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North Korea: Witness to Transformation

North Korea by Night I: Regional Developments

by and Travis Pope | April 8th, 2014 | 07:01 am
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We have all seen the satellite images of the Korean peninsula, with a lit-up South and nothing more than a small blip of light around Pyongyang. In fact this picture has been changing as can been in the 2012 composite image reproduced below; other cities and even smaller towns are visible. Therein lies a new way of tracking economic activity in North Korea that has been used elsewhere with interesting effect (see for example Small, Pozzi and Elvidge and Henderson and Weil): measuring changes in luminosity over time. In this post and several that follow, we take a look at some trends since 1992, beginning with the regional story.

North Korea at night

First, a few words about the data (a more technical note is appended below). In this analysis, we use the DMSP-OLS Stable Lights Dataset covering 1992-2012. In effect, this data set generates a single composite image of the country for each year and can be used as a proxy for economic activity across different parts of the country. The basic units of observation are the 186 local administrative areas defined by the Global Administrative Areas dataset.

The images remove background noise and ephemeral lights emitted from gas flares or fires, but there is one major drawback: the six satellite sensors are not sufficiently inter-calibrated to make direct year-to-year comparisons. What it is possible to do, however is to compare the shares of total light emanating from different jurisdictions (again, from local administrative areas up to regions). Put differently, we can’t say what the absolute growth of luminosity is in Pyongyang. But we can track changes in Pyongyang’s share of all light emanating from the country. As a result, the data is quite useful for showing changes in the spatial distribution of economic activity.

We begin in this post by looking at larger regional aggregations. We divide the country into five regions:

  • Pyongyang;
  • The Northwest, consisting of North Pyongan province, South Pyongan province, Chagang province, and the city of Sinuiju;
  • The Northeast, including the Hamgyeong provinces, Ryongyang province, and the Rason special city;
  • The Southeast including Gangweon province and the Kumgangsan special administrative area;
  • The Southwest, including North Hwanghae province, South Hwanghae province, and the city of Kaesong.

The figure below shows the three-year moving average of each region’s share of total national light from 1996 to 2011. The graph on the right omits Pyongyang, allowing us to see more clearly what is going on in the provinces.

share of light

Three important trends are visible. First, Pyongyang’s share declined from its 1996-97 peak—in the immediate aftermath of the famine–until the early-2000s. A possible interpretation: Pyongyang had been protected during the famine, but once it ended other areas began to recover. From 2003, however, the capital city’s share rebounded to mid-90s levels before turning down slightly in 2011.

Second, the biggest gainer in the immediate aftermath of the famine was the Northwest. The decline in Pyongyang’s relative share was matched almost exactly by strong relative growth in the Northwest region. The entirety of the Pyongyang-Sinuiju corridor, which connects the capital with China’s northeastern provinces, falls within this region. This uptick in luminosity corresponds with an inflection in China-DPRK trade that we have documented in earlier posts (see here, here, and here for some recent examples). If major infrastructure investments in this corridor continue, such as an express railway from Kaesong to Sinuiju, this momentum of this growth corridor could continue.

Third, the biggest relative loser in this story—particularly apparent when we omit Pyongyang—is the Northeast region. Not only was the region hit hardest by the famine but it has fared less well than the other regions since that time too. It is possible that this relative decline is overstated by the difficulty of capturing developments along the borders because of the technical problem of “blooming” across boundaries. Nonetheless, the division between the west and east coasts of the country is apparent.

Finally, although it is too early to call it a trend, it is interesting that Pyongyang’s share drops slightly in 2011 and that all other areas see increases, the Southeast more gradually, the Northeast and Northwest more sharply. A possible interpretation is that the economy has rebounded at least somewhat during the last three years.

In future posts, we will look more closely at developments across major cities and rural-urban differences over time.

 

Technical Note by Travis Pope:

The North Korean observations have been calibrated using the global dataset: I take the residuals of OLS regressions of global light output on global GDP and then scale the North Korean observations by the ratio of the residual to the observed annual light. This method removes a significant portion of the systematic sensor error, but the resulting time series still display year-to-year variation that cannot be explained by economic changes alone. Both systematic and random measurement error clearly persist.

I work around this problem by measuring the ratio of a geographic area’s lights to the total national lights in a given year. This relative measure allows for reliable comparisons between different areas within North Korea, as the systematic measurement bias in a given sensor is assumed to apply equally to different areas within the country in a given year. Thus, this ratio is a valid distributional measure, but does not allow for assessments of the actual luminosity levels. Those interested in further work in this vein are strongly encouraged to make use of the inter-calibration methods described in Baugh et al (2009) and Elvidge et al (2009).

To filter out random measurement variation in the sensors, we display three-year moving averages of the national share measure.

In addition to calibration issues, a second problem for luminosity analysis is light ‘blooming’. The outward dispersion of light causes grid cells adjacent to a major source of light to appear lit when in fact there is no light coming from that point in space. For example, the Pacific Ocean immediately west of Los Angeles appears to be abuzz with economic activity.

This is a considerable problem for North Korea, as one can see with the naked eye in the 2012 Stable Lights image above. Light from the relatively much brighter Chinese and South Korean border areas “bloom” into North Korea, particularly along the DMZ where the South Korean military has installed intense floodlights pointed directly at North Korea. I control for this problem with statistical methods established by Pinkovskiy (2013). Light of foreign origin is assumed to be a function of the light density in the neighboring foreign area. The North Korean light density within 10 kilometers of a border is deducted by the product of neighboring foreign light density and a coefficient of 0.23, the empirical rate of light dispersion. These methods provide reasonable controls, on average, but do not control for the varying rate of light dispersion due to the nature of the foreign light. Thus, observations bordering the DMZ appear to be overestimated because of the high-intensity South Korean military lights but urban observations in the area bordering the Chinese city of Dandong (Sinuiju in particular) appear to have been over-penalized and are thus underestimated. This point is critical for interpretation of results because the Northwest is found to have shown strong relative growth over the period, despite the fact that Sinuiju’s light output has been underestimated. Thus, results for the Northwest’s relative growth should be considered conservative estimates.

A third limitation on using this data for North Korea has to do with the absence of corresponding data on population or economic activity. Lights data is often used as a proxy for GDP or total economic activity in a given subnational jurisdiction. Growth in emitted lights suggests increased economic activity, but it is not clear if this increase is due to per capita growth or a population increase. In areas with reasonable economic data, we can use luminosity to inform population estimates; vice versa for areas with quality population data but poor economic data. In the case of North Korea, we have two unknown variables: reliable regional data on economic and population growth are both nonexistent. Strong relative growth should not be construed as evidence that incomes have grown; it may simply reflect differential population growth. But as a practical matter, we suspect this is not consequential, particularly given lack of significant geographical mobility in the country.

Comments (13)

rwendland,

Good question. The sensors always collect the lights data between 8:30pm and 10pm local time so shutting off the lights at midnight has no effect on this data.
The reason the images are not time-stamped is, as you mention, they are composites. Data is collected at different times and then all good quality images (no cloud cover, low moon glare, etc.) are averaged to produce the annualized image.

Travis Pope May 19, 2014 | 4:21 pm

Reply

According to a 2013 BBC report (see below), streetlights in Pyongyang are turned off around midnight. I wondered if the impact of such national streetlight policies would have a significant effect on using light to estimate economic development. I’ve looked at some papers but cannot see this issue being addressed.

In particular, I wonder if the well publicised photos of north/south Korea nighttime lights were taken before or after streetlights were switched off, or if they are a composite of data from different times. I haven’t been able to find photo timestamps to answer this.

The BBC report mentioned above is:

http://www.bbc.co.uk/news/magazine-20773542

rwendland May 18, 2014 | 11:00 am

Reply

Ildo:

Is your article available in English anywhere?

David Bell May 16, 2014 | 9:57 am

Reply

  • Pingback: North Korea by night: satellite images shed new light on the secretive state

  • Pingback: North Korea by night: satellite images shed new light on the secretive state | Kronosim

  • Ian,
    Thanks for your comment. You are absolutely correct about the inter-calibration methods you mentioned. Such a method was used to create the Stable Lights dataset I use here. However, there is still a great deal of variation that is clearly a result of the sensors so we use the National Share measure to side-step the issue altogether.
    If and when other scholars make use of more sophisticated techniques to approach North Korea questions at a more granular level, I’ll make sure to let Dr. Haggard know so he can link to them on the blog.

    Thanks,

    Travis

    Travis Pope April 14, 2014 | 1:36 pm

    Reply

    The issue of inter-calibration can be tackled in several ways (statistically, psuedo-empirically or physically). For most applications inter-calibration is performed using pseudo-invariant targets (deserts in the visible-infrared spectrum). What one might try is identify a stable target (e.g. a dark region as the ocean and a region and a relatively bright invariant target such as a polar glacier under full moon illumination).

    Ian Brown April 11, 2014 | 5:02 am

    Reply

    Nighttime satellite imagery of North Korea is certainly interesting, but a lot of media (not this blog) use it as a very simple pretext to show how North Korea is a black hole of underdevelopment surrounded by a sea of Eastasian tigers without looking further into it.

    But I think it’s an interesting question whether or not this very fact – that N.Korea is surrounded by economic powerhouses – overstates the contrast to a certain extent. Does this make the contrast clearer than that between other, similarly (un-)prosperous countries and their neighbors? What would happen if one compared pictures of North Korea with similar imagery of countries with similar GDP per capita and/or similar population densities? Countries with similar GDP per capita are overwhelmingly countries in Sub-Saharan Africa, and are located in very different geographical/economical contexts, but it could probably provide some interesting insights. Looking at population densities, the range of countries is more diverse (Switzerland, Italy, Nigeria, Luxembourg), and would likely be a complicating factor, but still relevant, I suppose.

    Ole Jakob April 10, 2014 | 12:09 pm

    Reply

    Ildo:

    Just to be clear: we are particularly concerned with the sharp contraction you found in 2007-8. We can’t think of any reason why this might have occurred, and we expect it is a result of the issues outlined by Travis.

    Thanks for noting your work; we will reference it in the future.

    SH

    Stephan Haggard April 9, 2014 | 11:50 am

    Reply

    Many thanks for your kind answers.
    Yes. In fact the inter-calibration issue is the critical point using these data, but I thought we can make a year-to-year comparison just within data from one sensor, not comparing data from various sensors. As you are no doubt aware, every satellite sensor of NOAA cover at least 3~5 years. In this manner, I thought we can track the direction of the SOL curve(up or down) year by year, and estimate the rise or fall of NK GDP. And this could be better than BOK’s ‘classical methodology’ at least.
    But anyway, now I think that the way you chose – regional comparison – can be far more appropriate. I appreciate your creative breakthrough, and actually envy you.^^

    Ildo Hwang April 9, 2014 | 9:51 am

    Reply

    Thanks for your comment and the link to your article. As you are no doubt aware, the inter-calibration issue can make comparing results across years misleading. There are sophisticated techniques for minimizing this problem but even these are not perfect. We will continue to work with this National Share measure to avoid the inter-calibration issue entirely. I agree with your hunch about the BOK data. If we do any work in that vein, we will make sure to cite your work. Thanks.

    Travis Pope April 8, 2014 | 10:14 pm

    Reply

    I congratulate the authors for this innovative use of data from satellite-based light sensors to analyze both temporal and regional variations in the luminosity of North Korea and thereby provide valuable verification of and insight into the economic trends and turning points in various parts of the country. I look forward to their posts of more refined results.

    John Despres April 8, 2014 | 2:18 pm

    Reply

    It’s really good to see your research on this.
    Actually, a few years ago I published an article about NK night lights and its economic activity for a Korean academic journal, and wrote some related stories on Dong-a Ilbo magazines.
    Here below are English abstract and links of them. Hope those could be helpful for further discussion.

    Abstract
    A Study on the Methodology
    for Observing North Korean Economy
    Using Night-time Satellite Imagery

    Hwang, Ildo(Dong-A Ilbo News Company)

    This article suggests new methodology for observing economic progress of North Korea using an analysis of SOL(Sum of Lights) from night satellite imagery taken by the US National Oceanic and Atmospheric Administration(NOAA) from 1992. The correlation between SOL and economic gross index in the third world countries has been proven by numerous precedented studies conducted outside Korea. This methodology has some advantages in that it is relatively free from limitations of other studies regarding macro-economic progress of North Korea based on the comments and opinions of North Korean refugees or other unproven assumptions. The result of SOL analysis shows it had steadily increased from the lowest on the collapse of the Socialistic Bloc of 1992, but decreased sharply in 1997 during the Great Famine. SOL increased slightly from 1998 again and retained a stable status in the beginning of 2000s; however, it collapsed in an unprecedented manner in 2007 and 2008. This progress presents a significant difference from the estimation released by the Bank of Korea. It might be a premature conviction that this difference proves some errors contained in the BOK data. However, considering the high corelation coefficient between SOL and macro-economic index shown by many precedented studies abroad, we can say that it is putting weight on repeated criticism raised on the credibility of the BOK data.
    Keywords: satellite imagery, North Korean economic gross, SOL(Sum of Lights)

    http://news.naver.com/main/read.nhn?mode=LSD&mid=sec&sid1=100&oid=262&aid=0000004435

    Ildo Hwang April 8, 2014 | 12:21 pm

    Reply

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