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North Korea: Witness to Transformation

North Korea by Night 3: The Urban-Rural Divide

by and Travis Pope | April 29th, 2014 | 07:06 am
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In two earlier posts, we reported on some preliminary efforts to use luminosity to measure spatial patterns of economic activity in North Korea. As we noted, we are not alone; see the interesting paper by Yong Suk Lee at Williams on the geographically uneven effects of sanctions.

Because of the way the satellite data is collected, it does not permit us to measure the absolute changes in luminosity over time (see the technical note appended below). But it does permit comparisons of the shares of total light emanating from different jurisdictions. In the first post we looked at large regional agglomerations and tracked the dominance of Pyongyang in total emissions of light in the country, accounting for roughly 50 percent of all visible light. Outside of Pyongyang, the most striking finding was the growth of the Northwest—the area encompassing the entirety of the Pyongyang-Sinuiju corridor—and the relative decline of the East Coast of the country. In the second post, we estimated per capita emissions in five major cities: Pyongyang, Chongjin, Nampo, Wonsan and Rason. We again found sharp regional differences, but with a twist: Nampo and Rason—two cities that are more open to international trade—had much higher per capita emissions than Chongjin and Wonsan; these findings also confirmed the disadvantaged status of the East Coast outside of Rason.

In this post we look at the urban-rural divide, again comparing relative shares. The measures suggest periods when either cities or rural areas are making relative gains.

Administrative areas are classified as urban or rural based on the Global Administrative Areas dataset’s classification. Urban areas are those listed as a City or a District (within a city). Rural areas were those classified as counties. This is a rough distinction: there are certainly small cities within some of these counties, but this distinction generally matches that between major cities and other areas. We also separate out Pyongyang for some comparisons. In these calculations, Pyongyang includes all areas within the “Pyongyang Directly Governed City” (평양직할시).

Below, we make three comparisons beginning with urban vs. rural including and excluding Pyongyang, and then we separate out Pyongyang and other urban areas and compare them to trends in the rural share. It should be noted that like our comparison of broad regions, the data sums to 1; the categories are mutually exclusive and exhaustive and account for all of the light emitted by the country.

luminosity post3 figure

A first interesting finding is that in the immediate aftermath of the famine (1997-2000), cities outside of Pyongyang gained at the expense of the rural areas; Pyongyang showed a modest relative decline. Rural areas did not start to show relative gains until the 2000-2005 period, when agriculture began to recover. But from 2005 forward, Pyongyang relative share of the country’s total luminosity increases at the expense of both other cities and rural areas. In our view, this is consistent with the conscious policy of channeling resources into the capital city and what we have called “the Pyongyang illusion”: the very much higher levels of income and wealth concentrated in the capital city when compared to both urban but particularly rural areas outside it.

It is too early to identify a trend, but in 2011, rural areas and particularly other cities show gains at Pyongyang’s expense. If this marks a spread of growth outside of the capital, that is a good thing.

 

Technical Notes 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 applied in Henderson et al (2012).

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 below. 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

Additional Note on Measuring Urban and Rural Luminosity

A concern for this analysis was the possibility that changes in the satellite sensors had non-linear effects across different luminosity levels. Different sensitivities could bias intensely lit areas relative to low-intensity areas in one direction or the other. If that were the case, one would expect Pyongyang and other cities to display a consistent positive correlation, but we find no evidence of this in the data. In fact, the Pyongyang, Rural, and Other Urban Areas graph demonstrates that Pyongyang and other major cities have mostly moved in opposing directions over the period, independent of the values in rural areas. As there is no consistent relationship between relative luminosity levels and the sensors used, we are confident that non-linear sensor effects are not driving these results.

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