Literature Review - Inequality and Poverty in the US

There is no approach to conceptualizing and quantifying poverty or inequality. The diversity of different approaches made and used in research reflects divergent opinions on what constitutes an acceptable basic standard of living and how the discrepancy between the poorest and wealthiest members of society should be defined. The analytical results drawn from poverty and inequality research might be influenced by the ideas and metrics chosen, which may be based on theoretical or pragmatic considerations. In either instance, it's important to outline their rationales.

History of Poverty in the United States

The Great Recession (GR) was the most highly fraught financial crisis the United States has faced since the 1930s Great Depression. In 2008, the stock and real estate markets collapsed, wiping out more than $15 trillion in public wealth, or about 10% of true aggregate public monetary resources. As the financial and non-financial sectors of the economy recovered, true Gross Domestic Product failed to expand in 2008 and declined by 2.6 percent in 2009, the largest decline in sixty years. Due to the abrupt halt in the country's monetary progress, a big number of laborers lost their jobs. Between December 2007 and December 2009, absolute nonfarm business decreased by 5.7 percent - a loss of 8.3 million jobs - and the unemployment rate peaked at 10%.

The emergency elicited a significant financial response. Despite the 'planned stabilizers' built into Unemployment Insurance, SNAP, and the cost framework, there were a few big arrangement adjustments in 2009 and 2010 that injected hundreds of billions of dollars into the economy (Burtless, 2009). The Troubled Asset Relief Program (TARP) stabilized the financial system by investing more than $400 billion in distressed assets or guaranteeing them, including large holdings in General Motors, AIG, and Citigroup. The American Recovery and Reinvestment Act (ARRA) provided financial assistance to state legislatures, reduced charges, extended TANF, SNAP, unemployment insurance, and the Earned Income Tax Credit, and additionally funded framework projects, infusing the economy with more than $700 billion in 2009 and 2010. It is generally agreed that these approach improvements, combined with the Federal Reserve Board's financial strategy actions, stabilized the economy and prevented the Extraordinary Recession from becoming a significantly more unpleasant financial event than it would have been in any case (CBO, 2013).

In the last part of 2009, the US economy began to grow again. First, Genuine Gross Domestic Item fell to its lowest point in the second quarter of 2009. Then, it began to rise again in the second half. At its peak in October 2009, the jobless rate was 10%, and the bottom of the jobless rate for nonfarm businesses came in December 2009. Since June 2009, when the NBER business-cycle dating council said the recession was over, the pace of growth has been slow, leaving a lot of people out of work more than four years after the end of the recession was announced.

They all had an effect on family income: the strong financial shocks in 2008 and 2009; the strategy used to deal with the Great Recession; the slow growth after that; and so on. This paper looks at the combined effects of those changes. In the years before the Great Recession, inequality in the United States rose. This paper looks into whether the recession or the growth had a different effect on those long-term patterns (Thompson and Smeeding, 2013). It talks about how the GR affects family pay inequality and poverty, mostly based on data from the Current Population Survey (CPS). We also look at how much the assessment and move system helped to lessen these effects in the GR. We look at how changes in the creation of pay between profit, capital, and moves changed.

Poverty and Inequality as a measure of Economic Resources

Income has been the most common way to measure poverty and inequality. When you get paid, you don't just get money from your job. You also get money from things like investment accounts, stock, state benefits, and annuity pay, to name a few things. It could also include payments from other families, like child support or settlements, and home building, like the value of the services provided by owner-occupiers as compared to the rent they pay.

A family is a group of people who live together, even if they aren't related. A typical definition of a family is "one person living alone, or a group of people who live together but aren't related who offer cooking facilities and a parlor, living room, or dining area" (ONS, 2016). There are two types of gross pay: one that includes money from the federal retirement aide system and one that does not. Overall pay, or "discretionary cashflow," is what the family gets from all of its taxes and benefits. It shows how much money the family has available for use and how much it has set aside as a reserve.

Income at the family level is also often changed or "equivalentized" to show how families of different sizes and organizations need and make different amounts of money to live the same way. This cycle takes into account more important connections that can be made between families with different arrangements (for instance, contrasting a solitary individual family and a group of four). It's used in the papers in this series that look at how pay inequality and different ideas of poverty are linked. This percentage of equivalized family net extra cash is used in those papers (Karagiannaki, 2017; Yang and Vizard, 2017).

Empirical studies on poverty and inequality

The expressions inequality and poverty are commonly used interchangeably in writing about income distribution. Increased inequality is interpreted as an increase in poverty as well, and vice versa. For instance, Dagdeviren et al.,(2000, 5) make reference to poverty-decreasing arrangements and then state: "the 'High Performing' Asian nations, prior to the late 1990s monetary crisis, combined rapid growth of per capita pay with moderately stable and low inequality," inferring that "the experience of the 'superior workers' suggested, in any case, that there may be strategy measures to cultivate the safe mix of rapid development and rapid poverty reduction." Additionally, it is just one model among a wide number.

Nonetheless, as is self-evident, a drop in poverty is not necessarily accompanied by a decline in inequality; it may, in fact, be accompanied by an increase in it. China witnessed a dramatic decline in poverty while seeing a dramatic increase in inequality. On the other hand, an increase in poverty may be accompanied by a decline in overall inequality. Finally, there may be widespread poverty in the general population but very little financial disparity.

Ravallion (1995) comments that there is no evidence that progress has been associated with an increase or decrease in the proclivity for inequality within non-industrial states.

De Janvry and Sadoulet (1999) disaggregate data on poverty and pay disparity in 12 Latin American countries between 1970 and 1994; they discover that wage growth reduces metropolitan and provincial poverty but not inequality. They also see an uneven influence of development on poverty and inequality, with recessions having a greater impact on both than similar pay expansions. According to De Janvry and Sadoulet (1999) metropolitan poverty is anti-recurrent, declining with wage growth and increasing during recessions. They do, however, remark that development is most effective in reducing urban poverty when disparity is not excessive. Thus, nations with high levels of inequality cannot rely on development to alleviate poverty. This result is consistent with Bourguignon's, as previously stated. Despite the fact that development cannot be considered to be unequalizing, development cannot be used to alleviate inequality at any stage.

De Janvry and Sadoulet's findings corroborate Bruno et al., (1996)  finding that the impact of development on inequality is unknown. Nonetheless, they draw attention to the fact that lower initial inequality increases the likelihood that development will alleviate poverty.

Quah (2002) considers the cases of China and India, which together contain 33% of the world's population. He concludes that comprehensive monetary development might occur simply by increasing inequality. Regardless of this, he maintains that development is unequivocally beneficial to the poor. He emphasizes that only at incomprehensibly high levels of inequality would financial progress be detrimental to poor people.

Besley and Burgess (2003, 11) establish a positive and critical relationship between inequality and a country's level of poverty. However, as Honohan (2004) points out, this association is practically redundant: assuming that the mean pay remains constant, the more of the public pay taken by the rich, the less is available for the others, and thus more persons are logically poor.

Kraay (2006) deconstructs changes in poverty into three components: a) changes in normal salaries; b) the relationship of poverty to development; and c) changes in pay circulation. He finds that in the short term, development in regular livelihoods accounts for around 70% of the variation in (headcount) poverty fluctuations, and more than 95% in the medium to long run. Thus, he argues that development is a vital tool for poverty reduction.

López and Servén (2006) employ a massive cross-country data set spanning nearly 40 years and covering both industrialized and developing countries to evaluate the erroneous hypothesis that the size dispersion of per capita pay may be represented by a lognormal thickness. The exact tests are consistent with the lognormal estimate of per capita pay appropriation. The logarithmic ordinariness of pay allocation enables the creators to decide a number of subjective and quantitative implications for the overall tasks of development and inequality reduction in poverty reduction under selected beginning conditions, using a variety of poverty metrics.

Housseima and Ben Rejeb (2012) examine board data from 52 agricultural nations from 1990 to 2005 to determine the key drivers of poverty reduction and demonstrate the relationship between poverty, inequality, and development. They discover that a one-point increase in per capita GDP results in a 0.40-point decrease in poverty rates. However, they note that increased levels of inequality increase the proportion of the impoverished in the population. The assessment results indicate that a one-point increase in the Gini coefficient results in a 3.26-point increase in the poverty rate. In this approach, growing inequality may impair monetary development's ability to alleviate poverty. However, Alvaredo and Gasparini (2013), establish a tenuous link between poverty and inequality. Between the headount ($2 line) and the Gini coefficient, the correlation coefficient is only 0.17.

The Poverty Trap

It has been said that there is a "poverty trap" that makes sense of why people who start out poor stay poor. There are a lot of self-sustaining systems that say that poverty makes more poverty. It started out with Nelson (1956), who wrote about a development model with low savings and venture rates at low wages. In this way, low levels of pay lead to low savings and speculation rates, which put countries in poverty. The same plan could be used to help people.

Kraay and McKenzie (2014) disagree with this idea, saying that even the first least fortunate 10% of countries have grown at a rate similar to that of the United States over the last 50 years. Be that as it may, this doesn't mean that there isn't a chance of a poverty trap if people who were poor at the start of the study stay poor for 50 years. The question is whether the poor's pay growth rate is faster than the non-pay poor's growth rate. Using data on poverty over time for 90 countries that grow food.

Ravallion (2012) says that even though their general poverty rate has been falling since at least 1980, the proportionate rate of decline hasn't been as high in the most poor countries as it has been in the rest of the world. The author says that the level of poverty that is already there affects how quickly people can get better, and that a high level of poverty also makes it more difficult for people to get better and get out of poverty. As Ravallion says, both effects make sense of the lack of poverty coming together. Nations that have a lot of poverty at the start don't have a faster rate of poverty decrease, which would make it possible for them to become one.

In her study of anti-poverty policies in the United States, Sawhill (1988) looked at the period from 1967 to 1985 and found that the rise in unemployment was one of the main factors that led to their failure. She also found that the chance of being poor in the United States is much higher if you are black, live in a female-headed household, or are a child under 18. When a child is born into a poor family, he or she spends a long time living in poverty. She found that segregation makes places where black poverty and inequality are higher than in places where white poverty and inequality are lower than in places where they are not segregated.

Husmann (2016) says that poverty is caused by being on the outside of social, political, economic, ecological, and biophysical systems. This means that people or groups are on the outside of social, political, economic, ecological, and biophysical systems. She uses this idea to make a marginality map of Ethiopia by putting seven indicators on top of each other that show different aspects of marginality. Marginality hotspots are found.

 

Research Methodology and Design

In the proposed study, I will use the quantitative method. A quantitative method is an approach to quantitative research that involves collecting numerical data and subsequently performing statistical analyses intended to show the strength of association among variables, determine the extent of differences among variables, or aggregate data using measures of central tendency, frequency, and dispersion (Konig et al., 2016). Quantitative research has several attributes that the proposed study meets. First, quantitative research is based on deductive logic, where researchers emphasize testing theory (Rutberg & Bouikidis, 2018). In connection with deductive logic as the basis, quantitative researchers are expected to use existing theories to develop hypotheses they wish to test. Third, a quantitative method involves conceptualization, operationalization, and measurement of critical variables (König et al., 2016). Notably, a quantitative research question should be reduced to specific variables that can be measured using clearly defined quantitative instruments (Rutberg & Bouikidis, 2018). Fourth, a quantitative method may involve examining differences or associations among variables or aggregating data through descriptive statistics such as means and frequencies (König et al., 2016).

In the proposed study, I intend to use a correlational design. According to Crawford (2014), a correlational design is an approach to quantitative research in which researchers focus on examining the relationship between two variables without controlling for other factors. As such, a correlational design is appropriate when the desired result of a quantitative study is the degree of association between two variables (Crawford, 2014). The correlational design is appropriate in the proposed study since I intend to establish an association between inequality and poverty.

Data Analysis

I will download the survey results from the only online GSS survey in an Excel format. Before embarking on the actual analysis, I will perform data preparation in Excel. Data preparation in Excel will involve averaging scale items to obtain a ratio score for each variable. After data preparation in Excel, I will import the prepared file into SPSS version 24 Software. In SPSS, data preparation will also be performed. Notably, data preparation in SPSS will involve assigning names that conform to the SPSS variable naming and labeling standards. For instance, SPSS does not allow variable names that contain white spaces; hence all variables with white spaces will be modified to suit this convention. Additionally, SPSS does not allow using the same name for two or more different variables. Lastly, I will ensure the variable names conform to the SPSS variable naming rules and are relevant to the actual measured variables they represent.

 

 

References

Alvaredo and Gasparini (2013). “Recent Trends in Inequality and Poverty in Developing Countries.” CEDLAS Working Paper Nº 151. URL: www.cedlas.econo.unlp.edu.ar/wp/wp-content/uploads/doc_cedlas151.pdf.

Besley, T. and Burgess, R. (2003a). Journal of Economic Perspectives. Vol. 17, Nº 3. Summer, 3-22.

Dagdeviren, H., van der Hoeven, R. and Weeks,J. (2000) “Redistribution Matters: Growth for Poverty Reduction.” URL: https://www.soas.ac.uk/economics/research/workingpapers/file28875.pdf

Bruno, M., Ravallion, M. and Squire L. (1996) “Equity and Growth in Developing Countries.Old and New Perspectives on the Policy Issues.” Policy Research Working Paper. The World Bank

De Janvry, A. and Sadoulet, E. (1999) “Growth, Poverty, and Inequality in Latin America: A Causal Analysis, 1970–94.” Conference on Social Protection and Poverty. Inter-American Development Bank. URL: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.200.2041&rep=rep1&type=pdf.

Honohan, P. (2004) “Inequality and Poverty.” Journal of Economic Perspectives. Vol. 18, Nº 2. Spring. 271-276.

König, J., Schmid, S., Löser, E., Neumann, O., Buchholz, S., & Kästner, R. (2016). Interplay of demographic variables, birth experience, and initial reactions in the prediction of symptoms of posttraumatic stress one year after giving birth. European journal of psych traumatology, 7(1), 32377.

Kraay, A. (2006): “When is Growth Pro-Poor? Evidence from a Panel of Countries”, Journal of Development Economics.Vol. 80, Issue 1, June, 198-227

Kraay, A., and McKenzie, D. (2014) “Do Poverty Traps Exist? Assessing the Evidence.” Journal of Economic Perspectives. Vol. 28, Nº3, 127-48

López, J.H. and Servén, L. (2006) “A Normal Relationship? Poverty, Growth, and Inequality.” World Bank Policy Research Working Paper 3814. URL: http://documents.worldbank.org/curated/en/620771468150322825/pdf/wps3814.pdf

Quah, D. (2002). One Third of the World’s Growth and Inequality. URL: http://eprints.lse.ac.uk/2019/1/One_Third_of_the_World's_Growth_and_Inequality.pdf

Ravallion, M. (1995) “Growth and poverty: Evidence for developing countries in the 1980s.” Economics Letters, Elsevier, vol. 48(3-4), 411-417, June.

Rutberg, S., & Bouikidis, C. D. (2018). Focusing on the fundamentals: A simplistic differentiation between qualitative and quantitative research. Nephrology Nursing Journal, 45(2), 209-213.