Magister Philosophiae - MPhil / The right for every woman to live free of violence is one of the basic human rights. Nevertheless, this right is still subject to violation on a massive and systematic scale around the world. At least one of three women around the world has reported been physical, sexual and emotional abuse by an intimate partner in her lifetime. Although, gender inequalities and discrimination are considered as the underlying factors of domestic violence, little is known about the contributions of the presence of sons and daughters at home, age, gender, education, marital status, working status, place of residence. Despite the fact that recommendations have been made both at the international and national levels to reduce intimate female abuse, the issue is still rampant in developing countries. The aim of this study was to identify and compare the impact of women’s socio-economic and demographic characteristics on domestic violence across seven countries (Cameroon, Ghana, Haiti, Liberia, Moldova, Nepal and Philippines). Frequencies and bivariate analyses were performed using the latest Demographic Health Surveys from 2005 to 2011. The findings established that on average 33.37 percent of women across the seven countries are abused. Domestic violence is a high concern in Cameroon. The educational level still remains a predicting factor of domestic violence across the countries under investigation except in Liberia. The number of living children was also identified as a predicting factor across the studied countries. Finally, a woman having a son or a daughter at home is more likely to expose the woman to intimate violence in Cameroon, Haiti, Moldova, Nepal and Philippines. All the countries are entrenched in a culture of male domination whereby women lack the freedom to decide on marital issues. As a recommendation, the government of each of the studied countries should be more proactive in reinforcing judicial system, policies and education that will help to curb the scourge of domestic violence. Furthermore, improving the level of literacy for women and educating men as the perpetrators of domestic violence will go a long way in abating this social ill.

This is a descriptive study of functional specialization in cities and its relation to certain social, economic and demographic variables.

<p>The study examines the participation of African immigrants in the South African labour force with the central question revolving around whether the immigrants create jobs through the establishment of their own businesses or take jobs from the locals. Analytical frame work used in this study includes descriptive statistics, chi-square test for association and standardized residuals, two-way analysis of variance and logistic regression. Demographic, locational and socio-economic characteristics were studied using descriptive and inferential statistical analysis. Two-factor analysis of variance was used to examine the differences on average in the African immigrants&rsquo / participation in the labour force, while logistic regression was used to investigate the effect of some demographic characteristics on employment and work status.</p>

<p>The study examines the participation of African immigrants in the South African labour force with the central question revolving around whether the immigrants create jobs through the establishment of their own businesses or take jobs from the locals. Analytical frame work used in this study includes descriptive statistics, chi-square test for association and standardized residuals, two-way analysis of variance and logistic regression. Demographic, locational and socio-economic characteristics were studied using descriptive and inferential statistical analysis. Two-factor analysis of variance was used to examine the differences on average in the African immigrants&rsquo / participation in the labour force, while logistic regression was used to investigate the effect of some demographic characteristics on employment and work status.</p>

Magister Philosophiae - MPhil / The study examines the participation of African immigrants in the South African labour force with the central question revolving around whether the immigrants create jobs through the establishment of their own businesses or take jobs from the locals. Analytical frame work used in this study includes descriptive statistics, chi-square test for association and standardized residuals, two-way analysis of variance and logistic regression. Demographic, locational and socio-economic characteristics were studied using descriptive and inferential statistical analysis. Two-factor analysis of variance was used to examine the differences on average in the African immigrants; participation in the labour force, while logistic regression was used to investigate the effect of some demographic characteristics on employment and work status. / South Africa

Thesis (M.Sc. Agriculture (Agricultural Economics)) -- University of Limpopo, 2022 / The poultry industry consists of the broiler and layer production. Most of the broiler chickens produced by smallholder farmers in villages are sold to local customers with lower degrees of processing, compared to large commercial farmers who have access to retail and export markets. The aim of this study was to analyse the determinants of gross margin and savings among small-scale broiler producers in Vhembe District of Limpopo Province. In the analyses the following objectives were performed; identifying and describing their socio-economic characteristics, assessing their gross margin, analysing the factors influencing their gross margin and lastly, by analysing the factors affecting savings among these farmers. The study was conducted in three municipalities (Makhado, Thulamela and Musina) under Vhembe District, where 60 respondents were purposively and randomly selected. The total number of households per municipality in Vhembe District were used to determine the exact number of broiler producers to be interviewed in each municipality due to insufficient data available regarding the total number of broiler producers in the district. The respondents were interviewed face to face using structured questionnaires. To achieve the study objectives the study used Descriptive statistics, Gross Margin analysis, Multiple Linear Regression and Logistic Regression model. The results of the study showed that the small-scale broiler producers in Vhembe District are profitable, with an average Gross Margin of R6470.78 per cycle. Six variables from Multiple Linear Regression analysis were found to have an influence on Gross Margin among small-scale broiler producers in Vhembe District. These variables were gender, primary economic activity, cost of day-old chicks, feed cost, electricity cost and labour cost. Seven variables from Logistic Regression analysis were found to have significant influence on savings. These variables were age, primary economic activity, monthly income, gross margin, feeds cost, cost of day-old chicks and vaccines. The study recommends that the broiler producers invest in other heating methods that do not require the use of electricity since it plays an important role towards the savings. The study further recommends that the Department of Agriculture should encourage the small-scale broiler producers to register their enterprise to be able to access extension services and other services from the department when necessary.

The dissertation examines the impact of Organizational Life Cycle (OLC) and the Country Socio Economic Characteristics (CSEC) on Top Management Team (TMT) Characteristics. The dissertation first elaborates and establishes the theoretical link between Organization Life Cycle, Country Socio-Economic Characteristics and characteristics of TMT. Second, a quantitative empirical study is conducted to test the OLC phases and CSEC impact on the TMT characteristics through characteristics. The dissertation outlines a detailed research methodology based on the state-of-art in the area of OLC, TMT and CSEC that will be implemented to answer the key research questions in regards to the scope of the doctoral thesis. Data set is collected from the 300 largest Swiss, German and Czech companies at year-end 2011, including detailed data on the country socio economic characteristics and career backgrounds of all TMT members (executive boards) at these companies at the end of 2011. A detailed procedure is developed to accurately classify organizations at different lifecycle phases, drawing extensively on existing literature and scales. Multilevel data analysis techniques are employed to understand how the different organization lifecycle phases influence both the level of TMT characteristics as well as changes in TMT composition and diversity due to inbound and outbound mobility of top managers over time. Substantial research synergies and knowledge transfer effects expected to emanate from this dissertation. In the dissertation regression and correlation analysis are applied for each phase of the companies' OLC in Switzerland, Germany and the Czech Republic. The dissertation states that more mature the company is more diversified the TMT are regardless the country. Also, the country impact has its own role in the relationship between the OLC and TMT characteristics which is expressed by the findings that Switzerland and Germany are more diversified than the Czech Republic in terms of TMT characteristics as gender diversity, age diversity, nationality diversity, education background of the TMTs, TMT dominant functions and TMT career length. The doctoral thesis contributes to the research by revealing relationships between TMT, CSEC and OLC theories. Also it develops methods and techniques for finding the interconnections between the OLC phases, CSEC with the TMT characteristics and the dissertation outlines the future research gaps in the area of TMT.

The study was conducted to describe how socio-economic characteristics (SEC) of women affect their utilization of maternal healthcare services in Abuja Municipal Areas Council (AMAC) in Abuja Nigeria. A non-experimental, facility-based cross-sectional survey was done. Data was collected using structured interviewer administered questionnaire in 5 district hospitals in AMAC. Sample size of 384 was calculated a priori based on the assumption that 50% of the target population utilized maternal healthcare services during their last pregnancy. Equal allocation of samples per facility was done. The ANC register was used as the sampling frame and proportionate allocation of samples per clinic days was undertaken in each facility. Data analysis included descriptive statistics, cross tabulations and measures of inequality. Logistic regression analysis was used to test the hypothesized relationship between socioeconomic characteristics (predictors) and maternal healthcare service utilization. Other than birth order that showed consistent effect, the results of this study indicated that the predictive effect (predisposing and enabling factors) of the SEC of women included in this study (age, education, birth order, location of residence, income group and coverage by health insurance) on maternal healthcare service utilization were not consistent when considered independently (bivariate analysis) as opposed to when considered together through logistic regression. In addition, the study revealed that there was inequality in the utilization of maternal healthcare services (ante-natal care - ANC, delivery care and post natal care - PNC, and contraceptive services) among women with different SEC, and the payment system for maternal healthcare services was regressive. Addressing these predictors in the natural co-existing state (as indicated by the logistic regression) is essential for equitable access and utilization of healthcare during pregnancy, delivery and the postnatal period, and for contraceptive services in AMAC, Abuja Nigeria. Targeted policy measures and programme actions guided by these findings are recommended to optimise returns on investment towards achieving national and global goals on maternal health in Nigeria / Health Studies / D. Litt. et Phil. (Health Studies)

The study was conducted to describe how socio-economic characteristics (SEC) of women affect their utilization of maternal healthcare services in Abuja Municipal Areas Council (AMAC) in Abuja Nigeria. A non-experimental, facility-based cross-sectional survey was done. Data was collected using structured interviewer administered questionnaire in 5 district hospitals in AMAC. Sample size of 384 was calculated a priori based on the assumption that 50% of the target population utilized maternal healthcare services during their last pregnancy. Equal allocation of samples per facility was done. The ANC register was used as the sampling frame and proportionate allocation of samples per clinic days was undertaken in each facility. Data analysis included descriptive statistics, cross tabulations and measures of inequality. Logistic regression analysis was used to test the hypothesized relationship between socioeconomic characteristics (predictors) and maternal healthcare service utilization. Other than birth order that showed consistent effect, the results of this study indicated that the predictive effect (predisposing and enabling factors) of the SEC of women included in this study (age, education, birth order, location of residence, income group and coverage by health insurance) on maternal healthcare service utilization were not consistent when considered independently (bivariate analysis) as opposed to when considered together through logistic regression. In addition, the study revealed that there was inequality in the utilization of maternal healthcare services (ante-natal care - ANC, delivery care and post natal care - PNC, and contraceptive services) among women with different SEC, and the payment system for maternal healthcare services was regressive. Addressing these predictors in the natural co-existing state (as indicated by the logistic regression) is essential for equitable access and utilization of healthcare during pregnancy, delivery and the postnatal period, and for contraceptive services in AMAC, Abuja Nigeria. Targeted policy measures and programme actions guided by these findings are recommended to optimise returns on investment towards achieving national and global goals on maternal health in Nigeria / Health Studies / D. Litt. et Phil. (Health Studies)

Text in English with summaries and keywords in English, Venda and Sepedi / Fuelwood remains a crucial source of energy among the vast majority of rural households because of its availability and affordability in comparison with most energy alternatives. Approximately 17 million people in South Africa live in communal lands where fuelwood can be harvested easily and freely by households, with 80% of the overall fuel consumed for domestic purposes extracted from burning fuelwood. The rapid-excess trends of fuelwood consumption – aggravated by population growth, agricultural and household settlement expansions – pose utmost challenges for community development. Overharvesting of fuelwood can result in fuelwood scarcity, loss of biodiversity, excessive land clearance and soil erosion. This study evaluated the factors that influence fuelwood consumption in households at the Thulamela Local Municipality. The study utilised mixed research methods, comprising quantitative and qualitative methods. A semi-structured questionnaire consisting of both closed and open-ended questions was used to collect data from the households. The collected data was mainly qualitative data (nominal and categorical data) and the researcher used the frequency menu to summarise the data and cross tabulation menu in the Statistical Package for Social Scientists (SPSS) version 25. For cross tabulation, the researcher used the Chi-square (χ2) test to measure the degree of association between two categorical variables. If the p-value is less than 0.05, there is a significant association between variables – thus, the variables dependent on each other. The study found that socio-economic characteristics such as monthly income, employment status, gender, educational level of the household head, number of employed household members, energy expenditure and type of occupation play a significant role in the factors that influence fuelwood consumption. As a result of these factors, fuelwood energy is still being used as a primary energy source by most households to meet their domestic needs for cooking and water heating – despite most of them being electrified. Additionally, lack of environmental education, the erratic electricity supply and staggering living conditions which drive widespread poverty in rural areas contribute to the extensive fuelwood consumption among households. The study highlighted the recommendations on mitigation measures that can be used to reduce extensive fuelwood consumption. These recommendations include encouraging the use of renewable energy and modern energy technologies such as biogas and solar energy, together with improved cooking stoves to help reduce overexploitation of natural resources and prevent indoor air pollution which is associated with heart disease and immortality. There is also a need to raise environmental awareness. It is through education that people’s perceptions, attitudes and behaviour regarding fuelwood consumption practices can be changed. The promotion of sustainable development through harvest control and afforestation can significantly reduce deforestation, loss of biodiversity, fuelwood scarcity and soil erosion. / Khuni dzi kha ḓi shumiswa sa tshiko tshihulwane tsha mafulufulu kha miṱa minzhi ya mahayani ngauri dzi a wanala na u swikelelea musi dzi tshi vhambedzwa na dziṅwe nḓila dza mafulufulu. Vhathu vha swikaho miḽioni dza 17 Afrika Tshipembe vha dzula mahayani hune vha kona u reḓa khuni hu si na vhuleme nahone nga mahala, ngeno zwivhaswa zwi swikaho 80% zwi shumiswaho miḓini zwi tshi bva kha khuni. Maitele maṅwe a tshihaḓu a kushumiselwe kwa khuni – a tshi ṋaṋiswa na nga nyaluwo ya vhathu, u engedzea ha vhulimi na vhupo ha vhudzulo – zwi ḓisa khaedu kha mveledziso ya tshitshavha. U reḓa khuni lwo kalulaho zwi nga vhanga ṱhahelelo ya khuni, u xelelwa nga mutshatshame wa zwi tshilaho, u ṱangula mavu na mukumbululo wa mavu. Ngudo iyi yo ḓiimisela u ela zwivhumbi zwi ṱuṱuwedzaho u shumiswa ha khuni miḓini ngei kha Masipala Wapo wa Thulamela. Ngudo yo shumisa ngona dza ṱhoḓisiso dzo ṱanganaho dzi re na ngona khwanthethivi na khwaḽithethivi. Mbudzisambekanywa dzo dzudzanywaho dzi re na mbudziso dza phindulo nthihi na dza phindulo ndapfu dzo shumiswa u kuvhanganya data miḓini. Data yo kuvhanganyiwaho kanzhi ndi yo sedzaho ndeme (ya tshivhalo na khethekanyo) ngeno muṱoḓisisi o shumisa menyu wa tshivhalo tsha zwithu u nweledza data na menyu wa thebulu dzi leluwaho kha Statistical Package for Social Scientists (SPSS) vesheni ya vhu 25. U itela thebulu dzi leluwaho, muṱoḓisisi o shumisa ndingo dza Chi-square (χ2) u ela tshikalo tsha nyelelano vhukati ha zwithu zwivhili zwo fhambanaho. Arali ndeme ya p i ṱhukhu kha 0.05, hu na u elana hu hulwane vhukati ha zwithu zwi vhambedzwaho – zwithu izwi zwi dovha zwa ṱalutshedzana. Ṱhoḓisiso yo wana uri zwiṱaluli zwa ikonomi na matshilisano sa mbuelo ya ṅwedzi, tshiimo mushumoni, mbeu, ḽeveḽe ya pfunzo ya ṱhoho ya muḓi, tshivhalo tsha vhathu vha shumaho muṱani, mbadelo dza fulufulu na mushumo une muthu a u shuma zwi na mushumo muhulwane kha zwithu zwi ṱuṱuwedzaho u shumiswa ha khuni. Nga ṅwambo wa zwithu izwi, khuni dzi kha ḓi shumiswa sa tshiko tshihulwane tsha fulufulu kha miḓi minzhi u swikelela ṱhoḓea dzavho dza hayani dza u bika na u wana u dudedza – naho vhunzhi havho vhe kha muḓagasi. Nṱhani ha izwo, u sa vha na pfunzo ya vhupo, nḓisedzo ya muḓagasi ine ya dzula i tshi shanduka na maga a kutshilele a konḓaho ane a vhanga vhushai ho andaho kha vhupo ha mahayani zwi vhanga u shumiseswa ha khuni miḓini. Ngudo dzo sumbedzisa themendelo kha maga a u lulamisa ane a nga shumiswa u fhungudza u shumiseswa ha khuni. Themendelo idzi dzi katela u ṱuṱuwedza tshumiso ya mafulufulu ḽo vusuludzwaho na thekhinoḽodzhi dza fulufulu dza musalauno sa bayogese na fulufulu ḽa masana a ḓuvha, kathihi na zwiṱofu zwa u bika zwo khwiniswaho u thusa u fhungudza u tambiseswa ha zwiko zwa mupo na u thivhela tshikafhadzo ya muya nga ngomu zwine zwa vhanga vhulwadze ha mbilu na dzimpfu. Hu na ṱhoḓea ya u ita mafulo a zwa vhupo. Ndi nga kha pfunzo hune kuvhonele kwa vhathu, kusedzele kwa zwithu na vhuḓifari havho maelana na kushumiselwe kwa khuni zwa nga shandukiswa. U bveledzwa ha mveledziso i sa nyeṱhi nga kha ndango ya khaṋo na u ṱavhiwa ha miri zwi nga fhungudza vhukuma u fhela ha maḓaka, u lozwiwa ha mutshatshame wa zwi tshilaho, u konḓa ha khuni na mukumbululo wa mavu. / Dikgong tša go bešwa di tšwela pele go ba methopo o bohlokwa wa enetši gareng ga bontši bja malapa a dinagamagaeng ka lebaka la ge di hwetšagala le go se ture ga tšona ge di bapetšwa le mekgwa ye mengwe ya enetši. Tekano ye e ka bago batho ba dimilione tše 17 ka Afrika Borwa ba dula mafelong a magaeng fao dikgong di ka kgonago go rengwa gabonolo le ka tokologo ke malapa a, fao e lego gore 80% ya palomoka ya dibešwa tšeo di šomišwago ka gae di hwetšwago go dikgong. Lebelo leo ka lona dikgong di hwetšago ka lona gore di tle di bešwe – leo le mpefatšwago ke go gola ga setšhaba, temo le go oketšega ga madulo a batho – le tliša ditlhohlo tše kgolo tlhabollong ya setšhaba. Go rema dikgong go fetišiša go ka feletša ka go hlaelela ga tšona, tahlegelo ya phedišano ya diphedi tša mehutahuta, go rema mehlare ka fao go fetišišago le kgogolego ya mobu. Dinyakišišo tše di ikemišeditše go sekaseka mabaka ao a huetšago go šomišwa ga dikgong ka malapeng ka Masepaleng wa Selegae wa Thulamela. Dinyakišišo tše di šomišitše mekgwa ya dinyakišišo ye e hlakantšwego, ye e lego wa dinyakišišo tša bontši le wa dinyakišišo tša boleng. Dipotšišonyakišišo tšeo di beakantšwego ka seripa tše di nago le bobedi dipotšišo tša di nago le dikgetho le dipotšišo tšeo di nyakago gore motho a fe maikutlo a gagwe di šomišitšwe go kgoboketša tshedimošo ka malapeng. Tshedimošo ye e kgobokeditšwego e bile kudu tshedimošo ya boleng (ya dipalo le ya go hlophiwa) gomme monyakišiši o šomišitše menyu wa bokgafetšakgafetša go dira kakaretšo ya tshedimošo le go menyu wa go bea dilo ka dintlha ka Sehlopheng sa Dipalopalo sa Bašomi ba tša Mahlale a Leago (SPSS) bešene ya 25. Go bea dilo ka dintlha, monyakišiši o šomišitše teko ya Chi-square (χ2) go ela bogolo bja kamano magareng ga diphapano tše pedi tša magoro. Ge p-value e le ye nnyane go 0.05, go na le kamano ye bohlokwa magareng ga diphapano – ke gore, diphapano di a hlalošana. Dinyakišišo di hweditše gore dipharologantši tša ekonomi ya setšhaba tša go swana le letseno la kgwedi ka kgwedi, maemo a mošomo, bong, maemo a thuto a hlogo ya lapa, palao ya maloko a ka lapeng ao a šomago, tšhomišo ya tšhelete go enetši le mohuta wa mošomo di raloka tema ye bohlokwa ka mabakeng ao a huetšago go šomišwa ga dikgong. Ka lebaka la mabaka a, enetši ya dikgong e sa šomišwa bjalo ka methopo o bohlokwa wa enetši ke malapa a mantši ka nepo ya go fihlelela dinyakwa tša bona tša ka gae tša go apea le go ruthufatša dintlo – go sa kgathale gore bontši bja tšona ke tša mohlagase. Godimo ga fao, tlhokego ya thuto ya mabapi le tikologo, kabo ya mohlagase ye e sa tshepišego le maemo a bophelo ao a hlobaetšago ao a hlohleletšago bohloki ka dinagamagaeng di tsenya letsogo go tšhomišo ya dikgong go fetišiša ka malapeng. Dinyakišišo di hlagiša ditšhišinyo tša mabapi le go fokotša tšhomišo ya dikgong go fetišiša. Ditšhišinyo tše di akaretšwa go hlohleletša tšhomišo ya mohlagase wa go dirišwa leswa le ditheknolotši tša enetši tša sebjalebjale tša go swana le gase ya tlhago le mohlagse wa sola, gotee le ditofo tša go apea tšeo di kaonafaditšwego ka nepo ya go fokotša go šomiša kudu methopo ya tlhago le go thibela tšhilafatšo ya moya ya ka dintlong e lego seo se amantšhwago le bolwetši bja pelo le mahu. Gape go na le tlhokego ya go tliša temošo ya tša tikologo. Ke ka go diriša thuto fao e lego gore maikutlo a batho, ditebelelo le maitshwaro a bona mabapi le ditiro tša tšhomišo ya dikgong a tlago fetošwa. Tšwetšopele ya tlhabollo ya go ya go iule ka taolo ya go rema dikgong le go bjala mehlare fao go ka fokotšago go rengwa ga mehlare, tahlegelo ya mehutahuta ya diphedi, tlhaelelo ya dikgong le kgogolego ya mobu. / Environmental Sciences / M. Sc. (Environment Management)