• Gini coefficients published by the United States Census Bureau indicate Utah has the third most equally distributed incomes in the U.S.
• The Ogden-Clearfield, UT (Weber, Morgan, Box Elder and Davis counties) metropolitan statistical area (MSA) shows the most equally-distributed incomes of any U.S. metro area.
• The Provo-Orem, UT (Utah and Juab counties) MSA also ranked in the top 10 with the most equally distributed incomes.
• Recent studies suggest income equality in developed countries is correlated with strong, consistent growth in gross domestic product.
• In the short-term, job growth shows no significant correlation with income distribution on a state or county basis.
• In most states and counties, income distribution appears to have become more unequal in recent years.
• Counties with significant mining employment tend to show more income-distribution equality.
Who gets the money? Measures of income equality attempt to quantify how income is dispersed throughout the population. In the U.S., those measures indicate that income inequality has been rising for several decades. In other words, a smaller and smaller percentage of the population is getting a larger and larger share of total income. Value judgments aside, how does income inequality affect the economy?
While rising income inequality is apparent, what’s not so clear is the effect of that inequality on economic growth. For decades, economists have debated and studied this relationship. Are unequal incomes good or bad for growth? Some studies (and theories) suggest a negative correlation between income inequality and growth in gross domestic product (GDP) while others find a positive relationship. Recent studies shed further light (or perhaps confusion) on the topic. However, they do indicate that in developed countries, consistent economic expansion is associated with more equally distributed incomes.
An International Monetary Fund study suggests that “equality appears to be an important ingredient in promoting and sustaining growth. The difference between countries that can sustain rapid growth for many years or even decades, and many others that see growth spurts fade quickly, may be the level of inequality.” In other words, income equality is linked to consistent, rapid economic growth.
A working paper from the World Bank found that income inequality has a significant negative effect on per capita gross domestic product (GDP) growth, but that that impact varies by the level of a country’s level of economic development. The study suggests that in poor countries, income inequality actually showed a positive effect on per capita GDP, while in richer countries, income equality was tied to more rapidly expanding GDP.
Finally, a recent study by the Organization for Economic Co-operation and Development (OECD) found that “the long-term increase in income inequality has curbed economic growth significantly.” Notable income equality in Utah Because of our notable income equality,
Utahns may want to place their hopes in that positive relationship between income equality and economic growth. In 2014, Utah ranked third among all states for equally distributed incomes. In addition, because it showed the highest income equality among U.S. metro areas, the Ogden-Clearfield, UT MSA was recently the focus of a Newsweek article. The Provo-Orem, UT MSA also ranked among the top 10 areas nationally for egalitarian incomes.
Defining income equality
How do we measure income equality? Typically, economists use the Gini coefficient. This is the statistical brainchild Italian Corrado Gini hatched in 1912. In the briefest possible terms, the Gini coefficient is a ratio used to measure income inequality. Gini coefficients range between “0” (everyone has the exact same income) and “1” (one person has all the income). A lower Gini coefficient indicates more equally distributed incomes, while a higher Gini coefficient signifies a more unequal dispersal.
Use the data visualization in this post to explore the different Gini coefficients for states and counties within Utah.
Remember that income does not equal wealth. Income measures revenue streams received by individuals including wages, profits, rents, pensions, government transfer payments, etc. (i.e., monies we receive). Wealth represents assets or monies we own. One could own a lot of property (wealth), but have little income (and vice versa). The Gini coefficient measures only income not wealth.
The Gini coefficient also says nothing about an area’s level of income. Two areas with identical Gini coefficients may maintain vastly different average/median incomes.
Utah’s place in the nation
According to data collected by the Census Bureau in the American Community Survey, Utah displayed the third lowest Gini coefficient in the nation in 2014. Utah’s Gini coefficient measured 0.4283 compared to the average U.S. figure of 0.4804. However, for the most part, there isn’t a particularly wide variation in state coefficients. Interestingly, the District of Columbia, New York state, Connecticut and Louisiana show the most unequal income distributions, while all the most-equal income distributions can be found in the West (in particular the Intermountain West) — Alaska, Wyoming, Utah and Idaho. States with less-equally distributed incomes tend to be located in the South and along the Eastern Seaboard. On the other hand, states with the most equally distributed incomes are generally located inland and north of the Mason-Dixon Line.
No correlation between income distribution and job growth in the short run
The lack of a good and easily accessible historical series limits the analysis of economic growth and income among states. Using job growth as a proxy for economic expansion, there appears scant correlation between a state’s Gini coefficient and growth in recent years. State job growth between 2006 and 2014 (which includes more than a full business cycle) seems more closely associated to the recent oil and gas boom than income equality. Many of the states with the most rapidly expanding employment bases (e.g., North Dakota, Texas, Utah, Colorado, Alaska) owe at least part of their expansion to resource-based industries.
Using the limited historical data from the American Community Survey (ACS), inequality appeared to increase in most states between 2006 and 2014. Only two areas — Hawaii and the District of Columbia — showed a decrease in their Gini coefficients during that eight-year period.
Counting county income equality
Within Utah, counties display a broad range of Gini coefficients. Use of the 2009-2013 ACS averages is necessary in order to obtain coefficients for all counties. Interestingly, the county with the lowest Gini coefficient — Morgan County, 0.3345 — shares a border with the county registering the highest Gini coefficient — Rich County, 0.4699. Not surprisingly, Summit County with its high-income residents and low-wage service workers shows the second highest Gini index in the state. Geographically, a clump of counties with relatively equal incomes can be found in the northwest side of the state.
As in the United States, there appears to be little correlation between a low Gini index and stronger job growth (or vice versa) among Utah counties during the most recent business cycle. On the other hand, counties with resource-dependent economies (and their associated higher-than-average wages) tend to display more equally-distributed incomes (e.g., Uintah, Duchesne and Emery counties). And, some of these counties (notably Uintah and Duchesne) have experienced strong job growth in recent years. However, in this case, higher mining wages seem to be the chicken that came before the lower Gini-coefficient egg. Moreover, these counties are currently losing employment rather than gaining it.
Population density does not seem to correlate with income equality. Several small counties have relatively unequal incomes while some population-dense counties show more income equality. Among counties with the largest populations, north beats out south. Weber and Davis counties generate more-equal income distributions that Salt Lake and Utah counties.
Because of the averaging method of ACS survey data for small counties, comparing county Gini coefficients over time is problematic. In fact, at this point in time, a comparison of averaged years requires comparing overlapping years (2006-2010 and 2009-2013). In addition, changes between the two coefficients are often within the margin of error for a particular area. However, this limited comparison does seem to suggest that income distributions in a majority of Utah’s counties have become less egalitarian over time. Interestingly, Wayne County which lost its largest employer during the time frame saw the largest gain in inequality. On the other hand, Kane County incomes appear to have become significantly more equally distributed.