PhD Applications Again

Three and a half years ago, I wrote a post about PhD applications. Since then, I have received a huge number of enquiries from prospective students. I now have two PhD students (Alrick Campbell and Panittra Ninpanit) and am on the committee/panel of two others (Anil Kavuri and Rohan Best). There are a couple of other good students who have applied but haven't come here because either their English test scores didn't meet our requirement or they couldn't get funding. We don't offer any new internal Crawford School scholarships at this point and I don't have any grant funds for PhD students. So, it is quite unlike applying for a PhD in the US where most students are funded by university sourced money in social sciences like economics. Here it is most likely that you will be funded by the Australian government one way or another, by your own government, or by an intergovernmental organization like the Asian Development Bank.*

As I mentioned in my previous post, also, unlike North America, applying to do a PhD in the social sciences and humanities here in Australia requires lining up a supervisor (=advisor) up front. Therefore, it is more like applying to do a PhD in the natural sciences and engineering in the US. Our formal process here also requires that potential students submit a research proposal, despite the fact that at ANU there is up to a year of coursework required in the economics program, which makes it seem more like a US PhD than most Australian PhD programs where you start doing research more or less straight away.

This is where I have been a bit frustrated by potential students submitting proposals that aren't at all related to the kind of research I do (despite this blog and my research webpage), proposals that are not very good, or being surprised that they need to submit a proposal because that isn't required to apply for a PhD in the US. Some of the latter seem like potentially good students. When I ask them for a proposal, the usual reaction is to write something rather quickly. I can't blame these students - when many programs around the world don't require a proposal, why should they invest a lot in writing one. One of the main reasons I did my PhD in the US rather than Britain was that I didn't know what to write a proposal about at the time. Another downside of a student submitting an upfront proposal is that they might then feel somewhat locked into that subject despite having written the proposal being a sunk cost. Alrick and Panittra were exceptions, having a pretty good proposal up front that was related to my research, which is why I agreed to supervise them.**

So, after receiving another off-the-wall topic from a prospective student this morning, I'm thinking of taking a radically new approach. Maybe, I should require students to submit a completed research paper (like we did when I was at RPI) instead of a  proposal for future research and then discuss this paper with the student to see how they think etc. I would require students to work on one of the broad areas I work on ("economic growth", "meta-analysis" etc.) and develop an actual proposal with them after they arrive here.

Or maybe the process is working exactly as it should? After all, I have had a few good applications and probably as many students as I should have. Any thoughts?

* Australian students can get an APA. Foreign students main option is the Australia Awards program. There are very few scholarships for students not from developing countries that Australia is interested in giving aid to. According to the government's Innovation Package, this will change dramatically.

** Students only need to line up the primary supervisor ahead of time. The other panel members usually join after the student has finished their coursework.

Follow-up on Anti-Vax

So, apparently my impression that anti-vax was a right wing cause (anti-government mandates/one-world government or whatever) was unusual and most people think it is a left-wing cause (anti big-pharma/pro natural remedies etc). Turns out that neither is the case and that there are people on both the left and the right (at least in the US) who are anti-vax. Actually, it seems that there is a slight maximum of concern about vaccines near the center of the political spectrum, as shown on this graph from the linked article:

 

Drivers of Industrial and Non-Industrial Greenhouse Gas Emissions to be Published in Ecological Economics

My paper with my former master's student Luis Sanchez has been accepted by Ecological Economics. This is one of the papers in the series using growth rates estimators of the income-emissions relationship that came out of my work on the IPCC 5th Assessment Report. This is the second paper I have published based on work done in our course: IDEC8011 Master's Research Essay. The previous one was with Jack Gregory who is now a PhD student at University of California, Davis. BTW, we previously submitted this paper to Nature Climate Change, Global Environmental Change, and Climatic Change in that order, with the first submission on 5 January 2015.

Long-run Estimates of Interfuel and Interfactor Elasticities

A new working paper coauthored with Chunbo Ma on estimating long-run elasticities. This is one of the major parts of our ARC DP12 project, the "Present" part of the title: "Energy Transitions: Past, Present, and Future". We just resubmitted the paper to a journal and I thought that was a good time to post a working paper with the benefit of some referee comments.

Both my meta-analysis of interfuel elasticities of substitution and Koetse et al.'s meta-analysis of the capital-energy elasticities of substitution show that elasticity estimates are dependent on the type of data – time series, panel, or cross-section – and the estimators used. Estimates that use time series data tend to be smallest in absolute value and those using cross-section data tend to be largest.

We review the econometric research that discusses how best to get long-run elasticity estimates from panel data. One suggestion is to use the between estimator, which is equivalent to an OLS regression on the average values over time for each country, firm etc. in the panel. Alternatively, Chirinko et al. (2011) argued in favor of estimating long-run elasticities of substitution using a long-run difference estimator, which is very similar to the "growth rates estimator" we have used recently.

We apply both these estimators to a Chinese dataset we have put together from both public and non-public data sources. We have data for 30 Chinese provinces over 11 years from 2000 to 2010. We estimate models for choice of fuels - interfuel substitution - and for the choice between capital, labor, and energy - interfactor substitution.

A big issue with the between estimator, which has made it relatively unpopular, is that it is particularly vulnerable to omitted variables bias. The big omitted variable in most production analysis is the state of technology. There is a lot of variation across provinces in productivity and prices and it seems that the two are correlated:

The first graph shows the price index for aggregate coal input that we constructed. Generally, coal is more expensive in Eastern China. The second graph shows an index of provincial total factor productivity, relative to Shanghai, which is the most productive province. Coastal provinces are the most productive - their distance to the technological frontier is low. To address this potential omitted variables bias, we add province level inefficiency and national technological change terms to the cost function equation. Chirinko et al. (2011) instead used instrumental variables estimation, but we found that their proposed instruments in many cases have very low or negative correlations with the targeted variables. We do use instrumental variables estimation, but this is due to the endogeneity inherent in our constructed coal and energy prices indices. We use Pindyck's (1979) approach to this. We also impose concavity on the cost function, if necessary.

The results show that demand for coal and electricity in China is very inelastic, while demand for diesel and gasoline is elastic. With the exception of gasoline and diesel, there are limited substitution possibilities among the fuels. Substitution possibilities are greater between energy and labor than between energy and capital. These results seem very intuitive to us. However, they are quite different to some previous studies for China, in particular the estimates in the paper by Hengyun Ma et al. (2008) Their estimates of the elasticities of substitution are negatively correlated with ours. Their study uses similar but older data, though we have improved the calculation of some variables. They use fixed effects estimation and don't impose concavity. These might be some of the reasons why our results differ. We also provide traditional fixed effects estimates with concavity imposed. These estimates are mostly close to zero. This suggests that the between and difference estimators are picking up longer-run behavior.

Which of these two estimators should we use in future? We can't give a definitive answer to that question but the difference estimator does seem to have some advantages. In particular, it allows cross-equation restrictions on the bias of technical change, which should result in better estimates of those parameters. So, that would be my first preference, though I am kind of reluctant to ignore the between variation in the data.

Influential Publications in Ecological Economics Revisited to be Published in... Ecological Economics

Our paper on the changes over the last decade in patterns of influence in ecological economics has been accepted for publication. Not very surprisingly the journal where it will be published is Ecological Economics. Elsevier have already sent me an e-mail saying that I should expect the proofs on 21 January! That is fast.

People's Ability to Delude Themselves is Amazing

The Australian reported a couple of days ago that the University of Wollongong gave a PhD for a thesis by an anti-vaccination activist, Judy Wilyman. It's the comments on the article where the delusion is amazing. Many people comment that it is totally outrageous that the University of Wollongong gave this PhD because obviously anti-vax is total nonsense and a conspiracy theory. At the same time, some of them are complaining that climate scepticism doesn't get sufficient respect from academia. Of course, climate scepticism is just as much nonsense and a conspiracy theory as anti-vax.* But these people believe that one of these theories is totally correct and the other totally bogus. I'm a bit surprised, as I thought that both these theories were right-wing anti-government theories. Apparently not in Australia?

This is, of course, exactly the same as people who are convinced that their religion is true and all other religions are false.

* I am open-minded about both anti-vaccination and climate change sceptical hypotheses. Also UFOs, yetis...

Between and Within

This will be obvious to anyone with a good understanding of econometrics, but it is quite stunning really to think that all the information you see in the first set of graphs in my previous post on the EKC is thrown away by fixed effects panel estimators. That is because the graphs plot the mean value over time in each country of the dependent variable against the mean value over time in each country of the explanatory variable. Fixed effects estimation first deducts these means from the data and then estimates the regression of the two residuals using ordinary least squares. This is why fixed effects is also called the "within estimator" because the "between (country) variation" you see in these graphs is ignored. Of course, you can estimate a model that just exploits this between variation using the between estimator.*

The reason the latter estimator is rarely used is because researchers are worried about omitted variables bias. Any omitted variables are subsumed in the error term while the fixed effects estimator eliminates their country specific means and so reduces the potential bias. Hauk and Wacziarg (2009), however, found that when there is also measurement error in the explanatory variables (which can also bias the regression estimates) the between estimator performs well compared to alternatives. Fixed effects estimation tends to inflate the effect of the measurement error.

Differenced estimators sweep out any country fixed effects in the differencing operation.** So they also remove all the between variation in the data. However, they do allow us to include country characteristics that are constant over time to explain differences in growth rates across countries, which standard fixed effects does not allow.***

* The linked paper was eventually published in Ecological Economics.
** For a two period panel, fixed effects and first differences produce identical results.
*** There are variations of fixed effects that can allow this.

The Environmental Kuznets Curve after 25 Years

This year marks the 25th anniversary of the release of the working paper: "Environmental Impacts of a North American Free Trade Agreement" by Gene Grossman and Alan Krueger, which launched the environmental Kuznets curve industry. I have a new working paper out whose title capitalizes on this milestone. This is my contribution to the special issue of the Journal of Bioeconomics based on the workshop at Griffith University that I attended in October. It's a mix between a survey of the literature and a summary of my recent research with various coauthors on the topic.

Despite the pretty pictures of the EKC in many economics textbooks, there isn't a lot of evidence for an inverted U-shape curve when you look at a cross-section of global data:

Carbon emissions from energy use and cement production and sulfur dioxide emissions both seem to be monotonically increasing in income per capita. Greenhouse gas emissions from agriculture and land-clearing (AFOLU, lower left) or particulate concentrations (bottom right) just seem to be amorphous clouds. In fact, we do find an EKC with an in sample income turning point for PM 2.5 pollution, but only when we look at changes over time in individual countries. Interestingly, Grossman and Krueger originally applied the EKC to ambient concentrations of pollutants and it is there that it seems to work best.

The paper promotes our new "growth rates" approach to modeling emissions. Here are graphs of the growth rates of pollution and income per capita that exactly match the traditional EKC graphs above:



There is a general tendency for declining economies to have mostly declining pollution and vice versa, though this effect is strongest for energy-related carbon emissions. The graphs for sulfur and AFOLU GHG emissions are both shifted down by comparison. There is a general tendency unrelated to growth for these pollutants to decline over time - a negative "time effect". Growth has a positive effect though on all three. PM 2.5 (lower right) is a different story. Here economic growth eventually brings down pollution. We don't find a significant negative time effect.

I first got interested in the EKC in November 1993 when I was sitting in Mick Common's office at the University of York where I'd recently started as a post-doc (though I was still working on my PhD). He literally drew the EKC on the back of an envelope and asked whether more growth would really improve the environment even if the EKC was true. I did the basic analysis really quickly but then it took us another couple of years to get the paper published in World Development.