How Autocracies Disrupt Unsanctioned Information Flows: The Role of State Power and Social Capital in North Korea

Figures & data

Figure 1. The experimental design.

Table 1. Conjoint Attributes and Values

Attributes	Values
Provider	Stranger
	Neighbor
	Friend
	Family
Acquisition Place	One’s own home
	Another person’s home
	Outside (park, street, etc.)
	Market
Distribution Media	North Korean phone/tablet
	CD/DVD
	USB
	Foreign device (phone etc.)
Content Subject	Not country-related
	International content
	South Korean content
	North Korean content
Content Form	Text
	Recorded file (audiobook etc.)
	Video
	Computer program or app
Target Age	Youth
	19–29
	30–49
	50+
Type	Fiction
	Nonfiction

Table 2. Background on Defector-migrant Sample

Statistic	N	Mean	St. Dev.	Min	Max
Women	313	0.73	0.45	0	1
Age	313	35.05	11.05	20	64
Origins: Borderlands^+	313	0.85	0.36	0	1
Migration and Resettlement^++
Year Defected from North Korea	313	2015	2.69	2004	2020
Years spent in China	313	1.70	1.90	1	19
Years spent in South Korea	313	5.00	2.38	0	17
Background in North Korea
Market Experience	313	0.67	0.47	0	1
Party Background (self)	313	0.09	0.29	0	1
Party Background (family)	313	0.65	0.48	0	1
Army Service	313	0.15	0.36	0	1
Socioeconomic Status in North Korea^+++
Upper	313	0.34	0.47	0	1
Middle	313	0.30	0.46	0	1
Low	313	0.26	0.44	0	1

⁺ Defined as the following jurisdictions: North Pyongan, Chagang, Ryanggang, North Hamgyong, and the special city of Rason.

⁺⁺ As noted, a limited number of participants (n = 7) left North Korea prior to 2010.

⁺⁺⁺ Status is defined by a question about material well-being prior to defection. Those who were able to afford expensive items such as cars and apartments or could easily purchase higher end durable goods like refrigerators are counted as “upper.” Those who could purchase basic goods without struggling are classified as “middle.” Those who can afford mere necessities (e.g., food) or struggle to get by on a day-to-day basis are counted as “low.”

Figure 2. Effects of foreign information profiles on the probability of use.

Note: For average marginal components effects (AMCEs), the estimates show the effects of the randomly assigned information attribute values on the probability of use. The point values from the marginal means (MMs) show the mean outcome of any given attribute level, averaged across all others. Estimates are based on the benchmark OLS model with clustered standard errors; bars represent 95% confidence intervals.

Figure 3. Marginal means of the interaction effects (provider * place).

Note: The point values from the marginal means (MMs) show the mean outcome of any given attribute level, averaged across all others. Estimates are based on the benchmark OLS model with clustered standard errors; bars represent 95% confidence intervals.

Figure 4. Estimated probability of foreign information use for selected profiles across the distribution.

Note: The figure shows the predicted probability of information profiles being preferred for use. Estimates show the minimum, 25th, 50th, 75th, and maximum percentiles of the distribution and are based on the benchmark OLS model with clustered standard errors; bars represent 95% confidence intervals.

Figure 5. Word-topic probabilities based on open-text answers.

Note: The figure shows the outcome of a three-cluster implementation of Latent Dirichlet Allocation (LDA). Values represent the probability of the word occurring given the topic (top 15 words shown).

Figure 6. Proportion who said they would share foreign information (per target recipient).

Figure 7. Effects of foreign information attributes on the probability of sharing.

Note: Estimates show the effects of the randomly assigned information attribute values on the probability of the information profile selected being shared with the target recipient. Estimates based on the benchmark OLS models; bars represent 95% confidence intervals.