What are the reasons for the decline in Median Household Income?

Answer

• Esri’s estimates, as well as available survey data, are discrete. In other words, households are grouped into finite categories. Esri’s methods must balance change in the distribution of households across income categories, as well as capture the overall trend in the market in the measures of median and average household income. Change in household income is not necessarily even across the income distribution. Discrete distributions can show seemingly large shifts in households in a category, but the underlying change in a household’s income is small.
• For categorical income data, median income is a derived measure that relies upon Pareto interpolation. Small changes to a distribution, particularly a bimodal or sparse distribution, can result in large changes to the median.
• Socio-demographic trends in household income are also a key component of Esri’s income model. Trends in income are also assessed for broader socio-demographic groups. The effect of geographic shifts and demographic shifts in household income combine to inform small area estimates. In trade areas that are undergoing socio-demographic change, the median income change can be unexpected. A growing area does not necessarily suggest that median income should increase. An aging population will see decline in their current incomes as they begin to receive fixed Social Security income. A diversifying population is likely to see an influx of younger, lower income families settle in the area.
• Esri’s five-year forecasts of household income rely on a historical evaluation of real income growth, during recessionary and recovery periods of the business cycle. Inflation is a key input to the forecast year model. Five-year inflation forecasts for the 2016/2021 period were revised significantly downward to accommodate the near-zero inflationary environment at the time of update. With historically low growth at the national level, median income decline is to be expected in the forecasts in some areas. Include the average incomes when analyzing any area.
• Esri relies on geometric retrieval to create reports for non-standard geographic areas, such as rings and drive times. The size of the trade area is directly proportional to the accuracy of the estimates generated from this retrieval method. In other words, larger trade areas that include more whole Census block groups will yield results more representative of the area than smaller trade areas.