April 21, 2014
Differences in State Safety Net Spending
By Jacob Berman, Associate Economist
The social safety net in the United States consists of dozens of anti-poverty programs on the local, state, and federal level that provide benefits to low-income households. Although anti-poverty programs are generally funded by the federal government, most are administered by states. State governments have broad discretion over the generosity of programs, so the level of benefits for any given household varies widely across regions. For example, the cut off for a single-parent household with three children to be eligible for Medicaid ranges from an annual income of $50,868 in Washington D.C. to $2,652 in Alabama. Similarly, the maximum weekly benefit for unemployment insurance ranges from $674 in Massachusetts to $235 in Mississippi.
One technique for comparing safety nets across states is to use eligibility rules to determine the benefits a hypothetical low-income household is likely to receive. However, as the number of states and programs under consideration grows, this calculation becomes more difficult because eligibility rules can be extremely complicated. For example, a full description of eligibility for the Temporary Assistance for Needy Families (TANF) program, sometimes referred to as welfare, requires a 250 page document that needs to be updated every year. Instead, I compare safety net programs using data on expenditures from the national accounts, and household income data from the Census Bureau's American Community Survey (ACS). I find that real benefits for low-income households in the most generous area, Vermont, are about two-and-a-half times greater than in the least generous area, Georgia.
Safety-net programs come in many different forms. Some programs (such as TANF) provide cash benefits which allow households to consume anything they choose, while others (such as Medicaid) provide in-kind benefits which only permit households to consume specific goods or services. Short-term programs (such as unemployment insurance) provide temporary aid, while others (such as disability insurance) are designed to provide more long-term support. Safety nets are meant to guarantee a minimum level of consumption and insure households against the risk of a large drop in market income.
My method for measuring the generosity of safety net programs is to add up the total amount spent on benefit transfers targeted at low-income households, and to divide it by the number of persons living in households below a given market income threshold. This approach has several strengths. First, my approach is comprehensive. The national accounts are the only data that include programs that are unique to all states and localities. Also, these data are derived from state outlays so they reflect households that actually collect benefits. Because take-up rates vary widely, some households do not receive benefits even though they are eligible. Second, my approach uses survey data for market income, which are accurate relative to survey data on transfers. Data on labor and capital income come from the ACS, which is the largest survey conducted by the federal government with over 3 million observations per year. Although using survey data on transfers would provide a clearer picture of which households receive benefits, the data are less reliable since the sample is much smaller and more likely to be affected by underreporting.
Since I am interested in the variance across states, I focus only on programs in which states have some discretion over benefits. These programs are as follows:
- Children’s Health Insurance Program (CHIP)
- Earned income credits
- Unemployment insurance
- Supplemental Security Income (SSI)
- Temporary Assistance for Needy Families (TANF)
- Supplemental Nutrition Assistance Program (SNAP)
- Special Supplemental Nutrition Program for Women, Infants, and Children (WIC)
- Worker’s compensation
- Temporary disability insurance
Social Security and Medicare, the two largest transfer programs, are not included since benefit eligibility is uniform across states and not targeted to low-income households. I define low-income households as any household in the bottom quartile of the national market income distribution. Using the 2012 ACS data, that cutoff is about $14,000. (Modest changes in the low income threshold do not affect the results.)
Following Census’ methodology, I drop persons living in group quarters since the concept of a household is not well-defined in this instance. In this exercise I am primarily interested in nonelderly adults and children, so I omit elderly, childless households from the sample. The real value of a transfer payment depends on the quantity of goods and services a household can purchase within their state. Since the price level varies across regions, the outlay data and the low-income threshold are adjusted using regional price parity multipliers for each state. This correction tends to make the safety net more generous in states dominated by rural communities, such as South Dakota, and less generous in states dominated by urban centers, such as New York.
Table 1 shows the average real transfer for a low-income person in the five most generous and least generous states. Vermont ranks as the most generous state with the average low-income person receiving about $26,000 in benefits. This is due largely to the fact that, using my measure, Vermont has the most generous Medicaid program and Medicaid accounts for about half of all of the programs I consider. Vermont also has its own refundable earned income credit and SSI program. Conversely, Georgia is at the bottom of the ranking since it has some of the most restrictive laws for Medicaid and TANF.
Table 2 highlights the results for states in the Seventh District. Iowa ranks as the most generous state and Michigan as the least generous. Overall, though, the differences between states in the region are small. Medicaid accounts for much of the difference, but income support programs also play a role. All states in the region offer a refundable earned income credit ranging from 34% of the federal credit in Wisconsin to 6% in Michigan. In Iowa, unemployment insurance replaces a high percentage of previous earnings, federal SSI recipients receive additional state funding, and SNAP benefits are not subject to household asset limits.
Figure 1 plots the relationship between the percentages of persons in a state defined to be “low-income” with the natural log of the average benefit. Average benefits are shown on a logarithmic scale since the marginal utility of benefits is assumed to decline as benefits increase. The blue line is the fit of an ordinary least squares (OLS) regression. The two variables are negatively correlated and statistically different from zero. That is, states with a large percentage of households earning low market income are also states that give the least generous benefits. Since the average poor person in high poverty states will tend to have less income than the average poor person in lower poverty states, we might expect a positive correlation since most programs tend to increase benefits as market income declines. Another reason we might expect a positive correlation is if more generous benefits strongly disincentivize work. Instead, these factors appear to be outweighed by the treatment of social insurance as a normal good; richer states are more willing to pay for the benefits that safety nets provide.
It is important to remember that there are many other types of state and local government policies that influence the welfare of low-income households. Tax policies also vary widely across states and can have powerful redistributive effects, particularly consumption taxes, which are regressive. Additionally, direct government purchases, such as the provision of education or transportation services, are not included in this exercise. Outside of the budget process, regulations influence the prices households pay for goods and services. For example, restrictive zoning laws tend to increase housing costs. Transfer payments are only part of the story. Developing a more complete accounting of the redistributive effects of state and local policies would be a valuable area for further research.Posted by Testa at 10:31 AM | Comments (0)
April 16, 2014
Seventh District Update
by Thom Walstrum and Scott Brave
A summary of economic conditions in the Seventh District from the latest release of the Beige Book and from other indicators of regional business activity:
• Overall conditions: Growth in economic activity in the Seventh District picked up in March, and contacts generally maintained their optimistic outlook for 2014.
• Consumer spending: Growth in consumer spending increased slightly in March, but remained modest. Sales of winter-related items were stronger than normal, while other sales categories, in particular light vehicles, picked up as the weather improved.
• Business Spending: Growth in business spending increased to a moderate pace in March. Growth in capital spending picked up. The pace of hiring increased, and while hiring plans decreased slightly, they remained positive.
• Construction and Real Estate: Growth in construction and real estate activity was modest in March. Although conditions improved, residential construction and real estate contacts reported that adverse weather continued to restrain growth. Demand for nonresidential construction grew at a moderate pace and commercial real estate activity continued to expand.
• Manufacturing: Growth in manufacturing production increased from a mild to moderate pace in March, with contacts from a number of industries reporting increased activity. The auto, aerospace, and energy industries remained a source of strength. Auto and steel production recovered from the weather-related slowdown, while demand for heavy machinery remained soft.
• Banking and finance: Credit conditions were again little changed on balance over the reporting period. Corporate financing costs decreased slightly, as bond spreads narrowed. Banking contacts reported moderate growth in business loan demand and modest growth in consumer loan demand.
• Prices and Costs: Cost pressures were mild. While energy and transportation costs remain elevated, they were lower than during the previous reporting period. Wage pressures were slightly lower and non-wage pressures moderated.
• Agriculture: The slow arrival of spring-like weather delayed fieldwork, but farmers were generally not too worried about the delay. Soybean prices rose relative to corn. The livestock sector moved further into the black, as milk, hog, and cattle prices increased.
The Midwest Economy Index (MEI) decreased to –0.03 in February from +0.32 in January, falling below zero for the first time since June 2013. Moreover, the relative MEI moved down to –0.01 in February from +0.23 in the previous month. February’s value for the relative MEI indicates that the Midwest economy was growing at a rate consistent with national economic growth.Posted by Testa at 1:10 PM | Comments (0)
April 3, 2014
Freight movement slows in January, while freight rates remain high—Is it the weather or something else?
The severity of this winter season has had a noticeably negative impact on everything from retail sales to industrial production. Roadway freight operations are no exception.
The effects of the extreme cold and heavy snow, which started last December and has continued into March of this year, seem to be showing up in some recent economic data on freight services. Chart 1 below contains the Transportation Services Index (TSI) for freight in the United States. The TSI contains freight data for most modes of freight transportation, including truck, rail, inland water, air, and pipeline. This index shows that on a seasonally adjusted basis, freight movement dropped in January by 2.8%. Since the data are adjusted for seasonality, the drop in January looks to be even more significant.
Though all modes of transportation have been affected by this winter’s weather, trucking arguably experienced the worst of it. Many firsthand reports (including my own) have indicated that ice and snow shut down routes in states that do not normally face such harsh wintry conditions. Extremely cold weather also made the loading and unloading of trucks more difficult, causing delays and disrupting normal schedules.
This winter’s disruptions to trucking operations were also accompanied by price spikes. According to DAT Solutions, spot rates (excluding long-term contractual prices) for dry vans, which account for the majority of long-haul freight, are up 17.6% from October 2014. These price spikes could be partially due to the severe winter weather and may only be temporary; however, some evidence points to shifting fundamentals that may be contributing to rising cost trends in the industry. Since the U.S. economy reached the bottom of the Great Recession (in mid-2009), the U.S. Bureau of Economic Analysis’s producer price index for long haul truck-borne freight has climbed at an average annual pace of 3.9%.
Many industry experts argue that tightening capacity together with rising costs in the trucking industry are driving up freight prices. As chart 2 shows, according to ACT Research, the so-called active population of heavy-duty (class 8) trucks has been declining steadily since 2007, even while the economic recovery has been ongoing.
ACT Research defines the active population of trucks as those trucks still in service that are 15 years of age or younger. The reason for this distinction is that once a vehicle reaches 15 years of age, it becomes much less likely to be used for hauling meaningful amounts of freight over long distances. So, at the same time the number of freight loads has been increasing on account of the recovering economy, the number of trucks available to carry those loads has been declining.
Another factor affecting freight rates has been the significant increase in truck prices. Truck prices started increasing in 2002 because of federally mandated diesel emission standards that required the costly development of new engine technologies. ACT Research analysts contend that since 2002 the cost of meeting these standards has added an estimated $30,000 to the cost of a new truck—a price increase of about 31%. Rising prices for new trucks have, in turn, made used trucks more attractive, causing their prices to go up as well. The average price for a used class 8 truck was higher in January of 2013 than ever before.
There is yet another factor that is likely to drive up costs for the trucking industry: the projection for a severe shortage of qualified truck drivers. The effects of the shortage, which has been in the making for some time, were somewhat mitigated during the most recent economic downturn. Since then, as freight activity has recovered, the driver shortage has become a more serious problem. A shortage of drivers, coupled with fewer trucks on the road, has tightened freight utilization rates, which are said to be approaching uncharted territory: Some estimates now have capacity utilization rates in the trucking industry in excess of 95%.
If, as I would argue, the recent slowdown in freight activity is due primarily to the severe winter weather, then missed deliveries will need to be managed. But this will not be easy. In the trucking industry, backlogs can be difficult to make up because there is only so much the trucking industry as a whole can ship—and only so much any one truck can haul (due to legal weight limit restrictions on most highways). Making up for the backlogs will result in added demands on a truck fleet that is already running at near-full capacity.
Based on this analysis, it doesn’t look like freight rates will be coming back down any time soon, especially if the economy keeps improving. As businesses moved to optimize their supply chains with techniques such as just-in-time inventory, freight has taken on an increasingly important role in their production processes. As a percent of total logistics expense for private business, trucking-related costs comprise 77.4% of transport costs and 48.6% of total logistics spending. Accordingly, when real gross domestic product (GDP) increases by 1%, some analysts estimate that the truck transportation needed to bring this about increases by 2 to 3%. Should the demand for hauling freight by truck grow dramatically, the trucking industry’s capacity would be strained under the current circumstances. When trucking capacity is strained, prices for those freight hauls that are not under long-term contract can jump. Given the changing fundamentals to the trucking industry discussed previously, some analysts argue that the recent price spikes for shipping freight via trucks will ultimately work their way into long-term contractual prices for hauling freight (which are predicted to reset throughout the year). Some estimates have the increase for contractual freight in the coming months to be in the range of 4% to 6%.
Rising capacity utilization for the trucking industry, increases in the costs of new trucking equipment, higher demand for qualified truck drivers, and a declining number of heavy-duty trucks in operation are some of the reasons that freight prices are on the rise. North American heavy-duty truck production is increasing to meet demand, but recently announced fuel economy standards will continue to add costs to the production of new vehicles—and, in turn, increase their sale prices. So while rising freight rates have historically been a good predictor of improved economic activity, there are other factors at work driving up rates at this time. It remains to be seen how all of this will affect consumer prices, but if these expected freight rate increases cannot be readily absorbed, they will have some impact on the consumer. For these reasons we will be keeping an eye on freight and freight rates in the months ahead—long after the snow has melted.
Just-in-time inventory is an inventory strategy employed by firms to increase their efficiency and decrease waste by receiving goods only as they are needed in the production process; this strategy reduces costs associated with carrying large inventories (of raw materials or finished goods, such as cars). (Return to text)Posted by Testa at 3:31 PM | Comments (0)