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Economics

 

[Note: Text that references rankings, charts, and data will be updated as work progresses on the 2020 Family Prosperity Index]

 

The Economics major index involves a complicated calculation of the factors that most directly affect the bottom line of family budgets: income and the means by which it is earned—jobs. These two data points go a long way—but not all the way—toward determining the prosperity of families in a given state. Other factors must be considered as well, including how and where income is earned.

 

Personal income can come from two sources: the private sector and the public sector. The distinction between the two sectors is important because only the private sector creates new income. The public sector, in contrast, can only redistribute income through taxes and spending. Public sector spending consists of direct transfers of money from taxpayers at large to people who qualify for entitlement programs (Medicare, Medicaid, Social Security, etc.) and government employee compensation, which occurs at the federal, state, and local levels.

 

There is a significant positive correlation between per household personal income and the share of income in a state that comes from the private sector.[1] Put simply, the larger the private sector in a particular state in comparison to the whole economy, the greater the per household personal income there. By contrast, the less important the private sector is, the lower the income; a one-percentage-point decrease in the size of the private sector is associated with a decrease in per household income of approximately $3,300.[2]
 

Of course, correlation does not equal causation, so the numbers do not, by themselves, prove that a larger role for government makes people poorer. But two states in the Northeast allow for a very strong natural comparison that does suggest that more government produces poverty. They are New Hampshire and Maine. These two states are similar in many ways—geography, climate, demographics, and culture—but they diverge significantly in their approach to public policy.

 

Between 1929 and 1950, Maine and New Hampshire had similar per household incomes, and the private sector was roughly the same percentage of each state’s total income (Chart 3). In 1951, though, Maine enacted a sales tax, which increased public sector spending and crowded out the private sector. After that point, New Hampshire’s per household income began to steadily pull away from Maine’s.

 

This trend accelerated in 1969 when Maine enacted an income tax. With this new source of revenue, Maine’s government was able to dramatically expand its welfare system—especially Medicaid, which the federal government put in place a few years before. In a 2015 ranking of the states based on the share of their population on Medicaid, Maine was ninth at 23 percent.[3]

 

In stark contrast, New Hampshire remains the only state in the Union not to have enacted a state or local sales tax or a state or local income tax (see Family Self-Sufficiency).

 

This difference in public policy between these two states has resulted in dramatic differences in the size of their private sectors. Between 1929 and 2016, Maine’s private sector shrank by 29 percent, going from 92 percent of personal income to 65.6 percent, giving it only the 41st-largest private sector in the country. The private sector in New Hampshire shrank in significance as well, but the decline was much smaller, with it going from 90.4 percent of the economy to 76.3 percent. New Hampshire now places second nationally in that measurement.

 

As a share of the state’s economy, the private sector in New Hampshire is 16 percent bigger than its counterpart in Maine—76.3 percent, compared to 65.6 percent. Consequently, New Hampshire’s per household income in 2016 is 36 percent higher than Maine’s—$136,891 and $101,020, respectively.

 

This difference in household income matters because it is an important economic measure of a family’s well-being. Higher levels of personal income mean that a family is able to buy more goods and services such as a home, a car, education, and healthcare.

 

We have adjusted the data on personal income in three ways to make comparisons across states and over time more valid:

 

First, personal income has to be adjusted for inflation, which erodes purchasing power over time. The data is shown in constant 2016 dollars.

 

Second, personal income has to be adjusted for differences in demographics. We divide the statewide data by the number of households, which, unlike the number of families, is tracked by the U.S. government. Comparing states on per capita personal income, by contrast, gives a statistical boost to states with an older population and fewer children. So for the purposes of the index, we use the household as an approximation for the family.

 

Third, income must also be adjusted for differences in purchasing power. It is common knowledge that the price of goods and services, especially housing, is generally higher in urban areas than in rural areas. Therefore, in nominal or unadjusted terms, states that have high nominal household personal income are also very likely to be high cost-of-living areas and vice versa.[4]
 

 

Of course, income must be earned and, for the vast majority of people, that comes through having a job. But jobs don’t appear out of thin air. They result from entrepreneurship. So understanding the strength of entrepreneurship in a state is essential to understanding how strongly jobs grow there — or don’t. As economist Tim Kane puts it:

 

“The oft-quoted American sports slogan, ‘Winning isn’t everything. It’s the only thing!’ could well be attributed to the economic importance of firm formation in creating jobs. A relatively new dataset from the U.S. government called Business Dynamics Statistics (BDS) confirms that startups aren’t everything when it comes to job growth. They’re the only thing.”[5]

 

Unfortunately, entrepreneurship has been in decline since the 1970s. Between 1977 and 2015, establishment births (new businesses) are down 40 percent and job births (new jobs) are down 48 percent. A study by the Economic Innovation Group has quantified the number of forgone jobs:

 

On average, each new business creates six new jobs in its first year. The U.S. economy launched 154,000 fewer companies in 2014 than it did in 2006—despite being 9.4 percent larger in terms of real GDP. That translates into 924,000 missing jobs in new companies in 2014 alone—setting aside the question whether established companies stepped in to fill any of the void. The cumulative jobs deficit from firms that were never born over the past eight years is even larger: 3.4 million jobs in 2014 by our conservative estimate.[6]

 

The study went on to identify several factors that have contributed to less entrepreneurship.  They included declining population growth; reduced startup capital due to less home equity; banking consolidation; regulatory burdens and tax complexity; the increasing use of noncompete agreements; occupational licensing; and the rising cost of living, among others. Occupational licensing, in particular, is a barrier to economic mobility and opportunity.[7]

 

Interestingly, the decline in entrepreneurial activity coincides with the breakdown in the family. Is this a coincidence? Perhaps not. A recent study found that “dinner table human capital” plays a critical role in entrepreneurial formation and success. In the words of its authors:

 

We document three new facts about entrepreneurship. First, a majority of male entrepreneurs start a firm in the same or closely related industry as their fathers’ industry of employment. Second, this tendency is correlated with intelligence: higher-IQ entrepreneurs are less likely to follow their fathers. Third, an entrepreneur that starts a firm in the same 5-digit industry as where his father was employed tends to outperform entrepreneurs in the same industry whose father did not work in that industry. We consider various explanations for these facts and conclude that “dinner table human capital”, where children obtain industry knowledge through their parents, is an important factor behind what type of firm is started and how well it performs.”[8]

 

The decline in entrepreneurship means that fewer people are employed by entrepreneurs. It also means that an important source of new-job growth has become less potent.

 

We are accustomed to thinking that a person is either employed or unemployed. But there are many shades of unemployment and, recognizing that, the Bureau of Labor Statistics has developed six different measures of unemployment, which it calls “Alternative Measures of Labor Utilization.”

 

For example, the breadwinner of a family fighting hard to make ends meet might be forced to take a part-time job in lieu of more stable full-time work. Economists refer to this as underemployment and it is captured in the “U6” measure, which is the broadest measure of un/underemployment.

 

Unemployment often imposes a financial shock on the family, but it can have unexpected costs as well. For instance, a recent study found that a 1-percentage-point increase in the unemployment rate in a county increased the opioid death rate there by 3.6 percent. The rate of emergency room visits for opioid overdoses, meanwhile, goes up by 7 percent when that happens.[9]

 

There is a growing segment of people, especially men, who are opting out of the workforce altogether. Evidence suggests that the rise in disability payments explains much of this practice. Existing measures of employment may need to be supplemented in the future.[10]

 

As shown in Chart 4 and Table 2:

 

Private Sector Share of Personal Income

 

The private sector share of personal income (or from here on, simply “private sector”) fell nationally by 5.5 percent from 75.6 percent in 2000 to 72.1 percent in 2018 (Chart 5). Of course, the private sector is still rebounding from the Great Recession and is likely to continue its improvement, albeit slowly, in the coming years.[11]

 

At the same time, states vary greatly when it comes to how important the private sector is to their economy. In 2018, the private sector composed 78.8 of the economy in Connecticut, the largest percentage of any state. At the other end of the scale was West Virginia, with a comparable number of 59.2 percent— or 76 percent of the Connecticut rate.

 

Chart 5 Private Sector Share of Personal Income 2000 to 2018.jpg

 

Nationally, the private sector’s importance has decreased 23 percent between 1929, when it accounted for 93 percent of personal income, to 2016, when it accounted for 71 percent (Chart 6). The chart also illustrates how the leading states of today compare to the U.S. average in past years.

 

For example, with 77.9 percent of its personal income being in the private sector, Connecticut was the highest-ranked state in 2016. That is equivalent to the U.S. average of 1967. Colorado’s private sector percentage (75 percent, ranked 5th) is equal to the 2000 U.S. average, and Washington’s number (72 percent, ranked 15th) is equal to the 2008 U.S. average.

 

In 31 states, the private sector is less important than it was in the country as a whole in 2016. West Virginia’s private sector share (57.3 percent) was the lowest in the country, and 19 percent below the U.S. average of 71 percent. It was also lower than the national private sector share at any point during the 1929-to-2016 time period.

 

Connecticut had the top score for the private sector sub-index (7.80), followed by New Jersey (7.35), Massachusetts (7.24), Illinois (7.24), and Utah (7.17). West Virginia had the lowest sub-index score (0.00). Other low-scoring states included New Mexico (0.68), Mississippi (1.14), Alaska (2.52), and Kentucky (2.78).

 

Real, Per Household Personal Income

 

Real, per household personal income increased nationally by 12 percent between 2000 and 2016, going from $115,451 to $128,892 (Chart 7). Given the correlation between the importance of the private sector and personal income, it should be no surprise that in 2016, Connecticut had the highest level of personal income at $175,465. It should also be no surprise that West Virginia had the lowest level at $85,950—or 49 percent of Connecticut’s income.[12]

 

Chart 7 Real Per Household Personal Income 2000 to 2018.JPG

 

Two states scored a perfect 10 for the personal income sub-index: California and Massachusetts. They were followed by Connecticut (9.98), New Jersey (9.92), and New York (8.97). West Virginia had the lowest personal income sub-index score (0.31). Other low-scoring states included New Mexico (1.13), Mississippi (1.36), Alabama (2.12), and Kentucky (2.15).

 

Cost of Living

 

The cost of living varies greatly across the 50 states (Chart 8). In 2017, Hawaii had the highest cost of living with an index value of 118.8, while Mississippi had the lowest cost of living with an index value of 85.2— or 72 percent of Hawaii’s value.[13]

 

Chart 8 Cost of Living 2008 to 2017.JPG

 

Alabama had the top score for the cost of living sub-index (7.40), followed by Mississippi (7.37), Arkansas (6.91), Missouri (6.76), and South Dakota (6.74). Hawaii had the lowest score (0.15). Other low-scoring states included New York (1.24), California (1.50), New Jersey (1.73), and Connecticut (2.46).

 

Note: Due to data limitations, the measure for the year-to-year change in the cost of living could only be measured in one-year increments, rather than in five-year increments, which would provide a more robust set of data.

 

Entrepreneurship

 

Charts 9 and 10  show how the various measures of entrepreneurship (establishment births and job births) changed nationally and varied across the 50 states from 2000 to 2015.[14]

 

Establishment births (as a percent of total establishments) decreased nationally by 11 percent from 11.4 percent in 2000 to 10.1 percent in 2015 (Chart 9). In 2015, Nevada had the greatest level of establishment births at 12.8 percent of all businesses, while West Virginia had the lowest level at 7.7 percent— or 60 percent of the Nevada rate.

 

Also, Chart 10 illustrates how establishment births have decreased 40 percent between 1977 (16.8 percent of all establishments) and 2015 (10.1 percent). It also compares how establishment births for the states in 2015 compared to the U.S. average over a recent 38-year period.

 

For example, establishment births played the largest role in Nevada in 2015, where they made up 12.8 percent of all establishments. But that was also equal to the U.S. average in 1997. Wyoming’s establishment birth rate in 2015 (10 percent, ranked 18th) was equal to the 2008 U.S. average.

 

In 33 states, the rate of establishment births was less than the U.S. average for 2015. West Virginia’s establishment birth rate (7.7 percent)—the lowest in the country—was 23 percent below the U.S. average (10.1 percent). It was also lower than the national establishment birth rate at any point during the 1977-to-2015 time period.

 

Job births (as a percent of total jobs) decreased nationally by 26 percent from 6.3 percent in 2000 to 4.6 percent in 2015 (Chart 11). In 2015, New Mexico had the greatest level of job births in the nation at 6.7 percent, while Wisconsin had the lowest level of at 3.3 percent— or 49 percent of the New Mexico rate.

 

Also, Chart 12 illustrates how the incidence of job births has decreased 48 percent between 1977 (8.9 percent of all jobs) and 2015 (4.6 percent). It also shows how establishment births for the states in 2015 compared to the U.S. average over time.

 

For example, jobs births were most important in New Mexico, where they represented 6.7 percent of all jobs in 2015. But the U.S. reached that number in 2003. The significance of jobs births to Indiana’s number of jobs (4.3 percent, ranked 25th) is equal to the 2011 U.S. average.

 

In 34 states, the number of job births as a percent of all jobs was below the 2015 U.S. average. Jobs births in Wisconsin (3.3 percent)—the lowest in the country—were 29 percent below the U.S. average (4.6 percent). That is also lower than the measurement for national job births at any point during the 1977- to-2015 time period.

 

Nevada had the top score for the entrepreneurship sub-index (10.00), followed by Florida (9.88), California (9.53), Delaware (9.28), and Utah (8.93). Iowa had the lowest score (1.00). Other low-scoring states included Vermont (1.42), Hawaii (1.67), Wisconsin (1.70), and Nebraska (2.19).

 

Note: The establishment births and job births were weighted equally in the entrepreneurship sub-index.

 

Unemployment

 

Charts 13 through 18 show how unemployment rates changed from 2003 (the first year of available data), both nationally and in the 50 states.[15] When economists study unemployment, they may mean one of six different measurements, which we describe below. The FPI gives each state a score based on a mixture of these measurements. The note at this section describes the mix.

 

The U1 unemployment rate (Chart 13) shows the number of people unemployed for 15 weeks or longer as a percent of the civilian labor force. U1 declined nationally from 2.3 percent in 2003 to 2 percent in 2016, a decline of 14 percent. In 2016, Illinois had the highest U1 unemployment rate at 2.9 percent, while South Dakota had the lowest rate at 0.8 percent— or 29 percent of the Illinois rate.

 

The U2 unemployment rate (Chart 14) measures the number of people who lost their job or took a temporary job as a percent of the civilian labor force. U2 decreased nationally from 3.3 percent in 2003 to 2.3 percent in 2016, a decline of 29 percent. In 2016, Alaska had the highest U2 unemployment rate at 3.8 percent, while South Dakota had the lowest rate at 1 percent— or 26 percent of the Alaska rate.

 

The U3 unemployment rate (Chart 15) measures the number of unemployed people as a percent of the civilian labor force. This is the official unemployment rate and the measure commonly cited in media outlets. U3 decreased nationally from 6 percent in 2003 to 4.9 percent in 2016, a decline of 19 percent. In 2016, New Mexico had the highest U3 unemployment rate at 6.8 percent, while New Hampshire had the lowest rate at 2.8 percent— or 41 percent of the New Mexico rate.

 

The U4 unemployment rate (Chart 16) measures the number of unemployed people plus the number of discouraged workers as a percent of the civilian labor force. U4 decreased nationally from 6.3 percent in 2003 to 5.2 percent in 2016, a decline of 17 percent. In 2015, New Mexico had the highest U4 unemployment rate at 7.3 percent, while New Hampshire had the lowest rate at 3 percent— or 41 percent of the New Mexico rate. (A “discouraged worker” is someone who has stopped looking for work since they believe there are no jobs available for them or that they would not qualify.)

 

The U5 unemployment rate (Chart 17) measures the number of unemployed people plus discouraged workers plus all other “marginally attached workers.” (A “marginally attached worker” is someone who has spent time looking for a job in previous year, but not within the last month.) U5 declined nationally from 7 percent in 2003 to 5.9 percent in 2016, a decline of 16 percent. In 2016, Alaska had the highest U5 unemployment rate at 8.4 percent, while South Dakota had the lowest rate at 3.5 percent— or 42 percent of the Alaska rate.

 

The U6 unemployment rate (Chart 18) measures the number of unemployed people plus all marginally attached workers plus people employed on a part-time basis for economic reasons (that is, they want but can’t find full-time employment) as a percent of the civilian labor force. U6 decreased nationally from 10.1 percent in 2003 to 9.6 percent in 2016, a decline of 5 percent. In 2016, Alaska had the highest U6 unemployment rate at 12.8 percent, while South Dakota had the lowest rate at 5.2 percent— or 41 percent of the Alaska rate.

 

New Hampshire had the top score for the unemployment sub-index (8.69), followed by South Dakota (8.62), Colorado (8.19), Hawaii (8.13), and Vermont (7.60). Alaska had the lowest score (0.60). Other low-scoring states included New Mexico (0.64), Louisiana (1.68), West Virginia (1.98), and Alabama (2.53).

 

Note: U3 was weighted 50% of sub-index while U1, U2, U4, U5, and U6 were weighted equally (10%) for the remainder of the unemployment sub-index.

 


[1]      Public sector activity crowds out private sector activity. For one example, see: Moody, J. Scott, “Expanding Medicaid Will Hurt North Carolina’s Families, Lower Income, and Reduce Jobs,” Federalism In Action, No. 5, March 23, 2015.

 

[2]      Alaska and Hawaii are excluded, as is common practice in state analysis, due to their unique economic characteristics.

 

[3]      “Medicaid State Fact Sheets,” Kaiser Family Foundation, June 16, 2017. https://www.kff.org/interactive/medicaid-state-fact-sheets/

 

[4]      The cost-of-living variable is often overlooked in policy discussions. For instance, the federal tax code adjusts for inflation, but does not do the same for the cost of living. As a result, federal tax payments can vary dramatically even if the real purchasing power of one’s income is the same.

 

[5]      Kane, Tim, “The Importance of Startups in Job Creation and Job Destruction,” Ewing Marion Kauffman Foundation, July 2010. http://www.kauffman.org/~/media/kauffman_org/research%20reports%20and%20covers/2010/07/firm_formation_importance_of_startups.pdf

 

[6]      “Dynamism in Retreat: Consequences for Regions, Markets, and Workers,” Economic Innovation Group, February 2017. http://eig.org/wp-content/uploads/2017/07/Dynamism-in-Retreat-A.pdf

 

[7]      Meehan, Andrew; Meehan, Brian; and Timmons, Edward, “Barriers to Mobility: Understanding the Relationship between Growth in Occupational Licensing and Economic Mobility,” Archbridge Institute, November 2017. https://www.archbridgeinstitute.org/wp-content/uploads/2017/11/Barriers-to-Mobility.pdf

 

[8]      Hvide, Hans K. and Oyer, Paul, “Dinner Table Human Capital and Entrepreneurship,” National Bureau of Economic Research, Working Paper 24198. http://www.nber.org/papers/w24198

 

[9]      Hollingsworth, Alex; Ruhm, Christopher J.; and Simon, Kosali, “Macroeconomic Conditions and Opioid Abuse,” Journal of Health Economics. Volume 56, December 2017, pp. 222-233. https://www.sciencedirect.com/science/article/pii/S0167629617303387?via%3Dihub

 

[10]  Winship, Scott, “What’s behind Declining Male Labor Force Participation: Fewer Good Jobs or Fewer Men Seeking Them?” Mercatus Center, 2017. https://www.mercatus.org/system/files/winship_malelaborparticipation_mr_v2.pdf

 

[11]     Regional Data, U.S. Department of Commerce: Bureau of Economic Analysis. http://www.bea.gov/itable/iTable.cfm?ReqID=70&step=1#reqid=70&step=1&isuri=1

 

[12]     Regional Data, U.S. Department of Commerce: Bureau of Economic Analysis. http://www.bea.gov/itable/iTable.cfm?ReqID=70&step=1#reqid=70&step=1&isuri=1

 

[13]     Regional Data, U.S. Department of Commerce: Bureau of Economic Analysis. http://www.bea.gov/itable/iTable.cfm?ReqID=70&step=1#reqid=70&step=1&isuri=1

 

[14]     Business Dynamics Statistics, U.S. Department of Commerce: Census Bureau. http://www.census.gov/ces/dataproducts/bds/data_estab.html

 

[15]     “Alternative Measures of Labor Underutilization for States,” U.S. Department of Labor: Bureau of Labor Statistics. http://www.bls.gov/lau/stalt_archived.htm