Tag Archives: economic growth

climate change is going to cause some economic damage

A letter in Nature says climate change is going to cause economic damage, and meeting the UN’s emissions targets would reduce that damage. Here’s the abstract, and the article itself is open access.

International climate change agreements typically specify global warming thresholds as policy targets1, but the relative economic benefits of achieving these temperature targets remain poorly understood2,3. Uncertainties include the spatial pattern of temperature change, how global and regional economic output will respond to these changes in temperature, and the willingness of societies to trade present for future consumption. Here we combine historical evidence4 with national-level climate5 and socioeconomic6 projections to quantify the economic damages associated with the United Nations (UN) targets of 1.5 °C and 2 °C global warming, and those associated with current UN national-level mitigation commitments (which together approach 3 °C warming7). We find that by the end of this century, there is a more than 75% chance that limiting warming to 1.5 °C would reduce economic damages relative to 2 °C, and a more than 60% chance that the accumulated global benefits will exceed US$20 trillion under a 3% discount rate (2010 US dollars). We also estimate that 71% of countries—representing 90% of the global population—have a more than 75% chance of experiencing reduced economic damages at 1.5 °C, with poorer countries benefiting most. Our results could understate the benefits of limiting warming to 1.5 °C if unprecedented extreme outcomes, such as large-scale sea level rise8, occur for warming of 2 °C but not for warming of 1.5 °C. Inclusion of other unquantified sources of uncertainty, such as uncertainty in secular growth rates beyond that contained in existing socioeconomic scenarios, could also result in less precise impact estimates. We find considerably greater reductions in global economic output beyond 2 °C. Relative to a world that did not warm beyond 2000–2010 levels, we project 15%–25% reductions in per capita output by 2100 for the 2.5–3 °C of global warming implied by current national commitments7, and reductions of more than 30% for 4 °C warming. Our results therefore suggest that achieving the 1.5 °C target is likely to reduce aggregate damages and lessen global inequality, and that failing to meet the 2 °C target is likely to increase economic damages substantially.

My head gets just a little twisted around thinking of reduced damages. This means the economy, and presumably our grandchildren’s quality of life, will be worse than it could have been if we started making an effort and investment now. But this doesn’t tell us if they will be absolutely better or worse off in a “future baseline” scenario compared to now, just that they will be worse off relative to that future baseline if we don’t take action than if we do. I think the various (very eye catching) graphs in this paper probably contain the answers to these questions, but I didn’t get it after an admittedly short few minutes staring at them, and I admit I didn’t read every word in the paper.

The other thing here is that we are taking a given climate scenario (1.5 or 3 degrees C warming for example), and talking about the benefits of those two future scenarios against each other. What I don’t see is the cost to the current generation if we choose to make this sacrifice, or even if it is a sacrifice at all. What investment would we have to make to achieve 1.5 vs. 3 degrees, and are there alternative investments we could make that could have a bigger payoff. I am not arguing against climate action, I am just questioning how this paper is communicating about costs and benefits in the present and in the future.

May 2018 in Review

Most frightening stories:

The idea of a soft landing where absolute dematerialization of the economy reduces our ecological footprint and sidesteps the consequences of climate change through innovation without serious pain may be wishful thinking.
Lake Powell is in increasingly deep (actually shallow, ha ha) trouble.
The American Academy of Pediatrics says climate change is already killing children.

Most hopeful stories:

There are some new ideas for detecting the potential for rapid ecological change or collapse of ecosystems.
Psychedelics might produce similar benefits to meditation.
Microgrids, renewables combined with the latest generation of batteries, are being tested in Puerto Rico.

Most interesting stories, that were not particularly frightening or hopeful, or perhaps were a mixture of both:

I learned about the stag hunt, a cousin of the prisoner’s dilemma in game theory.
Uber Air is looking a little more real. There are other ideas for autonomous urban helicopters too.
Connecticut is the latest U.S. state to join the National Popular Vote Compact.

dystopian Schumpeter meets Keynes

This article is about a serious attempt to consider climate change in a traditional economic model. Where does the dystopian part come in? Well, it sounds like the model suggests we are not going to innovate our way out of the consequences of climate change.

For these reasons, we develop the Dystopian Schumpeter meeting Keynes (DSK) model, which is the first attempt to provide a fully-fledged agent-based integrated assessment framework. It builds on Dosi et al. (2010, 2013, 2016) and extends the Keynes+Schumpeter (K+S) family of models, which account for endogenous growth, business cycles and crises. The model is composed by heterogeneous firms belonging to a capital-good industry and to a consumption-good sector. Firms are fed by an energy sector, which employ dirty or green power plants. The production activities of energy and manufacturing firms lead to CO2 emissions, which increase the Earth surface temperature in a non-linear way as in Sterman et al. (2013). Increasing temperatures trigger micro stochastic climate damages impacting in a heterogeneous way on workers’ labour productivity, and on the energy efficiency, capital stock and inventories of firms.

The DSK model accounts both for frequent and mild climate shocks and low-probability but extreme climate events. Technical change occurs both in the manufacturing and energy sectors. Innovation determines the cost of energy produced by dirty and green technologies, which, in turn, affect the energy-technology production mix and the total amount of CO2 emissions. In that, structural change of the economy is intimately linked to the climate dynamics. At the same time, climate shocks affect economic growth, business cycles, technical-change trajectories, green-house gas emissions, and global temperatures…

Simulation results show that the DSK model is able to replicate a wide array of micro and macro-economic stylized facts and climate-related statistical regularities. Moreover, the exploration of different climate shock scenarios reveals that the impact of climate change on economic performances is substantial, but highly heterogeneous, depending on the type of climate damages. More specifcally, climate shocks to labour productivity and capital stocks lead to the largest output losses and the highest economic instability, respectively. We also
find that the ultimate macroeconomic damages emerging from the aggregation of agent-level shocks are more severe than those obtained by standard IAMs, with the emergence of tipping-points and irreversible catastrophic events.

the French AI strategy

Other countries (than the United States) are developing strategies for how artificial intelligence will affect work, productivity, and growth in the near future.

France’s national strategy also reveals that Macron’s government is wrestling with how to ensure that AI supports inclusivity and diversity, and to make certain that its implementation is transparent. The French aren’t just theorizing; they’re taking action. France plans to invest 1.5 billion euros (almost $1.8 billion dollars) in the next five years in artificial intelligence research. The French are looking to create their own AI ecosystem, train the next generation of scientists and engineers, and make sure that their workforce is prepared for an automated future.

France isn’t alone. Last month, the European Union’s executive branch recommended its member states increase their public and private sector investment in AI. It also pledged billions in direct research spending. Meanwhile, China laid out its AI plan for global dominance last year, a plan that has also been backed up with massive investment. China’s goal is to lead the world in AI technology by 2030. Around the world, our global economic competitors are taking action on artificial intelligence.

It’s therefore striking that the United States doesn’t have a national artificial intelligence plan.

The fact that I don’t find it striking reflects my lowered expectations more than anything. We don’t really have a strategy for infrastructure or education either, for example.

dematerialization and decoupling

This paper is called Dematerialization, Decoupling, and Productivity Change. These are all buzzwords that will catch my eye. It makes a distinction between relative (ecological footprint is growing slower than the economy) and absolute (ecological footprint is not growing or is shrinking) decoupling. If you accept the concept that ecological footprint cannot grow forever, the distinction is important! This paper seems to cast doubt on the idea that there is any soft landing where absolute decoupling occurs automatically or by choice without significant pain.

The prospects for long-term sustainability depend on whether, and how much, we can absolutely decouple economic output from total energy and material throughput. While relative decoupling has occurred – that is, resource use has grown less quickly than the economy – absolute decoupling has not, raising the question whether it is possible. This paper proposes a novel explanation for why decoupling has not happened historically, drawing on a recent theory of cost-share induced productivity change and an extension of post-Keynesian pricing theory to natural resources. Cost-share induced productivity change and pricing behavior set up two halves of a dynamic, which we explore from a post-Keynesian perspective. In this dynamic, resource costs as a share of GDP move toward a stable level, at which the growth rate of resource productivity is typically less than the growth rate of GDP. This provides a parsimonious explanation of the prevalence of relative over absolute decoupling. The paper then presents some illustrative applications of the theory.

richest 1% now have 50%

The world’s richest 1% now officially have half the wealth, according to Project Censored, up from a mere 42.5% 10 years ago. The poorest half has less than 3%.

happy financial crisis anniversary

Happy 10-year anniversary to the 2008 financial crisis! The Week has a short summary of what caused it.

The bursting of the U.S. housing bubble triggered a chain reaction that nearly brought down the global financial system. Between 1997 and 2006, a combination of low interest rates, relaxed lending regulations, and government policies designed to encourage home buying fueled a housing boom that saw the average price for a U.S. home increase by 124 percent. Amid the speculative frenzy, financial institutions issued hundreds of billions of dollars in questionable loans to so-called subprime borrowers with poor credit histories. Borrowers’ ability to repay didn’t matter to lenders, because they were able to get subprime mortgages off their books by repackaging them into wildly complex derivative financial instruments like mortgage-backed securities and collateralized debt obligations. Corporate and institutional investors gobbled up these offerings, which not only offered attractive returns but also received high safety ratings from the major credit-rating agencies. In 2007 and 2008, the inevitable wave of foreclosures finally arrived — exposing the entire financial system to catastrophic losses…

The worst financial panic since the Great Depression. Already dangerously over-leveraged from years of risky bets, banks were unable to absorb the huge losses. The first big domino to fall was the investment bank Bear Stearns, which collapsed in March 2008. Later, Lehman Brothers filed for the largest bankruptcy in U.S. history, and the government bailed out insurance giant AIG, which had sold enormous amounts of credit default swaps insuring the bad investments. As panic spread, lending and investment screeched to a halt, and the country was plunged into the worst financial crisis since the stock market collapse of 1929…

The U.S. government took extraordinary measures to prevent a full-scale economic collapse. Under President George W. Bush, Congress approved a $700 billion bailout purchasing toxic assets to restore confidence in the market; under President Barack Obama, it authorized a $787 billion stimulus package to stimulate spending in the private sector. But massive damage had already been done. The economy slipped into a deep recession. The Dow Jones industrial average and the S&P 500 lost more than half their value. Unemployment peaked at roughly 10 percent by October 2009.

They say the system is safer now because of Dodd-Frank. Well, Dodd-Frank is under savage attack by our current administration, so I would not be too confident the system is safe. The article also explains that even though the economy has come back on average, Americans of average income and below are still feeling the effects and may never fully recover to where they would have been without the crisis.

the singularity is near…in China

This article in Economist says China wants to be a

“cyber superpower”—one that, within a dozen years, will lead the world in artificial intelligence (AI), quantum computing, semiconductors and the coming “5G” generation of mobile networks, not to mention synthetic biology and renewable energy.

This is a pretty good list of technologies of the future. Although they clearly have some potential military applications, they have many more civilian ones where everyone can gain at the same time. Personally I don’t think investing in the technologies of the future should be thought of as a zero sum game. It is more a question of whether the U.S. wants to keep up with its current peer group of the most advanced nations with the highest quality of life, a group it is still part of but in the middle of the pack and slipping toward the back, rather than out in front. If the idea of competing to lead in these technologies spurs the U.S. to action, that is okay with me. The article does have a few policy prescriptions:

Better that it should develop a broader policy to strengthen its technosystem, argues Ms Kania of CNAS. Instead of making it as closed as the Chinese one, which would seem to be Mr Trump’s preference, it needs to engage with allies such as Europe, Japan and Korea to spread open standards. It needs to build a shared digital infrastructure, such as common pools of key data for things like self-driving cars. And it needs to rediscover what has made it great in technology: investing in both basic and applied research and being an attractive destination for highly qualified immigrants (a requirement which, it must be admitted, the Trump administration is not well placed to meet).

I’ll offer a few more along these lines, if the U.S. would like to be a “cyber-superpower” a dozen years from now:

Small businesses and startups innovate, and they challenge lazy established big businesses to innovate. It needs to be much, much easier to start a business anywhere in the United States. It is not necessarily taxes and regulations, but the fact that there are too many complicated, confusing taxes and regulations fragmented among local, state, and federal entities. We need to figure this one out.
Economic growth requires continuous investment in human capital. People working toward an academic degree need an income, and the government needs to find a way to provide them with one. We need job skills training and retraining programs, and employers need to be heavily incentivized to train the workers they need in the skills they need. Skills-based immigration and guest worker programs can fill in the remaining gaps between the needed skills and available trained Americans.
Economic growth requires continuous investment in physical capital (what economists call “plants and equipment”) and in public infrastructure. For the former, tax incentives could be the answer, however unpopular they might be. For the latter, an infrastructure bank could be the answer, where the actual creation of the money supply is done through the issuance of infrastructure bonds.
Economic growth requires continuous innovation. On the private side, big tax incentives for research and development could be the answer, while on the public side, we could just turn on the taps for funding research, particularly at public universities. This has been slipping in recent decades from where it used to be.
I just mentioned a number of programs that require public spending, of course. I think they would pay for themselves in the long run, but in the short run new sources of revenue would be needed, however politically unpopular. I would look to a value added tax as the international best practice which the U.S. continues to ignore, and taxes on pollution and waste which have the added benefit of making us healthier and safer.
For any of these policies to have a prayer of getting through our political system, we would need a constitutional amendment making it clear that the right to free political speech applies only to human beings, not to corporations or dollars. Otherwise the United States will not be able to have these nice things.

one large or many smaller cities for maximum productivity

This paper looks at data from 306 cities in China to identify trends in how different sizes and densities of cities relative to each other affect economic productivity. The interesting finding is that it is best to have either one big low-density city or many smaller high-density ones.

How did urban polycentricity and dispersion affect economic productivity? A case study of 306 Chinese cities

This article aims to assess the impacts of urban spatial structure on economic productivity. Drawing upon detailed gridded population data of 306 Chinese cities at the prefecture level and above, we identify their urban (sub)centers through exploratory spatial data analysis, construct indicators to measure their degrees of polycentricity and dispersion, and model the impacts of spatial structure on urban productivity. A regression analysis reveals that economic productivity is significantly associated with urban spatial structure. Conditioning on other factors, higher degrees of dispersion are associated with lower level of urban productivity whereas the effects of polycentricity depend on urban population density. Less densely populated cities are likely to have higher productivity levels when they are more monocentric, while urban productivity of cities with high population density tend to benefit from a more polycentric structure. The paper concludes with spatial planning implications.

January 2018 in Review