The Manufacturing Construction Boom and Nonresidential Investment

Deputy Asst Secretary Tara Sinclair and Asst Sec Van Nostrand and Special Asst Gupta discuss the ongoing boom in manufacturing construction. Here’s one graph:

Source: Van Nostrand, Sinclair, Gupta (2023).

One doesn’t see movements like this in response to a non-macro policy. I wondered what this was in the context of overall private nonresidential fixed investment. here’s the picture. Instead of the construction data, I use BEA fixed investment data (which is a lot less than construction data for 2023Q1 — see appendix comparing the numbers).

Figure 1: Nonresidential fixed investment (bold black line), GDPNow for Q2 (red square), nonresidential fixed investment in manucturing structures (blue bar), in non-manufacturing structures (brown bar), and rest-of-nonresidential fixed investment (green bar), all in billions Ch.2012$ SAAR. Source: BEA, Atlanta Fed, and author’s calculations.

Looking forward, as of today, the Atlanta Fed’s GDPNow indicates the following for Q2 GDP and nonresidential fixed investment.

Figure 2: GDP (black, left log scale), GDPNow for Q2 (red square, left log scale), and nonresidential fixed investment (blue, right log scale), GDPNow implied nonresidential fixed investment (light blue square, right log scale), all in billions Ch.2012$ SAAR. NBER defined peak-to-trough recession dates shaded gray. Source: BEA, Atlanta Fed, NBER, and author’s calculations.

Even as GDP slows, investment is continuing to grow more rapidly, so that the log ratio of investment-to-GDP keeps on rising. GDPNow implies that continues into Q2. In the period of the Great Moderation, investment-to-GDP peaks before the NBER peak (aside from the Great Recession). This suggests that an onset of a recession is still some time off.

Appendix:

Here is the nominal version of the series used in the Treasury document, and the BEA series for investment in structures.

Figure 3: Construction in manufacturing (blue), and investment in nonresidential manufacturing structures (black), in billions $, SAAR. 2023Q2 observation is average of April and May data. NBER defined peak-to-trough recession dates shaded gray. Source: Census, BEA, and author’s calculations.

Brandsaas et al. (2023) argue that the Census series is likely to end up being more accurate, given that it is used to revise the BEA series.

 

 

24 thoughts on “The Manufacturing Construction Boom and Nonresidential Investment”

  1. pgl

    “One doesn’t see movements like this in response to a non-macro policy.”

    Supply siders have been telling us for the last 50 years that tax cuts for rich people would give us an investment boom. How did that work out?

      1. Moses Herzog

        @ Macroduck
        Yeah sure….. writing research papers which disprove Republican/MAGA lies is substantive work. But don’t you think it’s more fun making a 280 character Tweet insulting a female PhD Economist who writes about corporate profiteering and greedflation during a health pandemic?? Cannibalistic lefties say “Yes, YES I do. Why else would I be a lefty NYC snob or Madison WI snob if I can’t eat my own kind for lunch?? I mean, did you just ask me that……REALLY?!?!!”.

  2. Econned

    Menzie Chinn,
    A (not so) random question…
    Let’s assume an anonymous commenter on a blog were to make the following claim:
    “There has been no manufacturing construction boom. Additionally, there is no nonresidential investment at all. There is only residential investment.”
    While I do understand that you undoubtedly support that blog’s author to write a post (or even multiple posts) to refute the aforementioned claim, I’m wondering if such a comment would be a worthy topic for a peer-reviewed journal? Let us know!

    1. pgl

      Hey troll – stop chirping and start writing your first paper. The Journal of Basket Weaving is waiting.

    2. Moses Herzog

      The Menzie Stalker strikes again. Starting to wonder if this stalking has homo-erotic aspects?? Not that there’s anything wrong with that.

    1. Ivan

      Well he is trying to beat the former whiner in Chief for the GOP nomination. They are in a contest of who can be the loudest complaining “they are all so mean to me, its all their fault not mine”

  4. Ivan

    Again an indication of how successful the Biden administration has been. Trump was talking about bringing jobs back to the US – Biden is actually doing it.

  5. Macroduck

    Off topic, climate change and food security –

    A wandering jet streams is identified as risking widespread failure in food production:

    https://www.nature.com/articles/s41467-023-38906-7

    I’m pretty sure lots of people have put two and two together – much of the recent extreme weather we’ve experienced is due to a weak polar vortex destabilizing the jet stream. Extreme weather endangers crop yields. Why this paper does is address the modeling problem; models which don’t account for the risk of widespread simultaneous crop failure will underestimate famine risk.

    1. Macroduck

      Speaking of which, three scholars from Erasmus U. (check out the Dutch names on these guys) have taken a look at current practices in climate risk stress testing. The title of the article gives the gist:

      Climate risk stress tests underestimate potential financial sector losses

      Henk Jan Reinders, Dirk Schoenmaker, Mathijs Van Dijk 

      From the article:

      “Given the complexity of the link between climate shocks and financial sector outcomes, we conclude that all CRST (climate risk stress test) exercises to date have substantial drawbacks…We conclude that these limitations may well lead to a significant underestimation of potential system-wide financial losses.”

      https://cepr.org/voxeu/columns/climate-risk-stress-tests-underestimate-potential-financial-sector-losses

      1. Moses Herzog

        You confused this former “slip seater” semi-driver when you said “CRST”, because I thought that stood for Cedar Rapids Steel Transport. Or was it Cedar Rapids Slave Trade?? I forgot now. Well, maybe some semi-truck industry acronyms I intentionally forgot.

    2. Moses Herzog

      I haven’t read either of the papers you linked (hopefully I can find at least time to skim-read them). But I can tell you where I live, for this time of year (past Spring) the jet stream has been unusually far south on our continent (and in this region). It has increased weather instability and made for inordinate amounts of rain for the last roughly 2 weeks. I’m hoping this will actually end up being good for farmers, but some of it has led to flooding, which often times is not what farmers want.

  6. Macroduck

    Jobs data –

    Another very good report. Note ADP counted over twice as many new jobs in June, at 497,000, as did the establishment survey at 209,000. (Households reported 273,000 new jobs.) The goods producing sector is holding up OK, with the hiring diffusion index, at 56.3, the best in 3 months. That’s consistent with reports of a normalization in inventories, though I don’t see normalization all that clearly in the data:

    https://fred.stlouisfed.org/graph/?g=16Qtt

    Weekly payrolls (average hours times average hourly earnings) rose by a healthy 0.8% in June, the combined effect of hours and earnings posting decent gains:

    https://fred.stlouisfed.org/graph/?g=16QrJ

    The payroll gain almost certainly topped the rise in prices, so households got a real gain in purchasing power.

    1. Macroduck

      The understanding of the relationship between labor costs and inflation has evolved to something like this:

      “Several papers have now demonstrated that, at least in the aggregate, unit labor costs are not very helpful for forecasting inflation once we account for lagged inflation—certainly not as good as inflation is for forecasting growth in unit labor costs.”

      https://www.chicagofed.org/publications/chicago-fed-letter/2023/477

      Average hourly earnings also seem to have limited predictive power for inflation:

      https://fred.stlouisfed.org/graph/?g=16QXf

      Which is why I find it curious that so many headlines like these have been published today:

      “Markets dip on positive jobs data, which raises new fears of Fed interest rate hikes”

      “Expect more rate hikes from the Fed after the latest jobs report”

      “Fed Seen On Track to Raise Rates After Solid June Jobs Report”

      It’s true that the Fed is primed to hike rates. It should not be true that it has anything to do with one month’s wage data.

  8. pgl

    “The boom is principally driven by construction for computer, electronic, and electrical manufacturing—a relatively small share of manufacturing construction over the past few decades, but now a dominant component.”

    The US right after WWII led the world in developing these sectors but over time outsourced their production to emerging Asian nations such as Japan, South Korea, and Taiwan while there were still some US players. With learning by doing, we had seen decades of nations using all sorts of trade protection or domestic subsidies to leap frog the other players. WE may have received low prices from all of this but the US had a competitive disadvantage in these sectors.

    Our view on the benefits of outsourcing changed during the disruptions of the pandemic and how the 2021 rapid recovery led to certain shortages of key components of production. Now we did not see any recognition of these emerging trends under Trump but Team Biden has been all over this.

    But here’s a potential problem. The Asian tigers which now include China are quite upset at what they see as American industrial policy. Avoiding another stupid trade war may be difficult. I hope Team Biden can figure out how to promote US production without triggering Asian retaliation.

  12. ltr

    https://www.fao.org/worldfoodsituation/foodpricesindex/en/

    July 7, 2023

    FAO Food Price Index

    The FAO Food Price Index (FFPI) is a measure of the monthly change in international prices of a basket of food commodities. It consists of the average of five commodity group price indices (cereal, vegetable oil, dairy, meat, sugar) weighted by the average export shares of each of the groups over 2014-2016.

    Monthly release dates for 2023: 6 January, 3 February, 3 March, 7 April, 5 May, 2 June, 7 July, 4 August, 8 September, 6 October, 3 November, 8 December.

    FAO Food Price Index continues to fall

    The FAO Food Price Index (FFPI) averaged 122.3 points in June 2023, down 1.7 points (1.4 percent) from May, continuing the downward trend and averaging as much as 37.4 points (23.4 percent) below the peak it reached in March 2022. The month-on-month decline in the index in June reflected drops in the indices for sugar, vegetable oils, cereals and dairy products, while the meat price index remained virtually unchanged.

    The FAO Cereal Price Index averaged 126.6 points in June, down 2.7 points (2.1 percent) from May and as much as 39.7 points (23.9 percent) below its value a year ago. The month-on-month decline reflects a fall in the world prices of all major cereals. International coarse grain prices fell the most, down 3.4 percent since May. A fifth consecutive monthly decline in international maize prices was mostly driven by increased seasonal supplies from ongoing harvests in Argentina and Brazil. Amidst concerns over drought conditions, some rain at the end of the month in key maize producing areas of the United States of America also lessened the pressure on maize markets. Among other coarse grains, world prices of barley and sorghum also declined, influenced by spillover effects from maize and wheat markets. International wheat prices declined by 1.3 percent in June, as harvests in Northern Hemisphere countries started. Ample supplies in the Russian Federation, where also the wheat export tax decreased in the month of June, continued to weigh on prices, while improved crop conditions in the United States of America also contributed to the downward pressure on prices. International rice prices declined by 1.2 percent in June, amid subdued demand for non-Indica rice and efforts to attract export sales in Pakistan….

  13. ltr

    https://www.nature.com/articles/s41586-023-06185-3

    July 5, 2023

    Accurate medium-range global weather forecasting with 3D neural networks
    By Kaifeng Bi, Lingxi Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu & Qi Tian

    Abstract

    Weather forecasting is important for science and society. At present, the most accurate forecast system is the numerical weather prediction (NWP) method, which represents atmospheric states as discretized grids and numerically solves partial differential equations that describe the transition between those states. However, this procedure is computationally expensive. Recently, artificial-intelligence-based methods have shown potential in accelerating weather forecasting by orders of magnitude, but the forecast accuracy is still significantly lower than that of NWP methods. Here we introduce an artificial-intelligence-based method for accurate, medium-range global weather forecasting. We show that three-dimensional deep networks equipped with Earth-specific priors are effective at dealing with complex patterns in weather data, and that a hierarchical temporal aggregation strategy reduces accumulation errors in medium-range forecasting. Trained on 39 years of global data, our program, Pangu-Weather, obtains stronger deterministic forecast results on reanalysis data in all tested variables when compared with the world’s best NWP system, the operational integrated forecasting system of the European Centre for Medium-Range Weather Forecasts (ECMWF). Our method also works well with extreme weather forecasts and ensemble forecasts. When initialized with reanalysis data, the accuracy of tracking tropical cyclones is also higher than that of ECMWF-HRES.

  14. ltr

    https://english.news.cn/20230707/eb13a262317c47568477ea8b5996adb1/c.html

    July 7, 2023

    Chinese tech giant Huawei launches latest AI model

    GUANGZHOU — Chinese tech giant Huawei on Friday launched the latest version of its Pangu pre-trained deep learning AI model, Pangu 3.0. The announcement was made at the Huawei Cloud Developer Conference held in Dongguan, south China’s Guangdong Province.

    Pangu 3.0 has a three-tier architecture. The foundation layer, L0, has five different models: natural language processing, multimodal databases, computer vision, prediction and scientific computing. L0 provides various skills to meet the needs of different industry scenarios.

    The second layer, L1, provides a variety of industry-specific models, focusing on fields such as e-government, finance, manufacturing, mining and meteorology.

    The third layer, L2, provides multiple scenario-specific models for particular industry applications or business scenarios.

    Customers can also train models using their own datasets based on Huawei’s L0 or L1 Pangu layers.

    The Pangu series was created to serve industry needs, providing excellent services to customers in various sectors, said Zhang Ping’an, Huawei’s executive director and CEO of Huawei Cloud. He added that Pangu’s mission is to help customers effectively utilize and build large-scale models, enabling intelligent upgrades.

    First launched in 2021, Huawei’s Pangu series is a family of multiple large AI models that support a variety of natural language processing tasks, including text generation, text classification and conversation systems.

    Huawei said that the Pangu series has already been of immense value in numerous industries such as finance, manufacturing, pharmaceutical research and development, coal mining, and rail.

    On Thursday, a paper * on a Pangu weather AI model was published in Nature, one of the world’s top scientific journals. The paper described how to develop a precise and accurate global AI weather forecast system based on deep learning and train it using 43 years of weather data.

    In seconds, the model can accurately predict detailed meteorological features, including humidity, wind speed, temperature and sea level pressure, the paper said. This demonstrates Pangu’s high precision compared to traditional numerical prediction methods for forecasts, which could take anywhere from an hour to a week, and its prediction speed is 10,000 times faster.

    The company has also launched its Ascend AI cloud services, through which a single-card computer cluster can provide 2,000 petaflops of computing capacity, and a 1,000-card cluster can train a multi-billion parameter model for an uninterrupted 30 days. Huawei said that more reliable AI computing power has made large language models more accessible than ever to industry customers.

    * https://www.nature.com/articles/s41586-023-06185-3

  15. Macroduck

    Off topic, revisiting an issue discussed at length here in the past –

    Business dynamism, labour market concentration, and monopsony power

    Jonathan Hambur 

    A growing literature has demonstrated the effects that concentrated labour markets can have on workers’ bargaining power and wages. This column uses Australian administrative data to explore the role that firm dynamism plays in offsetting incumbent firms’ monopsony power. It shows that higher rates of firm entry and churn are associated with more outside options and job switching for workers, and therefore less monopsony power for concentrated incumbent firms. The declines in firm dynamism observed in many countries are therefore likely to have weighed on wages not only by lowering productivity, but also by lowering worker bargaining power.

    https://cepr.org/voxeu/columns/business-dynamism-labour-market-concentration-and-monopsony-power

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