How The Irs Income Tax Data Fails as a Method for Measuring Income Inequality and Poverty

Introduction

The use of IRS income tax data for measuring income inequality or poverty is fundamentally flawed as IRS income tax data is designed to assist in collecting revenue and not for compiling demographic data. Tax data mostly disregards the role of age and cost of living differences and needs to be corrected for these factors in order to be useful. Additionally, IRS taxable income itself is a poor measure of real income and does not include or is distorted by retirement and college funds, capital gains, and home ownership.

Income Distribution Over a Lifetime

One of the fundamental flaws with IRS income tax data is that it is collected annually and assessed based on that annual data, whereas individuals’ quality of living and relative poverty plays out over 50 or more tax years. Citing an article from Tax Foundation, it states, “An American earning the average adjusted gross income (AGI) for his age ends up in all five of the AGI quintiles throughout his lifetime” (Tax Foundation, 2023). What this means is that for the truly average taxpayer, the income quintile they fall into depends primarily on age, and the income quintile measurements become a population age measure, rather than a poverty measure. Students especially skew data as students generally have low income, despite being more likely to be higher income earners than their peers who joined the workforce immediately.

Regional Cost of Living Differences

Additionally, the IRS deals only with pure income and does not account for regional differences in living costs. The example given in the article is the case of Oakland, CA, which has a median income of $51,700, just shy of the national average of $53,000. Based on pure IRS taxable income, Oakland would appear to be a middle-class town, but, when adjusted for the cost of living, average income is only $42,000. This significant discrepancy reveals how IRS data can misrepresent the economic reality of different regions (Tax Foundation, 2023).

Limitations and Additional Factors

The article did an excellent job of exposing the dangers of over-reliance on statistics and the limitations of economic thinking. However, I believe that there are additional factors not mentioned by the article that contribute to the murkiness of the data. For example, in the section on the inaccuracy of non-wage income measurement, black market income was not taken into account. There are several million undocumented/illegal immigrants in the US who wouldn’t be included in IRS tax data and likely millions more individuals paid cash under the table and tax-free by their employers, and yet more who earn their income from crime.

Supply, Demand, and Relocation Decisions

I found, in the section on unequal prices and cost of living between regions, an interesting example of the laws of supply and demand. Quoting a Tax Foundation article, it states, “[E]conomist Lyman Stone found that people relocate, on net, not to the places with higher nominal incomes but to the places with higher price-adjusted incomes” (Stone, 2023). This demonstrates the sum total of increased demand for lower-priced products and the importance of price-adjusted income over raw income in individual decision-making. If we assume that Stone is correct, then, if an individual has a choice of two jobs with the same salary, one in an area with a high RPP, one in an area with low RPP, individuals would tend to choose the area with low RPP.

Age and Relocation

I believe this flow from high RPP areas to low RPP areas could also tie in with the substantial income differences based on age. A fresh college graduate is unlikely to move to a high RPP area unless they have a high-paying job offer, but moving to a low RPP area and working a lower-paying entry-level job is more likely. As said young college grad advances and begins earning more money, moving to a high RPP area or remaining in the same area as RPP increases becomes more feasible, and high RPP less of a disadvantage. This trend highlights the dynamic nature of economic mobility and the strategic decision-making involved in career and life choices.

Conclusion

The article elicits no strong emotional response from me. Much of the article was within my knowledge beforehand. For example, the cost of living disparity and RPP differences between states and regions, I had some experience with spending vacations with relatives in California. Indeed, the prices of eating a delicious In-N-Out burger were greater than a similar burger in Illinois. I was also aware of the differences in how capital gains income is taxed and collected. This experiential knowledge reinforces the importance of understanding regional economic conditions in assessing income data.

References

Stone, L. (2023). Relocation and Income: Economic Insights. Tax Foundation. Retrieved from https://taxfoundation.org

Tax Foundation. (2023). Understanding AGI and Income Inequality. Retrieved from https://taxfoundation.org