Special Report - Grit Won’t Quit: How Hiring for Passion + Perseverance May Help Reduce Turnover
CENTRAL WISCONSIN
FALL 2020 REPORT
GRIT WON’T QUIT How Hiring for Passion + Perseverance May Help Reduce Turnover by Nik Butz, Ph.D. and Reed Stratton, Ph.D. UWSP
Interesting (and Exciting) Times
Table of Contents
The Center for Business and Economic Insight staff is very excited about this digital version of our fall report!
Divisiveness and The Economic Challenges of 2021.......1-6 Kevin M. Bahr, CBEI Chief Analyst Economic Indicators.........................................................7-12 Scott Wallace, CBEI Director and Editor National Economic Statistics.............................................7-9 Table 1: Key Economic Indicators Table 2: Contributions to Percent Change in Real Gross Domestic Product Table 3: Lifecycle of the Expansion Labor Market Statistics....................................................... 10 Table 4: Labor Market Indicators from LAUS Table 5: WI Employment by Major Industry Sector County Economic Statistics. ...............................................11 Table 6: Help Wanted Advertising Table 7: Unemployment Claims Table 8: County Sales Tax Distribution Housing and Construction...................................................12 Table 9: National Affordability Index Table 10: Median Home Prices and Home Sales Table 11: Residential Construction Table 12: Nonresidential Construction Special Report................................................................ 13-19 Grit Won’t Quit: How Hiring for Passion + Perseverance May Help Reduce Turnover Nik Butz, Ph.D. and Reed Stratton, Ph.D. Column: Insight Spotlight.............................................. 20-21 Release the Data Analyst! Kurt Pflughoeft, Ph.D. Column: Inner View........................................................ 22-23 Thousand Lumens Productions Emma Fisher Column: Talent Matters....................................................... 24 Lyna Matesi, Ph.D. UW-Stevens Point MBA Program. ................. Inside Back Cover
Chief Analyst Kevin Bahr addresses the significant economic challenges America faces in 2021 during a time of extreme social and political divisiveness, focusing on the continued effects of COVID-19 and the
rapid growth in budget deficits and debt. Kevin will be exploring other economic challenges in his blog post which can be found at bit.ly/uwspcbeiblog . The Economic Indicators Report reveals the dramatic and rather unusual effects of COVID-19 on the national, state and regional economies. School of Business and Economics faculty Nik Butz and Reed Stratton, with the assistance of SBE students Abby Schmidt and Emily Gruber, present their research on the impact of grit on employee turnover for our Special Repor t. Here, you can find the slides from their presentation at the December 11 CBEI webinar meeting. This issue’s Insight Spotlight features Prof. Kurt Pflughoeft, director of the Center for Data Analytics, illustrating the tremendous value of data analytics in contributing to business success. In Inner View , CBEI Senior Research Assistant Emma Fisher talks with local entrepreneurs Jade Arnold and Zach Strenger about their business, Thousand Lumens Productions, which creates marketing videos for its clients. Our next presentation in May 2021 will be hosted on Zoom and our next report (also in May) will also be distributed electronically. The CBEI staff looks forward to the day when we can resume our normal, face-to-face biannual breakfast meetings. Let’s hope that the wide distribution of a vaccine next year will allow us to do so in December 2021!
CBEI Mission
CBEI Staff
The UW-Stevens Point Center for Business and Economic Insight (CBEI) promotes regional economic and community development through the provision of business and economic knowledge to local business, governmental, and community leaders. The primary areas of focus are Portage, Marathon and Wood counties.
Scott Wallace.................................... Director and Editor, CBEI Kevin Bahr................................................... Chief Analyst, CBEI Emma Fisher.........................Senior Research Assistant, CBEI Eva Donohoo................................... Publication Designer, CPS
The Central for Business and Economic Insight is made possible thanks to support from the UW-Stevens Point School of Buinsess and Economics.
The UW-Stevens Point School of Business and Economics creates career-ready graduates and leaders through applied learning. We serve the businesses, economy and people of the greater Central Wisconsin region. We specialize in preparing students for success by providing professional development experiences, access to employers, and in-demand skills.
Divisiveness and The Economic Challenges of 2021
Kevin M. Bahr CBEI Chief Analyst; Professor of Business School of Business and Economics
The divisiveness in America is significant and undeniable. It stretches across politics, race, gender, religion, and economic issues. This article takes a look at a few of the economic challenges of 2021, and also incorporates the divisiveness issue into the discussions. Check out the CBEI blog on uwsp.edu/cbei for an expanded discussion of the economic challenges of 2021. For many economic challenges, the overall economic health of the country would greatly benefit if divisions could be reduced. Not differences, but divisions. The blog includes links to the referenced sources for further information. Challenges can be met and the United States can accomplish much when political divisions are set aside and the focus is on helping the American people. The Bipartisan Policy Center provides an impressive list of historic accomplishments when Democrats and Republicans worked together to improve the lives of Americans. Listed below are some of those accomplishments: • 1964 Civil Rights Act – A civil rights bill proposed by congressional Democrats and supported by the Johnson administration needed significant bipartisan support to pass and become law. Democratic majority leader Mike Mansfield worked with his counterpart, Republican Senator Everett Dirksen, to get the bill passed. • 1969 Moon Landing - The National Aeronautics and Space Administration (NASA) was created in 1958 through bipartisan Congressional support. NASA led the development of the U.S. space program, resulting in the 1969 launch of Apollo 11 and Neil Armstrong walking on the moon. • 1983 Social Security Reform – In the early 1980s, the Social Security Trust Fund was trending toward a deficit. Party leaders Republican Senator Bob Dole and Democratic Senator Daniel Patrick Moynihan led a bipartisan group of legislators that were able to translate recommendations into legislation that financially strengthened and reformed Social Security. • 1990 Americans with Disabilities Act – Long overdue, the Act finally made it illegal to discriminate against people with disabilities. The Act was passed with bipartisan support; Republican Senator Bob Dole and Democrat George Mitchell played leading roles in the Act’s passage. • 2010 Tax Deal – The 2003 Bush Tax cuts were set to expire at the end of 2010, unless extended by Congress. The Obama administration worked a bipartisan compromise that kept the tax cuts in place but increased the rate in the top tax bracket.
And now, the challenges for 2021.
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Challenge #1 – COVID-19 and the 2021 Economy
The primary driver of U.S. economic growth in 2021 will likely be a repeat of 2020: COVID-19. The importance of the interrelationship between individual health, healthcare, and the economy has never been so visibly apparent. The United States economy typically chugs along at a pretty good pace unless there is a bump or shock to derail its progress. The key is to get the economy moving forward – precipitating a snowballing effect, where economic growth continues until something happens to stop it. When economic growth occurs, increased employment leads to more consumer spending, which leads to more economic growth. Consumer spending is generally the primary driver of U.S. economic growth, as it comprises approximately two-thirds of Gross Domestic Product (GDP). GDP is the benchmark for economic growth and measures the value of goods and services produced in a given period. Likewise, a snowballing effect can occur in the opposite direction. If consumer spending declines economic contraction continues until something happens to reverse it. If something happens to derail consumer spending, that’s where fiscal policy (spending by the U.S. government) and/or monetary policy (the Federal Reserve reducing interest rates) can be used to put consumer spending back on track. Fiscal policy can include spending on specific programs by the federal government and stimulus programs featuring direct payments to individuals. Although a variety of factors influence interest rates, the Federal Reserve’s monetary policy (buying and selling Treasury securities) strongly influences the movement of interest rates. The Federal Reserve targets the fed funds rate, a very short-term interest rate, which is the overnight borrowing rate between banks. When the Federal Reserve changes this rate, there is generally a rippling effect on other interest rates in the financial markets. Lower interest rates generally increase consumer spending. Higher interest rates generally lower consumer spending. The goal of the Federal Reserve – balance economic growth with acceptable levels of inflation. The table below shows U.S. economic growth as measured by the annual percentage change in GDP over the last 3 decades. As stated earlier, generally the U.S. economy chugs along unless there is a bump or shock to derail it. Over the last three decades economic growth was positive in 27 out of 30 years.
Annual Percentage Change in GDP (Source: Bureau of Economic Analysis)
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
-2.5
2.6
1.6
2.2
1.8
2.5
2.9
1.6
2.4
2.9
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
4.1
1.0
1.7
2.9
3.8
3.5
2.9
1.9
-0.1
-2.5
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
1.9
-0.1
3.5
2.8
4.0
2.7
3.8
4.4
4.5
4.8
An economic bump occurred in 1991 due to the Federal Reserve increasing interest rates and an oil price shock. By 1990 the rate of inflation (as measured by the change in the Consumer Price Index) exceeded 5%; the Federal Reserve countered by increasing the fed funds rate to lower consumer spending and reduce inflation. It might seem hard to believe given the low interest rates of the last decade, but in July 1990 the fed funds rate stood at 8.0%. An oil price shock also occurred as oil prices more than doubled in 1990 in response to the Iraqi invasion of Kuwait. Decreasing inflation and a reversal of oil prices led to a slashing of interest rates by the Federal Reserve from 8.0% in July 1990 to only 3.0% in September 1992. Low interest rates and the dot.com boom, the rise of internet and technology companies, paved the way for strong economic growth in the 1990s with four years of economic growth of 4.0% or greater.
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The United States began the new century with a variety of bumps to the economy. The dot.com bubble was over, with overhyped tech and internet stocks crashing back to reality. The technology heavy Nasdaq index declined over 75% between March 2000 and October 2002. The September 11, 2001 terrorist attacks contributed to the economic decline, as uncertainty and fear gripped the economy. In addition, the financial markets were plagued by accounting scandals (Enron). Although GDP growth was positive for the entire 2001 year at 1.0%, economic growth was negative for two quarters resulting in a minor recession. Percent Change from Quarter One Year Ago - Real GDP (Source: Bureau of Economic Analysis) 2018/Q1 2018/Q2 2018/Q3 2018/Q4 2019/Q1 2019/Q2 2019/Q3 2019/Q4 2020/Q1 2020/Q2 2020/Q3 3.1 3.3 3.1 2.5 2.3 2.0 2.1 2.3 0.3 9.0 2.9 The Bureau of Economic Analysis (BEA) began tracking quarterly GDP growth data in 1947. The 2020 second quarter drop was the worst on record, and much greater than the worst quarterly decline during the financial crisis. When comparing economic growth to the previous quarter of a year ago, the 2020 second quarter decline of -9.0% was significantly worse than the largest financial crisis quarterly decline, which was -3.9% in the second quarter of 2009. Third quarter 2020 GDP was still 2.9% lower than a year ago. Once again, a combination of fiscal and monetary policy was used to combat the effects of an economic shock. The Federal Reserve had already reduced interest rates 3 times in 2019 as the economy slowed due to trade wars and the effects of the tax cuts subsiding. However, in 2020, the fed funds rate was once again reduced to a historical low of 0.00-0.25% to counter the effects of COVID-19. In March, a $2 trillion fiscal stimulus package was passed that more than doubled the $800 billion stimulus package that was implemented in 2009 to overcome the financial crisis. The fiscal and monetary measures would help combat the problem, but they couldn’t solve the problem. COVID-19 put the brakes on consumer spending, resulting in an economic downturn in 2020. Unfortunately, COVID-19 also became political. There became an illusion of an economic choice: 1) limit the growth of the pandemic by closing the economy down and minimizing economic growth, or 2) maximize economic growth by completely opening-up the economy and accepting the spread of COVID-19. In reality, COVID-19 was going to impact to impact the economy, whether the economy was closed down or opened-up. The primary factor to future economic growth and minimizing any economic downturn was not about re-opening the economy, but getting the virus under control. If Americans felt safe shopping, traveling, and spending at retail businesses, consistent economic growth would return. The promising vaccine news in November will hopefully lead to a quick return to more normal conditions; however, even distribution of the vaccine will take some time. In the meantime, appropriate policy planning could allow fiscal policy to be used preemptively to offset the damaging economic effects of the virus on individuals and businesses, especially small business. The greater the unity of Americans in fighting the virus, the faster the virus will be controlled and the faster consistent economic growth returns. That’s the key for this pandemic, and any future pandemic. COVID-19 was going to impact the economy; the question was how much and how long it would impact the economy. The quicker the virus was controlled then the quicker consistent economic growth would return. Exactly when that happens is the big question for 2021.
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Challenge #2 – Controlling U.S. Debt and Federal Budget Deficits The Federal Deficit
The federal budget deficit refers to U.S. federal government spending exceeding government income. The necessity of the $2 trillion COVID-19 stimulus contributed to a record federal budget deficit for fiscal year 2020 (year ended September). The graph below puts some historical perspective on the current deficit relative to previous years. The graph shows the U.S. federal budget surplus or deficit since 1940. The shaded areas of the graph indicate an economic recession. For fiscal year 2020 the deficit topped $3.1 trillion, more than double the $1.4 trillion deficit that resulted in 2009 from the financial crisis. Federal budget deficits didn’t really start appearing on a regular basis until the 1970s, with the deficit increasing through the first half of the 1980s. Generally, an expanding economy leads to a decrease in the U.S. federal government budget deficit as tax revenues increase. The economic expansion in the 1980s led to the deficit declining slightly in the late 1980s, and the strong economic expansion in the 1990s led to budget surpluses. Following the recession of the early 2000s and the financial and economic crisis of the late 2000s, the deficit once again was reduced. However, the tax cuts of 2018 contributed to a growing budget deficit, and the 2020 stimulus brought the deficit to record levels.
Federal Budget Surplus or Deficit Annual amount of Federal Budget Surplus or Deficit in Millions of Dollars (1/1/40-9/30/20) Source: Federal Reserve Economic Database (FRED); U.S. Office of Management and Budget
Federal Debt To finance a budget deficit the U.S. government borrows money from the public through the issuance of U.S. government debt securities called U.S. Treasury securities. Buyers include individuals, institutional investors, certain mutual funds, and foreign investors and governments. Regarding the foreign investors, Japan and China are by far and away the major foreign investors in U.S. Treasury securities. As of August 2020, Japan owned nearly $1.3 trillion of U.S. Treasury securities while China owned nearly $1.1 trillion. Japan’s holdings accounted for approximately 18% of the Treasury debt held by foreign investors while China accounted for approximately 15%. The graph below puts some historical perspective on the current deficit relative to previous years. The graph shows the total federal debt held by the public. From 2000 to 2009, the federal debt approximately doubled, from approximately $5.8 trillion to $12.3 trillion. In the past decade, the federal debt level nearly doubled again, from $12.8 trillion in 2010 to over $23.2 trillion in the further quarter of 2019. In 2020, a spike in the debt level occurred. By the end of the second quarter in 2020, debt approached $26.5 trillion, a 14% increase relative to the fourth quarter of 2019.
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Center for Business and Economic Insight
Federal Debt: Total Public Debt Amount of Federal Debt in Millions of Dollars (1966–2020 2nd qtr.) Source: Federal Reserve Economic Database (FRED) based on data from the U.S. Treasury
Federal Debt and Income To better gauge the magnitude of federal debt outstanding, the amount of federal debt outstanding is often compared to the Gross Domestic Product (GDP). GDP not only measures output in the economy, it also reflects income. When goods and services are created, income is also created, split between individuals, corporations, and the government. Federal debt as a percentage of GDP is a measure of a country’s ability to pay its debt. If you have $100,000 of debt is it a lot? The answer is probably yes if your income is $50,000; the answer is probably no if your income is $50,000,000. The more income, the more debt you can generally financially afford. Federal debt as a percentage of GDP provides a relative measure as to how the federal debt financially burdens the country. The graph below puts some historical perspective on the amount of federal debt outstanding relative to the amount of income (GDP) generated in the U.S. Relatively speaking, federal deficits and increasing federal debt were not much of an issue until the 1980s. Federal debt as a percentage of GDP rose to over 50% by the end of the decade. In the 1990s, following a decade early recession, federal debt as a percentage of GDP topped 65%; federal budget surpluses helped reduce the level to approximately 55% by the end of the decade. The return of budget deficits after the turn of the century increased federal debt as a percentage of GDP to nearly 65% prior to the financial and economic crisis of 2007-2009; it increased to 84% be the end of the crisis. The COVID-19 economic contraction and stimulus brought federal debt as a percentage of GDP new heights. At the end of the second quarter of 2020, federal debt to GDP stood at 135%, a 50% increase from a decade earlier.
Federal Debt as a Percentage of U.S. GDP 1966–2020 2nd Qtr. Source: Federal Reserve Economic Database (FRED); U.S. Office of Management and Budget
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The Congressional Budget Office Analysis of Debt Does the United States have too much debt?
On September 21, 2020, the Congressional Budget Office (CBO) released its Long-Term Budget Outlook for the U.S. It presents the fiscal challenges that will be faced by future administrations. The CBO Budget Outlook includes the following assumptions • The economic impact of COVID-19 is included in the analysis. • Current laws affecting revenues and spending generally remain unchanged. • Individual tax rates revert to their 2017 levels in 2025 due to the 2018 tax bill sunset provisions. • The current corporate tax rate of 21% remains intact as it has no sunset provision. • Spending for Medicare and Social Security continues as scheduled even after their trust funds are exhausted. The following are key results taken directly from the CBO analysis: • “ Deficits . Even after the effects of the 2020 coronavirus pandemic fade, deficits in coming decades are projected to be large by historical standards. In CBO’s projections, deficits increase from 5 percent of gross domestic product (GDP) in 2030 to 13 percent by 2050—larger in every year than the average deficit of 3 percent of GDP over the past 50 years.” • “ Debt . The projected budget deficits would boost federal debt to 195 percent of GDP by 2050. • High and rising federal debt makes the economy more vulnerable to rising interest rates and, depending on how that debt is financed, rising inflation. The growing debt burden also raises borrowing costs, slowing the growth of the economy and national income, and it increases the risk of a fiscal crisis or a gradual decline in the value of Treasury securities.” • “ Spending . After the effects of increased spending associated with the pandemic dissipate, spending as a percentage of GDP rises in CBO’s projections. With growing debt and higher interest rates, net spending for interest nearly quadruples in relation to the size of the economy over the long term, accounting for most of the growth in total deficits. Also increasing are spending for Social Security (mainly owing to the aging of the population) and for Medicare and the other major health care programs (because of rising health care costs per person and, to a lesser degree, the aging of the population).” Summarizing the debt and deficit analysis by the CBO – both increase significantly. Federal debt explodes to 195% of GDP by 2050 if current government revenue and spending streams do not change. The U.S. is already well on the way to reaching that figure. A federal debt to GDP level of 195% could put the U.S. economy in an extremely precarious position, with increasing borrowing costs, potential inflation, increased interest rates, and even the possibility of a significant financial crisis. The debt challenges create policy challenges. Social Security programs are already under financial strain. The policy challenge is to balance tax revenues, the mix of those revenues (corporate and individual) and government spending on programs with an acceptable level of debt. Those are difficult challenges to meet prudently without political divisiveness, but with divisiveness those challenges may not be met.
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Center for Business and Economic Insight
ECONOMIC INDICATORS
Scott Wallace, Ph.D. Director and Editor, CBEI; Professor of Economics School of Business and Economics
The impacts of the pandemic on the economy differ greatly from typical recessions that result from the usual suspects like economic imbalances or bursting of an asset bubble. As Federal Reserve Chairman Jerome Powell has recognized, “the pandemic shock was essentially a case of a natural disaster hitting a healthy economy” (October 6, 2020).
National Economic Statistics Table 1 Key Economic Indicators 2020 Third Quarter
% ∆ Yr. Ago 1.72% -1.97% -7.30% 1.36%
Nominal Gross Domestic Product (in Trillions) Real Gross Domestic Product (in Billions) Industrial Production (2012 = 100) Consumer Price Index (1982 - 84 = 100)
$21.16
18,583.99
101.50 260.28
Description: • Nominal Gross Domestic Product (in Billions): The dollar value of all final goods and services produced in a year, using current prices • Real Gross Domestic Product (in Billions): The dollar value of all final goods and services produced in a year, using prices from a base year (2012) to adjust for inflation. • Industrial Production Index: Measures the changes in the volume of output (as a percentage of actual output in 2012) produced in the United States in manufacturing, mining, and electric & gas utilities. • Consumer Price Index: Measures the average monthly change in the price of a representative basket of goods and services bought by consumers Analysis: • The Third Quarter Real GDP number indicates that the economy is approximately 2% smaller than it was a year ago. This is certainly an improvement of where we were in the second quarter. See Table 2. • The Industrial Production Index number fell 109.5 to 101.5, a 7.3% drop over the last year.
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Table 2 Contributions to Percent Change in Real Gross Domestic Product (Seasonally Adjusted at Annual Rates) Line Percent Change at an Annual Rate 2019 Q2 2019 Q3 2019 Q4 2020 Q1 2020 Q2 2020 Q3 1 Gross Domestic Product Percentage Points at Annual Rates 1.5 2.6 2.4 -5 -31.4 33.1 2 Personal Consumption Expenditures 2.47 1.83 1.07 -4.75 -24.01 25.27 3 Goods 1.57 0.87 0.12 0.03 -2.06 9.24 4 Durable Goods 0.85 0.44 0.22 -0.93 0 5.18 5 Nondurable Goods 0.71 0.43 -0.1 0.97 -2.05 4.06 6 Services 0.9 0.96 0.96 -4.78 -21.95 16.04 7 Gross Private Domestic Investments -1.04 0.34 -0.64 -1.56 -8.77 11.58 8 Fixed Investment -0.07 0.42 0.17 -0.23 -5.27 4.96 9 Nonresidential 0.01 0.25 -0.04 -0.91 -3.67 2.88 10 Structures 0.05 0.11 -0.16 -0.11 -1.11 -0.43 11 Equipment -0.23 -0.1 -0.1 -0.91 -2.03 3.34 12 Intellectual Property Products 0.19 0.24 0.21 0.11 -0.53 -0.03 13 Residential -0.08 0.17 0.22 0.68 -1.6 2.09 14 Change in Private Inventories -0.97 -0.09 -0.82 -1.34 -3.5 6.62 15 Net Exports of Goods and Services -0.79 0.04 1.52 1.13 0.62 -3.09 16 Exports -0.54 0.1 0.39 -1.12 -9.51 4.9 17 Goods -0.74 0.23 0.19 -0.2 6.56 4.88 18 Services 0.2 -0.13 0.2 -0.92 -2.95 0.02 19 Imports -0.25 -0.06 1.13 2.25 10.13 -7.99 20 Goods -0.01 -0.08 1.15 1.36 7.32 -7.55 21 Services -0.24 0.02 -0.03 0.9 2.8 -0.43 22 Government Consumption Expenditures and Gross Investments 0.86 0.37 0.42 0.22 0.77 -0.68 23 Federal 0.58 0.31 0.26 0.1 1.17 -0.39 24 National Defense 0.17 0.22 0.26 -0.01 0.18 0.17 25 Nondefense 0.41 0.09 0 0.11 0.98 -0.55 26 State and Local 0.28 0.06 0.16 0.12 -0.4 -0.3 Bureau of Economic Analysis Description: • The above table decomposes percent changes in Real GDP into its components (consumption, investment, government, and net exports) and more specific subcomponents. Analysis: • It is important to recognize that the above table annualizes quarterly percent changes. Annualizing converts quarterly data to the approxi- mate annual equivalent. Doing so makes it easier to compare data sets with different reporting frequencies (i.e. quarterly vs annual data). • Annualizing data, however, can be a source of confusion. Note that for 2020 Q2, Gross Domestic Product fell by 31.4%! Despite the dra- matic impact of the pandemic on the economy, economic activity did not decrease by almost a third between the first and second quarters. Actually, total economic activity fell by about 9%, which is still a huge drop. Annualizing effectively projects how the economy would increase or decrease if that quarterly change continued to occur over the course of a year. • In addition, the 33.1% increase in GDP in 2020 Q3 might give one the impression that the economy had fully recovered and then some. Economic activity actually fell by 2.3% compared to 2020 Q1 numbers. It is important to consider the base when calculating percentage changes. For 2020 Q2, percent changes were calculated using the 2020 Q1 Real GDP of 19,010.8 (in billions) as the base. For 2020 Q3, the base was 17,302.5. • A 24.1 % fall in consumption expenditures accounted for most of the 31.4% decline in GDP for the second quarter. A 22% decline in the consumption of services was responsible for most of the fall in consumption. In most recessions, decreases in consumption of durable and nondurable goods typically account for most of the decline in consumption, with services often minimally impacted. This shows the unique effect of the pandemic on consumer behavior. • With the help of the CARES Act and elimination of lockdown restrictions, the economy sharply bounced back in Q3 2020. It will be important to see how surging COVID-19 cases and the expiration of federal relief measures affect growth in upcoming quarters.
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Center for Business and Economic Insight
Table 3 Lifecycle of the Expansion
14
6/20
12
10
9/09
9/20
8
6
4
2
0
38
38.5
39
39.5
40
40.5
41
41.5
Average Weekly Hours
Description: • Table 3 plots the unemployment rate against average weekly hours in manufacturing on a quarterly basis since the beginning of the current economic expansion in September 2009. • There are four phases: 1. In the initial phase of an expansion, unemployment is stable and remains high while there is a sharp rise in hours per week 2. In the second phase, the unemployment rate falls while hours per week tend to be relatively stable. • This graph visually demonstrates how the impact of the pandemic on the economy sharply differs from the more incremental changes that occur during a “normal” business cycle. In 2020 Q1, we see an increase in both the unemployment rate and average weekly hours which likely reflect the early effects of the pandemic in March. For 2020 Q2 (6/20), we see a much more dramatic impact of the pandemic with unemployment skyrocketing and average weekly hours dropping. For 2020 Q3 (9/20), we witness a rebound in economic activity, leading to a significant decrease in the unemployment rate and an increase in average weekly hours. 3. In the third phase, the unemployment rate is stable and hours per week decline 4.A contraction occurs when unemployment rises and hours per week falls Analysis:
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Labor Market Statistics
Table 4 Labor Market Indicators from LAUS and retrieved from WisConomy
Labor Force
Unemployment Rate
Employment
Labor Market Area
2020 Q3 (000) % ∆ Yr. Ago
2020 Q3
2019 Q3 2020 Q3 (000) % ∆ Yr. Ago
Portage County
37.7 14.0 73.8 35.2
-2.1% -2.0% 1.0% 0.6% 1.0% -2.4%
4.0% 4.2% 3.5% 4.7% 5.4% 7.9%
2.7% 2.8% 2.5% 3.1% 3.2% 3.5%
36.1 13.4 71.2 33.6
-3.6% -3.6% -0.1% -1.2% -1.1% -7.3%
City of Stevens Point Marathon County
Wood County
Wisconsin
3,141
2,967
United States
160,143
147,563
Description: • Labor Force: Includes all people over the age 16 who are either working or actively looking for work. • Unemployment Rate: The number of unemployed as a percentage of the labor force. Analysis: • While unemployment rates for Wisconsin and Central Wisconsin have increased over the last year, they remain well below the national unemployment rate. Table 5 Wisconsin Employment by Industry Sector from Payroll Employment Survey - CES and retrieved from WisConomy Nonfarm Jobs Construction Jobs Manufacturing Natural Resources and Mining Trade, Transport and Utilities Information Jobs 2020 Q3 (000) % ∆ Yr. Ago 2020 Q3 (000) % ∆ Yr. Ago 2020 Q3 (000) % ∆ Yr. Ago 2020 Q3 (000) % ∆ Yr. Ago 2020 Q3 (000) % ∆ Yr. Ago 2020 Q3 (000) % ∆ Yr. Ago 2,781 -6.87% 122 -2.05% 461 -4.68% 4 -13.89% 517 -3.00% 40 -17.80% Finance Jobs Business Services Education and Health Leisure and Hospitality Other Services Government % ∆ Yr. Ago 154 -6.50% 301 -7.57% 438 -5.86% 217 -29.82% 134 -13.24% 391 -3.19% Description: • Employment data are classified using the North American Industry Classification System (NAICS). The above table categorizes data according to major industry sectors Analysis: • The pandemic has had a disproportionate impact on service sectors like Leisure and Hospitality and Information Jobs. 2020 Q3 (000) % ∆ Yr. Ago 2020 Q3 (000) % ∆ Yr. Ago 2020 Q3 (000) % ∆ Yr. Ago 2020 Q3 (000) % ∆ Yr. Ago 2020 Q3 (000) % ∆ Yr. Ago 2020 Q3 (000)
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Center for Business and Economic Insight
County Economic Statistics Table 6 Help Wanted Advertising
Stevens Point
Wausau
Marshfield
Wisconsin Rapids
Lincoln County
Adams County
Index Value 2020 Q3
% ∆ Yr. Ago
Index Value 2020 Q3
% ∆ Yr. Ago
Index Value 2020 Q3
% ∆ Yr. Ago
Index Value 2020 Q3
% ∆ Yr. Ago
Index Value 2020 Q3
% ∆ Yr. Ago
Index Value 2020 Q3
% ∆ Yr. Ago
824.5 -50.30% 1031.5 -46.89% 509.5 -48.30% 564.5 -81.34% 795.00 -51.82% 509.5 -57.60% Description: • Presents index values for online job advertising for Stevens Point, Wausau, Marshfield, Wisconsin Rapids, Lincoln County, and Adams County Analysis: • Help Wanted Advertising is an important indicator of local labor market conditions. It tends to be a leading indicator of the beginning of recessions. That might not be the case this time because the steep declines in advertising may reflect the one-off impact of COVID-19 rather than changes in the underlying fundamentals of the economy.
Table 7 Unemployment Claims - Portage County Weekly Average 2020 Q3
% Change a From a Year Ago
New Claims Total Claims
332
621.70% 655.10%
2167
Description: • New Claims: Weekly average of new claims for 2020 Q3 • Total Claims: Weekly average of total claims for 2020 Q3 • Averages of new and total claims are used because of the volatility of weekly claims. Analysis:
• The dramatic increase in new claims and total claims in Portage County mirror what is happening at the national level. Despite an improving unemployment rate from reductions in temporary lay-offs, sustained high initial claims may indicate increases in permanent job losses.
Table 8 County Sales Tax Distribution Portage
Marathon
Wood
2020 Q3 (000) $2,008.4
% ∆ Yr. Ago
2020 Q3 (000)
% ∆ Yr. Ago
2020 Q3
% ∆ Yr. Ago
County Sales Tax Distribution
4.4% $3,697.6
6.1% $1,854.3
8.3%
Description: • The county sales tax rate of 0.5% is placed on retailers that make taxable retail sales. • Changes in county tax collections is a good indicator of changes in retail activity in Central Wisconsin. Analysis: • At first glance, the increase in county sales tax distributions from 2019 Q3 is somewhat surprising given the impact of the pandemic on retail activity. The increases may reflect the impact of the CARES Act and a pent-up demand effect from the declines in tax collections for May and June of 2020. For example, Portage County tax receipts fell by 5.9% and Marathon County’s tax receipts fell by 11.1% relative to May and June numbers in 2019.
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Housing and Construction Table 9 National Affordability Index (Third Quarter 2020)
Years
Median Price Existing Single Family Home
Mortgage Rate
Monthly P and I Payment
Payment as % of Income
Median Family Income
Qualifying Income
Composite
2019
274,600 316,200
4.04 2.95
1054 1,060
16
78,964 81,219
50,592 50,880
156.1 159.6
Sept. 2020
15.7
Description: • Composite Index measures affordability. An index of 150 means that a family earning the median family income has 150% of income necessary to quality for a conventional loan covering 80% of a median price family home. Analysis: • The median price of an existing home increased significantly over the last year while mortgage rates fell to historic lows. Affordability as measured by the composite index was little changed over the last year with home prices and interest rates moving in opposite directions.
Table 10 Median Home Prices and Home Sales
Wisconsin
Marathon
Portage
Wood
2020 Q3
% ∆ Yr. Ago
2020 Q3
% ∆ Yr. Ago
2020 Q3
% ∆ Yr. Ago
2020 Q3
% ∆ Yr. Ago
Median Home Prices
$230,100
12.8% $192,866 11.9% $217,193 17.12% $134,333 7.3%
Home Sales
28,479 10.6% 632
12.0% 281
-1.0% 293
8.0%
Table 11 Residential Construction: Stevens Point and Plover Area
Estimated Value of New Homes (000)
Number of Housing Units
Alteration Permits Issued
Estimated Value of Alterations (000)
Permits Issued
2020 Q3
2019 Q3
2020 Q3
% ∆ Yr. Ago
2020 Q3
2019 Q3
2020 Q3
2019 Q3
2020 Q3
% ∆ Yr. Ago
18
26 $6,173.84 -8.50% 19
24
569
443 $5,078.46 76.58%
Analysis: • The increase in both the number and estimated value of alterations may have been the result of the pandemic as households cancelled vacation plans and instead, stayed home engaging in home improvement projects.
Table 12 Nonresidential Construction: Stevens Point - Plover Area
Estimated Value of New Structures (000)
Alteration of Business Permits Issued
Estimated Value of Alterations (000)
Permits Issued
2020 Q3
2019 Q3
2020 Q3 503.0
% ∆ Yr. Ago -97.2%
2020 Q3
2019 Q3
2020 Q3
% ∆ Yr. Ago
3
14
157
121
$ 39,549.09
163.42%
Analysis: • While nonresidential construction numbers can be volatile from year to year, the steep decline in the number and value of new structures for the third quarter suggests that the pandemic may have played a role. Similar to residential construction, there was a dramatic increase in the estimated value of alterations this year over third quarter 2019.
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Center for Business and Economic Insight
SPECIAL REPORT
Grit Won’t Quit: How Hiring for Passion + Perseverance May Help Reduce Turnover
By Nik Butz, Ph.D. and Reed Stratton, Ph.D. UW-Stevens Point School of Business and Economics with assistance from SBE students Abby Schmidt ’20, MBA ’21 and Emily Gruber ’23
Turnover Hurts
“Soft” Costs
Distrust
Lost Clients
Wavering Commitment
Hindered Morale
Lost Innovation
$47,000 across industries
Nearly $14,000 per employee within companies
42 days and $4,129.00 for recruiting, interviewing, and onboarding
Financial Costs
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“To what extent might hiring for Grit as a criterion help reduce turnover in Central Wisconsin businesses? ” 1. What is “Grit?” 2. Current research about grit and retention 3. Research Process 4. Findings 5. Implications for your Business
Grit
“Effort and interest over years despite failure, adversity, and plateaus in progress.” - “Grit: Perseverance and Passion for Long Term Goals”
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Center for Business and Economic Insight
Previous Research
The Grit Effect - Eskreis-Winkler et al., 2014
Area
Controls
Success Rate for the “Gritty”
● ● ●
Military Soldiers
Level of physical fitness
38% more likely to pass elite special operations course
# of years in school
Intelligence
●
Sales Professionals
Conscientiousness
40% more likely to be retained
● Demographic variables (age, gender, race, and site/location)
● ● ● ●
High School Students
Academic conscientiousness & motivation
21% more likely to graduate from high school on time
Parent/teacher support
School safety Peer reports
Married Men
Personality traits such as ●
More likely to remain married
Openness to experience
● ● ● ●
Conscientiousness
Extraversion-introversion
Agreeableness
Neuroticism
True Grit - Robertson-Kraft & Duckworth, 2014 Background of the research: Predicting retention and effectiveness in novice teachers from low-income school districts. Performed two separate studies. 2nd study results for retention were inconclusive. Grit scores were based on evidence of grit in college activities and work experience listed on resumes. Results: Grittier teachers were more likely to be retained.
Controls
Success Rate for the “Gritty”
● ● ●
Study 1
SAT scores College GPA
More than twice as likely to be retained 60% more likely to outperform less gritty peers
Ratings of leadership potential
● ● ●
Study 2
SAT scores College GPA
Retention was nearly 100% for all subjects 64% more likely to outperform less gritty peers
Ratings of leadership potential
Research Process
Research Questions 1. How do turnover rates of companies
prioritizing Grit when hiring differ from those that do not prioritize Grit when hiring? 2. Given that Grit encompasses both interest
and effort, does one of those components predict retention more effectively than the other? 3. How does prioritization of Grit when hiring differ by organizational characteristics? 4. Can a hiring preference for Grit Interest, Grit Effort, or Grit Combined significantly predict retention?
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Measuring Grit & Turnover
Overcomes setbacks Finishes what they start Works at goals that take years Maintains focus (not distracted) Stable opinion of what’s important Sets/keeps goals that take years
Effort
Overall Grit
Interest
Going to the Source
● N = 52 managers in charge of making hiring decisions for companies in Central Wisconsin ○ # of Employees Median = 150; min 2; max 64,000 ○ Years in operation Median= 37; min = 2, max 201
Goods, 21%
Services, 66%
Other/Unspecified, 13%
Findings
Findings
Industry with High Turnover & Low Grit
1. How do turnover rates of companies prioritizing Grit when hiring differ
Industry with High Grit & Low Turnover
from those that do not prioritize Grit when hiring?
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Center for Business and Economic Insight
Slope of Best Fit Line appears flat
Findings
2. Given that Grit encompasses
Slope of Best Fit Line appears negative
both interest and effort, does one of those components predict retention more effectively than the other?
Slope of Best Fit Line appears positive
3. How does prioritization of Grit when hiring differ by organizational characteristics?
Findings
High Problem-Solving Skills associated with High Grit Effort
Significant relationship b/w High Grit Effort & Low Turnover Rate
Older had less turnover
4. Can a hiring preference for Grit Interest, Grit Effort, or Grit Combined significantly predict retention?
Findings
Together Grit Interest & Effort predict Turnover
Grit Effort was the strongest predictor significant at p < .01
Grit Effort remains a strong significant predictor, even when controlling for other commonly used hiring criteria
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Implications for Your Business
Hire For “Grit Combined”
● Why?
○ 42% decrease compared to average ○ Predicts 17% of turnover variability ○ Minimal time, minimal cost ○ Integrate as a criterion ○ Administer Grit Scale ○ Grit-behavior-based interview questions ○ Scenario about obstacle(s) ○ Attune to language
● How?
Favor “Grit Effort” the most
● Why?
○ Turnover rate fell by .41 units
● How?
○ Isolate effort-based questions ○ Raise continuity concerns on resumes ■ Don’t assume!
Newer Companies, Isolate for Grit Interest
● Why?
○ New companies experience higher turnover ○ Interest holds through structural changes ○ High interest yields retention in high-stress environments ● How? ○ Connect position to meaning on job posting ■ “Start with Why?” Simon Sinek ○ Isolate Grit scale responses ■ Question 1-6
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Center for Business and Economic Insight
Conclusion
Why Hire for Grit? ● Reduce turnover costs while saving ● Focus on controllable variables ● Predict whose most likely to commit ● Scale Grit-based hiring up or down ● Your competitors might be already doing it
Where To from Here? ● Converse with hiring specialists ● Revise job postings
● See for yourself ● Follow up with us
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SPOT LIGHT INSIGHT
Release the Data Analyst! Kurt Pflughoeft, Ph.D. Sentry Endowed Chair of Computational Analytics; Director of the Center for Data Analytics
Data analytics is all the buzz at many corporations subsuming areas such as business analytics and business intelligence. The goals of data analytics are broad, ranging from knowledge discovery to automated decision-making. The impact of this field has led to many benefits such as increased cross- and up-selling as well as identifying new business opportunities. Now, if for some reason, you are a bit skeptical about this field, you may ask, “Isn’t data analytics just a rebranding of past quantitative approaches like statistics, operations research or computer science?” The answer is: “Not quite.” Although these fields provide many contributions to data analytics, there have been great advances with regards to algorithms, software and hardware that have led to synergies which are leveraged by data analysts. Data analysts have a “can-do” attitude to arrive at solutions even if an unconventional approach may be needed. In fact, a few short years ago, the American Statistical Association, was worried that applied statistics was being eclipsed by fields like data analytics. The association made the following recommendations to its members: 1. Gain a deep understanding of the product/service that you are supporting. 2. Be more focused on predictions rather than merely inferring relationships. 3. Be willing to tackle large and messy problems. 4. Create more partnerships between the statistics and data analytic communities. These recommendations were patterned upon the successes of data analytics. For example, large and messy problems may be characterized by unsatisfied assumptions or by big, dirty and unstructured data. Many statisticians had not wanted to deal with such issues.
Likewise, managers don’t want to hear about excuses why a problem is too difficult to address; they want insights to guide their decision making. Solutions need not be optimal but “good-enough” solutions can be quite helpful. While working at a market research (MR) firm, I experienced some of the upheavals caused by data analytics. Most of our newer competitors were no longer other MR firms but rather data analytics firms. This observation was also evident from the confusion surrounding the revered Honomichl list of MR firms. Is a data analytics firm that tackles many MR problems an MR firm? Probably. An early example of the use of data analytics for an unconventional solution was the determination of audience share for TV programs. MR firms used many different techniques ranging from TV diaries to monitoring devices. The “audience share world” was reset when a professor simply analyzed tweets to come up with the same results. Tweets are now a standard way to determine, in part, audience share. A more recent example is the prediction of customer attrition by MR firms that measure satisfaction. Their method to determine potential defections relied on “Hot Alerts.” A “Hot Alert” identifies customers who may have given the client a low score on a key question. Although “Hot Alerts” have some benefits, they may be too late for intervention and they only address surveyed customers. Data analysts brought much more horsepower to identify potential defectors early on. For example, a TV broadcast company issued a request for proposal (RFP) to help with this task. The data analysts requested many more variables including call center contacts/notes, web site navigation, services bundles, customer viewing habits, customer age and location to name a few.
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Center for Business and Economic Insight
By using sophisticated modeling techniques, such as Random Forests, and big data ecosystems like Hadoop or Spark, data analysts could leverage all that data and make accurate predictions. In fact, the TV broadcast company was quite surprised how good the model was. It substantially outperformed their long-established internal model. In fact, you can see some of these competitions yourself on websites like Kaggle. An organization may post a particular problem and a dataset, often anonymized, for competition. The person or team that submits the best solution may win a prize. Some more recent Kaggle competitions include: • Two Sigma: Using the contents of newspaper articles to predict stock market performance. • Radboud University Medical Center: Detecting prostrate cancer from images of tissue samples. • NFL: Analyzing video to detect helmet impacts that occur on the field • Online Retailer (anonymous): Identifying high-valued customers that may benefit from a loyalty program. Data analysts perform four broad categories of analysis which are briefly mentioned below. Descriptive Analytics help summarize the past and current situations either visually or numerically. A common task might be to develop a key performance indicator (KPI) to help the firm keep track of successes or failures. Diagnostic Analytics determine why things have happened. For example, an anomaly may have been identified by the descriptive analysis and now its root cause needs to be identified. Predictive Analytics is all about forecasting the future. Needless to say, a difficult task. However, past successes, especially by recommender systems, have shown that limited short-term predictions are often successful. Recommender systems can be found on services such as Netflix or Amazon. Prescriptive Analytics can be the most challenging and may require much subject matter expertise. Here, a solution is proposed to address a problem or issue. A data analyst can add new perspectives and help the SME evaluate possible scenarios.
Here are a few notes about the use of data analytics to ensure success. 1. Business Goals: Identify your goals before engaging a data analyst. Projects that start out with find “something of interest” is like going down a rabbit hole. 2. Start Simple: Firms with no experience with data analytics are better off beginning with an easier project where a short-term win is more likely. 3. Data Preparation: Preparation can consume a lot of the project’s time. Estimates range from 50-80%. If you think your data is in relatively good shape because it is in a data base, you might be surprised to learn otherwise. Even with clean data, some analysis techniques require much data preparation. 4. Dead Data or Wishful Thinking: If you think that there is no relationship in the data, you may very well be right. Don’t expect a data analyst to magically find something. Most contributions from research are done in small incremental steps. 5. Think Outside of the Box: Radically new approaches should be contemplated when possible. It need not be quite as ingenious as the Mars Pathfinder inflatable airbag landing system but the use of tweets for TV audience share was quite good. 6. Good Luck: NO! Luck is not a prerequisite. Good planning and expertise will help ensure your success. In closing, I would be remiss if I didn’t mention the UW- Stevens Point data analytics program, which is patterned against some of the best data analytics and data science programs in the country. The UWSP program covers data preparation, machine learning, data mining, predictive analytics, big data analytics, statistics, R & Python programming, and database. Out of 120 credits required for college graduation, 70 of those credits are dedicated to data analytics; a very in depth major. Our program has faculty who not only have academic experience, but also are industry experts in data science, market research and computer science. Our product is our students and we are proud of what they represent. We have heard and seen from employers how our students are able to make contributions quite quickly to these organization. If you have any questions or would like to reach out, please contact, Kurt Pflughoeft, Sentry Endowed Chair of Computational Analytics and Director of the Center for Data Analytics, at kpflugho@uwsp.edu. The center offers data analytic services to businesses through faculty and students.
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