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Countering sanctions: The unequal geographic impact of economic sanctions in North Korea

How does an autocratic regime domestically counter the impact of economic sanctions? This column studies the impact of sanctions on North Korea using satellite night-time lights data and finds that the burden of sanctions falls on the vulnerable population and not the elites in power towards whom the sanctions are aimed. Sanctions that fail to change the leader’s behaviour likely increase inequality at a cost to the already marginalised hinterlands.

Economic sanctions have become a more frequent foreign policy tool in recent decades. North Korea and Iran have been sanctioned for their nuclear pursuit, Syria for mass civilian killings, and recently Russia for annexing Crimea. However, these sanctions do not seem to be effective in achieving their intended goals. Hufbauer et al. (2009) document 174 sanction cases and find that only 34% were at least partially successful with most of the successes happening before the 1970s. What explains such inefficacy of sanctions? Davis and Engerman (2003) point out that globalisation and the interdependency among countries have made sanctions less effective. But what happens inside the sanctioned country and could the inner workings of the sanctioned country explain why sanctions are ineffective? I examine this question in a recent paper in the context of North Korea (Lee 2014).

Sanctions are aimed at the elites in power. But, how would the elites respond to sanctions and in turn impact the domestic population? A stylised model predicts that, as long as non-compliance is not too costly, autocrats would respond to economic sanctions by redistributing resources to the economy’s more valuable urban sector. Since unequal resource distribution could trigger revolts and threaten the stability of the regime, the autocrat uses its military power to deter potential revolts. I empirically examine North Korea where sanctions have had no impact on changing the regime’s pursuit for nuclear weapons, and the autocrat maintains a strong control of the military and the people. North Korea’s isolation from the international world renders it an ideal case to study the domestic impact of sanctions. Furthermore, North Korea’s restriction on migration enables one to study urban elite capture without worrying about domestic migration that often confounds identification. However, data on North Korea is almost non-existent and what is available is often unreliable.1

Recent research innovations in the study of satellite night lights data provide one avenue around the data constraint. Researchers have found the satellite night lights to be a reliable proxy for economic activity in countries where economic data are sparse, particularly at sub-national levels (Henderson et al. 2012). I use the US Defense Meteorological Satellite Program’s lights data and create an average luminosity measure for each one arc minute by one arc minute grid, which translates to approximately a one mile by one mile grid, between 1992 and 2000. The very isolation and repressiveness of the North Korea makes lights data easier to study than it is elsewhere. This is because in North Korea the overall luminosity levels are lower, alleviating the problem of light over-saturation and top-coding that often limits the accuracy of studies of satellite data in other regions (Figure 1).

Figure 1. Satellite image of the Korean peninsula (2010)

Notes: The area covers 123 to 131 degree longitude and 32 to 44 degree latitude. Image is extracted for 2010.

I then document North Korea’s nuclear provocations and agreements that led countries and the UN to tighten or relax sanctions on North Korea. These events were triggered by North Korea’s nuclear ambitions, military first policy, and Juche ideology, an ideology that strives for international independence. I aggregate the events by year and type, i.e., trade, finance, aid or remittance, and travel sanctions, to create a sanctions index. So what does such an analysis reveal?

Figure 2, which plots the urban rural luminosity gap and the sanctions index, provides a stark description. The sanction index and the urban rural luminosity gap exhibit a similar U-shape, and the timing of the sanctions slightly precede the luminosity gap. The visual pattern hints to a direct impact of sanctions on the urban rural luminosity gap. Indeed in the econometric work, I find that an additional sanction increases the urban-rural luminosity gap by 1.07%, or using the elasticity estimate from Henderson et al. (2012), the urban-rural GDP difference by 0.21%.  This effect is comprised of an increase in luminosity by 0.57% or GDP by 0.11% in the cities, and a decrease in luminosity by 0.5% or GDP by 0.1% in the hinterlands. I find that most of the differential impact of sanctions on luminosity occurs within the urban core, the area within10km of the city center. The impact of sanctions on the urban-rural luminosity gap is robust to whether I use subsamples that cover different years, different latitudes, exclude borders, exclude unlit cells, include regional time trends, or use different sanctions indexes. Furthermore, the results are not driven by a general pattern of urbanisation and economic growth nor reverse causality.

Figure 2. Luminosity gap and the sanctions index

Notes: The left vertical axis measures the luminosity gap and the right vertical axis measures the sanctions index.

There is a tiered response among administrative cities that is consistent with urban elite capture. Luminosity increases more in the centre of power, Pyongyang, relative to province capitals, when sanctions increase. I map North Korea’s army corps and the main air force and navy bases but find no differential sanctions impact on the military base areas relative to the hinterlands. Lastly, the hinterlands also respond to sanctions as urban-rural inequality increases. I examine the fringe areas near the borders and the neighbouring countries. The border with South Korea is heavily militarised and the sanctions have no impact on South Korea. However, sanctions increase luminosity along the relatively porous Chinese border, both on the North Korean and Chinese side. As sanctions increase the hinterland population becomes economically deprived, and more may be prompted to engage in underground market activities along the border or migrate to China.

Overall, the empirical results indicate that when external sanctions generate hardships in the domestic economy, the urban elites, whether by disproportionately promoting economic activity or diverting electricity, shields from the negative impact at a cost to the hinterland population. In short, sanctions that fail to change the behaviour of leaders increase regional inequality at a cost to the already marginalised hinterlands.


Davis, L and S Engerman (2003), “History Lessons Sanctions: Neither War nor Peace.” Journal of Economic Perspectives, 17(2): 187-197.

Henderson J V, A Storeygard and D N Weil (2012), “Measuring Economic Growth From Outer Space.” The American Economic Review, 102(2): 994-1028.

Hufbauer G C, J Schott, K A Elliott and B Oeggm (2009) Economic Sanctions Reconsidered, 3rd Ed. Washington, DC: Institute for International Economics.

Lee, Y-S (2014), “Countering Sanctions: The Unequal Geographic Impact of Economic Sanctions on North Korea.” Stanford University FSI Working Paper.  


1 Two censuses have been conducted by North Korea under the supervision of the UN. However, the demography literature has found evidence that the North Korean census may have been manipulated to conceal the extent of its military population, refugee camps, and death during the famine.

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