Tuesday, July 29, 2014

Public Assistance Usage and Employment Patterns in Utah’s Refugee Population

Natalie Torosyan, Senior Economist

Each year, hundreds of refugees are resettled in Utah and receive assistance from a variety of sources. Among those sources is the Department of Workforce Services (DWS), which provides some form of public assistance or employment service to the majority of refugees in Utah. The Workforce Research and Analysis division at DWS recently published a research paper that profiles the segment of the refugee community in Utah served by DWS.

Refugees can access a number of public assistance programs through DWS. The public assistance programs that are described in this research paper are the following:
  • Supplement Nutritional Assistance Program, commonly known as food stamps
  • Medical
  • Child Care
  • Unemployment Insurance
  • Financial, which includes the Family Employment Program, Refugee Cash Assistance and General Assistance Cash Program
During the first four years after arrival in Utah, a refugee could have potentially received 48 months of public assistance from each of the programs listed above, or 240 overlapping months if taking each program individually. When summing the total number of months of public assistance across all of the different programs, refugees’ average public assistance usage was 42 months. The figure below, taken from the paper, plots the average public assistance usage and average four-year wages by country of origin. The size of the bubble indicates the proportion of the total refugee population that comes from a particular country of origin. The plot shows that Iraqis, the largest group of refugees, receive the most public assistance on average. The relatively small population of refugees from Cambodia tends to earn the highest wages during the first four years.

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The research paper also includes an analysis of industry sectors of employment, out-migration from the state, and the impact of the recession on the refugee population of Utah.

The full report can be accessed here.

Thursday, July 24, 2014

An Alternate Look at Utah’s Per Capita Personal Income

Mark Knold, Supervising Economist

In listings of the 50 states, Utah consistently ranks near the bottom in yearly per capita personal income. The knee-jerk conclusion is that Utah has low wages, or that what you can earn in Utah is muted. But is that a fair perception? What Utah’s per capita measure needs is a more transparent look.

Quickly we’ll define some terms. Personal income is the total income earned throughout Utah in a given year. In all states, the most prevalent way to generate income is through earnings from a job. But income can also come from retirement payments, dividends from investments, rental income, unemployment benefits, social security payments, or selling other assets (to pawnshops and on eBay, among others). It is the total extent of income amassed by Utah residents. Per capita is to take that total income and divide it by Utah’s total population. That brings it down to an individualized level that can be compared to other states. But what if you have the highest proportion of children in your population than any other state, as Utah does? Children generally don’t earn income. That is naturally going to lower your per capita calculation in relation to other states.

Generally, people 18 and over are the ones who generate a state’s total income, so let’s divide each state’s total income by the 18-and-over population. That way, we are only including the population segment that contributes to total income. Adjusting for age noticeably improves the picture for Utah.

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The image to the right has two graphs: the graph on the left highlights Utah’s position within the total-population per capita ranking in yellow; the graph on the right readjusts Utah’s position with just the 18-and-over population. Utah noticeably moves up in the rankings. In fact, Utah’s 45% increase between its general per capita value ($36,274) and its 18-and-over value ($52,501) is the largest percentage gain for any state. The more children you remove from the equation’s denominator, the more gain in per capita value.

That is not the end of the story. One could do an additional adjustment for the cost-of-living variations across states. Applying cost-of-living indices from this site and adjusting each state’s 18-and-over per capita accordingly, Utah advances even further, now finding itself in the middle of the pack.
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With the elimination of the non-income group (those below 18) and an additional adjustment for the various costs of living in each state, the story of Utah’s per capita income is brought into better proportion.

Even then, it is possible to add one more caveat. If you were to fill a room with a random sample of 30 year olds, and another equal-size room with a random sample of 50 year olds and calculate in each room the per capita income, which room would you expect to have the higher income? Due to 20 more years of work experience and earnings power, you should expect the 50-year-old room to have the higher income. That comparison is analogous to what you get when you compare Utah to the national average (which means most other states). The graph below shows that Utah has a higher percentage of its income-earning age at the younger end of the spectrum than does the nation as a whole. If it were possible to further adjust state incomes by age of income earners, Utah would probably inch up even more.

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Utah’s per capita income measure is cited regularly. Yet without a proper understanding of that variables’ nature and Utah’s unique underlying characteristics, a mistaken inference is often forthcoming.

Tuesday, July 22, 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 the 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

Lecia Parks Langston, Senior Economist

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.

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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.