1Appendix
In this appendix, we provide additional details on how we used ArcGIS to create ourglobal dataset of directed dyads, how we created measures of the proportion of border areas thatare mountainous and the number of border crossings in each border area, and the results ofadditional models.

Creating the global dataset
In the main text, we stated that we included all contiguous directed dyads and the dyads
with the four physically closest countries for each country, as calculated by ArcGIS. We
obtained the list of contiguous directed dyads from the Correlates of War project. For noncontiguous directed dyads, we pursued a longer process. First, we counted the number ofcountries (n) that share a border with each country. We then generated neighbor lists using
spatial weights matrices for the n+4 neighbors of each country. This allowed us to identify thefour closest non-contiguous countries for each country. Our directed dyad list of contiguous andnon-contiguous directed dyads goes from 1980-2016, so we accounted for state entry and exit in
the international system. Again, we relied on the Correlates of War project for data on state entry
and exit. Our analysis in this paper is confined to 1990-2016, so we only used directed dyads forthis time period.

2Creating measures of mountainous borders and border crossings
In order to account for geographic features of borders, we began by obtaining thelocations of all land borders. To do this, we: 1) Used the Intersect function in ArcGIS on theglobal polygon shapefile of countries from the cshapes package to create a line shapefile ofcountry borders; 2) Removed water borders and borders that did not exist before 2011; 3) Added
Correlates of War country codes for each border and verified that we had all directed dyads
represented as border lines.
Then, we turned all of our borders into “border areas.” To accomplish this, we added a 5
km buffer around all border lines. This step in ArcGIS reduced the directed dyads down toborder areas that only had one of the two directed dyads, so we had to keep this in mind at later
steps.
Our mountain variable is a continuous measure from zero to one. We use a binary
measure of whether 1 km grid cells are mountainous. This measure comes from the global rasterdata produced by Kapos, Rhind et al. (2000). Then, we calculated proportion of the border areathat is mountainous. For example, a value of 0.4 means that 40% of the border area ismountainous.
Our border crossing stations variable is a count of the number of border crossings in aborder area. Border crossing stations were identified by identifying locations where roads
intersect borders.1 This border crossing variable is thereby going to miss border crossings onroads that are not captured in existing spatial datasets. For a variety of reasons, road network
shapefiles miss the existence of many roads (Center for International Earth Science Information

1

Data was downloaded from: http://worldmap.harvard.edu/geoserver/wfs?outputFormat=SHAPEZIP&service=WFS&request=GetFeature&format_options=charset%3AUTF8&typename=geonode%3Aborder_crossings_phv&version=1.0.0

3Network - CIESIN - Columbia University and Information Technology Outreach Services ITOS - University of Georgia 2013). We expect therefore that our count of border crossings willtend to undercount crossings, and it will best reflect the existence of long-term crossing points.

Estimating the effects of border geography on barrier existence and refugee flows
In Table A1 and Table A2 below, we estimate poisson models to examine the effects ofborder geography on barrier existence and refugee flows. For our models of barrier existence inTable A2, we estimated models of all three of our barrier variables.
These models show that Mountainous Territory decreases the likelihood of having abarrier and decreases the size of refugee flows, on average. This supports the view thatmountains increase the difficulty of building barriers, and they deter people from crossing
borders. We do see in our case study of the United States-Mexico border that it is possible that if
countries with mountainous borders build barriers, then they will try to use the natural barrier ofmountains as a complement to the human-made barrier. This logic would merit additional
analysis though.
We also see that Border Crossing Stations increase the likelihood of having a barrier andincreases the size of refugee flows, on average. This supports our view that borders with more
official crossing points are easier to cross and are easier locations for countries to place barriers.
Then, we find that Land Boundary Length increases the likelihood of having a barrier andincreases the size of refugee flows, on average. The only exception is that boundary length is not
significantly related to refugee flows when using the Hassner and Wittenberg (2015) measure.
This also supports the view that longer borders are easier to cross and are easier locations forcountries to place barriers.

4

Table A1: Poisson regression for border geography on refugee flows(1)
VARIABLES
Logged Refugee Flows
Mountainous Territory
Border CrossingsLogged Boundary Length
Lagged and Logged Refugee Flows
Civil War Intensity, Origin
Polyarchy, Origin
Genocide, Origin
Political Terror Scale, Origin
Logged GDP per Capita, Origin
Logged Distance between Countries
Common Language
Colonial Tie
Constant-0.167***(0.033)0.009***(0.001)
0.042***(0.013)0.217***(0.003)0.301***(0.018)-0.653***(0.061)0.006(0.011)0.265***(0.013)-0.181***(0.010)-0.081***(0.014)0.184***(0.022)0.453***(0.044)-0.546(0.192)

Observations

18,100Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

5

Table A2: Logistic regression for border geography on barrier existence
(1)
(2)
(3)
VARIABLES
Hassner & Wittenberg Poast and Carter Avdan and Gelpi
Mountainous Territory-1.603***(0.136)0.017***(0.002)0.062(0.044)0.177**(0.075)-2.540***(0.187)-0.679***(0.154)0.134***-1.026***(0.143)0.013***(0.003)0.146***(0.051)-0.014(0.084)-1.827***(0.213)-0.727***(0.209)0.192***-1.090***(0.124)0.009***(0.002)0.262***(0.045)0.191***(0.074)-2.486***(0.176)-0.472***(0.111)
0.032(0.044)0.129***(0.049)0.004(0.043)0.215***(0.032)-0.076(0.049)-0.316***(0.084)-0.718***(0.227)-3.434***(0.670)(0.037)0.008(0.057)0.066(0.091)0.552***(0.173)-5.131***(0.769)(0.031)
0.033(0.050)-0.150*(0.079)-0.044(0.168)-7.279***(0.669)18,172
18,172Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

18,172

Border Crossings
Logged Land Boundary Length
Lagged Civil War Intensity, Origin
Lagged Polyarchy, Origin
Lagged Genocide, Origin
Lagged Political Terror Scale,OriginLagged and Logged GDP per
Capita, Origin
Logged Distance between Countries
Common Language
Colonial Tie
Constant

Observations

6

Negative binomial results
In Figure A1 below, we show the results of negative binomial regressions. This is thesame model specification as Figure 8 in the main text, except that this is negative binomial
instead of poisson. Here, we see that our negative binomial results are very similar to our poisson
results. Aside from the early 1990s, there is not a statistically significant relationship between
barriers and refugee flows.

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Figure A1: Negative binomial results for all dyads and contiguous dyads
All dyads

Contiguous dyads

8

OLS, Tobit, and OLS-IV results
In Figures A2, A3, and A4 below, we display model results from OLS, Tobit, and OLS
with instrumental variables. These linear models have some differences with our event countmodels. We do not discuss them in the main text because a normal distribution does not fit thedistribution of our dependent variable as well as a poisson distribution.
Here, the OLS models show that most model specifications yield an insignificant
relationship between barriers and refugee flows. When this relationship is statisticallysignificant, it is negative. This correlation, however, appears to be the result of endogeneity, as
we can see in Figure A3. Figure A3 shows that with the inclusion of an instrumental variable,
Logged Land Boundary Length, there are no model specifications with a statistically significant
relationship between barriers and refugee flows. In an effort to align the assumed distribution of
the dependent variable with its actual distribution, we also estimated tobit models in order tocreate Figure A4. Generally, these models mostly fail to detect a statistically significant
relationship between barriers and refugee flows.

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Figure A2: OLS results

Figure A3: OLS with instrumental variable

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Figure A4: Tobit results