The determinants of household poverty: the case of berehet woreda, amhara regional state, Ethiopia

Neway, Markew Mengiste; Massresha, Solomon Estifanos Article The determinants of household poverty: the case of berehet woreda, amhara regional state, Ethiopia Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Neway, Markew Mengiste; Massresha, Solomon Estifanos (2022) : The determinants of household poverty: the case of berehet woreda, amhara regional state, Ethiopia, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 10, Iss. 1, pp. 1-16, https://doi.org/10.1080/23322039.2022.2156090 This Version is available at: https://hdl.handle.net/10419/303891 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/oaef20 The determinants of household poverty: the case of berehet woreda, amhara regional state, Ethiopia Markew Mengiste Neway & Solomon Estifanos Massresha To cite this article: Markew Mengiste Neway & Solomon Estifanos Massresha (2022) The determinants of household poverty: the case of berehet woreda, amhara regional state, Ethiopia, Cogent Economics & Finance, 10:1, 2156090, DOI: 10.1080/23322039.2022.2156090 To link to this article: https://doi.org/10.1080/23322039.2022.2156090 © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 10 Dec 2022. Submit your article to this journal Article views: 2740 View related articles View Crossmark data Citing articles: 3 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE The determinants of household poverty: the case of berehet woreda, amhara regional state, Ethiopia Markew Mengiste Neway 1 * and Solomon Estifanos Massresha 1 Abstract: Nowadays, poverty is one of the most important issues that needs due attention in many developing countries like Ethiopia. Nonetheless, poverty in Ethiopia remains widespread in both rural and urban areas. Therefore, this study aimed to examine the determinants and status of poverty in Berehet Woreda. The study was conducted using a cross-sectional survey. To conduct the study, a sample of 384 households was selected using a stratified simple random sampling technique. Foster Greer Thorbecke’s Poverty Index was used to examine the extent and severity of poverty in the Woreda. Accordingly, about 36% of households in Woreda live below the poverty line, with an average poverty gap of 12% and poverty severity of about 7%. The binary logit model showed that household education status, dependency ratio, residential area, and access to credit were statistically significant in determining household poverty status. Since the poverty situation in Woreda was worse than the national average, the regional government should prioritize this Woreda and develop a special type of projects especially in rural areas that can lift the majority of the poor out of poverty. Subjects: Development Studies; Regional Development; Research Methods in Development Studies; Economics and Development; Economics Markew Mengiste Neway ABOUT THE AUTHOR Markew Mengiste Neway joined Berehet woreda Administrative office and working as the head of the office up to vice administrator of the woreda from 2006 up to 2015 after he holds his bachelor degree in Economics from College of business and economics Bahir Dar University, Ethiopia. After 9-year work experience, he got an incountry scholarship in Bahir Dar University. Now, he holds his master degree in Applied Development Economics on June 2017. After the accomplishment of Master degree he was engaged in teaching, research, and community service in Debre Markos University. Currently he is working in Debre Berhan University engaged in teaching, research, and community service. Specially, measurement and modeling of willingness to pay, food security, value chain analysis, income diversification, livelihood analysis, and poverty analysis are the author’s interest area of research. PUBLIC INTEREST STATEMENT Poverty is a major problem across the world and reducing poverty is not simple task. Some advocates of antipoverty programs claim that fighting poverty is a public good. This might be provided in the form of rigors research conducted by researcher on the level of poverty, sources of poverty and on the determinant of poverty. This would also leads to government intervention through designed policy are important. Societies also need a reduced poverty level accompanied by a number of constructive social impacts. Therefore, the research conducted on poverty would help the government or policy makers to meet the interest of the people by using recommended policy options. Neway & Massresha, Cogent Economics & Finance (2022), 10: 2156090 https://doi.org/10.1080/23322039.2022.2156090 Page 1 of 16 Received: 27 October 2020 Accepted: 04 December 2022 *Corresponding author: Markew Mengiste Neway, Department of Economics, Debre Berhan University, P.O. Box 445, Debre Berhan, Ethiopia E-mail: [email protected] Reviewing editor: Robert Read, Economics, University of Lancaster, United Kingdom Additional information is available at the end of the article © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Keywords: Berehet; determinant; logit model; poverty; severity JEL classification: I32 1. Introduction The concept of poverty refers to individuals or families who do not have enough resources to meet their current needs. The poor are vulnerable groups who lack access to adequate food, shelter, education, health and other services. However, the task of reducing poverty is quite challenging, as poverty means not only low levels of income/consumption and low levels of human development in terms of education and health care, but also feelings of powerlessness, vulnerability and fear because the poor are at greater risk of living on the brink of subsistence. Therefore, different policies and poverty eradication strategies should be interwoven to eradicate extreme hunger and poverty as propagated in the Sustainable Development Goals (SDGs) under Goal 1. Poverty can have many consequences, for instance, (Neville, 2011; Ratcliffe & Mckernan, 2010) pointed out that children growing up in poverty have much worse mental and cognitive development than children growing up in non-poor areas, and this is repeated in the same group. In extreme situations, poverty has always been seen as a curse on humanity (Kotler et al., 2006), particularly in less developed countries such as Ethiopia. As in every African country, poverty is widespread in Ethiopia, which is why poverty reduction becomes one of the main goals of the development efforts of all underdeveloped countries including Ethiopia (Bigsten et al., 2003), which in turn helps to maintain and promote social cohesion (Sharma & D, 2014; Teka et al., 2019). For example, in Ethiopia, where most people live below the poverty line, poverty is pervasive and persistent (Alemu et al., 2011). Along with severe poverty, Ethiopia is also threatened by high population growth associated with high unemployment rates, high dependency ratios and larger family sizes (Deressa & Sharma, 2014). As for Ethiopia’s poverty status, in 2000 45.5% of the population lived below the national poverty line, which fell to 30% in 2011. The same report also notes that the poverty rate fell from 45.5% in 2000 to 23.5% in 2016 (UNDP, 2018). In the same report, the poverty status based on the area of residence using the national poverty line in the year 2000 was 39.9% in urban and 45.4% in rural areas. Furthermore, the poverty status of the Amhara regional state where the study area is found was 41.8% in 2011 and 30.5% in 2014 (World Bank Group, 2015). Even if poverty gap and poverty severity indices in the year 2000 were declined from 10.1% and 3.9% to 3.7% and 1.4% in 2016 respectively (UNDP, 2018), the level of poverty remains widespread in Ethiopia, both in rural and urban areas. However, the rate at which poverty decline in urban areas is not the same as that of rural areas (Enquobahrie, 2004). In this regard, in urban areas poverty has declined from 26% in 2011 to 15% in 2016. Conversely, progress in rural areas has been more modest, with poverty falling from 30% to 26% over the same period (UNDP, 2018). Despite some improvements in recent years, poverty, along with other issues, is still becoming the most serious problem which affects the life of million in Ethiopia. For instance, a study conducted by (Hartline Grafton & Dean, 2017) points to a direct link between food insecurity and poverty, in which food-insecure households are poor. Additionally, (Teka et al., 2019) found a close link between poverty and income inequality in pastoral and agro-pastoral communities of Afar Regional state, Ethiopia. Berehet Woreda, where the study was conducted, is one of the most foodinsecure woreda in the northern Shewa Zone of Amhara Regional State which share a boarder with Afar Regional state. In addition, the poverty status in the study area is expected to be above the national and regional averages. Therefore, the incidence of poverty varies by social group, season, location, and region. In this sense, most studies of poverty have focused on poverty at the national or regional level (Bogale et al., 2005; Deressa & Sharma, 2014; Ermiyas et al., 2013). However, as far as the problem of poverty and its determinants is concerned, it is highly crucial to study the matter along specific area case in order to arrive at concrete results or solution. This is because the Neway & Massresha, Cogent Economics & Finance (2022), 10: 2156090 https://doi.org/10.1080/23322039.2022.2156090 Page 2 of 16 existing situations in one area may not be compatible with those in the other. For this reason, some of the policy implications that are proposed by pooling a group of people at national or regional level that are structurally diverse may not be effective in addressing the issue of poverty. Moreover, by its very nature, poverty is multifaceted, complex and pervasive. In this regard, poverty in the study area was further complicated by poor access to infrastructure, lack of government support, limited access to employment opportunities, and poor living conditions. Therefore, the main objective of the study is to determine the poverty status in Berehet Woreda, investigate rural–urban difference of poverty status and identify the determinant of poverty in the study area. Moreover, poverty alleviation has been an important policy debate in the international development literature in recent years and comprehensive development occurs when people have political, social, and economic power (Muhdin, 2015). However, as (Kotler et al., 2006) explained, the poor have often been viewed as a homogeneous mass to be addressed with standardized aid programs at the national level, which inevitably leads to discrepancies between local needs and solutions. For this reason the contribution of our study comes in two pervasive ways. First, most of the studies conducted on poverty at national and regional levels may not reflect the actual status of poverty in all parts of the country and fail to account poverty at grass root level. Second, to the best of the researcher’s knowledge, no study has been conducted on poverty in Berehet Woreda; therefore, our study adds to the available literature and sheds light for further research. As a result, potential stakeholders such as the government and organizations working on poverty alleviation will clearly understand and make evidence-based decisions. 2. Theoretical literature review Classical economists believed that poverty is highly associated with individual characteristics and a behavioral problem passes from generation to generation as a culture because of genetic factors, and they thought that the contribution of the government to combat poverty is minimal. On the other hand, the neo-classical theory assumed poverty results from unequal endowments in talents, skills, and capital. This theory considers poverty is a due to lack of capital in different forms, including human, physical, social, and institutional. Therefore, the empirical analysis of the determinant of poverty was conducted based on individual factors, institutional factors, and socioeconomic factors that determine the likelihood of the household to be poor or non-poor (Davis & Sanchez-martinez, 2014). 2.1. Empirical literature review on determinants of poverty 2.1.1. Household specific factors Different literature indicated that household head age (Beyene & Muche, 2010; Muleta & Deressa, 2014; Tesfaye & Getachew, 2018) has a positive and significant effect, that means as the age of the household increase, it increases the probability of being poor, which implies that older households are less likely to participate in productive activities. According to the life cycle theory of income, poverty will be higher for households headed by young and by old people. This is because productivity is low at a relatively young age, increases at middle age and then decreases again at old age. Contradictory to the above research finding, the research conducted by (Muhammedhussen, 2016) indicated that the age of the household has a negative and significant effect on reducing poverty. The sex of the household is another substantial determinant of poverty and has a negative impact on reducing poverty in the male-headed household (Bekele & Silshi Merid, 2020). This is because most of the time, females were engaged and occupied by non-productive activities, and they were deprived of vital and productive resources like land and other economic resources (Neway et al., 2022). Research conducted on determinants of rural poverty in Ethiopia the case of Dejen Woreda and Hong Kong proved that male headed households have low probability of being poor (Ermiyas et al., 2019; Peng et al., 2019). Neway & Massresha, Cogent Economics & Finance (2022), 10: 2156090 https://doi.org/10.1080/23322039.2022.2156090 Page 3 of 16 Furthermore, the study results on the determinant of poverty indicate that dependency ratio have a positive and significant effect in aggravating poverty (Ermiyas et al., 2019; Girma & Temesgen, 2018; Kassahun et al., 2022; Muleta & Deressa, 2014; Sinnathurai, 2013; Tesfaye & Getachew, 2018). Bringing a more unproductive household member into the family could exacerbate poverty as the household would struggle to meet food and other needs due to limited income. Therefore, households with more non-working members earning less income are at risk of falling into poverty (Eyasu & Yildiz, 2020). The other important household-specific variable that can influence household poverty status is the educational status of the household head. Education is considered as the most important determinant of household’s poverty in many studies (Awel & Brar, 2019; Bogale et al., 2005; Kassahun et al., 2022; Muleta & Deressa, 2014). Given that the main asset of the poor is their labour, and since the returns to labour are highly correlated with education, Garza–Rodríguez (2015) found an inverse relationship between education and poverty. In addition, (Eyasu & Yildiz, 2020) also found a positive correlation between education and poverty in both urban and rural areas. This means that improvement in education status increases earning potential and improve the occupational and geographic mobility of labour. The study from Hong Kong on determinant of poverty affirmed that household who have lower educational attainment have high probability of being poor (Peng et al., 2019). 2.1.2. Socio-economic factors The livestock sector also makes an important contribution to the economy and environmental protection: it restores income and other sources of crop production, absorbs income shocks caused by crop failures, generates a continuous income stream and employment opportunities, and reduces the seasonality of income, especially among the rural. Tropical Livestock unit as a measure of livestock owned by households found to have a negative and statistically significant association with poverty status of households in a number of studies (Alemaw et al., 2021; Awel & Brar, 2019; Girma & Temesgen, 2018; Kassahun et al., 2022; Muhammedhussen, 2016; Tesfaye & Getachew, 2018). Livestock owned as an essential asset of the household, can help households by bridging income and sometimes food gap by absorbs income shocks caused by crop failures, generates a continuous income stream and employment opportunities and it is also sources of wealth (Alemaw et al., 2021; Beyene & Muche, 2010; Ermiyas et al., 2019; Rehman et al., 2017). The religion of the head of household is also a major factor in poverty (Mberu et al., 2014). The Kenya study on patterns and determinants of poverty transitions in urban poor households showed that Muslims are less likely to escape poverty than Christians. The other study conducted in America (Ranjith & Rupasingha, 2012) showed the same result, and which indicating that Muslim households have a large family size, and this large family size contributed for poverty. The marital status of the household is another determinant of poverty. Married households has a better probability than none married one to get out of poverty (Heshmati et al., 2019). The phenomenon that married men earns higher average wages as compared to unmarried men, the so-called marriage premium. 2.1.3. Institutional factors Access to credit has been considered as one of the important variables of interest in poverty reduction. Access to credit services (Tesfaye & Getachew, 2018) has a negative and significant effect in reducing the probability of being poor. Access to credit services is the main source of financial capital that sustains rural livelihood. Therefore, enhancing and expanding rural credit services are important ingredient for farmers to fulfill their demand for modern farm inputs and enhance technology adoption. These directly increase the income of the household and help to escape out of poverty trap. Neway & Massresha, Cogent Economics & Finance (2022), 10: 2156090 https://doi.org/10.1080/23322039.2022.2156090 Page 4 of 16 2.1.4. Spatial factors Residential area (urban-rural) differential is one of the determinants of poverty. Rural residents are relatively worse off socio-economically than urban residents (Mberu et al., 2014). On the other hand, urban residents have higher consumption expenditure (income) than rural residents (Heshmati et al., 2019). Poverty alleviation also has been better achieved along with urban residents due to access to better education, infrastructure, and job opportunity result in a higher income than rural residents. 2.2. Measurement and decomposition of poverty The Foster-Greer-Thorbecke (FGT) index is a generalized poverty measure developed by Erik Thorbecke, Joel Greer, and James Foster (Foster et al., 1984). They also identify three categories of FGT contributions in poverty analysis to the measurement, to the axiomatic, and to the application. It also contributed to the design, implementation, and evaluation of prominent development programs (Foster et al., 2010). Even though there are competing measurements of poverty, researcher commonly used Foster Greer and Thorbecke poverty measure that has been found suitable for presenting information on poverty in an operationally convenient manner as compared to other unidimensional poverty measure such as Watts poverty index, Sen-Shorrocks-Thon index and Time taken to exit. On the other, Foster Greer and Thorbecke (FGT) poverty measure have desirable characteristics which are understandable and straightforward for policymakers (Foster & Greer Thorbecke, 2010). The FGT index has proven to be very useful for evaluating the extent of poverty across space and time in many studies. In this regard, many empirical studies focusing on the issue of poverty were bound to use FGT index since the measure is suitable to analyze inequality (average squared normalized poverty gap using P 2 squared coefficient of variation, renormalization of income gap (average poverty normalized gap) using P 1 and headcount ratio using P 0 (Foster et al., 1984). Most of the applications use the decomposability property of FGT measure to analyze the significant correlates of the incidence or headcount, depth, and severity of poverty and laid the ground for informed policy discussion to confront poverty (Foster et al., 2010). The FGT index as poverty measure is formulated as; Pα¼1 n∑q i¼11Yi Z � �α (1) Where ●P is a measure of absolute poverty ●α is the FGT parameter which may be interpreted as a measure of poverty α = 0,1,2 headcount, poverty gap and severity, respectively ●Y is total consumption expenditure per adult equivalent (i = 1,2, . . .,n) ●n is the total number of households in the sample ●q is the total number of poor households below the poverty line 3. Research methods 3.1. Sampling A two-stage stratified sampling method was used to select the sample respondents. In the first stage, one rural kebele from the nine Kebeles of the Woreda were randomly selected because the population in the Woreda is homogenous regarding religion, geographic location, and mode of living and one urban kebele it was selected. In the second stage, a total of 384 representative samples were selected from the two strata by using proportional random sampling i.e. 154 rural kebele and 250 from urban kebele. 3.2. Data type and collection method For this study, the following key data was extracted from primary sources for the period of 2017/ 18. In order to generate the information required for this study, a structured questionnaire was Neway & Massresha, Cogent Economics & Finance (2022), 10: 2156090 https://doi.org/10.1080/23322039.2022.2156090 Page 5 of 16 developed. The questionnaires were used as a significant source of information to collect data on different aspects of the household and individual characteristics related to determinants of household poverty. 3.3. Methods of data analysis 3.3.1. The setting of poverty line Poverty comparisons involve three main decisions: choosing a welfare measure; choice of a poverty line, and choosing a poverty index for aggregation (Appleton et al., 1999). Regarding the choice of welfare measure, the study chose consumption rather than income because consumption would be a better indicator of poverty measurement than income. Even if there is a great deal of literature on how to assess poverty the question remains unanswered to draw the line of poverty (Thorbecke, 2004). Currently, there are two main methods to set the poverty line: Cost of basic Needs Value (CBN) and Food-Energy-Intake (FEI). The cost of basic need approach is important for ensuring consistency or treating individuals of the same standard of living equally (World Bank Organization, 2001). The poverty line is also estimated by using the cost of basic need approach both food and non-food. In poverty analysis, there is no fixed poverty line instead we used the food poverty line of meeting 2300 kcal per person per day. W because an individual who has the same income shortfall does not’ mean that they have the same calorie shortfall. Any normal person needs a minimum energy requirement to perform regular physical life, estimated by Ethiopia nutrition and health research institute was used for this study. The study adopts the cost of basic need approach, which is the widely used method of setting the poverty line. The Monetary poverty line is constructed by using the cost of basic need approach, including the cost of basic food and non-food needs. A household is deemed as living in poverty if the daily per capita household food energy intake falls below 2,300 kcal and non-poor if the daily per capita household food energy intake falls above 2,300 kcal. This is done through estimation of the cost of bundle of good the average household consumed that give 2300 a daily kilo calorie to perform a good physical function. The consumption data from the household was collected to reflect the general pattern of food consumption to estimate the quantities of food items consumed by the average households and converted to monetary value in order to set the poverty line. Si¼αþβlog Yi Zi � �þε(2) Where ●Si = food share to total expenditure ●Yi = total expenditure ●Z i = food poverty line For households whose total expenditure is approximately equal to the food poverty line (Yi = Zi), the food share is α, and consequently, the non-food share of expenditure is (1—α). Thus, the poverty line is Z¼ZfþZnf (3) Where ●Z f food poverty line ●Z nf non-food poverty line Neway & Massresha, Cogent Economics & Finance (2022), 10: 2156090 https://doi.org/10.1080/23322039.2022.2156090 Page 6 of 16 Z¼Zfþ1αð ÞZf(4) Z¼Zf2αð Þ (5) All the above mathematical expressions would help identify the households as poor and non-poor. 3.3.2. The logistic regression model In order to capture the determinants of poverty, a logistic regression model is employed. This model is selected for the following reasons. First, it is easy to manipulate and simple to comprehend. Second, the dependent variable, the probability of being poor and non-poor, is dichotomous. Given the dependent variable of main interest that a household may be classified as poor or nonpoor, a binary logit model could be used for the analysis of the data. Based on the cost of basic need approach, the food poverty line and non-food expenditure is estimated 3961 and 4476 respectively Consider that a household is poor (Y = 1) if per capita household food and non-food expenditure is less than or equal 8437 Birr per year or non-poor (Y = 0) if the per capita household food and non-food expenditure is greater than 8437 Birr. Y�¼Xiβiþεi(6) Where Y� i Is the underlying latent variable that index the agricultural technology adoption εi is the stochastic error term, and β is a column vector of parameters that has to be estimated. Yi¼expXiβ 1þexpXiβ(7) OR Yi¼1 1þexp  ðXiβÞ(8) If we let X ik be the k th element of the vector independent variable X i , and β k be the k th element of β, then the marginal effect of a particular independent variable, X i , on the probability of the occurrence of the response is given by (Maddala, 1983): dYi dXi¼expXiβ 1þexpXiβ � ��βk(9) Yi¼1 1þeA(10) Ai¼β0þβ1AG þβ2HSXiþβ3Rlgniþβ4MRGiþβ5ATCSiþβ6Residiþβ7Educ02iþβ8Educ03i þβ9Educ04iþβ10Depeniþβ11TLUiþεi Where, ●Y i = Probability of being adopter in relation with the explanatory variables ●A i = A function of n explanatory variables ●βs = Unknown Parameters ●εi = Error/Stochastic term ●i = Individuals/Respondents in the study in which i =1, 2, 3, . . ., n =384 Neway & Massresha, Cogent Economics & Finance (2022), 10: 2156090 https://doi.org/10.1080/23322039.2022.2156090 Page 7 of 16 dams, deep wells, river diversions, and ponds have significant poverty-reducing effects that can help to alleviate poverty, particularly in rural areas). In urban areas, the government should focus on increasing productive employment, which can help the poor increase their income through social protection programs such as an urban safety net. Funding The author received no direct funding for this research; Berehet Woreda Adminstration [there is no ID]; Author details Markew Mengiste Neway 1 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0002-5553-7994 Solomon Estifanos Massresha 1 1 Department of Economics, Debre Berhan University, P.O. Box 445, Debre Berhan, Ethiopia. Disclosure statement No potential conflict of interest was reported by the authors. Data availability statement The data supporting this study’s findings are available from the corresponding author upon reasonable request. 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