determinants of poverty

Many variables can be considered as the determinants of income, and thus, of poverty. We can divide these variables into two general areas: the characteristics associated with the income generating potential of individuals and the characteristics associated with the geographic context in which the individual lives. The first kind of characteristics would include, for example, the assets owned by the individual, both physical and human, while the second type of characteristics would include, for example, the place in which the individual lives (urban or rural). However, there are severe problems in determining the direction of causality. Does poverty cause the characteristic or is it the presence of a given characteristic which causes poverty?. An example of this problem is whether poverty causes large households or a large household causes poverty. It is necessary to determine the direction of causality, but this is a difficult task that has not been solved yet due among other things to the unavailability of better data, especially panel data in developing countries. What we will try to do in this chapter is to get an approximation about the determinants of poverty, even if they could more properly be called the correlates of poverty.

This paper investigates the determinants of poverty and household welfare in South Africa using the National Income Dynamic Study data. South Africa offers a useful case due to its long and notorious history of high poverty levels, despite having decreased in recent years. The issue of poverty has been on the agenda of the South African government for many years. For example, in 2004 the Accelerated and Shared Growth Initiative for South Africa (ASGISA) acknowledged the challenges of prolonged poverty and other related problems (unemployment, and low earnings, and the jobless nature of economic growth). The New Growth Path policy announced by President Zuma in 2010 still raised similar issues – unemployment and poverty remains extremely high by international standards. The most recent government policy (the National Development Plan) introduced in 2013 as South Africa's long-term socio-economic development roadmap placed even more emphasis on similar issues and was viewed as a policy blueprint for eradicating poverty and reducing inequality in South Africa by 2030.

The determinants of poverty transitions in Europe and the role of duration dependence Andriopoulou, Eirini and Tsakloglou, Panagiotis Athens University of Economics and Business.[r]

Because the intensity of poverty, defined as shortfall, i.e., the poverty line minus income, is a fractional response variable taking the values from zero to 100 percent 2 , the determinants of poverty intensity were modeled by using a fractional regression model proposed by Papke and Wooldridge (1996). This approach was developed to deal with models containing fractional dependent variables bounded between zero and 100 percent. Papke and Wooldridge (1996) noted that a fractional logit estimator is appropriate for this type of response variable with cross sectional data. As demonstrated by Wagner (2001), the fractional logit approach, is the most appropriate approach because this model overcomes a lot of difficulties related to other more commonly used estimators such as OLS (ordinary least square) and TOBIT 3 . There have been an increasing number of studies applying the fractional logit/probit model to handle models containing a fractional response variable being bounded between zero and one (e.g, Cardoso et al., 2010; Gallaway, Olsen, & Mitchell, 2010; Jonasson, 2011; McGuinness & Wooden, 2009; Tuyen, Lim, Cameron, & Huong, 2014). Hence, following this approach, we applied the so-called fractional logit model:

This paper looks at the impact of population dynamics on poverty in elderly- headed households in the Philippines using data from the Family Income and Expenditure Survey (FIES) from 2000 to 2006. The population of the elderly, or those 60 years and above, has increased from 3.2 million in 1990 to 4.6 million in 2000. This group is growing at a rate of 3.6% per annum and estimated to reach 7 million in 2010. Data from the FIES shows that the percentage of the elderly who are poor is increasing since 2003. Moreover, the percentage of elderly-headed household belonging to the poorest 10% of all households has been on the rise since 1997. An econometric model based on the logistic regression shows that the presence of a young dependent (aged 14 years old or below) increases the probability that the elderly-headed household will become poor by about 9 percentage points, controlling for other factors such as income of the household, education, age and gender of the household head, income transfer from abroad and regional-specific characteristics. The results of the econometric model suggest that the high proportion of young dependents create negative effects on the welfare of the elderly-headed household by increasing the probability of that household being poor. From the point of view of policy, addressing the alarming poverty incidence in the country must include measures that will manage the country’s bourgeoning population and bring down the fertility rate to a level that is conducive to higher income growth.

Given that the main asset of the poor is their labor, and since the returns to labor are highly correlated with education, we would expect to find an inverse relationship between education and poverty. The results obtained for this variable in the multivariate analysis confirm the findings encoun- tered in the poverty profile of an inverse relationship between level of education and poverty. This result is in line with the general consensus in the literature about poverty and particularly with the results obtained for the case of Mexico by Cortés (1997), Székely (1998) and Garza-Rodrí- guez (2000). It can be seen in Table 4 that the odds of being poor for a household whose head has completed Junior High School education are 55% lower than those of a household whose head has no instruction.

In Table 3 we have presented the extent of land ownership and other productive assets. Land ownership is considered to be the most valuable asset in rural areas given the economic value, political power and social status attached to it (Khan 2004). Poverty among landless households is high and two thirds of households in Pakistan do not own land: this includes landless hares (labourers) and non-agriculture based households (Anwar et al. 2004). Contrary to the other rural areas of Pakistan, in Gilgit-Baltistan most of the households do own land. In case of our research site all of the households own land.Here the issue is not land ownership but its size due to high fragmentation and the lack of arable land resulting from the challenges of a fragile environment and lack of irrigation. These constraints are coupled with a lack of mechanisation, lack of access to markets and falling commodity prices (Ediger and Huafang 2006), since they limit the agricultural assets available to the households and ultimately result in a negative impact on their wellbeing.

Household heads working for government or semi-government, as well as being employers or self-employed are less likely to be poor compared to the unemployed. Scholars such as Rodriguez and Smith (1994); Fields et al. (2003); Rupasingha and Goetz (2007) and Ranathunga. and Gibson (2014) have also observed the similar results in relation to employment status and poverty. Similarly, households with agricultural land and that receive remittances are associated with a lower probability of being poor. Households in estate and rural sectors have a higher probability of being poor compared to households in the urban sector. The probability of being income poor for estate and rural households is higher by 2.1% and 3.2% respectively compared to the households who are in urban. The same pattern can be seen for multidimensional poverty as well. These findings related to income poverty are consistent with Gunawardena (2000); De Silva (2008); Gunatilaka et al. (2010); Deepawansa et al. (2011); Ranathunga. and Gibson (2014) and Jayathilaka et al. (2016). Moreover, the estimated models are overly statistically significant and also have considerably higher pseudo R 2 values.

Educated and healthy children are the building blocks for growing economies and positive societies. However, they are often powerless within their society and are restricted by their surroundings and poverty. Issues facing children in the Horn of Africa are: poverty, HIV/AIDS, disease, fear, illiteracy, drought/famine, homelessness, military enlistment, conflict, exploitation, marginalisation, gender discrimination, displacement, urbanization and hunger. Along with it, children have no education, inadequate food, and inadequate shelter, no support abused and mistreated, turn to crime, drugs and violence to survive Prostitution. “The issues that children faced will prevent them from becoming leaders of tomorrow. Lack of skills, health, education and care destroys their chances of positively impacting society in the future…” 7

In looking on level of education in relation to house head with head without education, the results shows that the likelihood of being poor is decreases by 0.583239 factor when house head attained primary education and decrease by 0.402036 when proceed to secondary education. This implies that, education is the important factors in reducing the impact of poverty at the household level. This result is consistent with that of Geda et al., (2005) who evidenced that poverty is strongly associated with the level of education and Maitra (2002) who evidenced that the education attainment of the household head has a significant impact in poverty status and standard of living of the household. The study reveals that education of head of household is an important factor on escaping poverty. This emphasize the need of putting more efforts on long-term cycle of empowering Zanzibar population with relevant knowledge and study skills and utilizing the surrounding environment in return of social and economic benefit. Large part of Zanzibar characterized by coral, small favorable agriculture area and surrounded by sea, and education should focus on what should have done on increasing household production both at micro and macro level.

These are national figures, including both rural and urban areas. Poverty in rural areas was much higher, 26 percent of the rural population were extremely poor and 29 percent moderately poor, meaning that more than half of Mexico’s rural population (55 percent) was poor in 1992. Although at first sight these figures seem exaggerated, as defined by the study itself, they include only the income needed to buy a minimum food consumption basket that meets minimum nutrition requirements (extreme poverty line) and the income needed to buy this food basket plus a minimum non-food basket ("intermediate” or moderate poverty).

Conclusion and Policy Implications: This study has examined the main determinants of poverty among rural farm households in Wolaita zone, Southern Ethiopia. The FGT index revealed that among the sampled households, about 64 percent households were found to be poor while only about 36 percent were found to be non-poor in the study area. This figure revealed that poverty in the study area is much higher than the proportion of population below the poverty line at the national level which was estimated to be 25.6 percent in rural part of the country in 2015/16.The gap and severity of poverty were found 5percent and 81 percent, respectively. In line with this, the descriptive statistics demonstrated that the two groups have significant difference in annual consumption expenditure per adult. That is, the mean expenditure for poor households is birr 3,344 while birr 13,167 is the mean consumption expenditure per adult for non-poor households. This confirms to the conventional fact that there is significant difference in consumption outlay between poor and non-poor households. The logistic regression model result also revealed that farm income, participation in off-farm activities, education status of the household head and market distance have a significant and negative association with the probability of the household being to be poor while households’ family size and sex of household head have significant and positive effect on the probability of the household being poor. Accordingly, the authors recommends family planning methods to reduce the burden of large family size, raising household income diversification to improve livelihood of the rural farm households, promoting rural off-farm employment opportunities and investment in rural infrastructure so as to reduce the poverty of farm households in the study area.

6 the determinants of poverty at the household level, using reduced form models of various structural relationships that affect poverty (Glewwe, 1991). The literature shows that regardless of the definition of poverty line, the most commonly used dependent variables in poverty functions are binary indicators (probit or logit regressions) of poverty status or measures of the poverty gap although the multiple regression model as a tool of analysis in those kind of studies has been criticized for number of drawbacks (Mok et al., 2007).

The Millennium Development Goals (MDGs) aims at halving by 2015 the percentage of world population in 1990 with income less than US $ 1 a day and halving the share of people who suffer from hunger. Being a developing nation, poverty reduction should be our foremost obligation. An appreciable decline has occurred recently, headcount decreased from 34.46 percent in 2000-01 to 23.94 in 2004-05 (Government of Pakistan, 2006-07). However, seeing only the statistics and the trends in poverty we can just observe that what happened to poverty in different periods and also the decomposition of poverty in different years gives us a more appropriate picture of the incidence of poverty. This knowledge is useful because it informs us whether poverty is increasing or decreasing overtime. But this information does not provide us the details of the causes of poverty. For instance, is poverty high due to low education attainment or large family size or due to any other reason? Here is a need of research about the determinants of poverty that are positively or negatively linked with the poverty status. This is the area where research can be most useful because firstly we have to understand the main determinants of poverty before designing the most efficient policy to reduce poverty in the country.

In terms of deprivation it connotes to lack of capability to fulfil many essential functions in human life. Apart from food, clothe and shelter a man needs education, health, sanitation and rural institutions (Tilak, 1993, Benerji, 2000, Janaiah et.al., 2000 and Kumari and Singh, 2009). Poverty is a social stigma and therefore it has received considerable attention in the development policies of the country. Efforts are being made to identify the critical factors that induce poverty and extinguish the effect of poverty mitigation plans in India. Jharkhand, being one of the most opulent states in terms of availability of natural resources ironically harbours a huge proportion of poor and thus it is imperative that the critical determinants of poverty in the state be analyzed and studied for a larger impact of development programmes. To examine various factors that inflict poverty, an ordered Probit regression was undertaken. The dependent variable(y), being a binary variable to determine probability the poor family is coded as one (1) and non-poor as zero (0). The probit computes maximum likelihood estimates of the parameters. The positive sign of estimate means a direct relationship with the dependent variable while negative sign shows an inverse relationship.

This paper investigates the determinants of poverty and household welfare in Sudan using the National Household Budget and Poverty Survey (NHBPS) data (2015). Poverty as a multidimensional concept includes monetary and nonmonetary characteristics. As a consequence of this multidimensional nature, there are used different measures which are different from one county to another. Even between people in a country observation for poverty change within regions and social and economic groups depending on their sources of income and determinants of well-being. People live in poverty when they are deprived of incomes and other living resources, such as goods, housing conditions, commodities and sanitation, services that can authorize them to have a role and build their own social life (Myftaraj, Zyka, and Bici, 2014).

Abstract Poverty is general scarcity or the state of one who lacks a certain amount of material possessions or money. Poverty is a multifaceted concept, which includes social, economic, and political elements. Poverty level can be calculated for households as well as for countries. United Nations developed a methodology to calculate the advanced poverty index for countries. The main purpose of this study is primarily to determine the determinants of poverty by conducting a strong literature survey and examining the United Nations methodology. By using the determinants of poverty, it is planned to calculate the poverty index and classify the households with an approach based on fuzzy logic. The applied methodology contains the weighting of the determinants of poverty by using fuzzy logic membership functions and the calculation of the poverty index of households. The weighting process of the determinants has recognized by taking the opinions of the expert academicians in the field of poverty. It is also considered to categorize the households, according to the level of poverty. For this purpose, it is determined a sample consisting of 120 households in Bayburt province. As a result of the analysis, it is aimed to examine the effect of factors determined the poverty and also provide suggestions to the researchers and policy makers.

Most of the poverty studies were based on multivariate regression analysis to identify the determinants of poverty at the household level, using reduced form models of various structural relationships (Glewwe, 1991). The literature indicates that regardless of the definition of the poverty line, the most commonly used dependent variables in poverty functions are dichotomous in nature or measures of the poverty gap. However, there is debate over the usefulness of poverty probit versus an OLS on consumption (Coudouel, Hentschel, & Wodon, 2002; Pradhan & Ravallion, 2000; Ravallian, 1996; Ravallion & Wodon, 1999; Wodon et al., 2001; World Bank, 2005). It is argued that that taking the dependent variable as a binary variable will lose a lot of information about the dependent variable and make the estimates of logit or probit regressions relatively sensitive to specification errors. However, there are some appropriate uses of probit or logit regressions (Coudouel et al., 2002, p. 45). Firstly, probit and logit regressions can be used to assess the predictive power of various variables used for means testing for targeting analysis. Secondly, probit or logit regressions can be used to analyse the determinants of transient versus chronic poverty where panel data are available.

The incidence of poverty in Nigeria has been on the increase in recent years despite impressive economic growth rate. This has been attributed to the failure of the recent impressive economic growth rates to benefit the poor. This means that the rapid economic growth of the past decade has not been able to reduce the incidence of poverty. More worrisome than the high rate of poverty in Nigerian is the spatial distribution of the incidence of poverty. While a higher percentage of Nigeria are poorer today than three decades ago, the northern half of Nigeria is far poorer than the southern half. This fact has been validated by different data sources on poverty in Nigeria. This study uses non-monetary determinants of poverty to validate this fact and by extension explain what drives the spatial distribution of poverty in Nigeria. This study shows that that demographic structure like household size, number of wives and wife age at first marriage correlate with high poverty rates. It also shows that household characteristics and welfare metrics like house type, source of water, sanitation condition and choice of cooking fuel also explain spatial distribution of poverty between southern and northern Nigeria. The study attributes the different outcomes of these determinants to both the cultural and religious beliefs of people of northern Nigeria which are rooted in the Islamic culture that has come to dominate this part of Nigeria for centuries. This study therefore recommends that this fact should be taken into consideration in designing poverty reduction strategy for Nigeria. There is the need for further research on how the northern half of Nigeria has been unable to overcome its cultural and historical background and modernise like southern Nigeria.

Given the poor economic performance of Guinea-Bissau over the last few years, including a severe recession toward the end of 2002, poverty is likely to be high and to have risen in recent years even compared to its high postconflict level. The first objective of this chapter is to estimate the share of the population in poverty in 2002, predict how it may have evolved since then, and assess the levels of growth that will be required to reduce poverty measures in the future. The chap- ter also provides a brief poverty profile and an analysis of the determinants of poverty using the 2002 nationally representative survey, which was recently made available for analysis. Geographic location, demographic structure (both household size and headship), employment (both in terms of sector and type), education, and migration all have potentially large effects on the consumption level and thereby poverty of households.