Author: tompepinsky

  • The Chinese Growth Differential

    This months dramatic volatility in the Chinese stock market has raised the question once more about after twenty years of breakneck economic growth, China’s growth trajectory is sustainable. Many consider a correction of some sort to be inevitable (see for example Brad Delong here). One strategy to look for signs that this is happening is to look at various indicators of economic activity within China, as James Hamilton has done. It is also useful, though, to step back a bit to see what we think Chinese growth ought to look like given what we know about economic growth in the rest of the world.

    I’ve done this by creating an empirical model of economic growth for all countries except for China. The model looks like this:

    gyt-t0 = yit0 + Xit + Dt + Dr

    where gyt-t0 is annual (geometric) growth in real GDP per capita over a five year period, yit0 is the initial level of real GDP per capita for that five year period, and Xit are the five-year averages of the other determinants of economic growth. Dt and Dr are period and region fixed effects, respectively. I’ve done this two ways, both including and excluding time and region effects. Naturally, country fixed effects would be nice, but including them effectively precludes me from being able to predict how Chinese growth might differ from growth elsewhere. So to be clear, any distinctive “Chinese” growth effect is captured in the “East Asia” dummy.

    These are what I’d call “appropriately naive Barro regressions.” They follow in the spirit of Barro’s classic Determinants of Economic Growth in modeling economic growth as a function of initial per capita GDP in levels plus measures of human capital, government policy, macroeconomic conditions, and regime type. They differ in that they do not even attempt to untangle issues of causality. No three-stage least squares using lags, colonial history, and regional dummies as instruments, or anything of the sort. Hence these regressions are “naive,” but that is “appropriate.”

    Using data from the World Development Indicators augmented by some additional variables from the QoG dataset, I estimated the two models described above for all countries in the world except for China. I then predicted what Chinese growth would look like using actual data from China. This gives us yearly predictions: in any year, given China’s level of per capita GDP and other variables, what is our best guess about its rate of growth in that year?

    I have plotted the resulting predictions here. The black series is the prediction that includes region and time effects, and the blue series ignores these. The red solid line is China’s actual economic growth.
    growth
    As you can see, since 1991 China has grown far faster than the appropriately naive model predicts. What is also true, though, is that the differences between China’s predicted and actual growth rates are narrowing considerably since 2011. This might be interpreted as a reversion to the “predicted” growth path based on the cross-national experience of the past forty years. What we observe, then, is a “Chinese growth differential.”
    differential
    That differential was lower in 2014 than it had been since the aftermath of Tiananmen.

    There are some other interesting things to note. The standard neoclassical growth model predicts convergence, in which countries growth rates slow as their GDPs rise. As Barro showed, the data do not support this simple story; they instead support a story of conditional convergence in which countries growth rates slow conditional on macroeconomic and political conditions (although see Rodrik recently and Quah not-so-recently for other views). The blue and black lines in the first graph above show a gentle increase in predicted growth rates. One interpretation of this is that, in a conditional convergence world, changes in living standards in China have actually outpacing the increases in GDP as determinants of economic growth. A wealthier China in 2010 should grow faster than China of 1990 due to the rapid increases in health and education.

    What does this mean? Recall that this prediction model cannot account for anything particular to China. So as a result it cannot tell us if any such Chinese particularity is durable or not, or if recent growth has been atypical relative to what China’s growth should be like. The estimated differential is relative to a counterfactual of all other countries’ growth experiences, not relative to some counterfactual version of China.

    But if we hold that caveat aside, as well as the problems of causal identification in naive (but appropriate!) growth regressions, it confirms that slower Chinese growth is to be expected. The interesting part is how this interacts with current events, in particular China’s stock market crisis and its political fallout. None of the above predictions suggests that a stock collapse is inevitable, but such a collapse might indeed hasten the shift toward to a more modest growth path. Check back in six months to see if that is the understatement of the year.

    NOTES

    For R code to produce these graphs, please see the first comment. Here are the full model results for Model 1 and Model 2.

    Dependent variable:
    growth
    (1) (2)
    log(GDPPC.Initial) -0.520*** -0.702***
    (0.093) (0.121)
    Fixed Capital Formation 0.154*** 0.153***
    (0.010) (0.010)
    Gov Final Cons Exp -0.060*** -0.055***
    (0.012) (0.011)
    Trade/GDP 0.008*** 0.011***
    (0.002) (0.002)
    Inflation -0.003*** -0.003***
    (0.0003) (0.0003)
    Life Expectancy 0.036** 0.036*
    (0.017) (0.021)
    Secondary Enrollment Rate 0.005 0.003
    (0.005) (0.006)
    Polity2 Score 0.043*** 0.044***
    (0.015) (0.017)
    Constant -0.054 3.199**
    (0.863) (1.244)
    Observations 867 867
    R2 0.377 0.454
    Adjusted R2 0.372 0.438
    Residual Std. Error 2.447 (df = 858) 2.314 (df = 841)
    F Statistic 65.009*** (df = 8; 858) 27.992*** (df = 25; 841)
    Note: *p<0.1; **p<0.05; ***p<0.01
  • Malaysia’s GE13, Long Form Research Blogging Redux, and Statistics versus Econometrics

    A little over two years ago, I wrote a post on “Long Form Research Blogging” related to my series of posts on Malaysia’s Thirteenth General Election. I wondered at the time if there would ever be a way to published all of that in an academic journal.

    Through an unusual set of coincidences, I have managed to cannibalize a good deal of those posts in an article that is now in print. What’s unusual about the process? Just that the article is a commentary on another article which appears in the Journal of East Asian Studies which makes what I think are some erroneous conclusions about the relationship between ethnicity and votes for the Barisan Nasional coalition. The full-text is not yet available from the publisher’s website. But I have made a copy of the original article, my comment, and the authors’ rejoinder available here: PDF. This is of course only for your own personal use, dear reader.

    I think that the contributions speak for themselves. But I do want to flag one issue in NRVP’s rejoinder which I find puzzling. They draw on a distinction between statistics and econometrics as describe by Rob Hyndman, and describe me as taking a statistical approach.

    The argument above that Pepinsky makes against our use of the fractional logit model is an example of the difference in disposition between taking either a theory-driven or data-driven approach. While largely similar, econometrics is predominantly theory driven while statistics tend to be data driven. Therefore, an econometrician develops a model based on economic (and other relevant) theories while a statistician may build a model after looking at datasets. The econometrician subsequently confronts the model with datasets to test the theory. The interested reader can refer to Rob Hyndman’s blog post1 for interesting insights into the differences between the two. In this context, it can be said that our econometric model is theory driven while Pepinsky’s model is data driven.

    This is odd to me because I thought that I was taking the econometric approach, and they were taking the statistical approach! I mean, contrast that quote above with the following argument from NRVP about why they have opted to use ethnic population totals, which I argue are theoretically inappropriate as a substitute for ethnic population shares:

    • It would make the article too technical, distracting the reader from
      the political issues at hand.
    • Interpretation of isometric log-ratio transformed variables is difficult,
      even in linear regression models, thereby making it hard to
      make useful inferences.
    • No work has been done on how the isometric log-ratio transformation
      can be performed on quadratic variables and for interaction variables

    In lieu of the above, we decided to go with ethnic population totals as our measure of ethnicity, as the sum constraint would at least somewhat be removed. However, we acknowledge that this is not the best way to model ethnicity, which Pepinsky has correctly and strongly pointed out. Nevertheless, in our opinion, it is the better choice to model the data.

    That looks to me like NRVP are prioritizing statistical procedure over coherent theory. My view is that we should not do that.

    Readers who have slogged the whole way through this post might also be interested in Andrew Gelman’s thoughts on statistics versus econometrics.