Monday, July 21, 2014

GDP: Looking Back at Q1 and Looking Forward to Q2

Tyson Smith, Regional Economist

Last month, the Bureau of Economic Analysis (BEA) revised down its first quarter 2014 Gross Domestic Product (GDP) estimates. GDP measures the total value of all goods and services produced in the economy during a given period, and is one of the broadest indicators of economic activity. According to BEA, the U.S. economy shrank 2.9 percent in the first three months of the year.

Since then, economic journalists like Andrew Flower and Ben Casselman at FiveThirtyEight.com have written extensively about the GDP estimates and the contraction’s potential impact. The question is: how much weight should we put in a single quarter of negative GDP change? The opinion among journalists, economists and financial experts is mixed. While there are few people willing to assert that the “sky is falling”, the news is certainly unwelcome considering the multi-year sluggishness of the recovery.

The Pessimistic Assessment:

First, let’s state the obvious; an expanding economy is better than a contracting one. While GDP is not a flawless measure of national economic health, it does correlate with increases in consumer spending, business investment and individual wealth. What makes this downward revision particularly alarming is the size of the contraction. The original estimate had GDP down 1 percent then, in June, the BEA moved it even further to negative 2.9 percent.

“Negative quarters are rare outside of recessions,” explains Casselman. “There have been only two other non-recessionary quarters since World War II when the economy shrank at a rate over 2 percent.” In both of those cases, the negative quarters immediately preceded a recession.

The following chart shows how the fall in GDP last quarter compares to recent history. The decrease is the largest of any outside of the recession. However, it is worth noting that GDP fell over 1 percent in the first quarter of 2011 and bounced back substantially thereafter.

Friday, July 18, 2014

Survey vs. Census—Revisiting the Data Timeliness and Accuracy Discussion

Carrie Mayne, Chief Economist

New data from the Quarterly Census of Employment and Wages was released since I last wrote about comparing the more timely survey data to the “true” but lagged census employment data.


With the addition of data for January, February, and March of 2014 we can see the pattern of undercounting jobs continues in the survey, although the margin is somewhat smaller starting in 2014. Has the survey become more accurate? Well, each year in February the survey estimates are refined through a benchmark process. Past years’ estimates are revised using the latest employment census data, and the model used to create the estimates going forward is recalibrated using the new information. As a result, we tend to see a pattern in the survey estimates where early months are fairly accurate but as time passes the accuracy wanes. In March for example, the difference between the census employment count and the survey estimate is only 300 which is essentially decimal dust when you are tracking 1.3 million jobs. But March is estimated only a few months after the model is benchmarked.

The June jobs estimate is 1,355,900, 3.5 percent growth above June of 2013. Now that we are out six months from model benchmarking we have to wonder: how accurate is that estimate? Forecasts based not on surveys but generated from models of historical employment patterns estimate June employment growth to be between 3.4 and 3.5 percent. This leads us to believe that even with half the year under our belts the survey estimates could still be on track. On the other hand, digging into the detail we see some signs of potential overestimating. Take for example construction employment. The most recent census data (March) shows construction employment at 73,325 which constitutes 6.7 percent growth over March of 2013. The June survey estimate has construction employment at 82,300, growth of 9.2 percent year over. As the weather warms up from March to June it is commonplace for construction employment to ramp up. But has it grown by almost 10,000 positions in three months? It seems more likely that the June estimate is slightly high.

Ideally, the monthly survey data would accurately estimate employment growth across the state and for all industries. But no statistical model or survey is perfect, so assessing the model’s accuracy whenever possible is essential.

Tuesday, July 15, 2014

Census Bureau Report Profiles Poverty

James Robson and Mark Knold, Senior Economists

A recently released U.S. Census Bureau report looks at poverty change across the United States, and it naturally shows poverty rose in Utah between 2000 and 2010. We say naturally because there were two national recessions that affected Utah during that decade, with the latter-half’s Great Recession being the nation’s worst economic setback since the 1930s Great Depression. Recessions reduce employment which in turn reduces income, and that increases the number of people with economic adversities. So the rise in poverty, though not welcome, is not a surprise. The report shows that all sections of the country saw poverty levels rise over the last decade, so this is not an isolated Utah issue.

Unlike in past decennial Censuses, the “2010 Census” is not a snapshot taken from a long-form survey on April 1, 2010. Instead, that data comes from the Census Bureau’s ongoing substitute, the American Community Survey, and the survey years used and averaged as the “2010 Census” cover 2008-2012. As that data spreads across a five-year period, the “2010 Census” measure covers the entire scope of the Great Recession—but not the recovery thereafter.

In a recent Deseret News article, this rise in poverty between 2000 and 2010 in Utah was noted. It then opined in relation to the current Utah employment growth and this rise in poverty; “Apparently, the poor are not getting many of the new jobs…”  Unfortunately, that is a mismatched statement. Utah’s strong economic jobs rebound has covered the last three years (2012-2014), with only 2012 having any overlap with the 2010 Census poverty measurement. Poverty measurements are a lagging statistic. In other words, we don’t have poverty measurement numbers yet that cover the period of Utah’s current job growth (2012 to the present). So to say that the current job growth is not benefitting the poor is to not properly match statistical data with the right time periods. Any statement on the poor and current job growth cannot be made until we have poverty measures that will cover the recent three years—and they will not be available for several more years.

One benefit of the Census Bureau report is that there is detailed information. One piece of depth shows census tracts where 20 percent or more of the population is poor. These are called high poverty areas. The attached map shows high poverty tracts across Utah. Most of the data can be taken at face value, but there is an occasional quirk. For example, in Salt Lake County, the area surrounding the University of Utah is a high poverty area. This is due to a college student population who are more focused on education than maximizing income. On paper, their income will label them as poor, yet their overall financial resources may not warrant them being in this category. The same caution can be applied in Utah County surrounding BYU and Logan with USU.

Poverty is not a welcome part of any community. Only a flourishing economy can reduce this obstacle; but even then, it takes time. Utah’s current job growth may not yet be deep enough to significantly lift the poor, but if prosperity continues, the setback of the Great Recession will eventually be reduced. Though we don't have the most current poverty rates, what we do have are improving indicators that signal economic progress—strong job growth, falling unemployment, and reduced public assistance caseloads. These point to an improving story about the economic well-being of Utah's families.

Monday, July 14, 2014

New race/ethnicity, gender and age population estimates available

The U.S. Census Bureau recently got down to demographic details with the release of its gender, age and race/ethnicity population estimates by county for 2013. This very extensive data release utilizes results from the American Community Survey to update population information. These estimates provide a fascinating peek into the demographic makeup of Utah’s counties. A few salient points follow after the jump:



Tuesday, July 8, 2014

Women in Manufacturing

Eric Martinson, Senior Economist

There is an interesting phenomenon I noticed within Utah’s manufacturing sector. It concerns two diverging trends regarding the female presence in manufacturing. The accompanying chart shows the two trends together. The blue line tracks female earnings within Utah’s manufacturing as a percentage of their male counterparts’ earnings from fourth quarter 1999 to second quarter 2013. The red line shows the female/male employment ratio.

(Click graph to enlarge)

Friday, June 20, 2014

Tradeoffs Between Timeliness and Accuracy

Carrie Mayne, Chief Economist

Adding up the 1.3 million or so jobs in the Utah economy may seem simple but in reality takes time.  To compile this information, analysts follow the same method used by every state in the country, which is defined by the U.S. Bureau of Labor Statistics (BLS). This method involves using quarterly reports submitted to the state’s unemployment insurance system, which accounts for the vast majority of jobs counts in the state.  This data collection process takes about four months after the end of that given quarter to complete, giving us a very accurate picture of the job market.  For example, we won’t know until November exactly how many jobs there are in our state today (June).

While a complete and accurate count is ideal, the cost of waiting several months to get the information  is not. BLS understands this and therefore conducts a more timely employment survey every month.  The survey includes a representative sample of employers across the state and various industries who are asked report their monthly employment.  Estimates of total employment are then calculated based on a model defined by BLS.  How well the estimates will reflect the “true” employment levels depends on:  1. How well the job creation of the sampled employers reflects the employers they are meant to represent, and 2. The accuracy of the model intended to explain the broader statewide jobs picture.

Each month when we report the job growth rate estimated from the monthly survey, we compare the estimate to all the other information we are studying to understand the current state of the economy and the trends that are driving economic activity.  Sometimes the survey lines up with our expectations. Other times we speculate that the monthly numbers either over- or under-estimate current activity.

Click to enlarge
So, what do we think about the latest numbers?  The accompanying graph shows the last twelve monthly survey job estimates along with the most recent six months of complete count data (the data that takes longer to produce).  Over this period, it seems the survey has been underestimating the job count by an average of about 2,600.   Does that mean the current estimates are also underestimating?  The unemployment rate, which has trended downward for quite some time, may indicate that job growth is actually stronger than what was estimated from the survey.

Only time will tell if our conjectures ring true.

Thursday, May 29, 2014

Regional Price Parities: Adjusting Wages for the Cost of Living

Tyson Smith, Regional Economist

We know that the dollar amount written on our paychecks obscures the true value of our wages.  For one, the price of goods and services generally increases over time, which means that a dollar today has more purchasing power than a dollar tomorrow. The economic term for price increases over time is inflation. If our wages do not keep pace with inflation, the value of our paycheck shrinks.

Secondly, we need to consider location when assessing the purchasing power of our wages. The price of a product or service in a specific region may be different than the price for the same product or service in a different location. Prices differ by location because geographical scarcities and localized consumer preferences can dramatically impact supply and demand[1].

                        (Click Image to Enlarge)
For example, most Americans spend a significant portion of their income on housing (rents, mortgages, etc.). In densely populated or fast growing regions, large numbers of people need housing. If the number of people looking for housing exceeds the number of accommodations available, then housing prices will be elevated. Conversely, regions with low population growth or density can more easily meet the housing needs of the population, which reduces the price of housing. The effects of geographical scarcity can be applied to every product and service in a local economy with varying degrees of impact on price levels.

The Bureau of Economic Analysis (BEA) uses Regional Price Parities (RPPs) to account for the cost of living in a specific location. RPPs measure the differences in the price levels of goods and services across states and metropolitan areas for a given year. RPPs are expressed as a percentage of the overall national price level, where the national average equals 100. State and metropolitan RPPs can be accessed on the BEA website, and the chart to the right shows the RPPs for each state in 2012. The 2012 RPP for Utah is 96.8, which means that average price levels in the state were 3.2 percent below the national average.

Wednesday, April 23, 2014

Customer Service Jobs Offer A Portal Into the Economy

Mark Knold, Supervising Economist

In a state with diverse job sectors—like Utah—each industry plays an important role supporting the broader economy. One trend we’re seeing right now is the significant growth and demand in the customer service field. Here’s why this is important.

Customer service jobs are estimated to have nearly 10,000 openings each year in Utah. They make up about 14% of our economy and can be found in a variety of company types all over the state. Customer service covers a broad range of occupations, but often includes cashiers, retail clerks, sales representatives, telemarketing and call center representatives, loan servicing, and other activities that may be either face-to-face customer service or telephone/internet-based interaction. While these entry-level jobs are often low-wage, they offer a gateway to future career opportunities.

Employers prefer to hire workers with job experience. New applicants are usually unknown to an employer, but showing an employment track record removes some of that unknown. So for those new to the labor force—like recent high school graduates—customer service positions are valuable openings through which inexperienced labor can make a breakthrough into the working world.

These jobs are common and plentiful across the entire United States, as sales transactions constitute a huge part of the everyday commerce in America. The skill set necessary for success is not particularly deep and easily obtainable. And people who desire flexible working hours or a means to gain work experience find these positions valuable for their current situation and for laying a foundation for future employment.

To help Utah’s plentiful supply of young workers make their breakthrough into the working world, the Department of Workforce Services is hosting a customer service career fair on April 28 at Weber High School.

Attendees will have the chance to meet employers in the customer service industry and learn about a variety of jobs—from entry level positions to management.

For more information about the event and the customer service industry in general, head to our website: jobs.utah.gov.

Friday, April 11, 2014

County by County Economic Diversity

Mark Knold, Supervising Economist

In the Summer 2013 issue of Statewide Local Insights, the feature article addressed the industrial diversity of the Utah economy. The analysis concluded that Utah has a very diverse economic base. This means its distribution of employment is desirably spread out across various industries. In other words, Utah has its industrial eggs in many different baskets. Through a grading tool called the Hachman Index, Utah’s economic diversity measured 97.6, meaning the Utah economy is 97.6 percent as diverse as the United States economy. The United States economy is viewed as the most diverse standard against which to measure a local economy and its industry employment distribution.

Peering more deeply into the Utah economy shows where that diversity is located. Taken as a whole, the Utah economy is quite diverse, but when you dissect the Utah economy by individual counties, you instead find more industrially-concentrated economies. The majority of the state’s diversification comes from Salt Lake County alone, which accounts for nearly half of all Utah employment. Add in the industrial diversity of fellow metro counties Davis, Utah and Weber—now 80 percent of the state economy—and the state’s overall diversity emerges. These four counties are the chief contributors to Utah’s economic diversity.

Click graph to enlarge
The graph lists Utah’s counties in descending order of their Hachman Index. Saying exactly where a county goes from being diverse to non-diverse is open to debate. Here we use a general rule that an economy at 80 percent (0.8 on chart) or more of the United States diversity will be labeled as diverse. This is a loose criterion, but is based upon observing the variation in economic performances across time. Economies at 80 percent or above have less economic variation; they weather the ups and downs of the business cycle better. Given this threshold, 23 counties are below, ranging from Tooele County’s 77 percent to Duchesne County’s 9 percent.

A diverse economy is an outcome of market factors of a given region. Rather than being artificially created, a local economy’s industrial diversity is developed organically depending on the size of the population and the endowment of natural resources. Thus, it is less likely for small population counties to be economically diverse given that they simply have a smaller distribution of residents and resources. So naturally, Utah’s numerous small counties will have less diverse economies.
Mapping the diversity reveals a regional look. The core of high diversity in the Wasatch Front metropolitan counties stands out. The counties surrounding those within the Wasatch Front then offer the first lower level of industrial diversity as part of their proximity to the urban population mass.