LINKAGE BETWEEN POVERTY, INEQUALITY, AND INCOME DISTRIBUTION: A CASE ON BANNU DISTRICT, PAKISTAN

Akhtar Gul¹ --- Fatima Zahra² --- Anum Iqbal³⁺ --- Tanbila Ghafoor⁴ --- Musaddiq Hussain⁵

¹Department of Economics, Government Post Graduate College Bannu, KPK, Pakistan.
²Department of Economics, Women University Multan, Punjab, Pakistan.
³University of Gujrat, Punjab, Pakistan.
⁴Department of Economics, University of Management and Technology (UMT) Lahore, Pakistan.
⁵Department of Finance, University of Science and Technology Bannu, KPK, Pakistan.

Keywords: Poverty, Income inequality, Basic needs of human life.

JEL Classification: B55, E00.

ARTICLE HISTORY: Received: 15 September 2020, Revised: 2 October 2020, Accepted: 19 October 2020, Published: 4 November 2020

Contribution/ Originality: The aim of the study is to investigate the association between poverty, inequality and income distribution evidence from Bannu District, Pakistan.

1. INTRODUCTION

Poverty and income inequality are features of every society, especially a capitalist society (Coburn, 2000; Judge, Fainshmidt, & Brown III, 2014; Milanovic, 1998). There are many causes of poverty, rather than a single cause because the nature of poverty is multi-dimensional. However, income equality is one dimensional. The fundamental cause of income inequality is capitalism. The capitalist class entirely exploits the working class. According to Karl Marx, “There are two classes in a society, labor and capitalist.” He ignored the middle class. Colonization is also a significant cause of poverty and income inequality in developing economies. (Ilemona, Akoji, & Matthew, 2013; Marx & Engels, 2002).

 Sixteenthcentury through to twentieth-century capitalist and imperialist society looted the resources of colonized society Philip (2004). Indian diplomat and scholar Shashi Tharoor defines the British outlook in his book, ‘An Era of Darkness: The British Empire in India’, during the early eighteenth century the sub-continent produced 27% of the world’s gross domestic product (GDP); British products merely hared 0.1% of the global GDP. The British left mid twentieth century, the sub-continent produced over 1% of the global GDP, and surprisingly, British produce accounted for over 10% of the global GDP (Tharoor, 2016). Therefore, during the colonial era the sub-continent faced over 20 famines (Gráda, 2009).

  Humanity has faced many problems since World War II. Many nations hinder their own economic development and prosperity, and poverty and income inequality are among the major problems. Berner et al, 2012)Scholars define poverty as “a situation lacking basic and essential human needs, including food, water, sanitation, clothing, shelter, health care and education” (Al Mamun, Hasan, & Rana, 2013; Spicker, 2007). There is no particular definition of poverty because there are many aspects. According to the United Nations Poverty Report 2016, a person earns just US$1 a day. Approximately,  seven billion people are breathing along with over 1.2 billion people living below the world poverty line. In the same vein, 1.2   billion people in the world earn less than $1 a day and 2.8 billion people earn less than $2 per day (Simpson, Wicken, Gallagher, Duncanson, & Oben, 2016).

Various researchers have tried to measure inequality and poverty but besides the existence of such long studies on poverty and income distribution a little effect have been made investigates the income inequality between and within different occupations.(Dynan & Ravina, 2007; Gwatkin, 2000; Thorbecke & Charumilind, 2002). The Survey of Asian Human Development, (2000) defined, “inequality as unequal distribution, whether that is income, consumption, or other wellbeing aspects of a population.”

Poverty and income inequality affect economic growth directly and indirectly and are related to each other. Poverty creates big change in society as compare to income distribution. Jamal (2006) stated that the high economic growth from 2000 to 2006 in most sectors was the main cause of poverty reduction, which leads to an increase in other areas, such as education, health, public spending and infrastructure (Farooq & Ghulam, 2011).

Americans have the highest average household incomes in the world, and its per capita income in 2013 was $53-144 The Gini coefficient, which measures inequality in the economy is 36.9%. The United Kingdom has a national income of £2.83 trillion, the per capita income is £43,829, and the Gini coefficient is 32.8, which shows that the UK’s income inequality is below that of the US. India is one of the leading countries in Asia with a national income of $1.996 trillion, a per capita income of $1584, and a Gini coefficient of 33.9 (Stevenson & Wolfers, 2013).

According to the Human Development Report of 2010, 22% of the population of Pakistan live below the poverty line and had a 32.7 Gini coefficient in 2006 (Khan & Bashir, 2012).

Asia is a highly populated region of the world with 4,282 million people living here, and the Asian economies are the some of the fastest-growing economies in the world. Over last two decades, from 1989 to 2013, the People’s Republic of China had an average growth rate of 9.8% per annum. Similarly, India’s annual average growth rate was 9% from 2003 to 2007, and Indonesia has had a 6% annual average growth rate since 2006 (Bag & Gupta, 2012; Chotikapanich, Griffiths, & Karunarathne, 2014; Felipe, Lanzafame, & Zhuang, 2014; Nagaraj, 2013). China has the second largest economy in the world, and India has the fifth largest economy in the world as well as the fourth largest market in the world. However, despite these positives factors, these countries also have greater income inequality and higher poverty levels (Park & Mercado, 2015).

According to the World Development Indicators, in 2013, approximately 12.5% and 31% of the population are living below the poverty line in the Middle East and South Asia respectively, and 21.7% in the Middle East and 66.7% of people in South Asia earn under $2 per day. Therefore, emerging economies (e.g. China, India, South Africa, and Indonesia) of the world also are facing poverty and income inequality crises. China faced severe poverty and high corruption in the 1950s. Since the 1950s, China has reduced the number of people living in deprivation, but11.80% of the population are still living below poverty line. India has the world’s fastest growing economy and has the fifth largest economy in the world. Over 30% of the Indian population lives in poverty, and 18.6 % of Indonesians are breathing polluted air. The population growth and unequal distribution between rural and urban areas increased in these countries (Hussain, Akram, Ghaffar, Qamar, & Ahmad, 2019; Park & Mercado, 2015).

There is a positive and strong correlation between poverty and income inequality. In the first decade of the twenty-first century inequality rose globally. Eighty percent of the global populations live in nations where income inequality is high. Only Five percent of the world’s income is earned by 40% of the most impoverished populations in the world, and the richest 20% of the population earns 75% of the world’s income (Rethinking Poverty Report on the World Social Situation, 2010).

The real GDP per capita in 1947 was Rs. 1, 476; after 54 years the per capita income rose to Rs. 5, 128 in 2001. However, according to caloric-based information, the poverty levels increased from 17.4%, 22.4% and 43.65% in1987-88, 1992-93 and 1998-99, respectively. According to the Human Development Report of 2000, the $2,250 per capita incomes and 31% of the population are living below the poverty line. Every third household in Pakistan does not have their basic needs met (Brohi, 2004).

Poverty is a result of unequal income distribution, and when people have a low income, they will have a low standard of living. The major differences in the levels of income in the same society affect the demand for health facilities, and education. History shows that only 20% of people globally have a comfortable lifestyle and the remaining 80% experience poverty, lack of education, hunger and live on less than $1 a day.

1.1. Relationship between poverty and income inequality

There is a direct association between poverty and income inequality; when income inequality raises poverty also rises. Income inequality means that income is not equally distributed among the population; rich people become richer and the poor will become poorer.

1.2. Objectives

1. To realize poverty and income inequality levels in Pakistan based on samples from Bannu District.
2. Policy recommendations based on the bases of results.

2. LITERATURE REVIEW

Several literature debates exist on macroeconomic problems associated with economic growth, such as poverty (Abduvaliev & Bustillo, 2020; Amin et al., 2020; Badibanga & Ulimwengu, 2020; Dauda, 2017; Ferreira, Leite, & Ravallion, 2010), inequality (Cuberes & Teignier, 2014; Hussain, 2020; Islam & McGillivray, 2020; Schober & Winter-Ebmer, 2011; Shin, 2012; Tchamyou, Asongu, & Odhiambo, 2019), unemployment (Abraham & Ozemhoka, 2017; Fountoulakis, 2020; Mohseni & Jouzaryan, 2016; Pasara & Garidzirai, 2020), and inflation (Hung, 2017; Hussain, Ahmad, Qamar, & Akram, 2019; Khan & Hanif, 2020).

For example, Heshmati (2004) investigated the link between income inequality, poverty, and globalization. He introduced indices for globalization and explained that how globalization works. The result indicated that the lower standards and slow globalization process in developing countries is due to limited poetical and personal engagement. The high standards and rapid globalization process in developed countries is because of their share and active participation in economic affairs worldwide, which enables them to reduce income inequality and poverty. (Santos-Paulino, 2012), investigated the effect of trade on poverty and income distribution theoretically as well as empirically. The statistical results indicated that the effect of trade on poverty alleviation varies from country to country. Free trade creates aggregate welfare but gains from trade are not equal for all participants. The results also indicated that the demand for factor income of production increases employment meaning poverty could be alleviated. Moreover, it suggested that lack of infrastructure; skills incomplete market and policy are barriers to achieving equal income distribution and poverty reduction.

(Dincer & Gunalp, 2008) investigated the effects of corruption on poverty and income inequality. The study used a special measure for corruption – a government official who was convicted of crimes – and compared it with the Gini coefficients of poverty and income inequality, means deviation, and the coefficient of variation and poverty rates. The results indicated that an increase in corruption lead to poverty and income inequality. Cheema and Sail (2012) investigated the long term relationship between poverty, income inequality and economics. The study used a time series and cross-sectional data collectively and found that growth and inequality played an important role in poverty alleviation and found that growth positively affected inequality in Pakistan. The author investigated whether growth is usually offset by the increase in inequality; then compared poverty and income inequality of rural and urban areas and concluded that poverty is high in both areas, but comparatively rural areas suffer more than urban areas. They recommended that the government should take measures to even out the distribution of income and growth.

Ahmad (2001) analyzed the estimation of distribution of income in various occupations in Pakistan, and also on a province level, by employing the Gini coefficient. He found that there was very high level of inequality among skilled workers and there was less inequality among professionals. The results indicated that the highest level of inequality was found in the North-West Frontier Province (now called KPK). Therefore, a low level of income inequality was found in Baluchistan compared to other provinces. (Fahad & Rehmat, 2013) investigated the impact of policies on poverty alleviation in Pakistan using time series data for the period from 1994  to 2005 The ordinary least square (OLS) method was used to analyze the data and the Gini coefficient was used to measure the inequity in  income distribution in Pakistan. Empirical results indicated that the per capita income, remittances, development expenditure and unemployment rate have a significant and positive effect on poverty alleviation. The study suggested that the government should increase expenditure in social and development projects, which would create employment and improve standards of living.

Anwar (2010) studied the role of growth and inequality to explain changes in poverty in Pakistan. In this study two main factors growth in average income and change in distribution of income affected the level of poverty in Pakistan showing that growth had a negative effect on poverty from 1999 to 2002; and in the second period, from 2002 to 2005, the absolute level reduced and income distribution did not worsen. So, it is suggested that growth is an essential strategy to reduce poverty in Pakistan.

Ali, Ramay, and Nas (2013) analyzed the determinants of income and income gaps between urban and rural Pakistan using secondary data from the ordinary last square (OLS) method. Their desired variables were provincial literacy, education, occupation, age, gender, and marital status. The results showed that literacy, education, and occupation were the main determinants of income in Pakistan. They found that low levels of education were prevalent in rural areas, and high levels of education in urban areas. They also found that fishery and agricultural workers earn much less comparatively, and the gap comes as a result of differing levels of literacy, education, occupation, and marital status.

3. DATA AND METHODOLOGY

This research used primary data for the desired variables, and collected the data via a questionnaire. After gathering the necessary data, regression analysis was conducted to examine the dependency of poverty on income and other independent variables. To find the inequality level, means deviation, standard deviation and coefficient of variation techniques were used. To make a comparison of the data a presentation technique was used, the data was demonstrated using a pie chart, histogram, and multi-bar chart. The variables of interest in this research are basic needs, education, and income inequality, and these were divided into two groups. A dependent variable is what you measure and what is affected during the experiment. Its response to a change is the independent variable, also called an output variable. In a scientific experiment you cannot have dependent variables without independent variables.

Table-1. Description of variables.

Variables	Descriptive
Dependent variable
Poverty	A lack of resources required covering basic human needs including healthcare, shelter, food, clothing, and education Scholars have defined poverty, as a low standard of living in a society at a given time.
Independent variables
Basic Needs	The minimum amount of resources necessary for survival, such as food, clothes, shelter, education, and health.
Health	The general condition of mind and body of a person. A criterion of health in this study is access to healthcare.
Education	A type of learning in which knowledge and skills are transferred from one generation to the next, through teaching and research. In this study the achievement of different levels of education is taken as an indicator of poverty reduction.
Income inequality	The gap between incomes of rich and poor people. In this study it is measured by standard deviation and coefficient of variation (CV).

3.1. Measurement of Poverty and Income

There are many methods and procedures through which poverty is measured, but in this study the following methods are used to measure poverty and income inequality in Bannu District.

3.2. Arithmetic Mean

The sum of all observations is divided by the numbers of observations to get the numerical value, which is known as the arithmetic mean, which is the central value of the data and used for comparison. Mathematically:

3.3. Variance

The arithmetic mean of the squares of deviation of all observations from their mean is called variance, and the positive square root of variance is called standard deviation. Symbolically:

3.4. Coefficient of Variation

A standard deviation as a percentage of the arithmetic mean of a dataset is known as the coefficient of variation. It is a relative measure of variation and was introduced by Karl Pearson.

These are the measures of dispersion that calculate the average variation or spread of data from their central value.

3.5. Sources of Data

Primary data was collected from different sources and methods, such as:

1. Direct personal investigation.
2. Indirect personal investigation.
3. Collection of data through nominator.
4. Through local sources.
5. Through questionnaires.
6. Via the internet (e-mails).

In this study, a questionnaire was developed and was personally filled in by households in Bannu District. A sample of 100 families was selected from in and around the city of Bannu.

4. RESULTS AND DISCUSSION

In this research, a sample of 200 middle class families were interviewed through questionnaires, and the average maximum and minimum levels of standard deviation and coefficient of variation of the desired variables were calculated with help of E-view software. The results are shown in the tables below.

4.1. Averages

Table 1 shows the average values of all the desired variables. It was found that 38% of people around Bannu city from middle class families are government servants, 2% are semi-government servants, 18% are employed in the private sector, and 40% are unemployed. The average income of these 200 families is RS 27,869 per month and their average overall expenditure is RS. 23,552 per month.

Table-2. Averages.

Variables	Averages
Governments servants	0.384614
Semi Governments servants	0.020513
Private	0.189744
Unemployed	0.405128
Income	27,869.74
Average expenditure	23,552.82
Food	1,091.28
Clothing	2,572.821
Rent	556.9231
Education	5,236.923
Health	3,192.564
Family size	8.220513
Dependent family members	6.7435
Family members earning	1.405128
No. of school-going children	2.943590
SSC	0.425641
HSSC	0.497436
BA/B.Sc	0.338462
M.A/M.Sc	0.184615
No. with access to doctors	0.969231
Females consulting female doctors	0.405128
Females consulting LHV	0.020513
Females consulting LEL	0.210256

The average food expenditure is RS 1,091, clothes expenditure is RS 2,572, education expenditure is RS 5,236, and health expenditure is RS 3,192 per month. The average family size in Bannu District with dependent family members is eight. Only one or two people from each family are earning, and regarding education, 42% of people are matriculate, 49% of people have an intermediate education level, 33% of people are graduates, and 18% of people have achieved a master’s level. If we look at health, it was found that 96% of people have access to doctors; it was also found that 40% of females consult female doctors in maternity cases and 2% percent of females consult lady health visitor (LHV), and 21% of females consult a local experienced lady to resolve maternity issues.

4.2. Explanation of Standard Deviation and Coefficient of Variation

In Table 3, the standard deviation and coefficient of variation are two different methods used to measure dispersion in the data. Here, two numerical methods were applied to measure the variation quantitatively and to measure inequality observed in income education and health facilities. The complete results are shown in Table 3, which lists the main desired variation in each variable. There is a 117.63% variation in the income of the population, demonstrating the inequality in Bannu District. There is a 160.57% variation in expenditure and 82.67% in educational expenditure, and a 91.039% variation in people’s health expenditure. In some cases, there is a 165.95% variation in the education of children and a 44.52% inequality in higher education, which is too high. The variation in the number of people with health conditions who need to use healthcare facilities is 559.80%, and an 82.31% inequality was found in the cases of females consulting female doctors regarding maternity issues. On the basis of averages, the poverty level is relatively high, and variations in income inequality, education and health facilities are even higher.

Table-3. Standard deviation and coefficient- of-variation.

Variables	Standard Deviation	Coefficient of Variation
Governments servants	0.487757	78.8538145
Semi government servants	0.142111	14.43449135
Private	0.393107	48.26777442
Unemployed	0.492180	82.31297493
Income	23690.90	117.6390091
Average expenditure	14667.52	160..5780664
Food expenditure	6506.743	162.7739101
Clothing expenditure	2413.884	106.5842849
Rentexpenditure	1190.119	46.79558095
Education expenditure	6334.122	82.67796231
Health expenditure	3506.784	91.03965343
Family size	3.276863	251.3255064
Dependent family members	2.964723	227.4610478
Family members earning	0.707088	198.7203856
No. of school-going children	1.773777	165.9503985
SSC	0.648836	65.6007065
HSSC	0.568724	87.46527314
BA/B.Sc	0.525933	64.35458509
M.A/M.Sc	0.414641	44.52405816
No. with access to doctors	0.173136	559.8090518
Females consulting female doctors	0.492180	82.31297493
Females consulting LHV	0.142111	14.43449135
Females consulting LEL	0.408540	51.4652176

5. CONCLUSION AND RECOMMENDATIONS

This research investigated the poverty level in the district of Bannu city and surrounding areas and found it to be relatively better regarding average monthly incomes. Expenditure rates are also good, they have a good level of education, 96% of people have access to doctors, and 42% of people have high school education. However, despite these facts, the variation in income level, which measures the income inequality, more than 117%, an 82% variation exists in education and health expenditure. There is also significant inequality of more than 91%, with over 60% of females who do not consult female doctors in maternal cases, 2% of females consult LHVs, and 21% of females consult a female who is experienced, in maternity, and there is a 40% unemployment rate in Bannu. The average family size is 8-9 and there is a dependency ratio, of 6-7, and only one or two people in the families are earning. So, there is a level of poverty in Bannu.

5.1. Policy Recommendations

It was found in this study that poverty and income inequality exist in Bannu District. To alleviate poverty the suggested measures below should be implemented.

1. To reduce the 40% unemployment and dependency burden, the government should create job opportunities in small scale production of goods.
2. The government should provide interest free loans to educated unemployed youth to help them start their own businesses.
3. To overcome the health problems, the government should increase the number of doctors in hospitals and provide medicine at the lowest possible prices to poverty-stricken people.
4. An awareness program should be started for females’ health and development.
5. New schools and colleges should be built in remote areas to increase the education levels within society. Admission to colleges and universities should be free to increase the number of people with a higher-level education.

Funding: This study received no specific financial support.

Competing Interests: The authors declare that they have no competing interests.

Acknowledgement: All authors contributed equally to the conception and design of the study.

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