The EV sector industrial subsidy figures released last week by @CSIS are inflated by at least ~$80B, mainly from poor assumptions made to calculate the NEV Sales Tax Exemption that (i) don't pass the sanity check and (ii) are out-of-whack with disclosed actuals amounts.
🧵
As disclosed in the CSIS analysis, the Sales Tax Exemption assumption is based on a very simple premise:
To incent purchases of NEVs (new energy vehicles i.e. BEVs+PHEVs) over ICE vehicles, most NEVs are exempt from the sales tax exemption, which is assumed to be 10%.
$39.5B of Sales Tax Exemption in '23 thus implies that there were at least $395B worth of NEVs sold in China based on CSIS assumptions.
Since we know how many NEVs were sold (~7.9M passenger and ~0.3M commercial), we can back out the implied average selling price (ASP).
CSIS discloses ASP assumption of ¥1.2M for large commercial vehicles. This would imply ~$54B in commercial vehicle NEV sales in '23, leaving $341B for light passenger vehicles.
Based on ~7.9M passenger NEVs sold in '23, this implies an ASP of $43k (¥310k).
This does not pass the sanity check, nor is it consistent with CSIS' own assumptions (passenger NEV ASP is ¥250k).
e.g. the best-selling models in China are compact and mid-size NEVs with ASPs in the ¥120-250k range.
In any case, we can also cross-check this assumption with actual disclosed figures.
The Ministry of Finance disclosed last year cumulative NEV Sales Tax Exemptions through 2022 of ~¥200 billion (~$29B) + another ~¥115 billion (~$16B) in 2023.
This suggests the CSIS estimate for Salex Tax Exemptions is overstated by 2.5-2.6x
Factoring in more accurate assumptions that are more in line with these actuals, I have done my own analysis on Chinese EV sector assumptions and arrived at ~$147B, ~$83B lower than the CSIS estimate.
Based on this, more interesting is what happens going forward IMO.
Per below, the NEV Sales Tax Exemption has been extended for 4 more years, but like the Buyer's Rebate before gradually eases out.
For example, the maximum exemption halves in 2026 to ¥15k per NEV.
I've taken the EV sector subsidies analysis out through 2030 assuming the Sales Tax Exemption is retired after this current program ends.
As you can see avg. subsidies per vehicle continue to fall gradually, from $2,700 in '23 to <$200 by '30.
If we take the model even further out to 2040, China will have spent ~$330B in total subsidies on the EV sector.
Paired against ~704M cumulative NEV sales through 2040, this would average out cumulatively to a subsidy of ~$468 per vehicle.
I think the lesson here is pretty clear and does not need overstated subsidy estimates to make the point.
The key to any successful industrial policy subsidy program is providing support to a domestic industry to achieve scale and profitability so subsidies can be gradually withdrawn over time.
The last point is key. If industrial policy and subsidies cannot achieve industry scale and self-sustaining profitability, you end up with a non-competitive sector that continues to suck up fiscal resources indefinitely.
The development of the Chinese EV sector has followed this principle.
Subsidies per car have fallen from >$20k per vehicle to ~$2k per vehicle over the last decade and will be effectively completed by 2027.
The question for Western policymakers is not about the need to develop and retain an EV industry using industrial policy and subsidies, like China has done. That should be pretty clear, the answer is yes.
It is about execution of said industrial policy. Specifically, are we seeing aggregate subsidies per NEV go down over time at a satisfactory pace?
Remember there are other new industries beyond EVs.
That China is a few years away from withdrawing subsidies from EVs just means that those fiscal resources will soon be available to support development of other future industries.
If one is stuck subsidizing old industries indefinitely this just means less fiscal resources available to spur development of new ones.
This is not the first time I’ve seen highly questionable assumptions in CSIS analysis on China.
Its analysis of COMAC development costs was even more egregious and off by 6-14x.
These numbers influence policy and decision-making. Isn’t it important that we get them right?
Fiscal revenue grew ~19% in the 2021-25 period compared to the 2016-2020 period (~3.5% nominal growth).
Note: IIRC fiscal revenue excludes categories like land sales, which are part of an auxiliary budget.
This provides a sense of relative priorities of various social welfare spending initiatives out of the general public budget (figures are over the 5-year period):
Scholars once worried that China's gender ratio imbalance would lead to a generation of surplus men, fueling crime, chaos, and even war.
What we got instead was ... this.
Certain China watchers busy penning missives on how this Paw-temkin Village is merely the latest example of the country's addiction to construction, penchant for capital misallocation and "politically entrenched elites" blocking efforts at structural reforms.
Chairman Xi declares, "Houses are for Humans, Not for Felines" and announces "Three Red Meows" regulations:
▪️ Moratorium of two cat-years on new construction
▪️ Catnip shall not exceed 25% of daily caloric intake
▪️ Kitty litter area capped at 10% of gross living space
Productivity is what ultimately drives per-capita economic growth and increases in living standards over the long run. This concept is one of the pillars of developmental economics.
I’ve come to realize one of the the fundamental issues with Pettis/Setser economic framing is misplaced reliance on accounting identities with little to no consideration of productivity effects.
To wit: nowhere in this thread is there any mention or consideration of how this sectoral shift impacts productivity.
In the short to medium run, there can certainly be supply-demand disequilibrium where “weak demand” is an issue.
e.g. in this 🧵 from a year ago I tried to quantify the headwinds from reverse wealth effect impact of the policy-driven pivot away from real estate to manufacturing since 2020 and how they could offset wage growth driven by underlying productivity growth enabled by sectoral shift.
But given enough time, markets adjust to find new equilibrium points. The more dynamic the economy, the quicker the adjustment.
Growth in per-capita income (and wealth) growth in the long run must be driven by primarily by increases in productivity. Since demand is derived from income 👇, this means productivity growth also drives demand.
In January, I speculated how "the most impactful outcome from DeepSeek's rise may ultimately be closer collaboration with Huawei and other chip designers".
We now have direct evidence of this collaboration, with potential standardization around UE8M0 the first major tangible result.
While some may dismiss this as technical or esoteric jargon relevant only to AI, computer science, or math enthusiasts ... I will try to explain here in plain language some key market and geopolitical implications of this development.
First I want to acknowledge others who are much closer to DeepSeek and AI for both raising, highlighting and explaining these recent developments, particularly @teortaxesTex @zephyr_z9 and @Compute_King
In this 🧵 I am merely synthesizing the insights and knowledge gained from following their timelines and trying to add value by layering on market and geopolitical insights.
In particular I highly recommend first digesting this post on the technical and strategic implications of UE8M0:
After three years of housing price declines — in line with forecasts like 👇 from two years ago — real estate has stabilized and from a GDP perspective, no longer a significant tailwind.
> “The state cannot allocate capital more efficiently than the market.”
An oft-repeated axiom chanted like a religious mantra and accepted by many as a universal truth.
But one that can be easily debunked with a straightforward contra-example from one of the most capital-intensive industries of them all: passenger rail.
China Railway (SOE) vs. Brightline (private)
CR HSR:
▪️ 48,000 km of greenfield track, predominantly elevated on viaducts
▪️ Serves 3.6B passengers annually
▪️ ¥550B of revenue on ¥5T of capital investment (9 years revenue payback)
▪️ 42 fatalities over 17+ years and 23B passenger rides
Brightline Florida:
▪️ 376 km of refurbished at-grade track
▪️ Serves 2.8M passengers per year
▪️ $187M revenue on at least $5.5B capital investment (29 years revenue payback)
▪️ Caused 182 fatalities in two-plus years of operation (hint: maybe you shouldn’t run fast trains over at-grade crossings).
Did the private company really do a better job allocating capital here? (rhetorical)
So no, I don’t think “the private sector is always better at allocating capital than the state sector” should be simply accepted as a universal truth, unchallenged.
It depends on the industry and the type of capital formation and the level of state/institutional capacity.
The more interesting, less-ideological exercise is to figure out the optimal ratio of state vs. private involvement on a sector-by-sector basis. This one requires actual nuance and complex thought.