What Happened to Labor's Share of National Income around 1970 and 2000?

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Notes

The Neoclassical Causal Model

 

Why is the US Labor Share in Income Declining and Will It Stop?

This page is UNDER CONSTRUCTION. Your comments and answers to questions would be appreciated. The topic is by no means simple and easy to analyze!

Except for a peak in the year 2000 (graphic above), Labor's Share of national Income (Q/L) in the United States has been consistently declining. There are two outstanding questions: (1) Why? and (2) When will it stop or possibly reverse? This post tests seven State Space models of the US economy to decide which model best explains the decline and forecasts what might happen in the future under each of the different models. The "best" model explains the labor share time paths as a result of inputs from the World System in which the US Economy is embedded.

The competing explanations for the decline in Labor's share are:

• Technological Change Technology can be embedded in the Capital Stock and cheaper Capital can be Substituted for Labor.
• Dynamics of the US Economy Shocks to the economic system (for example, technological shocks) can lead, over time, to changes in Labor's Share.
• Globalization and the World System Labor income can be driven down by cheap global competition and rewards for capital-labor substitution (using the KOF Index of Globalization).

In the graphic at the start of this post, six of the models are compared against a Random Walk, that is, no explanation.

In Neoclassical Economic Theory, Labor's Share of National Income and Capitals's share are constant parameters. At equilibrium, both are constant. Over time, differences can result from shocks and resulting system dynamics.

In Marxist Theory, Labor's share is driven to the subsistence level necessary to reproduce labor. Profit is whatever is left over after subsistence wages are paid.

ChatGPT summarizes it's findings as:

Here is a Causal Diagram of the ChatGPT explanation.

or a Simplified Model:

From the AIC Statistics below, the WL20-Input Model is best. However, the BAU model is a close second and the confidence intervals overlap.

Notes

Questions

1. What policy measures (if any) would you recommend to address the decline in Labor's Share of National Income?
2. Develop the Causal Diagram above in Neoclassical From (see see this post).
3. Derive the Simplified model of Labor's share from the Causal Diagram above using Loop Reduction Rules.
4. In the references below, do you find the World System mentioned directly or just indirectly? Do you think this is an important omission?
5. Is it necessary to go on to test the structural models (Solow-Swan vs. Marx-Ricardo) to understand the decline in Labor'ss Share?

References

NY Times (Sep 7, 2026) Why is Labor's Share of National Income Declining Is Technology the most important factor?

BLS, Second Quarter 2026, Revised, Economic News Release Labor productivity by sector

IMF (2017) What Explains the Decline of the U.S. Labor Share of Income? An Analysis of State and Industry Level Data we find that in addition to changes in labor institutions, technological change and different forms of trade integration lowered the labor share. In particular, the fall was largest, on average, in industries that saw: a high initial intensity of “routinizable” occupations; steep declines in unionization; a high level of competition from imports; and a high intensity of foreign input usage.\

NBER (2018) Is Automation Labor-Displacing? Productivity Growth, Employment, and the Labor Share We find that automation displaces employment and reduces labor's share of value-added in the industries in which it originates (a direct effect).

BLS (2020) Assessing the Impact of New Technologies on the Labor Market: Key Constructs, Gaps, and Data Collection Strategies for the Bureau of Labor Statistics Private and public decisions related to labor markets and working conditions are increasingly being influenced by technological considerations. Spurred by a wave of technological developments related to digitization, artificial intelligence (AI), and automation, governments around the world have declared that the creation and deployment of these technologies present both important opportunities and challenges to their citizens.

AER (2022) The Decline of the Labor Share: New Empirical Evidence We use time series techniques to estimate the importance of four main explanations for the decline of the US labor income share: rising firm markups, falling bargaining power of workers, higher investment-specific technology growth, and more automated production processes ... Our results point to automation as the main driver of the labor share.

 

Wikipedia Links

• Solow-Swan  an economic model of long-run economic growth. It attempts to explain long-run economic growth by looking at capital accumulation, labor or population growth, and increases in productivity largely driven by technological progress. At its core, it is an aggregate production function, often specified to be of Cobb–Douglas type, which enables the model "to make contact with microeconomics".
• Marxian Economics  a heterodox school of political economic thought. Its foundations can be traced back to Karl Marx's critique of political economy.
• Ricardo: Iron Law of Wages a proposed law of economics that asserts that real wages always tend, in the long run, toward the minimum wage necessary to sustain the life of the worker. The theory was first named by Ferdinand Lassalle in the mid-nineteenth century. Karl Marx and Friedrich Engels attribute the doctrine to Lassalle (notably in Marx's 1875 Critique of the Gotha Program), the idea to Thomas Malthus's (1798) An Essay on the Principle of Population, and the terminology to Goethe's "great, eternal iron laws" in Das Göttliche.
• Akaike Information Criterion (AIC) an estimator of prediction error and thereby relative quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models. Thus, AIC provides a means for model selection.
• Labor's Share the wage share or labor share is the part of national income, or the income of a particular economic sector, allocated to wages (labor). It is related to the capital or profit share, the part of income going to capital, which is also known as the K–Y ratio. The labor share is a key indicator for the distribution of income.

Solow-Swan

In the Solow-Swan Neoclassical economic model, wages (W) and profits (R) have fixed parameters in the model, w and r respectively.

Marx-Ricardo

In the Marx-Ricardo model, the Iron Law of Wages determines that, in the long run, the wage parameter, (w), is fixed at the level of subsistence as a result of market pressure and capitalist exploitation.

AIC Statistics

In terms of the AIC Statistics (smaller is better), the best model takes the World System (WL20 model) as input.

TECHE Forecast: Union Membership

Union Membership is forecast to be zero around 2060 as a result of Technical Efficiency Changes (TECHE). However,

There is not a lot of separation between the model AIC Statistics (above) and their confidence intervals.

TECHE Forecast: KOF Globalization

The KOF Index of Globalization is forecast to continue growing beyond 2100.

USL201 Forecast: W Index

The WL20 Model is in growth-and-collapse mode and is forecast to peak after 2050.

 

US (1960-2100) If Things are so Great, Why is Hardship in the US Increasing?

 

Measurable Hardship in the US (graphic above), is increasing and projected to increase until 2025. If US economic performance is so great, why is this happening? And, why does Hardship decrease after 2025 in the estimated model (see the Boiler Plate) above?

Quantitative Counterfactual Analysis indicates that impacts from growth in the World System are driving Hardship in the US. Decline in Hardship after 2025 is the effect of slowing growth in the World System. In other words, Growth in the World System has increased Hardship in the US even though the US is the Hegemonic leader of the World-System.

Part of the reason is that the current US Administration (Trump II) is cutting benefits for SNAP (Food Stamps) and other US Welfare programs. Why "millions of Americans" have to be on food assistance in the first place is a question that bothers me and the answer has to do with World-System forces.

The graphic at the beginning of this post is a forecast driven by outputs from the WL20 model. To determine the effect of World System forces, we can rerun the forecast with the BAU model (no input from the World System).

The BAU model shows Hardship reaching a high-level plateau after 2100. The Trump II Administration's actions to withdraw from the World-System and their attempts to cut US Welfare programs is an attempt to bring this Hardship Future into being. Increasing Hardship in the US was one of the prices of Hegemonic Leadership. 

The peak and decline of the World System would benefit the US if it still stays connected to the World-System. High levels of Hardship and World-System isolation are possibly a benefit to some elements of the US Right-Wing elite, but not most of the US Public. The Hardship Future will likely also be one of Conflict and Chaos--another potential benefit to the Right-Wing.

You can experiment yourself with the USL20 Hardship model here. For comparison, the System Matrices for the the USL20 Hardship model with World Input are presented in the Notes. Bootstrap 98% Confidence intervals for coefficients in the Systems Matrix (F) are provided in the code. Both models are stable.

Is there anything in the US Economy that controls Hardship or is it primarily driven by World System conditions? Economic theory doesn't have much (or anything) to say about Hardship. Controlling Hardship is essentially a Political Systems problem (among a lot of there things here), but current Neoliberal Ideology seems to rely on Economic growth to resolve all problems. In a future post, I'll investigate the topic of what (if anything) controls Hardship.

Notes

Hardship Index

The following indicators (from Shefner,  Roland and Pasdirtz, 2015) were used to construct the HARD (Hardship) Index. All data were taken from the World Development Indicators (WDI).

In addition to HARD1, two other indexes were constructed, HARD2 (dominated by Unemployment) and HARD3 (dominated by Inflation). Each index explained another 10% (0.841% and  0.933%, respectively) in the indicators. So, indeed, Unemployment and Inflation were important components of US Hardship, but the other indicators also played a role. Whether the US Electorate made a good choice in electing the TRUMP II administration to resolve their issues with hardship will have to wait and be seen over the next four years.

The Measurement Models for the WL20 Index and the US_HARD index are presented above. The time series forecast plots are presented below. The WL20 index first:

Growth in the World System (W1) peaks in 2040 and is forecast to decline after that. The Food-Market Index (W2=(LP+P.Wheat.-TEMP) declines throughout the period because Global Temperature is increasing and the Oil- Market-Environmental Index (W3=(P.OIL+P.WHEAT-OIL-EF-Earths) increases to a plateau in 2075.

And the the WL20 HARD index:

 

Overal Hardship (W1) increases through the period while W2 (Unemployment) and W3 (Poverty) are cyclical.

World-System (2015-2030) US Egg-Price Controversey

 

To be honest, I never thought I would be blogging about Egg Prices, but evidently the price of eggs and other grocery items may have helped Trump win a second term. Had Biden imposed price controls during his administration, there would have been a terrible outcry among pundits and economists. There appears to be no consensus about price controls and Neoliberalism insists on leaving the markets alone to set prices.

What interests me about the Egg-Price Controversy is that Americans do not seem to understand how markets work. For those that have gone to college, they must have been asleep in ECON 101 or have not connected Supply and Demand Curves to the US Capitalist System. Shocks such as COVD-19 or the Bird Flu (H5N1) Pandemic affect the supply of chickens (eggs) as herds are culled and, according to ECON 101, if supply decreases then prices increase. I guess Americans think prices will always be constant and if they aren't then it is the fault of the unlucky Presidential Administration in charge during the shock. 

Part of the problem here is the ECON 101 understanding of how markets work. Adjustment to Supply shocks is supposed to be instantaneous. But, time to adjustment is most likely a function of the size of the shock.

The graphic above shows shocks to Egg Prices from 1980 to the present. The shocks during COVID and the Bird Flu were very large by historical standards. There is not a lot of historical experience to predict how the market will respond, but it won't be instantaneous.

The graphic above shows the response of egg prices to systemic shocks.  The strongest forces (statistically) driving egg prices come from the World-System (something likely not discussed in ECON 101). The first graph above shows the Price of Eggs in January as a function of growth shocks to the World System. As the World System grows in response to shocks, egg prices increase. The assertion by Techno-Optimists that markets always reduce prices has to be tested in each market; it fails in the Market for Eggs.

The second graph shows shocks to the World-Market compared to Global Temperature. Regardless of what markets do, shocks to Global Temperature reduce egg prices (chickens must like a warmer climate, to a point). Finally, the third graph shows World-Market conditions compared to the Ecological Footprint. World-Market shocks increase egg prices. It takes almost ten years (at least in the model)  to work the shocks out of the system. Since, during the decade, there are likely to be more shocks, establishing dynamic causality is always difficult and contentious.

What is the Working Class in a Capitalist System to do? You are supposed to play by the rules and reduce your demand for eggs. Instead, you ask for the Socialist solution of Price Controls or elect a Far Right Wing Presidential Candidate to solve the problem who thinks that eggs come from a machine in the back room at McDonalds. Wouldn't it be easier and make more sense to switch to a vegan breakfast without eggs until everything blows over? Let the Price Gougers pound sand. Capitalism is certainly full of Contradictions that can make life miserable for consumers.

If you notice from the forecast at the beginning of this post, things are only going to get worse for Egg Prices! Shocks seem to be getting bigger as do the model's prediction intervals.  Keep in mind that no one knows the future and a model is not reality. The important issue is to understand how to protect yourself in a Capitalist System, a system that will not change soon and, when it does change, will produce massive shock waves that might make COVID and the Bird Flu look mild.

NOTES

Here is a more detailed look at the Error-Correcting Controllers (ECCs, a concept from Systems Theory not Economics) The second ECC shows how environmental conditions control egg prices (0.775 LP + 0.411 P.Wheat. + 0.241 P.Oil. - 0.235 TEMP) where LP is the Living Planet Index, P.Wheat. is the price of Wheat, P.Oil. is the price of Oil and TEMP is global temperature (see the Measurement Matrix above). In other words, favorable environmental conditions in the World System reduce the price of eggs. Finally, the third ECC shows another environmental World-Market controller  (0.712 P.Oil. + 0.461 P.Wheat - 0.241 Oil, - 0.293 EF) where Oil is World Oil production and EF is the Ecological Footprint. World Markets, when compared to Environmental conditions. Against a background of Egg Producer Price Gouging, World Markets and Environmental conditions are also taking their toll.

Another aspect of the Egg-Price controversy is the role of Technology. The Techno-Optimist Manifesto and  Neoliberalism both claim that Technology will drive prices down in a free market. I don't see that happening in the Egg Market. Technology (in the form of Vaccines) could reduce the impact of Bird Flu  but there is a problem: it is expensive to inoculate an entire herd of chickens, especially those that are going quickly to the slaughter house. There is some discussion of inoculating laying hens, but inoculation will only add to egg prices. Because there is a World market for chickens, culling the herd is (surprisingly) more cost effective since many countries will not accept chickens from infected herds. So, the effect of Technology (productivity increase) is not very clear in this market.

You can experiment  with the effect of Technology on Egg Prices here. You can see that the effect, at least in the short run, is not to lower prices as claimed by The Techno-Optimist Manifesto and  Neoliberalism. Sweeping claims about markets and technology always have to be tested.

World-System (1970-2060) US Debt Crisis

The US just had another debt crisis to join France (here), Germany (here) and Canada (here). Debt Crises have been quite the political spectacle, almost closing down the government in the US and toppling governments in European countries. Hitting the Debt Ceiling and Government shutdowns are nothing new for the US (here). Deficit Hawks have used the repeated crisis to impose Austerity on the US Government, threatening to dismantle Social Security, Health Care and Welfare programs while giving tax cuts to the wealthy. 

What is somewhat confusing about all this is that there is a branch of Economics called Modern Monetary Theory (MMT) that suggests that there can be no debt crises when governments control their own currency, as do the governments in the US, France, Germany and Canada. Populist  Deficit Hawks argue that everyone understands that we can accumulate too much debt and wind up in bankruptcy. MMT counters that if individuals go into too much debt they cannot simply print money to get out of debt as modern governments can. As long as there are slack resources in the US Economy, government deficit spending will not create inflation. If you are not familiar with the theoretical arguments, the controversies make interesting reading (here and here).

From the perspective of Systems Theory, Debt Crises reveal yet another Error Correcting Controller (ECC) that is being used to control outputs of the Political System. Regardless of theoretical and rational considerations, the DEBT ECC triggers an important feedback loop we need to understand. If governments have to go into debt to address the Climate Crisis or any other of the many Overlapping Crises, ideas about DEBT will assert themselves as a constraint on spending.

In the graphic above, I have displayed a history of US Debt from 1970 to the present and a forecast for the future out to 2060 by political administration. Debt has been fairly close to the (increasing) attractor path except during the Clinton Administration when it went down, during the Obama Administration when it went up and during the Trump I Administration when it went way up (above the 98% prediction interval) as a result of the COVID-19 Pandemic. The USL20 model's forecast for the future is that US Debt will be declining but with rather wide prediction intervals. Given the historical data, almost anything can happen.

Notes

Data are taken from the World Development Indicators (WDI). All variables are in standard scores. The methodology used to create forecasts is similar to the one used by the Atlanta Federal Reserves GDPNow app. Prediction intervals are generated using a Bootstrap algorithm in the R programming language. The Akaike Information Criterion (AIC) is used for model selection.

You can run the WL20W US BAU Model here. From my perspective, the future of US Debt depends on the future of the US economy, which is unknowable but about which I have a forecast (here).

 

US Retail Sales

Retail sales became the object of controversy this year when Paul Dales of Capital Economics challenged the "conventional wisdom" that Black Friday sales are a good predictor of yearly sales. In other places (here and here) I've described how the methodology used by Mr. Dales is probably flawed (I actually don't know what methodology was used but I made some guesses based on computer simulation). Another questionable part of Mr. Dale's work is the idea that any week during the year would be a good predictor of yearly sales. Sales forecasts would typically be based on some type of macro-economic time series model that uses multiyear data, the more the better. In this post, I will use multiple state space models to predict US retail sales. The results suggest a different story about what drives yearly sales in the US retail sector.

The Financial Forecast Center generates forecasts of US Retail Sales Growth Rates (here). The current forecast is displayed in the graphic above. The forecasts are generated with artificial intelligence software, not macro-economic models. As such, the FFC models do not have the biases associated with models based on a priori theoretical considerations. Their results show that retail sales growth rates have been declining since 2010 and are predicted to drop below 2% by 2013.

The FFC provides monthly, not seasonally adjusted, retail sales data (RSAFSNA) taken from the US Department of Commerce (here) in millions of dollars starting in 1992. When we plot that data for the period 2007-2012 (from the beginning of the financial crisis to the beginning of this year) we see very clearly that retail sales are elevated during the last two months of each year and that peak year-end sales pretty much follow trends for the rest of the year. The graph alone refutes the idea that Black Friday is a bunch of meaningless hype.

All this still begs the question of what is the best predictor of US retail sales. My approach is different from other forecasts that rely on models. Rather than advancing one model based on some argument, I test multiple models and choose the best one based on the AIC criterion: the best model is the one that predicts the historical data best with the fewest parameters. In this case, I tested the following models: (1) a random walk, R(t) = R(t-1) + E where R is Retail Sales and E is error, (2) a business-as-usual model, R(t) = a R(t-1) + E, (3) a state space model of the US economy, R(t) = a R(t-1) + S(US) + E where S(US) is the state of the US economy and (4) a state space model of the world economy,  R(t) = a R(t-1) + S(W) + E where S(W) is the state of the World economy. The states of the US and World economy are generated by the USL20 and WL20 models, respectively.

The rationale for these models is pretty straight forward: (1) given the high variability of the data, next year's sales may well be dominated by random error (the random walk), (2) if we smooth out some of the seasonal variability, average sales might just be a little bigger this year than last year (business-as-usual), (3) more realistically, US retail sales might depend on the entire state of the US economy and (4) given the globalization of world trade in retail services, the state of the world economy may be a better predictor of US sales.

The winner of the AIC competition was in fact the World economy model. The forecast with 98% prediction intervals from 2007-2015 is presented above. The best performance for retail sales was in 2007 and 2008. The worst performance was in 2009 and the first months of 2010. The sector has now pretty much recovered along with growth in the World economy. Returning to the FFC forecast of US retail sales growth rates, the forecast predicts an average annualized yearly growth rate of 2.07% for the period 2012-2015, very close to the current FFC number.

We've come a long way from the idea that Black Friday Retail Sales might be considered a reasonable predictor for yearly retail sales and learned that the US Retail Sector is part of the World economy. We've also learned that end-of-year sales are improbably (outside the 98% prediction interval) important to the sector. And, we've learned that setting up a single model to test a false problem is not a particularly useful way to conduct research.

TECHNICAL NOTE: The RSAFSNA.model is available here. Instructions for using forecasting models are available here. Once you have the R program installed and the dse, matlab, and scatterplot3d packages installed, you can run the following commands from the R console to display how well the best RSAFSNA.model fits the data.

> W <- "Absolute path for location of ws procedures"

> setwd(W)

> source("LibraryLoad.R")

> load(file="WL20v3_model")

> load(file="ws_procedures")

> 

> W <- "Absolute path for RSAFSNA model"

> setwd(W)

> load(file="US_RSAFSNA_model")

> m <- getModel(RSAFSNA.model,type="world index")

> tfplot(m)

You will notice that the model does not track the month-to-month variability in RSAFSNA. If we wanted to better predict things like end-of-year sales bumps, we could use seasonal dummy variables coded for the months of interest, for example November and December.

If you are interested in how the other models did, enter

> tfplot(m1 <- getModel(RSAFSNA.model,type="rw"))

> tfplot(m2 <- getModel(RSAFSNA.model,type="us index")) 

> tfplot(m3 <- getModel(RSAFSNA.model,type="bau"))

The plots actually seem to do a little better job of tracking the month-to-month variability. However, the AIC still suggests that the world model is best, but not by a huge amount.

> z <- bestTSestModel(list(m,m1,m2,m3))

Criterion value for all models based on data starting in period:  10 

5301.765 5354.228 5318.493 5377.438 

If you want to see the entire forecast with 98% bootstrap confidence intervals, enter

> b <- getModel(RSAFSNA.model,type="boot forecast")

> tfplot(b,m)