INVESTIGATION INTO THE CAUSES OF POVERTY IN EASTERN UGANDA

A CASE STUDY OF KISOZI SUB COUNTY, KAMULI DISTRICT

ABSTRACT

The study was carried out in Kisozi Sub County, Kamuli district with the purpose of investigating the causes of poverty in eastern Uganda. A case study of Kisozi sub county Kamuli district. The specific objectives of the study were; to determine how high cost of agricultural inputs lead to poverty in Eastern Uganda, to establish how lack of education leads to poverty in Eastern Uganda, to find out how low agricultural productivity leads to poverty in Eastern Uganda and to establish how unemployment leads to poverty in Eastern Uganda.

The study adopted a cross-sectional research design where quantitative and qualitative approaches of data collection methods were used to collect data from 80 respondents using questionnaires.

The study concluded that poverty can be caused through decrease in agricultural productivity, increase in the prices of agricultural inputs, low education, and unemployment among others. When farm incomes and the real wage rate increase, the rural non-farm economy grows, real household incomes increase and the percentage of the population living below poverty lines decreases. Nutritional status or other aspects of well being, such as health measures and education, may also improve. However, initial asset endowments, and land assets in particular, are significant determinants of households’ ability to access and effectively use productivity enhancing knowledge and technologies. Poor households face barriers to technology adoption and market access. The importance of productivity to agricultural sector growth to poverty reduction depends on a variety of contextual factors including the initial distribution of poverty, asset endowments, access to education, employment and the extent and nature of the poor’s participation in the agricultural sector.

It was recommended that non-cereal staples such as cassava, increased successful participation in high value crops. Also, more investment in education and training of rural citizens so as to improve the capacity of the rural labourforce, equip the youth with the knowledge and skills to secure good livelihood and break the cycle of poverty.

CHAPTER ONE

1.0 Introduction

This chapter shows the background to the study, statement of the problem, objectives of the study, research questions, and purpose of the study and the scope of the study.

1.1 Background to the study 

Poverty is a global menace that is grossly predominant in developing countries, with their attendant macroeconomic consequences. Hence every responsible government is expected to monitor their rates in the economy (Okafor, 2011). The higher the unemployment rate in an economy the higher would be the poverty level and associated welfare challenges.

The World Bank (2003) described poverty as denial of choices and opportunities, a violation of human dignity, lack of basic capacity to participate effectively in society, not having enough to feed and clothe a family, not having a school or clinic to go to; not having the land on which to grow one’s food or a job to earn one’s living and not having access to credit, insecurity, powerlessness and exclusion of individuals, households and communities, susceptibility to violence, and living on marginal or fragile environments, without access to clean water or sanitation. 

Aidelunuoghene (2014) and Oriahi and Aitufe (2010) noted the main causes of poverty to include income inequality, political instability, long-term ethnic conflict and civil unrest, lack of good governance, poor management of economic resources, low productive capacity, unemployment and corruption. The causes and effects of poverty interact, such that the variables that make people poor also create conditions that keep them poor. 

Obumneke (2012) enumerated factors responsible for poverty to include poor economic growth rate, adoption of untimely economic policy measures, wrong impression about technical and vocational studies, the neglect of the agricultural sector, and poor enabling environment. While Aitufe (2010) noted the main causes of poverty to include income inequality, political instability, long-term ethnic conflict and civil unrest, lack of good governance, poor management of economic resources, low productive capacity, unemployment and corruption. 

Uganda is one of the poorest countries with high levels of unemployment in the world with a per capita income of about US$350, and also one of the poorest in Sub-Sahara Africa. More than 80 percent of Uganda’s poor live in rural areas (UNHS IV, 2009/2010), and poverty alleviation programs in the last decade have been mainly implemented in Uganda such as Universal Primary Education, National Agricultural Advisory Services (NAADS), and Prosperity for All program to mention but a few, have been implemented in an effort to alleviate poverty and increase employment opportunities. The latest UNHS (20009/2010) reveals that while the number of people living below the poverty line in Uganda is falling, the number of poor people in Northern Uganda has not significantly reduced over the years. The National Agricultural Advisory Services (NAADS) is one of five core programs under the Program for Modernization of Agriculture (PMA) and the PMA mission is to “eradicate poverty by transforming subsistence agriculture to commercial agriculture”. Thus, the study sought to establish the causes of poverty in Eastern Uganda, in Kisozi Sub County, Kamuli district.

1.2 Problem statement

In the past years, many development strategies such as Uganda National Agricultural Advisory Services, Universal Primary education (UPE), Entandikwa Scheme, prosperity for All and many others, have been implemented in an effort to  reduce rural poverty.  According to the latest UNHS (2009- 2010) reveals that as the number of people living below the poverty line in Uganda is reducing, the numbers of poor people in the rural areas is not significantly reducing over the years. The question was therefore, why have incomes of the rural residents remained low? It was therefore important to investigate the causes of poverty in eastern Uganda.

1.3   Objectives of the study.

1.3.1 General objective.

The overall objective of the study was to investigate the causes of poverty in eastern Uganda. A case study of Kisozi sub county Kamuli district.

1.3.2 Specific   objectives.

1. To determine how high cost of agricultural inputs lead to poverty in Eastern Uganda.
2. To establish how lack of education leads to poverty in Eastern Uganda.
3. To find out how low agricultural productivity leads to poverty in Eastern Uganda
4. To establish how unemployment leads to poverty in Eastern Uganda

1.4 Research Questions

1. How does high cost of agricultural inputs lead to poverty in Eastern Uganda?
2. How does lack of education lead to poverty in Eastern Uganda?
3. How does low agricultural productivity lead to poverty in Eastern Uganda?
4. How does unemployment lead to poverty in Eastern Uganda?

1.5 Scope   of   the study

1.5.1 Content scope

The study was confined to investigate the causes of poverty in eastern Uganda. Specific emphasis was put on determining how high cost of agricultural inputs, lack of education, low agricultural productivity and unemployment lead to poverty in Eastern Uganda.

1.5.2 Geographical Scope

The study was carried out in Kisozi Sub County, Kamuli district. It consists of villages like, Kiyunga, Bugolo, Bulamuka, Buduuli among others, where a few respondents were selected and from which both men and women were requested to participate.

1.5.3 Time Scope

The study considered 2002-2017 as the period of body of knowledge to review literature.

1.6 Significance of the study

Through the determining the specific challenges that are faced and currently face in the implementation of poverty reduction strategies will be helpful making the implementation of such strategies easier in the future, as proponents will be able to anticipate potential barriers. This is basically a chance to assess the lessons learned by the government, to ensure that past mistakes will not be repeated, and past victories will be followed as an example. 

The study more so is expected to benefit the people of Kisozi Sub County and the entire Kamuli at large by providing information on how the causes of poverty in their region. This will be ensured by discussing with the respondents for five 5minutes.

The study more so will explore and recommend potential areas onto which the government    needs to put more effort in delivering services. More so policy makers will also benefit in the   manner that the findings will provide informed suggestion on how policy can be improved.  It   helps the researcher to build on his skills in the area of research

With improved and ease to implement policies to reduce poverty, more individuals will be able to access and benefit from those policies hence poverty will be easily eradicated.

CHAPTER TWO

LITERATURE REVIEW

2.0 Introduction

This chapter is all about the review of related literature on the study and it is done according to the objectives of the study.

2.1 Definitions of key terms

2.1.1 Poverty 

Adebayo (2013) referred to poverty as an enemy of man and a multi-dimensional phenomenon that affects many aspects of human conditions ranging from the physical, moral to the psychological, and humiliates and dehumanizes its victim. It is a state of being deficient in money or means of basic subsistence such as safe water, sanitation, solid waste collection, healthcare, schools and security. 

The World Bank (2003) described poverty as denial of choices and opportunities, a violation of human dignity, lack of basic capacity to participate effectively in society, not having enough to feed and clothe a family, not having a school or clinic to go to; not having the land on which to grow one’s food or a job to earn one’s living and not having access to credit, insecurity, powerlessness and exclusion of individuals, households and communities, susceptibility to violence, and living on marginal or fragile environments, without access to clean water or sanitation. 

2.2 How high cost of agricultural inputs leads to poverty 

Agricultural productivity determines the price of food, which then determines wage costs and the competitiveness of tradable goods leading to a confluence of effects that determine the real income effects of increased output for farming households (World Bank 2007). Increased agricultural output can change the relative prices of agricultural outputs in relation to substitute or complimentary products, as well as the costs of inputs to production (Irz et al. 2001). If increased output drives down product prices or the costs of production rise due to increased demand, than increased agricultural output may not translate into higher real farm income (Irz et al. 2001). Output growth may not increase farm household incomes if the price effects counteract the production gain, however food price effects depend upon the tradability of the food. Staple food crops in agriculturally based developing countries are largely nontradable because they consist of foods (cassava, sorghum, millet, etc.) that do not have international markets and because the domestic food economy remains relatively insulated by high transport and marketing costs. Since they are nontradable, their price is not influenced by competition in the international market (World Bank 2007).

The complexity of the pathways between increasing agricultural productivity and poverty reduction, in a semi-closed rural economy where food output is at least partially tradable (i.e. regionally). The price effects in the market for farm output determine the income effect of increased output for farm households. These price effects also send feedback to the producer that determines future desired output levels. Production decisions cause responses in the labor market that shift the demand for food as rural households can afford to consume more (or cannot afford to consume as much). The most favorable outcome for the poor occurs when the new general equilibrium increases both farm incomes and the real wage rate, spurring multiplier effects in the rural non-farm economy that increase real household incomes for farming and non-farming households and decrease poverty.

Technology reduces needed inputs, production costs will decrease (raising profits), but output may not be affected and employment could be reduced. If instead the technology raises yields, output and (most likely) employment will increase, but profits will not necessarily increase. Alternatively, if the technology raises labor productivity, wage rates will increase but probably at the expense of the quantity of labor employed, and with unclear effects on profits and output. A technology that permits expansion of cultivated area, might raise output, employment and profits, but is likely to lower yields. Finally, productivity gains may not result in poverty reduction if the decline in output prices outweighs the gain from increased productivity (Thirtle et al. 2001).

Poverty reduction depends on the production and consumption multipliers resulting from increased agricultural productivity. However, where income, asset endowments and land distribution are highly unequal, the majority of the benefits will accrue to the elite and the new resources generated will be directed towards imported or capital intensive consumer goods, rather than to locally produced, labor-intensive goods and services (Thirtle et al. 2001). Where inequality between the top and bottom income quintiles is greater, the income effect of agricultural growth is stronger for the highest quintile than the lowest. Where initial income inequality is smaller, agricultural growth contributes to an improvement in income distribution whereby the elasticity of poverty to agricultural growth declines successively with each higher income quintile (Mellor 1999). Additionally, inadequate access to land constrains the potential for poverty reduction through smallholder driven agricultural development.

Large productivity increases in staples could actually lead to a price collapse in staple food markets since the elasticity of demand is low and markets are typically thin. In such a case, increased output would drive prices down and undermine incentives for production. Thus, Staatz and Dembélé argue that increases in staple crop productivity need a complimentary increase in the production of a tradable good in order to stimulate increased income growth and demand for staples (Staatz and Dembélé 2008). Similarly, if the gains in total factor productivity do not outpace the decline in food prices, profitability will not be maintained and farmers may abandon productivity increasing technologies. In other words, poor net food selling households (producers) may become worse off when food prices fall due to the price inelastic demand for staple foods in most areas. Though increasing staple crop productivity will likely decrease overall poverty at the aggregate level since the urban poor and half of the rural poor, on average, are net food buyers (World Bank 2007). 

2.3 How lack of education leads to poverty 

Education and health endowments of the individuals are the necessary and important components of human capital which make them productive and raise their standard of living. Human capital is required for the effective utilization of physical and natural capitals, and technology and skills. Being a developing country Pakistan has owned the poverty reduction strategy paper, which is one of the main pillars of human capital. Without human capital formulation the goal of development or poverty elimination is inevitable and human capital accumulation is largely based upon education and skills attainment (Mughal,2007).

The other notable thing regarding the education’s significant role in poverty reduction is the direct linear relationship between education and earnings. In Pakistan, it has been found that monthly earnings of an individual worker increased by 7.3 percent with an additional year of schooling. Earnings will be increased by 37 percent with the attainment of ten years of schooling against no education. Moreover, each additional year of schooling level increased earnings by 3 percent at primary level, by 5 percent at secondary level, and by 7.1 to 8.2 percent at higher/tertiary level. Each additional year of technical training increased earnings by 2.5 percent. Therefore, it is quite evident that education can increase the earning potential of the poor and they become productive (Nasir and Nazli, 2000).

The educational attainment of household head is the critical determinant of household poverty in Pakistan. An increase in the educational level of the head of the household significantly reduces the chances of the household being poor (Qureshi and Arif, 2001). Moreover, an increase in the schooling of household heads not only has a positive impact on their productivity and earnings but also enhance the productivity of other members of the household perhaps through persuading them to be educated and/or skill-oriented (Abuka, Ego,  Opolot and Okello, 2007).

Not only poverty is concentrated in households with illiterate/less educated heads but also it is much harmful for the female-headed households as compared to the male headed ones. Female segment of our society is comparatively much deprived as compared to male one. On the other side, those female-headed poor households severely lack the basic requirements of life. Their housing, health, drinking water, sanitation facilities and garbage collection system all are in deplorable condition. All these things affect the productivity of poor persons and they can not come out of their vicious poverty circles. The provision of education can break this circle through giving a rise in earnings and fulfilling basic needs (Haq, 2005). A large portion of Pakistan’s population is dwelling in rural areas hence we must see the effect of education upon their productivity. In rural areas private returns to male education have an upward trend due to higher levels of education in labor markets for non-agricultural work. Wages to the farm-workers, who hired for the unskilled, manual work on the farm, are not responsive to education attainment (Kurosaki, 2001). Wages and productivity in non-farm activities rise with education at an increasing rate as education rises. On the other hand the farm productivity responds significantly only to the primary education (Kurosaki and Khan, (2006).

Examining separately the rural and urban chapters of Pakistan, it has been observed that in urban areas the education of the head of the household is negatively and dependency ratio is positively related with the poverty status of the household. In rural areas asset distribution especially land and livestock play an important role in differing poor and non-poor. The role of domestic and overseas transfers also appeared significant against poverty and its role is much more effective in urban areas (Jamal, 2005). 

Educational levels (primary, secondary and tertiary) are valuable in increasing the per capita expenditure of the household. As expenditures include the non-food items hence again education is relevant from the overall welfare point of view. Further, educational levels are significant elements in reducing the chances of the household to be poor (Okojie, 2002). It would be wrong to say that for growth, development and poverty reduction we should wait for the universalizing of primary education rather we should work upon the post-primary education because it has the same role as primary education. Primary education is the initial threshold of human capital but secondary and higher education, and investment in science and technology will give rise to acceleration and sustenance in economic growth and development. 

In India the analysis suggest that illiteracy, literacy and primary education are positively related with the poverty ratios on the other hand middle and secondary education are negatively related. Moreover, in the simple regression secondary and higher education is inversely related with poverty, therefore secondary and higher education is important in the inverse relation of education and poverty apart from primary education (Tilak, 2005). It has been seen that the likelihood of being poor is higher even for the lower level of education (Rodriguez and Smith, 1994). Sometimes the overall growth is more important for the welfare of poor as compared to the basic education provision hence income of poor raises one for one with average income (growth) but the primary education attainment has a very limited impact upon the income of the poor therefore they came up with the idea that growth is a prominent factor in eliminating poverty and primary education completion is not so much important. We have also such examples where education can’t approve their inverse relation with poverty. The reasons are the outside factors, which affects the inverse relationship. Evaluation of those factors considering the Southern African countries in 1990s and in the beginning of 2000, showed that although the educational indicators were appreciable like out of 24 the 12 states, whose data were available, the average completion rate of primary education was 84.6% and the drop out rate for secondary education was 15.4% in the years 1997-03. Also the adult literacy rate of the southern African states was 75% in 2000 whereas the emerging economies at that time had 74% and least developing countries had 52% but such statistics give no considerable improvement in poverty reduction. Poverty remained stagnated or increased in some cases. 

The other indicators of human deprivation including: the drinking water, fewer than five mortality rates, infants with low birth rates per 1000 and the prevalence of HIV have shown minor progress. The glaring facts unveiled the reasons like high unemployment rates (fall of monetary returns to education), limited access to productive resources like land and capital, rising HIV/AIDS, absence of sustained growth, high population growth rates which also demand more and more human capital, lower quality standards of education, too much dependence upon the structural adjustment programs of IMF that promotes reduction of government investment upon social services and infrastructure, paved the way towards deprivation (Senia, and Godwell, 2006). 

In the same direction, the failure of 1990s educational expansion to reduce poverty in Latin American countries divulges the reasons which are as follows: firstly, the inequality of educational opportunities, which results in the benefit to only those persons who were not so much poor. Secondly, according to one estimate the evaluated educational threshold for Latin American countries is 12 years of schooling but the government only emphasize upon the primary education. Thirdly, with the educational expansion the group of persons with higher education and high earnings increases and the educational level of the large labour force segment rises also but the former effect increases inequality (that causes poverty) and the later one does not. In 1990s the former effect dramatically dominates that is why poverty persists. Education definitely promotes social cohesion which gives rise to the fall of human poverty. But if inequality is rising in the society due to the factor mentioned earlier then it will generate social differentiation and distorts the process of human poverty obliteration (Bonal, 2007).

2.4 How low agricultural productivity leads to poverty 

Agricultural productivity is defined in several ways throughout the literature, including as general output per unit of input, farm yield by crop or total output per hectare, and output per worker. Regardless of which measure is used, empirical studies support the idea that improvements in agricultural productivity are important for poverty reduction (Mellor 1999). However, productivity growth can catalyze a wide range of direct and indirect effects that mediate the pathways to poverty alleviation (Thirtle et al. 2003). 

Agricultural productivity determines the price of food, which then determines wage costs and the competitiveness of tradable goods leading to a confluence of effects that determine the real income effects of increased output for farming households (World Bank 2007). Increased agricultural output can change the relative prices of agricultural outputs in relation to substitute or complimentary products, as well as the costs of inputs to production (Irz et al. 2001). If increased output drives down product prices or the costs of production rise due to increased demand, than increased agricultural output may not translate into higher real farm income (Irz et al. 2001). Output growth may not increase farm household incomes if the price effects counteract the production gain, however food price effects depend upon the tradability of the food. Staple food crops in agriculturally based developing countries are largely nontradable because they consist of foods (cassava, sorghum, millet, etc.) that do not have international markets and because the domestic food economy remains relatively insulated by high transport and marketing costs. Since they are nontradable, their price is not influenced by competition in the international market (World Bank 2007).

The complexity of the pathways between increasing agricultural productivity and poverty reduction, in a semi-closed rural economy where food output is at least partially tradable (i.e. regionally). The price effects in the market for farm output determine the income effect of increased output for farm households. These price effects also send feedback to the 

producer that determines future desired output levels. Production decisions cause responses in the labor market that shift the demand for food as rural households can afford to consume more (or cannot afford to consume as much). The most favorable outcome for the poor occurs when the new general equilibrium increases both farm incomes and the real wage rate, spurring multiplier effects in the rural non-farm economy that increase real household incomes for farming and non-farming households and decrease poverty.

Only one study (reported in both Thirtle et al., 2001 and Irz et al. 2001) models the direct relationship between agricultural productivity and changes in poverty measures at the macroeconomic level across countries. The authors examine the impact of land and labor productivity (yield and the land-to-labor ratio) as well as total factor productivity (agricultural value added) on the percentage of the population living on less than US$1 per day (the headcount index) using country-level data from the 2000 World Development Report. Thirtle et al.’s (2001) findings suggest that agricultural productivity growth has a robust and consistent impact on poverty for all productivity measures. They calculate that a 1% increase in productivity is associated with a decrease of 0.62% to 1.3% in the percent of the population below the US$1 per day poverty line. Additionally, the authors regress the productivity measures against the human development index and find that raising yields by 1% is associated with a 0.12% increase in the HDI (Thirtle et al. 2001). However, these data are single-year snapshots aggregated at the country level from multiple years, depending on each country’s most recent census, national household survey or index calculation, which limits the ability to make causal inferences.

Several factors mitigate the impact of agricultural productivity growth in nontradable goods (staple and other food crops) on poverty reduction, including the proportion of the poor participating in agriculture and the effect of productivity changes on food prices. The proportion of the rural poor engaged in farming varies geographically and many rural households are still net food buyers (Irz et al., 2001). While 77% of the poor in SSA are smallholder farmers, in Asia smallholders account for less than half of the poor, according to 1998 figures (Cox et al. 1998). 

Several studies provide evidence for the poverty reducing potential of agricultural productivity growth in staple crops. In Ethiopia, Diao and Pratt (2007) find that growth in staple crop productivity has greater potential for poverty reduction than any other agricultural or non-agricultural sector in their model. Minten and Barrett (2008) find similar evidence in Madagascar with regard to rice, which is largely nontradable due to high marketing and transport costs. Finally, Jayne et al. (2010) find that maize is the single most important crop in most smallholder farm incomes Kenya, Malawi, Zambia, and Mozambique, suggesting that productivity increases could result in poverty alleviation.

The strength of food price effects depends on the tradability of the good and the elasticity of demand. Where demand is quite inelastic, prices will fall more when production increases than where demand is more elastic. Where demand is more inelastic, a greater share of the benefits accrue to consumers. The size and openness of the market greatly determine the elasticity of demand (Thirtle et al. 2001). Where the staple crop sector is large and mostly nontradable (beyond regional trade), productivity gains will increase the aggregate food supply and drive down food prices (World Bank 2007, Thirtle et al. 2001). A negative correlation between per capita production and staple food prices has been observed in maize (Ethiopia, Ghana), sorghum (Burkina Faso, Mali, Sudan), cassava (Ghana), and weakly in millet (Burkina Faso, Mali, Sudan). Staple food prices have not followed this pattern in Kenya, however, where significant price interventions maintain stable prices and thus interrupt the market relationship between per capita production and food prices (World Bank 2007). While decreasing prices are not good for producers, Irz et al. (2001) highlight that recent market liberalizations have increased the tradability of goods, which probably increases producers’ share of the benefits from agricultural growth. This likely occurs because increased output at the local level is unlikely to affect prices when the good in question traded in a larger market (Irz et al. 2001).

The ability for productivity gains in tradable (export-oriented) agricultural goods to reduce poverty depends on the extent to which smallholders and poor households participate in production (World Bank 2007). The 2008 World Development Report (World Bank, 2007) notes that African countries have the potential to be competitive in the production of both traditional and new high value commodities. Specifically, there is potential for cocoa in Ghana, tea and flowers in Kenya, vegetables in Senegal, and fish in Uganda. Additionally, labor-intensive non-traditional exports can reduce poverty through employment opportunities, for instance in Kenyan horticulture production and vegetables in Senegal (World Bank 2007). And as Staatz and Dembélé (2008) articulate, if quality and time requirements can be met, there are few demand constraints to growth in high-value exports such as horticulture.

Increased agricultural production is likely to increase the demand for farm labor through increases in area cultivated, intensity of cultivation (labor use per unit of land), or frequency of cropping. The impact of farm labor opportunities on poverty reduction depends on the extent to which the rural poor depend on farm laboring for their livelihood. Labor dependency is higher in South Asia where between one-third and one-half of rural households are landless, but households with small plots of land or little working capital may depend significantly on laboring for their income even in Africa where landlessness is rarer (Irz et al. 2001). Technology also influences the scale of the change in labor demand. Some technologies increase labor productivity and decrease input requirements, while others allow for the expansion of cultivated area or multiple cropping per season. Evidence suggests that while it is impossible to predict the impact of a technology on labor requirements a priori, net growth in agricultural yields tends to raise the demand for farm labor (Irz et al. 2001).

2.5 How unemployment leads to poverty

Some scholars argue that unemployment is directly influenced by poverty (Saunders 2002; Ukpere and Slabert 2009; Apergiset al. 2011), others (Clifton and Marlar 2011) indicate that poorer countries do not always have higher unemployment rates. One can accept the scientific view that unemployment and poverty are two closely related problems facing the present world economy. 

Unemployment is exacerbating the economic crisis and reduces the overall purchasing power of the nation. This leads to poverty, which in turn, increases the debt burden and unemployment. Unemployment and poverty are more common in less developed countries. However, due to the global economical downturn, the recently-developed counties face their challenges (Clifton and Marlar 2011). 

One of the indicators of well-being is a low poverty rate. Selected welfare model and the implementation of social policy determine the lining standards and the expression levels of poverty, unemployment and social exclusion. Saunders (2002) states direct and indirect impact of unemployment on poverty and inequity. Finally, unemployment is destroying the funding base of welfare programs and increases poverty and social inequality. Namely unemployment worsens poverty. High poverty, as a rule, coexists with unemployment, thus the direct relationship between these two problems can be seen. However it is often discussed that relationship between unemployment and poverty depends on controversy. The analysis units to determine labor force status are individual, and poverty research focus on income units, thus, a person may have low income and still not be bankrupt until other family members have revenue that is shared – this is sufficient to say that the family is living above poverty line. Being unemployed does not necessary mean living below or above poverty line. 

Ukpere and Slabert (2009) in a conducted qualitative study found that there is a positive correlation between unemployment, currently wide spread globalization, income inequity and poverty. This view is shared by Tsaliki (2009), he states that efforts to increase labor flexibility by liberalizing labor market contributes to the polarization of income and increase poverty levels. The survey data of other authors (Apergiset al. 2011) showed that there is a two-way relationship between poverty and income inequity in both short and long terms. In the short term both income inequality and unemployment have a positive and statistically significant impact on poverty. It is worth noting that there is a correlation between export and poverty reduction. Since export and poverty ratio are asymmetrical, the export reduction may increase poverty and unemployment. Thus, each national export development strategy should include poverty reduction (Skae and Barclay 2007).

Clifton and Marlar (2011) Gallup media research should be distinguished from abundance of other research on unemployment and poverty. It was found that there is no significant relationship between unemployment rate and GDP per capita. Employment growth does not necessary reduce poverty. There is a tendency that in “productive” sectors even a small elevation in employment reduces poverty, but in “less productive” sectors a slightly bigger employment growth is needed (Hull 2009). 

Altogether, according to the works analyzed, it can be argued that a higher unemployment rate means that there are more unemployed people who may find themselves below the poverty line. However, unemployment and poverty is complex phenomenon and should be examined only by individual exiting conditions of a country (in particular – the structure of the family), but also by individual regions of the country. This issue is very relevant in Lithuania, where the region’s socio-economic development inequality is clearly noticeable. In 2011 unemployment in Vilnius region was 14,3 percent, while in Utena region – 23,2 percent (thus, there were 8,9 percent difference in interregional unemployment rate). A significant differentiation prevails in regional poverty indicators. A tendency is seen that in economic slowdown has reduced jobs and working hours in value adding areas. And only after economy recovered, people of these regions had a greater opportunity to return to the job market, get out of poverty, while the socio-economic difference between regions declined. It should be noted that this is positive for sustainable national development.

CHAPTER THREE

RESEARCH METHODOLOGY

3.1 Introduction

This chapter discusses the research design, data type and sources, sample size and selection, data collection tools/methods, data presentation and analysis, data collection procedure and limitation of the study.

3.2 Research Design

The study adopted a cross-sectional research design where quantitative and qualitative approaches of data collection methods were used. Cross-sectional design will allow for the study of the population at one specific time and the difference between the individual groups within the population to be compared (Opuko, 2000). The choice of this design is dependent on the nature of the study variables. According to Baron (2011), qualitative research design helps to capture qualitative data, based on qualitative aspects that may not be quantified. It aids in discovering the motives and desires or what people think and how they feel about a given subject or situation. This method involves an unstructured approach to inquiry and allows flexibility in all aspects of the research process. It is more appropriate to explore the nature of a problem, issue or phenomenon without quantifying it. While quantitative research is the systematic empirical investigation of observable phenomena via statistical, mathematical or computational techniques (Mugenda & Mugenda, 2003). According to Silverrman (2001), quantification gives greater confidence in the accuracy of conclusions derived from qualitative data; and it gives the reader a chance to think through the data on their own to cap on the researcher’s findings. The research used this method because it produces information only on the particular cases studied. 

3.3 Data type and sources

Data was collected from both primary and secondary sources.

Primary data was collected by use of questionnaires and interview guide. Secondary data was collected from published journals, reports, text books, and company records.

3.4 Sample Size, Selection and Procedure

The sample size was 80 respondents. The sample size was determined using Krejcie and Morgan (1970). A sample of 80 respondents was used because their response is enough to provide a clear view of the population.

Table 3.1: Sample Size and Composition

The study used both purposive sampling method and simple random sampling. Purposive sampling is one of the most cost-effective and time-effective sampling methods available, it may be the only appropriate method available if there are only limited number of primary data sources that can contribute to the study and this sampling technique can be effective in exploring anthropological situations where the discovery of meaning can benefit from an intuitive approach.

Simple stratified random sampling was also used to select samples from the population strata. It’s a method in which the population is divided into a number of divisions and a sample is drawn from division and such sample makes us the final sample. This technique was employed since it eases the making of proportionate samples, and therefore meaningful, comparisons between homogeneous sub-groups (Zikmund, 2003). 

3.5 Data collection methods and procedures

The study involved questionnaires and interview method.

The questionnaire was used because it is practical, also large amounts of information can be collected from a large number of people in a short period of time and in a relatively cost effective way, can be carried out by the researcher or by any number of people with limited affect to its validity and reliability, the results of the questionnaires can usually be quickly and easily quantified by either a researcher or through the use of a software package, can be analysed more ‘scientifically’ and objectively, when data has been quantified, it can be used to compare and contrast other research and may be used to measure change.

Interview guide was also used since it helps in the collection of more data as it allows the interaction of both the researcher and the respondents, any misunderstanding and mistake can be rectified easily in an interview. Interview can help to collect the fresh, new and primary information as needed.

3.6 Data management, presentation and analysis

3.6.1 Data management

This included all measures put in place to ensure that quality data is obtained. The management included data editing before leaving the area of study to ensure that there are no mistakes or areas left blank and if any mistakes are found they was corrected before leaving the field. The researcher also coded the interview and store them in the file for safety and locked in a place which can only be accessed by the researcher.  

3.6.2 Data presentation and analysis

3.6.2.1 Qualitative Data 

Data processing involved editing raw data to detect errors and omissions, classifying data according to common features, and tabulation to summarize and organize it. Data analysis involved the qualitative approach of identifying the major themes arising respondents’ answers; assigning of codes to the themes: classification of the themes under the main theme; and integrating the responses into the report in a more descriptive and analytical manner.

3.6.2.2 Quantitative Data

Manual editing of questionnaires was done to eliminate errors. After coding, tabulation was done to clearly present various responses and the interpretation. Frequencies and percentages were used to portray statistics used to analyze and interpret the findings of the study. Frequency tables, graphs and charts aided in presenting the data using statistical packages like Microsoft excel.

3.7 Limitations of the study

The researcher further was faced with a problem of some respondents not providing information for the study as information relating to the study variables, however to this, researcher explained to respondents that the information was only for the academic purpose while making them to understand the study variables.

The study was limited to a representative sample due to high expenses that were involved in terms of time and funds since this research were self-funded. However, the researcher strived to solicit for funds from family members and friends.

Another limitation was the scarcity of recent literature relating to the effect of tourism activities on people’s welfare due to lack of text books in the library. However, the researcher sourced information from the internet, newspapers and previous reports.

CHAPTER FOUR

PRESENTATION, ANALYSIS AND INTERPRETATION OF FINDINGS

4.0 Introduction

This chapter consists of the presentation, discussion and analysis of the findings from the study. It provides results which were analyzed from raw data collected in the field. It is in two categories; the first one represents the demographic characteristics of the respondents while the other category represents the responses of the questions that were asked concerning research objectives. The analysis was done and data is represented in form of tables, graphs and pie-charts.

4.1 Overview of the Study

The study was carried out Kisozi Sub County, Kamuli district – Eastern Uganda. Questionnaires and interview guides were designed to obtain data from a sample size of 80 was selected. The findings of the study were presented in accordance to the study objectives.

4.2 Demographic Characteristics of the Respondents

The background characteristics compiled show the sex, age, the education level and occupation. This data was analyzed and is presented below;

4.2.1 Findings on Sex of the respondents

Study respondents were required to indicate the sex of respondents because different gender types have different household responsibilities thus sex being vital in examining the welfare of households. The findings are presented below;

Figure 4.1: Sex of Respondents

Source: Primary Data 

From figure 4.1 above, it’s indicated, majority of respondents (52%) were males and the females were only 48% of the total respondents. This implies that men were found to be active in the study under investigation. However, both ideas were relevant for the study. Men are considered as heads of the families in that they have to be responsible for most of their families’ welfare like providing food, shelter as well as paying bills such as electricity, water.

4.2.2 Findings on age of respondents

The study sought to identify the age of respondents as some age brackets have many dependents under their care thus age being a major determinant in examining welfare of households. The findings are presented in the Table 4.1 below;

Table 4.1: Age of Respondents

Source: Primary Data

Table 4.1 shows that, the majority (40%) of the respondents were predominantly between the ages of 26 and 30 years. A significant percentage (25%) of the respondents were in the age bracket of 41 and 50years, 20% of the respondents were in the age bracket of 20 and 25years and another 15% of them were in the age group of 40 and above. 26-30years had the highest number because these are the most active age group hence they are actively involved in socio-economic activities, therefore they had rich experiences and could also appreciate the importance of the study. 

4.2.3 Findings on level of education 

It was vital to identify the education level of respondents because different levels of education lead to poverty differently. The findings are presented in Table 4.2 below;

Table 4.2: Level of education

Source: Primary Data

According to the study findings as in table 4.2, (17.5%) did not go to school, (23.75%) attained primary, (35%) attained secondary, (12%) of the respondents attained diploma level and 8.75% of the study respondents were degree holders. However, no respondents had postgraduate. The analysis above shows that most of the people completed secondary level and that very few respondents did not go to school. 

4.2.4 Findings on occupation of respondents  

It was vital to identify the occupation of respondents because people with different occupations are affected by poverty differently. The findings are presented in Table 4.3 below;

Table 4.3: Occupation of respondents

Source: Primary Data

Findings in table above, it was revealed that majority (61.25%) of respondents were farmers, 32.5% of them were business persons and only 6.25% of the study respondents were civil servants. This implies that the majority of the respondents engaged in farm activities most of which engage in subsistence farming where the grow crops for both home use and for use. Most households in Kisozi Sub County grow mostly maize, sugarcane and potatoes. 

4.3 How high cost of agricultural inputs leads to poverty

The first objective of the study sought to determine how high cost of agricultural inputs lead to poverty in Eastern Uganda. Various questions were posed to respondents and the following findings were obtained below;

4.3.1. Findings on whether respondents use fertilizers;

Respondents were asked to identify whether they used fertilizers. Results were obtained and are presented below;

Table 4.4: Findings on whether respondents use fertilizers;

Source: Primary Data

Findings from the table 4.4 above show that, 28.75% of the respondents mentioned that they use fertilizers and the remaining 71.25% of them do not use fertilizers. This implies that majority of the respondents do not apply fertilizers in their farms for several reasons as presented in Table 4.5 below;

Table 4.5: Findings on why people do not use fertilizers

Source: Primary Data

Findings as indicated in Table 4.5 above shows that, 41.25% of the respondents mentioned that they do not use fertilizers because they are expensive, 33.75% of them cited bad effects of fertilizers, 17.5% of the study respondents said ignorance and 7.5% revealed it’s tiresome to apply fertilizers. This implies that majority of the respondents said that fertilizers are expensive. One respondent was quoted saying:

“Fertilizers after a long period of time cause the land to become too old, which causes poor crop yields”.

4.3.2 Findings on whether respondents use pesticides

Respondents were asked to identify whether they used pesticides. Results were obtained and are presented below;

Table 4.6: Findings on whether respondents use pesticides;

Source: Primary Data

Results as indicated in table 4.6 above indicate that, 78.75% of the respondents said they do not apply pesticides while 21.25% of the study respondents indicated they use pesticides. This implied that majority of the people in Kisozi Sub County do not use pesticides as a form of agricultural input – thus, majority of the people use traditional methods of farming. However, according to study observations, many people get poor yields and this has made them to lag in poverty for a long period of time.

The study further asked respondents to indicate reasons why they do not utilize pesticides as an agricultural input, majority of them said that most pesticides are very expensive to buy for large fields, others revealed that pesticides need continuous usage, while others cited that pesticides are not effective as thy have big side effects.

These findings are in line with Thirtle et al (2001) who argued that technology reduces needed inputs and increases the prices of available inputs, production costs will decrease (raising profits), but output may not be affected and employment could be reduced. If instead the technology raises yields, output and (most likely) employment will increase, but profits will not necessarily increase. This has made farmers not use much input such as fertilisers, pesticides and other inputs.

4.3.4 Findings on whether respondents use machines such as tractors, ox ploughs

Respondents were asked to identify whether they used machines such as tractors, ox ploughs. Results were obtained and are presented below;

Table 4.7: Findings on whether respondents use machines such as tractors

Source: Primary Data

Table 4.7 above indicates that, 51.25% of the respondents said that they do not use machines in their agricultural activities, while 48.75% of them said they used machines. This implies that majority of people in Kisozi Sub County do not machines which is caused by several reasons as presented below;

Table 4.8: Findings on why respondents do not use machines such as tractors

Source: Primary Data

Table 4.8 above indicate that majority of the respondents cited high expenses involved in using machines, 26.25% of them said small farming practices carried out by people in Kisozi Sub County. This implies that using machines in farming is very expensive and involves a lot of requirements.

The above findings are in agreement with (Mellor 1999) who stated that inadequate access to capital constrains the potential for poverty reduction through smallholder driven agricultural development. This also leads to the inability of farmers to be able to use modernized methods of farming.

4.4 How lack of education leads to poverty

The study sought to assess how lack of education leads to poverty. Several questions were posed to respondents and the following results were obtained;

4.4.1 Ability to pay school fees

Respondents were asked to indicate whether they were able to pay school fees. The obtained results are presented below;

Majority of the respondents (81.25%) said yes, since majority had children studying in secondary levels, primary levels, and even others were able to pay university dues for their children. Only a few respondents (18.75%) said they were not able to pay school fees for their children. This is because their children were staying at home and was involving in home activities.

Respondents were also asked to indicate whether they have been able to pay scholastic materials and majority (81.25%) of them said that they were able to buy scholastic materials such as school uniforms, books, pens, and all other school materials. One respondent was quoted saying:

“I have been able to pay school fees for all the three children I have. However, I find a challenge in raising this school fees”.

Another respondent said that;

“The money I get from farming, I use it for school dues. However, farming alone is not enough to raise this money”.

From the above findings therefore, farming has been a major contributor to the education of children in most families in Kisozi Sub County. However, farmers find it hard to raise money for schools due to several reasons such as poor farming methods, low productivity among others. This has made them use all they get from farming to paying school fees, leaving nothing to save for future investments.

The attainment of education enhances the earning potential of individuals and consequently, the increased earnings will definitely help them to be out of poverty. 

Women are much more deprived and facing severe hardships in pulling themselves out of poverty as compared to men due to their unequal educational and employment opportunities. These findings are in line with Kurosaki and Khan (2006) who argued that not only poverty is concentrated in households with illiterate/less educated heads but also it is much harmful for the female-headed households as compared to the male headed ones. 

Also he said that wages to the farm-workers, who hired for the unskilled, manual work on the farm, are not responsive to education attainment (Kurosaki, 2001). The farm productivity responds significantly only to the primary education (Kurosaki and Khan, (2006).

4.5 How low agricultural productivity leads to poverty

Respondents were asked to determine how low agricultural productivity leads to poverty. Several responses were obtained and are presented below;

Majority of the respondents (93.5%) indicated that they produced approximately 3-10 kilograms bags of maize. Only a few (6.5%) of the study respondents indicated that the produce 10 and above bags of maize.

Also, 72.5% of the respondents indicated that they sold few produce due to the low productivity. Reasons obtained included; small piece of land, poor yields, drought.

Respondents indicated that low agricultural production increases the demand for farm labor through increases in area cultivated, intensity of cultivation (labor use per unit of land), or frequency of cropping. 

These findings are in line with Irz et al (2001) who stated that low agricultural production is likely to decrease the demand for farm labor through decreases in area cultivated, intensity of cultivation (labor use per unit of land), or frequency of cropping. The impact of farm labor opportunities on poverty reduction depends on the extent to which the rural poor depend on farm laboring for their livelihood. 

4.6. How unemployment leads to poverty

The fourth objective of the study was to determine how unemployment leads to poverty. Various responses were asked various questions concerning this and the results obtained are presented below;

4.6.1 Whether respondent was employed?

Respondents were asked to identify whether the respondent was employed. The findings are presented in table below;

Table 4.9: Findings on whether respondent was employed?

Source: Primary Data

From Table 4.9 above, (31.25%) of the study respondents were employed while the remaining (68.75%) indicated that they were not employed. Majority of the respondents were unemployed implying that they had little or no disposable incomes to enable them have access to their basic needs.

4.6.2. Kind of employment respondents did

The researcher sought to identify what respondents did for their survival. The findings are presented below;

Table 4.10: Kind of employment respondents did

Source: Primary Data

Findings in table above, it was revealed that majority (61.25%) of respondents were farmers, 32.5% of them were business persons and only 6.25% of the study respondents were civil servants. This implies that the majority of the respondents engaged in farm activities most of which engage in subsistence farming where the grow crops for both home use and for use. Most households in Kisozi Sub County grow mostly maize, sugarcane and potatoes. 

One respondent was quoted saying that;

“I always engage in farmland activities, I grow maize, cassava, potatoes, coffee and many others. These crops help me to be able to pay school fees, buy basic needs, pay utilities, medical bills and other bills”

4.6.3 Means of transport 

Respondents were asked to indicate the means of transport they used. Various responses were obtained and below the findings are presented;

Majority of the respondents (100%) used road transport to go to various places and carry out several activities. Respondents used bicycles, motor bikes and cars. One respondent said;

“I always walk when am going to short distances”

From findings, 72.5% of the study respondents used water transport. This is because they are next to River Nile, so they use boats, engine boats to cross to the side of the river where they either use ‘boda-bodas’ or bicycles for their transport.

One respondent said that;

“We have marrum roads which are in poor condition”

4.6.4 Type of house

Study respondents were required to indicate the type of house they stayed in. the findings are presented below;

Respondents (6.25%) stayed in properly finished houses while the remaining respondents (93.75%) of the study respondents stayed in either unfinished or grass hatched house.

Most respondents indicated that they had little incomes to build themselves proper houses. One respondent was quoted;

“The money I get from my business is not enough to pay all bills, buy all necessities and still build a proper house. That is why I stay in this kind of structure”.

The study findings agree with Ukpere and Slabert (2009) who conducted a qualitative study and found that there is a positive correlation between unemployment, currently wide spread globalization, income inequity and poverty. This view is shared by Tsaliki (2009), he states that efforts to increase labor flexibility by liberalizing labor market contributes to the polarization of income and increase poverty levels. The survey data of other authors (Apergiset al. 2011) showed that there is a two-way relationship between poverty and income inequity in both short and long terms. In the short term both income inequality and unemployment have a positive and statistically significant impact on poverty. It is worth noting that there is a correlation between export and poverty reduction. Since export and poverty ratio are asymmetrical, the export reduction may increase poverty and unemployment. Thus, each national export development strategy should include poverty reduction (Skae and Barclay 2007).

CHAPTER FIVE

DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS

5.0 Introduction

The data was analyzed using description and percentages. This chapter therefore presents the discussion of findings, conclusion and recommendations. 

5.1 Discussion of findings

5.1.1 How high cost of agricultural inputs leads to poverty

Findings from the table 4.4 showed that, majority of the respondents 71.25% do not apply fertilizers in their farms for several reasons such as fertilizers are expensive (41.25%), 33.75% of them cited bad effects of fertilizers. Also results in table 4.6 indicated that, 78.75% of the respondents do not use pesticides as a form of agricultural input – thus, majority of the people use traditional methods of farming. The findings are in agreement with (Mellor 1999) who stated that inadequate access to capital constrains the potential for poverty reduction through smallholder driven agricultural development. This also leads to the inability of farmers to be able to use modernized methods of farming.

Table 4.7 indicates that, 51.25% of the respondents do not use machines in their agricultural activities and Table 4.8 showed that majority of the respondents cited high expenses involved in using machines, 26.25% of them said small farming practices carried out by people in Kisozi Sub County. These findings are in line with Thirtle et al (2001) who argued that technology reduces needed inputs and increases the prices of available inputs, production costs will decrease (raising profits), but output may not be affected and employment could be reduced. If instead the technology raises yields, output and (most likely) employment will increase, but profits will not necessarily increase. This has made farmers not use much input such as fertilisers, pesticides and other inputs.

5.1.2 How lack of education leads to poverty

Majority of the respondents (81.25%) had children studying in secondary levels, primary levels, and even others were able to pay university dues for their children. Only a few respondents (18.75%) said they were not able to pay school fees for their children. This is because their children were staying at home and was involving in home activities. Majority (81.25%) were able to buy scholastic materials such as school uniforms, books, pens, and all other school materials. These findings are in line with Kurosaki and Khan (2006) who argued that not only poverty is concentrated in households with illiterate/less educated heads but also it is much harmful for the female-headed households as compared to the male headed ones. 

5.1.3 How low agricultural productivity leads to poverty

From results, majority of the respondents (93.5%) indicated that they produced approximately 3-10 kilograms bags of maize. Only a few (6.5%) of the study respondents indicated that the produce 10 and above bags of maize. Also, 72.5% of the respondents indicated that they sold few produce due to the low productivity. Reasons obtained included; small piece of land, poor yields, drought. These findings are in line with Irz et al (2001) who stated that low agricultural production is likely to decrease the demand for farm labor through decreases in area cultivated, intensity of cultivation (labor use per unit of land), or frequency of cropping. The impact of farm labor opportunities on poverty reduction depends on the extent to which the rural poor depend on farm laboring for their livelihood. 

5.1.4 How unemployment leads to poverty

Findings in Table 4.9 showed that (31.25%) of the study respondents were employed while the remaining (68.75%) indicated that they were not employed. Majority of the respondents were unemployed implying that they had little or no disposable incomes to enable them have access to their basic needs. It was revealed that majority (61.25%) of respondents were farmers, 32.5% of them were business persons and only 6.25% of the study respondents were civil servants. This implies that the majority of the respondents engaged in farm activities most of which engage in subsistence farming where the grow crops for both home use and for use. Most households in Kisozi Sub County grow mostly maize, sugarcane and potatoes. Majority of the respondents (100%) used road transport to go to various places and carry out several activities. Respondents used bicycles, motor bikes and cars. From findings, 72.5% of the study respondents used water transport. This is because they are next to River Nile, so they use boats, engine boats to cross to the side of the river where they either use ‘boda-bodas’ or bicycles for their transport. Respondents (6.25%) stayed in properly finished houses while the remaining respondents (93.75%) of the study respondents stayed in either unfinished or grass hatched house. The study findings agree with Ukpere and Slabert (2009) who conducted a qualitative study and found that there is a positive correlation between unemployment, currently wide spread globalization, income inequity and poverty. This view is shared by Tsaliki (2009), he states that efforts to increase labor flexibility by liberalizing labor market contributes to the polarization of income and increase poverty levels. The survey data of other authors (Apergiset al. 2011) showed that there is a two-way relationship between poverty and income inequity in both short and long terms. In the short term both income inequality and unemployment have a positive and statistically significant impact on poverty. It is worth noting that there is a correlation between export and poverty reduction. Since export and poverty ratio are asymmetrical, the export reduction may increase poverty and unemployment. Thus, each national export development strategy should include poverty reduction (Skae and Barclay 2007).

5.2 Conclusion

Poverty can be caused through decrease in agricultural productivity, increase in the prices of agricultural inputs, low education, and unemployment among others. When farm incomes and the real wage rate increase, the rural non-farm economy grows, real household incomes increase and the percentage of the population living below poverty lines decreases. Nutritional status or other aspects of well being, such as health measures and education, may also improve. However, initial asset endowments, and land assets in particular, are significant determinants of households’ ability to access and effectively use productivity enhancing knowledge and technologies. Poor households face barriers to technology adoption and market access. The importance of productivity to agricultural sector growth to poverty reduction depends on a variety of contextual factors including the initial distribution of poverty, asset endowments, access to education, employment and the extent and nature of the poor’s participation in the agricultural sector.

5.3 Recommendations

There is need for development of higher yielding maize varieties, increased production of non-cereal staples such as cassava, increased successful participation in high value crops.

Basing on the findings from the analysis of the data, the following strategies should be put into considerations so as to improve the rural incomes and reduce rural poverty.

Women should be empowered through adequate education and legal reforms to ensure equal opportunities that bring balance /equity in the levels of incomes between rural women and men which will gradually bring about growth in the rural sector.

More investment in education and training of rural citizens so as to improve the capacity of the rural labourforce, equip the youth with the knowledge and skills to secure good livelihood and break the cycle of poverty.

Establishment of more credit/loan schemes/money lending institutions and encouragement  of  the rural poor citizens to subscribe as members so as to acquire some capital for starting up  viable and lucrative investment.

5.4 Areas for further studies

The study recommends that further studies should be carried out on:

• The role played by the informal sector on people’s welfare
• The challenges faced by implementers of poverty reduction strategies  

REFERENCES

Adebayo (2013). Modeling Agricultural Trade and Policy Impacts in Less Developed Countries. OECD Food, Agriculture and Fisheries Working Papers, (11).

Aidelunuoghene, N. (2014). Agricultural Research, Productivity, and Food Prices in the Long Run. Science, 325(September), 4-5.

Barrett, Christopher B. 2005. Rural poverty dynamics: development policy implications. Agricultural Economics, 32(s1), 45-60. 

Bonal, F. (2007). Why Liberalization Alone Has Not Improved in Zambia: The Role of Asset Ownership and Working Capital Constraints (Policy Research Working Paper No. 2302). Washington, D.C.: World Bank.

Clifton, E. and Marlar, K. (2011). Smallholder market participation: Concepts and evidence from Eastern and Southern Africa. Food Policy 33(4): 299–317.

Clifton, T., and Marlar, H. (2011). The dynamics of agricultural production and the calorie-income relationship: Evidence from Pakistan. Journal of Econometrics

Coxhead, I. A., & Warr, P. G. (1998). Technical Change, Land Quality, and Income Distribution: A General Equilibrium Analysis. American Journal of Agricultural Economics, 73(2), 345-360. 

Dasgupta, P. (2003). World Poverty: Causes and Pathways [Conference Paper]. Proceedings of the World Bank Conference on Development Economics. Retrieved from http://www.econ.cam.ac.uk/faculty/dasgupta/worldpov.pdf 

Diao, X, & Pratt, A. (2007). Growth options and poverty reduction in Ethiopia – An economy-wide model analysis. Food Policy, 32(2), 205-228. 

Ellis, F. (2003). Livelihoods and Rural Poverty Reduction in Malawi. World Development, 31(9), 1495-1510. 

Irz E. (2001). The Economics of Poverty Traps and Persistent Poverty : An Asset-Based Approach. Journal of Development Studies, 43(2), 178-199.

Jamal, G (2005). The economics of poverty in poor countries. The Scandinavian journal of economics, 100(1), 41-68.

Minten, B., and Barrett, A. (2008). Consequences of a commodity boom in a controlled economy: Accumulation and redistribution in Kenya 1975–83. World Bank Economic Review 1(3): 489–513. 

Mughal, V. (2007). Growth and poverty reduction in Uganda, 1992-2000: Panel data evidence. Economic Policy, 1992-2000.

Nasir, B., and Nazli, C. (2000). The Role of Agriculture in Development: Implications for Sub-Saharan Africa (Research Report 153).

Obumneke, E. (2012). Pathways of Poverty Reduction Rural Development and Transmission Mechanisms in the Philippines [Research Report]. Asia and Pacific forum on poverty: Reforming policies and institutions for poverty reduction. Manila: Asian Development Bank.

Okojie, O. (2002). The role of cocoa in Ghana’s future development.

Oriahi, B., and Aitufe, I. (2010). Right Target, Wrong Mechanism? Agricultural Modernization and Poverty Reduction in Uganda. World Development,

Saunders, S. (2002). Fractal poverty traps. World Development, 34(1), 1-15. 

Senia, A., and Godwell, Y. (2006). IFPRI Agricultural Growth Linkages in Sub-Saharan Africa (Research Report 107). Washington, D.C.

Staatz and Dembélé 2008)

Thirtle, A. (2001). Non-farm income, gender, and inequality: evidence from rural Ghana and Uganda. Food Policy, 26(4), 405-420.

Tilak, R. (2005). Inequality and Growth Reconsidered: Lessons from East Asia. The World Bank Economic Review, 9(3), 477-508.

Ukpere, O., and Slabert, C. (2009). An analysis of income poverty effects in cash cropping economies in rural Mozambique: Blending econometric and economy-wide models [Thesis].

APPENDICES

APPENDIX I: QUESTIONNAIRE FOR PARENTS

I am Nambiro Rachael, a final year student pursuing a Bachelor’s Degree of Arts in Economics of Kyambogo University. This questionnaire serves to gather data concerning the “The causes of poverty in eastern Uganda, a case study of Kisozi sub county, Kamuli District”. You have been identified as one of the key respondents for this study. Therefore, I request for your cooperation. You will not be forced to answer any question that is against your will. The information you will provide will be treated with utmost confidentiality and will only be used for academic purposes. 

Thank you in advance 

CHAPTER A: BACKGROUND INFORMATION 

1. What is your sex?

Male            Female 

2. What is your age?    

(20-25)         (26-30)        (31-40)       (40 and above)

3. What is your highest level of education?

Masters                    Degree                Diploma                     Secondary        

Primary              None           others specify…………………………………..   

4. What is your occupation?

Farmer Businessman Civil Servant

Others Specify …………………………………..   

Chapter B: How high cost of agricultural inputs leads to poverty

6. Do you use fertilisers?

Yes

No

If yes, why?

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

If no, why?

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

7. Do you use pesticides?

Yes

No

If yes, why?

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

If no, why?

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

8. Do use high yielding manure?

Yes

No

If yes, why?

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

If no, why?

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

9. Do you use machines such as tractors?

Yes

No

If yes, why? 

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

If no, why? 

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

Chapter B: How lack of education lead to poverty

10. I have educated my children up to University.

Yes

No

If yes, why? 

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

If no, why? 

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

11. Am able to pay children’s school requirements

Yes

No

If yes, why? 

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

If no, why? 

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

12. Am able to pay school fees for my children

Yes

No

If yes, why? 

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

If no, why? 

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

Chapter D: How low agricultural productivity leads to poverty

13. How much harvest do you realize per acre of land?

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

14. Why?

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

15. How much do you sell?

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

16. How much do you use for subsistence

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

Chapter E: How unemployment lead to poverty

17. Are you employed or not?

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

18. Which kind of employment do you do?

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

19. Which means of transport do you use?

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

20. What kind type of house do you have?

…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

THANK YOU FOR YOUR TIME