Charles Steindel

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The two most comprehensive data sets on economic activity at the state level are personal income and payroll employment. The wage component of personal income (much the largest) is benchmarked to the QCEW as is, of course, payroll employment. As a general rule, other state-level data (including the nonwage components of personal income and household survey numbers) are much more tenuous, based on less frequent benchmarks and limited indicative numbers.

On March 22, BEA released initial estimate of the state personal income numbers for Q4 2017 and, naturally, for 2017 as a whole. For 2017 as a whole (year average 2017 vs. year average 2016), state growth in personal income ranged from -0.3 percent (North Dakota) to a whopping 4.8 percent in Washington. Aside from North Dakota, other states with particularly low growth included Iowa (0.3 percent), Alaska (0.4 percent), Kansas (1.0 percent), Nebraska (1.4 percent), South Dakota (1.4 percent), Connecticut (1.5 percent), Kentucky (1.6 percent), New Mexico (1.8 percent) and Wyoming (1.8 percent). States other than Washington with notably strong growth included Idaho (4.7 percent),Nevada (4.4 percent), Utah (4.4 percent), Arizona (4.3 percent), Colorado (4.1 percent), California (4.1 percent), Georgia (3.8 percent), Florida (3.8 percent), and North Carolina (3.8 percent). Clearly, growth in the West (with the obvious exception of Alaska, as well as Wyoming) outstripped the rest of the nation, especially the Plains states, where a drop in farm income had a notable impact.

Data on per capita income for 2017 was also released: there is considerable dispersion by state and region. Connecticut, despite its somewhat low aggregate income gain remains number 1 among the states, with income of $70,121 per head (Connecticut’s population growth is also quite low). Mississippi, number 50, had per capita income of $36,346, barely half that of the Nutmeg State’s residents (the gap from top to bottom would be even larger if DC’s $76,986 was included in the ranking). The differences in per capital income lead to some interesting contrasts in aggregate personal income between states which have clear population differences: New Jersey’s aggregate income is larger than Ohio’s, Connecticut’s is almost the same as Missouri’s, and Texas’ is only about 10 percent higher than New York’s despite Texas now having a population nearly 50 percent higher than New York.

Of course, these per capita figures do not take into account differences in prices and taxes across the states. BEA does produce estimates of real per capita disposable income by state, but these annual figures are released with a substantial lag. The price data by state are derived from estimates of state level PCE deflators, and these can’t be compiled until the state consumer spending numbers are produced. It is true that states with lower nominal pretax per capita incomes also tend to have lower tax burdens and lower prices, so the gaps are reduced somewhat when these adjustments are made, though there is not a wholesale reversal of rankings. It is often forgotten, though, that the personal income numbers are net of property tax—many “high-tax” states, such as New Jersey, are such because of high property taxes--though some may question the corresponding inclusion of imputed rental income in the computation of income). Moreover, no adjustments are made for differing levels of state and local government services consumed by residents without payment.

In the preliminary Q4 2017 results, state growth from Q3 ranged from 0.2 percent (North Dakota) to 1.5 percent (Nevada). These figures could well change substantively with the receipt of more comprehensive QCEW and other data.

State Employment 
On March 13 BLS released January labor market estimates by state, and on March 23 the February numbers came out. These figures include the regular benchmark revisions to the QCEW of the payroll numbers, which had been incorporated into the national figures in the February 2 release. In late February BLS had issued revised household data, reflecting the introduction of new Census Bureau information. New seasonal adjustment factors were also rolled out with these releases.

BLS introduced some changes into the payroll data with the January release. First, as was the case in the national report, industry figures are now reported on a 2017 NAICS basis, updated from the prior 2012 NAICS basis. Second, BLS is now applying the same concurrent seasonal adjustment methodology to state payroll data that it has been using for several years on the national data. As is the case in the national numbers, the updated seasonal factors will, prior to annual revisions, only be applied to the newly reported and prior two months.

BLS does not force the sum of state payroll employment to equal the national total (in the household survey the sum of the state values for the labor force and employment does equal the national totals for not seasonally adjusted series). Thus, there is a sense in which the “sum of the states” figure is a separate estimate of the national payroll situation. With both the national and state data now benchmarked to the QCEW, it is a good time to check on how they compare.

The answer is that the state sums have shown a bit less robust growth than the national aggregate. Over the course of 2017 (December 2016 to December 2017), the national release shows that nonfarm payroll employment rose 2.188 million; the sum of the states’ figure was up 2.073 million. While that gap looks fairly modest, it was considerably larger in 2016: the national total was up 2.344 million, more than 300 thousand greater than the 2.035 million reported by the sum of the states. The levels of the two aggregates, though, are quite close (in January, the sum of the states total was 147.838 million vs. the national figure of 147.864 million). Comparable comments can be made about the not-seasonally adjusted data.

Turning to the February figures, the household survey numbers suggest that regional differences in the tightness of job markets may be, despite much discussion, fairly limited at this point in the cycle. The state with the highest unemployment rate in January was Alaska (7.3 percent), compared to the low state, Hawaii (a remarkable percent)—a 5.2 percentage point gap. In the lower 48 states the range was a much narrower 3.2 percentage points: ranging from New Mexico’s 5.8 percent to 2.6 percent in both New Hampshire and North Dakota.

On the payroll side, Texas had the largest absolute increase in February (40,500 jobs); in percentage terms the largest gains were in Tennessee and Virginia (both up .5 percent). The sum of the states’ aggregate showed a larger increase in February than did the national report (350,000 vs 313,000). Despite the size of the aggregate gain, most states reported virtually no change in payrolls from January to February (a few report small losses); only 11 states had increases that were deemed statistically significant. These gainers were in the Northeast (Massachusetts, New York, and New Jersey), the Middle West (Illinois and Michigan), the Southeast (Florida, Georgia, South Carolina, Tennessee, and Virginia), as well as Texas.

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The two most comprehensive data sets on economic activity at the state level are personal income and payroll employment. The wage component of personal income (much the largest) is benchmarked to the QCEW as is, of course, payroll [...]

State Personal Income and Employment

<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<html><head>
</head>

	 
 
 
 
 


On March 13 BLS released January labor market estimates by state. These figures include the regular benchmark revisions to the QCEW of the payroll numbers, which had been incorporated into the national figures in the February 2 release. In late February BLS issued revised household data, reflecting the introduction of new Census Bureau information. New seasonal adjustment factors were also rolled out with these releases.

BLS introduced some changes into the payroll data with this release. First, as was the case in the national report, industry figures are now reported on a 2017 NAICS basis, updated from the prior 2012 NAICS basis. Second, BLS is now applying the same concurrent seasonal adjustment methodology to state payroll data that it has been using for several years on the national data. As is the case in the national numbers, the updated seasonal factors will, prior to annual revisions, only be applied to the newly reported and prior two months.

BLS does not force the sum of state payroll employment to equal the national total (in the household survey the sum of the state values for the labor force and employment does equal the national totals for not seasonally adjusted series). Thus, there is a sense in which the "sum of the states" figure is a separate estimate of the national payroll situation. With both the national and state data now benchmarked to the QCEW, it is a good time to check on how they compare.

The answer is that the state sums have shown a bit less robust growth than the national aggregate. Over the course of 2017 (December 2016 to December 2017), the national release shows that nonfarm payroll employment rose 2.188 million; the sum of the states' figure was up 2.073 million. While that gap looks fairly modest, it was considerably larger in 2016: the national total was up 2.344 million, more than 300 thousand greater than the 2.035 million reported by the sum of the states. The levels of the two aggregates, though, are quite close (in January, the sum of the states total was 147.750 million vs. the national figure of 147.864 million). Comparable comments can be made about the not-seasonally adjusted data.

Turning to the January figures, the state numbers generally showed less rich gains than the national estimate: the sum of states figure was up 170.5 thousand against the national increase of 239 thousand (as these things go, this was not such a marked discrepancy: last September's weak national increase of 14 thousand was dwarfed by the 106 thousand loss for the sum of the states, while in October the strong national increase of 271 thousand was small in comparison to the outsized 475 thousand gain for the sum of the states). Only 3 states-California, Maryland, and New Jersey-reported job gains that BLS saw as statistically significant (North Dakota had a statistically significant loss of jobs).

The household survey numbers suggest that regional differences in the tightness of job markets may be, despite much discussion, fairly limited at this point in the cycle. The state with the highest unemployment rate in January was Alaska (7.3%), compared to the low state, Hawaii (a remarkable 2.1%)-a 5.2 percentage point gap. In the lower 48 states the range was a much narrower 3.3 percentage points : going from New Mexico's 5.9% to 2.6% in both New Hampshire and North Dakota.

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On March 13 BLS released January labor market estimates by state. These figures include the regular benchmark revisions to the QCEW of the payroll numbers, which had been incorporated into the national figures in the February 2 [...]

January State Employment Report

Ratio of job openings to the number unemployed in the US now below pre-pandemic level

U.S. layoffs not showing as much cooling in labor markets as unemployment rate

While goods prices have moderated, service price inflation remains elevated and sticky

Since Jan 2023, BCEI US outlook has improved, worsened for Japan and the euro area

Persistent negative US economic surprises leading to big rate-cut expectations

Germany is Euro area weakling

Flash PMIs show good start to Q3 in US; loss of momentum in the euro area

Chinese economy on downtrend could threaten global economy

Working age population labor force participation near all-time high as retirements increase

Blue Chip forecasters look for widespread policy rate cuts just ahead

Flash June PMIs point to a widespread slowdown apart from the US

Announcement of snap French election is causing concern across Europe

The UK economy’s post-pandemic under-performance relative to its pre-pandemic trend

US Beveridge Curve back to near pre-pandemic levels

Negative economic surprises in the US but positive in Europe, especially the UK

Latest PMIs show US losing momentum while RoW is gaining momentum

Sticky services prices behind sticky overall PCE inflation

Flash April PMIs point to slower growth in the US but a pickup in Europe and Japan

Outlook strengthens for US while it weakens for other major economies

Crude petroleum prices are having a big impact on inflation expectations

Inflation surprising to upside in the US; by contrast to the downside in the euro area

 Gross Domestic Income is catching up with GDP

FOMC expects GDP growth above trend over the next three years

US core CPI inflation has begun to reaccelerate

Fiscal-policy aided factory building in the U.S. provided an unusually large boost to growth last year

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Shashwat Indeevar

The Yale Environmental Performance Index (EPI) 2024 Report states that there is a positive correlation between a country’s living standards and its progress toward achieving environmental goals. We wanted to explore that relationship in hopes of both shedding light on paths to making further progress toward environmental sustainability and highlighting any roadblocks. As Brookings noted in its commentary, “Developing Countries are Key to Climate Action,” it is imperative that any solutions for global climate change take into consideration the competing goal of increasing living standards in developing nations.

We ran regressions on 175 countries in cross section to quantify how living standards (as measured by 2023 per capita GDP) affect progress on environmental issues (as measured by the 2024 EPI). From these data, we can verify that per capita GDP is strongly correlated with EPI scores. Like the Yale study, we noticed that the gains from higher per capita GDP diminish as nations become wealthier. We were able to model this by taking logs of per capita income. As the chart shows, an increase in per capita GDP from 4 to 5 ($10,000 to $100,000) is associated with an increase of 15 points in the overall EPI score. The goodness of fit (adjusted R2) for this regression is 60.4 percent.

240903PS1.jpg

In general, affluent societies can afford to devote more resources to improve the environment. But there is a tradeoff: As the Yale report notes, higher per capita GDP gives a nation the means to produce/purchase more goods and services, implying more GHG emissions, not less. For each nation, the balance of these competing wants – more goods versus a cleaner environment – will determine the progress toward environmental goals.

We extended this line of inquiry by asking if the tradeoff differs for various environmental goals. The Yale data identify three main goals – environmental health, climate change, and ecosystem vitality. We regressed per capita GDP against these three EPI subcomponents separately. Our hypothesis was that the correlation would be strongest where the environmental issues are most tangible for nations’ citizens – environmental health. The wealthier a nation is, the more society will be willing to spend to buy clean air and water, waste treatment, and elimination of poisons. People see the local environment every day and it impacts their lives directly. In contrast, nations may be less inclined to devote resources to environmental issues where their populations are not immediately affected, such as climate change and ecosystem vitality.

It turns out that per capita GDP actually is significantly correlated with all three environmental goals – environment, climate, ecosystems. However, the three correlations are very different in terms of degree. The following table shows that the standard of living is a selective driver of environmental goals.

240903PS2.jpg

Increases in per capita GDP are correlated with huge gains in the environmental health score (EPI-Environment), and the regression has by far the closest fit. The impacts of per capita GDP on climate (EPI-Climate) and ecosystem (EPI-Ecosystem) goals are much more muted, with weaker goodness of fit. The strong correlation with the Environment component is the main driver of the correlation with the overall EPI. These results imply that higher living standards alone are not enough to bring about meaningful improvements on climate change and ecosystem vitality.

Based on IMF Greenhouse Gas Emissions, we estimate that developing countries account for 75.8 percent of current emissions. This is likely to skew further as these countries industrialize. Less affluent nations have little incentive to make progress on climate and ecosystem goals. As the Brookings commentary points out, with limited resources, these countries have development imperatives that both supersede and counter environmental goals.

The only way to get buy-in on climate initiatives from developing nations is for developed nations to offer aid. Developed nations need to help with industrialization in exchange for movement away from polluting production processes, especially the burning of coal, and toward cleaner technologies.

More generally, we believe climate goals need to become bigger priorities, even in developed nations. As with any public good, government needs to take the lead because people and businesses will act in their best interest individually, resulting in a free rider problem – people get very little incremental personal benefit from their own expenditures on these initiatives. Also, governments need to more effectively educate their populations that progress on climate goals will improve lives in the future.

Note: We accessed the Yale EPI and the IMF GHG datasets from the Haver ESG database and the comparable country-specific GDP per capita figures from the Haver WDI database.

**References:**
Bhattacharya, A., Kharas, H., McArthur, J. W., (2023). Commentary: Developing Countries Are Key to Climate Action. Washington, DC: Brookings. March 3, 2023. [https://www.brookings.edu/articles/developing-countries-are-key-to-climate-action/ ](https://www.brookings.edu/articles/developing-countries-are-key-to-climate-action/ )

Block, S., Emerson, J. W., Esty, D. C., de Sherbinin, A., Wendling, Z. A., et al. (2024). 2024 Environmental Performance Index. New Haven, CT: Yale Center for Environmental Law & Policy. epi.yale.edu

Climate Goals Hinge on Support for Developing Nations

Joseph G. Carson

The ratio of the home price to median household income has nearly doubled since 2000. It's counterfactual to think that would have occurred if the BLS (Bureau of Labor Statistics) had not changed in 1999 its survey of owner-occupied housing to measure implicit owner-rents. Unbeknownst to many, the change has led to a significant undercount of housing and overall CPI inflation for decades.

In 1999, BLS made a significant change by 'simplifying the housing survey' to measure owners-rents in the CPI. It no longer tracked two types of housing units, owners and renters, with different characteristics and, in many cases, various locations. BLS argued that this change was necessary because it could no longer find an adequate sample of owner-occupied units for rent. BLS stated that the change was made to ensure the accuracy of the data, but the actual outcome was an understatement of housing inflation and overall inflation.

During the housing boom of the early 2000s, a crucial period that underscored the importance of accurate inflation data, I debated with BLS at an annual economic conference in 2004. Our research found that the rental survey underestimated actual or experienced inflation by several hundred basis points each year in the owner-occupied housing market. My main point was that the owner and rental markets were two distinct housing markets with different vacancy rates, and the latter was a primary determinant of rent changes.

BLS countered, stating, "There is no clear theoretical relationship between the homeowner vacancy rate and owners' equivalent rent." I emphasized that the practical relationship states otherwise. When housing prices are rising rapidly, and there is a short supply of housing units, why would owner-rents track the rental market and not the owner-housing market? Different markets yield different inflation.

This practical relationship, often overlooked, is crucial to understanding the actual inflation rates in the owner housing market. During the housing boom of the early 2000s, owners' rents increased between 2% and 3% per year, as they were linked statistically to rents of primary residences, while house prices rose at double-digit rates. Even today, owners' rents are not tracking house price inflation.

Many factors are behind house price inflation. Yet, if consumer price inflation tracked actual house price inflation versus a "hypothetical measure," it is hard to argue against the view the fact that overall inflation would not be higher, as official and market interest rates would be as well, resulting in a median house price much below current levels.

The change in the housing rent survey is a much more significant BLS blunder than the recent controversy over the overstatement of payroll job growth, as it affects reported inflation, inflation adjustments to entitlement programs, and, in some cases, workers' wages and official and market interest rates.

Another BLS Blunder: Basing Inflation Measurement on Theory and Not Logic or Reason

The Federal Reserve Bank of Philadelphia’s state coincident indexes in July were soft. Connecticut was (again) on top with a .8 percent gain from June, but Alabama was the only other state with an increase of as much as .5 percent. A full 22 states saw declines, led by a plunge of 1 percent in Massachusetts (suggesting that the Hartford-Springfield MSA performance was near zero?). And, over the 3 months ending in July, 14 states were down, with Massachusetts off 1.5 percent, and Missouri and Montana seeing 1 percent drops. Connecticut was also on top at this horizon, with an increase of 2.2 percent (clearly, something odd must be happening around the intersection of I-84 and the Mass Pike…). Over the last 12 months, 6 states were down, and another 7 saw increases of less than 1 percent. Continuing the New England focus, Rhode Island’s index was off 1.7 percent. Arizona had a 4.6 percent increase, and 4 other states—mainly in the west—had gains of 3 percent or more.

The independently estimated national estimate of growth over the last 3 months (.4 percent) seems a bit lower than the state figures would suggest, but the 12-month result (2.4 percent) looks to be roughly in line with the state numbers.


State Coincident Indexes in July 2024

Viewpoints: March 2018

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State Personal Income and Employment

January State Employment Report