Using IRS income tax data to measure income inequality or poverty is fundamentally incorrect because IRS income tax data is designed to assist in revenue collection and not for compiling demographic data. Tax data in most cases ignore the role of age and cost-of-living differences and must be adjusted for these factors to be useful. Additionally, IRS taxable income itself is not an adequate measure of real income and does not include or is distorted by retirement and college funds, capital gains, and homeownership. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original EssayOne of the fundamental flaws of IRS income tax data is that it is collected annually and evaluated based on that annual data, while individuals' quality of life and relative poverty manifest themselves over 50 or more fiscal years. Citing an article from taxfoundation.org, the article states, “An American earning the average adjusted gross income (AGI) for his or her age ends up in all five AGI quintiles over his or her lifetime.” This means that, for the truly average taxpayer, the income quintile they fall into depends primarily on age, and income quintile measurements become a measure of the age of the population, rather than a measure of poverty. Students especially skew the data as they generally have a low income, despite being more likely to earn more than their peers who immediately entered the workforce. Furthermore, the IRS only deals with pure income and does not take into account regional differences in the cost of living. The example given in the article is the case of Oakland CA, which has a median income of $51,700, just under the national average of $53,000. Based on pure IRS taxable income, Oakland would appear to be a middle-class city, but, when adjusted for cost of living, the median income is only $42,000. The article did a great job exposing the dangers of over-reliance on statistics and the limitations of economic thinking. However, I believe there are additional factors not mentioned in the article that contribute to the darkness of the data. For example, in the section on the inaccuracy of measurement of non-wage income, black market income was not taken into account. There are several million undocumented/illegal immigrants in the United States who would not be included in IRS tax data, and likely millions more individuals who are paid cash under the table and exempt from taxes by their employers, and even more who earn the their income from crime. I discovered, in the section on the inequality of prices and the cost of living between regions, an interesting example of the laws of supply and demand. Citing an article from taxfoundation.org, the article states that “economist Lyman Stone found that people move, online, not to places with higher nominal incomes but to places with higher incomes adjusted for prices.” This demonstrates the sum total of increased demand for low-priced products and the importance of price-adjusted income versus raw income in individual decision making. If we assume that Stone is right, then if an individual has a choice between two jobs with the same salary, one in an area with a high RPP, one in an area with a low RPP, individuals would tend to choose the area with a low RPP. RPP. I believe this flow from high RPP areas to low RPP areas could also.