Sexual Competition, Mimetic Desire, and Neoliberal Market Society

In neoliberal market society, everyone faces the discipline of the market. In order to survive—ie in order to obtain the means to pay for the family’s food, rent, clothing, et cetera—ordinary people have to compete in the labor market. Investors have to compete with each other. And, of course, firms compete against their rivals. Indeed, even a pure monopolist has to worry about entry. A lot of ink has been spilt on the precariat workforce. But insecurity is the calling card of the neoliberal market society. Moreover, the closer we get to the neoliberal utopia, the fairer the verdict of the market. For the losers, there is no escape from the harsh sentence. So far we are still with Polanyi and Hoffer. But we must go further.

Houellebecq would suggest that we pay attention to the onset of ‘savage sexual competition’ as a result of the sexual revolution. In his account, the West made a wager with history that maximizing freedom would maximize happiness; and lost. (In Elementary Particles, Houellebecq articulates this pathos quite well but fails to surmount the last chapter problem. He picks up the same subject again in Soumission and this time gets his pathetic characters to ‘slouch towards Mecca‘ to escape their predicament.) For the neoliberal subject the rigors of sexual competition are, if anything, harsher than the rigors of the labor market. The liberalization of the sexual economy from the 1960s onwards must therefore be seen as an important component of the neoliberal condition. We need to investigate the market discipline faced by the neoliberal subject in the sexual marketplace. But we must go further still.

If we want to ground the analysis of neoliberal market society in the lived experience of the neoliberal subject, we must reckon with mimetic desire à la Girard. For we are not only enslaved by an obligation to enjoy. The pleasure principle is merely the beginning. What a neoliberal subject wants is not some independent draw from a fixed and exogenously given distribution. Nor are her desires merely correlated with those of others. She wants what others want because they want it. So she wants the iPhone X because, admit it, it’s cool. She knows what they will all think when they see the device in her hand. But that’s simply a case of ‘keeping up with the Joneses’—which is what we tend to find across consumer markets; a largely benign case of mimetic desire. Things get much more zero-sum when only a few can win and others must lose. The higher the college’s ranking, the more attractive the potential partner, the more prestigious the job etc, the more cut-throat it gets. Because desire is largely mimetic, neoliberal subjects are always found locked in mimetic rivalry—with their friends in high school, at work, or at a bar; and anonymously, with an amorphous mass of peers on job sites, dating platforms and so on and so forth.

In Girard’s scheme, mimetic rivalry engenders violence in archaic society. The instability intensifies until everyone gangs up against a single victim; a scapegoat, whose murder at the hands of the mob restores consensus and reestablishes the social peace. The ‘sacrificial crisis’ and the act of collective violence are not mythical, but real; a common solution arrived at by all archaic societies and ritualized as human sacrifice. This is the dark heart of our all-too-human past.

Neoliberal market society unleashes mimetic rivalry on an unprecedented scale. But it does so in a contained manner. Moreover, the modern state enjoys a near-absolute monopoly of violence. The violence engendered by the intensification of mimetic rivalry is therefore projected onto other domains—onto the political plane, onto video-games, on screen, onto 4chan, and perhaps most tragically, onto the neoliberal self. Perhaps that what is behind the curves studied by Case and Deaton.

If we want to interrogate the neoliberal condition, we must go beyond the whipsaw of global macro forces; beyond market discipline as currently understood in terms of the commodification of labor and capital. We need to start thinking about the market discipline faced by the neoliberal subject in the sexual marketplace as well. More generally, we need to take a broader view of the ever-intensifying market-like competition in neoliberal market society, eg for college admissions. We must get a handle on the attendant intensification of mimetic rivalry; the trauma thereby visited upon the neoliberal subject; and the socio-political consequences of that trauma.




Something very peculiar is going on at the top of the US wealth distribution

Inspired by the FT piece on the world’s überwealthy, I decided to explore the very top of the US wealth distribution. Figure 1 displays the average net worths in constant 2016 dollars of the top 1 percent, 0.1 percent, 0.01 percent, and 0.001 percent of the wealthiest adults in the United States over the past 100 years. (All data that appears in this post is from here.)


Figure 1. Average net worths at the top of the food chain.

We see that the neoliberal wealth boom is simply unprecedented. After fluctuating at historical levels until the 1980s, the fortunes of the richest Americans took off like a rocket. The rich have never been quite as rich as they are today.

Figure 2 zooms into the past twenty years. Within a very broad upward march, we can see dramatic fluctuations with the asset price booms of the late-1990s and the mid-2000s. But notice how the fortunes of the really rich had a different trajectory from the merely rich; especially over the past decade. Why?


Figure 2. Average net worth of the wealthiest over the past twenty years.

Figure 3 zooms in even further to 2004-2014 and allows us to examine the anomaly up close. While the total net worth of the top 1 percent and the 0.1 percent contracted sharply in 2009, that of the top 0.01 percent and the top 0.001 percent suffered only a mild correction. Concretely, while the former fell by 17 and 16 percent respectively in 2007-2009, the latter fell by only 3 and 4 percent. Why? Conversely, the former grew by 6 and 4 percent in 2010-2011, while the latter contracted by 17 and 13 percent. Why??


Figure 3. Average net worths of the top echelons.

Perhaps thresholds contain some information that may help us figure out what’s going on here. Figure 4 displays the threshold net wealth required for admission into these rarified echelons since the mid-1960s.


Figure 4. Threshold net worths for the upper echelons.

We see that fluctuations in the top 1 percent, 0.1 percent and 0.01 percent are similar: Rapid rises in the late-90s and mid-2000s booms and sharp corrections during the recessions. But quite strikingly even the bottom rung of the top 0.001 percent seem to have avoided a comparable loss in 2008-2009. Again, we zoom in to see what’s going on over the past decade or so. Figure 5 below displays the threshold net worths for the upper echelons over 2004-2014.


Figure 5. Threshold net worths for the upper echelons.

The evidence from the bottom rungs of the upper echelons is even more striking. It is clear that the very richest of individuals were able to protect themselves much better against global macro fluctuations than those right below them.

The 0.001 percent constitute the extreme top of the wealth distribution reported in the Piketty-Saez-Zucman database. The minimum personal net wealth in this rarified realm is a staggering $530 million. There are approximately 2,000 adults in the United States who clear that threshold. Their average net worth is $2.1 billion, up an astounding 792 percent since 1985. By comparison, the average net worth of the top 1 percent grew 322 percent and the average net worth of US households grew 289 percent over the same 30 year period 1985-2014.

More generally, over the past thirty years, the further up we go, the greater the gain. Table 1 displays the compounded rate of growth of average net worths in the upper echelons. The differentials may look small until you recall the magic of compounding. If your wealth grew at 7.2 percent instead of 4 percent, you’ll end up 2.5 times richer in thirty years. See the third column of Table 1 for exact figures.

Table 1: Accumulation rates.

Annual growth in net worth (1985-2014, compounded) In 30 years $1,000 invested at these rates accumulates to…
1 percent 4.03% $3,272
0.1 percent 5.04% $4,372
0.01 percent 6.14% $5,975
0.001 percent 7.24% $8,141

Piketty has shown in Capital that larger fortunes grow at higher rates. The Piketty-Saez-Zucman database corroborates that finding. One reason why that holds is that the truly wealthy have access to lucrative investment strategies unavailable to lesser investors. Another is that business equity accounts for a much greater portion of their net worth than that of lesser mortals. Yet another may be that they have easier access to leverage (which mechanically increases return on equity).

But all this still doesn’t explain how the truly rich enjoy greater protection against global macro fluctuations. Surely, they didn’t all short US housing in 2007? Perhaps the truly wealthy avoided the 2008-2009 bloodbath simply because housing is an insignificant portion of their portfolio?

It could also be that the billionaires who populate the very top of the food chain own serious equity in superstar firms which continued to perform relatively well through the financial crisis and the recession? Is that what explains this anomaly?

However this anomaly is resolved, one thing is clear. We should be very careful in extrapolating what we see in, say, the Forbes 400 to the rest of the rich. The oligarchs are in a class all by themselves.

Bonus round. In 2014Q4-2017Q1, US household wealth grew by 12.9 percent according to the Federal Reserve. If net personal wealth grew at the same rate it would be around $79 trillion. And if the shares of the upper echelons remain unchanged, the estimated aggregate net worths of the top 1 percent, 0.1 percent, 0.01 percent and 0.001 percent would be $29, $15, $8, and $4 trillion dollars respectively. That would place the aggregate net worth of the 1 percent at roughly the same level as the aggregate personal wealth of all US residents as late as 1990. See Figure 6 below.

Table 2: Aggregate wealth and wealth shares of the upper echelons (2014).

Aggregate Net Worth (trillions of 2016 dollars) Shares
1 percent 26 37%
0.1 percent 13 19%
0.01 percent 7 10%
0.001 percent 4 5%
Net Personal Wealth 70 100%

Figure 6. Aggregate personal wealth of US residents. 


Are Shocks to Housing Priced in the Cross-Section of Stock Returns?

In the previous post I argued that the risk premium on property is due to the fact that the marginal investor in housing is your average homeowner who finds it extraordinarily hard to diversify away the risk posed by her single-family home to her balance sheet. If I am right, this means that housing wealth is a systematic risk factor that ought to be priced in the cross-section of expected stock excess returns (ie, returns in excess of the risk-free rate).

Assume that the marginal investor in the stock market is your average homeowner. Since it is so hard for her to diversify away the risk posed by fluctuations in property values, she should value stocks that do well when property markets tank. Conversely, stocks whose returns covary with returns on property should be less valuable to her. Given our assumption that your average homeowner is the marginal investor in equities, expected returns on stocks whose returns covary strongly with property returns should be higher than expected returns on stocks whose returns covary weakly (or better yet, negatively) with property returns. This is what it means for shocks to housing to be priced in the cross-section of expected stock returns.

We want our risk factor to capture broad-based fluctuations in housing wealth. Ideally, we would use a quarterly time-series for total returns (including both rent and capital gains) on housing wealth owned directly by US households. I am unaware of the existence of such a dataset—if you know where I can find the data, please get in touch.

We can also instrument fluctuations in housing wealth by using a property price index. Here we use the US property price index reported by the Bank of International Settlements. For return data we use 250 test assets from Kenneth French’s website. (The same dataset I used in my paper, “The Risk Premium on Balance Sheet Capacity.”)

We’re now going to jump straight into the results. For our econometric strategy please see the appendix at the bottom.

Figure 1 displays a scatterplot of the cross-section of expected stock returns. Along the X axis we have the betas (the sensitivity of the portfolio’s return to property returns) for our 250 test assets, and along the Y axis we have the mean excess returns of the portfolios over the period 1975-2016.


Figure 1. Housing and the cross-section of expected stock excess returns.

Guys, this is not bad at all. Our single factor explains 20 percent of the cross-sectional variation in expected excess returns. By comparison, the celebrated Capital Asset Pricing Model, for instance, is a complete washout.


Figure 2. The CAPM fails catastrophically in explaining the cross-section of expected excess returns.

It is very hard for single factor models to exhibit such performance. Table 1 displays the results from the second pass. We see that the mean absolute pricing error is large because the zero-beta rate does not vanish. Indeed, at 1.8 percent per quarter it is simply not credible. But the risk premium on property returns is non-trivial and significant at the 5 percent level.

Table 1. Property returns and the cross-section

Estimate Std Error p-Value
Zero-beta rate 0.018 0.006 0.002
Property return 0.007 0.004 0.049
R^2 0.195
Adj-R^2 0.192
MAPE 0.022

I have a lot of professional stake in the failure of this model actually. I have argued that stock returns are explained by fluctuations in the risk-bearing capacity of the market-based financial intermediary sector. In other words, the central thrust of my work is to say that we ought to pay less attention to the small-fry and considerably greater attention to the risk appetite of the big fish, for that is what drives market-wide risk appetite. Fortunately for my thesis, property shocks do well, but not nearly as well as balance sheet capacity.

Figure 3 displays yet another scatterplot for the cross-section. On the X axis we have the factor betas (the sensitivities of the portfolios to balance sheet capacity) and on the Y axis we have, as usual, mean excess returns over 1975-2016.


Figure 3. Balance sheet capacity explains the cross-section of stock returns.

In Table 2 and Figure 3 we’re only looking at a single-factor model with balance sheet capacity as the sole systematic risk factor. That’s a parsimonious theory that says: exposure to fluctuations in the risk-bearing capacity of broker-dealers explains the cross-section of asset returns. The empirical evidence is pretty compelling that this is the right theory. We see that balance sheet capacity singlehandedly explains 44 percent of the cross-sectional variation in expected stock excess returns. What is also manifest is the vanishing of the zero-beta rate; and the attendant vanishing of the mean absolute pricing error. Other single factor models cannot even dream of competing with balance sheet capacity in terms of pricing error. Indeed, I have shown in my paper that even the pricing errors of standard multifactor benchmarks, Fama and French’s 3-factor model and Carhart’s 4-factor model, are significantly bigger than our single factor model’s 48 basis points. We can thus have good confidence that the evidence does not reject our parsimonious asset pricing model.

Table 2. The Primacy of Balance Sheet Capacity

Estimate Std Error p-Value
Zero-beta rate 0.002 0.010 0.440
Balance sheet capacity 0.095 0.038 0.007
R^2 0.442
Adj-R^2 0.440
MAPE 0.005

I know what you are thinking. If these things are priced in, there must be a way to make money off it. How do I get some of that juicy risk premium? Aren’t they non-traded factors? Yes, they are. But you can still harvest the risk premium on non-traded factors, eg by constructing factor mimicking portfolios. Briefly, you project your factor onto a bunch of traded portfolios and use the coefficients as weights to construct a portfolio that tracks your non-traded factor.

Figure 4 displays the risk-adjusted performance of portfolios that track benchmark risk factors and the two risk factors discussed in this essay. We report Sharpe ratios (the ratio of a portfolio’s mean excess return to the volatility of the portfolio return) rescaled by the volatility of the market portfolio for ease of interpretation.


Figure 4. Risk-adjusted performance of traded portfolios for size, market, value, momentum, property, and balance sheet capacity.

The results are consistent with our previous findings. The stock portfolio that tracks property outperforms standard benchmarks convincingly. In turn, the portfolio that tracks balance sheet capacity outperforms the portfolio that tracks property. But let’s be very clear about what Figure 4 does not say. There is no free lunch. More precisely, there is no risk-free arbitrage.

The existence of these two risk premiums imply instead that there is risk arbitrage. That is, you can obtain superior risk-adjusted returns than the market portfolio by systematically harvesting these risk premiums. The existence of the two risk premiums is due to structural features. Specifically, the property premium exists because non-rich homeowners must be compensated for their exposure to housing; while the risk premium on balance sheet capacity exists because of structural features of the market-based financial intermediary sector—features that I explain in detail in the introduction of my paper. Since we can expect these structural features to persist, we should therefore not expect these risk premiums to vanish (or perhaps even attenuate much) upon discovery.

Appendix. Cross-Sectional Asset Pricing

We can check whether any given risk factor is priced in the cross-section of excess returns using standard 2-pass regressions where you first project excess returns \left(R_{i,t}\right) onto the risk factor \left(f_t\right) in the time series to obtain factor betas \left(\beta_i\right) for assets i=1,\dots,N,

R_{i,t}=\alpha+\beta_i f_{t}+\varepsilon_{i,t}, \qquad t=1,\dots,T,

and then project mean excess returns \left(\bar R_i\right) onto the betas in the cross-section to obtain the price of risk \lambda,

\bar R_{i}=\gamma^{0}+\lambda\hat\beta_{i}+e_{i}, \qquad i=1,\dots,N.

The scalar \gamma^{0} is called the zero-beta rate. If there is no arbitrage, the zero-beta rate must vanish. If the zero-beta rate is statistically and economically different from zero, then that is a failure of the model. That’s why the mean absolute pricing error is a better metric for the failure of an asset pricing model than adjusted-R^2. It’s given by,

\text{MAPE}:=|\gamma^{0}|+\sum_{i=1}^{N}\omega_{i}|\hat e_{i}|,

where \omega_{i} are weights that we will discuss presently.

If you try this at home, you need to know that (1) ordinary least squares (OLS) is inefficient in the sense that the estimator no longer has the lowest variance among all linear unbiased estimators; (2) OLS standard errors are an order of magnitude too low (and the estimated coefficients are attenuated, though still consistent) because their computation assumes that the betas are known, whereas we are in fact estimating them with considerable noise in the first pass.

The solution to (1) is well-known. Simply use weighted least squares (WLS) where the weights are inversely proportional to the mean squared errors of the time-series regressions,

\omega_i \propto \left[\frac1{T}\sum_{t=1}^{T}\hat\varepsilon^2_{i,t}\right]^{-1},\qquad \sum_{i=1}^{N}\omega_{i}=1.

The solution to (2) is to use errors-in-variable (EIV) corrected standard errors. In our work, we always use WLS for the second pass and report EIV-corrected standard errors wherever appropriate.






Why Housing Has Outperformed Equities Over the Long Run

Jorda et al. are at it again. Over the past few years, they have constructed the most useful international macrofinancial dataset extending back to 1870 and covering 16 rich countries. The Policy Tensor has worked with the previous iteration of their dataset. I documented the reemergence of the financial cycle; the empirical law that all financial booms are, without exception, attended by real-estate booms; and that what explains medium-term fluctuations not just in real rates (a result originally obtained by Rey) but also in property returns, is the consumption to wealth ratio (equity returns on the other hand are explained by balance sheet capacity, not the consumption to wealth ratio).

There are two main findings in Jorda et al. (2017). First, they corroborate Piketty’s empirical law that the rate of return exceeds the growth rate. The gap is persistent and is only violated for any length of time during the world wars. Excluding these two ‘ultra-shortage of safe asset’-periods, the gap has averaged 4 percent per annum. That is definitely enough to relentlessly increase the ratio of wealth to income and drive stratification, as Piketty has shown.


Jorda et al. (2017)

The second finding is truly novel. Jorda et al. (2017) find that housing has dramatically outperformed equities over the long run. This is true not just in the aggregate but also at the country level.


Jorda et al. (2017)

Matt Klein over at Alphaville is truly puzzled by this failure of standard asset pricing theory. As he explains,

The ratio between the average yearly return above the short-term risk free rate and the annual standard deviation of those returns — the Sharpe Ratio— should be roughly equivalent across asset classes over long stretches time. There might be short periods when an asset class’s Sharpe ratio looks unusually high, especially in individual countries, but things tend to revert to their long-term average sooner or later.

More generally, the expectation of asset pricing theory is that Sharpe ratios should be roughly equal across not just asset classes but arbitrary portfolios as well. Deviations from equality imply the existence of extraordinary risk premia which ought to be eliminated through investors’ search for higher risk-adjusted returns.

This, of course, goes back to the hegemonic idea of Western thought. Competition serves as the organizing principle of evolutionary biology, economic theory, and international relations; as the cornerstone of America’s national ideology; and as the guiding star of modern governance and reform efforts. But there are some rather striking anomalies of this otherwise compelling broad-brush picture of the world—persistent sources of economic rents and the existence of substantial risk premia, eg on balance sheet capacity.

But I believe something much more elementary is going on with property. The next figure shows the global wealth portfolio. We see that housing constitutes the bulk of global wealth.


Jorda et al. (2017)

What explains the superior risk-adjusted performance of housing is the fact that housing assets are not, in fact, owned by the rich or market-based financial intermediaries like other asset classes, but quite broadly held by the small-fry. More precisely, the marginal investor in housing is your average homeowner who finds it extraordinarily hard to diversify away the risk posed by her single-family home to her balance sheet. Since it is so hard for her to diversify this risk away, she must be compensated for that risk.

Put another way, the risk premium on property is high because property returns are low when the marginal value of wealth to the marginal investor is high (ie, when times are bad for the average homeowner) and high precisely when the marginal value of wealth to the marginal investor is low (ie, when times are good for the average homeowner). This is as it should be given the relatively progressive vertical distribution of housing wealth.


How Long Can China Defy the Laws of Macrofinancial Gravitation?

The International Monetary Fund has warned that China’s debt is approaching “dangerous” levels. The Fund expects China’s non-financial sector debt to exceed 290 per cent of GDP by 2022, compared with 235 per cent last year. How long can China’s debt binge last? Recent research by economists at the Bank of International Settlements suggests not long.

Drehmann, Juselius and Korinek (2017) have emphasized the role played by debt service burdens in puncturing credit booms. There is an interesting lead-lag structure between new lending and debt service burdens. New lending mechanically increases the debt service burden, but the weight is felt with a lag.

When taking on new debt, borrowers commit to a pre-specified path of future debt service. This implies a predictable lag between credit booms and peaks in debt service which, in a panel of household debt in 17 countries, is four years on average.…Debt service peaks at a well-specified interval after the peak in new borrowing.…The reason is that debt service is a function of the stock of debt outstanding, which continues to grow even after the peak in new borrowing. 

Credit booms have a clear mechanical path. An exogenous increase in the risk-bearing capacity of the financial sector drives a lending boom. Credit-to-GDP ratios rise. Debt service ratios follow. At some point, debt service becomes too onerous to sustain, the lending boom is arrested and a financial crisis breaks out.

We illustrate these dynamics with the US experience. Figure 1 plots the detrended Credit-to-GDP gap and the Debt Service Ratio for the US private nonfinancial sector. We see that the credit gap peaked at 12.4 and the debt service ratio peaked at 18.4 just as the Great Recession began in the last quarter of 2007. The denouement of the credit boom triggered the onset of the Global Financial Crisis (GFC) as credit defaults made their way to dealer balance sheets.


Figure 1. US private nonfinancial debt service ratio and credit gap.


Figure 2 plots the Chinese nonfinancial sector’s credit gap and debt service ratio. As of 2016Q4, China’s credit gap was an astounding 24.6; twice as high as the peak American gap of 12.4. And China’s debt service ratio was 20.1 as of 2016Q4, already larger than America’s peak ratio of 18.4.


Figure 2. Chinese nonfinancial sector’s credit-to-GDP gap and debt service ratio.

The importance of these indicators comes from the fact that they are the strongest predictors of financial crises. In particular, the BIS researchers quoted above have shown that positive shocks to the debt service ratio significantly increase the probability of a financial crisis over the near term (1-3 years). They find that “debt service is the main channel through which new borrowing affects the probability of financial crises.”


Figure 3. Nonfinancial private debt service ratios in the United States and China.

Figure 3 displays the debt service ratios of the United States and China for 1999Q4-2016Q4. Since then the Chinese government has pushed through yet another credit expansion (which is what prompted the scolding from the IMF). It is hard to escape the conclusion that Beijing would find it hard to achieve a soft landing.

Mechanically, we know what will happen soon. A correction in property prices will destabilize the $28 trillion shadow banking flywheel built on top of real estate. Whether or not it leads to a dramatic implosion would depend on the strategy pursued by Beijing. Xi is definitely sold on Geithner’s financial Powell doctrine. But whether the crisis can be contained with techniques of financial fire-fighting that have evolved since 2007 remains to be seen. What is certain is that Beijing would have to absorb a significant portion of private liabilities onto the national balance sheet. As a result, public debt is likely to balloon.


Towards a Natural History of Capitalism: economic rents, regimes of accumulation, and oligarchy

Margin Call.png

Margin Call (2011)

This is an ongoing conversation with Ted Fertik. 

It was great talking today man. The first battle in the great war between Braudel and Marx was very productive. You have really helped me clarify my own thoughts. Tell me if this sounds like a reasonable offer for an armistice:

The labor theory of value explains some fraction of the variation in economic value and wage slavery is an important feature of the lived experience in modern times. I personally find a narrower Marxist frame quite useful: examine the strategies used by capitalism to deal with labor militancy within the larger political economy. On such home turf so to speak, attention to wage labor, labor’s share of labor productivity growth, and more generally the tug-of-war between labor and capital is decidedly warranted. But working with Braudel we can go much, much further. Here’s a strategy.

We pay attention to the connection between regimes of accumulation built on economic rents (ie, earnings over and above what would obtain under free market competition) on the one hand and oligarchy on the other. Think about this: If competition were the dominant fact of the market-capitalism quasi-object then the distribution of wealth ought to get more diffuse under greater competitive pressure and over time simply due to entropy.

But how then do we explain higher rates of accumulation precisely when competition is supposed to be the fiercest, ie in the modern neoliberal era?? The reality is that neoliberalism is an intensification of market discipline only for the losers while the winners have grown fat feasting on the anti-market.

Industrial concentration, rents, and oligarchic distributions of wealth track each other. The political economy of this threefold comovement demands interrogation. What ties the three together is the fact that regimes of rapid rates of wealth accumulation are built on the systematic harvesting of persistent economic rents. Rents serve not only as attractors of capitalism’s attention, as in the standard picture; but also as sources of high rates of accumulation itself.

This gets more interesting as you go into the details. For instance, oligopolistic firms share their rents with their employees. What explains the distribution of compensation of employees is interfirm variation rather than within-firm variation. In measuring the rates of accumulation, we therefore have to include what looks like extraordinary labor compensation (high salaries, bonuses and stock options) at oligopolistic firms (beginning with CEO compensation) as well as their supernormal profits.

The beauty of the neoliberal regime is that the capital market acts to ‘rationalize’ sectors into stable oligopolistic regimes. These rents find their way all the way to the coffers of core market-based financial intermediaries who use their privileged access to lucrative asset classes (which are unavailable to retail investors, eg fixed-income derivatives) and their privileged information on order flow and endogenous fluctuations, to corner some serious rent. That’s your market-based financial regime of accumulation. Then you have surveillance platform capitalism of the Valley, pace Zuboff. And so on and so froth throughout the history of capitalism. I think this is a powerful frame of reference.


Does the US Enjoy Nuclear Primacy Over North Korea?

The issue of nuclear deterrence in the Korean peninsula is usually posed in terms of how reliably the United States and its key regional allies can deter an attack by North Korea. That has it exactly the wrong way around. The question is not whether the US can deter North Korea; that’s a triviality. The real question is whether North Korea can deter the United States.

As I’ve explained at length before, strategic nuclear deterrence is extraordinarily stable when both sides enjoy an assured second-strike capability, ie the ability to retaliate with a devastating counterblow after having absorbed a massive first-strike. Under highly asymmetric conditions, nuclear deterrence is much less stable. If the stronger party can expect a splendid first-strike to destroy the deterrent of the weaker party with certainty, it is hard to see how the latter can deter the former. Of course, certainty is unachievable in practice. The question is just how certain the stronger party has to be for deterrence to fail. Of all the asymmetric deterrence scenarios, the confrontation between the unipole and North Korea is arguably the most one-sided. If North Korea can deter the United States, then deterrence is unlikely to fail anywhere else as well.

So does the United States enjoy nuclear primacy over North Korea? Concretely, if President Trump demanded a military solution to the “problem”, will the generals be able to put forward a viable operational plan to disarm North Korea without exposing the United States and its allies to catastrophic risk? Can the US take out the entire North Korean arsenal in counterforce strike before they have a chance to retaliate?? As usual, the devil is in the details.

The United States would enjoy nuclear primacy over North Korea if it could be near-certain of destroying North Korea’s nuclear capabilities in a splendid first-strike, or if it could be near-certain that it will be able to intercept North Korean missiles before they stuck populous cities and strategic bases of the United States and its regional allies. So we need to evaluate the capabilities of the both the United States and North Korea. Moreover, in order to assess the likelihood of deterrence failure, we also need to assess the balance of resolve. Having a first-strike capability is one thing; having the willingness to carry out a splendid first-strike quite another—much more is involved than simply hard power capabilities.

According to US intelligence, North Korea has an arsenal of some 60 odd nuclear warheads. More importantly, in the assessment of the intelligence community, North Korea has achieved miniaturization, ie they have figured out how to make the warheads small enough to mount them on an intercontinental ballistic missile (ICBM). The beleaguered state’s gains in missile knowhow have also crossed a critical threshold. In the assessment of analysts at the Middlebury Institute of International Studies, the July 4 test launch suggested that the missile had a range of 10,000km, putting the US west coast and midwest within reach. On July 28, North Korea fired a second test missile in a near-vertical trajectory that reached an altitude of 3,500km before falling harmlessly in the Sea of Japan. Analysts reckon that even New York, Boston and DC are now within operational reach of North Korean missiles. It’s still not clear whether they have figured out how to ensure that the warhead doesn’t burn up upon reentry into the dense lower atmosphere. It shouldn’t take long for them to achieve that capability. In any case, US strategists must assume that the North Koreans have the capability to strike the most populous US cities and, a fortori, US military bases in the region and the cities of its regional allies.

Can the United States be near-certain that its theater and intercontinental missile defense systems will be able to intercept North Korean missiles? The short answer is no. Of the last 5 tests of the ICBM ballistic missile defense system, 3 have failed. Theater missile defense (TMD) may perform better under test conditions, but is likely to fare even worse under actual warfighting conditions because North Korea has many more short-range platforms to strike Japan and especially South Korea (where it can even use artillery to deliver nuclear payloads) and they can deploy many more decoys to distract the systems. North Korea also has electronic warfare capabilities that can potentially interfere with TMD systems.

On the other hand the United States has a formidable repertoire of counterforce capabilities. The United States can use low-orbit platforms like satellites, air-breathing unmanned platforms (ie surveillance drones), and manned fixed-wing aircraft to generate near-continuous targeting solutions. The US can utilize a full-spectrum of air-, land- and sea-based launch platforms to take out North Korean targets. Lieber and Press (2017) have shown that North Korea would find it hard to ensure the survivability of its nuclear deterrent against the United States since the US can substitute seamlessly between multiple platforms for cueing, targeting, and strike solutions (“fire-control solutions”). In other words, the United States enjoys nuclear primacy over North Korea in the sense that the US can carry out a splendid first-strike against the North Korean arsenal with near-certainty.



But the problem is that the balance of resolve favors North Korea. No conceivable interest of the United States or its regional allies can be served by exposing themselves to North Korean nuclear strikes even an iota. Even though the US could take it out on demand, the very existence of the North Korean deterrent means that Japan and South Korea would resist a preemptive strike against North Korea. US assurances that the risk is small are quite unlikely to satisfy Seoul and Tokyo. Both allies would expect compelling reasons why they must run even a small risk of a nuclear attack.

Put another way, the United States is deterred because North Korea hasn’t raised the stakes enough to threaten a vital interest of South Korea and Japan. It is in the US interest to prevent a North Korean deterrent capable of striking US cities. But the US would find it impossible to convince Seoul and Tokyo that they need to run the risks necessary to achieve that goal. The United States cannot find a persuasive reason because there isn’t one. So much for nuclear primacy.