may be uncertain (Kaplinsky, 1991:54). The report will first examine the broad demographic changes in Region E noting the impact of these changes on the rural areas. Thereafter, the economic processes which characterise the rural areas will be discussed, in particular, employment, income levels and income distribution. This will feed into a discussion of the social processes which will include changing dynamics of migration, and a socio-economic profile of rural households. The paper concludes by briefly examining access and usage of basic services and facilities in the rural parts of Region E.

The purpose of this research is to develop and test a widely available, ready-to-use method for adjusting the Institute of Transportation Engineers (ITE) Trip Generation Handbook vehicle trip generation estimates for urban context using regional household travel survey data. The ITE Handbook has become the predominant method for estimating vehicle trips generated by different land uses or establishment, providing a method for data collection and vehicle trip estimation based on the size of the development (e.g. gross square footage, number of employees, number of dwelling units). These estimates are used in traffic impact analysis to assess the amount of impact the development will have on nearby transportation facilities and, the corresponding charges for mitigating the development's negative impacts, with roadway expansions, added turning bays, additional parking or traffic signalization, for example. The Handbook is often criticized, however, for its inability to account for variations in travel modes across urban contexts. For more than fifty years, ITE has collected suburban, vehicle-oriented data on trip generation for automobiles only. Despite the provision of warnings against application in urban areas, local governments continue to require the use of the ITE Handbook across all area-types. By over predicting vehicle traffic to developments in urban developments, developments may be overcharged to mitigate these developments locating in urban environments despite the lower automobile mode shares, discouraging infill development or densification. When ITE's Trip Generation Handbook overestimates the vehicle impact of a development, facilities are also overbuilt for the automobile traffic and diminishing the use of alternative modes. When ITE's TGH underestimates this impact, adjacent facilities may become oversaturated with traffic, pushing cars onto smaller facilities nearby. Currently, there is momentum amongst practitioners to improve these estimation techniques in urban contexts to help support smart growth and better plan for multiple modes. This research developed and tested a method to adjust ITE's Handbook vehicle trip generation estimates for changes in transportation mode shares in more urban contexts using information from household travel surveys. Mode share adjustments provide direct reductions to ITE's Handbook vehicle trip estimations. Household travel survey (HTS) data from three regions were collected: Portland, Oregon; Seattle, Washington; and Baltimore, Maryland. These data were used to estimate the automobile mode share rates across urban context using three different adjustment methodologies: (A) a descriptive table of mode shares across activity density ranges, (B) a binary logistic regression that includes a built environment description of urban context with the best predictive power, and (C) a binary logistic regression that includes a built environment description of urban context with high predictive power and land use policy-sensitivity. Each of these three methods for estimating the automobile mode share across urban context were estimated for each of nine land use categories, resulting in nine descriptive tables (Adjustment A) and eighteen regressions (Adjustments B and C). Additionally, a linear regression was estimated to predict vehicle occupancy rates across urban contexts for each of nine land use categories. 195 independently collected establishment-level vehicle trip generation data were collected in accordance with the ITE Handbook to validate and compare the performance of the three adjustment methods and estimations from the Handbook. Six land use categories (out of the nine estimated) were able to be tested. Out of all of the land uses tested and verified, ITE's Trip Generation Handbook appeared to have more accurate estimations for land uses that included residential condominiums/townhouses (LUC 230), supermarkets (LUC 850) and quality (sit-down) restaurants (LUC 931). Moderate or small improvements were observed when applying urban context adjustments to mid-rise apartments (LUC 223), high-turnover (sit-down) restaurants (LUC 932). The most substantial improvements occurred at high-rise apartments (LUC 222) and condominiums/townhouses (LUC 232), shopping centers (LUC 820), or coffee/donut (LUC 936) or bread/donut/bagel shops (LUC 939) without drive-through windows. The three methods proposed to estimate automobile mode share provides improvements to the Handbook rates for most infill developments in urban environments. For the land uses analyzed, it appeared a descriptive table of mode shares across activity density provided results with comparable improvements to the results from the more sophisticated binary logistic model estimations. Additional independently collected establishment-level data collections representing more land uses, time periods and time of days are necessary to determine how ITE's Handbook performs in other circumstances, including assessing the transferability of the vehicle trip end rates or mode share reductions across regions.

The transportation community is exploring how new "big'' databases constructed by companies or public administrative agencies can be used to better understand travelers' behaviors and better predict travelers' responses to various transportation policies. This thesis explores how a large targeted marketing database containing information about individuals’ socio-demographic characteristics, current residence attributes, and previous residential locations can be used to investigate research questions related to individuals' transportation preferences and the built environment. The first study examines how household vehicle ownership may be shaped by, or inferred from, previous behavior. Results show that individuals who have previously lived in dense ZIP codes or ZIP codes with more non-automobile commuting options are more likely to own fewer vehicles, all else equal. The second study uses autoregressive models that control for spatial dependence, correlation, and endogeneity to investigate whether investments in public transit infrastructure are associated with higher home values. Results show that willingness-to-pay estimates obtained from the general spatial Durbin model are less certain than comparable estimates obtained through ordinary least squares. The final study develops an empirical framework to examine a housing market's resilience to price volatility as a function of transportation accessibility. Two key modeling frameworks are considered. The first uses a spatial autoregressive model to investigate the relationship between a home's value, appreciation, and price stability while controlling for endogenous missing regressors. The second uses a latent class model that considers all these attributes simultaneously, but cannot control for endogeneity.

The level of savings in South Africa has been described as dismal and on the verge of becoming an economic crisis. Household savings has declined to a level of dissavingand is therefore in need of rectification. Savings can be broken down into household, corporate and government saving. The purpose of this research is to identify measures to improve household savings in South Africa. A critical review of household savings is conducted in chapter two utilising secondary data to examine household savings, identify factors affecting savings as well as establish measures to improve household savings. The study gathers primary data from 10leading economists and financial experts located in South Africa.A qualitative study is undertaken as it helps provide intrinsic information on the thoughts and opinions of the sample group on measures to improve household savings. The research has revealed that South African households are not saving sufficiently and that there are a few key factors affecting households savings. The key factors are indentified and investigated in the literature review and further examined by the respondents for their expert opinions. The respondents have identified thathousehold savings behaviours are insufficiently contributing to savings and there is a lack of a savings culture to encourage positive savings growth. Consumers are caught up in a web of consumerism with easy access to credit as a result of financial liberalisation. These two factors have created a debt trend and left many households in a downward spiral of debt. The respondents have identified the main factors affecting households as: savings culture,financial literacy, consumerism, income levels, education and interest rates. Measures identified to improve household savings are: Tax breaks, government incentives to saving, education, budgeting as well as developing a national culture of saving. These suggestions help outline a path for government, corporations and individuals to follow in achieving greater household savings. The research has outlined measures to improve household savings and stressed that there is no one single measure to rectify the savings dilemma, but rather it is to identify and acknowledge that the savings solution lies in addressing each of the factors affecting saving with a view to improving saving as a whole. / Thesis (MBA)-University of KwaZulu-Natal, 2011.

Univ., Diss.--Kiel, 2003. / Literaturverz. S. 157 - 168.

This essay concerns itself with the measurement of poverty. Through a lens of gender sensitivity, combined with three dimensions of gender, this essay analyses the quality of various SDG indicators. The paper has incorporated a methodology based on a qualitative content analysis, this method proves as an excellent way to analyse the strength of indicators relating to the SDG:s, as well as proves to work in symbiosis with the analytical framework of this essay. Through the analysis of this essay, it has been concluded that the instances as the SDG:s measure poverty proves to be insufficient. As the overall quality, combined with in some instances, the quantity of measurement instances holds sub-standard characteristics, the goal of leaving no one behind proves to be quite difficult to overcome.