From Wood to Coal: Directed Technical Change and the British Industrial Revolution

We have finally posted our long-promised paper on the Industrial Revolution as a CAMA Working Paper. This is the final paper from our ARC-funded DP12 project: "Energy Transitions: Past, Present and Future". The paper is coauthored with Jack Pezzey and Yingying Lu. We wrote our ARC proposal in 2011, but we "only" started work on the current model in late 2014 after I read Acemoglu's paper "Directed Technical Change" in detail on a flight back to Australia and figured out how to apply it to our case. We have presented the paper many times in seminars and conferences, though I will be presenting it again at the University of Sydney on April 6th.

The paper develops a directed technical change model of economic growth where there are two sectors of the economy each using a specific type of energy as well as machines and labor. The Malthus sector uses wood, which is only available in a fixed quantity per year, and the Solow sector uses coal, which is available at a fixed price. These assumptions are supported by the data. We don't think it is necessary to model coal as an explicitly non-renewable resource. As shallow deposits were worked out, technological change, including the development of the steam engine, allowed the exploitation of deeper deposits at more or less constant cost.

The names of the sectors come from the paper by Hansen and Prescott (2002): Malthus to Solow.  That paper assumes that technological change is exogenous and happens at a faster fixed rate in the Solow sector (which only uses labor and capital) than in the Malthus sector (which also uses a fixed quantity of land). The Solow sector is initially backward but because technical change is more rapid in that sector and it is not held back by fixed land, eventually it comes to dominate the economy in an industrial revolution.

Our paper updates this model for the 21st Century. In our model, technological change is endogenous, as is the speed with which it happens in each sector - the direction of technical change. We don't assume, a priori, that it is easier to find new ideas in the coal-using sector. In fact, we don't assume any differences between the sectors apart from the supply conditions of the two energy sources, which we explicitly model.

In most cases, an industrial revolution eventually happens. The most interesting case is when the elasticity of substitution between the outputs of the Malthus and Solow sector's is sufficiently high - based on our best guesses of the model parameters in Britain, greater than 2.9 - then it is possible if wood is relatively abundant for an economy to remain trapped forever in what we call Malthusian Sluggishness where growth is very low.* Population growth can push an economy out of this zone by raising the price of wood relative to coal and send the economy on a path to an industrial revolution.

These two phase diagrams show the two alternative paths an economy can take in the absence of population growth, depending on its initial endowment of knowledge and resources:

N is the ratio of knowledge in the Malthus sector (actually varieties of machines) to knowledge in the Solow sector. y is the ratio of output in the two sectors and e is the ratio of the price of wood to the price of coal. In the first diagram we see that an economy on an industrial revolution path first has rising wood prices relative to coal and also, initially, technical change is more rapid in the Malthus sector than in the Solow sector and so N rises too. In the long-run both these trends reverse and under Modern Economic Growth technical change is more rapid in the Solow sector and the relative price of wood falls. At the same time, we see in the second diagram that eventually the output of the Solow sector grows more rapidly than that of the Malthus sector so that y falls. The rate of economic growth also accelerates.

But an economy which starts out with a low relative wood price, e, or low relative knowledge in the Solow sector, N, can remain trapped with rising wood prices AND increasing specialization in the Malthus sector - rising y and N. Though there is coal lying underground, it is never exploited, even though switching to coal use would unleash more rapid economic growth in the long run. The myopic, but realistic, focus on near term profits from innovation discourages the required innovation in the Solow sector.

The core of the paper is a set of formal propositions laying out the logic of these findings but we also carry out simulations of the model calibrated to the British case over the period 1560-1900. Counterfactual simulations with more abundant wood, more expensive coal, more substitutability, less initial knowledge about using coal, or less population growth all delay the coming of the Industrial Revolution.

* We assume either that population is constant or treat its growth as exogenous.

Cohort Size and Cohort Age at Top US Economics Departments

I'm working on a new bibliometrics paper with Richard Tol. We are using Glenn Ellison's data set on economists at the top 50 U.S. economics departments as a testbed for our ideas. I had to compute the size of each year cohort for one of our calculations, and thought this graph of the number of economists at the 50 departments in each "academic age" year was interesting:

There isn't as sharp a post-tenure drop-off in numbers as you might expect, given the supposed strict tenure hurdle these departments impose. But as we can see the cohorts increase in size up to year 5, which might be explained by post-docs and other temporary appointments, or people even moving up the rankings after a few years at a lower ranked department. So, as a result, the tenure or out year would be spread over a few years too. On the other hand, as the data were collected in 2011, the Great Recession might also explain lower numbers for the first few years.

A post-retirement drop-off only really seems to occur after 39 years. The oldest person in the study by academic age was Arnold Harberger.

Two New Working Papers

We have just posted two new working papers: Technology Choices in the U.S. Electricity Industry before and after Market Restructuring and An Analysis of the Costs of Energy Saving and CO2 Mitigation in Rural Households in China.

The first paper, coauthored with Zsuzsanna Csereklyei, is the first to emerge from our ARC funded DP16 project.  Our goal was to look at the factors associated with the adoption of more or less energy efficient electricity generating technologies using a detailed US dataset. For example, combined cycle gas turbines are more energy efficient than regular gas turbines and supercritical coal boilers are more efficient than subcritical. Things are complicated by the different roles that these technologies play in the electricity system. Because regular gas turbines are less energy efficient but have lower capital costs they are mainly used to provide peaking power, while combined cycle turbines contribute more to baseload. So comparing combined cycle gas to subcritical coal makes more sense as a test of how various factors affect the choice of energy efficiency than comparing the two types of gas turbine technologies.

Additionally, some US regions underwent electricity market reform where either just wholesale or both wholesale and retail markets were liberalized, while other regions have retained integrated regulated utilities, which are typically guaranteed a rate of return on capital. Unless regulators press utilities to adopt energy efficient technologies there is much less incentive under rate of return than under wholesale markets to do so.

The graph shows that following widespread market reform at the end of the 20th Century there was big boom in investment in the two main natural gas technologies. More recently renewables have played an increasing role and there was a revival of investment in coal up to 2012. These trends are also partly driven by the lagged (because investment takes time) effects of fuel prices:

We find that electricity market deregulation resulted in significant immediate investment in various natural gas technologies, and a reduction in coal investments. However, market deregulation impacted less negatively on high efficiency coal technologies. In states that adopted wholesale electricity markets, high natural gas prices resulted in more investment in coal and renewable technologies.

There is also evidence that market liberalization encouraged investments into more efficient technologies. High efficiency coal technologies were less negatively affected by market
liberalization than less efficient coal technologies. Market liberalization also resulted in increased investment into high efficiency combined cycle gas. In summary the effect of liberalization is most negative for the least efficient coal technology and most positive for the most efficient natural gas technology.

The second paper is based on a survey of households in rural China and assesses the potential for energy conservation and carbon emissions mitigation when energy saving technologies are not fully implemented. In reality, appliances do not always survive for their designed lifetime and households often continue to use other older technologies alongside the new ones. The effect is to raise the cost of reducing energy use and emissions by a given amount. The paper computes marginal abatement cost curves under full and partial implementation of the new technologies.

The graph shows the marginal abatement cost curve for rural households in Hebei Province, scaled up from the survey and our analysis. Full-Scenario is the curve with full implementation of new technologies and OII-Scenario is with actual partial implementation. This analysis does not take into account any potential rebound effect of energy efficiency improvements.

The first author, Weishi Zhang, is a PhD student at the Chinese University of Hong Kong. She contacted me last year about possibly visiting ANU, and I supported her application for a scholarship to fund the visit (which unfortunately she didn't get), because I thought her research was some of the more interesting research on Chinese energy use and pollution that I had seen. I helped write the paper (and responses to referees in our revise and resubmit).

March Update

Just realized that we are already in the third month of the year and I haven't posted anything here yet! Things have been very busy with both work and family, so there hasn't been time to put out the blogposts only indirectly related to my research that I used to do - instead I'll usually tweet something on those topics - and research-wise things have either been at the relatively early research stages or the final publication stages. But there will soon be some new working papers going up and some blogposts here discussing them!

On the research front, in January we were mainly focused on putting the final touches on our climate change paper in time for the deadline for the special issue of the Journal of Econometrics. My coauthors want to wait for some feedback before posting a working paper on that. Then in February my collaborators Stephan Bruns and Alessio Moneta visited Canberra to work with me on modeling the economy-wide rebound effect as part of our ARC DP16 project. I spent the first half of the month working hard on the topic to prepare for their visit. We made good progress but it will be at least a few months till we have a paper on the topic ready. So far, it seems robust that the rebound effect is big. Then since they left, I've been catching up.

Recently, Paul Burke said: "You've already got three papers accepted this year - are you going to keep that pace up? ;)" He'd been keeping better count than me! Our original paper on the growth rates approach to modeling emissions and economic growth was accepted at Environment and Development Economics. Two related papers were also accepted - at Journal of Bioeconomics and Climatic Change. I also have three revise and resubmits to be working on... though one of those came in 2016... I'll put out one or two of those as working papers when we resubmit them.