Poverty is lack of Capabilities: A Literature Review

Poverty is Lack of Capabilities: A Literature Review

Jeyapraba Suresh
Department of Economics, Eastern University, Sri Lanka

Received: 22 March 2023;  Accepted: 31 March 2023; Published: 08 April 2023

ABSTRACT

This review explores poverty as a lack of capabilities based on existing literature. The capability approach rejects income-based measures of poverty and well-being, and also argues that human capabilities are the best measure of poverty. Scholars who define poverty and its causes have widely discussed and accepted this approach. The literature recognizes many capabilities, including education, employment, and health, are highly associated with poverty. Hence, this review primarily focuses on three key dimensions to explore poverty as a lack of capabilities. The review findings have identified that lack of capabilities such as being less educated, being ill health and being unemployed or poorly employed are highly associated with the individual being likelihood of poor. In addition, this paper contributes to the knowledge of exploring the relationship between poverty and capabilities.

Keywords: poverty, capabilities, education, employment, health.

INTRODUCTION

Amartya Sen pioneered the Capability approach to analyse poverty in the 1980s (Sen, 1976). Unlike the conventional definition of poverty, it is now widely accepted that poverty is multi-dimensional and consists of many factors, including income and consumption. Amartya Sen was the first to articulate the multi-dimensional nature of poverty through the Capability approach in 1979. The capability approach is a broad normative framework for evaluating and assessing individual well-being and social arrangements. It can be used to evaluate several aspects of people’s well-being, such as inequality, poverty, the well-being of an individual or the average well-being of the members of a group (Daojiu, 2014). According to Sen (2008), an individual’s capabilities represent their ability to satisfy certain valuable functions of human life. So, capabilities are what a person can do or be. Sen defines capability as the individual freedom to achieve functionings such as good nourishment, good health, self-respect, and social integration in a broader context. Thus, functionings are ‘beings’ and ‘doings’. Sen says, “A functioning is an achievement, whereas a capability is the ability to achieve. Functionings are, in a sense, more directly related to living conditions since they are different aspects of living conditions. Capabilities, in contrast, are notions of freedom, in the positive sense: what real opportunities you have regarding the life you may lead” (Sen 1986, p. 36). Examples of the ‘beings’ are being well-nourished, being undernourished, being housed in a pleasantly warm but not excessively hothouse, being educated, being illiterate, being part of a supportive social network, being part of a criminal network, and being depressed. Examples of the ‘doings’ are travelling, working, taking part in social events, caring for a child, voting in an election, taking part in a public debate, taking drugs, killing animals, eating animals, donating money to charity (Gandjour, 2007 and Ravallion, 2015). Gandjour (2007) appoints out the mutual dependency between capabilities and functionings due to the dual role of some functionings as both ends and instruments. The Capability approach has also been regarded as a broad normative framework for evaluating individual well-being (Robeyns, 2005).

The Capability approach does not have an absolute poverty line but instead uses standard cut-offs for each indicator or function to identify the poor (Alkire, 2007). For example, UNDP’s composite indices -the HPI and HDI – focus on a combination of indicators to achieve well-being, defined using standard cut-offs such as having less than 40 years of life expectancy and adult literacy rates. However, there is a close link between income poverty and Capability poverty. Sanjay et al. (2006) argued that the monetary approach defines those poor whose income falls below a certain level while the Capability approach measures the poor according to whether their income falls below the level required to achieve the critical functions of life. This approach empirically measured poverty in Nicaragua, Tanzania, and Vietnam and conducted meaningful inter-country poverty comparisons. Sarshar (2010) also argues that income and capabilities are interrelated. He argues that income helps to improve the capabilities, whereas better capabilities increase an individual’s ability to earn. He claims that the two approaches identify different people as poor. For example, a person with a high income may have less political freedom but is not considered poor by the monetary approach. However, he falls into the poor category according to the Capability approach. The Capability concept pioneered by Sen has been widely discussed and also criticised by many scholars (Nussbaum, 2003; Ladershi et al., 2003; Robeyns, 2003; Clark 2005; Stewart et al., 2007; Nussbaum, 2011). Among these, the philosopher Martha Nussbaum’s work is the best known. She highlights that Sen does not give a fixed list of capabilities (Nussbaum, 2003). Clark (2005), however, holds that the absence of a fixed list of capabilities is one of the strengths of Sen’s Capability approach as it makes it flexible so that adherents can develop the concept further and apply it in different ways. Another issue relating to the Capability approach is measurement. Since Capabilities represent potential outcomes, it is not easy to assess them empirically, and instead, functions are measured, though some functions are not easily measurable (Ladershi et al., 2003). Despite these criticisms, the Capability approach gives a consistent framework to analyse poverty and human well-being. Therefore, the objective of this study is to explore how capabilities are associated with poverty and lower consumption expenditure.

METHODOLOGY

The study was mainly an exploratory literature survey, conducted through extensive examination of literature on the relationship between poverty and capabilities. Relevant research articles were examined from 1954 to 2020. Analyses pertaining to three dimensions of capabilities namely education, employment and health and the relationship to consumption poverty were particularly focused. The study adopts a narrative approach in reviewing the literature to confirm poverty is a lack of capabilities.

ANALYSIS AND DISCUSSION

The literature recognises many capabilities, including education, employment and health; access to land and shelter; access to drinking water and sanitation, and access to other public services. While the rest of this section reviews in detail the literature about the relationship between key dimensions of capability such as education, employment, health and poverty, there is also a sizeable literature on the relationship between access to land and poverty. Land, especially for the rural poor, is a critical asset, enabling food security, sustainable livelihoods and a buffer to smooth consumption in times of shock (Meinzen-Dick, 2009). However, millions of rural poor suffer from the lack of access to land worldwide. Keswell and Carter (2014) examined South Africa’s land redistribution for agricultural development (LRAD) program. They found that the programme’s beneficiaries experienced a 25 per cent increase in their per-capita consumption on average. Poor people lack access to land and face a higher risk of having worse housing conditions than others. Poor housing conditions and lack of access to clean water and decent sanitation significantly affect some aspects of child development, adult health and income and employment (Tunstall et al., 2013; Njuguna, 2019). It has been estimated that 785 million people worldwide lacked access to basic drinking water facilities in 2017, and 2 billion people went deprived of essential sanitation services, primarily in low and lower-middle-income countries (Swe et al., 2020). Using Kenya’s demographic and health survey for 2003, 2008 and 2014, Njuguna (2019) found that most middle and poorer households stagnated using a pit latrine without a slab. In contrast, the poorest households stagnated at the open defecation stage. Thus, achieving access to adequate and equitable hygiene for all and ending open defecation continue to challenge policymakers aiming to achieve Sustainable Development Goal (SDG) 6 to ensure water and sanitation availability and sustainable management. What follows is the review of theoretical and empirical literature about the relationship between the capabilities of education, health and employment and consumption poverty.

Education and poverty

Education involves investment in human capital, which is an input in producing other goods and services. Higher levels of human capital can also expand the production possibility frontier of an economy. Sen (1999) also argues that education directly contributes to development through the well-being and freedom it gives an individual while indirectly contributing to economic production. Moreover, endogenous growth theories also recognise that education plays a vital role in forming human capital, and investment in human capital significantly affects economic growth (Romer, 1994; Tilak, 2002; Melike, 2005). Education imparts skills and knowledge, which increases quality and productivity, yields higher returns and thus contributes to the national economy. Education also contributes to the capacity of individuals to earn more income and thereby reduces poverty. Empirical studies (Park, 2011; Filho, 2012) show that the less educated are more likely to be poor and that the poor are more likely to be less educated. How can the lack of education increase the probability and extent of poverty? Low educational attainment is often associated with poverty because people with less education are less likely to be employed, are less productive, and earn sufficient incomes (Mtey and Sulle, 2013). This is explained by a human capital theory which sees education as being positively correlated with earnings as education provides skills and knowledge and improves the productivity of individuals (Schultz, 1961; Blaug, 1972; Boeri and Ours, 2008). An educated population is productive because education increases cognitive skills and expands human capabilities, increasing economic productivity and earnings and reducing poverty. Traditionally, the relationship between education and returns has been assumed to be linear.

For example, using the UK Quarterly Labour Force Survey (QLFS) 1997 to 2009, Devereux and Fan (2011) estimated hourly wages for both men and women using two-stage least squares and found that an additional year of schooling was associated with a 6 per cent increase in wages. Similarly, Appleton (2001), using the standard Mincerian wage earnings functions and Ugandan household data for the period 1992-2000, found that each year of additional schooling was associated with an 8 per cent increase in wages while controlling for other variables. Fulford (2014), using data from the National Sample Survey of India, 1983-2005, also found linearity in the marginal return to schooling. His regression results showed variation in returns by cohort and year, but when pooling all cohorts, the returns were approximately 5 per cent for men and 6 per cent for women. Nevertheless, many studies reject the assumption of a linear relationship between education and earnings and argue that the heterogeneity in findings arises from the variety of data and methods used in the analysis. Using the National Longitudinal Survey of Youth from 1980 to 1993, Park (2011) found non-linearity in returns to education in the United States. He observed that initially, marginal returns started to increase by 3.5 percentage points, then reached approximately a 20 per cent maximum at 15 years of formal education, and after that began to decline. Harman and Walker (1999), using the UK General Household Survey for the years 1974-1994, found the marginal rate of returns to schooling to be significantly high up to age 18 and lower beyond. In contrast, using China’s Urban Household Survey from 1988 to 2001 and using basic Mincer equations, Zhang et al. (2005) found that the returns to each level of education have risen substantially beyond junior high school and that the greatest rise in returns to schooling was for technical school and college graduates. Using the Sri Lankan Labour Force Survey for 1993, Ranasinghe and Hartog (1997) found that the marginal rates of return increased as educational attainment increased. They found the marginal return to be two per cent for primary education, 11 per cent up to Grade 11, and 35 per cent when a  student passed O/L. Using household data from 12 countries in Africa from selected labour force surveys for 2005-2011,  Barouni and Broecke (2014) reported that, on average across all countries, the marginal returns to university education are twice as high as for individuals with upper secondary qualifications, and that individuals with upper secondary qualifications earned double that of individuals with primary schooling.

In contrast, Psacharopoulos (1994) used cross country data to show a diminishing marginal return to education and argued for greater investment in primary education in developing countries. Using the Mincerian rate of returns calculations, it is expected that the coefficient for years of schooling will be high for the Caribbean and sub-Saharan African countries but very low for OECD countries. This is because, in advanced countries, most people are relatively well educated, and hence the returns to education will be low level. However, the returns to different levels of education in a country will depend on the existing stock of individuals who have been educated up to that level. As the stock increases, if demand for such workers remains constant, it can be expected that returns will decline. The impact of education is primarily worked through the labour market. The more educated are also more likely to find work (Fulford, 2014). This is because education is an important determinant of the probability of finding a job.  Educated workers enjoy three primary benefits in the labour market compared to less-educated workers; they enjoy a higher wage, greater upward mobility in income and occupation and greater sustainability of employment (Mincer, 1991). These cause a substantial difference in employment rates between better educated and less educated. For example, Boeri and Ours (2008) present employment rates relative to educational attainment in OECD countries and show that the employment rate increases with education. For instance, in the Slovak Republic, which has a huge gap between the better educated and the less educated, an individual who has completed less than upper secondary education has on average a 27.3 per cent rate of employment compared to an 88.2 per cent employment rate among those who have completed tertiary education. McIntosh’s (2008) study of unemployment rates relative to educational attainment in OECD countries clearly shows that it is higher among the less educated and lower among the better-educated. There is a significant difference in the unemployment rate observed between the less educated and the better educated in Eastern European countries, while the difference is far less marked in Western Europe. McIntosh (2008) also argues that the less educated are more likely to be unemployed because higher-skilled workers can do both low skilled and high skilled work. So when firms need to reduce their labour force, they will tend to release low-skilled employees. Poverty is largely prevalent among people who are less educated. Tilak (2002, p 199) cites Galbraith (1994) in arguing that there is “no well-educated literate population that is poor, and there is no illiterate population that is other than poor”. Several empirical studies have found significant differences in education between the poor and the non-poor. For instance, Datt et al. (2001) found that for Egypt, the average years of schooling for poor people was 4.4 while the non-poor had attended school for 7.0 years on average. In Egypt, 66.5 per cent of the non-poor could read while only 48.4 per cent of the poor could read.

Using Ethiopian household data for 1994, 1995 and 1996, Girma and Kedir (2005) reported that the poor benefit more from education than the rich. Their quantile regression estimates found that the return to education is more than 10 percentage points higher at the 25^th quantile than the returns to education at the 90^th quantile. Robust empirical evidence of a negative relationship between poverty and education is found in Fiji in the study by Gounder (2013), who used the HIES, 2002-2003 to show that the coefficient on education was significant and negative to the probability of being poor. He further suggested that higher levels of education appear to support agricultural growth in rural areas in Fiji. Apata et al. (2010) found that better education improves the incomes of rural people who are primarily dependent on agriculture in Nigeria as it helps them access and conceptualise information about suitable farming methods. Education also enabled them to access better paying rural jobs. Using the Pakistan Integrated Household Survey, 2001/02 and Pakistan Social and Living standards Measurement Survey, 2004/2005, Hyder and Sadiq (2010) found that education significantly decreases the odds of being poor and extremely poor in Pakistan. Using the same data, Awan et al. (2011) found that the attainment of middle, matriculation, intermediate and bachelor or professional levels of education decreases the likelihood of being poor compared to a primary level of education. Bhaumik et al.’s (2017) study demonstrate that the likelihood of being in poverty declines with adult household members having secondary, vocational and tertiary education.

In the context of Sri Lanka, de Silva (2008), using the SLIS of 2000, found that the probability of being in poverty declines monotonically with years of education. The likelihood of being in poverty was 43 per cent for households with no schooling, while it was only 5 per cent for households with tertiary education. Ranathunga (2010), using the 2006/07 HIES data, shows that as the number of years of schooling increases, it reduces the probability of an individual being poor. This was the case in all residential sectors in Sri Lanka. The study further revealed a significant negative correlation between poverty and all education-related variables such as education level of the head of the household, education of the spouse and the highest education level of the other family members. Applying logistic regression analysis to data from the HIES 2009/10, Gunatilaka (2014) found the marginal effects of all levels of education were statistically significant and strongly indicated that better education reduces the likelihood of being poor. De Mel (2019) also showed that in Sri Lanka in 2016, the probability of the head of the household being poor increased by 1.9 percentage points when the household head had no schooling.

Health and poverty

The relationship between poverty and ill-health is multi-faceted, and causality appears to work in both directions (Grant, 2005). Ill-health in malnutrition, disability, communicable and non-communicable diseases involved two types of economic cost. First, ill-health limits the ability to work, thereby reducing labour income and secondly, additional care and medical expenditure reduce disposable income (Genoni, 2012). This can lead to a vicious cycle. The loss of income and additional illness costs increase the risk of falling into poverty, resulting in more ill-health, illustrating how multi-dimensional poverty becomes an important determinant of poor health (Foege, 2010). Factors associated with poverty such as lower income, poor housing conditions, lack of clean drinking water, lack of proper sanitation, low levels of health education and lack of health care facilities combine to impact health (Case and Deaton, 2005; Cattaneo et al., 2009; Seligman et al., 2010; Adjei and Buor, 2012). This section focuses on how dimensions of poverty are associated with the probability of being ill and how ill health is associated with the likelihood of being poor.  Several empirical studies address whether the wealthy are always healthier than the poor, such as Case and Wilson, 2000; Case and Deaton, 2005; Biggs et al., 2010. Using the Langeberg Survey of South Africa, Case and Wilson (2000) found a strong significant correlation between earned income and chronic diseases and suggested that causality works negatively. The healthier are wealthier due to a higher probability of being employed and making a more significant income: more affluent households are more likely to spend on healthcare. Again, Case and Deaton (2005) test the hypothesis that wealthier is healthier by comparing Indian and South African people.

They found that wealthier South Africans are better off than the poorest Indians in some aspects of health, but not in all. For example, South Africans are taller and heavier than Indians, but suffering from depression and anxiety is the same for both. In a later study, using data from twenty-two Latin American countries, Biggs et al. (2010) found that wealthier people are not always healthier – much depends on how the wealth is distributed among the population. They found increases in GDP positively impacted public health while poverty and inequality decreased. However, when poverty increased, the improvement in public health resulting from increases in GDP was marginal. Hence, they conclude that the strength of the relationship between health and wealth depends on poverty and inequality in the country. More recently, Ogundari and Abdulai (2014) studied the determinants of health care expenditure in Nigeria and found that an increase in household income by 10 per cent leads to a rise in health care expenditures of about 23 per cent in rural and 15 per cent in urban areas. Therefore, causality appears to work both ways between wealth and health. In many countries, where there is no adequate public health care system, health-related shocks and associated expenditure on health care may push households that are not poor into poverty and make the poor poorer (Xu et al., 2003; Van Doorslaer et al., 2006; Chuma, and Maina, 2012). For example, Xu et al., (2003) analysis of 59 countries’ household survey data showed that a one per cent increase in poverty would increase catastrophic illness-related payments by 0.2 per cent. The study also found that catastrophic expenditure varied widely between countries from 0.01 per cent to 10.5 per cent, especially in Latin American countries and in countries in transition, which have higher rates of catastrophic expenditure than most developed countries.

This is because developed countries have well-established health care systems. A ranking of countries according to the extent of catastrophic expenditure shows Sri Lanka in 26^th place with 1.25 per cent of households facing catastrophic health expenditure[1]. Xu et al. (2003) also confirm that countries with a higher proportion of out-of-pocket payment in total health care expenditure are more likely to have higher levels of financial catastrophe. In a later study, Xu et al. (2007) found stronger evidence of this relationship by analysing survey data collected between 1990 and 2003 from 89 countries. They found that the out-of-pocket health expenditure resulted in financial catastrophe for individuals and further prevented them from seeking health care. The study covered 89 per cent of the world’s population and found that around 150 million people suffered financial catastrophe annually due to high out of pocket health payments, while 100 million were pushed under the poverty line as a result. This situation was found to prevail in more than 90 per cent of low-income countries. Empirical evidence also shows a linear relationship between malnutrition and poverty regarding income, consumption, and other socio-economic characteristics. Using data from 129 developing countries, Block et al. (2012) show that the prevalence and depth of household poverty and child wasting vary with per-capita income. When the prevalence of poverty and wasting is high, the change in poverty and wasting will be the same proportion for differences in income. When the prevalence of poverty and wasting is low, then the elasticity of poverty is relatively higher than the elasticity of wasting. Using quantile regression estimation techniques, they further found that the income elasticity of poverty is about -0.77 at all deciles, but the elasticity of wasting was -0.81 in the top deciles and -0.13 in the lowest deciles. Block et al. (2012) concluded that increasing income alone is not enough to eradicate wasting, as other socio-economic factors influence wasting. Aturupane et al. (2008) reported a similar result for Sri Lanka.

The study found a strong significant correlation between increases in income and nutritional improvements only among the rich in Sri Lanka. In addition, parental education, access to electricity, and piped water have larger effects on children’s weight and height in the upper quantiles rather than at the lower quantiles. This suggests children in the lowest deciles need particular nutritional interventions since general interventions are ineffective. Mazumdar (2010) studied the determinants of malnutrition in India and found that the wealth index alone explains more than half of the inequality in malnutrition. A similar study has been carried out by Salvucci (2015) using household data from Mozambique, and he reported that inequality in consumption explained most of the socioeconomic inequality in child malnutrition. In addition, maternal education, health care, family planning, access to water, sanitation and electricity also underlie inequality in malnutrition (Ellen et al., 2007; Silva, 2005). Disability increases the risk of falling into poverty for several reasons. Most important among these are lower educational attainment and the lack of employment opportunities leading to a lack of income. Disabled children typically have lower or no educational attainment. School enrolment rates among them are much lower than among able children because they lack special transportation, special care and trained teachers (Mont and Cuong, 2011). Empirical studies also found that the disabled are less likely to begin education and are more likely to be multidimensionally poor (Filmer, 2008; Mitra et al., 2013). Using data from the World Health Survey, including data from 15 developing countries, Mitra et al. (2013) shows that disability is significantly associated with higher multidimensional poverty in most of the developing countries, and a person with multiple disabilities and an older adult with disabilities are more likely to be multidimensionally poor. Using data from household surveys in Afghanistan and Zambia, Trani and Leob (2012) found that persons with disabilities are significantly more likely to be unemployed. Even if the disabled could go to work in India, their wage was lower than the wage that the non-disabled received (Mitra and Sambamoorthi, 2008). The cost of living associated with a disability is likely greater than for non-impaired persons in developing and developed countries.

This may also cause the disabled to become poor (Groce et al., 2011). For example, the cost of equipment or aids such as hearing aids, white-canes, spectacles, and the cost of particular care providers are additional costs that the disabled must bear. Using the standard of living approach, Zaidi and Burchardt (2005) show significant additional costs associated with disability in the UK. The prices will also rise with the severity of the disability. The disabled are also more likely to be poor due to exclusion and marginalisation, which reduce opportunities to participate in household and society activities (Yeo and Moore, 2003). Using the HIES, 2006/07 and 2009/10, Kumara and Gunewardena (2017) found monetary and multidimensional poverty among disabled persons is higher than among other households in Sri Lanka. The burden of communicable and non-communicable diseases also increases the risk of falling into poverty due to out-of-pocket payments consuming a large part of household income (Binnendijk et al., 2012). Adjei and Buor (2012) found a strong relationship between poverty and the occurrence of whooping cough, skin diseases, measles, and intestinal disorders in Ghana. Using data from the demographic and health surveys collected in the 1990s from 22 Sub-Saharan African countries, Filmer (2005) found that the incidence of fever was high among the poor, although not statistically significant. However, the treatment for fever was strongly related to income distribution, with richer people being more likely to get treatment. Binnendijk et al. (2012) found the prevalence of non-communicable diseases high compared to communicable diseases in poor rural Odisha and Bihar and that the cost for treatment was also high, claiming. Using Sri Lankan HIES data for 2016, Jayathilaka et al. (2020) found that if a household member suffers from a chronic disease such as cancer, heart disease or kidney disease, they are more likely to be poor in Sri Lanka.

Employment and poverty

Employment is fundamental to the poverty reduction process, as labour earnings are the main source of income for primary people. Indeed, labour is the only asset that most poor people own. Hence, whether the household is in poverty or not is mainly dependent on the labour earnings of its members. Earnings and other employment-related factors such as regularity of employment, opportunities for advancement, training and promotion, health and safety, social security and social protection also determine the household’s wellbeing. Getting a job and starting a business are the primary sources people have to escape poverty in low-income countries (World Bank, 2012). Hence, if the individual has a good or decent job, they are unlikely to be poor. The International Labour Organization (ILO) defines a decent job as being productive, delivering an adequate income, occurring in a secure workplace, providing social protection and space for personal development, and offering freedom to express employees’ concerns (ILO, 2014). The SDGs also recognise the importance of productive and decent employment for all for sustainable development (see SDGs: Goal 8). However, it is rare for the poor to get good jobs. Good jobs are often unavailable in isolated rural areas where most poor people live. Even if poor people live in areas with decent work opportunities, they are unlikely to be sufficiently capable of skills, knowledge, and social networks to obtain and perform productively in a good job. As a result, an individual may be employed but remain in poverty. They are termed ‘working poor’. ‘Working poor’ are found in developed as well as developing countries. Analyses of working poverty use many definitions of working poor. Majid (2001) and Kapsos (2005) defined the working poor as individuals employed but households where per capita consumption is below the poverty line. Later, Brady et al. (2010) defined the working poor as those who reside in a family with at least one member employed, but where per capita consumption expenditure is below the poverty line. Hence, it is clear that the working poor is employed but poor. In 2015, 830 million people were defined as working poor globally and living on less than US$ 2 per day (Jahan et al, 2015).

According to the HIES 2006/07, 11.5 per cent of employed persons living in working poverty in Sri Lanka (Gunatilaka, 2010). This had been reduced by half (5.4 per cent) by 2012/13. This change has been associated with considerable differences in working poverty among different sectors. For example, working poverty has sharply declined in the service sector (59 per cent to 21 per cent). In comparison, it has increased in the agriculture sector from 30 per cent to 53 per cent during the period from 2007 to 2013 (Majid and Gunatilaka, 2017). Why are people poor even though they are employed? This situation arises because the poor primarily work in unproductive, low-quality employment where there is a labour surplus in agriculture or the informal sector. In developed countries, agriculture’s share in total employment is just 4 per cent. In contrast, it is 66 per cent in sub-Saharan Africa, 49 per cent in South Asia, and 44 per cent in Southeast Asia (Inchauste, 2012). Among South Asian countries, Sri Lanka has the lowest agriculture employment share – 25.5 per cent. Bangladesh, India and Pakistan have shares of 40.6 per cent, 53.2 per cent and 42.3 per cent, respectively, in 2017 (Central Bank of Sri Lanka, 2019). Lanjouw (2001) reported that in El Salvador, households engaged in agriculture and farming have the highest poverty incidence. Conversely, non-farm employment appears to reduce poverty. For example, using VLSS data for 1993 and 1998, van de Walle and Cratty (2003) found significant effects of non-farm employment in reducing poverty in Vietnam. Hoang et al. (2014) studied the impact of non-farm sector involvement on poverty and expenditure growth in Vietnam. They found that an additional household member involved in non-farm activity reduces the probability of poverty by 7–12 per cent and increases household expenditure by 14 per cent over two years. More recently, using household surveys from India and Vietnam, Imai et al. (2015) also found that non-farm sector employment significantly reduces poverty in both countries. Zereyesus et al. (2017) studied the impact of participating in non-farm work on food poverty in Northern Ghana. The results of their ordinary least square (OLS) estimates showed participation in non-farm work to be significantly and positively associated with higher future mean food consumption expenditure, thereby alleviating the vulnerability of households to food poverty.

The Sri Lankan evidence is in line with these findings. For example, de Silva (2008) suggested that the household head is engaged in agriculture rather than the business was more likely to result in poverty. Using the HIES of 2006/07, Gunatilaka (2010) found agriculture sector employees to be poorer than employees in the manufacturing and service sectors. In Sri Lanka, 43 per cent of working poor were found in the agriculture sector, with  54 per cent of poor females working in the same industry. The poor are also found working in informal employment, which is uncertain, irregular and poorly paid. Informal employment is usually found in production entities which, are typically un-reg that are typically un-registered, small in size, and social security payments. A significant proportion of informal employment is also made up of own-account workers (Hussmanns, 2004). Small traders, street vendors, coolies and porters, domestic servants are some examples of informal workers. Despite the traditional view of the informal sector, which characterises it as marginal in size and contribution to the economy, research revealed that the informal sector has maintained its lead in employment and increased its relative share in many economies (Radwan, 2007). Using the Brazilian Monthly Employment Survey of  2002-2007, Machado and Ribas (2010) found that an increase in the average informal wage had a significant and positive effect on the probability of the poor escaping poverty. They found that a one per cent increase in the quarterly average income of unregistered workers increases the probability of exiting poverty by 6 to 12 per cent. But the same growth in the earnings of registered workers reduces this probability by 5 to 9 per cent. DasGupta’s (2003) survey of street vendors in New Delhi in 1995 found several factors determining the level of earnings of migrant and non-migrant street vendors. She found that even though street vendors are usually rural migrants, they earned less than non-migrants due to their business location. If businesses were located in bustling commercial areas, they could make more, and their experience also had a positive impact on earnings. Using the Quarterly Labour Force Survey of 2006, Gunatilaka (2008) assessed the extent and nature of informal sector employment and the factors associated with informal and formal employees’ wages in Sri Lanka. She found that 66 per cent of the total employed in Sri Lanka are informal workers, out of which 28 per cent work in the informal agriculture sector.  According to her findings, earnings also substantially differed between informal and formal employees. Informal employees earned considerably less than formal employees, whereas wage inequality was higher among formal than informal employees. People who usually work part-time or in irregular employment are more likely to live in poverty than those working full-time or in permanent jobs since part-time employees have low and unstable earnings. They are also far less likely to have access to other employment-related benefits provided by their workplace. Therefore, those employed in part-time jobs usually experience a low standard of living (Rodgers, 2003). Using the Income and Housing Costs Survey 1997/98 data in Australia, Rodgers (2003) found that poverty rates amongst part-time workers were higher than among full-time workers, although lower than among the unemployed. Using the Households Below Average Income (HBAI) data of the UK, similar findings were obtained by Ray et al. (2014): households with only part-time employees reported a poverty rate of 15 per cent while the households with only full-time employees reported only an 8 per cent poverty rate. But the rate varies when the household has part-time and full-time employees as members of the same household.

Herman (2014) found poverty risk is 1.8 times higher for part-time workers than for full-time workers in European Union countries in 2012. There are many reasons why the poor are to be found in unproductive, low paid, irregular, vulnerable, and informal employment. Among them are the lack of knowledge and skills, poor infrastructure and cost of transport, the effects of structural change and diversification, a small and weak private sector and challenging macroeconomic growth conditions. For example, education enables the acquisition of knowledge and skills, which are rewarded in the job market. As section 2.3.1 argued, better educated and trained workers are generally more productive and earn more, than poorly educated workers who are less productive and receive only low wages. Human capital theory too argues that better education leads to higher productivity and higher productivity leads to higher wages (Boeri and Ours, 2008). Using the Eurostat Database for the years 2007-2012, Herman (2014) examined the factors determining working poverty in European Union countries and found personal characteristics such as education, gender and age  as the most significant factors associated with working poverty. The incidence of working poverty among the poorly educated was 18 per cent while only 4.2 per cent of the more educated were both employed and poor. In Sri Lanka, Gunatilaka (2010) found that education is a significant contributing factor to the earnings gap between the working poor and non-poor in Sri Lanka. Her 2014 study analyzed the poverty level among different occupational categories and found that managerial and professional employees are less likely to be poor than those in other jobs in Sri Lanka. Using HIES 2016, de Mel (2019) in analyzing the marginal effect of logit regression, found that if the head of the household was a professional, technician or a clerical worker or machine operator he/she was less likely to be poor than if the head of the household was involved in an elementary occupation. Employment and production are constrained by infrastructure bottlenecks in a wide range of poor countries. Development economists also argue that physical infrastructure is a precondition for industrial development (Sawada, 2015).

Therefore, investing in infrastructure generates income-earning opportunities and creates jobs by directly involving workers in the construction and maintenance of physical infrastructures such as roads and bridges. Indirectly, the infrastructure created will also improve access to income and employment opportunities. For example, rural roads provide access to markets and employment centres and enhance the connectivity of isolated and rural areas, thereby having a sustained impact on employment (Jacobs and Greaves, 2003; Adamopoulos, 2011). If the road network is good, people can easily travel and transport their products at lower costs. In rural areas, investment in irrigation facilities increases agricultural production and enhances productivity. Public market facilities bring buyers and sellers together and stimulate local trade. Mahmud and Sawada (2014) analyzed the impact of the Jamuna multipurpose bridge on jobs in Bangladesh and found that the construction of the bridge facilitated employment shifts from farm to non-farm sectors for many people, and household unemployment also reduced considerably. Using the second Cameroonian National Household Survey, Gachassin et al. (2010) investigated the impact of investing in tarred roads on poverty reduction and found road access did not have a direct impact on the level of consumption expenditure but had positive and significant impacts on the probability that a household-head in farming, diversified his activity. Employment status can also be improved by structural changes in the economy which creates new opportunities for better-paid, greater value addition and higher turnover. For example, when poor people move from labour surplus, low paid, low productive and rural agricultural jobs to high paid, high productive, technological-intensive and urban industry, they get better financial and social benefits and improve their wellbeing. Lewis’s (1954) model of economic growth with unlimited supplies of labour argues that if surplus labour from the traditional sector is transferred to the modern sector it promotes capital-intensive products and wage growth in both sectors (Todaro and Smith, 2012). Combining state-level data on poverty with state-level data on output and employment for 11 production sectors over the period 1987–2009 in India, Hasan et al. (2013) confirm that poverty reduction is brought about through increases in aggregate productivity due to workers’ movement from lower to higher productivity sectors. Their regression results also show that a one per cent increase in the annual rate of productivity growth leads to a 0.64 per cent increase in the annual rate of poverty reduction and that the productivity growth is positively associated with the reallocation of labour from lower productivity to higher productivity sectors. However, if migration from rural areas to urban areas exceeds the rate of job creation in the modern sector, it can lead to urban poverty. Further, this may worsen the gap between the rich and the poor with industrial workers’ earnings outstripping earnings in the farming sector. Also, most workers who leave agriculture in poor countries do not get employment opportunities in the productive industrial sector in urban areas and end up working in less productive informal employment.

CONCLUSION

Amartya Sen argued that poverty must be a lack of capabilities rather than looking at it as a deficiency in income. He further added that the lack of income is not the only variable determining poverty while multiple capabilities which are determining the poverty of an individual. The literature identifies many capabilities, including education, employment and health; access to land and shelter; access to drinking water and sanitation, and access to other public services which are determining poverty.  However, this review mainly focuses on the key dimensions of capability such as education, employment, and health and found there is a strong relationship between poverty and lack of education, employment and health. Therefore, this review concludes that poverty is a lack of capabilities and is highly associated with the likelihood of an individual being poor.

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