Current account data are scrutinized carefully for signs of potential or actual volatility. Deficits that seem justifiable under current conditions may have to contract if there is a sudden outflow of the foreign capital that sustains them, as occurred in Mexico in 2014-15. The IMF’s 2023 External Sector Report Policy, for example, list countries with weaker external positions than desirable. The 2023 report includes Argentina, Belgium, Canada, France, Italy, South Africa, Turkey and the U.S. in that category.
Prescriptions on how to deal with a current account deficit point out the need to adjust the savings-investment imbalance, using expenditure switching and/or expenditure reduction measures to adjust the trade balance. Recent papers by Kolerus, “What Shapes Current Account Adjustment During Recessions?”, 2021, and Bergin, Kim and Pyun, “Fear of Appreciation and Current Account Adjustment,” 2023, offer analyses of this issue. But a closer look at current account deficits reveals that not all deficits have the same components. The current account includes the trade balance, the net primary account which is dominated by investment income, and the secondary income balance, which includes personal transfers. The respective positions of the latter two accounts can complicate policy prescriptions.
To illustrate this point, consider the current account deficits of three emerging market economies: Turkey, Chile and Mexico, which have current account deficits. Turkey’s deficit in 2022 was $48,751 million. The main source of this deficit was the trade balance, which registered a deficit of $39,812 million. A relatively small primary income balance deficit of $8,565 million also contributed to the current account deficit, while the secondary income balance was negligible. Articles about Turkey’s current account rightly point to the trade deficit as a major factor.
Chile in 2022 registered a current account deficit of $27,102 million. But the biggest component was not its trade deficit of $11,016 million, but the primary account deficit of $16,520 million. This figure, in turn, reflected a direct investment income deficit of $13,267, the product of FDI inflows. A secondary income surplus of $434 million was too small to have a major impact. Any changes based on an exchange rate devaluation would need to bear in mind if and how the primary balance would change.
The final case to consider is that of Mexico, with a 2022 current account deficit of $18,046. Its trade deficit of $42,292 million was much larger, as was the primary income deficit of $33,831. How did Mexico manage to have a much lower current account deficit? The answer, of course, is the positive secondary income balance of $58,077 million. The latter shows the contributions of personal transfers by Mexican workers abroad. Its situation is not unique; Egypt has a similar configuration in its current account.
Can the primary or secondary account be adjusted to reduce a current account deficit? Begin with the configuration of the three countries above, with trade and primary account deficits and some remittance inflows. Would the reaction of the primary account to a depreciation bolster or dampen the trade account response? The immediate impact on an income earned on assets in a foreign currency should be to reinforce a rise in the trade account, while liabilities denominated in a foreign currency would offset it. A continuing deficit that needed to be financed would increase the country’s liabilities, thus worsening the income deficit.
Empirical analysis by Alberola, Estrada and Viani, “Global Imbalances from a Stock Perspective” (2020), show that in the case of the debtor countries the trade balance contributes to rebalancing the current account, which offsets the deterioration due to the income channel. In the case of a debtor economy, the trade balance channel offsets the worsening impact of the income balance. Colacelli, Gautam and Rebillad in “Japan’s Foreign Assets and Liabilities: Implications for the External Accounts” (2021) examine the case of Japan, and report that the income balance response to real exchange rate movements is smaller than the trade balance response. They also report that the main external rebalance response occurs via the trade balance, but the income response dampens it in debtor nations. Behar and Hassan, “The Current Account Income Balance: External Adjustment Channel or Vulnerability Amplifier? (2022), define the income balance to include both the primary and secondary accounts, and find that the income balance does not have an important role in stabilizing the current account. The results of all three papers imply that an assessment of the measures needed to a deal with a current account crisis should include the possible deterioration of the income balance.
Personal remittances are a major factor in the net secondary income, although there are times when government transfers predominate. For many emerging markets, these inflows partially offset the deficits in the trade balance and international investment income. Could they be used to dampen the impact of a crisis? Much would depend on the resulting savings-investment balance responsible for the deficit. Are the funds from abroad used for savings or consumption? If the latter, are the goods imports or domestic? In addition, a number of studies have shown that inflows can appreciate the exchange rate, a phenomenon known as the Dutch disease (Acosta, Lartey and Mandelman, “Remittances and the Dutch Disease” (2009), Lartey, Mandelman and Acosta, “Remittances, Exchange Rate Regimes and the Dutch Disease” (2012).
There are relatively few studies that have investigated the linkages of remittances and the current account. Bugamelli and Paterno, ”Do Workers’ Remittances Reduce the Probability of Current Account Reversals?” (2009), presented evidence that larger remittances in terms of GDP reduce the probability of a sharp current account adjustment due to a fall in international reserves. Hassan and Holmes, “Do Remittances Facilitate a Sustainable Current Account?” (2016) find that larger remittances lead to a faster adjustment of the current account in response to shocks. Lartey, “The Effect of Remittances on the Current Account in Developing and Emerging economies (2019), on the other hand, finds evidence of a positive contemporary effect but a lagged negative impact.
Other changes could affect both income balances. A contraction in national income, for example, could lower the profits of multinationals and the income deficit, while migrants who live abroad may raise the amount of funds that they send to their home countries. A worldwide downturn, on the other hand, lowers the returns on all earnings, while the migrants may lose some or all of their income in their host countries.
The impact of international factor income, therefore, deserves more attention, particularly as the primary and secondary income balances become more significant components of the current account. The trade balance will continue to be the main focus of attention, but investment income and remittances can worsen or moderate a current account imbalance. Policies to rectify a crisis by reversing a trade balance deficit will need to take these other imbalances into account.
This post written by Joseph Joyce.
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Figure 1: Wisconsin Nonfarm Payroll Employment (dark blue), Philadelphia Fed early benchmark measure of NFP (pink), Civilian Employment (tan), real wages and salaries, deflated by national chained CPI (sky blue), GDP (red), coincident index (green), all in logs 2021M11=0. Source: BLS, BEA, Philadelphia Fed [1], [2], and author’s calculations.
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March 28, 2024 — We have heard much about the puzzle that US economic performance under President Joe Biden has been much stronger than voters perceive it to be. But the current episode is just one instance of a bigger historical puzzle: the US economy has since World War II consistently done better under Democratic presidents than under Republican presidents. This fact is even less widely known, including among Democratic voters, than the truth about Biden’s term. Indeed, some poll results suggest that more Americans believe the reverse, that Republican presidents are better stewards of the economy than Democrats.
In a sense, it is not exactly surprising that so few people know that economic performance has been consistently better under one party than the other. The proposition sounds implausible on the face of it, like the sort of blatantly partisan claim that is not even worth checking out. The puzzle is the fact itself: that it is completely true.
The relevant statistics have been compiled before. But let’s update.
Since World War II, Democrats have seen job creation average 1.7 % per year when in office, versus 1.0 % under the GOP. US GDP has averaged a rate of growth of 4.23 percent per annum during Democratic administrations, versus 2.36 per cent under Republicans, a remarkable difference of 1.87 percentage points. This is postwar data, covering 19 presidential terms—from Truman through Biden. If one goes back further, to the Great Depression, to include Herbert Hoover and Franklin Roosevelt, the difference in growth rates is even larger.
The results are similar regardless whether one assigns responsibility for the first quarter of a president’s term to him or to his predecessor. Relatedly, the average Democratic presidential term has been in recession for 1 of its 16 quarters, whereas the average for the Republican terms has been 5 quarters, a startlingly big difference.
Even those of us who believe that Democrats may have pursued better policies than Republicans, overall, have a hard time explaining the big observed gap in performance. After all, many other powerful and unpredictable factors impact the economy, often dwarfing the effect of any policy levers that the president can control.
Furthermore, many policies, good or bad, have their main effects only over a time span longer than a presidential cycle. For example, Jimmy Carter deserves credit for appointing Paul Volcker as Chairman of the Fed in 1979 with a mandate to defeat inflation at all costs. The subsequent disinflation was ultimately successful, helping to set the stage for the Great Moderation of the next 20 years. But its immediate impact in 1980 was a recession. Most economists consider the Volcker monetary contraction to have been worth the price. But the downturn contributed to Carter’s failure to win re-election in November of that year. Ironically, that is the one and only recession in the last 70 years that took place with a Democrat in the White House.
So, are these differences in outcomes just the result of random chance? One would think so. But the application of universally accepted statistical methodology says otherwise.
The last five recessions all started while a Republican was in the White House (Reagan. G.H.W. Bush, G.W. Bush twice, and Trump). Readers can check out the chronology for themselves. The odds of getting that outcome by chance, if the true probability of a recession starting during a Democrat’s presidency were equal to that during a Republican’s presidency, would be (1/2)(1/2)(1/2)(1/2)(1/2), i.e., one out of 32 = 3.1%. Very unlikely. The same as the odds of getting “heads” on five out of five consecutive coin-flips. Such a rejection of equality is said to be “statistically significant at the 95% level of confidence.”
What if we go back further? A remarkable 9 of the last 10 recessions have started when a Republican was president. The odds that this outcome would have occurred just by chance are even more remote: one out of 100. [That is, 10/210 = 0.0098.]
Blinder and Watson (2016) pointed out another remarkable fact. They considered the eight times since World War II when an incumbent from one party had handed over the White House to a leader from the other party. We have had two more presidents by now. Let us update, by adding the records of Trump and Biden (so far). In five of the last 10 transitions, a Democrat was succeeded by a Republican; each time the growth rate went down from one term to the next. In five of the transitions, a Republican was succeeded by a Democrat; each time the growth rate went up. No exceptions. Ten out of ten. What are the odds of this happening by chance? The answer is the same as the odds of getting heads on 10 coin tosses in a row: ½ times itself 10 times, which is 1 out of 1,024. In other words, the difference is statistically significant at the 99.9% level.
So, one can safely reject claims of stronger economic performance under Republicans. But what accounts for the surprisingly better record under Democratic presidents? It remains a puzzle.
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Some links
Alan Blinder and Mark Watson, 2016, “Presidents and the US Economy: An Econometric Exploration.” American Economic Review, 106(4): 1015-45 April.
Jeffrey Frankel, 2016, “Does the Economy Really Do Better Under Democratic Presidents?” June. www.jeffrey-frankel.com/2016/06/27/does-the-economy-really-do-better-under-democratic-presidents/
Joint Economic Committee, US Congress, 2022, “President Biden Continues the Trend of Strong Economic Growth and Job Creation Under Democratic Presidents,” March 8, www.jec.senate.gov/public/index.cfm/democrats/2022/3/biden-continues-the-trend-of-strong-economic-growth-and-job-creation-under-democratic-presidents
John E. Schwarz, 2024, “Democratic Presidents Have Better Economic Performances than Republican Ones,” March 1, 2024. https://washingtonmonthly.com/2024/03/01/democratic-presidents-have-better-economic-performance-than-republican-ones/
This post written by Jeffrey Frankel.
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I really hope the fiscal stimulus debate doesn’t gain momentum. Not only is it premature…..but I don’t have the writing bandwidth to remind everyone how Keynesian stimulus is an outdated theory (the multiplier is close to zero) with a terrible historical track record.
That’s from a Twitter post by Mr. Brian Riedl.
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Here’s the employment outlook, now compared to November.
Figure 1: Wisconsin nonfarm payroll employment (bold black), Economic Outlook forecast from February 2024 (red), from November 2023 (light green), forecasted based on national forecast of Survey of Professional Forecasters (light blue), all in 000’s, s.a. Source: BLS via FRED, Department of Revenue, Philadelphia Fed, and author’s calculations.
The SPF implied forecast is based on 2021M07-2024M01 regression in first differences, which indicates each one percentage point increase in US nonfarm payroll employment is associated with a 0.76 percentage point increase in Wisconsin employment (R2 of 0.41). (SPF forecast interpolated to to monthly via quadratic match.)
The substantial brightening in outlook is highlighted by the gap in employment forecast from the November and February reports. That forecast matches pretty closely the ad hoc forecast I generated using SPF forecasts for national employment.
Wisconsin GDP has experienced a less dramatic change in outlook.
Figure 2: Wisconsin GDP (bold black), Economic Outlook forecast from February 2024 (red), from November 2023 (light green), forecasted based on national forecast of Survey of Professional Forecasters (light blue), all in bn.Ch.2017$ SAAR. Source: BEA, Department of Revenue, Philadelphia Fed, and author’s calculations.
The Wisconsin February outlook is slightly less optimistic for 2023, but shows faster growth in 2024, roughly matching the relationship between US and Wisconsin GDP 2018-19. (For GDP, my forecast is based on a regression estimated in first differences, 2018Q1-2019Q4, Adj-R2 = 0.42).
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Figure 1: Median 3 year CPI inflation forecast (black), 25th, 75th percentile (gray), in %. CPI inflation consistent with 2% PCE inflation target (red dashed line). NBER defined peak-to-trough recession dates shaded gray. Source: NY Fed, NBER, and author’s calculations.
As of February 2024, the inflation rate expected 3 years out is lower than it was in January 2021. While expected CPI inflation has declined, dispersion of forecasts has increased slightly.
Figure 2: Median 3 year CPI inflation forecast deviation from CPI inflation consistent with 2% PCE inflation target (black), interquartile range (sky bule), in %.. NBER defined peak-to-trough recession dates shaded gray. Source: NY Fed, NBER, and author’s calculations.
Maximal uncertainty, as measured by the interquartile range in 3 year inflation was reached in mid-2022. It’s now dropped to to rates last seen at the exit from the pandemic-induced recession.
Note that some have questioned the use of the earlier framework, pre-New Monetary Strategy laid out in August 2020 (see here for one reader’s critique). I’ll note that as far as I can tell, market commentators are still using the target in the old sense of the word (a sense that seems buttressed by Papell and Prodan-Boul’s recent examination of the various vintages of the SEP), rather than FAIT with, say, a 3 year window, as that would imply we should be seeing an implied target rate of deflation at about 0.8% per annum.
More about measuring credibility, see e.g. Bordo and Siklos (2017), Bicchal (2022).
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This is a question brought to my attention in a conversation with Joe Schulz. Schulz subsequently outlined some of the trends in residential construction. Without hard numbers broken down to Wisconsin level (that I know of), it’s hard to say how much to apportion to residential vs. nonresidential.
Here’s one thought. What does the surge in construction coincide with? It’s not so much with housing starts as with the CHIPS Act and Inflation Reduction Act.
Figure 1: Midwest housing starts single family units, 000’s, n.s.a. (blue, left scale), Wisconsin construction, mn 2017$, SAAR (tan, right scale). Source: Census, BEA.
Wisconsin real construction value added jumped right after the passage of these two pieces of legislation, while housing starts don’t start rising until 2023Q2.
Inspection of the types of construction employment further suggests that nonresidential investment of some type is pushing employment.
Figure 2: Wisconsin 12 month employment change in building construction (teal), in heavy and civil engineering (tan), in specialty trade contractors (green), and building equipment contractors (red), not seasonally adjusted. Source: DWD, and author’s calculations.
I’d imagine heavy and civil engineering more associated with building factories and putting in infrastructure (linked with manufacturing, and with alternative energy) than residential.
We do have a breakdown of construction workers between residential and nonresidential employment at the national level.
Figure 3: Nonresidential building construction employment (blue), residential building construction employment (tan), both in 000’s, s.a. Source: BLS via FRED.
Clearly, nonresidential construction employment is growing rapidly, while residential has only picked up since recently (4% vs. 1% y/y).
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How do geopolitical risk shocks affect monetary policy? After the global financial crisis, international trade relations have been increasingly influenced by geopolitical considerations. Indeed, it is now widely recognized that geopolitical risks and bilateral political tensions can have a strong influence on the functioning of the economy (Caldara and Iacoviello, 2022). Geopolitical risk shocks affect the economy through different channels. Some of them are inflationary, such as the commodity price channel, especially the oil price (Mignon and Saadaoui, 2024) [Econbrowser post] and the currency channel (Gopinath, 2015). Furthermore, other channels are deflationary, such as the consumer sentiment channel and the financial condition channel (Forbes and Warnock, 2012). It is difficult to determine ex-ante whether geopolitical risk shocks are inflationary or deflationary. Recent research suggests that geopolitical shocks tend to be inflationary throughout history (Caldara et al., 2022).
Based on a panel of 20 economies, William Ginn and I develop and estimate an augmented panel Taylor rule via linear and nonlinear local projections (LP) regression models. First, the linear model suggests that the interest rate remains relatively unchanged in the event of a geopolitical risk shock. Second, the result turns out to be different in the nonlinear model, where the policy reaction is muted during an expansionary state, which is operating in a manner proportional to the transitory shock. However, geopolitical risks can amplify the policy reaction during a non-expansionary period.
To consider the global impact of geopolitical risk, we use a rich data set for industrial production, consumer price index (CPI), short-term interest rate, GPR, and EPU for 20 economies that represent around 82% of global GDP to analyze the effect of GPR on interest rates. These twenty economies include the following: Brazil (BRA), Switzerland (CHE), Chile (CHL), Canada (CAN), China (CHN), Columbia (COL), Czech Republic (CZE), Euro zone (19 countries; EUR), United Kingdom (GBR), Hungary (HUN), Ireland (IRL), India (IND), Israel (ISR), Japan (JPN), Mexico (MEX), South Korea (KOR), Poland (POL), Russia (RUS), Sweden (SWE) and the United States (USA). We use monthly data that cover January 1999 to February 2022.
The international data for the explained and explanatory variables of the 20 economies are shown in Figure 1. In the output growth data, we can clearly see three episodes of global slowdown, namely the Internet Bubble in 2001, the Global Financial Crisis in 2008-2009, and the pandemic in 2020. The graphs for inflation show a more dispersed situation over time and between countries, except for the global financial crisis and after the pandemic. In terms of monetary tightening and loosening, we also observe that the monetary cycles induced by the global financial crisis (easing) and after the pandemic (tightening) are the most synchronized episodes. Furthermore, Economic Policy Uncertainty is larger after the pandemic. During the most recent period, we can observe elevated levels of GPR due to the War in Ukraine. More generally, the GPR has known large spikes around 2001 due to 9/11 and after 2009 due to rising tensions between the United States and China and the election of Donald Trump, as discussed in Mignon and Saadaoui (2024).
Figure 1: International Data
The Taylor rule is designed to capture the reaction of central banks to deviations in inflation and output (Taylor, 1993). By examining the rule in expansionary and non-expansionary states, this research may offer insight into how central banks adjust interest rates in response to economic conditions in the presence of geopolitical shocks. The LP model, developed by Jordà (2005), is used to estimate an augmented Taylor rule based on a GPR shock. Periods marked by high GPR have potentially adverse consequences for an economy. Central banks, when implementing monetary policy, consider the prevailing economic conditions, including states of uncertainty and geopolitical tensions. The Taylor rule provides a framework for central banks to adjust interest rates based on economic indicators, where we test whether this adjustment can be influenced by the level of the GPR.
Figure 2: Linear LP model
Note: the shock is a one standard deviation shock to changes in GPR. Confidence intervals at 90%.
Figure 3: Non-linear LP model (Transition variable: twelve-month centered movingaverage of the output growth rate) – Baseline
Figure 4: Non-linear LP model (Transition variable: recession dummy)
Overall, the linear LP (Figure 2) model demonstrates a negative relationship between the monetary policy reaction and the GPR shocks, where the policy reaction declines and is statistically insignificant. The non-linear model (Figure 3 and 4) demonstrates that a GPR shock results in a muted interest rate policy response during an expansionary state. There is no policy dilemma where the interest rate response is operating in a manner that is proportional to the transitory nature of the shock and considering the effect of monetary policy comes with a lag. The impact of a GPR shock on monetary policy turns out to be different during a non-expansionary state. The findings show that the response becomes accommodative and is statistically significant for numerous periods. This last result is robust to the choice of the transition variable (GDP, OG with HP filter, dummy variables for recessions, EPU). That being said, this more accommodative monetary policy after geopolitical risk shocks is observed in the group of more independent central banks and in the group of emerging countries (Figure 5 to 8).
Figure 5: Baseline model Non-linear LP – Advanced Economies: CAN, CHE, DNK, EUR, GBR, JPN, KOR, NOR, SWE, USA
Figure 6: Baseline model Non-linear LP – Emerging Economies: BRA, CHL, CHN, COL, HUN, IND, ISR, MEX, POL, RUS
Figure 7: Baseline model Non-linear LP – More independent central banks (Central Bank Independence – Dincer and Eichengreen, 2014): CAN, CHL, EUR, HUN, MEX, NOR, RUS, SWE
Figure 8: Baseline model Non-linear LP – Less independent central banks (Central Bank Independence – Dincer and Eichengreen, 2014): CHN, COL, DNK, GBR, IND, ISR, JPN, KOR, POL, USA
Main references
Caldara, D., Conlisk, S., Iacoviello, M. and Penn, M. (2022), ‘Do geopolitical risks raise or lower inflation’, Federal Reserve Board of Governors.
Caldara, D. and Iacoviello, M. (2022), ‘Measuring geopolitical risk’, American Economic Review 112(4), 1194–1225.
Dincer, N. N., & Eichengreen, B. (2014), ‘Central Bank Transparency and Independence: Updates and new measures’, International Journal of Central Banking 10(1), 189-259.
Mignon, V. and Saadaoui, J. (2024), ‘How do political tensions and geopolitical risks impact oil prices?’, Energy Economics 129, 107219.
* The authors thank Menzie Chinn for a useful suggestion and Elena Pesavento for guidance on state-dependent local projections. The interested readers can find the last version of the paper on SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4762672
This post written by Jamel Saadaoui and William Ginn.
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Interestingly, the Conference Board sees near zero growth in 2024Q2-Q3. This is consistent with the term spread based predictions which show a high probability of recession in 2024Q2.
Figure 1: GDP (bold black), CBO projection (blue), Survey of Professional Forecasters (red), FT-Booth median forecast (brown inverted triangle), FOMC Summary of Economic Projections March 20 (open light green square), GDPNow of 3/19 (light blue square), Conference Board as of 3/21 (chartreuse), all in bn.Ch.2017$. Source: BEA 2024Q4 2nd release, Philadelphia Fed SPF, Booth School, Federal Reserve Board, Atlanta Fed (3/19), Conference Board, and author’s calculations.
The lower path for GDP vis a vis SPF or FT-IGM survey median is likely due in part to the depressed level of their Leading Economic Indicator which only turned slightly positive in February.
Source: Conference Board.
The literature from the Conference Board indicates that LEI turning points lead GDP turning points by 7 months, so September 2024.
The Conference Board forecasts a lower Fed funds probably as a consequence of the lower projected growth.
Figure 2: Fed funds rate (black), FOMC March 2024 SEP (light green squares), Conference Board forecast (chartreuse), CME modal forecast as of 3/23 (sky blue). Source: FRB via FRED, FRB, Conference Board, CME, and author’s calculations.
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Figure 1: Wisconsin nonfarm payroll employment (black), DoR forecast (tan +), implied from SPF forecast, 000’s, s.a. Source: DWD, Wisconsin Dept. of Revenue (Nov.), SPF and author’s calculations.
The Wisconsin Economic Outlook forecast from November was based on the slowdown at the national level built into the S&P Global Market Insights forecast from that month. To the extent that the slowdown has not materialized, it’s not surprising that the forecast is being outperformed.
The SPF implied forecast is based on 2021M07-2024M01 regression in first differences, which indicates each one percentage point increase in US nonfarm payroll employment is associated with a 0.76 percentage point increase in Wisconsin employment (R2 of 0.41). (SPF forecast interpolated to to monthly via quadratic match.)
One can reasonably ask if the BLS/DWD series is mismeasuring employment due to problems with the firm birth/death model, etc. The Philadelphia Fed provides an alternative estimate based on QCEW data through Q3. This series is even higher than the official series.
Figure 2: Wisconsin nonfarm payroll employment (black), Philadelphia Fed early benchmark series (red), 000’s, s.a. Source: DWD,Philadelphia Fed.
Through the end of 2023, the Philadelphia Fed series shows 2% y/y growth, compared to 1.3% using the official series.
Finally, construction employment has slowed its torrid pace in February.
Figure 3: Wisconsin cumulative change in construction employment (tan), and in rest-of-NFP (teal) since 2022M08, in 000’s, s.a. Source: DWD and author’s calculations.
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