Keywords

Intergenerational Mobility, Poverty, Marital Status, Age, Education, Family System, Pakistan, Probit Model.

Introduction

The world's population is divided into two groups based on economic factors: poor people on one side, and wealthy or mediocre people on the other, all of whom are well-organized and have access to a wide range of amenities. The poorest people live in developing countries, while the wealthy and middle classes live in developed countries. Because of rapid economic development and advanced technical infrastructure, developed countries have a relatively small proportion of the poor population. The share of national income contributed by the manufacturing and service sectors is very high. Manufacturing and service industries employ a substantial portion of the workforce.

Owing to high levels of unemployment, low rates of saving and capital growth, developing countries account for the majority of the world's poor population. Citizens have low standard of living and a high rate of population growth, as well as significant income gaps. Poverty is often passed on from generation to generation, a phenomenon known as intergenerational mobility of poverty (IMP). Poverty transmission to the next generation is long-term in developing countries, but it is short-term in developed countries. There are many reasons that people face; these act as roadblocks to breaking the poverty cycle. The intergenerational transmission of poverty is dependent on parental attitudes toward their children's backgrounds. People in developing countries have little assets, which makes it difficult for them to provide their children with a good education, adequate nutrition, and other basic needs. On a national and international level, there is a wealth of literature on intergenerational transmission of poverty.

Buvinic et al. (1992) sorted out the persistence of poverty by teenage mothers and their kids in the capital of Chile. There be a lack of hope in income from the death father, and the family's income was the only mother resources. They were unable to provide a better life for their kids in stipulations of nutrition, learning, and human resources as a result of these. They clarified that their earnings were unaffected by their mother's professional and vocational training. The mother's income will be increased, potentially breaking the poverty cycle. Mother's abilities had a negative relationship with chronic poverty as well. Desertion by the child's father can exacerbate poverty, whereas education of their children may alleviate it. Rodgers et al. (1995) proposed an experiential study of poverty transfer into different generation in the US, based on the 1968 to 1988 data. Equality of opportunity was violated by the transition of poverty from forefathers to their children. If parents' and children's poverty levels were independent, then the main source of transmission would be upward mobility among children of poor parents, but this would be much less than anticipated.

Horrell et al. (2001) highlighted the poverty transmission through shocks in life by income and capital in nineteenth-century United Kingdom. The loss of a father had an effect on the human capital as well as the education of their children. Being fatherless is a widespread tragedy that has resulted in children with low human resources, necessitating a significant increase in income to break the poverty cycle. Jackline (2001) studied the transmission of suffering through child labor in the United Kingdom. Child worked hard to ensure their families' and their own survival. Individual-level data from October 1988 was used in the analysis. A total of 10,000 households were sampled. Person jobs and socioeconomic characteristics were collected as part of the study. The sample ranged in age from 6 to 14 years old. The key result was that fathers who were themselves child laborers were more likely to include their children in the job. As a result, child labor was the source of poverty transmission from generation to generation. These children will work in low-wage jobs. The bivariate probit result revealed that children whose parents were child laborers were twice as likely to work. Schooling seemed to be crucial in contravention the poverty cycle.

Khawaja (2003) calculated that migration keen on and absent of the camp affects the poverty of the refugee camp populations in Jordan by the use of 1999 household survey data. For his study, he used binominal logistic regression. The findings revealed that migration not only source of poverty in camp. Capital investment such as economic dealings, social heritage, schooling, and demographic determinants like as family management and dependency ratio had a significant impact on poverty occurrence, had a minor impact. They discovered that by improving their human resources, they could break the poverty cycle, while increasing their dependency ratio had a positive effect on the cycle. Mckay et al. (2003) used qualitative and quantitative analyses to investigate the persistence of poverty in low-income countries. The chronic poor had a variety of characteristics, such as high dependency levels and a lack of assets, which contributed to their poverty. Human capital is able to break the poverty cycle by investing in education and training. The size of the household and the percentage of dependents had a positive effect on poverty transmission. Chronic poverty had a negative relationship with physical assets and well-paid salaries. Prices, conflict or war, climate or work, and serious illness or death are all external factors that have a direct impact on chronic poverty.

Airio et al. (2004) used the intergenerational transmission of poverty (IGTP) model to estimate the connections between poverty and social mobility. The data was collected from Finland's longitudinal census data file from 1970 to 1995. They came to the conclusion that poor families provided poor opportunities for their children, and that those who grew up in a poor family were nearly twice as likely to become poor adults as those who grew up in a non-poor family. During the 1990s economic crises, children of poor parents faced the same poverty risks as non-poor children, but non-poor children faced different risks after the downturn. Green et al. (2005) indicated the political and economic institutions that affected chronic poverty. Inequality, politics, growth theories, and social ties are all qualitative indicators that have pushed poverty from generation to generation. They came to the conclusion that increasing family consumption or wealth decreased the poverty. Poverty transmission into the next generation was caused by inequality, inefficient growth theories, social ties, and political uncertainty, all of which had a major connection. The poverty cycle had a negative relationship with income and consumption.

Ludwig et al. (2006) sorted out the association among culture and the Intergenerational Poverty (IGTP) in United States. The poor grandfather's community promoted marriage, employment, and faith as a means of overcoming the IGTP. They found how parental employment, faith, and wedding influence children's socioeconomic level as adults, as well as the possibility that changing these parental behavior measures will minimize poverty in coming generation. They calculated that all offspring were married, had religious parents, and employed, the cycle of poverty transmission would be broken more slowly. They proposed that encouraging parents of disadvantaged children to engage in positive social activity would help to prevent poverty transmission. The job of parents had a negative relationship with the poverty cycle; more income could break the poverty cycle. Since their children are not involved in substance abuse, alcohol abuse, delinquency, or other causes that can contribute to poverty, religious parents may be able to break the poverty cycle.

Sato et al. (2008) pointed out the experimental study of intergenerational poverty (IGTP) through by the perspective of mobilization of income in Asian state, Japan. They discovered that an individual from a low-income family was not capable to get a proper education due to poverty, so was unable to get work, resulting in a low income. Via Ordinal Logit regression, they discovered that a stumpy level of schooling resulted in a little level of job status and a low income occupation. Kabeer et al. (2009) studied children's education and poverty transmission through generations in urban Bangladesh. Children's education was a critical component in preventing poverty from being passed down over generations. Survey data are used to gather information from low-income households. They discovered that there is a difference in education between parents who send their children to teach and those who do not send their children to school. Parents' asset deficits are another reason for their children not attending school. Parents in Bangladesh had strong expectations for their sons rather than their daughters. As a result of these standards, approximately 65 percent of children have not finished their advanced education so have begun working at the under matriculation level of education.

Robert et al. (2009) investigated the association among poverty level and investment on child in less income families between 1970 and 1990. They discovered the causes that contribute to poverty in the long run, such as employment and education opportunities, ambitions of children and parents, and the availability of role models. Since their parents' income was low and they couldn't afford to spend heavily in their children's education, poor children attended schools with lower funding. According to the findings, adult children earn more than their parents, and the next generation earns half as much as the previous generation. The next generation of people who encountered poverty had a higher likelihood of having a bad childhood than those who had never experienced poverty. Pakpahan et al. (2009) established the poverty movement in Indonesia with the help of collection of panel data. They discovered that key aspects of child welfare, like as food, schooling, youngster labor, manner, assistance, and leadership, can influence poverty transfer. The key causes of intergenerational poverty transmission were haven’t material property and parental deprivation beyond poverty.

Bird et al. (2010) investigated the connection among capital investment of human such as schooling, conflicts, and intergenerational poverty (IGTP) in Uganda through the aid of the Q-squared methodology. They come to the conclusion that schooling aided new generations in escaping poverty and conflict. Conflicts had a positive relationship with the spread of poverty, while schooling had a negative relationship. Cooper et al. (2012) traced out the importance of assets passing down over the generations. They come to the conclusion that inheritance properties, land and gender equity have a greater impact on poverty transfer to their children. The equitable allocation of inherited physical properties has the potential to break poverty's resistance.

Bellani et al. (2013) pointed out the semi-parametric and non-parametric estimators of the Quantile treatment effects (QTE) to separate the distributional effects of growing up poor. They used EU-SILC data and elicited in order from parents such as age group, schooling, and profession. They discovered that organism poor as a child reduced the likelihood of earning money as an adult by about 3 thousand euro on average, and improved the likelihood of life form poor by approximately 3%. They calculated determinants with a point in time component, such as sexual category, birth time and quarter, household composition, maximum level of schooling, birth year, father and mother's main occupation, and number of siblings. They come to the conclusion that all variables that do not disrupt schooling, migration, or wealth have a substantial positive impact on the poverty cycle; otherwise, the poverty cycle could be broken by children's income and education.

After reviewing the previous literature it is observed that all studies explain the theoretical background for foreign countries. Hardly any study is found which has empirically analyzed the poverty transmission in Pakistan in terms of forefather to their offspring generation. This is actually gap of this study. Keep in view the importance, this study analyzing the causes to break the Intergenerational mobility of Poverty in Pakistan. Apart from Introduction in first section, data and methodology is given in second section, third section explains the results and concluding remarks are given in section four.

Data and Methodology

Data and Methods

For the reason of mobility of poverty into next generation, primary source of data has been taken from 30 districts of Pakistan. The data is collected randomly by interviews and survey. For the aim of this study, household heads are selected on the research basis. Total 300 household heads were selected in this survey in which 150 were those who are experienced the poverty from grandfather. 150 are those who had poor forefather but now they are emitted from poverty line and now they are middle or rich class family. Poverty is measured through Per Capita Income method based on National Poverty Line of Pakistan 2020. Analysis of the study is done by Descriptive Statistics and Correlation at Intermediate level and Probit Regression method is applied to see the relationship between the variables. The following method is used to measure the marginal effects in dependent variable due to change in explanatory variables.

Model Specification

IMP = f (ARE, AG, EDUCA, MAS, JOTF, HSIZE, DYRATIO,T ASSETS)

The function form of model can be written as;

MP=α+β₁.are+β₂.ag+β₃.educa+β₄.mas+β₅.jotf+β₆.hsize+β₇.dyratio+β₈.tassets+μi

The detail of description of the above mention variables are given in Table 1.

Table 1 List of the Variables Used in the Research
Variables	Explanation of the variables		Expected Relationship
IMP	Intergenerational mobility of Poverty	1= if respondent belongs from intergenerational poverty cycle 0= if respondent don’t belongs from intergenerational poverty cycle	Dependent Variable
Explanatory variables
ARE	Living Area	1= Respondent belongs from urban area 0= Otherwise	Negative
AG	Age of household head	A continuous variable	Negative
EDUCA	Complete year of Schooling of household head	A continuous variable	Negative
MAS	Marital status of household head	1= Married 0= Otherwise	Negative
JOTF	Family structure of household head	1= Joint family 0= Nuclear family	Negative
HSIZE	Family Size	A continuous variable	Positive
DYRATIO	Dependency ratio in family	A continuous variable	Positive
TASSETS	Total assets of household head	A continuous variable	Negative

Results and Discussion

Descriptive Statistics, Correlation, and Probit Regression Analysis are used to assess the findings. Table 2 shows the descriptive results. The results indicate that 50% of respondents believe poverty is passed on from generation to generation, with the highest and lowest values of intergenerational poverty transmission being 100.00 and 0.00 percent, respectively. Urban areas account for 30% of the survey, with maximum and minimum area values of 100.00 and 0.00 each, respectively. The average age of respondents is 44 years, with 72.00 and 23.00 years as the highest and minimum ages, respectively. The majority of respondents are barely middle pass, with maximum and minimum years of schooling of 16.00 and 1.00 years, respectively. The peak and least values of family composition were 100.00 and 0.00 percent, respectively, with 40 percent of people residing in joint families. The average household size was seven members, with the highest and lowest values of household size being 22.00 and 2.00 units, respectively. The dependence ratio is 0.6, with 100.00 and 0.00 percent as the maximum and minimum benefit values, respectively. The average amount of assets held by respondents in the selected region is around 4.6 million rupees, with the highest and lowest amounts being 72.2 million rupees and 0.00 rupees, respectively.

Table 2 Descriptive Summary
Variables	Mean	Maximum	Minimum
Intergenerational Mobility of Poverty	0.5	1	0
Area	0.3	1	0
Age	44.11	72	23
Education	6.27	16	1
Marital Status	0.53	1	0
Joint Family	0.4	1	0
Household Size	6.74	22	2
Dependency Ratio	0.6	1	0
Assets	4664827	72200000	0

A correlation matrix is created to verify the problem of multicollinearity, as shown in table 3. Since the correlation coefficient is less than 0.80, it is assumed that there is no multicollinearity between the dependent variables.

Table 3 Correlation Matrix
Correlation Probability	MAS	ARE	AG	EDUCA	HSIZE	JOTF	TASSETS	DYRATIO
MAS	1.00
ARE	-0.12	1.00
AG	0.04	-0.11	1.00
EDUCA	0.47	0.13	-0.15	1.00
HSIZE	-0.02	-0.04	0.39	-0.20	1.00
JOTF	-0.11	-0.02	0.27	-0.05	0.49	1.00
TASSETS	0.24	-0.10	0.16	0.24	0.20	0.12	1.00
DYRATIO	-0.39	-0.07	-0.14	-0.32	-0.10	-0.14	-0.33	1.00

The association among region and marriage status is -0.12, and the correlation between family size and marital status is -0.01, indicating a poor negative relationship. The correlation coefficient between family structure and marriage status is -0.11, indicating that these two variables have a negative relationship. The correlation value between total assets and marital status is 0.24, indicating an optimistic weak association between these variables, while the correlation value between dependency ratio and marriage status is -0.39, indicating a negative and moderate relationship. All variables value had less than 0.80, its means that there is no multicollinearity between the variables. Table 4 shows the results, which are divided into six columns. In the first column, independent variables are listed, and the marginal effects of regressor are calculated in the second column. The coefficient and standard error of the predictor variables are found in the third and fourth columns, respectively. The z-statistics value is shown in the second last column, and the importance of the individual predictor variables is shown in the last or sixth column. The table depicts overall poverty mobility over time.

Table 4 Probit Regression Estimation
Dependent Variable: Intergenerational mobility of poverty
Method: ML - Binary Probit econometric analysis
Variable	Marginal effects	Coefficient	Std. Error	z-Statistic	Prob.
C		7.31	3.11	2.33	0.03
ARE	-0.78	-3.11	1.23	-2.82	0.00
AG	-0.02	-0.09	0.10	-2.14	0.03
EDUCA	-0.25	-0.99	0.31	-3.74	0.01
MAS	-0.34	-1.37	0.59	-2.13	0.05
JOTF	-0.59	-2.37	0.99	-2.35	0.03
HSIZE	0.14	0.54	0.21	2.37	0.03
DYRATIO	1.41	5.65	2.01	2.91	0.00
TASSETS	-0.46	-1.84	0.01	-2.73	0.01
McFadden R-squared		0.89	Mean dependent var		0.49
LR statistic		379.87	Avg. log likelihood		-0.06
Prob (LR statistic)		0.01

Probit regression analysis is utilized to check the poverty of intergenerational mobility of poverty (1 if poverty transfer forefather to their offspring, 0 means did not poverty transfer forefather to their heirs). Since the likelihood value is 0.03 and the coefficient value is 7.31, the value of intercept is positive and statistically important. The approximate value of LR statistics is 379.87 unit, and his likelihood value is 0.01, indicating that it is meaningful at the 1 percent stage of significance. Based on our chosen explanatory variables, the levels of significance indicate that on the whole model is well suited, fine, and important. The McFadden R-squared value is 0.89, which represents the highest probability of the factors of the available data that we experimented in the field of study. With intergenerational mobility of poverty, it was calculated that the model is better-quality in describing the shift in regress and variable reasons by explanatory variables.

The results show that our regressor area has a negative relationship with intergenerational mobility of poverty. The area coefficient is -3.11, and the likelihood is 0.00, indicating that the matching variable has a statistically significant relationship with the dependent variable at the 1 percent level of significance. The marginal effects of area is -0.78 in city, indicating that respondents who live in inner-city areas are 78 percent less likely to be concerned in poverty transitioning from poor to poor. In fact, residency is linked to the accessibility of govt. and community services such as clean-fresh water, a sanitary system, and protection. In comparison to those who live in urban areas, people in rural areas face health risks and receive insufficient, low-quality education. They have effective tools that they can put to greater use or invest in. They put the money to good use in the production process. Harper et al. (2003); Baulch et al. (2002); Castaneda et al. (1999); Papanastasiou et al. (2010).

The key factor influencing poverty mobility is age, as well as historical periods. The relationship between age and poverty mobility is unfavorable. Age has a coefficient value of -0.09. The probability value of age is 0.03, indicating that there is a substantial connection between age and poverty transmission at the 5% level of significance. The marginal effects value of age is -0.02, indicate that 2% of respondents are less likely to be concerned about intergenerational mobility of poverty as poor to poor as they get older. People of a younger or older age have more experience than adults. The age of an individual has a big impact on their poverty level. Respondents' abilities and standing have improved as they have become older. Through the passing of time, household heads save a portion of their earnings or raise their work hours in order to escape from poverty. He raises more and increases his profits by putting their savings to good use. When he reaches at certain amount of wealth, he either invests in their children or in their love and care. So, as people get older, their poverty status affects whether they are wealthy or mediocre. Bellani et al. (2013); Papanastasiou et al. (2010); Sato et al. (2008).

Due to social and cultural norms, education plays a significant role in poverty mobility. The definition of affecting poverty transmission is given by the coefficient value of schooling, which is -0.99. These two variables have a negative relationship. Taking a 1 percent degree of significance, the probability value of education is 0.01, indicating that there is a important connection between education and transmission of poverty. The marginal effects of education is -0.25, indicating that 25% of household head are less likely to be involved in the poverty transition from poor to poor as their education level rises. Community expectations and education help respondents raise social values. When one's level of education rises, so does one's chance of landing a well-paying job. It makes learning, skills, and beliefs easier. The likelihood of a high income rises as respondents obtain a well-paid career via higher education. The high-earning work is the source of increased earnings and per capita income. When one's income rises as a result of schooling, the chances of escaping poverty rise as well. Horrell et al. (2001); Pakpahan et al. (2009); Baulch et al. (2002); Khwaja (2003); Papanastasiou et al. (2010); Lawson et al. (2006); Robert et al. (2009); Bezemer (2006); Davia et al. (2017); Korankye (2014); Harper et al. (2003).

The intergenerational transition of poverty is influenced by marital status. Because of the coefficient symbol, there is inversely association between these two variables. The value of the marital status unit coefficient is -1.37. The chance value of marriage status is 0.05. At a 5.00 percent level of significance, it shows a significant relationship with poverty transmission. The value of marginal effects is -0.34, indicate that getting married makes 34 percent of respondents have less likely to change their poverty transmission from poor to poor. The term "marital status" refers to a serious relationship with others. Married life offers the possibility of improving one's lifestyle. When a person marries, his income or care and guidance increase, according to the respondents. If one of the spouses is from a high social class or has political clout, the social and political prestige of the household head will undoubtedly rise. Household income rises as a result of the spouse's salary. If a spouse has a high level of schooling, her children are likely to have a high level of education as well. Poverty level shifts from poor forefather to mediocre or wealthy heirs as a result of schooling. Ludwig et al. (2006); Davia et al. (2017); Pakpahan et al. (2009).

A family arrangement is a living person's system in a home. There is an inversely association between the regressand variable intergenerational mobility of poverty and the regressor family arrangement. The probability value of family structure is 0.03, indicating a substantial relationship on poverty transmission at a level of significance of 5.00 percent. Household heads are less likely to be engaged with poverty status as poor to poor if they live in a joint family (59.00 percent on average). Treatment, protection, and expectations of respondents could all be improved in a joint family. If an individual is absent, there is no need to be concerned about her family. When an individual is alone and has no family, his security costs will rise. Food and shelter costs would be higher in a shared family than in a separate one. As a result, there is a greater risk of moving from a poor to a wealthy or mediocre position in a joint family. Papanastasiou et al. (2010); Sato et al. (2008); Moore et al. (2001); Bellani et al. (2008).

The size of a family has a direct relationship with intergenerational mobility of poverty. Household bulk is the number of family members, and the household size coefficient value is 0.54 units. The marginal impact of family size is 0.14, indicating that by raising their household size, 14.00 percent of household family are more likely to experience a shift in their poverty transmission from poor grandfather to poor. The chance value is 0.03. With a significance level of 5.00 percent, it has an important result on poverty transmission. If a family's size is small, there will be more resources for treatment, guidance, skills, and education. When a family's size is small, spending is low, and poor families can easily raise their children. When a poor family invests in their children's education and skills, the children's human capital increases. These children are then lifted out of poverty. However, if the population is large, there is a lack of education and human resources. Poor children are forced to engage in child labor or bonded labor. Because of the responsibility of guardian duties and the distribution of family support income that comes with a large family, they put little money into their children. Since parents' time and financial resources are inadequate, each new kid limits their capacity to assist their children in taking advantage of new opportunities (wolfe et al., 1982).

The quantity of inactive family members is referred to as the dependency ratio. It primarily consists of infants, the elderly and unproductive adults. The dependency ratio is defined as the proportion of inactive family members to the total number of family members. The dependence ratio has a probability value of 0.00, indicating a significant relationship with poverty status at a level of importance of 1.00 percent. The dependency ratio coefficient value is 5.65, indicating a positive relationship. The marginal effects of dependency ratio are 1.41. By increasing the dependence proportion, 141% of household heads are more likely to change their poverty resistance from poor forefather to poor. People are unable to save their capital due to the high dependency ratio. His investments or consumption are well in excess of his earnings. The household head with a high dependency ratio provides a low level of schooling, skills, and awareness to his family. The growth of human capital is very poor, and it is moving through the poverty cycle. Forefather of the poor respondents was poor, and the respondent fell into poverty as a result of the high dependency ratio (Khawaja, 2003; Baulch et al., 2002; Corcoran et al., 1985; Papanastasiou et al., 2010; Mckay et al., 2003; Lawson et al., 2006).

The most economical factor that affects poverty transmission is value of total assets. The possessions have a detrimental relationship with poverty. The assets' coefficient value is -1.84. At a significance level of 1.00 percent, there is a physically powerful association between explanatory variable assets and poverty resistance in generations. The -0.46 represents asset marginal effects. The respondents' poverty level was 46 percent less likely to move from poor to poor as their assets increased. Assets are a major factor in the transmission of poverty from generation to generation. An individual who is conscious of making productive use of resources can easily move from poverty to a wealthy or mediocre family. When it comes to money, there is a psychological component that is crucial. The high assts respondent should be knowledgeable and capable of making long-term strategic investments. When an individual makes long-term output decisions, his or her assets increase over time. Respondents with more assets invest in schooling, skills, and expertise in order to provide a better life for their human resources. These successful generations marry into a mediocre or wealthy family (Mckay et al., 2003; Kabeer et al., 2009; Corcoran, 1995; Cooper et al., 2012; Khawaja, 2003; Harper et al., 2003; Bhargava, 2003).

Conclusion and Policy Recommendation

The objective of this study is to examine the factors which are possible causes to break the Intergenerational mobility of Poverty in Pakistan. Considering the objectives, primary data has been taken from urban and rural areas of Pakistan. Simple Random sampling technique is utilized for the collection of data through interviews and questionnaire. Total 301 respondents were included in the sample in which 150 respondents were evident of Intergenerational mobility of Poverty (Whose forefathers were poor and heirs are also poor). 151 respondents are not evident of Intergenerational mobility of Poverty (Whose forefathers were poor but heirs are not poor). Poverty is measured through Per Capita Income method based on National Poverty Line of Pakistan 2020.

Analysis of the study is done by Descriptive Statistics and Correlation at Intermediate level and Logistic Regression method is applied to see the relationship between the variables. In this study, the dependent variable is Intergenerational Mobility of Poverty while Area, Age, Education, Household Size, Dependency Ratio, Assets, Marital Status and Joint Family are taken as explanatory variables. The results of Logistic Regression show that there will be no Intergenerational mobility of Poverty (Poverty will not transmit from one Generation to another Generation) as people are living in Urban Area, they are becoming more experienced, they are becoming educated, they are having good Assets value, they are Married and they are living in Joint Family system. There will be Poverty transmission from one generation to another generation if household size increases and dependency ratio increases in Pakistan.

On the basis of results, it may be suggested that there should be promotion of free education and family planning’s throughout the Pakistan especially in rural areas. In rural areas there are few earning opportunities due to this poverty transmits from generation to generation in rural areas. Conclusion of this study suggested that earning opportunities should be introduced in rural areas of Pakistan.

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