Tag: economics

Belt and Road

China’s Belt and Road Initiative is the most ambitious infrastructure investment effort in history. But is it also a plan to remake the global balance of power?

2023-10-02: Belt and Road is a giant failure

That’s just a mind-bogglingly bad long-term strategy for achieving global leadership. China’s leaders tout their country as the leader of the Global South, but they’re raiding developing countries like their own personal piggy bank. Throughout the whole saga of the Belt and Road, China’s government treated countries like Pakistan, Sri Lanka, and Zambia like Chinese provinces — assuming they could and would strongarm their populations into supporting new infrastructure, prioritizing economic throughput over efficiency and profitability, and counting on those other countries to take the hit when the projects went…er…south.

AI Services Economics

In the area of AI risk, many express great concern that the world may be taken over by a few big powerful AGI (artificial general intelligence) agents with opaque beliefs and values, who might arise suddenly via a fast local “foom” self-improvement process centered on one initially small system. I’ve argued in the past that such sudden local foom seems unlikely because innovation is rarely that lumpy.

UBI Might Hurt Poor

Replacing all current US safety net programs, including those aimed at senior citizens, with a truly universal basic income would result in “a massive distribution up the earnings distribution, along with a redistribution from the elderly and disabled towards those who are neither, primarily but not exclusively those without children.”

Clean energy jerbs

Fastest-growing jobs: solar panel installer, wind turbine techs

2021-04-14: The way to do this is not with silly nonsense like union jobs

What we need to produce are very cheap renewable technologies, ones so cheap that the poorer countries of the world will adopt them as well. If we insist on packing a lot of labor costs (“good jobs”) into our energy technologies, we will not come close to achieving that end.

I was disappointed and unnerved by recent comments from Brian Deese, President Joe Biden’s top economic adviser, who in the context of climate change remarked that “…investing in infrastructure can be one of the most effective ways to do that in a way that creates lots of jobs.” The correct Econ 101 answer, of course, is that a low-jobs energy infrastructure liberates labor to produce other goods and services for us, leading to higher overall output. Such policies remind me of the “make-work” fallacy, namely the view that the deliberate creation of domestic jobs (for instance through tariffs) will lead to a better economy. We will wind up with more good jobs in total if we seek to lower green energy prices, not raise them.

Geno-economics

The geno-economists seem confident that human genes have a measurable influence on human outcomes. But publicizing whatever predictive power does lie in our genes runs the risk of misleading the rest of us into believing that control of our genes is control of our future. They’re adamant that their motives are in forestalling the dystopian implications of the work, in fighting off misinformation and misguided policies. “The world in which we can predict all sorts of things about the future based on saliva samples — personality traits, cognitive abilities, life outcomes — is happening in the next 5 years. Now is the time to prepare for that.”

Ben Thompson

Taiwan, I think, struggles from having 1000s of years of Chinese bureaucracy behind it. Plus they were occupied by Japan for 50 years, so you’ve got that culture on top. Then you have this sclerotic corporate culture that the boss is always right, stay in the office until he goes home, and that sort of thing. It’s unhealthy. Whereas China — it’s much more bare-knuckled competition and “Figure out the right answer, figure it out quickly.” The competition there is absolutely brutal. It’s brutal in a way I think is hard for people to really comprehend, from the West. And that makes China, makes these companies really something to deal with.

Liberal Radicalism

We were able to compute the optimum level of the public good because we knew each individual’s utility function. In the real world each individual’s utility function is private information. Thus, to reach the social optimum we must solve 2 problems. The information problem and the free rider problem. The information problem is that no one knows the optimal quantity of the public good. The free rider problem is that no one is willing to pay for the public good. The government used the contribution levels under the top-up mechanism as a signal to decide how much of the public good to produce and almost magically the top-up function is such that citizens will voluntarily contribute exactly the amount that correctly signals how much society as a whole values the public good. Amazing!

SWF+UBD

Matt Bruenig has published an excellent proposal that the United States charter a large sovereign (“social”) wealth fund and use its profits to fund a universal basic dividend. the fund would have to grow to hold something like 64% of all assets, or 80% of US “net worth”, to finance a “full” UBI at a 4% per annum payout rate

Gentrification Is in the Stars

You know it when you see it, or perhaps smell it. Gentrification is that new dog park. It’s the Starbucks on the corner, the yoga studio, and the gradual rise in police presence. But it’s surprisingly hard to track the exact moment when a critical mass of more affluent people move into a neighborhood and tip property values up—the simplest, if not the most universally agreed upon, definition of the “G” word. Traditional public data sources can fail to pick up the rapid transformation that can occur in a community, since their records are usually updated on multi-year cycles. And government registries usually catalogue businesses in broad categories—you’re not going to find artisanal donut parlors or motorcycle lifestyle shops grouped together by the Census Bureau. A new working paper shows how review data can be used to quantify and track neighborhood change, putting a hard spine on what can otherwise be a soft science. Matching up a massive trove of business and service listings from the uber-popular reviews site against changes in housing prices and demographics, they found that reviews appears to work as a real-time forecaster of neighborhood change. You just have to look at the right types of listings.