Category: Politics

  • Regional Implications of the Russian Currency Crisis

    Unless something changes dramatically in the next couple of days, we will code this week as the onset of the 2014-15 Russian currency crisis. Using Quantmod, I’ve plotted below the evolution of the ruble-dollar exchange rate since 2010 along with 180-day Bollinger bands.

    The Indonesians have a verb for this kind of currency movement: anjlok, which my Echols and Shadily dictionary defines as “to plummet,” or figuratively, to “jump the rails.”

    One interpretation of the unfolding currency crisis is that this is bad news for Putin, and hence good news for US/Western foreign policy. Here are two reasons to be circumspect, at least at this stage.

    Recent History

    First, the historical context. Let’s look back another decade, shall we?

    Russia managed to ride out the 2008-09 Global Financial Crisis pretty well. One might look to this as evidence that Putin has the “room to move” to withstand an external shock of this sort, although I’d add two caveats to this view. For one, the ruble depreciation of 2008-09 looks a lot like a return to trend than a departure from trend (as it does now). But also, and the 2008-09 depreciation is probably a consequence of a flight to liquidity during the Global Financial Crisis, which is not true today.

    Regional Context

    A second reason to be skeptical is the external implications of a Russian ruble crisis. Let’s also look at XR movements in the Ukraine over the same time period.

    Just eyeballing this, it looks like the same pressures affecting Russia will be affecting the Ukraine too, which would mean that any harm that comes to the Russian economy would also spill over to its neighbors too, including those neighbors that are seen as friends of the U.S.

    Fortunately, we know that we should not just eyeball our time series, because non-stationary time series can be misleading. I’ve plotted below the daily changes in hryvnia/USD (blue) and ruble/USD exchange rates (red).

    It’s not so clear that they are related. We can also look at a scatterplot of day-on-day changes.

    There is only the slightest positive relationship here between hryvnia-USD movements and ruble-USD movements.

    So why write about this at all? Because it is also possible to show you this figure, from a time in which Ukrainian-Russian ties were not marred by invasions and things.

    The t-stat on the line of best fit is 5.6, and remains unchanged when controlling for lagged levels of each country’s exchange rate.*** This reflects the fact that during normal times, we expect there to be spillover effects from the Russian economy to the Ukrainian economy. (This figure is all the more striking given that the Ukraine was maintaining a quasi-fixed exchange rate during this period.) The economies are really tightly integrated: Russia is the Ukraine’s largest trading partner, after all.

    The takeaway thought is that currency crises have external consequences. Russia’s will too, and they might lead analysts to be careful of what they wish for, even as many of them happily watch the markets hammer Russia.

    Note

    *** Of course, I’d be curious to see a full ARIMAX/VECM model, and will post the R code to get all the data together in the first comment on this post.

  • The Evolution of Indonesia’s Political Economy in One Amazing Figure

    My colleague Sharon Poczter and I are finishing up a new paper that analyzes the career backgrounds of a large sample of Indonesian political elites. This means cataloguing thousands of careers on elites’ CVs in order to classify them into various types, such as military, private sector, and so on. Because we have such a rich source of data, we can also analyze phenomena such as individuals with multiple types of employment over their lifetime; the predictive power of gender, religion, partisan affiliation, and education; and differences in career patterns over time.

    The last bit means exploiting differences in career histories based on elites’ ages (or implied ages for elites who have died). This is a crude form of cohort analysis, and it allows us to ask if older political elites tended to have different career paths than younger ones. We do this by fitting a series of logistic regression models predicting whether an individual has one of four types of backgrounds—private sector, bureaucracy, government, and military—using with a cubic polynomial of age as the main predictor alongside a series of demographic and political covariates. Here, we’ve plotted the predicted probability that an elite of a particular age has each of the four employment types. We’ve also included 95% confidence intervals. The result is striking.
    margins

    Among older elites—those who rose to prominence under the New Order—military and bureaucratic backgrounds predominate. Among younger elites, who we can infer rose to prominence under the late New Order period or under democratic rule due to their age, private sector backgrounds predominate. This isn’t exactly the “Rise of Capital” that Richard Robison described: he considered the links between indigenous capital and the state to be much tighter than what this figure is describing, and I am not aware of his predicting a decline of military and state as avenues to political power alongside the rise of capital. But the findings are related, and they help to make sense of how Indonesia’s political economy has changed over the past fifty years.